From 03ee67bfd34b9e872b33eb05fef5db83410b16f3 Mon Sep 17 00:00:00 2001
From: WDevelopsWebApps <97454358+WDevelopsWebApps@users.noreply.github.com>
Date: Wed, 28 Sep 2022 10:53:40 +0200
Subject: add advanced saving for save button

---
 modules/images.py |  5 ++++-
 modules/ui.py     | 37 +++++++++++++++++++++++++++++--------
 2 files changed, 33 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 9458bf8d..923f81df 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -290,7 +290,10 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[cfg]", str(p.cfg_scale))
         x = x.replace("[width]", str(p.width))
         x = x.replace("[height]", str(p.height))
-        x = x.replace("[styles]", sanitize_filename_part(", ".join(p.styles), replace_spaces=False))
+        #currently disabled if using the save button, will work otherwise 
+        # if enabled it will cause a bug because styles is not included in the save_files data dictionary
+        if hasattr(p, "styles"):
+            x = x.replace("[styles]", sanitize_filename_part(", ".join(p.styles), replace_spaces=False))
         x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
     x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
diff --git a/modules/ui.py b/modules/ui.py
index 7db8edbd..87a86a45 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -28,6 +28,7 @@ import modules.gfpgan_model
 import modules.codeformer_model
 import modules.styles
 import modules.generation_parameters_copypaste
+from modules.images import apply_filename_pattern, get_next_sequence_number
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -90,13 +91,26 @@ def send_gradio_gallery_to_image(x):
 
 
 def save_files(js_data, images, index):
-    import csv
-
-    os.makedirs(opts.outdir_save, exist_ok=True)
-
+    import csv    
     filenames = []
 
+    #quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it
+    class MyObject:
+        def __init__(self, d=None):
+            if d is not None:
+                for key, value in d.items():
+                    setattr(self, key, value)
+
     data = json.loads(js_data)
+    p = MyObject(data)
+    path = opts.outdir_save
+    save_to_dirs = opts.save_to_dirs
+
+    if save_to_dirs:
+        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt)
+        path = os.path.join(opts.outdir_save, dirname)
+
+    os.makedirs(path, exist_ok=True)
     
     if index > -1 and opts.save_selected_only and (index > 0 or not opts.return_grid): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
         images = [images[index]]
@@ -107,11 +121,18 @@ def save_files(js_data, images, index):
         writer = csv.writer(file)
         if at_start:
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
-
-        filename_base = str(int(time.time() * 1000))
+        file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
+        if file_decoration != "":
+            file_decoration = "-" + file_decoration.lower()
+        file_decoration = apply_filename_pattern(file_decoration, p, p.seed, p.prompt)
+        truncated = (file_decoration[:240] + '..') if len(file_decoration) > 240 else file_decoration
+        filename_base = truncated
+
+        basecount = get_next_sequence_number(path, "")
         for i, filedata in enumerate(images):
-            filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + ".png"
-            filepath = os.path.join(opts.outdir_save, filename)
+            file_number = f"{basecount+i:05}"
+            filename = file_number + filename_base + ".png"
+            filepath = os.path.join(path, filename)
 
             if filedata.startswith("data:image/png;base64,"):
                 filedata = filedata[len("data:image/png;base64,"):]
-- 
cgit v1.2.3


From c938679de7b87b4f14894d9f57fe0f40dd6e3c06 Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Wed, 28 Sep 2022 22:14:13 -0300
Subject: Fix memory leak and reduce memory usage

---
 modules/codeformer_model.py |  6 ++++--
 modules/devices.py          |  3 ++-
 modules/extras.py           |  2 ++
 modules/gfpgan_model.py     | 11 +++++------
 modules/processing.py       | 33 ++++++++++++++++++++++++++-------
 5 files changed, 39 insertions(+), 16 deletions(-)

(limited to 'modules')

diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index 8fbdea24..2177291a 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -89,7 +89,7 @@ def setup_codeformer():
                             output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
                             restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
                         del output
-                        torch.cuda.empty_cache()
+                        devices.torch_gc()
                     except Exception as error:
                         print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
                         restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
@@ -106,7 +106,9 @@ def setup_codeformer():
                     restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
 
                 if shared.opts.face_restoration_unload:
-                    self.net.to(devices.cpu)
+                    self.net = None
+                    self.face_helper = None
+                    devices.torch_gc()
 
                 return restored_img
 
diff --git a/modules/devices.py b/modules/devices.py
index 07bb2339..df63dd88 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,4 +1,5 @@
 import torch
+import gc
 
 # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
 from modules import errors
@@ -17,8 +18,8 @@ def get_optimal_device():
 
     return cpu
 
-
 def torch_gc():
+    gc.collect()
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()
diff --git a/modules/extras.py b/modules/extras.py
index 9a825530..38b86167 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -98,6 +98,8 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
 
         outputs.append(image)
 
+    devices.torch_gc()
+
     return outputs, plaintext_to_html(info), ''
 
 
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index 44c5dc6c..b1288f0c 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -49,6 +49,7 @@ def gfpgan():
 
 
 def gfpgan_fix_faces(np_image):
+    global loaded_gfpgan_model
     model = gfpgan()
 
     np_image_bgr = np_image[:, :, ::-1]
@@ -56,7 +57,9 @@ def gfpgan_fix_faces(np_image):
     np_image = gfpgan_output_bgr[:, :, ::-1]
 
     if shared.opts.face_restoration_unload:
-        model.gfpgan.to(devices.cpu)
+        del model
+        loaded_gfpgan_model = None
+        devices.torch_gc()
 
     return np_image
 
@@ -83,11 +86,7 @@ def setup_gfpgan():
                 return "GFPGAN"
 
             def restore(self, np_image):
-                np_image_bgr = np_image[:, :, ::-1]
-                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgan().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
-                np_image = gfpgan_output_bgr[:, :, ::-1]
-
-                return np_image
+                return gfpgan_fix_faces(np_image)
 
         shared.face_restorers.append(FaceRestorerGFPGAN())
     except Exception:
diff --git a/modules/processing.py b/modules/processing.py
index 4ecdfcd2..de5cda79 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -12,7 +12,7 @@ import cv2
 from skimage import exposure
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking
+from modules import devices, prompt_parser, masking, lowvram
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
 from modules.shared import opts, cmd_opts, state
@@ -335,7 +335,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         if state.job_count == -1:
             state.job_count = p.n_iter
 
-        for n in range(p.n_iter):
+    for n in range(p.n_iter):
+        with torch.no_grad(), precision_scope("cuda"), ema_scope():
             if state.interrupted:
                 break
 
@@ -368,22 +369,32 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
+            del samples_ddim
+
+            if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+                lowvram.send_everything_to_cpu()
+
+            devices.torch_gc()
+
             if opts.filter_nsfw:
                 import modules.safety as safety
                 x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
 
-            for i, x_sample in enumerate(x_samples_ddim):
+        for i, x_sample in enumerate(x_samples_ddim):
+            with torch.no_grad(), precision_scope("cuda"), ema_scope():
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
 
-                if p.restore_faces:
+            if p.restore_faces:
+                with torch.no_grad(), precision_scope("cuda"), ema_scope():
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
                         images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
 
-                    devices.torch_gc()
-
                     x_sample = modules.face_restoration.restore_faces(x_sample)
 
+                devices.torch_gc()
+
+            with torch.no_grad(), precision_scope("cuda"), ema_scope():
                 image = Image.fromarray(x_sample)
 
                 if p.color_corrections is not None and i < len(p.color_corrections):
@@ -411,8 +422,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 infotexts.append(infotext(n, i))
                 output_images.append(image)
 
-            state.nextjob()
+        del x_samples_ddim 
 
+        devices.torch_gc()
+
+        state.nextjob()
+
+    with torch.no_grad(), precision_scope("cuda"), ema_scope():
         p.color_corrections = None
 
         index_of_first_image = 0
@@ -648,4 +664,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         if self.mask is not None:
             samples = samples * self.nmask + self.init_latent * self.mask
 
+        del x
+        devices.torch_gc()
+
         return samples
-- 
cgit v1.2.3


From c2d5b29040132c171bc4d77f1f63da972306f22c Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Thu, 29 Sep 2022 01:14:54 -0300
Subject: Move silu to sd_hijack

---
 modules/sd_hijack.py | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index bfbd07f9..4bc58fa2 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -12,6 +12,7 @@ from ldm.util import default
 from einops import rearrange
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
+from torch.nn.functional import silu
 
 
 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
@@ -100,14 +101,6 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
-def nonlinearity_hijack(x):
-    # swish
-    t = torch.sigmoid(x)
-    x *= t
-    del t
-
-    return x
-
 def cross_attention_attnblock_forward(self, x):
         h_ = x
         h_ = self.norm(h_)
@@ -245,11 +238,12 @@ class StableDiffusionModelHijack:
         m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
         self.clip = m.cond_stage_model
 
+        ldm.modules.diffusionmodules.model.nonlinearity = silu
+
         if cmd_opts.opt_split_attention_v1:
             ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
         elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
             ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
-            ldm.modules.diffusionmodules.model.nonlinearity = nonlinearity_hijack
             ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
 
         def flatten(el):
-- 
cgit v1.2.3


From e82ea202997cbcd2ab72891cd075d9ba270eb67d Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 15:26:18 -0500
Subject: Optimize model loader

Child classes only get populated to __subclassess__ when they are imported. We don't actually need to import any of them to webui any more, so clean up webUI imports and make sure loader imports children.

Also, fix command line paths not actually being passed to the scalers.
---
 modules/modelloader.py | 19 ++++++++++++++++---
 1 file changed, 16 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/modelloader.py b/modules/modelloader.py
index 1106aeb7..b1721671 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -4,7 +4,6 @@ import importlib
 from urllib.parse import urlparse
 
 from basicsr.utils.download_util import load_file_from_url
-
 from modules import shared
 from modules.upscaler import Upscaler
 from modules.paths import script_path, models_path
@@ -120,16 +119,30 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
 
 
 def load_upscalers():
+    sd = shared.script_path
+    # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
+    # so we'll try to import any _model.py files before looking in __subclasses__
+    modules_dir = os.path.join(sd, "modules")
+    for file in os.listdir(modules_dir):
+        if "_model.py" in file:
+            model_name = file.replace("_model.py", "")
+            full_model = f"modules.{model_name}_model"
+            try:
+                importlib.import_module(full_model)
+            except:
+                pass
     datas = []
+    c_o = vars(shared.cmd_opts)
     for cls in Upscaler.__subclasses__():
         name = cls.__name__
         module_name = cls.__module__
         module = importlib.import_module(module_name)
         class_ = getattr(module, name)
-        cmd_name = f"{name.lower().replace('upscaler', '')}-models-path"
+        cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
         opt_string = None
         try:
-            opt_string = shared.opts.__getattr__(cmd_name)
+            if cmd_name in c_o:
+                opt_string = c_o[cmd_name]
         except:
             pass
         scaler = class_(opt_string)
-- 
cgit v1.2.3


From 8deae077004f0332ca607fc3a5d568b1a4705bec Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 15:28:37 -0500
Subject: Add ScuNET DeNoiser/Upscaler

Q&D Implementation of ScuNET, thanks to our handy model loader. :P

https://github.com/cszn/SCUNet
---
 modules/scunet_model.py      |  90 +++++++++++++++
 modules/scunet_model_arch.py | 265 +++++++++++++++++++++++++++++++++++++++++++
 modules/shared.py            |   1 +
 3 files changed, 356 insertions(+)
 create mode 100644 modules/scunet_model.py
 create mode 100644 modules/scunet_model_arch.py

(limited to 'modules')

diff --git a/modules/scunet_model.py b/modules/scunet_model.py
new file mode 100644
index 00000000..7987ac14
--- /dev/null
+++ b/modules/scunet_model.py
@@ -0,0 +1,90 @@
+import os.path
+import sys
+import traceback
+
+import PIL.Image
+import numpy as np
+import torch
+from basicsr.utils.download_util import load_file_from_url
+
+import modules.upscaler
+from modules import shared, modelloader
+from modules.paths import models_path
+from modules.scunet_model_arch import SCUNet as net
+
+
+class UpscalerScuNET(modules.upscaler.Upscaler):
+    def __init__(self, dirname):
+        self.name = "ScuNET"
+        self.model_path = os.path.join(models_path, self.name)
+        self.model_name = "ScuNET GAN"
+        self.model_name2 = "ScuNET PSNR"
+        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
+        self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
+        self.user_path = dirname
+        super().__init__()
+        model_paths = self.find_models(ext_filter=[".pth"])
+        scalers = []
+        add_model2 = True
+        for file in model_paths:
+            if "http" in file:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(file)
+            if name == self.model_name2 or file == self.model_url2:
+                add_model2 = False
+            try:
+                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
+                scalers.append(scaler_data)
+            except Exception:
+                print(f"Error loading ScuNET model: {file}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+        if add_model2:
+            scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
+            scalers.append(scaler_data2)
+        self.scalers = scalers
+
+    def do_upscale(self, img: PIL.Image, selected_file):
+        torch.cuda.empty_cache()
+
+        model = self.load_model(selected_file)
+        if model is None:
+            return img
+
+        device = shared.device
+        img = np.array(img)
+        img = img[:, :, ::-1]
+        img = np.moveaxis(img, 2, 0) / 255
+        img = torch.from_numpy(img).float()
+        img = img.unsqueeze(0).to(shared.device)
+
+        img = img.to(device)
+        with torch.no_grad():
+            output = model(img)
+        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
+        output = 255. * np.moveaxis(output, 0, 2)
+        output = output.astype(np.uint8)
+        output = output[:, :, ::-1]
+        torch.cuda.empty_cache()
+        return PIL.Image.fromarray(output, 'RGB')
+
+    def load_model(self, path: str):
+        device = shared.device
+        if "http" in path:
+            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
+                                          progress=True)
+        else:
+            filename = path
+        if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
+            print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
+            return None
+
+        model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
+        model.load_state_dict(torch.load(filename), strict=True)
+        model.eval()
+        for k, v in model.named_parameters():
+            v.requires_grad = False
+        model = model.to(device)
+
+        return model
+
diff --git a/modules/scunet_model_arch.py b/modules/scunet_model_arch.py
new file mode 100644
index 00000000..972a2639
--- /dev/null
+++ b/modules/scunet_model_arch.py
@@ -0,0 +1,265 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+from timm.models.layers import trunc_normal_, DropPath
+
+
+class WMSA(nn.Module):
+    """ Self-attention module in Swin Transformer
+    """
+
+    def __init__(self, input_dim, output_dim, head_dim, window_size, type):
+        super(WMSA, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.head_dim = head_dim
+        self.scale = self.head_dim ** -0.5
+        self.n_heads = input_dim // head_dim
+        self.window_size = window_size
+        self.type = type
+        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
+
+        self.relative_position_params = nn.Parameter(
+            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
+
+        self.linear = nn.Linear(self.input_dim, self.output_dim)
+
+        trunc_normal_(self.relative_position_params, std=.02)
+        self.relative_position_params = torch.nn.Parameter(
+            self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
+                                                                                                                 2).transpose(
+                0, 1))
+
+    def generate_mask(self, h, w, p, shift):
+        """ generating the mask of SW-MSA
+        Args:
+            shift: shift parameters in CyclicShift.
+        Returns:
+            attn_mask: should be (1 1 w p p),
+        """
+        # supporting sqaure.
+        attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
+        if self.type == 'W':
+            return attn_mask
+
+        s = p - shift
+        attn_mask[-1, :, :s, :, s:, :] = True
+        attn_mask[-1, :, s:, :, :s, :] = True
+        attn_mask[:, -1, :, :s, :, s:] = True
+        attn_mask[:, -1, :, s:, :, :s] = True
+        attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
+        return attn_mask
+
+    def forward(self, x):
+        """ Forward pass of Window Multi-head Self-attention module.
+        Args:
+            x: input tensor with shape of [b h w c];
+            attn_mask: attention mask, fill -inf where the value is True;
+        Returns:
+            output: tensor shape [b h w c]
+        """
+        if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+        x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
+        h_windows = x.size(1)
+        w_windows = x.size(2)
+        # sqaure validation
+        # assert h_windows == w_windows
+
+        x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
+        qkv = self.embedding_layer(x)
+        q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
+        sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
+        # Adding learnable relative embedding
+        sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
+        # Using Attn Mask to distinguish different subwindows.
+        if self.type != 'W':
+            attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
+            sim = sim.masked_fill_(attn_mask, float("-inf"))
+
+        probs = nn.functional.softmax(sim, dim=-1)
+        output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
+        output = rearrange(output, 'h b w p c -> b w p (h c)')
+        output = self.linear(output)
+        output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
+
+        if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
+                                                 dims=(1, 2))
+        return output
+
+    def relative_embedding(self):
+        cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
+        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
+        # negative is allowed
+        return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
+
+
+class Block(nn.Module):
+    def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+        """ SwinTransformer Block
+        """
+        super(Block, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        assert type in ['W', 'SW']
+        self.type = type
+        if input_resolution <= window_size:
+            self.type = 'W'
+
+        self.ln1 = nn.LayerNorm(input_dim)
+        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.ln2 = nn.LayerNorm(input_dim)
+        self.mlp = nn.Sequential(
+            nn.Linear(input_dim, 4 * input_dim),
+            nn.GELU(),
+            nn.Linear(4 * input_dim, output_dim),
+        )
+
+    def forward(self, x):
+        x = x + self.drop_path(self.msa(self.ln1(x)))
+        x = x + self.drop_path(self.mlp(self.ln2(x)))
+        return x
+
+
+class ConvTransBlock(nn.Module):
+    def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+        """ SwinTransformer and Conv Block
+        """
+        super(ConvTransBlock, self).__init__()
+        self.conv_dim = conv_dim
+        self.trans_dim = trans_dim
+        self.head_dim = head_dim
+        self.window_size = window_size
+        self.drop_path = drop_path
+        self.type = type
+        self.input_resolution = input_resolution
+
+        assert self.type in ['W', 'SW']
+        if self.input_resolution <= self.window_size:
+            self.type = 'W'
+
+        self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
+                                 self.type, self.input_resolution)
+        self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+        self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+
+        self.conv_block = nn.Sequential(
+            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
+            nn.ReLU(True),
+            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
+        )
+
+    def forward(self, x):
+        conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
+        conv_x = self.conv_block(conv_x) + conv_x
+        trans_x = Rearrange('b c h w -> b h w c')(trans_x)
+        trans_x = self.trans_block(trans_x)
+        trans_x = Rearrange('b h w c -> b c h w')(trans_x)
+        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
+        x = x + res
+
+        return x
+
+
+class SCUNet(nn.Module):
+    # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
+    def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
+        super(SCUNet, self).__init__()
+        if config is None:
+            config = [2, 2, 2, 2, 2, 2, 2]
+        self.config = config
+        self.dim = dim
+        self.head_dim = 32
+        self.window_size = 8
+
+        # drop path rate for each layer
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
+
+        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
+
+        begin = 0
+        self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution)
+                        for i in range(config[0])] + \
+                       [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[0]
+        self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution // 2)
+                        for i in range(config[1])] + \
+                       [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[1]
+        self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution // 4)
+                        for i in range(config[2])] + \
+                       [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[2]
+        self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                      'W' if not i % 2 else 'SW', input_resolution // 8)
+                       for i in range(config[3])]
+
+        begin += config[3]
+        self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution // 4)
+                      for i in range(config[4])]
+
+        begin += config[4]
+        self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution // 2)
+                      for i in range(config[5])]
+
+        begin += config[5]
+        self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution)
+                      for i in range(config[6])]
+
+        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
+
+        self.m_head = nn.Sequential(*self.m_head)
+        self.m_down1 = nn.Sequential(*self.m_down1)
+        self.m_down2 = nn.Sequential(*self.m_down2)
+        self.m_down3 = nn.Sequential(*self.m_down3)
+        self.m_body = nn.Sequential(*self.m_body)
+        self.m_up3 = nn.Sequential(*self.m_up3)
+        self.m_up2 = nn.Sequential(*self.m_up2)
+        self.m_up1 = nn.Sequential(*self.m_up1)
+        self.m_tail = nn.Sequential(*self.m_tail)
+        # self.apply(self._init_weights)
+
+    def forward(self, x0):
+
+        h, w = x0.size()[-2:]
+        paddingBottom = int(np.ceil(h / 64) * 64 - h)
+        paddingRight = int(np.ceil(w / 64) * 64 - w)
+        x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
+
+        x1 = self.m_head(x0)
+        x2 = self.m_down1(x1)
+        x3 = self.m_down2(x2)
+        x4 = self.m_down3(x3)
+        x = self.m_body(x4)
+        x = self.m_up3(x + x4)
+        x = self.m_up2(x + x3)
+        x = self.m_up1(x + x2)
+        x = self.m_tail(x + x1)
+
+        x = x[..., :h, :w]
+
+        return x
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index 8428c7a3..a48b995a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -40,6 +40,7 @@ parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory wi
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
 parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))
 parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
+parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(model_path, 'ScuNET'))
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
-- 
cgit v1.2.3


From abdbf1de646f007b6d76cfb3f416fdfaadb57903 Mon Sep 17 00:00:00 2001
From: Liam <liamthekerr@gmail.com>
Date: Thu, 29 Sep 2022 14:40:47 -0400
Subject: token counters now update when roll artist and style buttons are
 pressed
 https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/1194#issuecomment-1261203893

---
 modules/ui.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 15572bb0..5eea1860 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -539,6 +539,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             roll.click(
                 fn=roll_artist,
+                _js="roll_artist_txt2img",
                 inputs=[
                     txt2img_prompt,
                 ],
@@ -743,6 +744,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             roll.click(
                 fn=roll_artist,
+                _js="roll_artist_img2img",
                 inputs=[
                     img2img_prompt,
                 ],
@@ -753,6 +755,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
             style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
+            style_js_funcs = ["update_style_txt2img", "update_style_img2img"]
 
             for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
                 button.click(
@@ -764,9 +767,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                     outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
                 )
 
-            for button, (prompt, negative_prompt), (style1, style2) in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns):
+            for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
                 button.click(
                     fn=apply_styles,
+                    _js=js_func,
                     inputs=[prompt, negative_prompt, style1, style2],
                     outputs=[prompt, negative_prompt, style1, style2],
                 )
-- 
cgit v1.2.3


From ff8dc1908af088d0ed43fb85baad662733c5ca9c Mon Sep 17 00:00:00 2001
From: Liam <liamthekerr@gmail.com>
Date: Thu, 29 Sep 2022 15:47:06 -0400
Subject: fixed token counter for prompt editing

---
 modules/ui.py | 20 +++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 5eea1860..6bf28562 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -11,6 +11,7 @@ import time
 import traceback
 import platform
 import subprocess as sp
+from functools import reduce
 
 import numpy as np
 import torch
@@ -32,6 +33,7 @@ import modules.gfpgan_model
 import modules.codeformer_model
 import modules.styles
 import modules.generation_parameters_copypaste
+from modules.prompt_parser import get_learned_conditioning_prompt_schedules
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -345,8 +347,11 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
         outputs=[seed, dummy_component]
     )
 
-def update_token_counter(text):
-    tokens, token_count, max_length = model_hijack.tokenize(text)
+def update_token_counter(text, steps):
+    prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
+    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+    prompts = [prompt_text for step,prompt_text in flat_prompts]
+    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
     style_class = ' class="red"' if (token_count > max_length) else ""
     return f"<span {style_class}>{token_count}/{max_length}</span>"
 
@@ -364,8 +369,7 @@ def create_toprow(is_img2img):
                     roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
                     paste = gr.Button(value=paste_symbol, elem_id="paste")
                     token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
-                    hidden_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-                    hidden_button.click(fn=update_token_counter, inputs=[prompt], outputs=[token_counter])
+                    token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
 
                 with gr.Column(scale=10, elem_id="style_pos_col"):
                     prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
@@ -396,7 +400,7 @@ def create_toprow(is_img2img):
                 prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
                 save_style = gr.Button('Create style', elem_id="style_create")
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button
 
 
 def setup_progressbar(progressbar, preview, id_part):
@@ -419,7 +423,7 @@ def setup_progressbar(progressbar, preview, id_part):
 
 def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
 
         with gr.Row(elem_id='txt2img_progress_row'):
@@ -568,9 +572,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
+            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             with gr.Column(scale=1):
@@ -793,6 +798,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 (denoising_strength, "Denoising strength"),
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, img2img_paste_fields, img2img_prompt)
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
-- 
cgit v1.2.3


From 3c6a049fc3c6b54ada3736710a7e86663ea7f3d9 Mon Sep 17 00:00:00 2001
From: Liam <liamthekerr@gmail.com>
Date: Fri, 30 Sep 2022 12:12:44 -0400
Subject: consolidated token counter functions

---
 modules/ui.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 6bf28562..40c08984 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -543,7 +543,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             roll.click(
                 fn=roll_artist,
-                _js="roll_artist_txt2img",
+                _js="update_txt2img_tokens",
                 inputs=[
                     txt2img_prompt,
                 ],
@@ -749,7 +749,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             roll.click(
                 fn=roll_artist,
-                _js="roll_artist_img2img",
+                _js="update_img2img_tokens",
                 inputs=[
                     img2img_prompt,
                 ],
@@ -760,7 +760,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
             style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
-            style_js_funcs = ["update_style_txt2img", "update_style_img2img"]
+            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
 
             for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
                 button.click(
-- 
cgit v1.2.3


From bdaa36c84470adbdce3e98c01a69af5e95adfb02 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Fri, 30 Sep 2022 23:53:25 -0400
Subject: When device is MPS, use CPU for GFPGAN instead

GFPGAN will not work if the device is MPS, so default to CPU instead.
---
 modules/devices.py      | 2 +-
 modules/gfpgan_model.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 07bb2339..08bb26d6 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -34,7 +34,7 @@ errors.run(enable_tf32, "Enabling TF32")
 
 
 device = get_optimal_device()
-device_codeformer = cpu if has_mps else device
+device_gfpgan = device_codeformer = cpu if device.type == 'mps' else device
 
 
 def randn(seed, shape):
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index bb30d733..fcd8544a 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -21,7 +21,7 @@ def gfpgann():
     global loaded_gfpgan_model
     global model_path
     if loaded_gfpgan_model is not None:
-        loaded_gfpgan_model.gfpgan.to(shared.device)
+        loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
         return loaded_gfpgan_model
 
     if gfpgan_constructor is None:
@@ -36,8 +36,8 @@ def gfpgann():
     else:
         print("Unable to load gfpgan model!")
         return None
-    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
-    model.gfpgan.to(shared.device)
+    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
+    model.gfpgan.to(devices.device_gfpgan)
     loaded_gfpgan_model = model
 
     return model
-- 
cgit v1.2.3


From 4c2478a68a4f11959fe4887d38e0436eac19f97e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 18:30:53 +0100
Subject: add script reload method

---
 modules/scripts.py | 9 +++++++++
 1 file changed, 9 insertions(+)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 7c3bd5e7..3c14b9e3 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -165,3 +165,12 @@ class ScriptRunner:
 
 scripts_txt2img = ScriptRunner()
 scripts_img2img = ScriptRunner()
+
+def reload_scripts(basedir):
+  global scripts_txt2img,scripts_img2img
+
+  scripts_data.clear()
+  load_scripts(basedir)
+
+  scripts_txt2img = ScriptRunner()
+  scripts_img2img = ScriptRunner()
-- 
cgit v1.2.3


From 4f8490cd5630823ac44de8b5c5e4325bdbbea7fa Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 18:33:31 +0100
Subject: add restart button

---
 modules/ui.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 15572bb0..ec6aaa28 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1002,6 +1002,17 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             _js='function(){}'
         )
 
+        def request_restart():
+          settings_interface.gradio_ref.do_restart = True
+
+        restart_gradio = gr.Button(value='Restart Gradio and Refresh Scripts')
+        restart_gradio.click(
+            fn=request_restart,
+            inputs=[],
+            outputs=[],
+            _js='function(){document.body.innerHTML=\'<h1 style="color:white;text-align: center;">Reloading</h1>\';setTimeout(function(){location.reload()},2000)}'
+        )
+        
         if column is not None:
             column.__exit__()
 
@@ -1026,7 +1037,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         css += css_hide_progressbar
 
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
-
+        
+        settings_interface.gradio_ref = demo
+        
         with gr.Tabs() as tabs:
             for interface, label, ifid in interfaces:
                 with gr.TabItem(label, id=ifid):
-- 
cgit v1.2.3


From 121ed7d36febe94995774973b5edc1ba2ba84aad Mon Sep 17 00:00:00 2001
From: Alexandre Simard <as.simard@outlook.com>
Date: Sat, 1 Oct 2022 14:04:20 -0400
Subject: Add progress bar for SwinIR in cmd

I do not know how to add them to the UI...
---
 modules/swinir_model.py | 27 +++++++++++++++------------
 1 file changed, 15 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index 41fda5a7..9bd454c6 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -5,6 +5,7 @@ import numpy as np
 import torch
 from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
+from tqdm import tqdm
 
 from modules import modelloader
 from modules.paths import models_path
@@ -122,18 +123,20 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
     E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
     W = torch.zeros_like(E, dtype=torch.half, device=device)
 
-    for h_idx in h_idx_list:
-        for w_idx in w_idx_list:
-            in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
-            out_patch = model(in_patch)
-            out_patch_mask = torch.ones_like(out_patch)
-
-            E[
-            ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-            ].add_(out_patch)
-            W[
-            ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-            ].add_(out_patch_mask)
+    with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
+        for h_idx in h_idx_list:
+            for w_idx in w_idx_list:
+                in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+                out_patch = model(in_patch)
+                out_patch_mask = torch.ones_like(out_patch)
+
+                E[
+                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                ].add_(out_patch)
+                W[
+                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                ].add_(out_patch_mask)
+                pbar.update(1)
     output = E.div_(W)
 
     return output
-- 
cgit v1.2.3


From afaa03c5fd05f48ed9c9f15558ea6f0bc4f61628 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 22:43:45 +0100
Subject: add redefinition guard to gradio_routes_templates_response

---
 modules/ui.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index ec6aaa28..fd057916 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1219,12 +1219,13 @@ for filename in sorted(os.listdir(jsdir)):
         javascript += f"\n<script>{jsfile.read()}</script>"
 
 
-def template_response(*args, **kwargs):
-    res = gradio_routes_templates_response(*args, **kwargs)
-    res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
-    res.init_headers()
-    return res
+if 'gradio_routes_templates_response' not in globals():
+  def template_response(*args, **kwargs):
+      res = gradio_routes_templates_response(*args, **kwargs)
+      res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
+      res.init_headers()
+      return res
 
+  gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
+  gradio.routes.templates.TemplateResponse = template_response
 
-gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
-gradio.routes.templates.TemplateResponse = template_response
-- 
cgit v1.2.3


From 6048002dade91b82b1ce9fea3c6ff5b5c1f8c990 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 23:10:07 +0100
Subject: Add scope warning to refresh button

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index fd057916..72846a12 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1005,7 +1005,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         def request_restart():
           settings_interface.gradio_ref.do_restart = True
 
-        restart_gradio = gr.Button(value='Restart Gradio and Refresh Scripts')
+        restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
         restart_gradio.click(
             fn=request_restart,
             inputs=[],
-- 
cgit v1.2.3


From 027c5aae5546ff3650347cb3c2b87df4415ab900 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 23:29:26 +0100
Subject: update reloading message style

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 72846a12..7b2359c2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1010,7 +1010,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             fn=request_restart,
             inputs=[],
             outputs=[],
-            _js='function(){document.body.innerHTML=\'<h1 style="color:white;text-align: center;">Reloading</h1>\';setTimeout(function(){location.reload()},2000)}'
+            _js='function(){document.body.innerHTML=\'<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>\';setTimeout(function(){location.reload()},2000)}'
         )
         
         if column is not None:
-- 
cgit v1.2.3


From 0aa354bd5e811e2b41b17a3052cf5d4c8190d533 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 00:13:47 +0100
Subject: remove styling from python side

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 7b2359c2..cb859ac4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1010,7 +1010,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             fn=request_restart,
             inputs=[],
             outputs=[],
-            _js='function(){document.body.innerHTML=\'<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>\';setTimeout(function(){location.reload()},2000)}'
+            _js='function(){restart_reload()}'
         )
         
         if column is not None:
-- 
cgit v1.2.3


From cf33268d686986a24f2e04eb615f01ed53bfe308 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 01:18:42 +0100
Subject: add script body only refresh

---
 modules/scripts.py | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 3c14b9e3..788397f5 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -162,10 +162,33 @@ class ScriptRunner:
 
         return processed
 
+    def reload_sources(self):
+      for si,script in list(enumerate(self.scripts)):
+        with open(script.filename, "r", encoding="utf8") as file:
+            args_from = script.args_from
+            args_to   = script.args_to 
+            filename  = script.filename
+            text = file.read()
+
+            from types import ModuleType
+            compiled = compile(text, filename, 'exec')
+            module = ModuleType(script.filename)
+            exec(compiled, module.__dict__)
+
+            for key, script_class in module.__dict__.items():
+                if type(script_class) == type and issubclass(script_class, Script):
+                    self.scripts[si] = script_class()
+                    self.scripts[si].filename = filename
+                    self.scripts[si].args_from = args_from
+                    self.scripts[si].args_to = args_to
 
 scripts_txt2img = ScriptRunner()
 scripts_img2img = ScriptRunner()
 
+def reload_script_body_only():
+  scripts_txt2img.reload_sources()
+  scripts_img2img.reload_sources()
+
 def reload_scripts(basedir):
   global scripts_txt2img,scripts_img2img
 
-- 
cgit v1.2.3


From 07e40ad7f23472fc1c781fe1cc6c1ee403413918 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 01:19:55 +0100
Subject: add custom script body only refresh option

---
 modules/ui.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index cb859ac4..eb7c0585 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1012,6 +1012,17 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             outputs=[],
             _js='function(){restart_reload()}'
         )
+
+        def reload_scripts():
+          modules.scripts.reload_script_body_only()
+
+        reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='primary')
+        reload_script_bodies.click(
+            fn=reload_scripts,
+            inputs=[],
+            outputs=[],
+            _js='function(){}'
+        )
         
         if column is not None:
             column.__exit__()
-- 
cgit v1.2.3


From 2deea867814272f1f089b60e9ba8d587c16b2fb1 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 01:36:30 +0100
Subject: Put reload buttons in row and add secondary style

---
 modules/ui.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index eb7c0585..963a2c61 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1002,27 +1002,30 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             _js='function(){}'
         )
 
-        def request_restart():
-          settings_interface.gradio_ref.do_restart = True
+        with gr.Row():
+          reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+          restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
 
-        restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-        restart_gradio.click(
-            fn=request_restart,
-            inputs=[],
-            outputs=[],
-            _js='function(){restart_reload()}'
-        )
 
         def reload_scripts():
           modules.scripts.reload_script_body_only()
 
-        reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='primary')
         reload_script_bodies.click(
             fn=reload_scripts,
             inputs=[],
             outputs=[],
             _js='function(){}'
         )
+
+        def request_restart():
+          settings_interface.gradio_ref.do_restart = True
+
+        restart_gradio.click(
+            fn=request_restart,
+            inputs=[],
+            outputs=[],
+            _js='function(){restart_reload()}'
+        )
         
         if column is not None:
             column.__exit__()
-- 
cgit v1.2.3


From 3cf1a96006daffedb8ecd0ae142eca4c4da06105 Mon Sep 17 00:00:00 2001
From: RnDMonkey <ryandrutledge@gmail.com>
Date: Sat, 1 Oct 2022 21:11:03 -0700
Subject: added safety for blank directory naming patterns

---
 modules/images.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index f1aed5d6..e7894b4c 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -311,7 +311,7 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[cfg]", str(p.cfg_scale))
         x = x.replace("[width]", str(p.width))
         x = x.replace("[height]", str(p.height))
-        x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]), replace_spaces=False))
+        x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "No styles", replace_spaces=False))
         x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
     x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
@@ -374,7 +374,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
     if save_to_dirs:
-        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt)
+        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ ')
         path = os.path.join(path, dirname)
 
     os.makedirs(path, exist_ok=True)
-- 
cgit v1.2.3


From 70f526704721a303ae045f6406439dcceee4302e Mon Sep 17 00:00:00 2001
From: RnDMonkey <ryandrutledge@gmail.com>
Date: Sat, 1 Oct 2022 21:18:15 -0700
Subject: use os.path.normpath for better safety checking

---
 modules/images.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index e7894b4c..5ef7eb92 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -374,8 +374,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
     if save_to_dirs:
-        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ ')
-        path = os.path.join(path, dirname)
+        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt)
+        path = os.path.normpath(os.path.join(path, dirname))
 
     os.makedirs(path, exist_ok=True)
 
-- 
cgit v1.2.3


From 32edf1732f27a1fad5133667c22b948adda1b070 Mon Sep 17 00:00:00 2001
From: RnDMonkey <ryandrutledge@gmail.com>
Date: Sat, 1 Oct 2022 21:37:14 -0700
Subject: os.path.normpath wasn't working, reverting to manual strip

---
 modules/images.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 5ef7eb92..4998e92c 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -374,8 +374,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
     if save_to_dirs:
-        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt)
-        path = os.path.normpath(os.path.join(path, dirname))
+        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
+        path = os.path.join(path, dirname)
 
     os.makedirs(path, exist_ok=True)
 
-- 
cgit v1.2.3


From 820f1dc96b1979d7e92170c161db281ee8bd988b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 15:03:39 +0300
Subject: initial support for training textual inversion

---
 modules/devices.py                             |   3 +-
 modules/processing.py                          |  13 +-
 modules/sd_hijack.py                           | 324 ++++---------------------
 modules/sd_hijack_optimizations.py             | 164 +++++++++++++
 modules/sd_models.py                           |   4 +-
 modules/shared.py                              |   3 +-
 modules/textual_inversion/dataset.py           |  76 ++++++
 modules/textual_inversion/textual_inversion.py | 258 ++++++++++++++++++++
 modules/textual_inversion/ui.py                |  32 +++
 modules/ui.py                                  | 139 +++++++++--
 10 files changed, 717 insertions(+), 299 deletions(-)
 create mode 100644 modules/sd_hijack_optimizations.py
 create mode 100644 modules/textual_inversion/dataset.py
 create mode 100644 modules/textual_inversion/textual_inversion.py
 create mode 100644 modules/textual_inversion/ui.py

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 07bb2339..ff82f2f6 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -32,10 +32,9 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-
 device = get_optimal_device()
 device_codeformer = cpu if has_mps else device
-
+dtype = torch.float16
 
 def randn(seed, shape):
     # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
diff --git a/modules/processing.py b/modules/processing.py
index 7eeb5191..8223423a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -56,7 +56,7 @@ class StableDiffusionProcessing:
         self.prompt: str = prompt
         self.prompt_for_display: str = None
         self.negative_prompt: str = (negative_prompt or "")
-        self.styles: str = styles
+        self.styles: list = styles or []
         self.seed: int = seed
         self.subseed: int = subseed
         self.subseed_strength: float = subseed_strength
@@ -271,7 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
-        "Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
+        "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
     }
 
     generation_params.update(p.extra_generation_params)
@@ -295,8 +295,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     fix_seed(p)
 
-    os.makedirs(p.outpath_samples, exist_ok=True)
-    os.makedirs(p.outpath_grids, exist_ok=True)
+    if p.outpath_samples is not None:
+        os.makedirs(p.outpath_samples, exist_ok=True)
+
+    if p.outpath_grids is not None:
+        os.makedirs(p.outpath_grids, exist_ok=True)
 
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
 
@@ -323,7 +326,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
 
     if os.path.exists(cmd_opts.embeddings_dir):
-        model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)
+        model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     infotexts = []
     output_images = []
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index fa7eaeb8..fd57e5c5 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -6,244 +6,41 @@ import torch
 import numpy as np
 from torch import einsum
 
-from modules import prompt_parser
+import modules.textual_inversion.textual_inversion
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared
 from modules.shared import opts, device, cmd_opts
 
-from ldm.util import default
-from einops import rearrange
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 
+attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
+diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
+diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
 
-# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
-def split_cross_attention_forward_v1(self, x, context=None, mask=None):
-    h = self.heads
 
-    q = self.to_q(x)
-    context = default(context, x)
-    k = self.to_k(context)
-    v = self.to_v(context)
-    del context, x
+def apply_optimizations():
+    if cmd_opts.opt_split_attention_v1:
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
+        ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
 
-    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
-    for i in range(0, q.shape[0], 2):
-        end = i + 2
-        s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
-        s1 *= self.scale
+def undo_optimizations():
+    ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward
+    ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
+    ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
-        s2 = s1.softmax(dim=-1)
-        del s1
-
-        r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
-        del s2
-
-    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
-    del r1
-
-    return self.to_out(r2)
-
-
-# taken from https://github.com/Doggettx/stable-diffusion
-def split_cross_attention_forward(self, x, context=None, mask=None):
-    h = self.heads
-
-    q_in = self.to_q(x)
-    context = default(context, x)
-    k_in = self.to_k(context) * self.scale
-    v_in = self.to_v(context)
-    del context, x
-
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
-    del q_in, k_in, v_in
-
-    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-
-    stats = torch.cuda.memory_stats(q.device)
-    mem_active = stats['active_bytes.all.current']
-    mem_reserved = stats['reserved_bytes.all.current']
-    mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
-    mem_free_torch = mem_reserved - mem_active
-    mem_free_total = mem_free_cuda + mem_free_torch
-
-    gb = 1024 ** 3
-    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
-    modifier = 3 if q.element_size() == 2 else 2.5
-    mem_required = tensor_size * modifier
-    steps = 1
-
-    if mem_required > mem_free_total:
-        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
-        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
-        #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
-
-    if steps > 64:
-        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
-        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
-                           f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
-
-    slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-    for i in range(0, q.shape[1], slice_size):
-        end = i + slice_size
-        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
-
-        s2 = s1.softmax(dim=-1, dtype=q.dtype)
-        del s1
-
-        r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
-        del s2
-
-    del q, k, v
-
-    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
-    del r1
-
-    return self.to_out(r2)
-
-def nonlinearity_hijack(x):
-    # swish
-    t = torch.sigmoid(x)
-    x *= t
-    del t
-
-    return x
-
-def cross_attention_attnblock_forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q1 = self.q(h_)
-        k1 = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q1.shape
-
-        q2 = q1.reshape(b, c, h*w)
-        del q1
-
-        q = q2.permute(0, 2, 1)   # b,hw,c
-        del q2
-
-        k = k1.reshape(b, c, h*w) # b,c,hw
-        del k1
-
-        h_ = torch.zeros_like(k, device=q.device)
-
-        stats = torch.cuda.memory_stats(q.device)
-        mem_active = stats['active_bytes.all.current']
-        mem_reserved = stats['reserved_bytes.all.current']
-        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
-        mem_free_torch = mem_reserved - mem_active
-        mem_free_total = mem_free_cuda + mem_free_torch
-
-        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
-        mem_required = tensor_size * 2.5
-        steps = 1
-
-        if mem_required > mem_free_total:
-            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
-
-        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-        for i in range(0, q.shape[1], slice_size):
-            end = i + slice_size
-
-            w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-            w2 = w1 * (int(c)**(-0.5))
-            del w1
-            w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
-            del w2
-
-            # attend to values
-            v1 = v.reshape(b, c, h*w)
-            w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)
-            del w3
-
-            h_[:, :, i:end] = torch.bmm(v1, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-            del v1, w4
-
-        h2 = h_.reshape(b, c, h, w)
-        del h_
-
-        h3 = self.proj_out(h2)
-        del h2
-
-        h3 += x
-
-        return h3
 
 class StableDiffusionModelHijack:
-    ids_lookup = {}
-    word_embeddings = {}
-    word_embeddings_checksums = {}
     fixes = None
     comments = []
-    dir_mtime = None
     layers = None
     circular_enabled = False
     clip = None
 
-    def load_textual_inversion_embeddings(self, dirname, model):
-        mt = os.path.getmtime(dirname)
-        if self.dir_mtime is not None and mt <= self.dir_mtime:
-            return
-
-        self.dir_mtime = mt
-        self.ids_lookup.clear()
-        self.word_embeddings.clear()
-
-        tokenizer = model.cond_stage_model.tokenizer
-
-        def const_hash(a):
-            r = 0
-            for v in a:
-                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
-            return r
-
-        def process_file(path, filename):
-            name = os.path.splitext(filename)[0]
-
-            data = torch.load(path, map_location="cpu")
-
-            # textual inversion embeddings
-            if 'string_to_param' in data:
-                param_dict = data['string_to_param']
-                if hasattr(param_dict, '_parameters'):
-                    param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11
-                assert len(param_dict) == 1, 'embedding file has multiple terms in it'
-                emb = next(iter(param_dict.items()))[1]
-            # diffuser concepts
-            elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
-                assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
-
-                emb = next(iter(data.values()))
-                if len(emb.shape) == 1:
-                    emb = emb.unsqueeze(0)
-
-            self.word_embeddings[name] = emb.detach().to(device)
-            self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1)*100)&0xffff:04x}'
-
-            ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
-
-            first_id = ids[0]
-            if first_id not in self.ids_lookup:
-                self.ids_lookup[first_id] = []
-            self.ids_lookup[first_id].append((ids, name))
-
-        for fn in os.listdir(dirname):
-            try:
-                fullfn = os.path.join(dirname, fn)
-
-                if os.stat(fullfn).st_size == 0:
-                    continue
-
-                process_file(fullfn, fn)
-            except Exception:
-                print(f"Error loading emedding {fn}:", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-                continue
-
-        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
+    embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
 
     def hijack(self, m):
         model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
@@ -253,12 +50,7 @@ class StableDiffusionModelHijack:
 
         self.clip = m.cond_stage_model
 
-        if cmd_opts.opt_split_attention_v1:
-            ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
-        elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-            ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
-            ldm.modules.diffusionmodules.model.nonlinearity = nonlinearity_hijack
-            ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
+        apply_optimizations()
 
         def flatten(el):
             flattened = [flatten(children) for children in el.children()]
@@ -296,7 +88,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
         super().__init__()
         self.wrapped = wrapped
-        self.hijack = hijack
+        self.hijack: StableDiffusionModelHijack = hijack
         self.tokenizer = wrapped.tokenizer
         self.max_length = wrapped.max_length
         self.token_mults = {}
@@ -317,7 +109,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
@@ -339,28 +130,19 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             while i < len(tokens):
                 token = tokens[i]
 
-                possible_matches = self.hijack.ids_lookup.get(token, None)
+                embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
-                if possible_matches is None:
+                if embedding is None:
                     remade_tokens.append(token)
                     multipliers.append(weight)
+                    i += 1
                 else:
-                    found = False
-                    for ids, word in possible_matches:
-                        if tokens[i:i + len(ids)] == ids:
-                            emb_len = int(self.hijack.word_embeddings[word].shape[0])
-                            fixes.append((len(remade_tokens), word))
-                            remade_tokens += [0] * emb_len
-                            multipliers += [weight] * emb_len
-                            i += len(ids) - 1
-                            found = True
-                            used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
-                            break
-
-                    if not found:
-                        remade_tokens.append(token)
-                        multipliers.append(weight)
-                i += 1
+                    emb_len = int(embedding.vec.shape[0])
+                    fixes.append((len(remade_tokens), embedding))
+                    remade_tokens += [0] * emb_len
+                    multipliers += [weight] * emb_len
+                    used_custom_terms.append((embedding.name, embedding.checksum()))
+                    i += emb_len
 
         if len(remade_tokens) > maxlen - 2:
             vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
@@ -431,32 +213,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 while i < len(tokens):
                     token = tokens[i]
 
-                    possible_matches = self.hijack.ids_lookup.get(token, None)
+                    embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
                     mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
                     if mult_change is not None:
                         mult *= mult_change
-                    elif possible_matches is None:
+                        i += 1
+                    elif embedding is None:
                         remade_tokens.append(token)
                         multipliers.append(mult)
+                        i += 1
                     else:
-                        found = False
-                        for ids, word in possible_matches:
-                            if tokens[i:i+len(ids)] == ids:
-                                emb_len = int(self.hijack.word_embeddings[word].shape[0])
-                                fixes.append((len(remade_tokens), word))
-                                remade_tokens += [0] * emb_len
-                                multipliers += [mult] * emb_len
-                                i += len(ids) - 1
-                                found = True
-                                used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
-                                break
-
-                        if not found:
-                            remade_tokens.append(token)
-                            multipliers.append(mult)
-
-                    i += 1
+                        emb_len = int(embedding.vec.shape[0])
+                        fixes.append((len(remade_tokens), embedding))
+                        remade_tokens += [0] * emb_len
+                        multipliers += [mult] * emb_len
+                        used_custom_terms.append((embedding.name, embedding.checksum()))
+                        i += emb_len
 
                 if len(remade_tokens) > maxlen - 2:
                     vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
@@ -464,6 +237,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     overflowing_words = [vocab.get(int(x), "") for x in ovf]
                     overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
                     hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+
                 token_count = len(remade_tokens)
                 remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
                 remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
@@ -484,7 +258,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         else:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
 
-
         self.hijack.fixes = hijack_fixes
         self.hijack.comments = hijack_comments
 
@@ -517,14 +290,19 @@ class EmbeddingsWithFixes(torch.nn.Module):
 
         inputs_embeds = self.wrapped(input_ids)
 
-        if batch_fixes is not None:
-            for fixes, tensor in zip(batch_fixes, inputs_embeds):
-                for offset, word in fixes:
-                    emb = self.embeddings.word_embeddings[word]
-                    emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
-                    tensor[offset+1:offset+1+emb_len] = self.embeddings.word_embeddings[word][0:emb_len]
+        if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
+            return inputs_embeds
+
+        vecs = []
+        for fixes, tensor in zip(batch_fixes, inputs_embeds):
+            for offset, embedding in fixes:
+                emb = embedding.vec
+                emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
+                tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
+
+            vecs.append(tensor)
 
-        return inputs_embeds
+        return torch.stack(vecs)
 
 
 def add_circular_option_to_conv_2d():
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
new file mode 100644
index 00000000..9c079e57
--- /dev/null
+++ b/modules/sd_hijack_optimizations.py
@@ -0,0 +1,164 @@
+import math
+import torch
+from torch import einsum
+
+from ldm.util import default
+from einops import rearrange
+
+
+# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
+def split_cross_attention_forward_v1(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+    k = self.to_k(context)
+    v = self.to_v(context)
+    del context, x
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
+    for i in range(0, q.shape[0], 2):
+        end = i + 2
+        s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
+        s1 *= self.scale
+
+        s2 = s1.softmax(dim=-1)
+        del s1
+
+        r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
+        del s2
+
+    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+    del r1
+
+    return self.to_out(r2)
+
+
+# taken from https://github.com/Doggettx/stable-diffusion
+def split_cross_attention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q_in = self.to_q(x)
+    context = default(context, x)
+    k_in = self.to_k(context) * self.scale
+    v_in = self.to_v(context)
+    del context, x
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    del q_in, k_in, v_in
+
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+
+    stats = torch.cuda.memory_stats(q.device)
+    mem_active = stats['active_bytes.all.current']
+    mem_reserved = stats['reserved_bytes.all.current']
+    mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
+    mem_free_torch = mem_reserved - mem_active
+    mem_free_total = mem_free_cuda + mem_free_torch
+
+    gb = 1024 ** 3
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
+    modifier = 3 if q.element_size() == 2 else 2.5
+    mem_required = tensor_size * modifier
+    steps = 1
+
+    if mem_required > mem_free_total:
+        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
+        #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
+
+    if steps > 64:
+        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+                           f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
+
+    slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+    for i in range(0, q.shape[1], slice_size):
+        end = i + slice_size
+        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
+
+        s2 = s1.softmax(dim=-1, dtype=q.dtype)
+        del s1
+
+        r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+        del s2
+
+    del q, k, v
+
+    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+    del r1
+
+    return self.to_out(r2)
+
+def nonlinearity_hijack(x):
+    # swish
+    t = torch.sigmoid(x)
+    x *= t
+    del t
+
+    return x
+
+def cross_attention_attnblock_forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q1 = self.q(h_)
+        k1 = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q1.shape
+
+        q2 = q1.reshape(b, c, h*w)
+        del q1
+
+        q = q2.permute(0, 2, 1)   # b,hw,c
+        del q2
+
+        k = k1.reshape(b, c, h*w) # b,c,hw
+        del k1
+
+        h_ = torch.zeros_like(k, device=q.device)
+
+        stats = torch.cuda.memory_stats(q.device)
+        mem_active = stats['active_bytes.all.current']
+        mem_reserved = stats['reserved_bytes.all.current']
+        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
+        mem_free_torch = mem_reserved - mem_active
+        mem_free_total = mem_free_cuda + mem_free_torch
+
+        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
+        mem_required = tensor_size * 2.5
+        steps = 1
+
+        if mem_required > mem_free_total:
+            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+
+        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+        for i in range(0, q.shape[1], slice_size):
+            end = i + slice_size
+
+            w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+            w2 = w1 * (int(c)**(-0.5))
+            del w1
+            w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
+            del w2
+
+            # attend to values
+            v1 = v.reshape(b, c, h*w)
+            w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)
+            del w3
+
+            h_[:, :, i:end] = torch.bmm(v1, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+            del v1, w4
+
+        h2 = h_.reshape(b, c, h, w)
+        del h_
+
+        h3 = self.proj_out(h2)
+        del h2
+
+        h3 += x
+
+        return h3
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2539f14c..5b3dbdc7 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -8,7 +8,7 @@ from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader
+from modules import shared, modelloader, devices
 from modules.paths import models_path
 
 model_dir = "Stable-diffusion"
@@ -134,6 +134,8 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
     if not shared.cmd_opts.no_half:
         model.half()
 
+    devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
+
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpint = checkpoint_file
 
diff --git a/modules/shared.py b/modules/shared.py
index ac968b2d..ac0bc480 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -78,6 +78,7 @@ class State:
     current_latent = None
     current_image = None
     current_image_sampling_step = 0
+    textinfo = None
 
     def interrupt(self):
         self.interrupted = True
@@ -88,7 +89,7 @@ class State:
         self.current_image_sampling_step = 0
         
     def get_job_timestamp(self):
-        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
 
 
 state = State()
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
new file mode 100644
index 00000000..7e134a08
--- /dev/null
+++ b/modules/textual_inversion/dataset.py
@@ -0,0 +1,76 @@
+import os
+import numpy as np
+import PIL
+import torch
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+import random
+import tqdm
+
+
+class PersonalizedBase(Dataset):
+    def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
+
+        self.placeholder_token = placeholder_token
+
+        self.size = size
+        self.width = width
+        self.height = height
+        self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+
+        self.dataset = []
+
+        with open(template_file, "r") as file:
+            lines = [x.strip() for x in file.readlines()]
+
+        self.lines = lines
+
+        assert data_root, 'dataset directory not specified'
+
+        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
+        print("Preparing dataset...")
+        for path in tqdm.tqdm(self.image_paths):
+            image = Image.open(path)
+            image = image.convert('RGB')
+            image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
+
+            filename = os.path.basename(path)
+            filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')
+            filename_tokens = [token for token in filename_tokens if token.isalpha()]
+
+            npimage = np.array(image).astype(np.uint8)
+            npimage = (npimage / 127.5 - 1.0).astype(np.float32)
+
+            torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
+            torchdata = torch.moveaxis(torchdata, 2, 0)
+
+            init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
+
+            self.dataset.append((init_latent, filename_tokens))
+
+        self.length = len(self.dataset) * repeats
+
+        self.initial_indexes = np.arange(self.length) % len(self.dataset)
+        self.indexes = None
+        self.shuffle()
+
+    def shuffle(self):
+        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, i):
+        if i % len(self.dataset) == 0:
+            self.shuffle()
+
+        index = self.indexes[i % len(self.indexes)]
+        x, filename_tokens = self.dataset[index]
+
+        text = random.choice(self.lines)
+        text = text.replace("[name]", self.placeholder_token)
+        text = text.replace("[filewords]", ' '.join(filename_tokens))
+
+        return x, text
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
new file mode 100644
index 00000000..c0baaace
--- /dev/null
+++ b/modules/textual_inversion/textual_inversion.py
@@ -0,0 +1,258 @@
+import os
+import sys
+import traceback
+
+import torch
+import tqdm
+import html
+import datetime
+
+from modules import shared, devices, sd_hijack, processing
+import modules.textual_inversion.dataset
+
+
+class Embedding:
+    def __init__(self, vec, name, step=None):
+        self.vec = vec
+        self.name = name
+        self.step = step
+        self.cached_checksum = None
+
+    def save(self, filename):
+        embedding_data = {
+            "string_to_token": {"*": 265},
+            "string_to_param": {"*": self.vec},
+            "name": self.name,
+            "step": self.step,
+        }
+
+        torch.save(embedding_data, filename)
+
+    def checksum(self):
+        if self.cached_checksum is not None:
+            return self.cached_checksum
+
+        def const_hash(a):
+            r = 0
+            for v in a:
+                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
+            return r
+
+        self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
+        return self.cached_checksum
+
+class EmbeddingDatabase:
+    def __init__(self, embeddings_dir):
+        self.ids_lookup = {}
+        self.word_embeddings = {}
+        self.dir_mtime = None
+        self.embeddings_dir = embeddings_dir
+
+    def register_embedding(self, embedding, model):
+
+        self.word_embeddings[embedding.name] = embedding
+
+        ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0]
+
+        first_id = ids[0]
+        if first_id not in self.ids_lookup:
+            self.ids_lookup[first_id] = []
+        self.ids_lookup[first_id].append((ids, embedding))
+
+        return embedding
+
+    def load_textual_inversion_embeddings(self):
+        mt = os.path.getmtime(self.embeddings_dir)
+        if self.dir_mtime is not None and mt <= self.dir_mtime:
+            return
+
+        self.dir_mtime = mt
+        self.ids_lookup.clear()
+        self.word_embeddings.clear()
+
+        def process_file(path, filename):
+            name = os.path.splitext(filename)[0]
+
+            data = torch.load(path, map_location="cpu")
+
+            # textual inversion embeddings
+            if 'string_to_param' in data:
+                param_dict = data['string_to_param']
+                if hasattr(param_dict, '_parameters'):
+                    param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11
+                assert len(param_dict) == 1, 'embedding file has multiple terms in it'
+                emb = next(iter(param_dict.items()))[1]
+            # diffuser concepts
+            elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
+                assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
+
+                emb = next(iter(data.values()))
+                if len(emb.shape) == 1:
+                    emb = emb.unsqueeze(0)
+            else:
+                raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
+
+            vec = emb.detach().to(devices.device, dtype=torch.float32)
+            embedding = Embedding(vec, name)
+            embedding.step = data.get('step', None)
+            self.register_embedding(embedding, shared.sd_model)
+
+        for fn in os.listdir(self.embeddings_dir):
+            try:
+                fullfn = os.path.join(self.embeddings_dir, fn)
+
+                if os.stat(fullfn).st_size == 0:
+                    continue
+
+                process_file(fullfn, fn)
+            except Exception:
+                print(f"Error loading emedding {fn}:", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+                continue
+
+        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
+
+    def find_embedding_at_position(self, tokens, offset):
+        token = tokens[offset]
+        possible_matches = self.ids_lookup.get(token, None)
+
+        if possible_matches is None:
+            return None
+
+        for ids, embedding in possible_matches:
+            if tokens[offset:offset + len(ids)] == ids:
+                return embedding
+
+        return None
+
+
+
+def create_embedding(name, num_vectors_per_token):
+    init_text = '*'
+
+    cond_model = shared.sd_model.cond_stage_model
+    embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
+
+    ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
+    embedded = embedding_layer(ids.to(devices.device)).squeeze(0)
+    vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
+
+    for i in range(num_vectors_per_token):
+        vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
+
+    fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
+    assert not os.path.exists(fn), f"file {fn} already exists"
+
+    embedding = Embedding(vec, name)
+    embedding.step = 0
+    embedding.save(fn)
+
+    return fn
+
+
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
+    assert embedding_name, 'embedding not selected'
+
+    shared.state.textinfo = "Initializing textual inversion training..."
+    shared.state.job_count = steps
+
+    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+
+    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name)
+
+    if save_embedding_every > 0:
+        embedding_dir = os.path.join(log_directory, "embeddings")
+        os.makedirs(embedding_dir, exist_ok=True)
+    else:
+        embedding_dir = None
+
+    if create_image_every > 0:
+        images_dir = os.path.join(log_directory, "images")
+        os.makedirs(images_dir, exist_ok=True)
+    else:
+        images_dir = None
+
+    cond_model = shared.sd_model.cond_stage_model
+
+    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
+    with torch.autocast("cuda"):
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+
+    hijack = sd_hijack.model_hijack
+
+    embedding = hijack.embedding_db.word_embeddings[embedding_name]
+    embedding.vec.requires_grad = True
+
+    optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
+
+    losses = torch.zeros((32,))
+
+    last_saved_file = "<none>"
+    last_saved_image = "<none>"
+
+    ititial_step = embedding.step or 0
+    if ititial_step > steps:
+        return embedding, filename
+
+    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    for i, (x, text) in pbar:
+        embedding.step = i + ititial_step
+
+        if embedding.step > steps:
+            break
+
+        if shared.state.interrupted:
+            break
+
+        with torch.autocast("cuda"):
+            c = cond_model([text])
+            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+
+            losses[embedding.step % losses.shape[0]] = loss.item()
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        pbar.set_description(f"loss: {losses.mean():.7f}")
+
+        if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
+            last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
+            embedding.save(last_saved_file)
+
+        if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
+            last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
+
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                prompt=text,
+                steps=20,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+            )
+
+            processed = processing.process_images(p)
+            image = processed.images[0]
+
+            shared.state.current_image = image
+            image.save(last_saved_image)
+
+            last_saved_image += f", prompt: {text}"
+
+        shared.state.job_no = embedding.step
+
+        shared.state.textinfo = f"""
+<p>
+Loss: {losses.mean():.7f}<br/>
+Step: {embedding.step}<br/>
+Last prompt: {html.escape(text)}<br/>
+Last saved embedding: {html.escape(last_saved_file)}<br/>
+Last saved image: {html.escape(last_saved_image)}<br/>
+</p>
+"""
+
+    embedding.cached_checksum = None
+    embedding.save(filename)
+
+    return embedding, filename
+
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
new file mode 100644
index 00000000..ce3677a9
--- /dev/null
+++ b/modules/textual_inversion/ui.py
@@ -0,0 +1,32 @@
+import html
+
+import gradio as gr
+
+import modules.textual_inversion.textual_inversion as ti
+from modules import sd_hijack, shared
+
+
+def create_embedding(name, nvpt):
+    filename = ti.create_embedding(name, nvpt)
+
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+    return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
+
+
+def train_embedding(*args):
+
+    try:
+        sd_hijack.undo_optimizations()
+
+        embedding, filename = ti.train_embedding(*args)
+
+        res = f"""
+Training {'interrupted' if shared.state.interrupted else 'finished'} after {embedding.step} steps.
+Embedding saved to {html.escape(filename)}
+"""
+        return res, ""
+    except Exception:
+        raise
+    finally:
+        sd_hijack.apply_optimizations()
diff --git a/modules/ui.py b/modules/ui.py
index 15572bb0..57aef6ff 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -21,6 +21,7 @@ import gradio as gr
 import gradio.utils
 import gradio.routes
 
+from modules import sd_hijack
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
@@ -32,6 +33,7 @@ import modules.gfpgan_model
 import modules.codeformer_model
 import modules.styles
 import modules.generation_parameters_copypaste
+import modules.textual_inversion.ui
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -142,8 +144,8 @@ def save_files(js_data, images, index):
     return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
-def wrap_gradio_call(func):
-    def f(*args, **kwargs):
+def wrap_gradio_call(func, extra_outputs=None):
+    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
         run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
         if run_memmon:
             shared.mem_mon.monitor()
@@ -159,7 +161,10 @@ def wrap_gradio_call(func):
             shared.state.job = ""
             shared.state.job_count = 0
 
-            res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+            if extra_outputs_array is None:
+                extra_outputs_array = [None, '']
+
+            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
 
         elapsed = time.perf_counter() - t
 
@@ -179,6 +184,7 @@ def wrap_gradio_call(func):
         res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
 
         shared.state.interrupted = False
+        shared.state.job_count = 0
 
         return tuple(res)
 
@@ -187,7 +193,7 @@ def wrap_gradio_call(func):
 
 def check_progress_call(id_part):
     if shared.state.job_count == 0:
-        return "", gr_show(False), gr_show(False)
+        return "", gr_show(False), gr_show(False), gr_show(False)
 
     progress = 0
 
@@ -219,13 +225,19 @@ def check_progress_call(id_part):
         else:
             preview_visibility = gr_show(True)
 
-    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image
+    if shared.state.textinfo is not None:
+        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
+    else:
+        textinfo_result = gr_show(False)
+
+    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
 
 
 def check_progress_call_initial(id_part):
     shared.state.job_count = -1
     shared.state.current_latent = None
     shared.state.current_image = None
+    shared.state.textinfo = None
 
     return check_progress_call(id_part)
 
@@ -399,13 +411,16 @@ def create_toprow(is_img2img):
     return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste
 
 
-def setup_progressbar(progressbar, preview, id_part):
+def setup_progressbar(progressbar, preview, id_part, textinfo=None):
+    if textinfo is None:
+        textinfo = gr.HTML(visible=False)
+
     check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
     check_progress.click(
         fn=lambda: check_progress_call(id_part),
         show_progress=False,
         inputs=[],
-        outputs=[progressbar, preview, preview],
+        outputs=[progressbar, preview, preview, textinfo],
     )
 
     check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
@@ -413,11 +428,14 @@ def setup_progressbar(progressbar, preview, id_part):
         fn=lambda: check_progress_call_initial(id_part),
         show_progress=False,
         inputs=[],
-        outputs=[progressbar, preview, preview],
+        outputs=[progressbar, preview, preview, textinfo],
     )
 
 
-def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
+def create_ui(wrap_gradio_gpu_call):
+    import modules.img2img
+    import modules.txt2img
+
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
@@ -483,7 +501,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
             txt2img_args = dict(
-                fn=txt2img,
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
                 _js="submit",
                 inputs=[
                     txt2img_prompt,
@@ -675,7 +693,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             )
 
             img2img_args = dict(
-                fn=img2img,
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
                 _js="submit_img2img",
                 inputs=[
                     dummy_component,
@@ -828,7 +846,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
 
         submit.click(
-            fn=run_extras,
+            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
             _js="get_extras_tab_index",
             inputs=[
                 dummy_component,
@@ -878,7 +896,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                     pnginfo_send_to_img2img = gr.Button('Send to img2img')
 
         image.change(
-            fn=wrap_gradio_call(run_pnginfo),
+            fn=wrap_gradio_call(modules.extras.run_pnginfo),
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
@@ -887,7 +905,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
                 gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
-                
+
                 with gr.Row():
                     primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")
                     secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
@@ -896,10 +914,96 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
                 save_as_half = gr.Checkbox(value=False, label="Safe as float16")
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
-            
+
             with gr.Column(variant='panel'):
                 submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
 
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+    with gr.Blocks() as textual_inversion_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column():
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
+
+                    new_embedding_name = gr.Textbox(label="Name")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding = gr.Button(value="Create", variant='primary')
+
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
+                    train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                    learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
+                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+                    steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=1000, precision=0)
+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=1000, precision=0)
+
+                    with gr.Row():
+                        with gr.Column(scale=2):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            with gr.Row():
+                                interrupt_training = gr.Button(value="Interrupt")
+                                train_embedding = gr.Button(value="Train", variant='primary')
+
+            with gr.Column():
+                progressbar = gr.HTML(elem_id="ti_progressbar")
+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+
+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
+                ti_progress = gr.HTML(elem_id="ti_progress", value="")
+                ti_outcome = gr.HTML(elem_id="ti_error", value="")
+                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
+
+        create_embedding.click(
+            fn=modules.textual_inversion.ui.create_embedding,
+            inputs=[
+                new_embedding_name,
+                nvpt,
+            ],
+            outputs=[
+                train_embedding_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        train_embedding.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_embedding_name,
+                learn_rate,
+                dataset_directory,
+                log_directory,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        interrupt_training.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
     def create_setting_component(key):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
@@ -1011,6 +1115,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (textual_inversion_interface, "Textual inversion", "ti"),
         (settings_interface, "Settings", "settings"),
     ]
 
@@ -1044,11 +1149,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         
         def modelmerger(*args):
             try:
-                results = run_modelmerger(*args)
+                results = modules.extras.run_modelmerger(*args)
             except Exception as e:
                 print("Error loading/saving model file:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
-                modules.sd_models.list_models() #To remove the potentially missing models from the list
+                modules.sd_models.list_models()  # to remove the potentially missing models from the list
                 return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
             return results
 
-- 
cgit v1.2.3


From 0114057ad672a581bd0b598870b58b674b1a3624 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 15:49:42 +0300
Subject: fix incorrect use of glob in modelloader for #1410

---
 modules/modelloader.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/modelloader.py b/modules/modelloader.py
index 8c862b42..015aeafa 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -43,7 +43,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
         for place in places:
             if os.path.exists(place):
                 for file in glob.iglob(place + '**/**', recursive=True):
-                    full_path = os.path.join(place, file)
+                    full_path = file
                     if os.path.isdir(full_path):
                         continue
                     if len(ext_filter) != 0:
-- 
cgit v1.2.3


From 0758f6e641b5790ce566a998d43e0ea74a627766 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 17:24:50 +0300
Subject: fix --ckpt option breaking model selection

---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5b3dbdc7..9259d69e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -69,7 +69,7 @@ def list_models():
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
         checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
-        shared.opts.sd_model_checkpoint = title
+        shared.opts.data['sd_model_checkpoint'] = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
     for filename in model_list:
-- 
cgit v1.2.3


From 88ec0cf5571883d84abd09196652b3679e359f2e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 19:40:51 +0300
Subject: fix for incorrect embedding token length calculation (will break
 seeds that use embeddings, you're welcome!) add option to input
 initialization text for embeddings

---
 modules/sd_hijack.py                           |  8 ++++----
 modules/textual_inversion/textual_inversion.py | 13 +++++--------
 modules/textual_inversion/ui.py                |  4 ++--
 modules/ui.py                                  |  2 ++
 4 files changed, 13 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index fd57e5c5..3fa06242 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -130,7 +130,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             while i < len(tokens):
                 token = tokens[i]
 
-                embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
                 if embedding is None:
                     remade_tokens.append(token)
@@ -142,7 +142,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     remade_tokens += [0] * emb_len
                     multipliers += [weight] * emb_len
                     used_custom_terms.append((embedding.name, embedding.checksum()))
-                    i += emb_len
+                    i += embedding_length_in_tokens
 
         if len(remade_tokens) > maxlen - 2:
             vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
@@ -213,7 +213,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 while i < len(tokens):
                     token = tokens[i]
 
-                    embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
                     mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
                     if mult_change is not None:
@@ -229,7 +229,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                         remade_tokens += [0] * emb_len
                         multipliers += [mult] * emb_len
                         used_custom_terms.append((embedding.name, embedding.checksum()))
-                        i += emb_len
+                        i += embedding_length_in_tokens
 
                 if len(remade_tokens) > maxlen - 2:
                     vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index c0baaace..0c50161d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -117,24 +117,21 @@ class EmbeddingDatabase:
         possible_matches = self.ids_lookup.get(token, None)
 
         if possible_matches is None:
-            return None
+            return None, None
 
         for ids, embedding in possible_matches:
             if tokens[offset:offset + len(ids)] == ids:
-                return embedding
+                return embedding, len(ids)
 
-        return None
+        return None, None
 
 
-
-def create_embedding(name, num_vectors_per_token):
-    init_text = '*'
-
+def create_embedding(name, num_vectors_per_token, init_text='*'):
     cond_model = shared.sd_model.cond_stage_model
     embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
 
     ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
-    embedded = embedding_layer(ids.to(devices.device)).squeeze(0)
+    embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
     vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
 
     for i in range(num_vectors_per_token):
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index ce3677a9..66c43ffb 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -6,8 +6,8 @@ import modules.textual_inversion.textual_inversion as ti
 from modules import sd_hijack, shared
 
 
-def create_embedding(name, nvpt):
-    filename = ti.create_embedding(name, nvpt)
+def create_embedding(name, initialization_text, nvpt):
+    filename = ti.create_embedding(name, nvpt, init_text=initialization_text)
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
diff --git a/modules/ui.py b/modules/ui.py
index 3b81a4f7..eca50df0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -954,6 +954,7 @@ def create_ui(wrap_gradio_gpu_call):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
 
                     new_embedding_name = gr.Textbox(label="Name")
+                    initialization_text = gr.Textbox(label="Initialization text", value="*")
                     nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
 
                     with gr.Row():
@@ -997,6 +998,7 @@ def create_ui(wrap_gradio_gpu_call):
             fn=modules.textual_inversion.ui.create_embedding,
             inputs=[
                 new_embedding_name,
+                initialization_text,
                 nvpt,
             ],
             outputs=[
-- 
cgit v1.2.3


From 71fe7fa49f5eb1a2c89932a9d217ed153c12fc8b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 19:56:37 +0300
Subject: fix using aaaa-100 embedding when the prompt has aaaa-10000 and you
 have both aaaa-100 and aaaa-10000 in the directory with embeddings.

---
 modules/textual_inversion/textual_inversion.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 0c50161d..9d2241ce 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -57,7 +57,8 @@ class EmbeddingDatabase:
         first_id = ids[0]
         if first_id not in self.ids_lookup:
             self.ids_lookup[first_id] = []
-        self.ids_lookup[first_id].append((ids, embedding))
+
+        self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)
 
         return embedding
 
-- 
cgit v1.2.3


From 4ec4af6e0b7addeee5221a03f32d117ccdc875d9 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 20:15:25 +0300
Subject: add checkpoint info to saved embeddings

---
 modules/textual_inversion/textual_inversion.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 9d2241ce..1183aab7 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,7 +7,7 @@ import tqdm
 import html
 import datetime
 
-from modules import shared, devices, sd_hijack, processing
+from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
 
@@ -17,6 +17,8 @@ class Embedding:
         self.name = name
         self.step = step
         self.cached_checksum = None
+        self.sd_checkpoint = None
+        self.sd_checkpoint_name = None
 
     def save(self, filename):
         embedding_data = {
@@ -24,6 +26,8 @@ class Embedding:
             "string_to_param": {"*": self.vec},
             "name": self.name,
             "step": self.step,
+            "sd_checkpoint": self.sd_checkpoint,
+            "sd_checkpoint_name": self.sd_checkpoint_name,
         }
 
         torch.save(embedding_data, filename)
@@ -41,6 +45,7 @@ class Embedding:
         self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
         return self.cached_checksum
 
+
 class EmbeddingDatabase:
     def __init__(self, embeddings_dir):
         self.ids_lookup = {}
@@ -96,6 +101,8 @@ class EmbeddingDatabase:
             vec = emb.detach().to(devices.device, dtype=torch.float32)
             embedding = Embedding(vec, name)
             embedding.step = data.get('step', None)
+            embedding.sd_checkpoint = data.get('hash', None)
+            embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
             self.register_embedding(embedding, shared.sd_model)
 
         for fn in os.listdir(self.embeddings_dir):
@@ -249,6 +256,10 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
 
+    checkpoint = sd_models.select_checkpoint()
+
+    embedding.sd_checkpoint = checkpoint.hash
+    embedding.sd_checkpoint_name = checkpoint.model_name
     embedding.cached_checksum = None
     embedding.save(filename)
 
-- 
cgit v1.2.3


From 3ff0de2c594b786ef948a89efb1814c59bb42117 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 20:23:40 +0300
Subject: added --disable-console-progressbars to disable progressbars in
 console disabled printing prompts to console by default, enabled by
 --enable-console-prompts

---
 modules/img2img.py     | 4 +++-
 modules/sd_samplers.py | 8 ++++++--
 modules/shared.py      | 7 +++++--
 modules/txt2img.py     | 4 +++-
 4 files changed, 17 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index 03e934e9..f4455c90 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -103,7 +103,9 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpaint_full_res_padding=inpaint_full_res_padding,
         inpainting_mask_invert=inpainting_mask_invert,
     )
-    print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
+
+    if shared.cmd_opts.enable_console_prompts:
+        print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
 
     p.extra_generation_params["Mask blur"] = mask_blur
 
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 92522214..9316875a 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -77,7 +77,9 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs):
     state.sampling_steps = len(sequence)
     state.sampling_step = 0
 
-    for x in tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs):
+    seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
+
+    for x in seq:
         if state.interrupted:
             break
 
@@ -207,7 +209,9 @@ def extended_trange(sampler, count, *args, **kwargs):
     state.sampling_steps = count
     state.sampling_step = 0
 
-    for x in tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs):
+    seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
+
+    for x in seq:
         if state.interrupted:
             break
 
diff --git a/modules/shared.py b/modules/shared.py
index 5a591dc9..1bf7a6c1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -58,6 +58,9 @@ parser.add_argument("--opt-channelslast", action='store_true', help="change memo
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
+parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
+parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+
 
 cmd_opts = parser.parse_args()
 device = get_optimal_device()
@@ -320,14 +323,14 @@ class TotalTQDM:
         )
 
     def update(self):
-        if not opts.multiple_tqdm:
+        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
             return
         if self._tqdm is None:
             self.reset()
         self._tqdm.update()
 
     def updateTotal(self, new_total):
-        if not opts.multiple_tqdm:
+        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
             return
         if self._tqdm is None:
             self.reset()
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 5368e4d0..d4406c3c 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -34,7 +34,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         denoising_strength=denoising_strength if enable_hr else None,
     )
 
-    print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
+    if cmd_opts.enable_console_prompts:
+        print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
+
     processed = modules.scripts.scripts_txt2img.run(p, *args)
 
     if processed is None:
-- 
cgit v1.2.3


From 6365a41f5981efa506dfe4e8fa878b43ca2d8d0c Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Sun, 2 Oct 2022 12:58:17 -0500
Subject: Update esrgan_model.py

Use alternate ESRGAN Model download path.
---
 modules/esrgan_model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index ea91abfe..4aed9283 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -73,8 +73,8 @@ def fix_model_layers(crt_model, pretrained_net):
 class UpscalerESRGAN(Upscaler):
     def __init__(self, dirname):
         self.name = "ESRGAN"
-        self.model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
-        self.model_name = "ESRGAN 4x"
+        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
+        self.model_name = "ESRGAN_4x"
         self.scalers = []
         self.user_path = dirname
         self.model_path = os.path.join(models_path, self.name)
-- 
cgit v1.2.3


From a1cde7e6468f80584030525a1b07cbf0f4ee42eb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 21:09:10 +0300
Subject: disabled SD model download after multiple complaints

---
 modules/sd_models.py            | 18 ++++++++----------
 modules/textual_inversion/ui.py |  2 +-
 2 files changed, 9 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 9259d69e..9a6b568f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -13,9 +13,6 @@ from modules.paths import models_path
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
-model_name = "sd-v1-4.ckpt"
-model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1"
-user_dir = None
 
 CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
 checkpoints_list = {}
@@ -30,12 +27,10 @@ except Exception:
     pass
 
 
-def setup_model(dirname):
-    global user_dir
-    user_dir = dirname
+def setup_model():
     if not os.path.exists(model_path):
         os.makedirs(model_path)
-    checkpoints_list.clear()
+
     list_models()
 
 
@@ -45,7 +40,7 @@ def checkpoint_tiles():
 
 def list_models():
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=user_dir, ext_filter=[".ckpt"], download_name=model_name)
+    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])
 
     def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
@@ -106,8 +101,11 @@ def select_checkpoint():
 
     if len(checkpoints_list) == 0:
         print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
-        print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
-        print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
+        if shared.cmd_opts.ckpt is not None:
+            print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
+        print(f" - directory {model_path}", file=sys.stderr)
+        if shared.cmd_opts.ckpt_dir is not None:
+            print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
         print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
         exit(1)
 
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 66c43ffb..633037d8 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -22,7 +22,7 @@ def train_embedding(*args):
         embedding, filename = ti.train_embedding(*args)
 
         res = f"""
-Training {'interrupted' if shared.state.interrupted else 'finished'} after {embedding.step} steps.
+Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps.
 Embedding saved to {html.escape(filename)}
 """
         return res, ""
-- 
cgit v1.2.3


From 852fd90c0dcda9cb5fbbfdf0c7308ce58034935c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 21:22:20 +0300
Subject: emergency fix for disabling SD model download after multiple
 complaints

---
 modules/sd_models.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 9a6b568f..5f992064 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -45,8 +45,8 @@ def list_models():
     def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
 
-        if user_dir is not None and abspath.startswith(user_dir):
-            name = abspath.replace(user_dir, '')
+        if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
+            name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
         elif abspath.startswith(model_path):
             name = abspath.replace(model_path, '')
         else:
-- 
cgit v1.2.3


From e808096cf641d868f88465515d70d40fc46125d4 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 19:26:06 +0100
Subject: correct indent

---
 modules/scripts.py | 48 +++++++++++++++++++++++++-----------------------
 modules/ui.py      | 25 ++++++++++++-------------
 2 files changed, 37 insertions(+), 36 deletions(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 788397f5..45230f9a 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -163,37 +163,39 @@ class ScriptRunner:
         return processed
 
     def reload_sources(self):
-      for si,script in list(enumerate(self.scripts)):
-        with open(script.filename, "r", encoding="utf8") as file:
-            args_from = script.args_from
-            args_to   = script.args_to 
-            filename  = script.filename
-            text = file.read()
+        for si, script in list(enumerate(self.scripts)):
+            with open(script.filename, "r", encoding="utf8") as file:
+                args_from = script.args_from
+                args_to = script.args_to
+                filename = script.filename
+                text = file.read()
 
-            from types import ModuleType
-            compiled = compile(text, filename, 'exec')
-            module = ModuleType(script.filename)
-            exec(compiled, module.__dict__)
+                from types import ModuleType
 
-            for key, script_class in module.__dict__.items():
-                if type(script_class) == type and issubclass(script_class, Script):
-                    self.scripts[si] = script_class()
-                    self.scripts[si].filename = filename
-                    self.scripts[si].args_from = args_from
-                    self.scripts[si].args_to = args_to
+                compiled = compile(text, filename, 'exec')
+                module = ModuleType(script.filename)
+                exec(compiled, module.__dict__)
+
+                for key, script_class in module.__dict__.items():
+                    if type(script_class) == type and issubclass(script_class, Script):
+                        self.scripts[si] = script_class()
+                        self.scripts[si].filename = filename
+                        self.scripts[si].args_from = args_from
+                        self.scripts[si].args_to = args_to
 
 scripts_txt2img = ScriptRunner()
 scripts_img2img = ScriptRunner()
 
 def reload_script_body_only():
-  scripts_txt2img.reload_sources()
-  scripts_img2img.reload_sources()
+    scripts_txt2img.reload_sources()
+    scripts_img2img.reload_sources()
+
 
 def reload_scripts(basedir):
-  global scripts_txt2img,scripts_img2img
+    global scripts_txt2img, scripts_img2img
 
-  scripts_data.clear()
-  load_scripts(basedir)
+    scripts_data.clear()
+    load_scripts(basedir)
 
-  scripts_txt2img = ScriptRunner()
-  scripts_img2img = ScriptRunner()
+    scripts_txt2img = ScriptRunner()
+    scripts_img2img = ScriptRunner()
diff --git a/modules/ui.py b/modules/ui.py
index 963a2c61..6b30f84b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1003,12 +1003,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         )
 
         with gr.Row():
-          reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-          restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
 
 
         def reload_scripts():
-          modules.scripts.reload_script_body_only()
+            modules.scripts.reload_script_body_only()
 
         reload_script_bodies.click(
             fn=reload_scripts,
@@ -1018,7 +1018,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         )
 
         def request_restart():
-          settings_interface.gradio_ref.do_restart = True
+            settings_interface.gradio_ref.do_restart = True
 
         restart_gradio.click(
             fn=request_restart,
@@ -1234,12 +1234,11 @@ for filename in sorted(os.listdir(jsdir)):
 
 
 if 'gradio_routes_templates_response' not in globals():
-  def template_response(*args, **kwargs):
-      res = gradio_routes_templates_response(*args, **kwargs)
-      res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
-      res.init_headers()
-      return res
-
-  gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
-  gradio.routes.templates.TemplateResponse = template_response
-
+    def template_response(*args, **kwargs):
+        res = gradio_routes_templates_response(*args, **kwargs)
+        res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
+        res.init_headers()
+        return res
+
+    gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
+    gradio.routes.templates.TemplateResponse = template_response
-- 
cgit v1.2.3


From 91f327f22bb2feb780c424c74723cc0629dc34a1 Mon Sep 17 00:00:00 2001
From: Lopyter <git@lopyter.dev>
Date: Sun, 2 Oct 2022 18:15:31 +0200
Subject: make save to dirs optional for imgs saved from ui

---
 modules/shared.py | 1 +
 modules/ui.py     | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 1bf7a6c1..785e7af6 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -173,6 +173,7 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
     "grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"),
     "directories_filename_pattern": OptionInfo("", "Directory name pattern"),
     "directories_max_prompt_words": OptionInfo(8, "Max prompt words", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
+    "use_save_to_dirs_for_ui": OptionInfo(False, "Use \"Save images to a subdirectory\" option for images saved from UI"),
 }))
 
 options_templates.update(options_section(('upscaling', "Upscaling"), {
diff --git a/modules/ui.py b/modules/ui.py
index 78a15d83..8912deff 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -113,7 +113,7 @@ def save_files(js_data, images, index):
 
     p = MyObject(data)
     path = opts.outdir_save
-    save_to_dirs = opts.save_to_dirs
+    save_to_dirs = opts.use_save_to_dirs_for_ui
 
     if save_to_dirs:
         dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt)
-- 
cgit v1.2.3


From c4445225f79f1c57afe52358ff4b205864eb7aac Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 21:50:14 +0300
Subject: change wording for options

---
 modules/shared.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 785e7af6..7246eadc 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -170,10 +170,10 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
 
 options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
     "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
-    "grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"),
+    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
+    "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
     "directories_filename_pattern": OptionInfo("", "Directory name pattern"),
-    "directories_max_prompt_words": OptionInfo(8, "Max prompt words", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
-    "use_save_to_dirs_for_ui": OptionInfo(False, "Use \"Save images to a subdirectory\" option for images saved from UI"),
+    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
 }))
 
 options_templates.update(options_section(('upscaling', "Upscaling"), {
-- 
cgit v1.2.3


From c7543d4940da672d970124ae8f2fec9de7bdc1da Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 22:41:21 +0300
Subject: preprocessing for textual inversion added

---
 modules/interrogate.py                         |  1 +
 modules/textual_inversion/preprocess.py        | 75 ++++++++++++++++++++++++++
 modules/textual_inversion/textual_inversion.py |  1 +
 modules/textual_inversion/ui.py                | 14 +++--
 modules/ui.py                                  | 36 +++++++++++++
 5 files changed, 124 insertions(+), 3 deletions(-)
 create mode 100644 modules/textual_inversion/preprocess.py

(limited to 'modules')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index f62a4745..eed87144 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -21,6 +21,7 @@ Category = namedtuple("Category", ["name", "topn", "items"])
 
 re_topn = re.compile(r"\.top(\d+)\.")
 
+
 class InterrogateModels:
     blip_model = None
     clip_model = None
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
new file mode 100644
index 00000000..209e928f
--- /dev/null
+++ b/modules/textual_inversion/preprocess.py
@@ -0,0 +1,75 @@
+import os
+from PIL import Image, ImageOps
+import tqdm
+
+from modules import shared, images
+
+
+def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
+    size = 512
+    src = os.path.abspath(process_src)
+    dst = os.path.abspath(process_dst)
+
+    assert src != dst, 'same directory specified as source and desitnation'
+
+    os.makedirs(dst, exist_ok=True)
+
+    files = os.listdir(src)
+
+    shared.state.textinfo = "Preprocessing..."
+    shared.state.job_count = len(files)
+
+    if process_caption:
+        shared.interrogator.load()
+
+    def save_pic_with_caption(image, index):
+        if process_caption:
+            caption = "-" + shared.interrogator.generate_caption(image)
+        else:
+            caption = ""
+
+        image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
+        subindex[0] += 1
+
+    def save_pic(image, index):
+        save_pic_with_caption(image, index)
+
+        if process_flip:
+            save_pic_with_caption(ImageOps.mirror(image), index)
+
+    for index, imagefile in enumerate(tqdm.tqdm(files)):
+        subindex = [0]
+        filename = os.path.join(src, imagefile)
+        img = Image.open(filename).convert("RGB")
+
+        if shared.state.interrupted:
+            break
+
+        ratio = img.height / img.width
+        is_tall = ratio > 1.35
+        is_wide = ratio < 1 / 1.35
+
+        if process_split and is_tall:
+            img = img.resize((size, size * img.height // img.width))
+
+            top = img.crop((0, 0, size, size))
+            save_pic(top, index)
+
+            bot = img.crop((0, img.height - size, size, img.height))
+            save_pic(bot, index)
+        elif process_split and is_wide:
+            img = img.resize((size * img.width // img.height, size))
+
+            left = img.crop((0, 0, size, size))
+            save_pic(left, index)
+
+            right = img.crop((img.width - size, 0, img.width, size))
+            save_pic(right, index)
+        else:
+            img = images.resize_image(1, img, size, size)
+            save_pic(img, index)
+
+        shared.state.nextjob()
+
+    if process_caption:
+        shared.interrogator.send_blip_to_ram()
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1183aab7..d4e250d8 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,6 +7,7 @@ import tqdm
 import html
 import datetime
 
+
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 633037d8..f19ac5e0 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -2,24 +2,31 @@ import html
 
 import gradio as gr
 
-import modules.textual_inversion.textual_inversion as ti
+import modules.textual_inversion.textual_inversion
+import modules.textual_inversion.preprocess
 from modules import sd_hijack, shared
 
 
 def create_embedding(name, initialization_text, nvpt):
-    filename = ti.create_embedding(name, nvpt, init_text=initialization_text)
+    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
 
 
+def preprocess(*args):
+    modules.textual_inversion.preprocess.preprocess(*args)
+
+    return "Preprocessing finished.", ""
+
+
 def train_embedding(*args):
 
     try:
         sd_hijack.undo_optimizations()
 
-        embedding, filename = ti.train_embedding(*args)
+        embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
 
         res = f"""
 Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps.
@@ -30,3 +37,4 @@ Embedding saved to {html.escape(filename)}
         raise
     finally:
         sd_hijack.apply_optimizations()
+
diff --git a/modules/ui.py b/modules/ui.py
index 8912deff..e7bde53b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -961,6 +961,8 @@ def create_ui(wrap_gradio_gpu_call):
         with gr.Row().style(equal_height=False):
             with gr.Column():
                 with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
 
                     new_embedding_name = gr.Textbox(label="Name")
@@ -974,6 +976,24 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create", variant='primary')
 
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
+
+                    process_src = gr.Textbox(label='Source directory')
+                    process_dst = gr.Textbox(label='Destination directory')
+
+                    with gr.Row():
+                        process_flip = gr.Checkbox(label='Flip')
+                        process_split = gr.Checkbox(label='Split into two')
+                        process_caption = gr.Checkbox(label='Add caption')
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            run_preprocess = gr.Button(value="Preprocess", variant='primary')
+
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
@@ -1018,6 +1038,22 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
+        run_preprocess.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                process_src,
+                process_dst,
+                process_flip,
+                process_split,
+                process_caption,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ],
+        )
+
         train_embedding.click(
             fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
-- 
cgit v1.2.3


From 6785331e22d6a488fbf5905fab56d7fec867e038 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 22:59:01 +0300
Subject: keep textual inversion dataset latents in CPU memory to save a bit of
 VRAM

---
 modules/textual_inversion/dataset.py           | 2 ++
 modules/textual_inversion/textual_inversion.py | 3 +++
 modules/ui.py                                  | 4 ++--
 3 files changed, 7 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 7e134a08..e8394ff6 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -8,6 +8,7 @@ from torchvision import transforms
 
 import random
 import tqdm
+from modules import devices
 
 
 class PersonalizedBase(Dataset):
@@ -47,6 +48,7 @@ class PersonalizedBase(Dataset):
             torchdata = torch.moveaxis(torchdata, 2, 0)
 
             init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
+            init_latent = init_latent.to(devices.cpu)
 
             self.dataset.append((init_latent, filename_tokens))
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index d4e250d8..8686f534 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -212,7 +212,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
         with torch.autocast("cuda"):
             c = cond_model([text])
+
+            x = x.to(devices.device)
             loss = shared.sd_model(x.unsqueeze(0), c)[0]
+            del x
 
             losses[embedding.step % losses.shape[0]] = loss.item()
 
diff --git a/modules/ui.py b/modules/ui.py
index e7bde53b..d9d02ece 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1002,8 +1002,8 @@ def create_ui(wrap_gradio_gpu_call):
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
-                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=1000, precision=0)
-                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=1000, precision=0)
+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
 
                     with gr.Row():
                         with gr.Column(scale=2):
-- 
cgit v1.2.3


From 166283653cfe7521a422c91e8fb801f3ecb4adc8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 23:18:13 +0300
Subject: remove LDSR warning

---
 modules/paths.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/paths.py b/modules/paths.py
index ceb80417..606f7d66 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -20,7 +20,6 @@ path_dirs = [
     (os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []),
     (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
     (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
-    (os.path.join(sd_path, '../latent-diffusion'), 'LDSR.py', 'LDSR', []),
     (os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
 ]
 
-- 
cgit v1.2.3


From 138662734c25dab4e73e632b7eaff9ad9c0ce2b4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 3 Oct 2022 07:57:59 +0300
Subject: use dropdown instead of radio for img2img upscaler selection

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 7246eadc..2a599e9c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -183,7 +183,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
-    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
-- 
cgit v1.2.3


From e615d4f9d101e2712c7c2d0e3e8feb19cb430c74 Mon Sep 17 00:00:00 2001
From: Hanusz Leszek <leszek.hanusz@gmail.com>
Date: Sun, 2 Oct 2022 21:08:23 +0200
Subject: Convert folder icon surrogate pair to valid utf8

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index d9d02ece..16432151 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -69,7 +69,7 @@ random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
 reuse_symbol = '\u267b\ufe0f'  # ♻️
 art_symbol = '\U0001f3a8'  # 🎨
 paste_symbol = '\u2199\ufe0f'  # ↙
-folder_symbol = '\uD83D\uDCC2'
+folder_symbol = '\U0001f4c2'  # 📂
 
 def plaintext_to_html(text):
     text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-- 
cgit v1.2.3


From 34c638142eaa57f89b86545ba3c72085036398bb Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 30 Sep 2022 22:38:14 +0100
Subject: Fixed when eta = 0

Unexpected behavior when using eta = 0 in something like XY, but your default eta was set to something not 0.
---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 9316875a..dbf570d2 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -127,7 +127,7 @@ class VanillaStableDiffusionSampler:
         return res
 
     def initialize(self, p):
-        self.eta = p.eta or opts.eta_ddim
+        self.eta = p.eta if p.eta is not None else opts.eta_ddim
 
         for fieldname in ['p_sample_ddim', 'p_sample_plms']:
             if hasattr(self.sampler, fieldname):
-- 
cgit v1.2.3


From 36ea4ac0f5844e5c8dec124edbdb714ccdd6013c Mon Sep 17 00:00:00 2001
From: RnDMonkey <ryandrutledge@gmail.com>
Date: Sun, 2 Oct 2022 22:21:16 -0700
Subject: moved no-style return outside join function

---
 modules/images.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index bba55158..1a046aca 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -315,7 +315,7 @@ def apply_filename_pattern(x, p, seed, prompt):
         #currently disabled if using the save button, will work otherwise 
         # if enabled it will cause a bug because styles is not included in the save_files data dictionary
         if hasattr(p, "styles"):
-            x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"] or "None"), replace_spaces=False))
+            x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
 
         x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
-- 
cgit v1.2.3


From 6491b09c24ea77f1f69990ea80a216f9ce319589 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 3 Oct 2022 08:53:52 +0300
Subject: use existing function for gfpgan

---
 modules/gfpgan_model.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index bb30d733..dd3fbcab 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -97,11 +97,7 @@ def setup_model(dirname):
                 return "GFPGAN"
 
             def restore(self, np_image):
-                np_image_bgr = np_image[:, :, ::-1]
-                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgann().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
-                np_image = gfpgan_output_bgr[:, :, ::-1]
-
-                return np_image
+                return gfpgan_fix_faces(np_image)
 
         shared.face_restorers.append(FaceRestorerGFPGAN())
     except Exception:
-- 
cgit v1.2.3


From 43a74fa595003321200a40bd2431e56c245e75ed Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 3 Oct 2022 11:48:19 +0300
Subject: batch processing for img2img with an empty output directory, by
 request

---
 modules/img2img.py | 7 +++++--
 modules/ui.py      | 2 +-
 2 files changed, 6 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index f4455c90..2ff8e261 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -23,8 +23,10 @@ def process_batch(p, input_dir, output_dir, args):
 
     print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
 
+    save_normally = output_dir == ''
+
     p.do_not_save_grid = True
-    p.do_not_save_samples = True
+    p.do_not_save_samples = not save_normally
 
     state.job_count = len(images) * p.n_iter
 
@@ -48,7 +50,8 @@ def process_batch(p, input_dir, output_dir, args):
                 left, right = os.path.splitext(filename)
                 filename = f"{left}-{n}{right}"
 
-            processed_image.save(os.path.join(output_dir, filename))
+            if not save_normally:
+                processed_image.save(os.path.join(output_dir, filename))
 
 
 def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
diff --git a/modules/ui.py b/modules/ui.py
index 16432151..55f7aa95 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -658,7 +658,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.TabItem('Batch img2img', id='batch'):
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.{hidden}</p>")
+                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
                         img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
                         img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
 
-- 
cgit v1.2.3


From 2865ef4b9ab16d56326cc805541bebcf01d099bc Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 3 Oct 2022 13:10:03 +0300
Subject: fix broken date in TI

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 8686f534..cd9f3498 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -164,7 +164,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
 
-    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name)
+    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
 
     if save_embedding_every > 0:
         embedding_dir = os.path.join(log_directory, "embeddings")
-- 
cgit v1.2.3


From 5ef0baf5eaec7f21a1666af424405cbee19f3764 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 08:52:11 +0300
Subject: add support for gelbooru tags in filenames for textual inversion

---
 modules/textual_inversion/dataset.py    | 7 +++++--
 modules/textual_inversion/preprocess.py | 4 +++-
 2 files changed, 8 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index e8394ff6..7c44ea5b 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -9,6 +9,9 @@ from torchvision import transforms
 import random
 import tqdm
 from modules import devices
+import re
+
+re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
@@ -38,8 +41,8 @@ class PersonalizedBase(Dataset):
             image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
 
             filename = os.path.basename(path)
-            filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')
-            filename_tokens = [token for token in filename_tokens if token.isalpha()]
+            filename_tokens = os.path.splitext(filename)[0]
+            filename_tokens = re_tag.findall(filename_tokens)
 
             npimage = np.array(image).astype(np.uint8)
             npimage = (npimage / 127.5 - 1.0).astype(np.float32)
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 209e928f..f545a993 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -26,7 +26,9 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
         if process_caption:
             caption = "-" + shared.interrogator.generate_caption(image)
         else:
-            caption = ""
+            caption = filename
+            caption = os.path.splitext(caption)[0]
+            caption = os.path.basename(caption)
 
         image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
         subindex[0] += 1
-- 
cgit v1.2.3


From eeab7aedf532680a6ae9058ee272450bb07e41eb Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 4 Oct 2022 04:24:35 -0400
Subject: Add --use-cpu command line option

Remove MPS detection to use CPU for GFPGAN / CodeFormer and add a --use-cpu command line option.
---
 modules/devices.py      | 5 ++---
 modules/esrgan_model.py | 9 ++++-----
 modules/scunet_model.py | 8 ++++----
 modules/shared.py       | 9 +++++++--
 4 files changed, 17 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 5d9c7a07..b5a0cd29 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,8 +1,8 @@
 import torch
 
-# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
 from modules import errors
 
+# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
 has_mps = getattr(torch, 'has_mps', False)
 
 cpu = torch.device("cpu")
@@ -32,8 +32,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = get_optimal_device()
-device_gfpgan = device_codeformer = cpu if device.type == 'mps' else device
+device = device_gfpgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
 dtype = torch.float16
 
 def randn(seed, shape):
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 4aed9283..d17e730f 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -6,8 +6,7 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgam_model_arch as arch
-from modules import shared, modelloader, images
-from modules.devices import has_mps
+from modules import shared, modelloader, images, devices
 from modules.paths import models_path
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
@@ -97,7 +96,7 @@ class UpscalerESRGAN(Upscaler):
         model = self.load_model(selected_model)
         if model is None:
             return img
-        model.to(shared.device)
+        model.to(devices.device_esrgan)
         img = esrgan_upscale(model, img)
         return img
 
@@ -112,7 +111,7 @@ class UpscalerESRGAN(Upscaler):
             print("Unable to load %s from %s" % (self.model_path, filename))
             return None
 
-        pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
+        pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None)
         crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
 
         pretrained_net = fix_model_layers(crt_model, pretrained_net)
@@ -127,7 +126,7 @@ def upscale_without_tiling(model, img):
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(shared.device)
+    img = img.unsqueeze(0).to(devices.device_esrgan)
     with torch.no_grad():
         output = model(img)
     output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index 7987ac14..fb64b740 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -8,7 +8,7 @@ import torch
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
-from modules import shared, modelloader
+from modules import devices, modelloader
 from modules.paths import models_path
 from modules.scunet_model_arch import SCUNet as net
 
@@ -51,12 +51,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         if model is None:
             return img
 
-        device = shared.device
+        device = devices.device_scunet
         img = np.array(img)
         img = img[:, :, ::-1]
         img = np.moveaxis(img, 2, 0) / 255
         img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(shared.device)
+        img = img.unsqueeze(0).to(device)
 
         img = img.to(device)
         with torch.no_grad():
@@ -69,7 +69,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         return PIL.Image.fromarray(output, 'RGB')
 
     def load_model(self, path: str):
-        device = shared.device
+        device = devices.device_scunet
         if "http" in path:
             filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
                                           progress=True)
diff --git a/modules/shared.py b/modules/shared.py
index 2a599e9c..7899ab8d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -12,7 +12,7 @@ import modules.interrogate
 import modules.memmon
 import modules.sd_models
 import modules.styles
-from modules.devices import get_optimal_device
+import modules.devices as devices
 from modules.paths import script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -46,6 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
+parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[])
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -63,7 +64,11 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print
 
 
 cmd_opts = parser.parse_args()
-device = get_optimal_device()
+
+devices.device, devices.device_gfpgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
+(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
+
+device = devices.device
 
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
 parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
-- 
cgit v1.2.3


From 27ddc24fdee1fbe709054a43235ab7f9c51b3e9f Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 4 Oct 2022 05:18:17 -0400
Subject: Add BSRGAN to --add-cpu

---
 modules/bsrgan_model.py | 6 +++---
 modules/devices.py      | 2 +-
 modules/shared.py       | 6 +++---
 3 files changed, 7 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
index e62c6657..3bd80791 100644
--- a/modules/bsrgan_model.py
+++ b/modules/bsrgan_model.py
@@ -8,7 +8,7 @@ import torch
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
-from modules import shared, modelloader
+from modules import devices, modelloader
 from modules.bsrgan_model_arch import RRDBNet
 from modules.paths import models_path
 
@@ -44,13 +44,13 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
         model = self.load_model(selected_file)
         if model is None:
             return img
-        model.to(shared.device)
+        model.to(devices.device_bsrgan)
         torch.cuda.empty_cache()
         img = np.array(img)
         img = img[:, :, ::-1]
         img = np.moveaxis(img, 2, 0) / 255
         img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(shared.device)
+        img = img.unsqueeze(0).to(devices.device_bsrgan)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/devices.py b/modules/devices.py
index b5a0cd29..b7899632 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -32,7 +32,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = device_gfpgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
+device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
 dtype = torch.float16
 
 def randn(seed, shape):
diff --git a/modules/shared.py b/modules/shared.py
index 7899ab8d..95b98a06 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -46,7 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[])
+parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[])
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -65,8 +65,8 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print
 
 cmd_opts = parser.parse_args()
 
-devices.device, devices.device_gfpgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
-(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
+devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
+(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
 
 device = devices.device
 
-- 
cgit v1.2.3


From dc9c5a97742e3a34d37da7108642d8adc0dc5858 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 4 Oct 2022 05:22:50 -0400
Subject: Modify --add-cpu description

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 95b98a06..25aff5b0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -46,7 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[])
+parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
-- 
cgit v1.2.3


From 6c6ae28bf5fd1e8bc3e8f64a3430b6f29f338f77 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 12:32:22 +0300
Subject: send all three of GFPGAN's and codeformer's models to CPU memory
 instead of just one for #1283

---
 modules/codeformer_model.py | 12 ++++++++++--
 modules/devices.py          | 10 ++++++++++
 modules/gfpgan_model.py     | 14 ++++++++++++--
 modules/processing.py       | 16 +++++++++-------
 4 files changed, 41 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index a29f3855..e6d9fa4f 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -69,10 +69,14 @@ def setup_model(dirname):
 
                 self.net = net
                 self.face_helper = face_helper
-                self.net.to(devices.device_codeformer)
 
                 return net, face_helper
 
+            def send_model_to(self, device):
+                self.net.to(device)
+                self.face_helper.face_det.to(device)
+                self.face_helper.face_parse.to(device)
+
             def restore(self, np_image, w=None):
                 np_image = np_image[:, :, ::-1]
 
@@ -82,6 +86,8 @@ def setup_model(dirname):
                 if self.net is None or self.face_helper is None:
                     return np_image
 
+                self.send_model_to(devices.device_codeformer)
+
                 self.face_helper.clean_all()
                 self.face_helper.read_image(np_image)
                 self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
@@ -113,8 +119,10 @@ def setup_model(dirname):
                 if original_resolution != restored_img.shape[0:2]:
                     restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
 
+                self.face_helper.clean_all()
+
                 if shared.opts.face_restoration_unload:
-                    self.net.to(devices.cpu)
+                    self.send_model_to(devices.cpu)
 
                 return restored_img
 
diff --git a/modules/devices.py b/modules/devices.py
index ff82f2f6..12aab665 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,3 +1,5 @@
+import contextlib
+
 import torch
 
 # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
@@ -57,3 +59,11 @@ def randn_without_seed(shape):
 
     return torch.randn(shape, device=device)
 
+
+def autocast():
+    from modules import shared
+
+    if dtype == torch.float32 or shared.cmd_opts.precision == "full":
+        return contextlib.nullcontext()
+
+    return torch.autocast("cuda")
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index dd3fbcab..5586b554 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -37,22 +37,32 @@ def gfpgann():
         print("Unable to load gfpgan model!")
         return None
     model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
-    model.gfpgan.to(shared.device)
     loaded_gfpgan_model = model
 
     return model
 
 
+def send_model_to(model, device):
+    model.gfpgan.to(device)
+    model.face_helper.face_det.to(device)
+    model.face_helper.face_parse.to(device)
+
+
 def gfpgan_fix_faces(np_image):
     model = gfpgann()
     if model is None:
         return np_image
+
+    send_model_to(model, devices.device)
+
     np_image_bgr = np_image[:, :, ::-1]
     cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
     np_image = gfpgan_output_bgr[:, :, ::-1]
 
+    model.face_helper.clean_all()
+
     if shared.opts.face_restoration_unload:
-        model.gfpgan.to(devices.cpu)
+        send_model_to(model, devices.cpu)
 
     return np_image
 
diff --git a/modules/processing.py b/modules/processing.py
index 0a4b6198..9cbecdd8 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1,4 +1,3 @@
-import contextlib
 import json
 import math
 import os
@@ -330,9 +329,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     infotexts = []
     output_images = []
-    precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
-    ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)
-    with torch.no_grad(), precision_scope("cuda"), ema_scope():
+
+    with torch.no_grad():
         p.init(all_prompts, all_seeds, all_subseeds)
 
         if state.job_count == -1:
@@ -351,8 +349,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
             #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
             #c = p.sd_model.get_learned_conditioning(prompts)
-            uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
-            c = prompt_parser.get_learned_conditioning(prompts, p.steps)
+            with devices.autocast():
+                uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
+                c = prompt_parser.get_learned_conditioning(prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
@@ -361,7 +360,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if p.n_iter > 1:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
-            samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+            with devices.autocast():
+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength).to(devices.dtype)
+
             if state.interrupted:
 
                 # if we are interruped, sample returns just noise
@@ -386,6 +387,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                     devices.torch_gc()
 
                     x_sample = modules.face_restoration.restore_faces(x_sample)
+                    devices.torch_gc()
 
                 image = Image.fromarray(x_sample)
 
-- 
cgit v1.2.3


From 2f1b61d97987ae0a52a7dfc6bc99c68928bdb594 Mon Sep 17 00:00:00 2001
From: dan <guaneec@gmail.com>
Date: Mon, 3 Oct 2022 19:25:36 +0800
Subject: Allow nested structures inside schedules

---
 modules/prompt_parser.py | 119 +++++++++++++++++++++--------------------------
 1 file changed, 53 insertions(+), 66 deletions(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index e811eb9e..99c8ed99 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -1,20 +1,11 @@
 import re
 from collections import namedtuple
 import torch
+from lark import Lark, Transformer, Visitor
+import functools
 
 import modules.shared as shared
 
-re_prompt = re.compile(r'''
-(.*?)
-\[
-    ([^]:]+):
-    (?:([^]:]*):)?
-    ([0-9]*\.?[0-9]+)
-]
-|
-(.+)
-''', re.X)
-
 # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
 # will be represented with prompt_schedule like this (assuming steps=100):
 # [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
@@ -25,61 +16,57 @@ re_prompt = re.compile(r'''
 
 
 def get_learned_conditioning_prompt_schedules(prompts, steps):
-    res = []
-    cache = {}
-
-    for prompt in prompts:
-        prompt_schedule: list[list[str | int]] = [[steps, ""]]
-
-        cached = cache.get(prompt, None)
-        if cached is not None:
-            res.append(cached)
-            continue
-
-        for m in re_prompt.finditer(prompt):
-            plaintext = m.group(1) if m.group(5) is None else m.group(5)
-            concept_from = m.group(2)
-            concept_to = m.group(3)
-            if concept_to is None:
-                concept_to = concept_from
-                concept_from = ""
-            swap_position = float(m.group(4)) if m.group(4) is not None else None
-
-            if swap_position is not None:
-                if swap_position < 1:
-                    swap_position = swap_position * steps
-                swap_position = int(min(swap_position, steps))
-
-            swap_index = None
-            found_exact_index = False
-            for i in range(len(prompt_schedule)):
-                end_step = prompt_schedule[i][0]
-                prompt_schedule[i][1] += plaintext
-
-                if swap_position is not None and swap_index is None:
-                    if swap_position == end_step:
-                        swap_index = i
-                        found_exact_index = True
-
-                    if swap_position < end_step:
-                        swap_index = i
-
-            if swap_index is not None:
-                if not found_exact_index:
-                    prompt_schedule.insert(swap_index, [swap_position, prompt_schedule[swap_index][1]])
-
-                for i in range(len(prompt_schedule)):
-                    end_step = prompt_schedule[i][0]
-                    must_replace = swap_position < end_step
-
-                    prompt_schedule[i][1] += concept_to if must_replace else concept_from
-
-        res.append(prompt_schedule)
-        cache[prompt] = prompt_schedule
-        #for t in prompt_schedule:
-        #    print(t)
-
-    return res
+    grammar = r"""
+    start: prompt
+    prompt: (emphasized | scheduled | weighted | plain)*
+    !emphasized: "(" prompt ")"
+            | "(" prompt ":" prompt ")"
+            | "[" prompt "]"
+    scheduled: "[" (prompt ":")? prompt ":" NUMBER "]"
+    !weighted: "{" weighted_item ("|" weighted_item)* "}"
+    !weighted_item: prompt (":" prompt)?
+    plain: /([^\\\[\](){}:|]|\\.)+/
+    %import common.SIGNED_NUMBER -> NUMBER
+    """
+    parser = Lark(grammar, parser='lalr')
+    def collect_steps(steps, tree):
+        l = [steps]
+        class CollectSteps(Visitor):
+            def scheduled(self, tree):
+                tree.children[-1] = float(tree.children[-1])
+                if tree.children[-1] < 1:
+                    tree.children[-1] *= steps
+                tree.children[-1] = min(steps, int(tree.children[-1]))
+                l.append(tree.children[-1])
+        CollectSteps().visit(tree)
+        return sorted(set(l))
+    def at_step(step, tree):
+        class AtStep(Transformer):
+            def scheduled(self, args):
+                if len(args) == 2:
+                    before, after, when = (), *args
+                else:
+                    before, after, when = args
+                yield before if step <= when else after
+            def start(self, args):
+                def flatten(x):
+                    if type(x) == str:
+                        yield x
+                    else:
+                        for gen in x:
+                            yield from flatten(gen)
+                return ''.join(flatten(args[0]))
+            def plain(self, args):
+                yield args[0].value
+            def __default__(self, data, children, meta):
+                for child in children:
+                    yield from child
+        return AtStep().transform(tree)
+    @functools.cache
+    def get_schedule(prompt):
+        tree = parser.parse(prompt)
+        return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
+    return [get_schedule(prompt) for prompt in prompts]
 
 
 ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
-- 
cgit v1.2.3


From 61652461242951966e5b4cee83ce359cefa91c17 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 14:23:22 +0300
Subject: support interrupting after the previous change

---
 modules/processing.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 9cbecdd8..6f5599c7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -361,7 +361,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             with devices.autocast():
-                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength).to(devices.dtype)
+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
 
             if state.interrupted:
 
@@ -369,6 +369,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 # use the image collected previously in sampler loop
                 samples_ddim = shared.state.current_latent
 
+            samples_ddim = samples_ddim.to(devices.dtype)
+
             x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
-- 
cgit v1.2.3


From d5bba20a58f43a9f984bb67b4e17f48661f6b818 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 14:35:12 +0300
Subject: ignore errors in parse for purposes of token counting for #1564

---
 modules/ui.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 55f7aa95..20dc8c37 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -386,14 +386,22 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
         outputs=[seed, dummy_component]
     )
 
+
 def update_token_counter(text, steps):
-    prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
+    try:
+        prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
+    except Exception:
+        # a parsing error can happen here during typing, and we don't want to bother the user with
+        # messages related to it in console
+        prompt_schedules = [[[steps, text]]]
+
     flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
-    prompts = [prompt_text for step,prompt_text in flat_prompts]
+    prompts = [prompt_text for step, prompt_text in flat_prompts]
     tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
     style_class = ' class="red"' if (token_count > max_length) else ""
     return f"<span {style_class}>{token_count}/{max_length}</span>"
 
+
 def create_toprow(is_img2img):
     id_part = "img2img" if is_img2img else "txt2img"
 
-- 
cgit v1.2.3


From accd00d6b8258c12b5168918a4c546b02357924a Mon Sep 17 00:00:00 2001
From: Justin Riddiough <jriddiough@gmail.com>
Date: Tue, 4 Oct 2022 01:14:28 -0500
Subject: Explain how to use second progress bar in pycharm

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 25aff5b0..11bdf01a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -200,7 +200,7 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
 options_templates.update(options_section(('system', "System"), {
     "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
     "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
-    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
+    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. In PyCharm select 'emulate terminal in console output'."),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-- 
cgit v1.2.3


From ea6b0d98a64290a0305e27126ea59ce1da7959a2 Mon Sep 17 00:00:00 2001
From: Justin Riddiough <jriddiough@gmail.com>
Date: Tue, 4 Oct 2022 06:38:45 -0500
Subject: Remove pycharm note, fix typo

---
 modules/shared.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 11bdf01a..a7d13b2d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -200,7 +200,7 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
 options_templates.update(options_section(('system', "System"), {
     "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
     "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
-    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. In PyCharm select 'emulate terminal in console output'."),
+    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
@@ -209,7 +209,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-    "enable_emphasis": OptionInfo(True, "Eemphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+    "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-- 
cgit v1.2.3


From eec1b39bd54711ca31e43022d2d6ac8c6d7281da Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Tue, 4 Oct 2022 20:16:52 +0900
Subject: Apply prompt pattern last

---
 modules/images.py | 39 ++++++++++++++++++++-------------------
 1 file changed, 20 insertions(+), 19 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index bba55158..5b56c7e3 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -287,6 +287,25 @@ def apply_filename_pattern(x, p, seed, prompt):
     if seed is not None:
         x = x.replace("[seed]", str(seed))
 
+    if p is not None:
+        x = x.replace("[steps]", str(p.steps))
+        x = x.replace("[cfg]", str(p.cfg_scale))
+        x = x.replace("[width]", str(p.width))
+        x = x.replace("[height]", str(p.height))
+
+        #currently disabled if using the save button, will work otherwise
+        # if enabled it will cause a bug because styles is not included in the save_files data dictionary
+        if hasattr(p, "styles"):
+            x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"] or "None"), replace_spaces=False))
+
+        x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
+
+    x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
+    x = x.replace("[date]", datetime.date.today().isoformat())
+    x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
+    x = x.replace("[job_timestamp]", shared.state.job_timestamp)
+
+    # Apply [prompt] at last. Because it may contain any replacement word.^M
     if prompt is not None:
         x = x.replace("[prompt]", sanitize_filename_part(prompt))
         if "[prompt_no_styles]" in x:
@@ -295,7 +314,7 @@ def apply_filename_pattern(x, p, seed, prompt):
                 if len(style) > 0:
                     style_parts = [y for y in style.split("{prompt}")]
                     for part in style_parts:
-                        prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')                        
+                        prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
             prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
             x = x.replace("[prompt_no_styles]", sanitize_filename_part(prompt_no_style, replace_spaces=False))
 
@@ -306,24 +325,6 @@ def apply_filename_pattern(x, p, seed, prompt):
                 words = ["empty"]
             x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))
 
-    if p is not None:
-        x = x.replace("[steps]", str(p.steps))
-        x = x.replace("[cfg]", str(p.cfg_scale))
-        x = x.replace("[width]", str(p.width))
-        x = x.replace("[height]", str(p.height))
-        
-        #currently disabled if using the save button, will work otherwise 
-        # if enabled it will cause a bug because styles is not included in the save_files data dictionary
-        if hasattr(p, "styles"):
-            x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"] or "None"), replace_spaces=False))
-
-        x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
-
-    x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
-    x = x.replace("[date]", datetime.date.today().isoformat())
-    x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
-    x = x.replace("[job_timestamp]", shared.state.job_timestamp)
-
     if cmd_opts.hide_ui_dir_config:
         x = re.sub(r'^[\\/]+|\.{2,}[\\/]+|[\\/]+\.{2,}', '', x)
 
-- 
cgit v1.2.3


From 52cef36f6ba169a8e606ecdcaed73d47378f0e8e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 16:54:31 +0300
Subject: emergency fix for img2img

---
 modules/processing.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 6f5599c7..e9c45394 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -331,7 +331,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     output_images = []
 
     with torch.no_grad():
-        p.init(all_prompts, all_seeds, all_subseeds)
+        with devices.autocast():
+            p.init(all_prompts, all_seeds, all_subseeds)
 
         if state.job_count == -1:
             state.job_count = p.n_iter
-- 
cgit v1.2.3


From 957e29a8e9cb8ca069799ec69263e188c89ed6a6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 17:23:48 +0300
Subject: option to not show images in web ui

---
 modules/img2img.py | 3 +++
 modules/shared.py  | 1 +
 modules/txt2img.py | 3 +++
 3 files changed, 7 insertions(+)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index 2ff8e261..da212d72 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -129,4 +129,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     if opts.samples_log_stdout:
         print(generation_info_js)
 
+    if opts.do_not_show_images:
+        processed.images = []
+
     return processed.images, generation_info_js, plaintext_to_html(processed.info)
diff --git a/modules/shared.py b/modules/shared.py
index a7d13b2d..ff4e5fa3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -229,6 +229,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
+    "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
diff --git a/modules/txt2img.py b/modules/txt2img.py
index d4406c3c..e985242b 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -48,5 +48,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     if opts.samples_log_stdout:
         print(generation_info_js)
 
+    if opts.do_not_show_images:
+        processed.images = []
+
     return processed.images, generation_info_js, plaintext_to_html(processed.info)
 
-- 
cgit v1.2.3


From e1b128d8e46bddb9c0b2fd3ee0eefd57e0527ee0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 17:36:39 +0300
Subject: do not touch p.seed/p.subseed during processing #1181

---
 modules/processing.py | 26 +++++++++++++++++---------
 1 file changed, 17 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index e9c45394..8180c63d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -248,9 +248,16 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     return x
 
 
+def get_fixed_seed(seed):
+    if seed is None or seed == '' or seed == -1:
+        return int(random.randrange(4294967294))
+
+    return seed
+
+
 def fix_seed(p):
-    p.seed = int(random.randrange(4294967294)) if p.seed is None or p.seed == '' or p.seed == -1 else p.seed
-    p.subseed = int(random.randrange(4294967294)) if p.subseed is None or p.subseed == '' or p.subseed == -1 else p.subseed
+    p.seed = get_fixed_seed(p.seed)
+    p.subseed = get_fixed_seed(p.subseed)
 
 
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
@@ -292,7 +299,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         
     devices.torch_gc()
 
-    fix_seed(p)
+    seed = get_fixed_seed(p.seed)
+    subseed = get_fixed_seed(p.subseed)
 
     if p.outpath_samples is not None:
         os.makedirs(p.outpath_samples, exist_ok=True)
@@ -311,15 +319,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     else:
         all_prompts = p.batch_size * p.n_iter * [p.prompt]
 
-    if type(p.seed) == list:
-        all_seeds = p.seed
+    if type(seed) == list:
+        all_seeds = seed
     else:
-        all_seeds = [int(p.seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
+        all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
 
-    if type(p.subseed) == list:
-        all_subseeds = p.subseed
+    if type(subseed) == list:
+        all_subseeds = subseed
     else:
-        all_subseeds = [int(p.subseed) + x for x in range(len(all_prompts))]
+        all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0):
         return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
-- 
cgit v1.2.3


From 1eb588cbf19924333b88beaa1ac0041904966640 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 18:02:01 +0300
Subject: remove functools.cache as some people are having issues with it

---
 modules/prompt_parser.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 99c8ed99..5d58c4ed 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -29,6 +29,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     %import common.SIGNED_NUMBER -> NUMBER
     """
     parser = Lark(grammar, parser='lalr')
+
     def collect_steps(steps, tree):
         l = [steps]
         class CollectSteps(Visitor):
@@ -40,6 +41,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
                 l.append(tree.children[-1])
         CollectSteps().visit(tree)
         return sorted(set(l))
+
     def at_step(step, tree):
         class AtStep(Transformer):
             def scheduled(self, args):
@@ -62,11 +64,13 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
                 for child in children:
                     yield from child
         return AtStep().transform(tree)
-    @functools.cache
+    
     def get_schedule(prompt):
         tree = parser.parse(prompt)
         return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
-    return [get_schedule(prompt) for prompt in prompts]
+
+    promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
+    return [promptdict[prompt] for prompt in prompts]
 
 
 ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
-- 
cgit v1.2.3


From 90e911fd546e76f879b38a764473569911a0f845 Mon Sep 17 00:00:00 2001
From: Rae Fu <rraefu@gmail.com>
Date: Tue, 4 Oct 2022 09:49:51 -0600
Subject: prompt_parser: allow spaces in schedules, add test, log/ignore errors

Only build the parser once (at import time) instead of for each step.

doctest is run by simply executing modules/prompt_parser.py
---
 modules/processing.py    |  10 ++--
 modules/prompt_parser.py | 139 ++++++++++++++++++++++++++++++-----------------
 2 files changed, 95 insertions(+), 54 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 8180c63d..bb94033b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -84,7 +84,7 @@ class StableDiffusionProcessing:
         self.s_tmin = opts.s_tmin
         self.s_tmax = float('inf')  # not representable as a standard ui option
         self.s_noise = opts.s_noise
-        
+
         if not seed_enable_extras:
             self.subseed = -1
             self.subseed_strength = 0
@@ -296,7 +296,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         assert(len(p.prompt) > 0)
     else:
         assert p.prompt is not None
-        
+
     devices.torch_gc()
 
     seed = get_fixed_seed(p.seed)
@@ -359,8 +359,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
             #c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
-                uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
-                c = prompt_parser.get_learned_conditioning(prompts, p.steps)
+                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
+                c = prompt_parser.get_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
@@ -527,7 +527,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()
-        
+
         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
 
         return samples
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 5d58c4ed..a3b12421 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -1,10 +1,7 @@
 import re
 from collections import namedtuple
-import torch
-from lark import Lark, Transformer, Visitor
-import functools
 
-import modules.shared as shared
+import lark
 
 # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
 # will be represented with prompt_schedule like this (assuming steps=100):
@@ -14,25 +11,48 @@ import modules.shared as shared
 # [75, 'fantasy landscape with a lake and an oak in background masterful']
 # [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
 
+schedule_parser = lark.Lark(r"""
+!start: (prompt | /[][():]/+)*
+prompt: (emphasized | scheduled | plain | WHITESPACE)*
+!emphasized: "(" prompt ")"
+        | "(" prompt ":" prompt ")"
+        | "[" prompt "]"
+scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
+WHITESPACE: /\s+/
+plain: /([^\\\[\]():]|\\.)+/
+%import common.SIGNED_NUMBER -> NUMBER
+""")
 
 def get_learned_conditioning_prompt_schedules(prompts, steps):
-    grammar = r"""
-    start: prompt
-    prompt: (emphasized | scheduled | weighted | plain)*
-    !emphasized: "(" prompt ")"
-            | "(" prompt ":" prompt ")"
-            | "[" prompt "]"
-    scheduled: "[" (prompt ":")? prompt ":" NUMBER "]"
-    !weighted: "{" weighted_item ("|" weighted_item)* "}"
-    !weighted_item: prompt (":" prompt)?
-    plain: /([^\\\[\](){}:|]|\\.)+/
-    %import common.SIGNED_NUMBER -> NUMBER
     """
-    parser = Lark(grammar, parser='lalr')
+    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
+    >>> g("test")
+    [[10, 'test']]
+    >>> g("a [b:3]")
+    [[3, 'a '], [10, 'a b']]
+    >>> g("a [b: 3]")
+    [[3, 'a '], [10, 'a b']]
+    >>> g("a [[[b]]:2]")
+    [[2, 'a '], [10, 'a [[b]]']]
+    >>> g("[(a:2):3]")
+    [[3, ''], [10, '(a:2)']]
+    >>> g("a [b : c : 1] d")
+    [[1, 'a b  d'], [10, 'a  c  d']]
+    >>> g("a[b:[c:d:2]:1]e")
+    [[1, 'abe'], [2, 'ace'], [10, 'ade']]
+    >>> g("a [unbalanced")
+    [[10, 'a [unbalanced']]
+    >>> g("a [b:.5] c")
+    [[5, 'a  c'], [10, 'a b c']]
+    >>> g("a [{b|d{:.5] c")  # not handling this right now
+    [[5, 'a  c'], [10, 'a {b|d{ c']]
+    >>> g("((a][:b:c [d:3]")
+    [[3, '((a][:b:c '], [10, '((a][:b:c d']]
+    """
 
     def collect_steps(steps, tree):
         l = [steps]
-        class CollectSteps(Visitor):
+        class CollectSteps(lark.Visitor):
             def scheduled(self, tree):
                 tree.children[-1] = float(tree.children[-1])
                 if tree.children[-1] < 1:
@@ -43,13 +63,10 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
         return sorted(set(l))
 
     def at_step(step, tree):
-        class AtStep(Transformer):
+        class AtStep(lark.Transformer):
             def scheduled(self, args):
-                if len(args) == 2:
-                    before, after, when = (), *args
-                else:
-                    before, after, when = args
-                yield before if step <= when else after
+                before, after, _, when = args
+                yield before or () if step <= when else after
             def start(self, args):
                 def flatten(x):
                     if type(x) == str:
@@ -57,16 +74,22 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
                     else:
                         for gen in x:
                             yield from flatten(gen)
-                return ''.join(flatten(args[0]))
+                return ''.join(flatten(args))
             def plain(self, args):
                 yield args[0].value
             def __default__(self, data, children, meta):
                 for child in children:
                     yield from child
         return AtStep().transform(tree)
-    
+
     def get_schedule(prompt):
-        tree = parser.parse(prompt)
+        try:
+            tree = schedule_parser.parse(prompt)
+        except lark.exceptions.LarkError as e:
+            if 0:
+                import traceback
+                traceback.print_exc()
+            return [[steps, prompt]]
         return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
 
     promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
@@ -77,8 +100,7 @@ ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at
 ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])
 
 
-def get_learned_conditioning(prompts, steps):
-
+def get_learned_conditioning(model, prompts, steps):
     res = []
 
     prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
@@ -92,7 +114,7 @@ def get_learned_conditioning(prompts, steps):
             continue
 
         texts = [x[1] for x in prompt_schedule]
-        conds = shared.sd_model.get_learned_conditioning(texts)
+        conds = model.get_learned_conditioning(texts)
 
         cond_schedule = []
         for i, (end_at_step, text) in enumerate(prompt_schedule):
@@ -105,12 +127,13 @@ def get_learned_conditioning(prompts, steps):
 
 
 def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
-    res = torch.zeros(c.shape, device=shared.device, dtype=next(shared.sd_model.parameters()).dtype)
+    param = c.schedules[0][0].cond
+    res = torch.zeros(c.shape, device=param.device, dtype=param.dtype)
     for i, cond_schedule in enumerate(c.schedules):
         target_index = 0
-        for curret_index, (end_at, cond) in enumerate(cond_schedule):
+        for current, (end_at, cond) in enumerate(cond_schedule):
             if current_step <= end_at:
-                target_index = curret_index
+                target_index = current
                 break
         res[i] = cond_schedule[target_index].cond
 
@@ -148,23 +171,26 @@ def parse_prompt_attention(text):
       \\ - literal character '\'
       anything else - just text
 
-    Example:
-
-        'a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).'
-
-    produces:
-
-    [
-        ['a ', 1.0],
-        ['house', 1.5730000000000004],
-        [' ', 1.1],
-        ['on', 1.0],
-        [' a ', 1.1],
-        ['hill', 0.55],
-        [', sun, ', 1.1],
-        ['sky', 1.4641000000000006],
-        ['.', 1.1]
-    ]
+    >>> parse_prompt_attention('normal text')
+    [['normal text', 1.0]]
+    >>> parse_prompt_attention('an (important) word')
+    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+    >>> parse_prompt_attention('(unbalanced')
+    [['unbalanced', 1.1]]
+    >>> parse_prompt_attention('\(literal\]')
+    [['(literal]', 1.0]]
+    >>> parse_prompt_attention('(unnecessary)(parens)')
+    [['unnecessaryparens', 1.1]]
+    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+    [['a ', 1.0],
+     ['house', 1.5730000000000004],
+     [' ', 1.1],
+     ['on', 1.0],
+     [' a ', 1.1],
+     ['hill', 0.55],
+     [', sun, ', 1.1],
+     ['sky', 1.4641000000000006],
+     ['.', 1.1]]
     """
 
     res = []
@@ -206,4 +232,19 @@ def parse_prompt_attention(text):
     if len(res) == 0:
         res = [["", 1.0]]
 
+    # merge runs of identical weights
+    i = 0
+    while i + 1 < len(res):
+        if res[i][1] == res[i + 1][1]:
+            res[i][0] += res[i + 1][0]
+            res.pop(i + 1)
+        else:
+            i += 1
+
     return res
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
+else:
+    import torch  # doctest faster
-- 
cgit v1.2.3


From b32852ef037251eb3d846af76e2965594e1ac7a5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 20:49:54 +0300
Subject: add editor to img2img

---
 modules/shared.py | 1 +
 modules/ui.py     | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index ff4e5fa3..e52c9b1d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -55,6 +55,7 @@ parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide dire
 parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="color-sketch")
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
diff --git a/modules/ui.py b/modules/ui.py
index 20dc8c37..6cd6761b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -644,7 +644,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
                     with gr.TabItem('img2img', id='img2img'):
-                        init_img = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil")
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool)
 
                     with gr.TabItem('Inpaint', id='inpaint'):
                         init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")
-- 
cgit v1.2.3


From ef40e4cd4d383a3405e03f1da3f5b5a1820a8f53 Mon Sep 17 00:00:00 2001
From: xpscyho <xpsychonauticonx@gmail.com>
Date: Tue, 4 Oct 2022 15:12:38 -0400
Subject: Display time taken in mins, secs when relevant Fixes #1656

---
 modules/ui.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 6cd6761b..de6342a4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -196,6 +196,11 @@ def wrap_gradio_call(func, extra_outputs=None):
             res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
 
         elapsed = time.perf_counter() - t
+        elapsed_m = int(elapsed // 60)
+        elapsed_s = elapsed % 60
+        elapsed_text = f"{elapsed_s:.2f}s"
+        if (elapsed_m > 0):
+            elapsed_text = f"{elapsed_m}m "+elapsed_text
 
         if run_memmon:
             mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
@@ -210,7 +215,7 @@ def wrap_gradio_call(func, extra_outputs=None):
             vram_html = ''
 
         # last item is always HTML
-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
+        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
 
         shared.state.interrupted = False
         shared.state.job_count = 0
-- 
cgit v1.2.3


From 82380d9ac18614c87bebba1b4cfd4b147cc76a18 Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Tue, 4 Oct 2022 22:28:50 -0300
Subject: Removing parts no longer needed to fix vram

---
 modules/devices.py    |  3 +--
 modules/processing.py | 21 ++++++++-------------
 2 files changed, 9 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 6db4e57c..0158b11f 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,7 +1,6 @@
 import contextlib
 
 import torch
-import gc
 
 from modules import errors
 
@@ -20,8 +19,8 @@ def get_optimal_device():
 
     return cpu
 
+
 def torch_gc():
-    gc.collect()
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()
diff --git a/modules/processing.py b/modules/processing.py
index e7f9c85e..f666ba81 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -345,8 +345,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         if state.job_count == -1:
             state.job_count = p.n_iter
 
-    for n in range(p.n_iter):
-        with torch.no_grad(), precision_scope("cuda"), ema_scope():
+        for n in range(p.n_iter):
             if state.interrupted:
                 break
 
@@ -395,22 +394,19 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 import modules.safety as safety
                 x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
 
-        for i, x_sample in enumerate(x_samples_ddim):
-            with torch.no_grad(), precision_scope("cuda"), ema_scope():
+            for i, x_sample in enumerate(x_samples_ddim):
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
 
-            if p.restore_faces:
-                with torch.no_grad(), precision_scope("cuda"), ema_scope():
+                if p.restore_faces:
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
                         images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
 
-                    x_sample = modules.face_restoration.restore_faces(x_sample)
                     devices.torch_gc()
 
-                devices.torch_gc()
+                    x_sample = modules.face_restoration.restore_faces(x_sample)
+                    devices.torch_gc()
 
-            with torch.no_grad(), precision_scope("cuda"), ema_scope():
                 image = Image.fromarray(x_sample)
 
                 if p.color_corrections is not None and i < len(p.color_corrections):
@@ -438,13 +434,12 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 infotexts.append(infotext(n, i))
                 output_images.append(image)
 
-        del x_samples_ddim 
+            del x_samples_ddim 
 
-        devices.torch_gc()
+            devices.torch_gc()
 
-        state.nextjob()
+            state.nextjob()
 
-    with torch.no_grad(), precision_scope("cuda"), ema_scope():
         p.color_corrections = None
 
         index_of_first_image = 0
-- 
cgit v1.2.3


From bbdbbd36eda870cf0bd49fdf28476c78919a123e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 5 Oct 2022 04:43:05 +0100
Subject: shared.state.interrupt when restart is requested

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index de6342a4..523ab25b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1210,6 +1210,7 @@ def create_ui(wrap_gradio_gpu_call):
         )
 
         def request_restart():
+            shared.state.interrupt()
             settings_interface.gradio_ref.do_restart = True
 
         restart_gradio.click(
-- 
cgit v1.2.3


From 59a2b9e5afc27d2fda72069ca0635070535d18fe Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Wed, 5 Oct 2022 20:50:10 +0200
Subject: deepdanbooru interrogator

---
 modules/deepbooru.py | 60 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 modules/ui.py        | 24 ++++++++++++++++-----
 2 files changed, 79 insertions(+), 5 deletions(-)
 create mode 100644 modules/deepbooru.py

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
new file mode 100644
index 00000000..958b1c3d
--- /dev/null
+++ b/modules/deepbooru.py
@@ -0,0 +1,60 @@
+import os.path
+from concurrent.futures import ProcessPoolExecutor
+
+import numpy as np
+import deepdanbooru as dd
+import tensorflow as tf
+
+
+def _load_tf_and_return_tags(pil_image, threshold):
+    this_folder = os.path.dirname(__file__)
+    model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28')
+    if not os.path.exists(model_path):
+        return "Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru"
+
+    tags = dd.project.load_tags_from_project(model_path)
+    model = dd.project.load_model_from_project(
+        model_path, compile_model=True
+    )
+
+    width = model.input_shape[2]
+    height = model.input_shape[1]
+    image = np.array(pil_image)
+    image = tf.image.resize(
+        image,
+        size=(height, width),
+        method=tf.image.ResizeMethod.AREA,
+        preserve_aspect_ratio=True,
+    )
+    image = image.numpy()  # EagerTensor to np.array
+    image = dd.image.transform_and_pad_image(image, width, height)
+    image = image / 255.0
+    image_shape = image.shape
+    image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
+
+    y = model.predict(image)[0]
+
+    result_dict = {}
+
+    for i, tag in enumerate(tags):
+        result_dict[tag] = y[i]
+
+
+
+    result_tags_out = []
+    result_tags_print = []
+    for tag in tags:
+        if result_dict[tag] >= threshold:
+            result_tags_out.append(tag)
+            result_tags_print.append(f'{result_dict[tag]} {tag}')
+
+    print('\n'.join(sorted(result_tags_print, reverse=True)))
+
+    return ', '.join(result_tags_out)
+
+
+def get_deepbooru_tags(pil_image, threshold=0.5):
+    with ProcessPoolExecutor() as executor:
+        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold)
+        ret = f.result()  # will rethrow any exceptions
+    return ret
\ No newline at end of file
diff --git a/modules/ui.py b/modules/ui.py
index 20dc8c37..ae98219a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -23,6 +23,7 @@ import gradio.utils
 import gradio.routes
 
 from modules import sd_hijack
+from modules.deepbooru import get_deepbooru_tags
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
@@ -312,6 +313,11 @@ def interrogate(image):
     return gr_show(True) if prompt is None else prompt
 
 
+def interrogate_deepbooru(image):
+    prompt = get_deepbooru_tags(image)
+    return gr_show(True) if prompt is None else prompt
+
+
 def create_seed_inputs():
     with gr.Row():
         with gr.Box():
@@ -439,15 +445,17 @@ def create_toprow(is_img2img):
                     outputs=[],
                 )
 
-            with gr.Row():
+            with gr.Row(scale=1):
                 if is_img2img:
-                    interrogate = gr.Button('Interrogate', elem_id="interrogate")
+                    interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+                    deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
                 else:
                     interrogate = None
+                    deepbooru = None
                 prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
                 save_style = gr.Button('Create style', elem_id="style_create")
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -476,7 +484,7 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.txt2img
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
 
         with gr.Row(elem_id='txt2img_progress_row'):
@@ -628,7 +636,7 @@ def create_ui(wrap_gradio_gpu_call):
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             with gr.Column(scale=1):
@@ -785,6 +793,12 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[img2img_prompt],
             )
 
+            img2img_deepbooru.click(
+                fn=interrogate_deepbooru,
+                inputs=[init_img],
+                outputs=[img2img_prompt],
+            )
+
             save.click(
                 fn=wrap_gradio_call(save_files),
                 _js="(x, y, z) => [x, y, selected_gallery_index()]",
-- 
cgit v1.2.3


From 1506fab29ad54beb9f52236912abc432209c8089 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Wed, 5 Oct 2022 21:15:08 +0200
Subject: removing problematic tag

---
 modules/deepbooru.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 958b1c3d..841cb9c5 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -38,13 +38,12 @@ def _load_tf_and_return_tags(pil_image, threshold):
 
     for i, tag in enumerate(tags):
         result_dict[tag] = y[i]
-
-
-
     result_tags_out = []
     result_tags_print = []
     for tag in tags:
         if result_dict[tag] >= threshold:
+            if tag.startswith("rating:"):
+                continue
             result_tags_out.append(tag)
             result_tags_print.append(f'{result_dict[tag]} {tag}')
 
-- 
cgit v1.2.3


From 17a99baf0c929e5df4dfc4b2a96aa3890a141112 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Wed, 5 Oct 2022 22:05:24 +0200
Subject: better model search

---
 modules/deepbooru.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 841cb9c5..a64fd9cd 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -9,8 +9,15 @@ import tensorflow as tf
 def _load_tf_and_return_tags(pil_image, threshold):
     this_folder = os.path.dirname(__file__)
     model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28')
-    if not os.path.exists(model_path):
-        return "Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru"
+
+    model_good = False
+    for path_candidate in [model_path, os.path.dirname(model_path)]:
+        if os.path.exists(os.path.join(path_candidate, 'project.json')):
+            model_path = path_candidate
+            model_good = True
+    if not model_good:
+        return ("Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/"
+                "deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru")
 
     tags = dd.project.load_tags_from_project(model_path)
     model = dd.project.load_model_from_project(
-- 
cgit v1.2.3


From c26732fbee2a57e621ac22bf70decf7496daa4cd Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 5 Oct 2022 23:16:27 +0300
Subject: added support for AND from
 https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/

---
 modules/processing.py    |   2 +-
 modules/prompt_parser.py | 114 ++++++++++++++++++++++++++++++++++++++++++++---
 modules/sd_samplers.py   |  35 ++++++++++-----
 modules/ui.py            |   6 ++-
 4 files changed, 138 insertions(+), 19 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index bb94033b..d8c6b8d5 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -360,7 +360,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             #c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
-                c = prompt_parser.get_learned_conditioning(shared.sd_model, prompts, p.steps)
+                c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index a3b12421..f7420daf 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -97,10 +97,26 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
 
 
 ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
-ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])
 
 
 def get_learned_conditioning(model, prompts, steps):
+    """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
+    and the sampling step at which this condition is to be replaced by the next one.
+
+    Input:
+    (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)
+
+    Output:
+    [
+        [
+            ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886,  0.0229, -0.0523,  ..., -0.4901, -0.3066,  0.0674], ..., [ 0.3317, -0.5102, -0.4066,  ...,  0.4119, -0.7647, -1.0160]], device='cuda:0'))
+        ],
+        [
+            ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886,  0.0229, -0.0522,  ..., -0.4901, -0.3067,  0.0673], ..., [-0.0192,  0.3867, -0.4644,  ...,  0.1135, -0.3696, -0.4625]], device='cuda:0')),
+            ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886,  0.0229, -0.0522,  ..., -0.4901, -0.3067,  0.0673], ..., [-0.7352, -0.4356, -0.7888,  ...,  0.6994, -0.4312, -1.2593]], device='cuda:0'))
+        ]
+    ]
+    """
     res = []
 
     prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
@@ -123,13 +139,75 @@ def get_learned_conditioning(model, prompts, steps):
         cache[prompt] = cond_schedule
         res.append(cond_schedule)
 
-    return ScheduledPromptBatch((len(prompts),) + res[0][0].cond.shape, res)
+    return res
+
+
+re_AND = re.compile(r"\bAND\b")
+re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?\s*(?:\d+|\d*\.\d+)?))?\s*$")
+
+
+def get_multicond_prompt_list(prompts):
+    res_indexes = []
+
+    prompt_flat_list = []
+    prompt_indexes = {}
+
+    for prompt in prompts:
+        subprompts = re_AND.split(prompt)
+
+        indexes = []
+        for subprompt in subprompts:
+            text, weight = re_weight.search(subprompt).groups()
+
+            weight = float(weight) if weight is not None else 1.0
+
+            index = prompt_indexes.get(text, None)
+            if index is None:
+                index = len(prompt_flat_list)
+                prompt_flat_list.append(text)
+                prompt_indexes[text] = index
+
+            indexes.append((index, weight))
+
+        res_indexes.append(indexes)
+
+    return res_indexes, prompt_flat_list, prompt_indexes
+
+
+class ComposableScheduledPromptConditioning:
+    def __init__(self, schedules, weight=1.0):
+        self.schedules: list[ScheduledPromptConditioning] = schedules
+        self.weight: float = weight
+
+
+class MulticondLearnedConditioning:
+    def __init__(self, shape, batch):
+        self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
+        self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
 
 
-def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
-    param = c.schedules[0][0].cond
-    res = torch.zeros(c.shape, device=param.device, dtype=param.dtype)
-    for i, cond_schedule in enumerate(c.schedules):
+def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
+    """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
+    For each prompt, the list is obtained by splitting the prompt using the AND separator.
+
+    https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
+    """
+
+    res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
+
+    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
+
+    res = []
+    for indexes in res_indexes:
+        res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
+
+    return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
+
+
+def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
+    param = c[0][0].cond
+    res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
+    for i, cond_schedule in enumerate(c):
         target_index = 0
         for current, (end_at, cond) in enumerate(cond_schedule):
             if current_step <= end_at:
@@ -140,6 +218,30 @@ def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
     return res
 
 
+def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
+    param = c.batch[0][0].schedules[0].cond
+
+    tensors = []
+    conds_list = []
+
+    for batch_no, composable_prompts in enumerate(c.batch):
+        conds_for_batch = []
+
+        for cond_index, composable_prompt in enumerate(composable_prompts):
+            target_index = 0
+            for current, (end_at, cond) in enumerate(composable_prompt.schedules):
+                if current_step <= end_at:
+                    target_index = current
+                    break
+
+            conds_for_batch.append((len(tensors), composable_prompt.weight))
+            tensors.append(composable_prompt.schedules[target_index].cond)
+
+        conds_list.append(conds_for_batch)
+
+    return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
+
+
 re_attention = re.compile(r"""
 \\\(|
 \\\)|
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index dbf570d2..d27c547b 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -109,9 +109,12 @@ class VanillaStableDiffusionSampler:
         return 0
 
     def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
-        cond = prompt_parser.reconstruct_cond_batch(cond, self.step)
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
 
+        assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
+        cond = tensor
+
         if self.mask is not None:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)
             x_dec = img_orig * self.mask + self.nmask * x_dec
@@ -183,19 +186,31 @@ class CFGDenoiser(torch.nn.Module):
         self.step = 0
 
     def forward(self, x, sigma, uncond, cond, cond_scale):
-        cond = prompt_parser.reconstruct_cond_batch(cond, self.step)
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
 
+        batch_size = len(conds_list)
+        repeats = [len(conds_list[i]) for i in range(batch_size)]
+
+        x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+        sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+        cond_in = torch.cat([tensor, uncond])
+
         if shared.batch_cond_uncond:
-            x_in = torch.cat([x] * 2)
-            sigma_in = torch.cat([sigma] * 2)
-            cond_in = torch.cat([uncond, cond])
-            uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
-            denoised = uncond + (cond - uncond) * cond_scale
+            x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
         else:
-            uncond = self.inner_model(x, sigma, cond=uncond)
-            cond = self.inner_model(x, sigma, cond=cond)
-            denoised = uncond + (cond - uncond) * cond_scale
+            x_out = torch.zeros_like(x_in)
+            for batch_offset in range(0, x_out.shape[0], batch_size):
+                a = batch_offset
+                b = a + batch_size
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
+
+        denoised_uncond = x_out[-batch_size:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
 
         if self.mask is not None:
             denoised = self.init_latent * self.mask + self.nmask * denoised
diff --git a/modules/ui.py b/modules/ui.py
index 523ab25b..9620350f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -34,7 +34,7 @@ import modules.gfpgan_model
 import modules.codeformer_model
 import modules.styles
 import modules.generation_parameters_copypaste
-from modules.prompt_parser import get_learned_conditioning_prompt_schedules
+from modules import prompt_parser
 from modules.images import apply_filename_pattern, get_next_sequence_number
 import modules.textual_inversion.ui
 
@@ -394,7 +394,9 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
 
 def update_token_counter(text, steps):
     try:
-        prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
+        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+
     except Exception:
         # a parsing error can happen here during typing, and we don't want to bother the user with
         # messages related to it in console
-- 
cgit v1.2.3


From 4320f386d9641c7c234589c4cb0c0c6cbeb156ad Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Wed, 5 Oct 2022 22:39:32 +0200
Subject: removing underscores and colons

---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index a64fd9cd..fb5018a6 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -56,7 +56,7 @@ def _load_tf_and_return_tags(pil_image, threshold):
 
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return ', '.join(result_tags_out)
+    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
 
 
 def get_deepbooru_tags(pil_image, threshold=0.5):
-- 
cgit v1.2.3


From f8e41a96bb30a04dd5e294c7e1178c1c3b09d481 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 5 Oct 2022 23:52:05 +0300
Subject: fix various float parsing errors

---
 modules/prompt_parser.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index f7420daf..800b12c7 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -143,8 +143,7 @@ def get_learned_conditioning(model, prompts, steps):
 
 
 re_AND = re.compile(r"\bAND\b")
-re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?\s*(?:\d+|\d*\.\d+)?))?\s*$")
-
+re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
 
 def get_multicond_prompt_list(prompts):
     res_indexes = []
-- 
cgit v1.2.3


From 20f8ec877a99ce2ebf193cb1e2e773cfc77b7c41 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 00:09:32 +0300
Subject: remove type annotations in new code because presumably they don't
 work in 3.7

---
 modules/prompt_parser.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 800b12c7..ee4c5d02 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -175,14 +175,14 @@ def get_multicond_prompt_list(prompts):
 
 class ComposableScheduledPromptConditioning:
     def __init__(self, schedules, weight=1.0):
-        self.schedules: list[ScheduledPromptConditioning] = schedules
+        self.schedules = schedules  # : list[ScheduledPromptConditioning]
         self.weight: float = weight
 
 
 class MulticondLearnedConditioning:
     def __init__(self, shape, batch):
         self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
-        self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
+        self.batch = batch  # : list[list[ComposableScheduledPromptConditioning]]
 
 
 def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
@@ -203,7 +203,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne
     return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
 
 
-def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
+def reconstruct_cond_batch(c, current_step):  # c: list[list[ScheduledPromptConditioning]]
     param = c[0][0].cond
     res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
     for i, cond_schedule in enumerate(c):
-- 
cgit v1.2.3


From 34c358d10d52817f7a889ae4c52096ee654f3fe6 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 5 Oct 2022 22:11:30 +0100
Subject: use typing.list in prompt_parser.py for wider python version support

---
 modules/prompt_parser.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 800b12c7..fdfa21ae 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -1,6 +1,6 @@
 import re
 from collections import namedtuple
-
+from typing import List
 import lark
 
 # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
@@ -175,14 +175,14 @@ def get_multicond_prompt_list(prompts):
 
 class ComposableScheduledPromptConditioning:
     def __init__(self, schedules, weight=1.0):
-        self.schedules: list[ScheduledPromptConditioning] = schedules
+        self.schedules: List[ScheduledPromptConditioning] = schedules
         self.weight: float = weight
 
 
 class MulticondLearnedConditioning:
     def __init__(self, shape, batch):
         self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
-        self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
+        self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
 
 
 def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
@@ -203,7 +203,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne
     return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
 
 
-def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
+def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
     param = c[0][0].cond
     res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
     for i, cond_schedule in enumerate(c):
-- 
cgit v1.2.3


From 55400c981b7c1389482057a35ed6ea11f08da194 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 6 Oct 2022 03:11:15 +0100
Subject: Set gradio-img2img-tool default to 'editor'

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index e52c9b1d..bab0fe6e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -55,7 +55,7 @@ parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide dire
 parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="color-sketch")
+parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
-- 
cgit v1.2.3


From 2499fb4e1910d31ff12c24110f161b20641b8835 Mon Sep 17 00:00:00 2001
From: Raphael Stoeckli <github@rabanti.ch>
Date: Wed, 5 Oct 2022 21:57:18 +0200
Subject: Add sanitizer for captions in Textual inversion

---
 modules/textual_inversion/preprocess.py | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index f545a993..4f3df4bd 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,5 +1,8 @@
+from cmath import log
 import os
 from PIL import Image, ImageOps
+import platform
+import sys
 import tqdm
 
 from modules import shared, images
@@ -25,6 +28,7 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
     def save_pic_with_caption(image, index):
         if process_caption:
             caption = "-" + shared.interrogator.generate_caption(image)
+            caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
         else:
             caption = filename
             caption = os.path.splitext(caption)[0]
@@ -75,3 +79,27 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
 
     if process_caption:
         shared.interrogator.send_blip_to_ram()
+
+def sanitize_caption(base_path, original_caption, suffix):
+    operating_system = platform.system().lower()
+    if (operating_system == "windows"):
+        invalid_path_characters = "\\/:*?\"<>|"
+        max_path_length = 259
+    else:
+        invalid_path_characters = "/" #linux/macos
+        max_path_length = 1023
+    caption = original_caption
+    for invalid_character in invalid_path_characters:
+        caption = caption.replace(invalid_character, "")
+    fixed_path_length = len(base_path) + len(suffix) 
+    if fixed_path_length + len(caption) <= max_path_length:
+        return caption
+    caption_tokens = caption.split()
+    new_caption = ""
+    for token in caption_tokens:
+        last_caption = new_caption
+        new_caption = new_caption + token + " "
+        if (len(new_caption) + fixed_path_length - 1  > max_path_length):
+            break
+    print(f"\nPath will be too long. Truncated caption: {original_caption}\nto: {last_caption}", file=sys.stderr)
+    return last_caption.strip()
-- 
cgit v1.2.3


From 4288e53fc2ea25fa49715bf5b7f14603553c9e38 Mon Sep 17 00:00:00 2001
From: Raphael Stoeckli <github@rabanti.ch>
Date: Wed, 5 Oct 2022 23:11:32 +0200
Subject: removed unused import, fixed typo

---
 modules/textual_inversion/preprocess.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 4f3df4bd..f1c002a2 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,4 +1,3 @@
-from cmath import log
 import os
 from PIL import Image, ImageOps
 import platform
@@ -13,7 +12,7 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
 
-    assert src != dst, 'same directory specified as source and desitnation'
+    assert src != dst, 'same directory specified as source and destination'
 
     os.makedirs(dst, exist_ok=True)
 
-- 
cgit v1.2.3


From 5f24b7bcf4a074fbdec757617fcd1bc82e76551b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 12:08:48 +0300
Subject: option to let users select which samplers they want to hide

---
 modules/processing.py  | 13 ++++++-------
 modules/sd_samplers.py | 19 +++++++++++++++++--
 modules/shared.py      | 15 +++++++++------
 3 files changed, 32 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index d8c6b8d5..e01c8b3f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -11,9 +11,8 @@ import cv2
 from skimage import exposure
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking
+from modules import devices, prompt_parser, masking, sd_samplers
 from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.face_restoration
@@ -110,7 +109,7 @@ class Processed:
         self.width = p.width
         self.height = p.height
         self.sampler_index = p.sampler_index
-        self.sampler = samplers[p.sampler_index].name
+        self.sampler = sd_samplers.samplers[p.sampler_index].name
         self.cfg_scale = p.cfg_scale
         self.steps = p.steps
         self.batch_size = p.batch_size
@@ -265,7 +264,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params = {
         "Steps": p.steps,
-        "Sampler": samplers[p.sampler_index].name,
+        "Sampler": sd_samplers.samplers[p.sampler_index].name,
         "CFG scale": p.cfg_scale,
         "Seed": all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
@@ -478,7 +477,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.firstphase_height_truncated = int(scale * self.height)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@@ -521,7 +520,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         shared.state.nextjob()
 
-        self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
@@ -556,7 +555,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.nmask = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
-        self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
         crop_region = None
 
         if self.image_mask is not None:
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d27c547b..2e1f7715 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -32,12 +32,27 @@ samplers_data_k_diffusion = [
     if hasattr(k_diffusion.sampling, funcname)
 ]
 
-samplers = [
+all_samplers = [
     *samplers_data_k_diffusion,
     SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
     SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
 ]
-samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']]
+
+samplers = []
+samplers_for_img2img = []
+
+
+def set_samplers():
+    global samplers, samplers_for_img2img
+
+    hidden = set(opts.hide_samplers)
+    hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive'])
+
+    samplers = [x for x in all_samplers if x.name not in hidden]
+    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
+
+
+set_samplers()
 
 sampler_extra_params = {
     'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
diff --git a/modules/shared.py b/modules/shared.py
index bab0fe6e..ca2e4c74 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,6 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
+from modules import sd_samplers
 from modules.paths import script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -238,14 +239,16 @@ options_templates.update(options_section(('ui', "User interface"), {
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-  "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-  "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-  "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
-  's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-  's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-  's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}),
+    "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
+    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
 }))
 
+
 class Options:
     data = None
     data_labels = options_templates
-- 
cgit v1.2.3


From 2d3ea42a2d1e909bbccdb6b49561b187c60a9402 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 13:21:12 +0300
Subject: workaround for a mysterious bug where prompt weights can't be matched

---
 modules/prompt_parser.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index a7a6aa31..f00256f2 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -156,7 +156,9 @@ def get_multicond_prompt_list(prompts):
 
         indexes = []
         for subprompt in subprompts:
-            text, weight = re_weight.search(subprompt).groups()
+            match = re_weight.search(subprompt)
+
+            text, weight = match.groups() if match is not None else (subprompt, 1.0)
 
             weight = float(weight) if weight is not None else 1.0
 
-- 
cgit v1.2.3


From 71901b3d3bea1d035bf4a7229d19356b4b062151 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Wed, 5 Oct 2022 14:30:57 +0300
Subject: add karras scheduling variants

---
 modules/sd_samplers.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 2e1f7715..8d6eb762 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -26,6 +26,17 @@ samplers_k_diffusion = [
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
 ]
 
+if opts.show_karras_scheduler_variants:
+    k_diffusion.sampling.sample_dpm_2_ka = k_diffusion.sampling.sample_dpm_2
+    k_diffusion.sampling.sample_dpm_2_ancestral_ka = k_diffusion.sampling.sample_dpm_2_ancestral
+    k_diffusion.sampling.sample_lms_ka = k_diffusion.sampling.sample_lms
+    samplers_k_diffusion_ka = [
+        ('LMS K Scheduling', 'sample_lms_ka', ['k_lms_ka']),
+        ('DPM2 K Scheduling', 'sample_dpm_2_ka', ['k_dpm_2_ka']),
+        ('DPM2 a K Scheduling', 'sample_dpm_2_ancestral_ka', ['k_dpm_2_a_ka']),
+    ]
+    samplers_k_diffusion.extend(samplers_k_diffusion_ka)
+
 samplers_data_k_diffusion = [
     SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
     for label, funcname, aliases in samplers_k_diffusion
@@ -345,6 +356,8 @@ class KDiffusionSampler:
 
         if p.sampler_noise_scheduler_override:
           sigmas = p.sampler_noise_scheduler_override(steps)
+        elif self.funcname.endswith('ka'):
+          sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
         else:
           sigmas = self.model_wrap.get_sigmas(steps)
         x = x * sigmas[0]
-- 
cgit v1.2.3


From 3ddf80a9db8793188e2fe9488233d2b272cceb33 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Wed, 5 Oct 2022 14:31:51 +0300
Subject: add variant setting

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index ca2e4c74..9e4860a2 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -236,6 +236,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+    "show_karras_scheduler_variants": OptionInfo(True, "Show Karras scheduling variants for select samplers. Try these variants if your K sampled images suffer from excessive noise."),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-- 
cgit v1.2.3


From 5993df24a1026225cb8af89237547c1d9101ce69 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 14:12:52 +0300
Subject: integrate the new samplers PR

---
 modules/processing.py  |  7 +++---
 modules/sd_samplers.py | 59 ++++++++++++++++++++++++++------------------------
 modules/shared.py      |  1 -
 3 files changed, 35 insertions(+), 32 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index e01c8b3f..e567956c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -477,7 +477,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.firstphase_height_truncated = int(scale * self.height)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@@ -520,7 +520,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         shared.state.nextjob()
 
-        self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
+
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
@@ -555,7 +556,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.nmask = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
-        self.sampler = sd_samplers.samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
         crop_region = None
 
         if self.image_mask is not None:
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 8d6eb762..497df943 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -13,46 +13,46 @@ from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 
 
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases'])
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
 
 samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a']),
-    ('Euler', 'sample_euler', ['k_euler']),
-    ('LMS', 'sample_lms', ['k_lms']),
-    ('Heun', 'sample_heun', ['k_heun']),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2']),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
-    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),
-    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),
+    ('Euler', 'sample_euler', ['k_euler'], {}),
+    ('LMS', 'sample_lms', ['k_lms'], {}),
+    ('Heun', 'sample_heun', ['k_heun'], {}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
+    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
+    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
+    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
 ]
 
-if opts.show_karras_scheduler_variants:
-    k_diffusion.sampling.sample_dpm_2_ka = k_diffusion.sampling.sample_dpm_2
-    k_diffusion.sampling.sample_dpm_2_ancestral_ka = k_diffusion.sampling.sample_dpm_2_ancestral
-    k_diffusion.sampling.sample_lms_ka = k_diffusion.sampling.sample_lms
-    samplers_k_diffusion_ka = [
-        ('LMS K Scheduling', 'sample_lms_ka', ['k_lms_ka']),
-        ('DPM2 K Scheduling', 'sample_dpm_2_ka', ['k_dpm_2_ka']),
-        ('DPM2 a K Scheduling', 'sample_dpm_2_ancestral_ka', ['k_dpm_2_a_ka']),
-    ]
-    samplers_k_diffusion.extend(samplers_k_diffusion_ka)
-
 samplers_data_k_diffusion = [
-    SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
-    for label, funcname, aliases in samplers_k_diffusion
+    SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+    for label, funcname, aliases, options in samplers_k_diffusion
     if hasattr(k_diffusion.sampling, funcname)
 ]
 
 all_samplers = [
     *samplers_data_k_diffusion,
-    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
-    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
+    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
+    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
 ]
 
 samplers = []
 samplers_for_img2img = []
 
 
+def create_sampler_with_index(list_of_configs, index, model):
+    config = list_of_configs[index]
+    sampler = config.constructor(model)
+    sampler.config = config
+    
+    return sampler
+
+
 def set_samplers():
     global samplers, samplers_for_img2img
 
@@ -130,6 +130,7 @@ class VanillaStableDiffusionSampler:
         self.step = 0
         self.eta = None
         self.default_eta = 0.0
+        self.config = None
 
     def number_of_needed_noises(self, p):
         return 0
@@ -291,6 +292,7 @@ class KDiffusionSampler:
         self.stop_at = None
         self.eta = None
         self.default_eta = 1.0
+        self.config = None
 
     def callback_state(self, d):
         store_latent(d["denoised"])
@@ -355,11 +357,12 @@ class KDiffusionSampler:
         steps = steps or p.steps
 
         if p.sampler_noise_scheduler_override:
-          sigmas = p.sampler_noise_scheduler_override(steps)
-        elif self.funcname.endswith('ka'):
-          sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
+            sigmas = p.sampler_noise_scheduler_override(steps)
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
         else:
-          sigmas = self.model_wrap.get_sigmas(steps)
+            sigmas = self.model_wrap.get_sigmas(steps)
+
         x = x * sigmas[0]
 
         extra_params_kwargs = self.initialize(p)
diff --git a/modules/shared.py b/modules/shared.py
index 9e4860a2..ca2e4c74 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -236,7 +236,6 @@ options_templates.update(options_section(('ui', "User interface"), {
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-    "show_karras_scheduler_variants": OptionInfo(True, "Show Karras scheduling variants for select samplers. Try these variants if your K sampled images suffer from excessive noise."),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-- 
cgit v1.2.3


From be71115b1a1201d04f0e2a11e718fb31cbd26474 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 6 Oct 2022 01:09:44 +0100
Subject: Update shared.py

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index ca2e4c74..9f7c6efe 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -236,6 +236,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+    "show_progress_in_title": OptionInfo(False, "Show generation progress in window title."),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-- 
cgit v1.2.3


From fec71e4de24b65b0f205a3c071b71651bbcb0dfc Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 6 Oct 2022 01:35:07 +0100
Subject: Default window title progress updates on

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 9f7c6efe..5c16f025 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -236,7 +236,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-    "show_progress_in_title": OptionInfo(False, "Show generation progress in window title."),
+    "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-- 
cgit v1.2.3


From 0bb458f0ca06a7be27cf1a1003c536d1f06a5bd3 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Wed, 5 Oct 2022 01:19:50 +0900
Subject: Removed duplicate image saving codes

Use `modules.images.save_image()` instead.
---
 modules/images.py |  7 ++++---
 modules/ui.py     | 46 ++++++++++------------------------------------
 2 files changed, 14 insertions(+), 39 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index c2fadab9..810f1446 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -353,7 +353,7 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
-def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix=""):
+def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
     if short_filename or prompt is None or seed is None:
         file_decoration = ""
     elif opts.save_to_dirs:
@@ -377,7 +377,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     else:
         pnginfo = None
 
-    save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
+    if save_to_dirs is None:
+        save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
     if save_to_dirs:
         dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
@@ -431,4 +432,4 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
             file.write(info + "\n")
 
-
+    return fullfn
diff --git a/modules/ui.py b/modules/ui.py
index 9620350f..4f18126f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -35,7 +35,7 @@ import modules.codeformer_model
 import modules.styles
 import modules.generation_parameters_copypaste
 from modules import prompt_parser
-from modules.images import apply_filename_pattern, get_next_sequence_number
+from modules.images import save_image
 import modules.textual_inversion.ui
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
@@ -114,20 +114,13 @@ def save_files(js_data, images, index):
     p = MyObject(data)
     path = opts.outdir_save
     save_to_dirs = opts.use_save_to_dirs_for_ui
-
-    if save_to_dirs:
-        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt)
-        path = os.path.join(opts.outdir_save, dirname)
-
-    os.makedirs(path, exist_ok=True)
-  
+    extension: str = opts.samples_format
+    start_index = 0
 
     if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
 
         images = [images[index]]
-        infotexts = [data["infotexts"][index]]
-    else:
-        infotexts = data["infotexts"]
+        start_index = index
 
     with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
         at_start = file.tell() == 0
@@ -135,37 +128,18 @@ def save_files(js_data, images, index):
         if at_start:
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
 
-        file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
-        if file_decoration != "":
-            file_decoration = "-" + file_decoration.lower()
-        file_decoration = apply_filename_pattern(file_decoration, p, p.seed, p.prompt)
-        truncated = (file_decoration[:240] + '..') if len(file_decoration) > 240 else file_decoration
-        filename_base = truncated
-        extension = opts.samples_format.lower()
-
-        basecount = get_next_sequence_number(path, "")
-        for i, filedata in enumerate(images):
-            file_number = f"{basecount+i:05}"
-            filename = file_number + filename_base + f".{extension}"
-            filepath = os.path.join(path, filename)
-
-
+        for image_index, filedata in enumerate(images, start_index):
             if filedata.startswith("data:image/png;base64,"):
                 filedata = filedata[len("data:image/png;base64,"):]
 
             image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))
-            if opts.enable_pnginfo and extension == 'png':
-                pnginfo = PngImagePlugin.PngInfo()
-                pnginfo.add_text('parameters', infotexts[i])
-                image.save(filepath, pnginfo=pnginfo)
-            else:
-                image.save(filepath, quality=opts.jpeg_quality)
 
-            if opts.enable_pnginfo and extension in ("jpg", "jpeg", "webp"):
-                piexif.insert(piexif.dump({"Exif": {
-                    piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(infotexts[i], encoding="unicode")
-                }}), filepath)
+            is_grid = image_index < p.index_of_first_image
+            i = 0 if is_grid else (image_index - p.index_of_first_image)
+
+            fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
+            filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
 
         writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
-- 
cgit v1.2.3


From dbc8a4d35129b08eab30776bbbaf3a2e7ac10a6c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 20:27:50 +0300
Subject: add generation parameters to images shown in web ui

---
 modules/processing.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index de818d5b..8faf9095 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -430,7 +430,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 if opts.samples_save and not p.do_not_save_samples:
                     images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
 
-                infotexts.append(infotext(n, i))
+                text = infotext(n, i)
+                infotexts.append(text)
+                image.info["parameters"] = text
                 output_images.append(image)
 
             del x_samples_ddim 
@@ -447,7 +449,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             grid = images.image_grid(output_images, p.batch_size)
 
             if opts.return_grid:
-                infotexts.insert(0, infotext())
+                text = infotext()
+                infotexts.insert(0, text)
+                grid.info["parameters"] = text
                 output_images.insert(0, grid)
                 index_of_first_image = 1
 
-- 
cgit v1.2.3


From cf7c784fcc0c84a8a4edd8d3aca4dda4c7025c43 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Fri, 7 Oct 2022 00:19:52 +0900
Subject: Removed duplicate defined models_path

Use `modules.paths.models_path` instead `modules.shared.model_path`.
---
 modules/shared.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 5c16f025..25bb6e6c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -14,11 +14,10 @@ import modules.sd_models
 import modules.styles
 import modules.devices as devices
 from modules import sd_samplers
-from modules.paths import script_path, sd_path
+from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
-model_path = os.path.join(script_path, 'models')
 parser = argparse.ArgumentParser()
 parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
@@ -36,14 +35,14 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
-parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(model_path, 'Codeformer'))
-parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(model_path, 'GFPGAN'))
-parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
-parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))
-parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
-parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(model_path, 'ScuNET'))
-parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
-parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))
+parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
+parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
+parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
+parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
+parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
+parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
+parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
+parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-- 
cgit v1.2.3


From 070b7d60cf5dac6387b3bfc8f3b3977b620e4fd5 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Wed, 5 Oct 2022 02:13:09 +0900
Subject: Added styles to Processed

So `[styles]` pattern can use in saving image UI.
---
 modules/images.py     | 7 +------
 modules/processing.py | 2 ++
 2 files changed, 3 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 810f1446..fa0714fd 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -292,12 +292,7 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[cfg]", str(p.cfg_scale))
         x = x.replace("[width]", str(p.width))
         x = x.replace("[height]", str(p.height))
-
-        #currently disabled if using the save button, will work otherwise
-        # if enabled it will cause a bug because styles is not included in the save_files data dictionary
-        if hasattr(p, "styles"):
-            x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
-
+        x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
         x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
     x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
diff --git a/modules/processing.py b/modules/processing.py
index 8faf9095..706dbfa8 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -121,6 +121,7 @@ class Processed:
         self.denoising_strength = getattr(p, 'denoising_strength', None)
         self.extra_generation_params = p.extra_generation_params
         self.index_of_first_image = index_of_first_image
+        self.styles = p.styles
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -165,6 +166,7 @@ class Processed:
             "extra_generation_params": self.extra_generation_params,
             "index_of_first_image": self.index_of_first_image,
             "infotexts": self.infotexts,
+            "styles": self.styles,
         }
 
         return json.dumps(obj)
-- 
cgit v1.2.3


From 1cc36d170ac15e7f04208df32db27af1b10c867c Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Wed, 5 Oct 2022 02:17:15 +0900
Subject: Added job_timestamp to Processed

So `[job_timestamp]` pattern can use in saving image UI.
---
 modules/images.py     | 2 +-
 modules/processing.py | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index fa0714fd..669d76af 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -298,7 +298,7 @@ def apply_filename_pattern(x, p, seed, prompt):
     x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
     x = x.replace("[date]", datetime.date.today().isoformat())
     x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
-    x = x.replace("[job_timestamp]", shared.state.job_timestamp)
+    x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp))
 
     # Apply [prompt] at last. Because it may contain any replacement word.^M
     if prompt is not None:
diff --git a/modules/processing.py b/modules/processing.py
index 706dbfa8..f773a30e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -122,6 +122,7 @@ class Processed:
         self.extra_generation_params = p.extra_generation_params
         self.index_of_first_image = index_of_first_image
         self.styles = p.styles
+        self.job_timestamp = state.job_timestamp
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -167,6 +168,7 @@ class Processed:
             "index_of_first_image": self.index_of_first_image,
             "infotexts": self.infotexts,
             "styles": self.styles,
+            "job_timestamp": self.job_timestamp,
         }
 
         return json.dumps(obj)
-- 
cgit v1.2.3


From 405c8171d1acbb994084d98770bbcb97d01d9406 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Thu, 6 Oct 2022 00:59:04 +0900
Subject: Prefer using `Processed.sd_model_hash` attribute when filename
 pattern

---
 modules/images.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 669d76af..29c5ee24 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -295,7 +295,7 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
         x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
-    x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
+    x = x.replace("[model_hash]", getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash))
     x = x.replace("[date]", datetime.date.today().isoformat())
     x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
     x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp))
-- 
cgit v1.2.3


From b34b25b4c941819d34f29be6c4c1ec01e64585b4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 23:27:01 +0300
Subject: karras samplers for img2img?

---
 modules/sd_samplers.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 497df943..df17e93c 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -338,9 +338,11 @@ class KDiffusionSampler:
         steps, t_enc = setup_img2img_steps(p, steps)
 
         if p.sampler_noise_scheduler_override:
-          sigmas = p.sampler_noise_scheduler_override(steps)
+            sigmas = p.sampler_noise_scheduler_override(steps)
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
         else:
-          sigmas = self.model_wrap.get_sigmas(steps)
+            sigmas = self.model_wrap.get_sigmas(steps)
 
         noise = noise * sigmas[steps - t_enc - 1]
         xi = x + noise
-- 
cgit v1.2.3


From f174fb29228a04955fb951b32b0bab79e33ec2b8 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 05:21:49 +0300
Subject: add xformers attention

---
 modules/sd_hijack_optimizations.py | 39 +++++++++++++++++++++++++++++++++++++-
 1 file changed, 38 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index ea4cfdfc..da1b76e1 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,7 +1,9 @@
 import math
 import torch
 from torch import einsum
-
+import xformers.ops
+import functorch
+xformers._is_functorch_available=True
 from ldm.util import default
 from einops import rearrange
 
@@ -92,6 +94,41 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
+def _maybe_init(self, x):
+    """
+    Initialize the attention operator, if required We expect the head dimension to be exposed here, meaning that x
+    : B, Head, Length
+    """
+    if self.attention_op is not None:
+        return
+    _, M, K = x.shape
+    try:
+        self.attention_op = xformers.ops.AttentionOpDispatch(
+            dtype=x.dtype,
+            device=x.device,
+            k=K,
+            attn_bias_type=type(None),
+            has_dropout=False,
+            kv_len=M,
+            q_len=M,
+        ).op
+    except NotImplementedError as err:
+        raise NotImplementedError(f"Please install xformers with the flash attention / cutlass components.\n{err}")
+
+def xformers_attention_forward(self, x, context=None, mask=None):
+    h = self.heads
+    q_in = self.to_q(x)
+    context = default(context, x)
+    k_in = self.to_k(context)
+    v_in = self.to_v(context)
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    del q_in, k_in, v_in
+    self._maybe_init(q)
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    return self.to_out(out)
+
 def cross_attention_attnblock_forward(self, x):
         h_ = x
         h_ = self.norm(h_)
-- 
cgit v1.2.3


From 2eb911b056ce6ff4434f673366782ed34f2b2f12 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 05:22:28 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index a6fa890c..6221ed5a 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -20,12 +20,17 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 
 def apply_optimizations():
-    ldm.modules.diffusionmodules.model.nonlinearity = silu
-
     if cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
+    if cmd_opts.opt_split_attention:
+        ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
+        ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
+    elif not cmd_opts.disable_opt_xformers_attention:
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
+        ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init
+        ldm.modules.attention.CrossAttention.attention_op = None
+        ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
 
-- 
cgit v1.2.3


From da4ab2707b4cb0611cf181ba248a271d1937433e Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 05:23:06 +0300
Subject: Update shared.py

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 25bb6e6c..8cc3b2fe 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -43,6 +43,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
 parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
+parser.add_argument("--disable-opt-xformers-attention", action='store_true', help="force-disables xformers attention optimization")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-- 
cgit v1.2.3


From 35d6b231628d18d53d166c3a92fea1523e88d51e Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 05:31:53 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 6221ed5a..a006c0a3 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -20,17 +20,16 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 
 def apply_optimizations():
+    ldm.modules.diffusionmodules.model.nonlinearity = silu
     if cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     if cmd_opts.opt_split_attention:
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
-        ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
     elif not cmd_opts.disable_opt_xformers_attention:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init
         ldm.modules.attention.CrossAttention.attention_op = None
-        ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
 
-- 
cgit v1.2.3


From 5303df24282ba06abb34a423f2967354d37d078e Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 06:01:14 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index a006c0a3..ddacb0ad 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -23,10 +23,10 @@ def apply_optimizations():
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     if cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    if cmd_opts.opt_split_attention:
+    elif cmd_opts.opt_split_attention:
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
-    elif not cmd_opts.disable_opt_xformers_attention:
+    elif not cmd_opts.disable_opt_xformers_attention and not cmd_opts.opt_split_attention:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init
         ldm.modules.attention.CrossAttention.attention_op = None
-- 
cgit v1.2.3


From 5e3ff846c56dc8e1d5c76ea04a8f2f74d7da07fc Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 06:38:01 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index ddacb0ad..cbdb9d3c 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -26,7 +26,7 @@ def apply_optimizations():
     elif cmd_opts.opt_split_attention:
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
-    elif not cmd_opts.disable_opt_xformers_attention and not cmd_opts.opt_split_attention:
+    elif not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip):
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init
         ldm.modules.attention.CrossAttention.attention_op = None
-- 
cgit v1.2.3


From bad7cb29cecac51c5c0f39afec332b007ed73133 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 7 Oct 2022 10:17:52 +0300
Subject: added support for hypernetworks (???)

---
 modules/hypernetwork.py            | 55 ++++++++++++++++++++++++++++++++++++++
 modules/sd_hijack_optimizations.py | 17 ++++++++++--
 modules/shared.py                  |  9 ++++++-
 3 files changed, 78 insertions(+), 3 deletions(-)
 create mode 100644 modules/hypernetwork.py

(limited to 'modules')

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
new file mode 100644
index 00000000..9ed1eed9
--- /dev/null
+++ b/modules/hypernetwork.py
@@ -0,0 +1,55 @@
+import glob
+import os
+import torch
+from modules import devices
+
+
+class HypernetworkModule(torch.nn.Module):
+    def __init__(self, dim, state_dict):
+        super().__init__()
+
+        self.linear1 = torch.nn.Linear(dim, dim * 2)
+        self.linear2 = torch.nn.Linear(dim * 2, dim)
+
+        self.load_state_dict(state_dict, strict=True)
+        self.to(devices.device)
+
+    def forward(self, x):
+        return x + (self.linear2(self.linear1(x)))
+
+
+class Hypernetwork:
+    filename = None
+    name = None
+
+    def __init__(self, filename):
+        self.filename = filename
+        self.name = os.path.splitext(os.path.basename(filename))[0]
+        self.layers = {}
+
+        state_dict = torch.load(filename, map_location='cpu')
+        for size, sd in state_dict.items():
+            self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
+
+
+def load_hypernetworks(path):
+    res = {}
+
+    for filename in glob.iglob(path + '**/*.pt', recursive=True):
+        hn = Hypernetwork(filename)
+        res[hn.name] = hn
+
+    return res
+
+def apply(self, x, context=None, mask=None, original=None):
+
+
+    if CrossAttention.hypernetwork is not None and context.shape[2] in CrossAttention.hypernetwork:
+        if context.shape[1] == 77 and CrossAttention.noise_cond:
+            context = context + (torch.randn_like(context) * 0.1)
+        h_k, h_v = CrossAttention.hypernetwork[context.shape[2]]
+        k = self.to_k(h_k(context))
+        v = self.to_v(h_v(context))
+    else:
+        k = self.to_k(context)
+        v = self.to_v(context)
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index ea4cfdfc..d9cca485 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -5,6 +5,8 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange
 
+from modules import shared
+
 
 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
 def split_cross_attention_forward_v1(self, x, context=None, mask=None):
@@ -42,8 +44,19 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     q_in = self.to_q(x)
     context = default(context, x)
-    k_in = self.to_k(context) * self.scale
-    v_in = self.to_v(context)
+
+    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k_in = self.to_k(hypernetwork_layers[0](context))
+        v_in = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k_in = self.to_k(context)
+        v_in = self.to_v(context)
+
+    k_in *= self.scale
+
     del context, x
 
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
diff --git a/modules/shared.py b/modules/shared.py
index 25bb6e6c..879d8424 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers
+from modules import sd_samplers, hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -76,6 +76,12 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 
 config_filename = cmd_opts.ui_settings_file
 
+hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
+
+
+def selected_hypernetwork():
+    return hypernetworks.get(opts.sd_hypernetwork, None)
+
 
 class State:
     interrupted = False
@@ -206,6 +212,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
+    "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
-- 
cgit v1.2.3


From d15b3ec0013c10f02f0fb80e8448bac8872a151f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 7 Oct 2022 10:40:22 +0300
Subject: support loading VAE

---
 modules/sd_models.py | 8 ++++++++
 1 file changed, 8 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5f992064..8f794b47 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -134,6 +134,14 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
 
     devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
 
+    vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+    if os.path.exists(vae_file):
+        print(f"Loading VAE weights from: {vae_file}")
+        vae_ckpt = torch.load(vae_file, map_location="cpu")
+        vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+
+        model.first_stage_model.load_state_dict(vae_dict)
+
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpint = checkpoint_file
 
-- 
cgit v1.2.3


From 97bc0b9504572d2df80598d0b694703bcd626de6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 7 Oct 2022 13:22:50 +0300
Subject: do not stop working on failed hypernetwork load

---
 modules/hypernetwork.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index 9ed1eed9..c5cf4afa 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -1,5 +1,8 @@
 import glob
 import os
+import sys
+import traceback
+
 import torch
 from modules import devices
 
@@ -36,8 +39,12 @@ def load_hypernetworks(path):
     res = {}
 
     for filename in glob.iglob(path + '**/*.pt', recursive=True):
-        hn = Hypernetwork(filename)
-        res[hn.name] = hn
+        try:
+            hn = Hypernetwork(filename)
+            res[hn.name] = hn
+        except Exception:
+            print(f"Error loading hypernetwork {filename}", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
 
     return res
 
-- 
cgit v1.2.3


From f7c787eb7c295c27439f4fbdf78c26b8389560be Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 7 Oct 2022 16:39:51 +0300
Subject: make it possible to use hypernetworks without opt split attention

---
 modules/hypernetwork.py | 42 ++++++++++++++++++++++++++++++++++--------
 modules/sd_hijack.py    |  6 ++++--
 2 files changed, 38 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index c5cf4afa..c7b86682 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -4,7 +4,12 @@ import sys
 import traceback
 
 import torch
-from modules import devices
+
+from ldm.util import default
+from modules import devices, shared
+import torch
+from torch import einsum
+from einops import rearrange, repeat
 
 
 class HypernetworkModule(torch.nn.Module):
@@ -48,15 +53,36 @@ def load_hypernetworks(path):
 
     return res
 
-def apply(self, x, context=None, mask=None, original=None):
 
+def attention_CrossAttention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
 
-    if CrossAttention.hypernetwork is not None and context.shape[2] in CrossAttention.hypernetwork:
-        if context.shape[1] == 77 and CrossAttention.noise_cond:
-            context = context + (torch.randn_like(context) * 0.1)
-        h_k, h_v = CrossAttention.hypernetwork[context.shape[2]]
-        k = self.to_k(h_k(context))
-        v = self.to_v(h_v(context))
+    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k = self.to_k(hypernetwork_layers[0](context))
+        v = self.to_v(hypernetwork_layers[1](context))
     else:
         k = self.to_k(context)
         v = self.to_v(context)
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+    if mask is not None:
+        mask = rearrange(mask, 'b ... -> b (...)')
+        max_neg_value = -torch.finfo(sim.dtype).max
+        mask = repeat(mask, 'b j -> (b h) () j', h=h)
+        sim.masked_fill_(~mask, max_neg_value)
+
+    # attention, what we cannot get enough of
+    attn = sim.softmax(dim=-1)
+
+    out = einsum('b i j, b j d -> b i d', attn, v)
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    return self.to_out(out)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index a6fa890c..d68f89cc 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -8,7 +8,7 @@ from torch import einsum
 from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
 from modules.shared import opts, device, cmd_opts
 
 import ldm.modules.attention
@@ -20,6 +20,8 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 
 def apply_optimizations():
+    undo_optimizations()
+
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
     if cmd_opts.opt_split_attention_v1:
@@ -30,7 +32,7 @@ def apply_optimizations():
 
 
 def undo_optimizations():
-    ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward
+    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
-- 
cgit v1.2.3


From 54fa613c8391e3973cca9d94cdf539061932508b Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Fri, 7 Oct 2022 20:37:43 +0200
Subject: loading tf only in interrogation process

---
 modules/deepbooru.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index fb5018a6..79dc59bd 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,12 +1,13 @@
 import os.path
 from concurrent.futures import ProcessPoolExecutor
 
-import numpy as np
-import deepdanbooru as dd
-import tensorflow as tf
 
 
 def _load_tf_and_return_tags(pil_image, threshold):
+    import deepdanbooru as dd
+    import tensorflow as tf
+    import numpy as np
+
     this_folder = os.path.dirname(__file__)
     model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28')
 
-- 
cgit v1.2.3


From fa2ea648db81f5723bb5d722f2fe0ebd7dfc319a Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Fri, 7 Oct 2022 20:46:38 +0200
Subject: even more powerfull fix

---
 modules/deepbooru.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 79dc59bd..60094336 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -60,8 +60,13 @@ def _load_tf_and_return_tags(pil_image, threshold):
     return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
 
 
+def subprocess_init_no_cuda():
+    import os
+    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+
 def get_deepbooru_tags(pil_image, threshold=0.5):
-    with ProcessPoolExecutor() as executor:
-        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold)
+    with ProcessPoolExecutor(initializer=subprocess_init_no_cuda) as executor:
+        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
         ret = f.result()  # will rethrow any exceptions
     return ret
\ No newline at end of file
-- 
cgit v1.2.3


From 5f12e7efd92ad802742f96788b4be3249ad02829 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Fri, 7 Oct 2022 20:58:30 +0200
Subject: linux test

---
 modules/deepbooru.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 60094336..781b2249 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,6 +1,6 @@
 import os.path
 from concurrent.futures import ProcessPoolExecutor
-
+from multiprocessing import get_context
 
 
 def _load_tf_and_return_tags(pil_image, threshold):
@@ -66,7 +66,8 @@ def subprocess_init_no_cuda():
 
 
 def get_deepbooru_tags(pil_image, threshold=0.5):
-    with ProcessPoolExecutor(initializer=subprocess_init_no_cuda) as executor:
+    context = get_context('spawn')
+    with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
         f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
         ret = f.result()  # will rethrow any exceptions
     return ret
\ No newline at end of file
-- 
cgit v1.2.3


From 12c4d5c6b5bf9dd50d0601c36af4f99b65316d58 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 7 Oct 2022 23:22:22 +0300
Subject: hypernetwork training mk1

---
 modules/hypernetwork.py              |  88 ------------
 modules/hypernetwork/hypernetwork.py | 267 +++++++++++++++++++++++++++++++++++
 modules/hypernetwork/ui.py           |  43 ++++++
 modules/sd_hijack.py                 |   4 +-
 modules/sd_hijack_optimizations.py   |   3 +-
 modules/shared.py                    |  13 +-
 modules/textual_inversion/ui.py      |   1 -
 modules/ui.py                        |  58 +++++++-
 8 files changed, 374 insertions(+), 103 deletions(-)
 delete mode 100644 modules/hypernetwork.py
 create mode 100644 modules/hypernetwork/hypernetwork.py
 create mode 100644 modules/hypernetwork/ui.py

(limited to 'modules')

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
deleted file mode 100644
index c7b86682..00000000
--- a/modules/hypernetwork.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import glob
-import os
-import sys
-import traceback
-
-import torch
-
-from ldm.util import default
-from modules import devices, shared
-import torch
-from torch import einsum
-from einops import rearrange, repeat
-
-
-class HypernetworkModule(torch.nn.Module):
-    def __init__(self, dim, state_dict):
-        super().__init__()
-
-        self.linear1 = torch.nn.Linear(dim, dim * 2)
-        self.linear2 = torch.nn.Linear(dim * 2, dim)
-
-        self.load_state_dict(state_dict, strict=True)
-        self.to(devices.device)
-
-    def forward(self, x):
-        return x + (self.linear2(self.linear1(x)))
-
-
-class Hypernetwork:
-    filename = None
-    name = None
-
-    def __init__(self, filename):
-        self.filename = filename
-        self.name = os.path.splitext(os.path.basename(filename))[0]
-        self.layers = {}
-
-        state_dict = torch.load(filename, map_location='cpu')
-        for size, sd in state_dict.items():
-            self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
-
-
-def load_hypernetworks(path):
-    res = {}
-
-    for filename in glob.iglob(path + '**/*.pt', recursive=True):
-        try:
-            hn = Hypernetwork(filename)
-            res[hn.name] = hn
-        except Exception:
-            print(f"Error loading hypernetwork {filename}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-
-    return res
-
-
-def attention_CrossAttention_forward(self, x, context=None, mask=None):
-    h = self.heads
-
-    q = self.to_q(x)
-    context = default(context, x)
-
-    hypernetwork = shared.selected_hypernetwork()
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is not None:
-        k = self.to_k(hypernetwork_layers[0](context))
-        v = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
-    if mask is not None:
-        mask = rearrange(mask, 'b ... -> b (...)')
-        max_neg_value = -torch.finfo(sim.dtype).max
-        mask = repeat(mask, 'b j -> (b h) () j', h=h)
-        sim.masked_fill_(~mask, max_neg_value)
-
-    # attention, what we cannot get enough of
-    attn = sim.softmax(dim=-1)
-
-    out = einsum('b i j, b j d -> b i d', attn, v)
-    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-    return self.to_out(out)
diff --git a/modules/hypernetwork/hypernetwork.py b/modules/hypernetwork/hypernetwork.py
new file mode 100644
index 00000000..a3d6a47e
--- /dev/null
+++ b/modules/hypernetwork/hypernetwork.py
@@ -0,0 +1,267 @@
+import datetime
+import glob
+import html
+import os
+import sys
+import traceback
+import tqdm
+
+import torch
+
+from ldm.util import default
+from modules import devices, shared, processing, sd_models
+import torch
+from torch import einsum
+from einops import rearrange, repeat
+import modules.textual_inversion.dataset
+
+
+class HypernetworkModule(torch.nn.Module):
+    def __init__(self, dim, state_dict=None):
+        super().__init__()
+
+        self.linear1 = torch.nn.Linear(dim, dim * 2)
+        self.linear2 = torch.nn.Linear(dim * 2, dim)
+
+        if state_dict is not None:
+            self.load_state_dict(state_dict, strict=True)
+        else:
+            self.linear1.weight.data.fill_(0.0001)
+            self.linear1.bias.data.fill_(0.0001)
+            self.linear2.weight.data.fill_(0.0001)
+            self.linear2.bias.data.fill_(0.0001)
+
+        self.to(devices.device)
+
+    def forward(self, x):
+        return x + (self.linear2(self.linear1(x)))
+
+
+class Hypernetwork:
+    filename = None
+    name = None
+
+    def __init__(self, name=None):
+        self.filename = None
+        self.name = name
+        self.layers = {}
+        self.step = 0
+        self.sd_checkpoint = None
+        self.sd_checkpoint_name = None
+
+        for size in [320, 640, 768, 1280]:
+            self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
+
+    def weights(self):
+        res = []
+
+        for k, layers in self.layers.items():
+            for layer in layers:
+                layer.train()
+                res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
+
+        return res
+
+    def save(self, filename):
+        state_dict = {}
+
+        for k, v in self.layers.items():
+            state_dict[k] = (v[0].state_dict(), v[1].state_dict())
+
+        state_dict['step'] = self.step
+        state_dict['name'] = self.name
+        state_dict['sd_checkpoint'] = self.sd_checkpoint
+        state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
+
+        torch.save(state_dict, filename)
+
+    def load(self, filename):
+        self.filename = filename
+        if self.name is None:
+            self.name = os.path.splitext(os.path.basename(filename))[0]
+
+        state_dict = torch.load(filename, map_location='cpu')
+
+        for size, sd in state_dict.items():
+            if type(size) == int:
+                self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
+
+        self.name = state_dict.get('name', self.name)
+        self.step = state_dict.get('step', 0)
+        self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
+        self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
+
+
+def load_hypernetworks(path):
+    res = {}
+
+    for filename in glob.iglob(path + '**/*.pt', recursive=True):
+        try:
+            hn = Hypernetwork()
+            hn.load(filename)
+            res[hn.name] = hn
+        except Exception:
+            print(f"Error loading hypernetwork {filename}", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    return res
+
+
+def attention_CrossAttention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+
+    hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        hypernetwork_k, hypernetwork_v = hypernetwork_layers
+
+        self.hypernetwork_k = hypernetwork_k
+        self.hypernetwork_v = hypernetwork_v
+
+        context_k = hypernetwork_k(context)
+        context_v = hypernetwork_v(context)
+    else:
+        context_k = context
+        context_v = context
+
+    k = self.to_k(context_k)
+    v = self.to_v(context_v)
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+    if mask is not None:
+        mask = rearrange(mask, 'b ... -> b (...)')
+        max_neg_value = -torch.finfo(sim.dtype).max
+        mask = repeat(mask, 'b j -> (b h) () j', h=h)
+        sim.masked_fill_(~mask, max_neg_value)
+
+    # attention, what we cannot get enough of
+    attn = sim.softmax(dim=-1)
+
+    out = einsum('b i j, b j d -> b i d', attn, v)
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    return self.to_out(out)
+
+
+def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
+    assert hypernetwork_name, 'embedding not selected'
+
+    shared.hypernetwork = shared.hypernetworks[hypernetwork_name]
+
+    shared.state.textinfo = "Initializing hypernetwork training..."
+    shared.state.job_count = steps
+
+    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+
+    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
+
+    if save_hypernetwork_every > 0:
+        hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
+        os.makedirs(hypernetwork_dir, exist_ok=True)
+    else:
+        hypernetwork_dir = None
+
+    if create_image_every > 0:
+        images_dir = os.path.join(log_directory, "images")
+        os.makedirs(images_dir, exist_ok=True)
+    else:
+        images_dir = None
+
+    cond_model = shared.sd_model.cond_stage_model
+
+    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
+    with torch.autocast("cuda"):
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+
+    hypernetwork = shared.hypernetworks[hypernetwork_name]
+    weights = hypernetwork.weights()
+    for weight in weights:
+        weight.requires_grad = True
+
+    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
+
+    losses = torch.zeros((32,))
+
+    last_saved_file = "<none>"
+    last_saved_image = "<none>"
+
+    ititial_step = hypernetwork.step or 0
+    if ititial_step > steps:
+        return hypernetwork, filename
+
+    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    for i, (x, text) in pbar:
+        hypernetwork.step = i + ititial_step
+
+        if hypernetwork.step > steps:
+            break
+
+        if shared.state.interrupted:
+            break
+
+        with torch.autocast("cuda"):
+            c = cond_model([text])
+
+            x = x.to(devices.device)
+            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+            del x
+
+            losses[hypernetwork.step % losses.shape[0]] = loss.item()
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        pbar.set_description(f"loss: {losses.mean():.7f}")
+
+        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
+            hypernetwork.save(last_saved_file)
+
+        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
+            last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
+
+            preview_text = text if preview_image_prompt == "" else preview_image_prompt
+
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                prompt=preview_text,
+                steps=20,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+            )
+
+            processed = processing.process_images(p)
+            image = processed.images[0]
+
+            shared.state.current_image = image
+            image.save(last_saved_image)
+
+            last_saved_image += f", prompt: {preview_text}"
+
+        shared.state.job_no = hypernetwork.step
+
+        shared.state.textinfo = f"""
+<p>
+Loss: {losses.mean():.7f}<br/>
+Step: {hypernetwork.step}<br/>
+Last prompt: {html.escape(text)}<br/>
+Last saved embedding: {html.escape(last_saved_file)}<br/>
+Last saved image: {html.escape(last_saved_image)}<br/>
+</p>
+"""
+
+    checkpoint = sd_models.select_checkpoint()
+
+    hypernetwork.sd_checkpoint = checkpoint.hash
+    hypernetwork.sd_checkpoint_name = checkpoint.model_name
+    hypernetwork.save(filename)
+
+    return hypernetwork, filename
+
+
diff --git a/modules/hypernetwork/ui.py b/modules/hypernetwork/ui.py
new file mode 100644
index 00000000..525f978c
--- /dev/null
+++ b/modules/hypernetwork/ui.py
@@ -0,0 +1,43 @@
+import html
+import os
+
+import gradio as gr
+
+import modules.textual_inversion.textual_inversion
+import modules.textual_inversion.preprocess
+from modules import sd_hijack, shared
+
+
+def create_hypernetwork(name):
+    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
+    assert not os.path.exists(fn), f"file {fn} already exists"
+
+    hypernetwork = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
+    hypernetwork.save(fn)
+
+    shared.reload_hypernetworks()
+    shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None)
+
+    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
+
+
+def train_hypernetwork(*args):
+
+    initial_hypernetwork = shared.hypernetwork
+
+    try:
+        sd_hijack.undo_optimizations()
+
+        hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args)
+
+        res = f"""
+Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
+Hypernetwork saved to {html.escape(filename)}
+"""
+        return res, ""
+    except Exception:
+        raise
+    finally:
+        shared.hypernetwork = initial_hypernetwork
+        sd_hijack.apply_optimizations()
+
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index d68f89cc..ec8c9d4b 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -8,7 +8,7 @@ from torch import einsum
 from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared
 from modules.shared import opts, device, cmd_opts
 
 import ldm.modules.attention
@@ -32,6 +32,8 @@ def apply_optimizations():
 
 
 def undo_optimizations():
+    from modules.hypernetwork import hypernetwork
+
     ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index d9cca485..3f32e020 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -45,8 +45,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
     q_in = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.selected_hypernetwork()
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+    hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None)
 
     if hypernetwork_layers is not None:
         k_in = self.to_k(hypernetwork_layers[0](context))
diff --git a/modules/shared.py b/modules/shared.py
index 879d8424..c5a893e8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, hypernetwork
+from modules import sd_samplers
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -28,6 +28,7 @@ parser.add_argument("--no-half", action='store_true', help="do not switch the mo
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
@@ -76,11 +77,15 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 
 config_filename = cmd_opts.ui_settings_file
 
-hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
 
+def reload_hypernetworks():
+    from modules.hypernetwork import hypernetwork
+    hypernetworks.clear()
+    hypernetworks.update(hypernetwork.load_hypernetworks(cmd_opts.hypernetwork_dir))
 
-def selected_hypernetwork():
-    return hypernetworks.get(opts.sd_hypernetwork, None)
+
+hypernetworks = {}
+hypernetwork = None
 
 
 class State:
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index f19ac5e0..c57de1f9 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -22,7 +22,6 @@ def preprocess(*args):
 
 
 def train_embedding(*args):
-
     try:
         sd_hijack.undo_optimizations()
 
diff --git a/modules/ui.py b/modules/ui.py
index 4f18126f..051908c1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -37,6 +37,7 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
+import modules.hypernetwork.ui
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -965,6 +966,18 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create", variant='primary')
 
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
+
+                    new_hypernetwork_name = gr.Textbox(label="Name")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_hypernetwork = gr.Button(value="Create", variant='primary')
+
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
 
@@ -986,6 +999,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                    train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
                     learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
@@ -993,15 +1007,12 @@ def create_ui(wrap_gradio_gpu_call):
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
+                    preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
 
                     with gr.Row():
-                        with gr.Column(scale=2):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            with gr.Row():
-                                interrupt_training = gr.Button(value="Interrupt")
-                                train_embedding = gr.Button(value="Train", variant='primary')
+                        interrupt_training = gr.Button(value="Interrupt")
+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
+                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
 
             with gr.Column():
                 progressbar = gr.HTML(elem_id="ti_progressbar")
@@ -1027,6 +1038,18 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
+        create_hypernetwork.click(
+            fn=modules.hypernetwork.ui.create_hypernetwork,
+            inputs=[
+                new_hypernetwork_name,
+            ],
+            outputs=[
+                train_hypernetwork_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
         run_preprocess.click(
             fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
@@ -1062,12 +1085,33 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
+        train_hypernetwork.click(
+            fn=wrap_gradio_gpu_call(modules.hypernetwork.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_hypernetwork_name,
+                learn_rate,
+                dataset_directory,
+                log_directory,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                preview_image_prompt,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
         interrupt_training.click(
             fn=lambda: shared.state.interrupt(),
             inputs=[],
             outputs=[],
         )
 
+
     def create_setting_component(key):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
-- 
cgit v1.2.3


From c9cc65b201679ea43c763b0d85e749d40bbc5433 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 04:09:18 +0300
Subject: switch to the proper way of calling xformers

---
 modules/sd_hijack_optimizations.py | 28 +++-------------------------
 1 file changed, 3 insertions(+), 25 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index da1b76e1..7fb4a45e 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -94,39 +94,17 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
-def _maybe_init(self, x):
-    """
-    Initialize the attention operator, if required We expect the head dimension to be exposed here, meaning that x
-    : B, Head, Length
-    """
-    if self.attention_op is not None:
-        return
-    _, M, K = x.shape
-    try:
-        self.attention_op = xformers.ops.AttentionOpDispatch(
-            dtype=x.dtype,
-            device=x.device,
-            k=K,
-            attn_bias_type=type(None),
-            has_dropout=False,
-            kv_len=M,
-            q_len=M,
-        ).op
-    except NotImplementedError as err:
-        raise NotImplementedError(f"Please install xformers with the flash attention / cutlass components.\n{err}")
-
 def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
     context = default(context, x)
     k_in = self.to_k(context)
     v_in = self.to_v(context)
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
-    self._maybe_init(q)
-    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
 
-    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    out = rearrange(out, 'b n h d -> b n (h d)', h=h)
     return self.to_out(out)
 
 def cross_attention_attnblock_forward(self, x):
-- 
cgit v1.2.3


From b70eaeb2005a5a9593119e7fd32b8072c2a208d5 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 04:10:35 +0300
Subject: delete broken and unnecessary aliases

---
 modules/sd_hijack.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index cbdb9d3c..0e99c319 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -21,16 +21,14 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 def apply_optimizations():
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if cmd_opts.opt_split_attention_v1:
+    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip):
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
+    elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif cmd_opts.opt_split_attention:
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
-    elif not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip):
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-        ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init
-        ldm.modules.attention.CrossAttention.attention_op = None
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
 
 def undo_optimizations():
-- 
cgit v1.2.3


From f2055cb1d4ce45d7aaacc49d8ab5bec7791a8f47 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sat, 8 Oct 2022 01:47:02 -0400
Subject: Add hypernetwork support to split cross attention v1

* Add hypernetwork support to split_cross_attention_forward_v1
* Fix device check in esrgan_model.py to use devices.device_esrgan instead of shared.device
---
 modules/esrgan_model.py            |  2 +-
 modules/sd_hijack_optimizations.py | 18 ++++++++++++++----
 2 files changed, 15 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index d17e730f..28548124 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -111,7 +111,7 @@ class UpscalerESRGAN(Upscaler):
             print("Unable to load %s from %s" % (self.model_path, filename))
             return None
 
-        pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None)
+        pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
         crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
 
         pretrained_net = fix_model_layers(crt_model, pretrained_net)
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index d9cca485..3351c740 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -12,13 +12,22 @@ from modules import shared
 def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     h = self.heads
 
-    q = self.to_q(x)
+    q_in = self.to_q(x)
     context = default(context, x)
-    k = self.to_k(context)
-    v = self.to_v(context)
+
+    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k_in = self.to_k(hypernetwork_layers[0](context))
+        v_in = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k_in = self.to_k(context)
+        v_in = self.to_v(context)
     del context, x
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    del q_in, k_in, v_in
 
     r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
     for i in range(0, q.shape[0], 2):
@@ -31,6 +40,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
 
         r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
         del s2
+    del q, k, v
 
     r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
     del r1
-- 
cgit v1.2.3


From 5d54f35c583bd5a3b0ee271a862827f1ca81ef09 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 11:55:02 +0300
Subject: add xformers attnblock and hypernetwork support

---
 modules/sd_hijack_optimizations.py | 20 ++++++++++++++++++--
 1 file changed, 18 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 7fb4a45e..c78d5838 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -98,8 +98,14 @@ def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
     context = default(context, x)
-    k_in = self.to_k(context)
-    v_in = self.to_v(context)
+    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+    if hypernetwork_layers is not None:
+        k_in = self.to_k(hypernetwork_layers[0](context))
+        v_in = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k_in = self.to_k(context)
+        v_in = self.to_v(context)
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
@@ -169,3 +175,13 @@ def cross_attention_attnblock_forward(self, x):
         h3 += x
 
         return h3
+    
+    def xformers_attnblock_forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q1 = self.q(h_).contiguous()
+        k1 = self.k(h_).contiguous()
+        v = self.v(h_).contiguous()
+        out = xformers.ops.memory_efficient_attention(q1, k1, v)
+        out = self.proj_out(out)
+        return x+out
-- 
cgit v1.2.3


From 76a616fa6b814c681eaf6edc87eb3001b8c2b6be Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 11:55:38 +0300
Subject: Update sd_hijack_optimizations.py

---
 modules/sd_hijack_optimizations.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index c78d5838..ee58c7e4 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -176,7 +176,7 @@ def cross_attention_attnblock_forward(self, x):
 
         return h3
     
-    def xformers_attnblock_forward(self, x):
+def xformers_attnblock_forward(self, x):
         h_ = x
         h_ = self.norm(h_)
         q1 = self.q(h_).contiguous()
-- 
cgit v1.2.3


From 91d66f5520df416db718103d460550ad495e952d Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 11:56:01 +0300
Subject: use new attnblock for xformers path

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 0e99c319..3da8c8ce 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -23,7 +23,7 @@ def apply_optimizations():
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip):
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif cmd_opts.opt_split_attention:
-- 
cgit v1.2.3


From 616b7218f7c469d25c138634472017a7e18e742e Mon Sep 17 00:00:00 2001
From: leko <rexx0520@gmail.com>
Date: Fri, 7 Oct 2022 23:09:21 +0800
Subject: fix: handles when state_dict does not exist

---
 modules/sd_models.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 8f794b47..9409d070 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -122,7 +122,11 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
     pl_sd = torch.load(checkpoint_file, map_location="cpu")
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
-    sd = pl_sd["state_dict"]
+    
+    if "state_dict" in pl_sd:
+        sd = pl_sd["state_dict"]
+    else:
+        sd = pl_sd
 
     model.load_state_dict(sd, strict=False)
 
-- 
cgit v1.2.3


From 706d5944a075a6523ea7f00165d630efc085ca22 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 13:38:57 +0300
Subject: let user choose his own prompt token count limit

---
 modules/processing.py |  6 ++++++
 modules/sd_hijack.py  | 13 +++++++------
 modules/shared.py     |  5 +++--
 3 files changed, 16 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index f773a30e..d814d5ac 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -123,6 +123,7 @@ class Processed:
         self.index_of_first_image = index_of_first_image
         self.styles = p.styles
         self.job_timestamp = state.job_timestamp
+        self.max_prompt_tokens = opts.max_prompt_tokens
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -141,6 +142,7 @@ class Processed:
         self.all_subseeds = all_subseeds or [self.subseed]
         self.infotexts = infotexts or [info]
 
+
     def js(self):
         obj = {
             "prompt": self.prompt,
@@ -169,6 +171,7 @@ class Processed:
             "infotexts": self.infotexts,
             "styles": self.styles,
             "job_timestamp": self.job_timestamp,
+            "max_prompt_tokens": self.max_prompt_tokens,
         }
 
         return json.dumps(obj)
@@ -266,6 +269,8 @@ def fix_seed(p):
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
+    max_tokens = getattr(p, 'max_prompt_tokens', opts.max_prompt_tokens)
+
     generation_params = {
         "Steps": p.steps,
         "Sampler": sd_samplers.samplers[p.sampler_index].name,
@@ -281,6 +286,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
+        "Max tokens": (None if max_tokens == shared.vanilla_max_prompt_tokens else max_tokens)
     }
 
     generation_params.update(p.extra_generation_params)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index d68f89cc..340329c0 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -18,7 +18,6 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
 
-
 def apply_optimizations():
     undo_optimizations()
 
@@ -83,7 +82,7 @@ class StableDiffusionModelHijack:
             layer.padding_mode = 'circular' if enable else 'zeros'
 
     def tokenize(self, text):
-        max_length = self.clip.max_length - 2
+        max_length = opts.max_prompt_tokens - 2
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, max_length
 
@@ -94,7 +93,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         self.wrapped = wrapped
         self.hijack: StableDiffusionModelHijack = hijack
         self.tokenizer = wrapped.tokenizer
-        self.max_length = wrapped.max_length
         self.token_mults = {}
 
         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
@@ -116,7 +114,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
-        maxlen = self.wrapped.max_length
+        maxlen = opts.max_prompt_tokens
 
         if opts.enable_emphasis:
             parsed = prompt_parser.parse_prompt_attention(line)
@@ -191,7 +189,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def process_text_old(self, text):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
-        maxlen = self.wrapped.max_length
+        maxlen = self.wrapped.max_length  # you get to stay at 77
         used_custom_terms = []
         remade_batch_tokens = []
         overflowing_words = []
@@ -268,8 +266,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         if len(used_custom_terms) > 0:
             self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
 
+        position_ids_array = [min(x, 75) for x in range(len(remade_batch_tokens[0])-1)] + [76]
+        position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
+
         tokens = torch.asarray(remade_batch_tokens).to(device)
-        outputs = self.wrapped.transformer(input_ids=tokens)
+        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
         z = outputs.last_hidden_state
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
diff --git a/modules/shared.py b/modules/shared.py
index 879d8424..864e772c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -118,8 +118,8 @@ prompt_styles = modules.styles.StyleDatabase(styles_filename)
 interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
-# This was moved to webui.py with the other model "setup" calls.
-# modules.sd_models.list_models()
+
+vanilla_max_prompt_tokens = 77
 
 
 def realesrgan_models_names():
@@ -221,6 +221,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
+    "max_prompt_tokens": OptionInfo(vanilla_max_prompt_tokens, f"Max prompt token count. Two tokens are reserved for for start and end. Default is {vanilla_max_prompt_tokens}. Setting this to a different value will result in different pictures for same seed.", gr.Number, {"precision": 0}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.3


From 786d9f63aaa4515df82eb2cf357ea92f3dae1e29 Mon Sep 17 00:00:00 2001
From: Trung Ngo <codem01@gmail.com>
Date: Tue, 4 Oct 2022 22:56:30 -0500
Subject: Add button to skip the current iteration

---
 modules/img2img.py    | 4 ++++
 modules/processing.py | 4 ++++
 modules/shared.py     | 5 +++++
 modules/ui.py         | 8 ++++++++
 4 files changed, 21 insertions(+)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index da212d72..e60b7e0f 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -32,6 +32,10 @@ def process_batch(p, input_dir, output_dir, args):
 
     for i, image in enumerate(images):
         state.job = f"{i+1} out of {len(images)}"
+        if state.skipped:
+            state.skipped = False
+            state.interrupted = False
+            continue
 
         if state.interrupted:
             break
diff --git a/modules/processing.py b/modules/processing.py
index d814d5ac..6805039c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -355,6 +355,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             state.job_count = p.n_iter
 
         for n in range(p.n_iter):
+            if state.skipped:
+                state.skipped = False
+                state.interrupted = False
+            
             if state.interrupted:
                 break
 
diff --git a/modules/shared.py b/modules/shared.py
index 864e772c..7f802bd9 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -84,6 +84,7 @@ def selected_hypernetwork():
 
 
 class State:
+    skipped = False
     interrupted = False
     job = ""
     job_no = 0
@@ -96,6 +97,10 @@ class State:
     current_image_sampling_step = 0
     textinfo = None
 
+    def skip(self):
+        self.skipped = True
+        self.interrupted = True
+
     def interrupt(self):
         self.interrupted = True
 
diff --git a/modules/ui.py b/modules/ui.py
index 4f18126f..e3e62fdd 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -191,6 +191,7 @@ def wrap_gradio_call(func, extra_outputs=None):
         # last item is always HTML
         res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
 
+        shared.state.skipped = False
         shared.state.interrupted = False
         shared.state.job_count = 0
 
@@ -411,9 +412,16 @@ def create_toprow(is_img2img):
 
         with gr.Column(scale=1):
             with gr.Row():
+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
                 interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
                 submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
 
+                skip.click(
+                    fn=lambda: shared.state.skip(),
+                    inputs=[],
+                    outputs=[],
+                )
+
                 interrupt.click(
                     fn=lambda: shared.state.interrupt(),
                     inputs=[],
-- 
cgit v1.2.3


From 00117a07efbbe8482add12262a179326541467de Mon Sep 17 00:00:00 2001
From: Trung Ngo <codem01@gmail.com>
Date: Sat, 8 Oct 2022 05:33:21 -0500
Subject: check specifically for skipped

---
 modules/img2img.py     | 2 --
 modules/processing.py  | 3 +--
 modules/sd_samplers.py | 4 ++--
 modules/shared.py      | 1 -
 4 files changed, 3 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index e60b7e0f..24126774 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -34,8 +34,6 @@ def process_batch(p, input_dir, output_dir, args):
         state.job = f"{i+1} out of {len(images)}"
         if state.skipped:
             state.skipped = False
-            state.interrupted = False
-            continue
 
         if state.interrupted:
             break
diff --git a/modules/processing.py b/modules/processing.py
index 6805039c..3657fe69 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -357,7 +357,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         for n in range(p.n_iter):
             if state.skipped:
                 state.skipped = False
-                state.interrupted = False
             
             if state.interrupted:
                 break
@@ -385,7 +384,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             with devices.autocast():
                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
 
-            if state.interrupted:
+            if state.interrupted or state.skipped:
 
                 # if we are interruped, sample returns just noise
                 # use the image collected previously in sampler loop
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index df17e93c..13a8b322 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -106,7 +106,7 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs):
     seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
 
     for x in seq:
-        if state.interrupted:
+        if state.interrupted or state.skipped:
             break
 
         yield x
@@ -254,7 +254,7 @@ def extended_trange(sampler, count, *args, **kwargs):
     seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
 
     for x in seq:
-        if state.interrupted:
+        if state.interrupted or state.skipped:
             break
 
         if sampler.stop_at is not None and x > sampler.stop_at:
diff --git a/modules/shared.py b/modules/shared.py
index 7f802bd9..ca462628 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -99,7 +99,6 @@ class State:
 
     def skip(self):
         self.skipped = True
-        self.interrupted = True
 
     def interrupt(self):
         self.interrupted = True
-- 
cgit v1.2.3


From 4999eb2ef9b30e8c42ca7e4a94d4bbffe4d1f015 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 14:25:47 +0300
Subject: do not let user choose his own prompt token count limit

---
 modules/processing.py |  5 -----
 modules/sd_hijack.py  | 25 ++++++++++++-------------
 modules/shared.py     |  3 ---
 3 files changed, 12 insertions(+), 21 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 3657fe69..d5162ddc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -123,7 +123,6 @@ class Processed:
         self.index_of_first_image = index_of_first_image
         self.styles = p.styles
         self.job_timestamp = state.job_timestamp
-        self.max_prompt_tokens = opts.max_prompt_tokens
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -171,7 +170,6 @@ class Processed:
             "infotexts": self.infotexts,
             "styles": self.styles,
             "job_timestamp": self.job_timestamp,
-            "max_prompt_tokens": self.max_prompt_tokens,
         }
 
         return json.dumps(obj)
@@ -269,8 +267,6 @@ def fix_seed(p):
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
-    max_tokens = getattr(p, 'max_prompt_tokens', opts.max_prompt_tokens)
-
     generation_params = {
         "Steps": p.steps,
         "Sampler": sd_samplers.samplers[p.sampler_index].name,
@@ -286,7 +282,6 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
-        "Max tokens": (None if max_tokens == shared.vanilla_max_prompt_tokens else max_tokens)
     }
 
     generation_params.update(p.extra_generation_params)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 340329c0..2c1332c9 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -36,6 +36,13 @@ def undo_optimizations():
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
 
+def get_target_prompt_token_count(token_count):
+    if token_count < 75:
+        return 75
+
+    return math.ceil(token_count / 10) * 10
+
+
 class StableDiffusionModelHijack:
     fixes = None
     comments = []
@@ -84,7 +91,7 @@ class StableDiffusionModelHijack:
     def tokenize(self, text):
         max_length = opts.max_prompt_tokens - 2
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
-        return remade_batch_tokens[0], token_count, max_length
+        return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
 
 
 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
@@ -114,7 +121,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
-        maxlen = opts.max_prompt_tokens
 
         if opts.enable_emphasis:
             parsed = prompt_parser.parse_prompt_attention(line)
@@ -146,19 +152,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     used_custom_terms.append((embedding.name, embedding.checksum()))
                     i += embedding_length_in_tokens
 
-        if len(remade_tokens) > maxlen - 2:
-            vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
-            ovf = remade_tokens[maxlen - 2:]
-            overflowing_words = [vocab.get(int(x), "") for x in ovf]
-            overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
-            hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-
         token_count = len(remade_tokens)
-        remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
-        remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
+        prompt_target_length = get_target_prompt_token_count(token_count)
+        tokens_to_add = prompt_target_length - len(remade_tokens) + 1
 
-        multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
-        multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
+        remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
+        multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
 
         return remade_tokens, fixes, multipliers, token_count
 
diff --git a/modules/shared.py b/modules/shared.py
index ca462628..475d7e52 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -123,8 +123,6 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
 
-vanilla_max_prompt_tokens = 77
-
 
 def realesrgan_models_names():
     import modules.realesrgan_model
@@ -225,7 +223,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-    "max_prompt_tokens": OptionInfo(vanilla_max_prompt_tokens, f"Max prompt token count. Two tokens are reserved for for start and end. Default is {vanilla_max_prompt_tokens}. Setting this to a different value will result in different pictures for same seed.", gr.Number, {"precision": 0}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.3


From 77f4237d1c3af1756e7dab2699e3dcebad5619d6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 15:25:59 +0300
Subject: fix bugs related to variable prompt lengths

---
 modules/sd_hijack.py   | 14 +++++++++-----
 modules/sd_samplers.py | 35 ++++++++++++++++++++++++++++-------
 2 files changed, 37 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 2c1332c9..7e7fde0f 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -89,7 +89,6 @@ class StableDiffusionModelHijack:
             layer.padding_mode = 'circular' if enable else 'zeros'
 
     def tokenize(self, text):
-        max_length = opts.max_prompt_tokens - 2
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
 
@@ -174,7 +173,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if line in cache:
                 remade_tokens, fixes, multipliers = cache[line]
             else:
-                remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+                token_count = max(current_token_count, token_count)
 
                 cache[line] = (remade_tokens, fixes, multipliers)
 
@@ -265,15 +265,19 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         if len(used_custom_terms) > 0:
             self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
 
-        position_ids_array = [min(x, 75) for x in range(len(remade_batch_tokens[0])-1)] + [76]
+        target_token_count = get_target_prompt_token_count(token_count) + 2
+
+        position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
         position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
 
-        tokens = torch.asarray(remade_batch_tokens).to(device)
+        remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
+        tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
         outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
         z = outputs.last_hidden_state
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-        batch_multipliers = torch.asarray(batch_multipliers).to(device)
+        batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
+        batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
         original_mean = z.mean()
         z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
         new_mean = z.mean()
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 13a8b322..eade0dbb 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -142,6 +142,16 @@ class VanillaStableDiffusionSampler:
         assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
         cond = tensor
 
+        # for DDIM, shapes must match, we can't just process cond and uncond independently;
+        # filling unconditional_conditioning with repeats of the last vector to match length is
+        # not 100% correct but should work well enough
+        if unconditional_conditioning.shape[1] < cond.shape[1]:
+            last_vector = unconditional_conditioning[:, -1:]
+            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
+            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
+        elif unconditional_conditioning.shape[1] > cond.shape[1]:
+            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
+
         if self.mask is not None:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)
             x_dec = img_orig * self.mask + self.nmask * x_dec
@@ -221,18 +231,29 @@ class CFGDenoiser(torch.nn.Module):
 
         x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
-        cond_in = torch.cat([tensor, uncond])
 
-        if shared.batch_cond_uncond:
-            x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
+        if tensor.shape[1] == uncond.shape[1]:
+            cond_in = torch.cat([tensor, uncond])
+
+            if shared.batch_cond_uncond:
+                x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
+            else:
+                x_out = torch.zeros_like(x_in)
+                for batch_offset in range(0, x_out.shape[0], batch_size):
+                    a = batch_offset
+                    b = a + batch_size
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
         else:
             x_out = torch.zeros_like(x_in)
-            for batch_offset in range(0, x_out.shape[0], batch_size):
+            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
+            for batch_offset in range(0, tensor.shape[0], batch_size):
                 a = batch_offset
-                b = a + batch_size
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
+                b = min(a + batch_size, tensor.shape[0])
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])
+
+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
 
-        denoised_uncond = x_out[-batch_size:]
+        denoised_uncond = x_out[-uncond.shape[0]:]
         denoised = torch.clone(denoised_uncond)
 
         for i, conds in enumerate(conds_list):
-- 
cgit v1.2.3


From 7001bffe0247804793dfabb69ac96d832572ccd0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 15:43:25 +0300
Subject: fix AND broken for long prompts

---
 modules/prompt_parser.py | 9 +++++++++
 1 file changed, 9 insertions(+)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index f00256f2..15666073 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -239,6 +239,15 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
 
         conds_list.append(conds_for_batch)
 
+    # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes
+    # and won't be able to torch.stack them. So this fixes that.
+    token_count = max([x.shape[0] for x in tensors])
+    for i in range(len(tensors)):
+        if tensors[i].shape[0] != token_count:
+            last_vector = tensors[i][-1:]
+            last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
+            tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
+
     return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
 
 
-- 
cgit v1.2.3


From 772db721a52da374d627b60994222051f26c27a7 Mon Sep 17 00:00:00 2001
From: ddPn08 <pyn.g.1125@gmail.com>
Date: Fri, 7 Oct 2022 23:02:07 +0900
Subject: fix glob path in hypernetwork.py

---
 modules/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index c7b86682..7f062242 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -43,7 +43,7 @@ class Hypernetwork:
 def load_hypernetworks(path):
     res = {}
 
-    for filename in glob.iglob(path + '**/*.pt', recursive=True):
+    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
         try:
             hn = Hypernetwork(filename)
             res[hn.name] = hn
-- 
cgit v1.2.3


From 5f85a74b00c0154bfd559dc67edfa7e30342b7c9 Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Fri, 7 Oct 2022 17:48:34 -0400
Subject: fix bug where when using prompt composition, hijack_comments
 generated before the final AND will be dropped

---
 modules/processing.py | 1 +
 modules/sd_hijack.py  | 5 ++++-
 2 files changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index d5162ddc..8240ee27 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -313,6 +313,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         os.makedirs(p.outpath_grids, exist_ok=True)
 
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
+    modules.sd_hijack.model_hijack.clear_comments()
 
     comments = {}
 
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 7e7fde0f..ba808a39 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -88,6 +88,9 @@ class StableDiffusionModelHijack:
         for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
             layer.padding_mode = 'circular' if enable else 'zeros'
 
+    def clear_comments(self):
+        self.comments = []
+
     def tokenize(self, text):
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
@@ -260,7 +263,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
 
         self.hijack.fixes = hijack_fixes
-        self.hijack.comments = hijack_comments
+        self.hijack.comments += hijack_comments
 
         if len(used_custom_terms) > 0:
             self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
-- 
cgit v1.2.3


From 26b459a3799c5cdf71ca8ed5315a99f69c69f02c Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:20:04 +0300
Subject: default to split attention if cuda is available and xformers is not

---
 modules/sd_hijack.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 3da8c8ce..04adcf03 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -21,12 +21,12 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 def apply_optimizations():
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip):
+    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip or shared.xformers_available):
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    elif cmd_opts.opt_split_attention:
+    elif cmd_opts.opt_split_attention or torch.cuda.is_available():
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
-- 
cgit v1.2.3


From ddfa9a97865c732193023a71521c5b7b53d8571b Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:20:41 +0300
Subject: add xformers_available shared variable

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 8cc3b2fe..6ed4b802 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -74,7 +74,7 @@ device = devices.device
 
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
 parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
-
+xformers_available = False
 config_filename = cmd_opts.ui_settings_file
 
 
-- 
cgit v1.2.3


From 69d0053583757ce2942d62de81e8b89e6be07840 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:21:40 +0300
Subject: update sd_hijack_opt to respect new env variables

---
 modules/sd_hijack_optimizations.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index ee58c7e4..be09ec8f 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,9 +1,14 @@
 import math
 import torch
 from torch import einsum
-import xformers.ops
-import functorch
-xformers._is_functorch_available=True
+try:
+    import xformers.ops
+    import functorch
+    xformers._is_functorch_available = True
+    shared.xformers_available = True
+except:
+    print('Cannot find xformers, defaulting to split attention. Try setting --xformers in your webui-user file if you wish to install it.')
+    continue
 from ldm.util import default
 from einops import rearrange
 
-- 
cgit v1.2.3


From 970de9ee6891ff586821d0d80dde01c2f6c681b3 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:29:43 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 04adcf03..5b30539f 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -21,7 +21,7 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 def apply_optimizations():
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip or shared.xformers_available):
+    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip) and shared.xformers_available:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
-- 
cgit v1.2.3


From 7ff1170a2e11b6f00f587407326db0b9f8f51adf Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 16:33:39 +0300
Subject: emergency fix for xformers (continue + shared)

---
 modules/sd_hijack_optimizations.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index e43e2c7a..05023b6f 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,19 +1,19 @@
 import math
 import torch
 from torch import einsum
-try:
-    import xformers.ops
-    import functorch
-    xformers._is_functorch_available = True
-    shared.xformers_available = True
-except:
-    print('Cannot find xformers, defaulting to split attention. Try setting --xformers in your webui-user file if you wish to install it.')
-    continue
+
 from ldm.util import default
 from einops import rearrange
 
 from modules import shared
 
+try:
+    import xformers.ops
+    import functorch
+    xformers._is_functorch_available = True
+    shared.xformers_available = True
+except Exception:
+    print('Cannot find xformers, defaulting to split attention. Try adding --xformers commandline argument to your webui-user file if you wish to install it.')
 
 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
 def split_cross_attention_forward_v1(self, x, context=None, mask=None):
-- 
cgit v1.2.3


From dc1117233ef8f9b25ff1ac40b158f20b70ba2fcb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 17:02:18 +0300
Subject: simplify xfrmers options: --xformers to enable and that's it

---
 modules/sd_hijack.py               |  2 +-
 modules/sd_hijack_optimizations.py | 20 +++++++++++++-------
 modules/shared.py                  |  2 +-
 3 files changed, 15 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 5d93f7f6..91e98c16 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -22,7 +22,7 @@ def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip) and shared.xformers_available:
+    if cmd_opts.xformers and shared.xformers_available and not torch.version.hip:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 05023b6f..d23d733b 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,4 +1,7 @@
 import math
+import sys
+import traceback
+
 import torch
 from torch import einsum
 
@@ -7,13 +10,16 @@ from einops import rearrange
 
 from modules import shared
 
-try:
-    import xformers.ops
-    import functorch
-    xformers._is_functorch_available = True
-    shared.xformers_available = True
-except Exception:
-    print('Cannot find xformers, defaulting to split attention. Try adding --xformers commandline argument to your webui-user file if you wish to install it.')
+if shared.cmd_opts.xformers:
+    try:
+        import xformers.ops
+        import functorch
+        xformers._is_functorch_available = True
+        shared.xformers_available = True
+    except Exception:
+        print("Cannot import xformers", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
 
 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
 def split_cross_attention_forward_v1(self, x, context=None, mask=None):
diff --git a/modules/shared.py b/modules/shared.py
index d68df751..02cb2722 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -43,7 +43,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
 parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
-parser.add_argument("--disable-opt-xformers-attention", action='store_true', help="force-disables xformers attention optimization")
+parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-- 
cgit v1.2.3


From 27032c47df9c07ac21dd5b89fa7dc247bb8705b6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 17:10:05 +0300
Subject: restore old opt_split_attention/disable_opt_split_attention logic

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 91e98c16..335a2bcf 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -27,7 +27,7 @@ def apply_optimizations():
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    elif cmd_opts.opt_split_attention or torch.cuda.is_available():
+    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
-- 
cgit v1.2.3


From 4f33289d0fc5aa3a197f4a4c926d03d44f0d597e Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Sat, 8 Oct 2022 22:48:15 +0900
Subject: Fixed typo

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index e3e62fdd..ffd75f6a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -946,7 +946,7 @@ def create_ui(wrap_gradio_gpu_call):
                 custom_name = gr.Textbox(label="Custom Name (Optional)")
                 interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
                 interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
-                save_as_half = gr.Checkbox(value=False, label="Safe as float16")
+                save_as_half = gr.Checkbox(value=False, label="Save as float16")
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
             with gr.Column(variant='panel'):
-- 
cgit v1.2.3


From cfc33f99d47d1f45af15499e5965834089d11858 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 17:28:58 +0300
Subject: why did you do this

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 335a2bcf..ed271976 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -28,7 +28,7 @@ def apply_optimizations():
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-        ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
 
-- 
cgit v1.2.3


From 017b6b8744f0771e498656ec043e12d5cc6969a7 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 17:27:21 +0300
Subject: check for ampere

---
 modules/sd_hijack.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index ed271976..5e266d5e 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -22,9 +22,10 @@ def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if cmd_opts.xformers and shared.xformers_available and not torch.version.hip:
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+    if cmd_opts.xformers and shared.xformers_available and torch.version.cuda:
+        if torch.cuda.get_device_capability(shared.device) == (8, 6):
+            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
+            ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-- 
cgit v1.2.3


From cc0258aea7b6605be3648900063cfa96ed7c5ffa Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 17:44:53 +0300
Subject: check for ampere without destroying the optimizations. again.

---
 modules/sd_hijack.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 5e266d5e..a3e374f0 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -22,10 +22,9 @@ def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if cmd_opts.xformers and shared.xformers_available and torch.version.cuda:
-        if torch.cuda.get_device_capability(shared.device) == (8, 6):
-            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-            ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+    if cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6):
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-- 
cgit v1.2.3


From a5550f0213c3f145b1c984816ebcef92c48853ee Mon Sep 17 00:00:00 2001
From: Artem Zagidulin <p0chta@yandex.ru>
Date: Wed, 5 Oct 2022 19:10:39 +0300
Subject: alternate prompt

---
 modules/prompt_parser.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 15666073..919d5d31 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -13,13 +13,14 @@ import lark
 
 schedule_parser = lark.Lark(r"""
 !start: (prompt | /[][():]/+)*
-prompt: (emphasized | scheduled | plain | WHITESPACE)*
+prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
 !emphasized: "(" prompt ")"
         | "(" prompt ":" prompt ")"
         | "[" prompt "]"
 scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
+alternate: "[" prompt ("|" prompt)+ "]"
 WHITESPACE: /\s+/
-plain: /([^\\\[\]():]|\\.)+/
+plain: /([^\\\[\]():|]|\\.)+/
 %import common.SIGNED_NUMBER -> NUMBER
 """)
 
@@ -59,6 +60,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
                     tree.children[-1] *= steps
                 tree.children[-1] = min(steps, int(tree.children[-1]))
                 l.append(tree.children[-1])
+            def alternate(self, tree):
+                l.extend(range(1, steps+1))
         CollectSteps().visit(tree)
         return sorted(set(l))
 
@@ -67,6 +70,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
             def scheduled(self, args):
                 before, after, _, when = args
                 yield before or () if step <= when else after
+            def alternate(self, args):
+                yield next(args[(step - 1)%len(args)])
             def start(self, args):
                 def flatten(x):
                     if type(x) == str:
-- 
cgit v1.2.3


From 01f8cb44474e454903c11718e6a4f33dbde34bb8 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Sat, 8 Oct 2022 18:02:56 +0200
Subject: made deepdanbooru optional, added to readme, automatic download of
 deepbooru model

---
 modules/deepbooru.py | 20 ++++++++++----------
 modules/shared.py    |  1 +
 modules/ui.py        | 19 ++++++++++++-------
 3 files changed, 23 insertions(+), 17 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 781b2249..7e3c0618 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -9,16 +9,16 @@ def _load_tf_and_return_tags(pil_image, threshold):
     import numpy as np
 
     this_folder = os.path.dirname(__file__)
-    model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28')
-
-    model_good = False
-    for path_candidate in [model_path, os.path.dirname(model_path)]:
-        if os.path.exists(os.path.join(path_candidate, 'project.json')):
-            model_path = path_candidate
-            model_good = True
-    if not model_good:
-        return ("Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/"
-                "deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru")
+    model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
+    if not os.path.exists(os.path.join(model_path, 'project.json')):
+        # there is no point importing these every time
+        import zipfile
+        from basicsr.utils.download_util import load_file_from_url
+        load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
+                           model_path)
+        with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
+            zip_ref.extractall(model_path)
+        os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
 
     tags = dd.project.load_tags_from_project(model_path)
     model = dd.project.load_model_from_project(
diff --git a/modules/shared.py b/modules/shared.py
index 02cb2722..c87b726e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -44,6 +44,7 @@ parser.add_argument("--scunet-models-path", type=str, help="Path to directory wi
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
+parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
diff --git a/modules/ui.py b/modules/ui.py
index 30583fe9..c5c11c3c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -23,9 +23,10 @@ import gradio.utils
 import gradio.routes
 
 from modules import sd_hijack
-from modules.deepbooru import get_deepbooru_tags
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts
+if cmd_opts.deepdanbooru:
+    from modules.deepbooru import get_deepbooru_tags
 import modules.shared as shared
 from modules.sd_samplers import samplers, samplers_for_img2img
 from modules.sd_hijack import model_hijack
@@ -437,7 +438,10 @@ def create_toprow(is_img2img):
             with gr.Row(scale=1):
                 if is_img2img:
                     interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-                    deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+                    if cmd_opts.deepdanbooru:
+                        deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+                    else:
+                        deepbooru = None
                 else:
                     interrogate = None
                     deepbooru = None
@@ -782,11 +786,12 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[img2img_prompt],
             )
 
-            img2img_deepbooru.click(
-                fn=interrogate_deepbooru,
-                inputs=[init_img],
-                outputs=[img2img_prompt],
-            )
+            if cmd_opts.deepdanbooru:
+                img2img_deepbooru.click(
+                    fn=interrogate_deepbooru,
+                    inputs=[init_img],
+                    outputs=[img2img_prompt],
+                )
 
             save.click(
                 fn=wrap_gradio_call(save_files),
-- 
cgit v1.2.3


From f9c5da159245bb1e7603b3c8b9e0703bcb1c2ff5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 19:05:19 +0300
Subject: add fallback for xformers_attnblock_forward

---
 modules/sd_hijack_optimizations.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index d23d733b..dba21192 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -211,6 +211,7 @@ def cross_attention_attnblock_forward(self, x):
         return h3
     
 def xformers_attnblock_forward(self, x):
+    try:
         h_ = x
         h_ = self.norm(h_)
         q1 = self.q(h_).contiguous()
@@ -218,4 +219,6 @@ def xformers_attnblock_forward(self, x):
         v = self.v(h_).contiguous()
         out = xformers.ops.memory_efficient_attention(q1, k1, v)
         out = self.proj_out(out)
-        return x+out
+        return x + out
+    except NotImplementedError:
+        return cross_attention_attnblock_forward(self, x)
-- 
cgit v1.2.3


From 3061cdb7b610d4ba7f1ea695d9d6364b591e5bc7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 19:22:15 +0300
Subject: add --force-enable-xformers option and also add messages to console
 regarding cross attention optimizations

---
 modules/sd_hijack.py | 6 +++++-
 modules/shared.py    | 1 +
 2 files changed, 6 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index a3e374f0..307cc67d 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -22,12 +22,16 @@ def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6):
+
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6)):
+        print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
+        print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
+        print("Applying cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
diff --git a/modules/shared.py b/modules/shared.py
index 02cb2722..8f941226 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -44,6 +44,7 @@ parser.add_argument("--scunet-models-path", type=str, help="Path to directory wi
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
+parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-- 
cgit v1.2.3


From 15c4278f1a18b8104e135dd82690d10cff39a2e7 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 8 Oct 2022 17:50:01 +0100
Subject: TI preprocess wording
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

I had to check the code to work out what splitting was 🤷🏿
---
 modules/ui.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index ffd75f6a..d52d74c6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -980,9 +980,9 @@ def create_ui(wrap_gradio_gpu_call):
                     process_dst = gr.Textbox(label='Destination directory')
 
                     with gr.Row():
-                        process_flip = gr.Checkbox(label='Flip')
-                        process_split = gr.Checkbox(label='Split into two')
-                        process_caption = gr.Checkbox(label='Add caption')
+                        process_flip = gr.Checkbox(label='Create flipped copies')
+                        process_split = gr.Checkbox(label='Split oversized images into two')
+                        process_caption = gr.Checkbox(label='Use CLIP caption as filename')
 
                     with gr.Row():
                         with gr.Column(scale=3):
-- 
cgit v1.2.3


From b458fa48fe5734a872bca83061d702609cb52940 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 8 Oct 2022 17:56:28 +0100
Subject: Update ui.py

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index d52d74c6..b09359aa 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -982,7 +982,7 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
                         process_split = gr.Checkbox(label='Split oversized images into two')
-                        process_caption = gr.Checkbox(label='Use CLIP caption as filename')
+                        process_caption = gr.Checkbox(label='Use BLIP caption as filename')
 
                     with gr.Row():
                         with gr.Column(scale=3):
-- 
cgit v1.2.3


From 1371d7608b402d6f15c200ec2f5fde4579836a05 Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sat, 8 Oct 2022 14:28:22 -0400
Subject: Added ability to ignore last n layers in FrozenCLIPEmbedder

---
 modules/sd_hijack.py | 11 +++++++++--
 modules/shared.py    |  1 +
 2 files changed, 10 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 307cc67d..f12a9696 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -281,8 +281,15 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
         tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
-        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
-        z = outputs.last_hidden_state
+
+        tmp = -opts.CLIP_ignore_last_layers
+        if (opts.CLIP_ignore_last_layers == 0):
+            outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
+            z = outputs.last_hidden_state
+        else:
+            outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp)
+            z = outputs.hidden_states[tmp]
+            z = self.wrapped.transformer.text_model.final_layer_norm(z)
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
diff --git a/modules/shared.py b/modules/shared.py
index 8f941226..af8dc744 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -225,6 +225,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
+    'CLIP_ignore_last_layers': OptionInfo(0, "Ignore last layers of CLIP model", gr.Slider, {"minimum": 0, "maximum": 5, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.3


From e6e42f98df2c928c4f49351ad6b466387ce87d42 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 19:25:10 +0300
Subject: make --force-enable-xformers work without needing --xformers

---
 modules/sd_hijack_optimizations.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index dba21192..c4396bb9 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -10,7 +10,7 @@ from einops import rearrange
 
 from modules import shared
 
-if shared.cmd_opts.xformers:
+if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
         import xformers.ops
         import functorch
-- 
cgit v1.2.3


From 3b2141c5fb6a3c2b8ab4b1e759a97ead77260129 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 22:21:15 +0300
Subject: add 'Ignore last layers of CLIP model' option as a parameter to the
 infotext

---
 modules/processing.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 8240ee27..515fc91a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -123,6 +123,7 @@ class Processed:
         self.index_of_first_image = index_of_first_image
         self.styles = p.styles
         self.job_timestamp = state.job_timestamp
+        self.clip_skip = opts.CLIP_ignore_last_layers
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -141,7 +142,6 @@ class Processed:
         self.all_subseeds = all_subseeds or [self.subseed]
         self.infotexts = infotexts or [info]
 
-
     def js(self):
         obj = {
             "prompt": self.prompt,
@@ -170,6 +170,7 @@ class Processed:
             "infotexts": self.infotexts,
             "styles": self.styles,
             "job_timestamp": self.job_timestamp,
+            "clip_skip": self.clip_skip,
         }
 
         return json.dumps(obj)
@@ -267,6 +268,8 @@ def fix_seed(p):
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
+    clip_skip = getattr(p, 'clip_skip', opts.CLIP_ignore_last_layers)
+
     generation_params = {
         "Steps": p.steps,
         "Sampler": sd_samplers.samplers[p.sampler_index].name,
@@ -282,6 +285,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
+        "Clip skip": None if clip_skip==0 else clip_skip,
     }
 
     generation_params.update(p.extra_generation_params)
-- 
cgit v1.2.3


From 610a7f4e1480c0ffeedb2a07dc27ae86bf03c3a8 Mon Sep 17 00:00:00 2001
From: Edouard Leurent <eleurent@gmail.com>
Date: Sat, 8 Oct 2022 16:49:43 +0100
Subject: Break after finding the local directory of stable diffusion

Otherwise, we may override it with one of the next two path (. or ..) if it is present there, and then the local paths of other modules (taming transformers, codeformers, etc.) wont be found in sd_path/../.

Fix https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/1085
---
 modules/paths.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/paths.py b/modules/paths.py
index 606f7d66..0519caa0 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -12,6 +12,7 @@ possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'),
 for possible_sd_path in possible_sd_paths:
     if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')):
         sd_path = os.path.abspath(possible_sd_path)
+        break
 
 assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)
 
-- 
cgit v1.2.3


From 432782163ae53e605470bcefc9a6f796c4556912 Mon Sep 17 00:00:00 2001
From: Aidan Holland <thehappydinoa@gmail.com>
Date: Sat, 8 Oct 2022 15:12:24 -0400
Subject: chore: Fix typos

---
 modules/interrogate.py       | 4 ++--
 modules/processing.py        | 2 +-
 modules/scunet_model_arch.py | 4 ++--
 modules/sd_models.py         | 4 ++--
 modules/sd_samplers.py       | 4 ++--
 modules/shared.py            | 6 +++---
 modules/swinir_model_arch.py | 2 +-
 modules/ui.py                | 4 ++--
 8 files changed, 15 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index eed87144..635e266e 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -140,11 +140,11 @@ class InterrogateModels:
 
             res = caption
 
-            cilp_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+            clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
 
             precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
             with torch.no_grad(), precision_scope("cuda"):
-                image_features = self.clip_model.encode_image(cilp_image).type(self.dtype)
+                image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
 
                 image_features /= image_features.norm(dim=-1, keepdim=True)
 
diff --git a/modules/processing.py b/modules/processing.py
index 515fc91a..31220881 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -386,7 +386,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
             if state.interrupted or state.skipped:
 
-                # if we are interruped, sample returns just noise
+                # if we are interrupted, sample returns just noise
                 # use the image collected previously in sampler loop
                 samples_ddim = shared.state.current_latent
 
diff --git a/modules/scunet_model_arch.py b/modules/scunet_model_arch.py
index 972a2639..43ca8d36 100644
--- a/modules/scunet_model_arch.py
+++ b/modules/scunet_model_arch.py
@@ -40,7 +40,7 @@ class WMSA(nn.Module):
         Returns:
             attn_mask: should be (1 1 w p p),
         """
-        # supporting sqaure.
+        # supporting square.
         attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
         if self.type == 'W':
             return attn_mask
@@ -65,7 +65,7 @@ class WMSA(nn.Module):
         x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
         h_windows = x.size(1)
         w_windows = x.size(2)
-        # sqaure validation
+        # square validation
         # assert h_windows == w_windows
 
         x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 9409d070..a09866ce 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -147,7 +147,7 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
         model.first_stage_model.load_state_dict(vae_dict)
 
     model.sd_model_hash = sd_model_hash
-    model.sd_model_checkpint = checkpoint_file
+    model.sd_model_checkpoint = checkpoint_file
 
 
 def load_model():
@@ -175,7 +175,7 @@ def reload_model_weights(sd_model, info=None):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
 
-    if sd_model.sd_model_checkpint == checkpoint_info.filename:
+    if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index eade0dbb..6e743f7e 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -181,7 +181,7 @@ class VanillaStableDiffusionSampler:
 
         self.initialize(p)
 
-        # existing code fails with cetain step counts, like 9
+        # existing code fails with certain step counts, like 9
         try:
             self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
         except Exception:
@@ -204,7 +204,7 @@ class VanillaStableDiffusionSampler:
 
         steps = steps or p.steps
 
-        # existing code fails with cetin step counts, like 9
+        # existing code fails with certain step counts, like 9
         try:
             samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
         except Exception:
diff --git a/modules/shared.py b/modules/shared.py
index af8dc744..2dc092d6 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -141,9 +141,9 @@ class OptionInfo:
         self.section = None
 
 
-def options_section(section_identifer, options_dict):
+def options_section(section_identifier, options_dict):
     for k, v in options_dict.items():
-        v.section = section_identifer
+        v.section = section_identifier
 
     return options_dict
 
@@ -246,7 +246,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
-    "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+    "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
 }))
 
diff --git a/modules/swinir_model_arch.py b/modules/swinir_model_arch.py
index 461fb354..863f42db 100644
--- a/modules/swinir_model_arch.py
+++ b/modules/swinir_model_arch.py
@@ -166,7 +166,7 @@ class SwinTransformerBlock(nn.Module):
 
     Args:
         dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
+        input_resolution (tuple[int]): Input resolution.
         num_heads (int): Number of attention heads.
         window_size (int): Window size.
         shift_size (int): Shift size for SW-MSA.
diff --git a/modules/ui.py b/modules/ui.py
index b09359aa..b51af121 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -38,7 +38,7 @@ from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
 
-# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
 
@@ -102,7 +102,7 @@ def save_files(js_data, images, index):
     import csv    
     filenames = []
 
-    #quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it
+    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
     class MyObject:
         def __init__(self, d=None):
             if d is not None:
-- 
cgit v1.2.3


From 050a6a798cec90ae2f881c2ddd3f0221e69907dc Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 23:26:48 +0300
Subject: support loading .yaml config with same name as model support EMA
 weights in processing (????)

---
 modules/processing.py |  2 +-
 modules/sd_models.py  | 30 +++++++++++++++++++++++-------
 2 files changed, 24 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 31220881..4fea6d56 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -347,7 +347,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     infotexts = []
     output_images = []
 
-    with torch.no_grad():
+    with torch.no_grad(), p.sd_model.ema_scope():
         with devices.autocast():
             p.init(all_prompts, all_seeds, all_subseeds)
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index a09866ce..cb3982b1 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -14,7 +14,7 @@ from modules.paths import models_path
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
 checkpoints_list = {}
 
 try:
@@ -63,14 +63,20 @@ def list_models():
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
         shared.opts.data['sd_model_checkpoint'] = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
     for filename in model_list:
         h = model_hash(filename)
         title, short_model_name = modeltitle(filename, h)
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
+
+        basename, _ = os.path.splitext(filename)
+        config = basename + ".yaml"
+        if not os.path.exists(config):
+            config = shared.cmd_opts.config
+
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
 
 
 def get_closet_checkpoint_match(searchString):
@@ -116,7 +122,10 @@ def select_checkpoint():
     return checkpoint_info
 
 
-def load_model_weights(model, checkpoint_file, sd_model_hash):
+def load_model_weights(model, checkpoint_info):
+    checkpoint_file = checkpoint_info.filename
+    sd_model_hash = checkpoint_info.hash
+
     print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
     pl_sd = torch.load(checkpoint_file, map_location="cpu")
@@ -148,15 +157,19 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
+    model.sd_checkpoint_info = checkpoint_info
 
 
 def load_model():
     from modules import lowvram, sd_hijack
     checkpoint_info = select_checkpoint()
 
-    sd_config = OmegaConf.load(shared.cmd_opts.config)
+    if checkpoint_info.config != shared.cmd_opts.config:
+        print(f"Loading config from: {shared.cmd_opts.config}")
+
+    sd_config = OmegaConf.load(checkpoint_info.config)
     sd_model = instantiate_from_config(sd_config.model)
-    load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+    load_model_weights(sd_model, checkpoint_info)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
@@ -178,6 +191,9 @@ def reload_model_weights(sd_model, info=None):
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
+    if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
+        return load_model()
+
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.send_everything_to_cpu()
     else:
@@ -185,7 +201,7 @@ def reload_model_weights(sd_model, info=None):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+    load_model_weights(sd_model, checkpoint_info)
 
     sd_hijack.model_hijack.hijack(sd_model)
 
-- 
cgit v1.2.3


From 5841990b0df04906da7321beef6f7f7902b7d57b Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 05:38:38 +0100
Subject: Update textual_inversion.py

---
 modules/textual_inversion/textual_inversion.py | 25 ++++++++++++++++++++++---
 1 file changed, 22 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index cd9f3498..f6316020 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,6 +7,9 @@ import tqdm
 import html
 import datetime
 
+from PIL import Image, PngImagePlugin
+import base64
+from io import BytesIO
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
@@ -80,7 +83,15 @@ class EmbeddingDatabase:
         def process_file(path, filename):
             name = os.path.splitext(filename)[0]
 
-            data = torch.load(path, map_location="cpu")
+            data = []
+
+            if filename.upper().endswith('.PNG'):
+                embed_image = Image.open(path)
+                if 'sd-embedding' in embed_image.text:
+                    embeddingData = base64.b64decode(embed_image.text['sd-embedding'])
+                    data = torch.load(BytesIO(embeddingData), map_location="cpu")
+            else:
+                data = torch.load(path, map_location="cpu")
 
             # textual inversion embeddings
             if 'string_to_param' in data:
@@ -156,7 +167,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -244,7 +255,15 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
             image = processed.images[0]
 
             shared.state.current_image = image
-            image.save(last_saved_image)
+
+            if save_image_with_stored_embedding:
+                info = PngImagePlugin.PngInfo()
+                info.add_text("sd-embedding", base64.b64encode(open(last_saved_file,'rb').read()))
+                image.save(last_saved_image, "PNG", pnginfo=info)
+            else:
+                image.save(last_saved_image)
+
+
 
             last_saved_image += f", prompt: {text}"
 
-- 
cgit v1.2.3


From cd8673bd9b2e59bddefee8d307340d643695fe11 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 05:40:57 +0100
Subject: add embed embedding to ui

---
 modules/ui.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index b51af121..a5983204 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1001,7 +1001,8 @@ def create_ui(wrap_gradio_gpu_call):
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
+                    
                     with gr.Row():
                         with gr.Column(scale=2):
                             gr.HTML(value="")
@@ -1063,6 +1064,7 @@ def create_ui(wrap_gradio_gpu_call):
                 create_image_every,
                 save_embedding_every,
                 template_file,
+                save_image_with_stored_embedding,
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.3


From c77c89cc83c618472ad352cf8a28fde28c3a1377 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 10:23:31 +0300
Subject: make main model loading and model merger use the same code

---
 modules/extras.py    |  6 +++---
 modules/sd_models.py | 14 +++++++++-----
 2 files changed, 12 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 1d9e64e5..ef6e6de7 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -169,9 +169,9 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
 
     print(f"Loading {secondary_model_info.filename}...")
     secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
-   
-    theta_0 = primary_model['state_dict']
-    theta_1 = secondary_model['state_dict']
+
+    theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
+    theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
 
     theta_funcs = {
         "Weighted Sum": weighted_sum,
diff --git a/modules/sd_models.py b/modules/sd_models.py
index cb3982b1..18fb8c2e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -122,6 +122,13 @@ def select_checkpoint():
     return checkpoint_info
 
 
+def get_state_dict_from_checkpoint(pl_sd):
+    if "state_dict" in pl_sd:
+        return pl_sd["state_dict"]
+
+    return pl_sd
+
+
 def load_model_weights(model, checkpoint_info):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
@@ -131,11 +138,8 @@ def load_model_weights(model, checkpoint_info):
     pl_sd = torch.load(checkpoint_file, map_location="cpu")
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
-    
-    if "state_dict" in pl_sd:
-        sd = pl_sd["state_dict"]
-    else:
-        sd = pl_sd
+
+    sd = get_state_dict_from_checkpoint(pl_sd)
 
     model.load_state_dict(sd, strict=False)
 
-- 
cgit v1.2.3


From 4e569fd888f8e3c5632a072d51abbb6e4d17abd6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 10:31:47 +0300
Subject: fixed incorrect message about loading config; thanks anon!

---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 18fb8c2e..2101b18d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -169,7 +169,7 @@ def load_model():
     checkpoint_info = select_checkpoint()
 
     if checkpoint_info.config != shared.cmd_opts.config:
-        print(f"Loading config from: {shared.cmd_opts.config}")
+        print(f"Loading config from: {checkpoint_info.config}")
 
     sd_config = OmegaConf.load(checkpoint_info.config)
     sd_model = instantiate_from_config(sd_config.model)
-- 
cgit v1.2.3


From 5ab7e88d9b0bb0125af9f7237242a00a93360ce5 Mon Sep 17 00:00:00 2001
From: aoirusann <82883326+aoirusann@users.noreply.github.com>
Date: Sat, 8 Oct 2022 13:09:29 +0800
Subject: Add `Download` & `Download as zip`

---
 modules/ui.py | 39 ++++++++++++++++++++++++++++++++++-----
 1 file changed, 34 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index b51af121..fe7f10a7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -98,9 +98,10 @@ def send_gradio_gallery_to_image(x):
     return image_from_url_text(x[0])
 
 
-def save_files(js_data, images, index):
+def save_files(js_data, images, do_make_zip, index):
     import csv    
     filenames = []
+    fullfns = []
 
     #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
     class MyObject:
@@ -141,10 +142,22 @@ def save_files(js_data, images, index):
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
+            fullfns.append(fullfn)
 
         writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
 
-    return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+    # Make Zip
+    if do_make_zip:
+        zip_filepath = os.path.join(path, "images.zip")
+
+        from zipfile import ZipFile
+        with ZipFile(zip_filepath, "w") as zip_file:
+            for i in range(len(fullfns)):
+                with open(fullfns[i], mode="rb") as f:
+                    zip_file.writestr(filenames[i], f.read())
+        fullfns.insert(0, zip_filepath)
+
+    return fullfns, '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
 def wrap_gradio_call(func, extra_outputs=None):
@@ -521,6 +534,12 @@ def create_ui(wrap_gradio_gpu_call):
                         button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
                         open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
 
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+                    
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False)
+
                 with gr.Group():
                     html_info = gr.HTML()
                     generation_info = gr.Textbox(visible=False)
@@ -570,13 +589,15 @@ def create_ui(wrap_gradio_gpu_call):
 
             save.click(
                 fn=wrap_gradio_call(save_files),
-                _js="(x, y, z) => [x, y, selected_gallery_index()]",
+                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
                 inputs=[
                     generation_info,
                     txt2img_gallery,
+                    do_make_zip,
                     html_info,
                 ],
                 outputs=[
+                    download_files,
                     html_info,
                     html_info,
                     html_info,
@@ -701,6 +722,12 @@ def create_ui(wrap_gradio_gpu_call):
                         button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
                         open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
 
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+                    
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False)
+
                 with gr.Group():
                     html_info = gr.HTML()
                     generation_info = gr.Textbox(visible=False)
@@ -776,13 +803,15 @@ def create_ui(wrap_gradio_gpu_call):
 
             save.click(
                 fn=wrap_gradio_call(save_files),
-                _js="(x, y, z) => [x, y, selected_gallery_index()]",
+                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
                 inputs=[
                     generation_info,
                     img2img_gallery,
-                    html_info
+                    do_make_zip,
+                    html_info,
                 ],
                 outputs=[
+                    download_files,
                     html_info,
                     html_info,
                     html_info,
-- 
cgit v1.2.3


From 14192c5b207b16b1ec7a4c9c4ea538d1a6811a4d Mon Sep 17 00:00:00 2001
From: aoirusann <yimodo.siki@gmail.com>
Date: Sun, 9 Oct 2022 13:01:10 +0800
Subject: Support `Download` for txt files.

---
 modules/images.py | 39 +++++++++++++++++++++++++++++++++++++--
 modules/ui.py     |  5 ++++-
 2 files changed, 41 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 29c5ee24..c0a90676 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -349,6 +349,38 @@ def get_next_sequence_number(path, basename):
 
 
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
+    '''Save an image.
+
+    Args:
+        image (`PIL.Image`):
+            The image to be saved.
+        path (`str`):
+            The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
+        basename (`str`):
+            The base filename which will be applied to `filename pattern`.
+        seed, prompt, short_filename, 
+        extension (`str`):
+            Image file extension, default is `png`.
+        pngsectionname (`str`):
+            Specify the name of the section which `info` will be saved in.
+        info (`str` or `PngImagePlugin.iTXt`):
+            PNG info chunks.
+        existing_info (`dict`):
+            Additional PNG info. `existing_info == {pngsectionname: info, ...}`
+        no_prompt:
+            TODO I don't know its meaning.
+        p (`StableDiffusionProcessing`)
+        forced_filename (`str`):
+            If specified, `basename` and filename pattern will be ignored.
+        save_to_dirs (bool):
+            If true, the image will be saved into a subdirectory of `path`.
+
+    Returns: (fullfn, txt_fullfn)
+        fullfn (`str`):
+            The full path of the saved imaged.
+        txt_fullfn (`str` or None):
+            If a text file is saved for this image, this will be its full path. Otherwise None.
+    '''
     if short_filename or prompt is None or seed is None:
         file_decoration = ""
     elif opts.save_to_dirs:
@@ -424,7 +456,10 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg")
 
     if opts.save_txt and info is not None:
-        with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
+        txt_fullfn = f"{fullfn_without_extension}.txt"
+        with open(txt_fullfn, "w", encoding="utf8") as file:
             file.write(info + "\n")
+    else:
+        txt_fullfn = None
 
-    return fullfn
+    return fullfn, txt_fullfn
diff --git a/modules/ui.py b/modules/ui.py
index fe7f10a7..debd8873 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -138,11 +138,14 @@ def save_files(js_data, images, do_make_zip, index):
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
 
-            fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
             fullfns.append(fullfn)
+            if txt_fullfn:
+                filenames.append(os.path.basename(txt_fullfn))
+                fullfns.append(txt_fullfn)
 
         writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
 
-- 
cgit v1.2.3


From 122d42687b97ec4df4c2a8c335d2de385cd1f1a1 Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sat, 8 Oct 2022 22:37:35 -0400
Subject: Fix VRAM Issue by only loading in hypernetwork when selected in
 settings

---
 modules/hypernetwork.py            | 23 +++++++++++++++--------
 modules/sd_hijack_optimizations.py |  6 +++---
 modules/shared.py                  |  7 ++-----
 3 files changed, 20 insertions(+), 16 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index 7f062242..19f1c227 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -40,18 +40,25 @@ class Hypernetwork:
             self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
 
 
-def load_hypernetworks(path):
+def list_hypernetworks(path):
     res = {}
-
     for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
+        name = os.path.splitext(os.path.basename(filename))[0]
+        res[name] = filename
+    return res
+
+
+def load_hypernetwork(filename):
+    print(f"Loading hypernetwork {filename}")
+    path = shared.hypernetworks.get(filename, None)
+    if (path is not None):
         try:
-            hn = Hypernetwork(filename)
-            res[hn.name] = hn
+            shared.loaded_hypernetwork = Hypernetwork(path)
         except Exception:
-            print(f"Error loading hypernetwork {filename}", file=sys.stderr)
+            print(f"Error loading hypernetwork {path}", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
-
-    return res
+    else:
+        shared.loaded_hypernetwork = None
 
 
 def attention_CrossAttention_forward(self, x, context=None, mask=None):
@@ -60,7 +67,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     q = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork = shared.loaded_hypernetwork
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
 
     if hypernetwork_layers is not None:
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index c4396bb9..634fb4b2 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -28,7 +28,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     q_in = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork = shared.loaded_hypernetwork
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
 
     if hypernetwork_layers is not None:
@@ -68,7 +68,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
     q_in = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork = shared.loaded_hypernetwork
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
 
     if hypernetwork_layers is not None:
@@ -132,7 +132,7 @@ def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
     context = default(context, x)
-    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork = shared.loaded_hypernetwork
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
     if hypernetwork_layers is not None:
         k_in = self.to_k(hypernetwork_layers[0](context))
diff --git a/modules/shared.py b/modules/shared.py
index b2c76a32..9dce6cb7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -79,11 +79,8 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 xformers_available = False
 config_filename = cmd_opts.ui_settings_file
 
-hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
-
-
-def selected_hypernetwork():
-    return hypernetworks.get(opts.sd_hypernetwork, None)
+hypernetworks = hypernetwork.list_hypernetworks(os.path.join(models_path, 'hypernetworks'))
+loaded_hypernetwork = None
 
 
 class State:
-- 
cgit v1.2.3


From 03e570886f430f39020e504aba057a95f2e62484 Mon Sep 17 00:00:00 2001
From: frostydad <64224601+Cyberes@users.noreply.github.com>
Date: Sat, 8 Oct 2022 18:13:13 -0600
Subject: Fix incorrect sampler name in output

---
 modules/processing.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 4fea6d56..6b8664a0 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1,3 +1,4 @@
+
 import json
 import math
 import os
@@ -46,6 +47,12 @@ def apply_color_correction(correction, image):
     return image
 
 
+def get_correct_sampler(p):
+    if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
+        return sd_samplers.samplers
+    elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
+        return sd_samplers.samplers_for_img2img
+
 class StableDiffusionProcessing:
     def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
         self.sd_model = sd_model
@@ -272,7 +279,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params = {
         "Steps": p.steps,
-        "Sampler": sd_samplers.samplers[p.sampler_index].name,
+        "Sampler": get_correct_sampler(p)[p.sampler_index].name,
         "CFG scale": p.cfg_scale,
         "Seed": all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
-- 
cgit v1.2.3


From ef93acdc731b7a2b3c13651b6de1bce58af989d4 Mon Sep 17 00:00:00 2001
From: frostydad <64224601+Cyberes@users.noreply.github.com>
Date: Sat, 8 Oct 2022 18:15:35 -0600
Subject: remove line break

---
 modules/processing.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 6b8664a0..7fa1144e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1,4 +1,3 @@
-
 import json
 import math
 import os
-- 
cgit v1.2.3


From 1ffeb42d38d9276dc28918189d32f60d593a162c Mon Sep 17 00:00:00 2001
From: Nicolas Noullet <nicolas.noullet@gmail.com>
Date: Sun, 9 Oct 2022 00:18:45 +0200
Subject: Fix typo

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 9dce6cb7..dffa0094 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -238,7 +238,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
-    "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
-- 
cgit v1.2.3


From e2930f9821c197da94e208b5ae73711002844efc Mon Sep 17 00:00:00 2001
From: Tony Beeman <beeman@gmail.com>
Date: Fri, 7 Oct 2022 17:46:39 -0700
Subject: Fix for Prompts_from_file showing extra textbox.

---
 modules/scripts.py | 30 ++++++++++++++++++++++++++----
 1 file changed, 26 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 45230f9a..d8f87927 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -1,4 +1,5 @@
 import os
+from pydoc import visiblename
 import sys
 import traceback
 
@@ -31,6 +32,15 @@ class Script:
     def show(self, is_img2img):
         return True
 
+
+    # Called when the ui for this script has been shown.
+    # Useful for hiding some controls, since the scripts module sets visibility to
+    # everything to true. The parameters will be the parameters returned by the ui method
+    # The return value should be gradio updates, similar to what you would return
+    # from a Gradio event handler.
+    def on_show(self, *args):
+        return [ui.gr_show(True)] * len(args)
+
     # This is where the additional processing is implemented. The parameters include
     # self, the model object "p" (a StableDiffusionProcessing class, see
     # processing.py), and the parameters returned by the ui method.
@@ -125,20 +135,32 @@ class ScriptRunner:
             inputs += controls
             script.args_to = len(inputs)
 
-        def select_script(script_index):
+        def select_script(*args):
+            script_index = args[0]
+            on_show_updates = []
             if 0 < script_index <= len(self.scripts):
                 script = self.scripts[script_index-1]
                 args_from = script.args_from
                 args_to = script.args_to
+                script_args = args[args_from:args_to]
+                on_show_updates = wrap_call(script.on_show, script.filename, "on_show", *script_args)
             else:
                 args_from = 0
                 args_to = 0
 
-            return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
+            ret = [ ui.gr_show(True)] # always show the dropdown
+            for i in range(1, len(inputs)):
+                if (args_from <= i < args_to):
+                    ret.append( on_show_updates[i - args_from] )
+                else:
+                    ret.append(ui.gr_show(False))
+            return ret
+
+            # return [ui.gr_show(True if (i == 0) else on_show_updates[i - args_from] if args_from <= i < args_to else False) for i in range(len(inputs))]
 
         dropdown.change(
             fn=select_script,
-            inputs=[dropdown],
+            inputs=inputs,
             outputs=inputs
         )
 
@@ -198,4 +220,4 @@ def reload_scripts(basedir):
     load_scripts(basedir)
 
     scripts_txt2img = ScriptRunner()
-    scripts_img2img = ScriptRunner()
+    scripts_img2img = ScriptRunner()
\ No newline at end of file
-- 
cgit v1.2.3


From 86cb16886f8f48169cee4658ad0c5e5443beed2a Mon Sep 17 00:00:00 2001
From: Tony Beeman <beeman@gmail.com>
Date: Fri, 7 Oct 2022 23:51:50 -0700
Subject: Pull Request Code Review Fixes

---
 modules/scripts.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index d8f87927..8dfd4de9 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -1,5 +1,4 @@
 import os
-from pydoc import visiblename
 import sys
 import traceback
 
-- 
cgit v1.2.3


From cbf6dad02d04d98e5a2d5e870777ab99b5796b2d Mon Sep 17 00:00:00 2001
From: Tony Beeman <beeman@gmail.com>
Date: Sat, 8 Oct 2022 10:40:30 -0700
Subject: Handle case where on_show returns the wrong number of arguments

---
 modules/scripts.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 8dfd4de9..7d89979d 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -143,6 +143,8 @@ class ScriptRunner:
                 args_to = script.args_to
                 script_args = args[args_from:args_to]
                 on_show_updates = wrap_call(script.on_show, script.filename, "on_show", *script_args)
+                if (len(on_show_updates) != (args_to - args_from)):
+                    print("Error in custom script (" + script.filename + "): on_show() method should return the same number of arguments as ui().", file=sys.stderr)
             else:
                 args_from = 0
                 args_to = 0
@@ -150,13 +152,14 @@ class ScriptRunner:
             ret = [ ui.gr_show(True)] # always show the dropdown
             for i in range(1, len(inputs)):
                 if (args_from <= i < args_to):
-                    ret.append( on_show_updates[i - args_from] )
+                    if (i - args_from) < len(on_show_updates):
+                        ret.append( on_show_updates[i - args_from] )
+                    else:
+                        ret.append(ui.gr_show(True))
                 else:
                     ret.append(ui.gr_show(False))
             return ret
 
-            # return [ui.gr_show(True if (i == 0) else on_show_updates[i - args_from] if args_from <= i < args_to else False) for i in range(len(inputs))]
-
         dropdown.change(
             fn=select_script,
             inputs=inputs,
-- 
cgit v1.2.3


From ab4fe4f44c3d2675a351269fe2ff1ddeac557aa6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 11:59:41 +0300
Subject: hide filenames for save button by default

---
 modules/ui.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 8071b1cb..e1ab2665 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -162,7 +162,7 @@ def save_files(js_data, images, do_make_zip, index):
                     zip_file.writestr(filenames[i], f.read())
         fullfns.insert(0, zip_filepath)
 
-    return fullfns, '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
 def wrap_gradio_call(func, extra_outputs=None):
@@ -553,7 +553,7 @@ def create_ui(wrap_gradio_gpu_call):
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
                     
                     with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False)
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
                 with gr.Group():
                     html_info = gr.HTML()
@@ -741,7 +741,7 @@ def create_ui(wrap_gradio_gpu_call):
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
                     
                     with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False)
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
                 with gr.Group():
                     html_info = gr.HTML()
-- 
cgit v1.2.3


From 0241d811d23427b99f6b1eda1540bdf8d87963d5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 12:04:44 +0300
Subject: Revert "Fix for Prompts_from_file showing extra textbox."

This reverts commit e2930f9821c197da94e208b5ae73711002844efc.
---
 modules/scripts.py | 32 ++++----------------------------
 1 file changed, 4 insertions(+), 28 deletions(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 7d89979d..45230f9a 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -31,15 +31,6 @@ class Script:
     def show(self, is_img2img):
         return True
 
-
-    # Called when the ui for this script has been shown.
-    # Useful for hiding some controls, since the scripts module sets visibility to
-    # everything to true. The parameters will be the parameters returned by the ui method
-    # The return value should be gradio updates, similar to what you would return
-    # from a Gradio event handler.
-    def on_show(self, *args):
-        return [ui.gr_show(True)] * len(args)
-
     # This is where the additional processing is implemented. The parameters include
     # self, the model object "p" (a StableDiffusionProcessing class, see
     # processing.py), and the parameters returned by the ui method.
@@ -134,35 +125,20 @@ class ScriptRunner:
             inputs += controls
             script.args_to = len(inputs)
 
-        def select_script(*args):
-            script_index = args[0]
-            on_show_updates = []
+        def select_script(script_index):
             if 0 < script_index <= len(self.scripts):
                 script = self.scripts[script_index-1]
                 args_from = script.args_from
                 args_to = script.args_to
-                script_args = args[args_from:args_to]
-                on_show_updates = wrap_call(script.on_show, script.filename, "on_show", *script_args)
-                if (len(on_show_updates) != (args_to - args_from)):
-                    print("Error in custom script (" + script.filename + "): on_show() method should return the same number of arguments as ui().", file=sys.stderr)
             else:
                 args_from = 0
                 args_to = 0
 
-            ret = [ ui.gr_show(True)] # always show the dropdown
-            for i in range(1, len(inputs)):
-                if (args_from <= i < args_to):
-                    if (i - args_from) < len(on_show_updates):
-                        ret.append( on_show_updates[i - args_from] )
-                    else:
-                        ret.append(ui.gr_show(True))
-                else:
-                    ret.append(ui.gr_show(False))
-            return ret
+            return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
 
         dropdown.change(
             fn=select_script,
-            inputs=inputs,
+            inputs=[dropdown],
             outputs=inputs
         )
 
@@ -222,4 +198,4 @@ def reload_scripts(basedir):
     load_scripts(basedir)
 
     scripts_txt2img = ScriptRunner()
-    scripts_img2img = ScriptRunner()
\ No newline at end of file
+    scripts_img2img = ScriptRunner()
-- 
cgit v1.2.3


From 6f6798ddabe10d320fe8ea05edf0fdcef0c51a8e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 12:33:37 +0300
Subject: prevent a possible code execution error (thanks, RyotaK)

---
 modules/ui.py | 9 +++++++++
 1 file changed, 9 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index e1ab2665..dad509f3 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1153,6 +1153,15 @@ def create_ui(wrap_gradio_gpu_call):
     component_dict = {}
 
     def open_folder(f):
+        if not os.path.isdir(f):
+            print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+            return
+
         if not shared.cmd_opts.hide_ui_dir_config:
             path = os.path.normpath(f)
             if platform.system() == "Windows":
-- 
cgit v1.2.3


From 0609ce06c0778536cb368ac3867292f87c6d9fc7 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Fri, 7 Oct 2022 03:36:08 +0900
Subject: Removed duplicate definition model_path

---
 modules/bsrgan_model.py     | 2 --
 modules/esrgan_model.py     | 2 --
 modules/ldsr_model.py       | 2 --
 modules/realesrgan_model.py | 2 --
 modules/scunet_model.py     | 2 --
 modules/swinir_model.py     | 2 --
 modules/upscaler.py         | 7 ++++---
 7 files changed, 4 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
index 3bd80791..737e1a76 100644
--- a/modules/bsrgan_model.py
+++ b/modules/bsrgan_model.py
@@ -10,13 +10,11 @@ from basicsr.utils.download_util import load_file_from_url
 import modules.upscaler
 from modules import devices, modelloader
 from modules.bsrgan_model_arch import RRDBNet
-from modules.paths import models_path
 
 
 class UpscalerBSRGAN(modules.upscaler.Upscaler):
     def __init__(self, dirname):
         self.name = "BSRGAN"
-        self.model_path = os.path.join(models_path, self.name)
         self.model_name = "BSRGAN 4x"
         self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
         self.user_path = dirname
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 28548124..3970e6e4 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -7,7 +7,6 @@ from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgam_model_arch as arch
 from modules import shared, modelloader, images, devices
-from modules.paths import models_path
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
@@ -76,7 +75,6 @@ class UpscalerESRGAN(Upscaler):
         self.model_name = "ESRGAN_4x"
         self.scalers = []
         self.user_path = dirname
-        self.model_path = os.path.join(models_path, self.name)
         super().__init__()
         model_paths = self.find_models(ext_filter=[".pt", ".pth"])
         scalers = []
diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
index 1c1070fc..8c4db44a 100644
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@@ -7,13 +7,11 @@ from basicsr.utils.download_util import load_file_from_url
 from modules.upscaler import Upscaler, UpscalerData
 from modules.ldsr_model_arch import LDSR
 from modules import shared
-from modules.paths import models_path
 
 
 class UpscalerLDSR(Upscaler):
     def __init__(self, user_path):
         self.name = "LDSR"
-        self.model_path = os.path.join(models_path, self.name)
         self.user_path = user_path
         self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
         self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py
index dc0123e0..3ac0b97a 100644
--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@@ -8,14 +8,12 @@ from basicsr.utils.download_util import load_file_from_url
 from realesrgan import RealESRGANer
 
 from modules.upscaler import Upscaler, UpscalerData
-from modules.paths import models_path
 from modules.shared import cmd_opts, opts
 
 
 class UpscalerRealESRGAN(Upscaler):
     def __init__(self, path):
         self.name = "RealESRGAN"
-        self.model_path = os.path.join(models_path, self.name)
         self.user_path = path
         super().__init__()
         try:
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index fb64b740..36a996bf 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -9,14 +9,12 @@ from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
 from modules import devices, modelloader
-from modules.paths import models_path
 from modules.scunet_model_arch import SCUNet as net
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
     def __init__(self, dirname):
         self.name = "ScuNET"
-        self.model_path = os.path.join(models_path, self.name)
         self.model_name = "ScuNET GAN"
         self.model_name2 = "ScuNET PSNR"
         self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index 9bd454c6..fbd11f84 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -8,7 +8,6 @@ from basicsr.utils.download_util import load_file_from_url
 from tqdm import tqdm
 
 from modules import modelloader
-from modules.paths import models_path
 from modules.shared import cmd_opts, opts, device
 from modules.swinir_model_arch import SwinIR as net
 from modules.upscaler import Upscaler, UpscalerData
@@ -25,7 +24,6 @@ class UpscalerSwinIR(Upscaler):
                          "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
                          "-L_x4_GAN.pth "
         self.model_name = "SwinIR 4x"
-        self.model_path = os.path.join(models_path, self.name)
         self.user_path = dirname
         super().__init__()
         scalers = []
diff --git a/modules/upscaler.py b/modules/upscaler.py
index d9d7c5e2..34672be7 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -36,10 +36,11 @@ class Upscaler:
         self.half = not modules.shared.cmd_opts.no_half
         self.pre_pad = 0
         self.mod_scale = None
-        if self.name is not None and create_dirs:
+
+        if self.model_path is not None and self.name:
             self.model_path = os.path.join(models_path, self.name)
-            if not os.path.exists(self.model_path):
-                os.makedirs(self.model_path)
+        if self.model_path and create_dirs:
+            os.makedirs(self.model_path, exist_ok=True)
 
         try:
             import cv2
-- 
cgit v1.2.3


From bd833409ac7b8337040d521f6b65ced51e1b2ea8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 13:10:15 +0300
Subject: additional changes for saving pnginfo for #1803

---
 modules/extras.py     | 4 ++++
 modules/processing.py | 6 ++++--
 2 files changed, 8 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index ef6e6de7..39dd3806 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -98,6 +98,10 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
                           no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,
                           forced_filename=image_name if opts.use_original_name_batch else None)
 
+        if opts.enable_pnginfo:
+            image.info = existing_pnginfo
+            image.info["extras"] = info
+
         outputs.append(image)
 
     devices.torch_gc()
diff --git a/modules/processing.py b/modules/processing.py
index 7fa1144e..2c991317 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -451,7 +451,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
                 text = infotext(n, i)
                 infotexts.append(text)
-                image.info["parameters"] = text
+                if opts.enable_pnginfo:
+                    image.info["parameters"] = text
                 output_images.append(image)
 
             del x_samples_ddim 
@@ -470,7 +471,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if opts.return_grid:
                 text = infotext()
                 infotexts.insert(0, text)
-                grid.info["parameters"] = text
+                if opts.enable_pnginfo:
+                    grid.info["parameters"] = text
                 output_images.insert(0, grid)
                 index_of_first_image = 1
 
-- 
cgit v1.2.3


From f4578b343ded3b8ccd1879ea0c0b3cdadfcc3a5f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 13:23:30 +0300
Subject: fix model switching not working properly if there is a different yaml
 config

---
 modules/sd_models.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2101b18d..d0c74dd8 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -196,7 +196,8 @@ def reload_model_weights(sd_model, info=None):
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
-        return load_model()
+        shared.sd_model = load_model()
+        return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.send_everything_to_cpu()
-- 
cgit v1.2.3


From 77a719648db515f10136e8b8483d5b16bda2eaeb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 13:48:04 +0300
Subject: fix logic error in #1832

---
 modules/upscaler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/upscaler.py b/modules/upscaler.py
index 34672be7..6ab2fb40 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -37,7 +37,7 @@ class Upscaler:
         self.pre_pad = 0
         self.mod_scale = None
 
-        if self.model_path is not None and self.name:
+        if self.model_path is None and self.name:
             self.model_path = os.path.join(models_path, self.name)
         if self.model_path and create_dirs:
             os.makedirs(self.model_path, exist_ok=True)
-- 
cgit v1.2.3


From ad4de819c43997f2666b5bad95301f5c37f9018e Mon Sep 17 00:00:00 2001
From: victorca25 <victorca25@users.noreply.github.com>
Date: Sun, 9 Oct 2022 13:02:12 +0200
Subject: update ESRGAN architecture and model to support all ESRGAN models in
 the DB, BSRGAN and real-ESRGAN models

---
 modules/bsrgan_model.py      |  76 -------
 modules/bsrgan_model_arch.py | 102 ----------
 modules/esrgam_model_arch.py |  80 --------
 modules/esrgan_model.py      | 190 ++++++++++++------
 modules/esrgan_model_arch.py | 463 +++++++++++++++++++++++++++++++++++++++++++
 5 files changed, 591 insertions(+), 320 deletions(-)
 delete mode 100644 modules/bsrgan_model.py
 delete mode 100644 modules/bsrgan_model_arch.py
 delete mode 100644 modules/esrgam_model_arch.py
 create mode 100644 modules/esrgan_model_arch.py

(limited to 'modules')

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
deleted file mode 100644
index 737e1a76..00000000
--- a/modules/bsrgan_model.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import os.path
-import sys
-import traceback
-
-import PIL.Image
-import numpy as np
-import torch
-from basicsr.utils.download_util import load_file_from_url
-
-import modules.upscaler
-from modules import devices, modelloader
-from modules.bsrgan_model_arch import RRDBNet
-
-
-class UpscalerBSRGAN(modules.upscaler.Upscaler):
-    def __init__(self, dirname):
-        self.name = "BSRGAN"
-        self.model_name = "BSRGAN 4x"
-        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
-        self.user_path = dirname
-        super().__init__()
-        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
-        scalers = []
-        if len(model_paths) == 0:
-            scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4)
-            scalers.append(scaler_data)
-        for file in model_paths:
-            if "http" in file:
-                name = self.model_name
-            else:
-                name = modelloader.friendly_name(file)
-            try:
-                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
-                scalers.append(scaler_data)
-            except Exception:
-                print(f"Error loading BSRGAN model: {file}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-        self.scalers = scalers
-
-    def do_upscale(self, img: PIL.Image, selected_file):
-        torch.cuda.empty_cache()
-        model = self.load_model(selected_file)
-        if model is None:
-            return img
-        model.to(devices.device_bsrgan)
-        torch.cuda.empty_cache()
-        img = np.array(img)
-        img = img[:, :, ::-1]
-        img = np.moveaxis(img, 2, 0) / 255
-        img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(devices.device_bsrgan)
-        with torch.no_grad():
-            output = model(img)
-        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-        output = 255. * np.moveaxis(output, 0, 2)
-        output = output.astype(np.uint8)
-        output = output[:, :, ::-1]
-        torch.cuda.empty_cache()
-        return PIL.Image.fromarray(output, 'RGB')
-
-    def load_model(self, path: str):
-        if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
-                                          progress=True)
-        else:
-            filename = path
-        if not os.path.exists(filename) or filename is None:
-            print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
-            return None
-        model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4)  # define network
-        model.load_state_dict(torch.load(filename), strict=True)
-        model.eval()
-        for k, v in model.named_parameters():
-            v.requires_grad = False
-        return model
-
diff --git a/modules/bsrgan_model_arch.py b/modules/bsrgan_model_arch.py
deleted file mode 100644
index cb4d1c13..00000000
--- a/modules/bsrgan_model_arch.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import functools
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.nn.init as init
-
-
-def initialize_weights(net_l, scale=1):
-    if not isinstance(net_l, list):
-        net_l = [net_l]
-    for net in net_l:
-        for m in net.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
-                m.weight.data *= scale  # for residual block
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.BatchNorm2d):
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias.data, 0.0)
-
-
-def make_layer(block, n_layers):
-    layers = []
-    for _ in range(n_layers):
-        layers.append(block())
-    return nn.Sequential(*layers)
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    def __init__(self, nf=64, gc=32, bias=True):
-        super(ResidualDenseBlock_5C, self).__init__()
-        # gc: growth channel, i.e. intermediate channels
-        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
-        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
-        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    '''Residual in Residual Dense Block'''
-
-    def __init__(self, nf, gc=32):
-        super(RRDB, self).__init__()
-        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
-
-    def forward(self, x):
-        out = self.RDB1(x)
-        out = self.RDB2(out)
-        out = self.RDB3(out)
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4):
-        super(RRDBNet, self).__init__()
-        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
-        self.sf = sf
-
-        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
-        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
-        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        #### upsampling
-        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        if self.sf==4:
-            self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        fea = self.conv_first(x)
-        trunk = self.trunk_conv(self.RRDB_trunk(fea))
-        fea = fea + trunk
-
-        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        if self.sf==4:
-            fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.HRconv(fea)))
-
-        return out
\ No newline at end of file
diff --git a/modules/esrgam_model_arch.py b/modules/esrgam_model_arch.py
deleted file mode 100644
index e413d36e..00000000
--- a/modules/esrgam_model_arch.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# this file is taken from https://github.com/xinntao/ESRGAN
-
-import functools
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def make_layer(block, n_layers):
-    layers = []
-    for _ in range(n_layers):
-        layers.append(block())
-    return nn.Sequential(*layers)
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    def __init__(self, nf=64, gc=32, bias=True):
-        super(ResidualDenseBlock_5C, self).__init__()
-        # gc: growth channel, i.e. intermediate channels
-        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
-        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
-        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    '''Residual in Residual Dense Block'''
-
-    def __init__(self, nf, gc=32):
-        super(RRDB, self).__init__()
-        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
-
-    def forward(self, x):
-        out = self.RDB1(x)
-        out = self.RDB2(out)
-        out = self.RDB3(out)
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
-        super(RRDBNet, self).__init__()
-        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
-
-        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
-        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
-        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        #### upsampling
-        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        fea = self.conv_first(x)
-        trunk = self.trunk_conv(self.RRDB_trunk(fea))
-        fea = fea + trunk
-
-        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.HRconv(fea)))
-
-        return out
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 3970e6e4..a49e2258 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -5,68 +5,115 @@ import torch
 from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
-import modules.esrgam_model_arch as arch
+import modules.esrgan_model_arch as arch
 from modules import shared, modelloader, images, devices
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
 
-def fix_model_layers(crt_model, pretrained_net):
-    # this code is adapted from https://github.com/xinntao/ESRGAN
-    if 'conv_first.weight' in pretrained_net:
-        return pretrained_net
 
-    if 'model.0.weight' not in pretrained_net:
-        is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
-        if is_realesrgan:
-            raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
-        else:
-            raise Exception("The file is not a ESRGAN model.")
+def mod2normal(state_dict):
+    # this code is copied from https://github.com/victorca25/iNNfer
+    if 'conv_first.weight' in state_dict:
+        crt_net = {}
+        items = []
+        for k, v in state_dict.items():
+            items.append(k)
+
+        crt_net['model.0.weight'] = state_dict['conv_first.weight']
+        crt_net['model.0.bias'] = state_dict['conv_first.bias']
+
+        for k in items.copy():
+            if 'RDB' in k:
+                ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
+                if '.weight' in k:
+                    ori_k = ori_k.replace('.weight', '.0.weight')
+                elif '.bias' in k:
+                    ori_k = ori_k.replace('.bias', '.0.bias')
+                crt_net[ori_k] = state_dict[k]
+                items.remove(k)
+
+        crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
+        crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
+        crt_net['model.3.weight'] = state_dict['upconv1.weight']
+        crt_net['model.3.bias'] = state_dict['upconv1.bias']
+        crt_net['model.6.weight'] = state_dict['upconv2.weight']
+        crt_net['model.6.bias'] = state_dict['upconv2.bias']
+        crt_net['model.8.weight'] = state_dict['HRconv.weight']
+        crt_net['model.8.bias'] = state_dict['HRconv.bias']
+        crt_net['model.10.weight'] = state_dict['conv_last.weight']
+        crt_net['model.10.bias'] = state_dict['conv_last.bias']
+        state_dict = crt_net
+    return state_dict
+
+
+def resrgan2normal(state_dict, nb=23):
+    # this code is copied from https://github.com/victorca25/iNNfer
+    if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
+        crt_net = {}
+        items = []
+        for k, v in state_dict.items():
+            items.append(k)
+
+        crt_net['model.0.weight'] = state_dict['conv_first.weight']
+        crt_net['model.0.bias'] = state_dict['conv_first.bias']
+
+        for k in items.copy():
+            if "rdb" in k:
+                ori_k = k.replace('body.', 'model.1.sub.')
+                ori_k = ori_k.replace('.rdb', '.RDB')
+                if '.weight' in k:
+                    ori_k = ori_k.replace('.weight', '.0.weight')
+                elif '.bias' in k:
+                    ori_k = ori_k.replace('.bias', '.0.bias')
+                crt_net[ori_k] = state_dict[k]
+                items.remove(k)
+
+        crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
+        crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
+        crt_net['model.3.weight'] = state_dict['conv_up1.weight']
+        crt_net['model.3.bias'] = state_dict['conv_up1.bias']
+        crt_net['model.6.weight'] = state_dict['conv_up2.weight']
+        crt_net['model.6.bias'] = state_dict['conv_up2.bias']
+        crt_net['model.8.weight'] = state_dict['conv_hr.weight']
+        crt_net['model.8.bias'] = state_dict['conv_hr.bias']
+        crt_net['model.10.weight'] = state_dict['conv_last.weight']
+        crt_net['model.10.bias'] = state_dict['conv_last.bias']
+        state_dict = crt_net
+    return state_dict
+
+
+def infer_params(state_dict):
+    # this code is copied from https://github.com/victorca25/iNNfer
+    scale2x = 0
+    scalemin = 6
+    n_uplayer = 0
+    plus = False
+
+    for block in list(state_dict):
+        parts = block.split(".")
+        n_parts = len(parts)
+        if n_parts == 5 and parts[2] == "sub":
+            nb = int(parts[3])
+        elif n_parts == 3:
+            part_num = int(parts[1])
+            if (part_num > scalemin
+                and parts[0] == "model"
+                and parts[2] == "weight"):
+                scale2x += 1
+            if part_num > n_uplayer:
+                n_uplayer = part_num
+                out_nc = state_dict[block].shape[0]
+        if not plus and "conv1x1" in block:
+            plus = True
+
+    nf = state_dict["model.0.weight"].shape[0]
+    in_nc = state_dict["model.0.weight"].shape[1]
+    out_nc = out_nc
+    scale = 2 ** scale2x
+
+    return in_nc, out_nc, nf, nb, plus, scale
 
-    crt_net = crt_model.state_dict()
-    load_net_clean = {}
-    for k, v in pretrained_net.items():
-        if k.startswith('module.'):
-            load_net_clean[k[7:]] = v
-        else:
-            load_net_clean[k] = v
-    pretrained_net = load_net_clean
-
-    tbd = []
-    for k, v in crt_net.items():
-        tbd.append(k)
-
-    # directly copy
-    for k, v in crt_net.items():
-        if k in pretrained_net and pretrained_net[k].size() == v.size():
-            crt_net[k] = pretrained_net[k]
-            tbd.remove(k)
-
-    crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
-    crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
-
-    for k in tbd.copy():
-        if 'RDB' in k:
-            ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
-            if '.weight' in k:
-                ori_k = ori_k.replace('.weight', '.0.weight')
-            elif '.bias' in k:
-                ori_k = ori_k.replace('.bias', '.0.bias')
-            crt_net[k] = pretrained_net[ori_k]
-            tbd.remove(k)
-
-    crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
-    crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
-    crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
-    crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
-    crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
-    crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
-    crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
-    crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
-    crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
-    crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
-
-    return crt_net
 
 class UpscalerESRGAN(Upscaler):
     def __init__(self, dirname):
@@ -109,20 +156,39 @@ class UpscalerESRGAN(Upscaler):
             print("Unable to load %s from %s" % (self.model_path, filename))
             return None
 
-        pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
-        crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
+        state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
+
+        if "params_ema" in state_dict:
+            state_dict = state_dict["params_ema"]
+        elif "params" in state_dict:
+            state_dict = state_dict["params"]
+            num_conv = 16 if "realesr-animevideov3" in filename else 32
+            model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
+            model.load_state_dict(state_dict)
+            model.eval()
+            return model
+
+        if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
+            nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
+            state_dict = resrgan2normal(state_dict, nb)
+        elif "conv_first.weight" in state_dict:
+            state_dict = mod2normal(state_dict)
+        elif "model.0.weight" not in state_dict:
+            raise Exception("The file is not a recognized ESRGAN model.")
+
+        in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
 
-        pretrained_net = fix_model_layers(crt_model, pretrained_net)
-        crt_model.load_state_dict(pretrained_net)
-        crt_model.eval()
+        model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
+        model.load_state_dict(state_dict)
+        model.eval()
 
-        return crt_model
+        return model
 
 
 def upscale_without_tiling(model, img):
     img = np.array(img)
     img = img[:, :, ::-1]
-    img = np.moveaxis(img, 2, 0) / 255
+    img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
     img = img.unsqueeze(0).to(devices.device_esrgan)
     with torch.no_grad():
diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py
new file mode 100644
index 00000000..bc9ceb2a
--- /dev/null
+++ b/modules/esrgan_model_arch.py
@@ -0,0 +1,463 @@
+# this file is adapted from https://github.com/victorca25/iNNfer
+
+import math
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+####################
+# RRDBNet Generator
+####################
+
+class RRDBNet(nn.Module):
+    def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
+            act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
+            finalact=None, gaussian_noise=False, plus=False):
+        super(RRDBNet, self).__init__()
+        n_upscale = int(math.log(upscale, 2))
+        if upscale == 3:
+            n_upscale = 1
+
+        self.resrgan_scale = 0
+        if in_nc % 16 == 0:
+            self.resrgan_scale = 1
+        elif in_nc != 4 and in_nc % 4 == 0:
+            self.resrgan_scale = 2
+
+        fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
+        rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+            norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
+            gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
+        LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
+
+        if upsample_mode == 'upconv':
+            upsample_block = upconv_block
+        elif upsample_mode == 'pixelshuffle':
+            upsample_block = pixelshuffle_block
+        else:
+            raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
+        if upscale == 3:
+            upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
+        else:
+            upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
+        HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
+        HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
+
+        outact = act(finalact) if finalact else None
+
+        self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
+            *upsampler, HR_conv0, HR_conv1, outact)
+
+    def forward(self, x, outm=None):
+        if self.resrgan_scale == 1:
+            feat = pixel_unshuffle(x, scale=4)
+        elif self.resrgan_scale == 2:
+            feat = pixel_unshuffle(x, scale=2)
+        else:
+            feat = x
+
+        return self.model(feat)
+
+
+class RRDB(nn.Module):
+    """
+    Residual in Residual Dense Block
+    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
+    """
+
+    def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
+            spectral_norm=False, gaussian_noise=False, plus=False):
+        super(RRDB, self).__init__()
+        # This is for backwards compatibility with existing models
+        if nr == 3:
+            self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+                    gaussian_noise=gaussian_noise, plus=plus)
+            self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+                    gaussian_noise=gaussian_noise, plus=plus)
+            self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+                    gaussian_noise=gaussian_noise, plus=plus)
+        else:
+            RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+                                              norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+                                              gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
+            self.RDBs = nn.Sequential(*RDB_list)
+
+    def forward(self, x):
+        if hasattr(self, 'RDB1'):
+            out = self.RDB1(x)
+            out = self.RDB2(out)
+            out = self.RDB3(out)
+        else:
+            out = self.RDBs(x)
+        return out * 0.2 + x
+
+
+class ResidualDenseBlock_5C(nn.Module):
+    """
+    Residual Dense Block
+    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
+    Modified options that can be used:
+        - "Partial Convolution based Padding" arXiv:1811.11718
+        - "Spectral normalization" arXiv:1802.05957
+        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. 
+            {Rakotonirina} and A. {Rasoanaivo}
+    """
+
+    def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
+            spectral_norm=False, gaussian_noise=False, plus=False):
+        super(ResidualDenseBlock_5C, self).__init__()
+
+        self.noise = GaussianNoise() if gaussian_noise else None
+        self.conv1x1 = conv1x1(nf, gc) if plus else None
+
+        self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+            spectral_norm=spectral_norm)
+        self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+            spectral_norm=spectral_norm)
+        self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+            spectral_norm=spectral_norm)
+        self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+            spectral_norm=spectral_norm)
+        if mode == 'CNA':
+            last_act = None
+        else:
+            last_act = act_type
+        self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
+            norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
+            spectral_norm=spectral_norm)
+
+    def forward(self, x):
+        x1 = self.conv1(x)
+        x2 = self.conv2(torch.cat((x, x1), 1))
+        if self.conv1x1:
+            x2 = x2 + self.conv1x1(x)
+        x3 = self.conv3(torch.cat((x, x1, x2), 1))
+        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
+        if self.conv1x1:
+            x4 = x4 + x2
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        if self.noise:
+            return self.noise(x5.mul(0.2) + x)
+        else:
+            return x5 * 0.2 + x
+
+
+####################
+# ESRGANplus
+####################
+
+class GaussianNoise(nn.Module):
+    def __init__(self, sigma=0.1, is_relative_detach=False):
+        super().__init__()
+        self.sigma = sigma
+        self.is_relative_detach = is_relative_detach
+        self.noise = torch.tensor(0, dtype=torch.float)
+
+    def forward(self, x):
+        if self.training and self.sigma != 0:
+            self.noise = self.noise.to(x.device)
+            scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
+            sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
+            x = x + sampled_noise
+        return x 
+
+def conv1x1(in_planes, out_planes, stride=1):
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+####################
+# SRVGGNetCompact
+####################
+
+class SRVGGNetCompact(nn.Module):
+    """A compact VGG-style network structure for super-resolution.
+    This class is copied from https://github.com/xinntao/Real-ESRGAN
+    """
+
+    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
+        super(SRVGGNetCompact, self).__init__()
+        self.num_in_ch = num_in_ch
+        self.num_out_ch = num_out_ch
+        self.num_feat = num_feat
+        self.num_conv = num_conv
+        self.upscale = upscale
+        self.act_type = act_type
+
+        self.body = nn.ModuleList()
+        # the first conv
+        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
+        # the first activation
+        if act_type == 'relu':
+            activation = nn.ReLU(inplace=True)
+        elif act_type == 'prelu':
+            activation = nn.PReLU(num_parameters=num_feat)
+        elif act_type == 'leakyrelu':
+            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+        self.body.append(activation)
+
+        # the body structure
+        for _ in range(num_conv):
+            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
+            # activation
+            if act_type == 'relu':
+                activation = nn.ReLU(inplace=True)
+            elif act_type == 'prelu':
+                activation = nn.PReLU(num_parameters=num_feat)
+            elif act_type == 'leakyrelu':
+                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+            self.body.append(activation)
+
+        # the last conv
+        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
+        # upsample
+        self.upsampler = nn.PixelShuffle(upscale)
+
+    def forward(self, x):
+        out = x
+        for i in range(0, len(self.body)):
+            out = self.body[i](out)
+
+        out = self.upsampler(out)
+        # add the nearest upsampled image, so that the network learns the residual
+        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
+        out += base
+        return out
+
+
+####################
+# Upsampler
+####################
+
+class Upsample(nn.Module):
+    r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
+    The input data is assumed to be of the form
+    `minibatch x channels x [optional depth] x [optional height] x width`.
+    """
+
+    def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
+        super(Upsample, self).__init__()
+        if isinstance(scale_factor, tuple):
+            self.scale_factor = tuple(float(factor) for factor in scale_factor)
+        else:
+            self.scale_factor = float(scale_factor) if scale_factor else None
+        self.mode = mode
+        self.size = size
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
+
+    def extra_repr(self):
+        if self.scale_factor is not None:
+            info = 'scale_factor=' + str(self.scale_factor)
+        else:
+            info = 'size=' + str(self.size)
+        info += ', mode=' + self.mode
+        return info
+
+
+def pixel_unshuffle(x, scale):
+    """ Pixel unshuffle.
+    Args:
+        x (Tensor): Input feature with shape (b, c, hh, hw).
+        scale (int): Downsample ratio.
+    Returns:
+        Tensor: the pixel unshuffled feature.
+    """
+    b, c, hh, hw = x.size()
+    out_channel = c * (scale**2)
+    assert hh % scale == 0 and hw % scale == 0
+    h = hh // scale
+    w = hw // scale
+    x_view = x.view(b, c, h, scale, w, scale)
+    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
+
+
+def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
+                        pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
+    """
+    Pixel shuffle layer
+    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
+    Neural Network, CVPR17)
+    """
+    conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
+                        pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
+    pixel_shuffle = nn.PixelShuffle(upscale_factor)
+
+    n = norm(norm_type, out_nc) if norm_type else None
+    a = act(act_type) if act_type else None
+    return sequential(conv, pixel_shuffle, n, a)
+
+
+def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
+                pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
+    """ Upconv layer """
+    upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
+    upsample = Upsample(scale_factor=upscale_factor, mode=mode)
+    conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
+                        pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
+    return sequential(upsample, conv)
+
+
+
+
+
+
+
+
+####################
+# Basic blocks
+####################
+
+
+def make_layer(basic_block, num_basic_block, **kwarg):
+    """Make layers by stacking the same blocks.
+    Args:
+        basic_block (nn.module): nn.module class for basic block. (block)
+        num_basic_block (int): number of blocks. (n_layers)
+    Returns:
+        nn.Sequential: Stacked blocks in nn.Sequential.
+    """
+    layers = []
+    for _ in range(num_basic_block):
+        layers.append(basic_block(**kwarg))
+    return nn.Sequential(*layers)
+
+
+def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
+    """ activation helper """
+    act_type = act_type.lower()
+    if act_type == 'relu':
+        layer = nn.ReLU(inplace)
+    elif act_type in ('leakyrelu', 'lrelu'):
+        layer = nn.LeakyReLU(neg_slope, inplace)
+    elif act_type == 'prelu':
+        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
+    elif act_type == 'tanh':  # [-1, 1] range output
+        layer = nn.Tanh()
+    elif act_type == 'sigmoid':  # [0, 1] range output
+        layer = nn.Sigmoid()
+    else:
+        raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
+    return layer
+
+
+class Identity(nn.Module):
+    def __init__(self, *kwargs):
+        super(Identity, self).__init__()
+
+    def forward(self, x, *kwargs):
+        return x
+
+
+def norm(norm_type, nc):
+    """ Return a normalization layer """
+    norm_type = norm_type.lower()
+    if norm_type == 'batch':
+        layer = nn.BatchNorm2d(nc, affine=True)
+    elif norm_type == 'instance':
+        layer = nn.InstanceNorm2d(nc, affine=False)
+    elif norm_type == 'none':
+        def norm_layer(x): return Identity()
+    else:
+        raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
+    return layer
+
+
+def pad(pad_type, padding):
+    """ padding layer helper """
+    pad_type = pad_type.lower()
+    if padding == 0:
+        return None
+    if pad_type == 'reflect':
+        layer = nn.ReflectionPad2d(padding)
+    elif pad_type == 'replicate':
+        layer = nn.ReplicationPad2d(padding)
+    elif pad_type == 'zero':
+        layer = nn.ZeroPad2d(padding)
+    else:
+        raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
+    return layer
+
+
+def get_valid_padding(kernel_size, dilation):
+    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
+    padding = (kernel_size - 1) // 2
+    return padding
+
+
+class ShortcutBlock(nn.Module):
+    """ Elementwise sum the output of a submodule to its input """
+    def __init__(self, submodule):
+        super(ShortcutBlock, self).__init__()
+        self.sub = submodule
+
+    def forward(self, x):
+        output = x + self.sub(x)
+        return output
+
+    def __repr__(self):
+        return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
+
+
+def sequential(*args):
+    """ Flatten Sequential. It unwraps nn.Sequential. """
+    if len(args) == 1:
+        if isinstance(args[0], OrderedDict):
+            raise NotImplementedError('sequential does not support OrderedDict input.')
+        return args[0]  # No sequential is needed.
+    modules = []
+    for module in args:
+        if isinstance(module, nn.Sequential):
+            for submodule in module.children():
+                modules.append(submodule)
+        elif isinstance(module, nn.Module):
+            modules.append(module)
+    return nn.Sequential(*modules)
+
+
+def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
+               pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
+               spectral_norm=False):
+    """ Conv layer with padding, normalization, activation """
+    assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)
+    padding = get_valid_padding(kernel_size, dilation)
+    p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
+    padding = padding if pad_type == 'zero' else 0
+
+    if convtype=='PartialConv2D':
+        c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+               dilation=dilation, bias=bias, groups=groups)
+    elif convtype=='DeformConv2D':
+        c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+               dilation=dilation, bias=bias, groups=groups)
+    elif convtype=='Conv3D':
+        c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+                dilation=dilation, bias=bias, groups=groups)
+    else:
+        c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+                dilation=dilation, bias=bias, groups=groups)
+
+    if spectral_norm:
+        c = nn.utils.spectral_norm(c)
+
+    a = act(act_type) if act_type else None
+    if 'CNA' in mode:
+        n = norm(norm_type, out_nc) if norm_type else None
+        return sequential(p, c, n, a)
+    elif mode == 'NAC':
+        if norm_type is None and act_type is not None:
+            a = act(act_type, inplace=False)
+        n = norm(norm_type, in_nc) if norm_type else None
+        return sequential(n, a, p, c)
-- 
cgit v1.2.3


From 542a3d3a4a00c1383fbdaf938ceefef87cf834bb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 14:33:22 +0300
Subject: fix btoken hypernetworks in XY plot

---
 modules/hypernetwork.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index 19f1c227..498bc9d8 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -49,15 +49,18 @@ def list_hypernetworks(path):
 
 
 def load_hypernetwork(filename):
-    print(f"Loading hypernetwork {filename}")
     path = shared.hypernetworks.get(filename, None)
-    if (path is not None):
+    if path is not None:
+        print(f"Loading hypernetwork {filename}")
         try:
             shared.loaded_hypernetwork = Hypernetwork(path)
         except Exception:
             print(f"Error loading hypernetwork {path}", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
     else:
+        if shared.loaded_hypernetwork is not None:
+            print(f"Unloading hypernetwork")
+
         shared.loaded_hypernetwork = None
 
 
-- 
cgit v1.2.3


From d6d10a37bfd21568e74efb46137f906da96d5fdb Mon Sep 17 00:00:00 2001
From: William Moorehouse <moorehousew@gmail.com>
Date: Sun, 9 Oct 2022 04:58:40 -0400
Subject: Added extended model details to infotext

---
 modules/processing.py | 3 +++
 modules/sd_models.py  | 3 ++-
 modules/shared.py     | 1 +
 3 files changed, 6 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 2c991317..d1bcee4a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -284,6 +284,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
+        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_name else shared.sd_model.sd_model_name),
+        "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name),
+        "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
diff --git a/modules/sd_models.py b/modules/sd_models.py
index d0c74dd8..3fa42329 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,7 +4,7 @@ import sys
 from collections import namedtuple
 import torch
 from omegaconf import OmegaConf
-
+from pathlib import Path
 
 from ldm.util import instantiate_from_config
 
@@ -158,6 +158,7 @@ def load_model_weights(model, checkpoint_info):
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
         model.first_stage_model.load_state_dict(vae_dict)
+        model.sd_model_vae_name = Path(vae_file).stem
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
diff --git a/modules/shared.py b/modules/shared.py
index dffa0094..ca63f7d8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -242,6 +242,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
+    "add_extended_model_details_to_info": OptionInfo(False, "Add extended model details to generation information (model name, VAE, hypernetwork)"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-- 
cgit v1.2.3


From 006791c13d70e582eee766b7d0499e9821a86bf9 Mon Sep 17 00:00:00 2001
From: William Moorehouse <moorehousew@gmail.com>
Date: Sun, 9 Oct 2022 05:09:18 -0400
Subject: Fix grabbing the model name for infotext

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index d1bcee4a..c035c990 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -284,7 +284,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_name else shared.sd_model.sd_model_name),
+        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name),
         "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name),
         "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
-- 
cgit v1.2.3


From 594cbfd8fbe4078b43ceccf01509eeef3d6790c6 Mon Sep 17 00:00:00 2001
From: William Moorehouse <moorehousew@gmail.com>
Date: Sun, 9 Oct 2022 07:27:11 -0400
Subject: Sanitize infotext output (for now)

---
 modules/processing.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index c035c990..049f3769 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -284,9 +284,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name),
-        "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name),
-        "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork),
+        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
+        "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name.replace(',', '').replace(':', '')),
+        "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork.replace(',', '').replace(':', '')),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.3


From e6e8cabe0c9c335e0d72345602c069b198558b53 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 14:57:48 +0300
Subject: change up #2056 to make it work how i want it to plus make xy plot
 write correct values to images

---
 modules/processing.py | 5 ++---
 modules/sd_models.py  | 2 --
 modules/shared.py     | 2 +-
 3 files changed, 3 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 049f3769..04aed989 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -284,9 +284,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name.replace(',', '').replace(':', '')),
-        "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork.replace(',', '').replace(':', '')),
+        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 3fa42329..e63d3c29 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,7 +4,6 @@ import sys
 from collections import namedtuple
 import torch
 from omegaconf import OmegaConf
-from pathlib import Path
 
 from ldm.util import instantiate_from_config
 
@@ -158,7 +157,6 @@ def load_model_weights(model, checkpoint_info):
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
         model.first_stage_model.load_state_dict(vae_dict)
-        model.sd_model_vae_name = Path(vae_file).stem
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
diff --git a/modules/shared.py b/modules/shared.py
index ca63f7d8..6ecc2503 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -242,7 +242,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
-    "add_extended_model_details_to_info": OptionInfo(False, "Add extended model details to generation information (model name, VAE, hypernetwork)"),
+    "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-- 
cgit v1.2.3


From 9d1138e2940c4ddcd2685bcba12c7d407e9e0ec5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 15:08:10 +0300
Subject: fix typo in filename for ESRGAN arch

---
 modules/esrgam_model_arch.py | 80 --------------------------------------------
 modules/esrgan_model.py      |  2 +-
 modules/esrgan_model_arch.py | 80 ++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 81 insertions(+), 81 deletions(-)
 delete mode 100644 modules/esrgam_model_arch.py
 create mode 100644 modules/esrgan_model_arch.py

(limited to 'modules')

diff --git a/modules/esrgam_model_arch.py b/modules/esrgam_model_arch.py
deleted file mode 100644
index e413d36e..00000000
--- a/modules/esrgam_model_arch.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# this file is taken from https://github.com/xinntao/ESRGAN
-
-import functools
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def make_layer(block, n_layers):
-    layers = []
-    for _ in range(n_layers):
-        layers.append(block())
-    return nn.Sequential(*layers)
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    def __init__(self, nf=64, gc=32, bias=True):
-        super(ResidualDenseBlock_5C, self).__init__()
-        # gc: growth channel, i.e. intermediate channels
-        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
-        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
-        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    '''Residual in Residual Dense Block'''
-
-    def __init__(self, nf, gc=32):
-        super(RRDB, self).__init__()
-        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
-
-    def forward(self, x):
-        out = self.RDB1(x)
-        out = self.RDB2(out)
-        out = self.RDB3(out)
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
-        super(RRDBNet, self).__init__()
-        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
-
-        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
-        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
-        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        #### upsampling
-        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        fea = self.conv_first(x)
-        trunk = self.trunk_conv(self.RRDB_trunk(fea))
-        fea = fea + trunk
-
-        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.HRconv(fea)))
-
-        return out
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 3970e6e4..46ad0da3 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -5,7 +5,7 @@ import torch
 from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
-import modules.esrgam_model_arch as arch
+import modules.esrgan_model_arch as arch
 from modules import shared, modelloader, images, devices
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py
new file mode 100644
index 00000000..e413d36e
--- /dev/null
+++ b/modules/esrgan_model_arch.py
@@ -0,0 +1,80 @@
+# this file is taken from https://github.com/xinntao/ESRGAN
+
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def make_layer(block, n_layers):
+    layers = []
+    for _ in range(n_layers):
+        layers.append(block())
+    return nn.Sequential(*layers)
+
+
+class ResidualDenseBlock_5C(nn.Module):
+    def __init__(self, nf=64, gc=32, bias=True):
+        super(ResidualDenseBlock_5C, self).__init__()
+        # gc: growth channel, i.e. intermediate channels
+        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
+        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
+        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+        # initialization
+        # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    def forward(self, x):
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        return x5 * 0.2 + x
+
+
+class RRDB(nn.Module):
+    '''Residual in Residual Dense Block'''
+
+    def __init__(self, nf, gc=32):
+        super(RRDB, self).__init__()
+        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
+
+    def forward(self, x):
+        out = self.RDB1(x)
+        out = self.RDB2(out)
+        out = self.RDB3(out)
+        return out * 0.2 + x
+
+
+class RRDBNet(nn.Module):
+    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
+        super(RRDBNet, self).__init__()
+        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
+
+        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
+        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
+        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        #### upsampling
+        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        fea = self.conv_first(x)
+        trunk = self.trunk_conv(self.RRDB_trunk(fea))
+        fea = fea + trunk
+
+        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
+        fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
+        out = self.conv_last(self.lrelu(self.HRconv(fea)))
+
+        return out
-- 
cgit v1.2.3


From 875ddfeecfaffad9eee24813301637cba310337d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 17:58:43 +0300
Subject: added guard for torch.load to prevent loading pickles with unknown
 content

---
 modules/paths.py  |  1 +
 modules/safe.py   | 89 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 modules/shared.py |  1 +
 3 files changed, 91 insertions(+)
 create mode 100644 modules/safe.py

(limited to 'modules')

diff --git a/modules/paths.py b/modules/paths.py
index 0519caa0..1e7a2fbc 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -1,6 +1,7 @@
 import argparse
 import os
 import sys
+import modules.safe
 
 script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
 models_path = os.path.join(script_path, "models")
diff --git a/modules/safe.py b/modules/safe.py
new file mode 100644
index 00000000..2d2c1371
--- /dev/null
+++ b/modules/safe.py
@@ -0,0 +1,89 @@
+# this code is adapted from the script contributed by anon from /h/
+
+import io
+import pickle
+import collections
+import sys
+import traceback
+
+import torch
+import numpy
+import _codecs
+import zipfile
+
+
+def encode(*args):
+    out = _codecs.encode(*args)
+    return out
+
+
+class RestrictedUnpickler(pickle.Unpickler):
+    def persistent_load(self, saved_id):
+        assert saved_id[0] == 'storage'
+        return torch.storage._TypedStorage()
+
+    def find_class(self, module, name):
+        if module == 'collections' and name == 'OrderedDict':
+            return getattr(collections, name)
+        if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
+            return getattr(torch._utils, name)
+        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage']:
+            return getattr(torch, name)
+        if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
+            return getattr(torch.nn.modules.container, name)
+        if module == 'numpy.core.multiarray' and name == 'scalar':
+            return numpy.core.multiarray.scalar
+        if module == 'numpy' and name == 'dtype':
+            return numpy.dtype
+        if module == '_codecs' and name == 'encode':
+            return encode
+        if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
+            import pytorch_lightning.callbacks
+            return pytorch_lightning.callbacks.model_checkpoint
+        if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint':
+            import pytorch_lightning.callbacks.model_checkpoint
+            return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint
+        if module == "__builtin__" and name == 'set':
+            return set
+
+        # Forbid everything else.
+        raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
+
+
+def check_pt(filename):
+    try:
+
+        # new pytorch format is a zip file
+        with zipfile.ZipFile(filename) as z:
+            with z.open('archive/data.pkl') as file:
+                unpickler = RestrictedUnpickler(file)
+                unpickler.load()
+
+    except zipfile.BadZipfile:
+
+        # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
+        with open(filename, "rb") as file:
+            unpickler = RestrictedUnpickler(file)
+            for i in range(5):
+                unpickler.load()
+
+
+def load(filename, *args, **kwargs):
+    from modules import shared
+
+    try:
+        if not shared.cmd_opts.disable_safe_unpickle:
+            check_pt(filename)
+
+    except Exception:
+        print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+        print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
+        print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr)
+        return None
+
+    return unsafe_torch_load(filename, *args, **kwargs)
+
+
+unsafe_torch_load = torch.load
+torch.load = load
diff --git a/modules/shared.py b/modules/shared.py
index 6ecc2503..3d7f08e1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -65,6 +65,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 
 
 cmd_opts = parser.parse_args()
-- 
cgit v1.2.3


From 9ecea0a8d6bdc434755e11128487fd62f1ff130f Mon Sep 17 00:00:00 2001
From: Artem Zagidulin <p0chta@yandex.ru>
Date: Sun, 9 Oct 2022 16:14:56 +0300
Subject: fix missing png info when Extras Batch Process

---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 39dd3806..41e8612c 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -29,7 +29,7 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
     if extras_mode == 1:
         #convert file to pillow image
         for img in image_folder:
-            image = Image.fromarray(np.array(Image.open(img)))
+            image = Image.open(img)
             imageArr.append(image)
             imageNameArr.append(os.path.splitext(img.orig_name)[0])
     else:
-- 
cgit v1.2.3


From 6c383d2e82045fc4475d665f83bdeeac8fd844d9 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 22:24:07 +0300
Subject: show model selection setting on top of page

---
 modules/shared.py |  5 +++--
 modules/ui.py     | 54 +++++++++++++++++++++++++++++++++++++++++++++---------
 2 files changed, 48 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 3d7f08e1..270fa402 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -131,13 +131,14 @@ def realesrgan_models_names():
 
 
 class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False):
         self.default = default
         self.label = label
         self.component = component
         self.component_args = component_args
         self.onchange = onchange
         self.section = None
+        self.show_on_main_page = show_on_main_page
 
 
 def options_section(section_identifier, options_dict):
@@ -214,7 +215,7 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
     "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
diff --git a/modules/ui.py b/modules/ui.py
index dad509f3..2231a8ed 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1175,10 +1175,13 @@ Requested path was: {f}
         changed = 0
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if not opts.same_type(value, opts.data_labels[key].default):
-                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+            if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
+                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
+            if comp == dummy_component:
+                continue
+
             comp_args = opts.data_labels[key].component_args
             if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
                 continue
@@ -1196,6 +1199,21 @@ Requested path was: {f}
 
         return f'{changed} settings changed.', opts.dumpjson()
 
+    def run_settings_single(value, key):
+        if not opts.same_type(value, opts.data_labels[key].default):
+            return gr.update(visible=True), opts.dumpjson()
+
+        oldval = opts.data.get(key, None)
+        opts.data[key] = value
+
+        if oldval != value:
+            if opts.data_labels[key].onchange is not None:
+                opts.data_labels[key].onchange()
+
+        opts.save(shared.config_filename)
+
+        return gr.update(value=value), opts.dumpjson()
+
     with gr.Blocks(analytics_enabled=False) as settings_interface:
         settings_submit = gr.Button(value="Apply settings", variant='primary')
         result = gr.HTML()
@@ -1203,6 +1221,8 @@ Requested path was: {f}
         settings_cols = 3
         items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
 
+        quicksettings_list = []
+
         cols_displayed = 0
         items_displayed = 0
         previous_section = None
@@ -1225,10 +1245,14 @@ Requested path was: {f}
 
                     gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
 
-                component = create_setting_component(k)
-                component_dict[k] = component
-                components.append(component)
-                items_displayed += 1
+                if item.show_on_main_page:
+                    quicksettings_list.append((i, k, item))
+                    components.append(dummy_component)
+                else:
+                    component = create_setting_component(k)
+                    component_dict[k] = component
+                    components.append(component)
+                    items_displayed += 1
 
         request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
         request_notifications.click(
@@ -1242,7 +1266,6 @@ Requested path was: {f}
             reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
             restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
 
-
         def reload_scripts():
             modules.scripts.reload_script_body_only()
 
@@ -1289,7 +1312,11 @@ Requested path was: {f}
         css += css_hide_progressbar
 
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
-        
+        with gr.Row(elem_id="quicksettings"):
+            for i, k, item in quicksettings_list:
+                component = create_setting_component(k)
+                component_dict[k] = component
+
         settings_interface.gradio_ref = demo
         
         with gr.Tabs() as tabs:
@@ -1306,7 +1333,16 @@ Requested path was: {f}
             inputs=components,
             outputs=[result, text_settings],
         )
-        
+
+        for i, k, item in quicksettings_list:
+            component = component_dict[k]
+
+            component.change(
+                fn=lambda value, k=k: run_settings_single(value, key=k),
+                inputs=[component],
+                outputs=[component, text_settings],
+            )
+
         def modelmerger(*args):
             try:
                 results = modules.extras.run_modelmerger(*args)
-- 
cgit v1.2.3


From e59c66c0088422b27f64b401ef42c242f836725a Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sat, 8 Oct 2022 16:32:05 -0400
Subject: Optimized code for Ignoring last CLIP layers

---
 modules/sd_hijack.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index f12a9696..4a2d2153 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -282,14 +282,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
         tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
 
-        tmp = -opts.CLIP_ignore_last_layers
-        if (opts.CLIP_ignore_last_layers == 0):
-            outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
-            z = outputs.last_hidden_state
-        else:
-            outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp)
-            z = outputs.hidden_states[tmp]
-            z = self.wrapped.transformer.text_model.final_layer_norm(z)
+        tmp = -opts.CLIP_stop_at_last_layers
+        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp)
+        z = outputs.hidden_states[tmp]
+        z = self.wrapped.transformer.text_model.final_layer_norm(z)
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
-- 
cgit v1.2.3


From a14f7bf113a2af9e06a1c4d06c2efa244f9c5730 Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sat, 8 Oct 2022 16:33:06 -0400
Subject: Corrected CLIP Layer Ignore description and updated its range to the
 max possible

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 270fa402..1995a99a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -225,7 +225,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-    'CLIP_ignore_last_layers': OptionInfo(0, "Ignore last layers of CLIP model", gr.Slider, {"minimum": 0, "maximum": 5, "step": 1}),
+    'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.3


From ec2bd9be75865c9f3a8c898163ab381688c03b6e Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sat, 8 Oct 2022 17:28:42 -0400
Subject: Fix issues with CLIP ignore option name change

---
 modules/processing.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 04aed989..92a105a2 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -129,7 +129,7 @@ class Processed:
         self.index_of_first_image = index_of_first_image
         self.styles = p.styles
         self.job_timestamp = state.job_timestamp
-        self.clip_skip = opts.CLIP_ignore_last_layers
+        self.clip_skip = opts.CLIP_stop_at_last_layers
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -274,7 +274,7 @@ def fix_seed(p):
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
-    clip_skip = getattr(p, 'clip_skip', opts.CLIP_ignore_last_layers)
+    clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
 
     generation_params = {
         "Steps": p.steps,
-- 
cgit v1.2.3


From ad3ae441081155dcd4fde805279e5082ca264695 Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sun, 9 Oct 2022 04:32:40 -0400
Subject: Updated code for legibility

---
 modules/sd_hijack.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 4a2d2153..7793d25b 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -284,8 +284,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         tmp = -opts.CLIP_stop_at_last_layers
         outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp)
-        z = outputs.hidden_states[tmp]
-        z = self.wrapped.transformer.text_model.final_layer_norm(z)
+        if tmp < -1:
+            z = outputs.hidden_states[tmp]
+            z = self.wrapped.transformer.text_model.final_layer_norm(z)
+        else:
+            z = outputs.last_hidden_state
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
-- 
cgit v1.2.3


From 1824e9ee3ab4f94aee8908a62ea2569a01aeb3d7 Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sun, 9 Oct 2022 14:15:43 -0400
Subject: Removed unnecessary tmp variable

---
 modules/sd_hijack.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 7793d25b..437acce4 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -282,10 +282,9 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
         tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
 
-        tmp = -opts.CLIP_stop_at_last_layers
-        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp)
-        if tmp < -1:
-            z = outputs.hidden_states[tmp]
+        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers)
+        if opts.CLIP_stop_at_last_layers > 1:
+            z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
             z = self.wrapped.transformer.text_model.final_layer_norm(z)
         else:
             z = outputs.last_hidden_state
-- 
cgit v1.2.3


From 8d340cfb884e1dbff5b6f477f4ecf7d104279115 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 22:30:59 +0300
Subject: do not add clip skip to parameters if it's 1 or 0

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 92a105a2..94d2dd62 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -293,7 +293,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
-        "Clip skip": None if clip_skip==0 else clip_skip,
+        "Clip skip": None if clip_skip <= 1 else clip_skip,
     }
 
     generation_params.update(p.extra_generation_params)
-- 
cgit v1.2.3


From fa0c5eb81b72bc1e562d0b9bbd92f30945d78b4e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 20:41:22 +0100
Subject: Add pretty image captioning functions

---
 modules/images.py | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 29c5ee24..10963dc7 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -428,3 +428,34 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             file.write(info + "\n")
 
     return fullfn
+
+def addCaptionLines(lines,image,initialx,textfont):
+    draw = ImageDraw.Draw(image)
+    hstart =initialx
+    for fill,line in lines:
+        fontSize = 32
+        font = ImageFont.truetype(textfont, fontSize)
+        _,_,w, h = draw.textbbox((0,0),line,font=font)
+        fontSize =  min( int(fontSize * ((image.size[0]-35)/w) ), 28)
+        font = ImageFont.truetype(textfont, fontSize)
+        _,_,w,h = draw.textbbox((0,0),line,font=font)
+        draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill)
+        hstart += h
+    return hstart
+
+def captionImge(image,prelines,postlines,background=(51, 51, 51),font=None):
+    if font is None:
+        try:
+            font = ImageFont.truetype(opts.font or Roboto, fontsize)
+            font = opts.font or Roboto
+        except Exception:
+            font = Roboto
+
+    sampleImage = image
+    background = Image.new("RGBA", (sampleImage.size[0],sampleImage.size[1]+1024), background)
+    hoffset = addCaptionLines(prelines,background,5,font)+16
+    background.paste(sampleImage,(0,hoffset))
+    hoffset = hoffset+sampleImage.size[1]+8
+    hoffset = addCaptionLines(postlines,background,hoffset,font)
+    background = background.crop((0,0,sampleImage.size[0],hoffset+8))
+    return background
-- 
cgit v1.2.3


From a65476718f08a35f527b973ef731e6f488bace5e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 23:38:49 +0300
Subject: add DoubleStorage to list of allowed classes for pickle

---
 modules/safe.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/safe.py b/modules/safe.py
index 2d2c1371..4d06f2a5 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -27,7 +27,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             return getattr(collections, name)
         if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
             return getattr(torch._utils, name)
-        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage']:
+        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
             return getattr(torch, name)
         if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
             return getattr(torch.nn.modules.container, name)
-- 
cgit v1.2.3


From 03694e1f9915e34cf7d9a31073f1a1a9def2909f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 21:58:14 +0100
Subject: add embedding load and save from b64 json

---
 modules/textual_inversion/textual_inversion.py | 30 ++++++++++++++++++--------
 1 file changed, 21 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index f6316020..1b7f8906 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,9 +7,11 @@ import tqdm
 import html
 import datetime
 
-from PIL import Image, PngImagePlugin
+from PIL import Image,PngImagePlugin
+from ..images import captionImge
+import numpy as np
 import base64
-from io import BytesIO
+import json
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
@@ -87,9 +89,9 @@ class EmbeddingDatabase:
 
             if filename.upper().endswith('.PNG'):
                 embed_image = Image.open(path)
-                if 'sd-embedding' in embed_image.text:
-                    embeddingData = base64.b64decode(embed_image.text['sd-embedding'])
-                    data = torch.load(BytesIO(embeddingData), map_location="cpu")
+                if 'sd-ti-embedding' in embed_image.text:
+                    data = embeddingFromB64(embed_image.text['sd-ti-embedding'])
+                    name = data.get('name',name)
             else:
                 data = torch.load(path, map_location="cpu")
 
@@ -258,13 +260,23 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
             if save_image_with_stored_embedding:
                 info = PngImagePlugin.PngInfo()
-                info.add_text("sd-embedding", base64.b64encode(open(last_saved_file,'rb').read()))
-                image.save(last_saved_image, "PNG", pnginfo=info)
+                data = torch.load(last_saved_file)
+                info.add_text("sd-ti-embedding", embeddingToB64(data))
+
+                pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))]
+                
+                caption_checkpoint_hash = data.get('sd_checkpoint','UNK')
+                caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNK'
+                caption_stepcount = data.get('step',0)
+                caption_stepcount = caption_stepcount if caption_stepcount else 0
+
+                post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(caption_checkpoint_hash,
+                                                                                                  caption_stepcount))]
+                captioned_image = captionImge(image,prelines=pre_lines,postlines=post_lines)
+                captioned_image.save(last_saved_image, "PNG", pnginfo=info)
             else:
                 image.save(last_saved_image)
 
-
-
             last_saved_image += f", prompt: {text}"
 
         shared.state.job_no = embedding.step
-- 
cgit v1.2.3


From 969bd8256e5b4f1007d3cc653723d4ad50a92528 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:02:28 +0100
Subject: add alternate checkpoint hash source

---
 modules/textual_inversion/textual_inversion.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1b7f8906..d7813084 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -265,8 +265,11 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
                 pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))]
                 
-                caption_checkpoint_hash = data.get('sd_checkpoint','UNK')
-                caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNK'
+                caption_checkpoint_hash = data.get('sd_checkpoint')
+                if caption_checkpoint_hash is None:
+                    caption_checkpoint_hash = data.get('hash')
+                caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNKNOWN'
+                
                 caption_stepcount = data.get('step',0)
                 caption_stepcount = caption_stepcount if caption_stepcount else 0
 
-- 
cgit v1.2.3


From 5d12ec82d3e13f5ff4c55db2930e4e10aed7015a Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:05:09 +0100
Subject: add encoder and decoder classes

---
 modules/textual_inversion/textual_inversion.py | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index d7813084..44d4e08b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -16,6 +16,27 @@ import json
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
+class EmbeddingEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, torch.Tensor):
+            return {'EMBEDDINGTENSOR':obj.cpu().detach().numpy().tolist()}
+        return json.JSONEncoder.default(self, o)
+
+class EmbeddingDecoder(json.JSONDecoder):
+    def __init__(self, *args, **kwargs):
+        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
+    def object_hook(self, d):
+        if 'EMBEDDINGTENSOR' in d:
+            return torch.from_numpy(np.array(d['EMBEDDINGTENSOR']))
+        return d
+
+def embeddingToB64(data):
+    d = json.dumps(data,cls=EmbeddingEncoder)
+    return base64.b64encode(d.encode())
+
+def EmbeddingFromB64(data):
+    d = base64.b64decode(data)
+    return json.loads(d,cls=EmbeddingDecoder)
 
 class Embedding:
     def __init__(self, vec, name, step=None):
-- 
cgit v1.2.3


From d0184b8f76ce492da699f1926f34b57cd095242e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:06:12 +0100
Subject: change json tensor key name

---
 modules/textual_inversion/textual_inversion.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 44d4e08b..ae8d207d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -19,15 +19,15 @@ import modules.textual_inversion.dataset
 class EmbeddingEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, torch.Tensor):
-            return {'EMBEDDINGTENSOR':obj.cpu().detach().numpy().tolist()}
+            return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
         return json.JSONEncoder.default(self, o)
 
 class EmbeddingDecoder(json.JSONDecoder):
     def __init__(self, *args, **kwargs):
         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
     def object_hook(self, d):
-        if 'EMBEDDINGTENSOR' in d:
-            return torch.from_numpy(np.array(d['EMBEDDINGTENSOR']))
+        if 'TORCHTENSOR' in d:
+            return torch.from_numpy(np.array(d['TORCHTENSOR']))
         return d
 
 def embeddingToB64(data):
-- 
cgit v1.2.3


From 66846105103cfc282434d0dc2102910160b7a633 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:06:42 +0100
Subject: correct case on embeddingFromB64

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ae8d207d..d2b95fa3 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -34,7 +34,7 @@ def embeddingToB64(data):
     d = json.dumps(data,cls=EmbeddingEncoder)
     return base64.b64encode(d.encode())
 
-def EmbeddingFromB64(data):
+def embeddingFromB64(data):
     d = base64.b64decode(data)
     return json.loads(d,cls=EmbeddingDecoder)
 
-- 
cgit v1.2.3


From 96f1e6be59316ec640cab2435fa95b3688194906 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:14:50 +0100
Subject: source checkpoint hash from current checkpoint

---
 modules/textual_inversion/textual_inversion.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index d2b95fa3..b16fa84e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -286,10 +286,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
                 pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))]
                 
-                caption_checkpoint_hash = data.get('sd_checkpoint')
-                if caption_checkpoint_hash is None:
-                    caption_checkpoint_hash = data.get('hash')
-                caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNKNOWN'
+                checkpoint = sd_models.select_checkpoint()
+                caption_checkpoint_hash = checkpoint.hash
                 
                 caption_stepcount = data.get('step',0)
                 caption_stepcount = caption_stepcount if caption_stepcount else 0
-- 
cgit v1.2.3


From 01fd9cf0d28d8b71a113ab1aa62accfe7f0d9c51 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:17:02 +0100
Subject: change source of step count

---
 modules/textual_inversion/textual_inversion.py | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index b16fa84e..e4f339b8 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -285,15 +285,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
                 info.add_text("sd-ti-embedding", embeddingToB64(data))
 
                 pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))]
-                
                 checkpoint = sd_models.select_checkpoint()
-                caption_checkpoint_hash = checkpoint.hash
-                
-                caption_stepcount = data.get('step',0)
-                caption_stepcount = caption_stepcount if caption_stepcount else 0
-
-                post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(caption_checkpoint_hash,
-                                                                                                  caption_stepcount))]
+                post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(checkpoint.hash,
+                                                                                                     embedding.step))]
                 captioned_image = captionImge(image,prelines=pre_lines,postlines=post_lines)
                 captioned_image.save(last_saved_image, "PNG", pnginfo=info)
             else:
-- 
cgit v1.2.3


From 0ac3a07eecbd7b98f3a19d01dc46f02dcda3443b Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 00:05:36 +0100
Subject: add caption image with overlay

---
 modules/images.py | 46 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 10963dc7..4a4fc977 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -459,3 +459,49 @@ def captionImge(image,prelines,postlines,background=(51, 51, 51),font=None):
     hoffset = addCaptionLines(postlines,background,hoffset,font)
     background = background.crop((0,0,sampleImage.size[0],hoffset+8))
     return background
+
+def captionImageOverlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
+    from math import cos
+
+    image = srcimage.copy()
+
+    if textfont is None:
+        try:
+            textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
+            textfont = opts.font or Roboto
+        except Exception:
+            textfont = Roboto
+
+    factor = 1.5
+    gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0))
+    for y in range(image.size[1]):
+        mag = 1-cos(y/image.size[1]*factor)
+        mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
+        gradient.putpixel((0, y), (0,0,0,int(mag*255)))
+    image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
+
+    draw = ImageDraw.Draw(image)
+    fontSize = 32
+    font = ImageFont.truetype(textfont, fontSize)
+    padding = 10
+
+    _,_,w, h = draw.textbbox((0,0),title,font=font)
+    fontSize =  min( int(fontSize * (((image.size[0]*0.75)-(padding*4))/w) ), 72)
+    font = ImageFont.truetype(textfont, fontSize)
+    _,_,w,h = draw.textbbox((0,0),title,font=font)
+    draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230))
+
+    _,_,w, h = draw.textbbox((0,0),footerLeft,font=font)
+    fontSizeleft =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
+    _,_,w, h = draw.textbbox((0,0),footerMid,font=font)
+    fontSizemid =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
+    _,_,w, h = draw.textbbox((0,0),footerRight,font=font)
+    fontSizeright =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
+
+    font = ImageFont.truetype(textfont, min(fontSizeleft,fontSizemid,fontSizeright))
+
+    draw.text((padding,image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255,255,255,230))
+    draw.text((image.size[0]/2,image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255,255,255,230))
+    draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230))
+
+    return image
-- 
cgit v1.2.3


From d6a599ef9ba18a66ae79b50f2945af5788fdda8f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 00:07:52 +0100
Subject: change caption method

---
 modules/textual_inversion/textual_inversion.py | 30 ++++++++++++++++++--------
 1 file changed, 21 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e4f339b8..21596e78 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -8,7 +8,7 @@ import html
 import datetime
 
 from PIL import Image,PngImagePlugin
-from ..images import captionImge
+from ..images import captionImageOverlay
 import numpy as np
 import base64
 import json
@@ -212,6 +212,12 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
     else:
         images_dir = None
 
+    if create_image_every > 0 and save_image_with_stored_embedding:
+        images_embeds_dir = os.path.join(log_directory, "image_embeddings")
+        os.makedirs(images_embeds_dir, exist_ok=True)
+    else:
+        images_embeds_dir = None
+        
     cond_model = shared.sd_model.cond_stage_model
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
@@ -279,19 +285,25 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
             shared.state.current_image = image
 
-            if save_image_with_stored_embedding:
+            if save_image_with_stored_embedding and os.path.exists(last_saved_file):
+                
+                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
+
                 info = PngImagePlugin.PngInfo()
                 data = torch.load(last_saved_file)
                 info.add_text("sd-ti-embedding", embeddingToB64(data))
 
-                pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))]
+                title = "<{}>".format(data.get('name','???'))
                 checkpoint = sd_models.select_checkpoint()
-                post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(checkpoint.hash,
-                                                                                                     embedding.step))]
-                captioned_image = captionImge(image,prelines=pre_lines,postlines=post_lines)
-                captioned_image.save(last_saved_image, "PNG", pnginfo=info)
-            else:
-                image.save(last_saved_image)
+                footer_left = checkpoint.model_name
+                footer_mid = '[{}]'.format(checkpoint.hash)
+                footer_right = '[{}]'.format(embedding.step)
+
+                captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right)
+
+                captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+            
+            image.save(last_saved_image)
 
             last_saved_image += f", prompt: {text}"
 
-- 
cgit v1.2.3


From e2c2925eb4d634b186de2c76798162ec56e2f869 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 00:12:53 +0100
Subject: remove braces from steps

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 21596e78..9a18ee5c 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -297,7 +297,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '[{}]'.format(embedding.step)
+                footer_right = '{}'.format(embedding.step)
 
                 captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right)
 
-- 
cgit v1.2.3


From 6435691bb11c5a35703720bfd2a875f24c066f86 Mon Sep 17 00:00:00 2001
From: Justin Maier <Zipp425@gmail.com>
Date: Sun, 9 Oct 2022 19:26:52 -0600
Subject: Add "Scale to" option to Extras

---
 modules/extras.py | 28 +++++++++++++++++++++++-----
 modules/ui.py     | 38 +++++++++++++++++++++++++-------------
 2 files changed, 48 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 41e8612c..83ca7049 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -1,3 +1,4 @@
+import math
 import os
 
 import numpy as np
@@ -19,7 +20,7 @@ import gradio as gr
 cached_images = {}
 
 
-def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
     devices.torch_gc()
 
     imageArr = []
@@ -67,8 +68,23 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
             info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
             image = res
 
+        if resize_mode == 1:
+            upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
+            crop_info = " (crop)" if upscaling_crop else ""
+            info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
+
+            def crop_upscaled_center(image, resize_w, resize_h):
+                left = int(math.ceil((image.width - resize_w) / 2))
+                right = image.width - int(math.floor((image.width - resize_w) / 2))
+                top = int(math.ceil((image.height - resize_h) / 2))
+                bottom = image.height - int(math.floor((image.height - resize_h) / 2))
+
+                image = image.crop((left, top, right, bottom))
+                return image
+
+
         if upscaling_resize != 1.0:
-            def upscale(image, scaler_index, resize):
+            def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
                 small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
                 pixels = tuple(np.array(small).flatten().tolist())
                 key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
@@ -77,15 +93,17 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
                 if c is None:
                     upscaler = shared.sd_upscalers[scaler_index]
                     c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+                    if mode == 1 and crop:
+                        c = crop_upscaled_center(c, resize_w, resize_h)
                     cached_images[key] = c
 
                 return c
 
             info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
-            res = upscale(image, extras_upscaler_1, upscaling_resize)
+            res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
 
             if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-                res2 = upscale(image, extras_upscaler_2, upscaling_resize)
+                res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
                 info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
                 res = Image.blend(res, res2, extras_upscaler_2_visibility)
 
@@ -190,7 +208,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
             theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
-    
+
     for key in theta_1.keys():
         if 'model' in key and key not in theta_0:
             theta_0[key] = theta_1[key]
diff --git a/modules/ui.py b/modules/ui.py
index 2231a8ed..4bb2892b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -101,7 +101,7 @@ def send_gradio_gallery_to_image(x):
 
 
 def save_files(js_data, images, do_make_zip, index):
-    import csv    
+    import csv
     filenames = []
     fullfns = []
 
@@ -551,7 +551,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.Row():
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-                    
+
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
@@ -739,7 +739,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.Row():
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-                    
+
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
@@ -903,7 +903,15 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.TabItem('Batch Process'):
                         image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
 
-                upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
+                with gr.Tabs(elem_id="extras_resize_mode"):
+                    with gr.TabItem('Scale by'):
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
+                    with gr.TabItem('Scale to'):
+                        with gr.Group():
+                            with gr.Row():
+                                upscaling_resize_w = gr.Number(label="Width", value=512)
+                                upscaling_resize_h = gr.Number(label="Height", value=512)
+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
 
                 with gr.Group():
                     extras_upscaler_1 = gr.Radio(label='Upscaler 1', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
@@ -934,6 +942,7 @@ def create_ui(wrap_gradio_gpu_call):
             fn=wrap_gradio_gpu_call(modules.extras.run_extras),
             _js="get_extras_tab_index",
             inputs=[
+                dummy_component,
                 dummy_component,
                 extras_image,
                 image_batch,
@@ -941,6 +950,9 @@ def create_ui(wrap_gradio_gpu_call):
                 codeformer_visibility,
                 codeformer_weight,
                 upscaling_resize,
+                upscaling_resize_w,
+                upscaling_resize_h,
+                upscaling_crop,
                 extras_upscaler_1,
                 extras_upscaler_2,
                 extras_upscaler_2_visibility,
@@ -951,14 +963,14 @@ def create_ui(wrap_gradio_gpu_call):
                 html_info,
             ]
         )
-     
+
         extras_send_to_img2img.click(
             fn=lambda x: image_from_url_text(x),
             _js="extract_image_from_gallery_img2img",
             inputs=[result_images],
             outputs=[init_img],
         )
-        
+
         extras_send_to_inpaint.click(
             fn=lambda x: image_from_url_text(x),
             _js="extract_image_from_gallery_img2img",
@@ -1286,7 +1298,7 @@ Requested path was: {f}
             outputs=[],
             _js='function(){restart_reload()}'
         )
-        
+
         if column is not None:
             column.__exit__()
 
@@ -1318,12 +1330,12 @@ Requested path was: {f}
                 component_dict[k] = component
 
         settings_interface.gradio_ref = demo
-        
+
         with gr.Tabs() as tabs:
             for interface, label, ifid in interfaces:
                 with gr.TabItem(label, id=ifid):
                     interface.render()
-        
+
         if os.path.exists(os.path.join(script_path, "notification.mp3")):
             audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
 
@@ -1456,10 +1468,10 @@ Requested path was: {f}
 
             if getattr(obj,'custom_script_source',None) is not None:
               key = 'customscript/' + obj.custom_script_source + '/' + key
-            
+
             if getattr(obj, 'do_not_save_to_config', False):
                 return
-            
+
             saved_value = ui_settings.get(key, None)
             if saved_value is None:
                 ui_settings[key] = getattr(obj, field)
@@ -1483,10 +1495,10 @@ Requested path was: {f}
 
         if type(x) == gr.Textbox:
             apply_field(x, 'value')
-        
+
         if type(x) == gr.Number:
             apply_field(x, 'value')
-        
+
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
     visit(extras_interface, loadsave, "extras")
-- 
cgit v1.2.3


From cc92dc1f8d73dd4d574c4c8ccab78b7fc61e440b Mon Sep 17 00:00:00 2001
From: ssysm <me@theeditorstudio.com>
Date: Sun, 9 Oct 2022 23:17:29 -0400
Subject: add vae path args

---
 modules/sd_models.py | 2 +-
 modules/shared.py    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index cb3982b1..b6979432 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -147,7 +147,7 @@ def load_model_weights(model, checkpoint_info):
 
     devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
 
-    vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+    vae_file = shared.cmd_opts.vae_path or os.path.splitext(checkpoint_file)[0] + ".vae.pt"
     if os.path.exists(vae_file):
         print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location="cpu")
diff --git a/modules/shared.py b/modules/shared.py
index 2dc092d6..52ccfa6e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -64,7 +64,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
-
+parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 
 cmd_opts = parser.parse_args()
 
-- 
cgit v1.2.3


From 1f92336be768d235c18a82acb2195b7135101ae7 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Sun, 9 Oct 2022 23:58:18 -0500
Subject: refactored the deepbooru module to improve speed on running multiple
 interogations in a row.  Added the option to generate deepbooru tags for
 textual inversion preproccessing.

---
 modules/deepbooru.py                    | 84 +++++++++++++++++++++++++--------
 modules/textual_inversion/preprocess.py | 22 ++++++++-
 modules/ui.py                           | 52 ++++++++++++++------
 3 files changed, 122 insertions(+), 36 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 7e3c0618..cee4a3b4 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,21 +1,74 @@
 import os.path
 from concurrent.futures import ProcessPoolExecutor
-from multiprocessing import get_context
+import multiprocessing
 
 
-def _load_tf_and_return_tags(pil_image, threshold):
+def get_deepbooru_tags(pil_image, threshold=0.5):
+    """
+    This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
+    """
+    from modules import shared  # prevents circular reference
+    create_deepbooru_process(threshold)
+    shared.deepbooru_process_return["value"] = -1
+    shared.deepbooru_process_queue.put(pil_image)
+    while shared.deepbooru_process_return["value"] == -1:
+        time.sleep(0.2)
+    release_process()
+    return ret
+
+
+def deepbooru_process(queue, deepbooru_process_return, threshold):
+    model, tags = get_deepbooru_tags_model()
+    while True: # while process is running, keep monitoring queue for new image
+        pil_image = queue.get()
+        if pil_image == "QUIT":
+            break
+        else:
+            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold)
+
+
+def create_deepbooru_process(threshold=0.5):
+    """
+    Creates deepbooru process.  A queue is created to send images into the process.  This enables multiple images
+    to be processed in a row without reloading the model or creating a new process.  To return the data, a shared
+    dictionary is created to hold the tags created.  To wait for tags to be returned, a value of -1 is assigned
+    to the dictionary and the method adding the image to the queue should wait for this value to be updated with
+    the tags.
+    """
+    from modules import shared  # prevents circular reference
+    shared.deepbooru_process_manager = multiprocessing.Manager()
+    shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
+    shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
+    shared.deepbooru_process_return["value"] = -1
+    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold))
+    shared.deepbooru_process.start()
+
+
+def release_process():
+    """
+    Stops the deepbooru process to return used memory
+    """
+    from modules import shared  # prevents circular reference
+    shared.deepbooru_process_queue.put("QUIT")
+    shared.deepbooru_process.join()
+    shared.deepbooru_process_queue = None
+    shared.deepbooru_process = None
+    shared.deepbooru_process_return = None
+    shared.deepbooru_process_manager = None
+
+def get_deepbooru_tags_model():
     import deepdanbooru as dd
     import tensorflow as tf
     import numpy as np
-
     this_folder = os.path.dirname(__file__)
     model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
     if not os.path.exists(os.path.join(model_path, 'project.json')):
         # there is no point importing these every time
         import zipfile
         from basicsr.utils.download_util import load_file_from_url
-        load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
-                           model_path)
+        load_file_from_url(
+            r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
+            model_path)
         with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
             zip_ref.extractall(model_path)
         os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
@@ -24,7 +77,13 @@ def _load_tf_and_return_tags(pil_image, threshold):
     model = dd.project.load_model_from_project(
         model_path, compile_model=True
     )
+    return model, tags
 
+
+def get_deepbooru_tags_from_model(model, tags, pil_image, threshold=0.5):
+    import deepdanbooru as dd
+    import tensorflow as tf
+    import numpy as np
     width = model.input_shape[2]
     height = model.input_shape[1]
     image = np.array(pil_image)
@@ -57,17 +116,4 @@ def _load_tf_and_return_tags(pil_image, threshold):
 
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
-
-
-def subprocess_init_no_cuda():
-    import os
-    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-
-
-def get_deepbooru_tags(pil_image, threshold=0.5):
-    context = get_context('spawn')
-    with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
-        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
-        ret = f.result()  # will rethrow any exceptions
-    return ret
\ No newline at end of file
+    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
\ No newline at end of file
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index f1c002a2..9f63c9a4 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -3,11 +3,14 @@ from PIL import Image, ImageOps
 import platform
 import sys
 import tqdm
+import time
 
 from modules import shared, images
+from modules.shared import opts, cmd_opts
+if cmd_opts.deepdanbooru:
+    import modules.deepbooru as deepbooru
 
-
-def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
+def preprocess(process_src, process_dst, process_flip, process_split, process_caption, process_caption_deepbooru=False):
     size = 512
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
@@ -24,10 +27,21 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
     if process_caption:
         shared.interrogator.load()
 
+    if process_caption_deepbooru:
+        deepbooru.create_deepbooru_process()
+
     def save_pic_with_caption(image, index):
         if process_caption:
             caption = "-" + shared.interrogator.generate_caption(image)
             caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
+        elif process_caption_deepbooru:
+            shared.deepbooru_process_return["value"] = -1
+            shared.deepbooru_process_queue.put(image)
+            while shared.deepbooru_process_return["value"] == -1:
+                time.sleep(0.2)
+            caption = "-" + shared.deepbooru_process_return["value"]
+            caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
+            shared.deepbooru_process_return["value"] = -1
         else:
             caption = filename
             caption = os.path.splitext(caption)[0]
@@ -79,6 +93,10 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
     if process_caption:
         shared.interrogator.send_blip_to_ram()
 
+    if process_caption_deepbooru:
+        deepbooru.release_process()
+
+
 def sanitize_caption(base_path, original_caption, suffix):
     operating_system = platform.system().lower()
     if (operating_system == "windows"):
diff --git a/modules/ui.py b/modules/ui.py
index 2231a8ed..179e3a83 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1034,6 +1034,9 @@ def create_ui(wrap_gradio_gpu_call):
                         process_flip = gr.Checkbox(label='Create flipped copies')
                         process_split = gr.Checkbox(label='Split oversized images into two')
                         process_caption = gr.Checkbox(label='Use BLIP caption as filename')
+                        if cmd_opts.deepdanbooru:
+                            process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename')
+
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1086,21 +1089,40 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
-        run_preprocess.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                process_src,
-                process_dst,
-                process_flip,
-                process_split,
-                process_caption,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ],
-        )
+        if cmd_opts.deepdanbooru:
+            # if process_caption_deepbooru is None, it will cause an error, as a result only include it if it is enabled
+            run_preprocess.click(
+                fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+                _js="start_training_textual_inversion",
+                inputs=[
+                    process_src,
+                    process_dst,
+                    process_flip,
+                    process_split,
+                    process_caption,
+                    process_caption_deepbooru,
+                ],
+                outputs=[
+                    ti_output,
+                    ti_outcome,
+                ],
+            )
+        else:
+            run_preprocess.click(
+                fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+                _js="start_training_textual_inversion",
+                inputs=[
+                    process_src,
+                    process_dst,
+                    process_flip,
+                    process_split,
+                    process_caption,
+                ],
+                outputs=[
+                    ti_output,
+                    ti_outcome,
+                ],
+            )
 
         train_embedding.click(
             fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
-- 
cgit v1.2.3


From 8acc901ba3a252dc6ab4fabcb41644cf64d1774c Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 10 Oct 2022 00:38:55 -0400
Subject: Newer versions of PyTorch use TypedStorage instead

Pytorch 1.13 and later will rename _TypedStorage to TypedStorage, so check for TypedStorage and use _TypedStorage if it is not available. Currently this is needed so that nightly builds of PyTorch work correctly.
---
 modules/safe.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/safe.py b/modules/safe.py
index 4d06f2a5..05917463 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -12,6 +12,10 @@ import _codecs
 import zipfile
 
 
+# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
+TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
+
+
 def encode(*args):
     out = _codecs.encode(*args)
     return out
@@ -20,7 +24,7 @@ def encode(*args):
 class RestrictedUnpickler(pickle.Unpickler):
     def persistent_load(self, saved_id):
         assert saved_id[0] == 'storage'
-        return torch.storage._TypedStorage()
+        return TypedStorage()
 
     def find_class(self, module, name):
         if module == 'collections' and name == 'OrderedDict':
-- 
cgit v1.2.3


From 8a7c07a2140c98bceca858087525d77fd0352fda Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 10 Oct 2022 15:39:39 +0800
Subject: show image history

---
 modules/images_history.py | 90 +++++++++++++++++++++++++++++++++++++++++++++++
 modules/ui.py             | 11 ++++--
 2 files changed, 99 insertions(+), 2 deletions(-)
 create mode 100644 modules/images_history.py

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
new file mode 100644
index 00000000..23d83557
--- /dev/null
+++ b/modules/images_history.py
@@ -0,0 +1,90 @@
+import os
+def get_recent_images(is_img2img, dir_name, page_index, step):
+	page_index = int(page_index)
+	f_list = os.listdir(dir_name)
+	file_list = []
+	for file in f_list:
+		if file[-4:] == ".txt":
+			continue
+		file_list.append(file)	
+	file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+	num = 24
+	max_page_index = len(file_list) // num + 1	
+	page_index = max_page_index if page_index == -1 else page_index + step
+	page_index = 1 if page_index < 1 else page_index	
+	page_index = max_page_index if page_index > max_page_index else page_index
+	idx_frm = (page_index - 1) * num
+	file_list = file_list[idx_frm:idx_frm + num]
+	print(f"Loading history page {page_index}")	
+	return [os.path.join(dir_name, file) for file in file_list], page_index, file_list
+def first_page_click(is_img2img, dir_name):
+	return get_recent_images(is_img2img, dir_name, 1, 0)
+def end_page_click(is_img2img, dir_name):
+	return get_recent_images(is_img2img, dir_name, -1, 0)
+def prev_page_click(is_img2img, dir_name, page_index):
+	return get_recent_images(is_img2img, dir_name, page_index, -1)
+def next_page_click(is_img2img, dir_name, page_index):	
+	return get_recent_images(is_img2img, dir_name, page_index, 1)
+def page_index_change(is_img2img, dir_name, page_index):	
+	return get_recent_images(is_img2img, dir_name, page_index, 0)
+def show_image_info(num, filenames):
+	return filenames[int(num)]
+def delete_image(is_img2img, dir_name, name, page_index, filenames):
+	path = os.path.join(dir_name, name)		
+	if os.path.exists(path):
+		print(f"Delete file {path}")
+		os.remove(path)	
+	i = 0
+	for f in filenames:
+		if f == name:
+			break
+		i += 1
+	images, page_index, file_list = get_recent_images(is_img2img, dir_name, page_index, 0)
+	current_file =  file_list[i] if i < len(file_list) else None
+	return images, page_index, file_list, current_file
+
+
+def show_images_history(gr, opts, is_img2img):
+		def id_name(is_img2img, name):	
+			return ("img2img" if is_img2img else "txt2img") + "_" + name
+		with gr.Row():				
+			if is_img2img:
+				dir_name = opts.outdir_img2img_samples
+			else:
+				dir_name = opts.outdir_txt2img_samples							
+			first_page = gr.Button('First Page', elem_id=id_name(is_img2img,"images_history_first_page"))
+			prev_page = gr.Button('Prev Page') 
+			page_index = gr.Number(value=1)
+			next_page = gr.Button('Next Page') 
+			end_page = gr.Button('End Page')  
+		with gr.Row():	      
+			delete = gr.Button('Delete')
+			Send = gr.Button('Send')
+		with gr.Row():	
+			with gr.Column(elem_id=id_name(is_img2img,"images_history")):	
+				history_gallery = gr.Gallery(label="Images history").style(grid=6)
+				img_file_name = gr.Textbox()	
+				img_file_info = gr.Textbox(dir_name)
+				img_path = gr.Textbox(dir_name, visible=False)					
+				set_index = gr.Button('set_index',  elem_id=id_name(is_img2img,"images_history_set_index"))
+				is_img2img_flag = gr.Checkbox(is_img2img, visible=False)
+		filenames = gr.State()
+		first_page.click(first_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames])
+		next_page.click(next_page_click, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index, filenames])
+		prev_page.click(prev_page_click, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index, filenames])		
+		end_page.click(end_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames])
+		page_index.submit(page_index_change, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index, filenames])
+		set_index.click(show_image_info, _js="images_history_get_current_img",inputs=[is_img2img_flag, filenames], outputs=img_file_name)	
+		delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames], outputs=[history_gallery, page_index, filenames,img_file_name]) 
+		#page_index.change(page_index_change, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index])
+		
+def create_history_tabs(gr, opts):
+	with gr.Blocks(analytics_enabled=False) as images_history:
+		with gr.Tabs() as tabs:			
+			with gr.Tab("txt2img history", id="images_history_txt2img"):
+				with gr.Blocks(analytics_enabled=False) as images_history_txt2img:   
+					show_images_history(gr, opts, is_img2img=False) 
+			with gr.Tab("img2img history", id="images_history_img2img"):
+				with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+					show_images_history(gr, opts, is_img2img=True)
+	return images_history
diff --git a/modules/ui.py b/modules/ui.py
index 4f18126f..8762fcf5 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -37,6 +37,7 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
+import modules.images_history as img_his
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -499,7 +500,6 @@ def create_ui(wrap_gradio_gpu_call):
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
             with gr.Column(variant='panel'):
-
                 with gr.Group():
                     txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
                     txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
@@ -516,6 +516,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     html_info = gr.HTML()
                     generation_info = gr.Textbox(visible=False)
+                
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -607,6 +608,7 @@ def create_ui(wrap_gradio_gpu_call):
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
+        
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
@@ -696,6 +698,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     html_info = gr.HTML()
                     generation_info = gr.Textbox(visible=False)
+                
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -1126,8 +1129,10 @@ def create_ui(wrap_gradio_gpu_call):
 
         opts.save(shared.config_filename)
 
-        return f'{changed} settings changed.', opts.dumpjson()
+        return f'{changed} settings changed.', opts.dumpjson()    
 
+    
+    images_history = img_his.create_history_tabs(gr, opts)    
     with gr.Blocks(analytics_enabled=False) as settings_interface:
         settings_submit = gr.Button(value="Apply settings", variant='primary')
         result = gr.HTML()
@@ -1206,7 +1211,9 @@ def create_ui(wrap_gradio_gpu_call):
         (pnginfo_interface, "PNG Info", "pnginfo"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (textual_inversion_interface, "Textual inversion", "ti"),
+        (images_history, "History", "images_history"),
         (settings_interface, "Settings", "settings"),
+
     ]
 
     with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
-- 
cgit v1.2.3


From 3110f895b2718a3a25aae419fdf5c87c177ec9f4 Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 17:07:46 +0900
Subject: Textual Inversion: Added custom training image size and number of
 repeats per input image in a single epoch

---
 modules/textual_inversion/dataset.py           |  6 +++---
 modules/textual_inversion/preprocess.py        |  4 ++--
 modules/textual_inversion/textual_inversion.py | 15 ++++++++++++---
 modules/ui.py                                  |  8 +++++++-
 4 files changed, 24 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 7c44ea5b..acc4ce59 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -15,13 +15,13 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
+    def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
 
         self.placeholder_token = placeholder_token
 
         self.size = size
-        self.width = width
-        self.height = height
+        self.width = size
+        self.height = size
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
         self.dataset = []
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index f1c002a2..b3de6fd7 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -7,8 +7,8 @@ import tqdm
 from modules import shared, images
 
 
-def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
-    size = 512
+def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption):
+    size = process_size
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index cd9f3498..e34dc2e8 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,6 +6,7 @@ import torch
 import tqdm
 import html
 import datetime
+import math
 
 
 from modules import shared, devices, sd_hijack, processing, sd_models
@@ -156,7 +157,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -182,7 +183,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -200,6 +201,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
     if ititial_step > steps:
         return embedding, filename
 
+    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
+    epoch_len = (tr_img_len * num_repeats) + tr_img_len
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, (x, text) in pbar:
         embedding.step = i + ititial_step
@@ -223,7 +227,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
             loss.backward()
             optimizer.step()
 
-        pbar.set_description(f"loss: {losses.mean():.7f}")
+        epoch_num = math.floor(embedding.step / epoch_len)
+        epoch_step = embedding.step - (epoch_num * epoch_len)
+
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
 
         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
@@ -236,6 +243,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
                 sd_model=shared.sd_model,
                 prompt=text,
                 steps=20,
+                height=training_size,
+                width=training_size,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
diff --git a/modules/ui.py b/modules/ui.py
index 2231a8ed..f821fd8d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1029,6 +1029,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
+                    process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1043,13 +1044,15 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                 with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+                    training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
 
@@ -1092,6 +1095,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 process_src,
                 process_dst,
+                process_size,
                 process_flip,
                 process_split,
                 process_caption,
@@ -1110,7 +1114,9 @@ def create_ui(wrap_gradio_gpu_call):
                 learn_rate,
                 dataset_directory,
                 log_directory,
+                training_size,
                 steps,
+                num_repeats,
                 create_image_every,
                 save_embedding_every,
                 template_file,
-- 
cgit v1.2.3


From 8ec069e64df48f8f202f8b93a08e91b69448eb39 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 03:23:24 -0500
Subject: removed duplicate run_preprocess.click by creating
 run_preprocess_inputs list and appending deepbooru variable to input list if
 in scope

---
 modules/ui.py | 49 +++++++++++++++++--------------------------------
 1 file changed, 17 insertions(+), 32 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 179e3a83..22ca74c2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1089,40 +1089,25 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
+        run_preprocess_inputs = [
+            process_src,
+            process_dst,
+            process_flip,
+            process_split,
+            process_caption,
+        ]
         if cmd_opts.deepdanbooru:
             # if process_caption_deepbooru is None, it will cause an error, as a result only include it if it is enabled
-            run_preprocess.click(
-                fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-                _js="start_training_textual_inversion",
-                inputs=[
-                    process_src,
-                    process_dst,
-                    process_flip,
-                    process_split,
-                    process_caption,
-                    process_caption_deepbooru,
-                ],
-                outputs=[
-                    ti_output,
-                    ti_outcome,
-                ],
-            )
-        else:
-            run_preprocess.click(
-                fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-                _js="start_training_textual_inversion",
-                inputs=[
-                    process_src,
-                    process_dst,
-                    process_flip,
-                    process_split,
-                    process_caption,
-                ],
-                outputs=[
-                    ti_output,
-                    ti_outcome,
-                ],
-            )
+            run_preprocess_inputs.append(process_caption_deepbooru)
+        run_preprocess.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=run_preprocess_inputs,
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ],
+        )
 
         train_embedding.click(
             fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
-- 
cgit v1.2.3


From 4ee7519fc2e459ce8eff1f61f1655afba393357c Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 17:31:33 +0900
Subject: Fixed progress bar output for epoch

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e34dc2e8..769682ea 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -228,7 +228,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             optimizer.step()
 
         epoch_num = math.floor(embedding.step / epoch_len)
-        epoch_step = embedding.step - (epoch_num * epoch_len)
+        epoch_step = embedding.step - (epoch_num * epoch_len) + 1
 
         pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
 
-- 
cgit v1.2.3


From 2f94331df2cb1181439adecc28cfd758049f6501 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 03:34:00 -0500
Subject: removed change in last commit, simplified to adding the visible
 argument to process_caption_deepbooru and it set to False if deepdanbooru
 argument is not set

---
 modules/ui.py | 22 ++++++++++------------
 1 file changed, 10 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 22ca74c2..f8adafb3 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1036,7 +1036,8 @@ def create_ui(wrap_gradio_gpu_call):
                         process_caption = gr.Checkbox(label='Use BLIP caption as filename')
                         if cmd_opts.deepdanbooru:
                             process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename')
-
+                        else:
+                            process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename', visible=False)
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1089,20 +1090,17 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
-        run_preprocess_inputs = [
-            process_src,
-            process_dst,
-            process_flip,
-            process_split,
-            process_caption,
-        ]
-        if cmd_opts.deepdanbooru:
-            # if process_caption_deepbooru is None, it will cause an error, as a result only include it if it is enabled
-            run_preprocess_inputs.append(process_caption_deepbooru)
         run_preprocess.click(
             fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
-            inputs=run_preprocess_inputs,
+            inputs=[
+                process_src,
+                process_dst,
+                process_flip,
+                process_split,
+                process_caption,
+                process_caption_deepbooru
+            ],
             outputs=[
                 ti_output,
                 ti_outcome,
-- 
cgit v1.2.3


From 23f2989799ee3911d2959cfceb74b921f20c9a51 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 10 Oct 2022 18:33:49 +0800
Subject: images history over

---
 modules/images_history.py | 141 +++++++++++++++++++++++++++-------------------
 modules/ui.py             |   9 ++-
 2 files changed, 92 insertions(+), 58 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 23d83557..0e0a48f3 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,5 +1,6 @@
 import os
-def get_recent_images(is_img2img, dir_name, page_index, step):
+def get_recent_images(dir_name, page_index, step, image_index):
+	print(image_index)
 	page_index = int(page_index)
 	f_list = os.listdir(dir_name)
 	file_list = []
@@ -8,7 +9,7 @@ def get_recent_images(is_img2img, dir_name, page_index, step):
 			continue
 		file_list.append(file)	
 	file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
-	num = 24
+	num = 48
 	max_page_index = len(file_list) // num + 1	
 	page_index = max_page_index if page_index == -1 else page_index + step
 	page_index = 1 if page_index < 1 else page_index	
@@ -16,75 +17,101 @@ def get_recent_images(is_img2img, dir_name, page_index, step):
 	idx_frm = (page_index - 1) * num
 	file_list = file_list[idx_frm:idx_frm + num]
 	print(f"Loading history page {page_index}")	
-	return [os.path.join(dir_name, file) for file in file_list], page_index, file_list
-def first_page_click(is_img2img, dir_name):
-	return get_recent_images(is_img2img, dir_name, 1, 0)
-def end_page_click(is_img2img, dir_name):
-	return get_recent_images(is_img2img, dir_name, -1, 0)
-def prev_page_click(is_img2img, dir_name, page_index):
-	return get_recent_images(is_img2img, dir_name, page_index, -1)
-def next_page_click(is_img2img, dir_name, page_index):	
-	return get_recent_images(is_img2img, dir_name, page_index, 1)
-def page_index_change(is_img2img, dir_name, page_index):	
-	return get_recent_images(is_img2img, dir_name, page_index, 0)
-def show_image_info(num, filenames):
-	return filenames[int(num)]
-def delete_image(is_img2img, dir_name, name, page_index, filenames):
+	image_index = int(image_index)
+	if image_index < 0 or image_index > len(file_list)  - 1:
+		current_file = None 
+		hide_image = None
+	else:
+		current_file =  file_list[int(image_index)]
+		hide_image = os.path.join(dir_name, current_file)
+	return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image
+def first_page_click(dir_name, page_index, image_index):
+	return get_recent_images(dir_name, 1, 0, image_index)
+def end_page_click(dir_name, page_index, image_index):
+	return get_recent_images(dir_name, -1, 0, image_index)
+def prev_page_click(dir_name, page_index, image_index):
+	return get_recent_images(dir_name, page_index, -1, image_index)
+def next_page_click(dir_name, page_index, image_index):	
+	return get_recent_images(dir_name, page_index, 1, image_index)
+def page_index_change(dir_name, page_index, image_index):	
+	return get_recent_images(dir_name, page_index, 0, image_index)
+
+def show_image_info(num, image_path, filenames):
+	file = filenames[int(num)]
+	return file, num, os.path.join(image_path, file)
+def delete_image(is_img2img, dir_name, name, page_index, filenames, image_index):
+	print("filename", name)
 	path = os.path.join(dir_name, name)		
 	if os.path.exists(path):
 		print(f"Delete file {path}")
 		os.remove(path)	
-	i = 0
-	for f in filenames:
-		if f == name:
-			break
-		i += 1
-	images, page_index, file_list = get_recent_images(is_img2img, dir_name, page_index, 0)
-	current_file =  file_list[i] if i < len(file_list) else None
-	return images, page_index, file_list, current_file
+	images, page_index, file_list, current_file, hide_image = get_recent_images(dir_name, page_index, 0, image_index)
+	return images, page_index, file_list, current_file, hide_image
 
 
-def show_images_history(gr, opts, is_img2img):
+def show_images_history(gr, opts, is_img2img, run_pnginfo, switch_dict):
 		def id_name(is_img2img, name):	
 			return ("img2img" if is_img2img else "txt2img") + "_" + name
-		with gr.Row():				
-			if is_img2img:
-				dir_name = opts.outdir_img2img_samples
-			else:
-				dir_name = opts.outdir_txt2img_samples							
-			first_page = gr.Button('First Page', elem_id=id_name(is_img2img,"images_history_first_page"))
-			prev_page = gr.Button('Prev Page') 
-			page_index = gr.Number(value=1)
-			next_page = gr.Button('Next Page') 
-			end_page = gr.Button('End Page')  
-		with gr.Row():	      
-			delete = gr.Button('Delete')
-			Send = gr.Button('Send')
-		with gr.Row():	
-			with gr.Column(elem_id=id_name(is_img2img,"images_history")):	
-				history_gallery = gr.Gallery(label="Images history").style(grid=6)
-				img_file_name = gr.Textbox()	
-				img_file_info = gr.Textbox(dir_name)
-				img_path = gr.Textbox(dir_name, visible=False)					
-				set_index = gr.Button('set_index',  elem_id=id_name(is_img2img,"images_history_set_index"))
-				is_img2img_flag = gr.Checkbox(is_img2img, visible=False)
-		filenames = gr.State()
-		first_page.click(first_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames])
-		next_page.click(next_page_click, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index, filenames])
-		prev_page.click(prev_page_click, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index, filenames])		
-		end_page.click(end_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames])
-		page_index.submit(page_index_change, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index, filenames])
-		set_index.click(show_image_info, _js="images_history_get_current_img",inputs=[is_img2img_flag, filenames], outputs=img_file_name)	
-		delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames], outputs=[history_gallery, page_index, filenames,img_file_name]) 
+		if is_img2img:
+			dir_name = opts.outdir_img2img_samples
+		else:
+			dir_name = opts.outdir_txt2img_samples
+		with gr.Row():	 
+				first_page = gr.Button('First', elem_id=id_name(is_img2img,"images_history_first_page"))
+				prev_page = gr.Button('Prev') 
+				page_index = gr.Number(value=1, label="Page Index")
+				next_page = gr.Button('Next') 
+				end_page = gr.Button('End')	
+		with gr.Row(elem_id=id_name(is_img2img,"images_history")):			
+			with gr.Row():	 
+				with gr.Column():
+					history_gallery = gr.Gallery(show_label=False).style(grid=6)
+				with gr.Column():	 
+					with gr.Row():	      
+						delete = gr.Button('Delete')
+						pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+						pnginfo_send_to_img2img = gr.Button('Send to img2img')
+					with gr.Row():
+						with gr.Column():
+							img_file_info = gr.Textbox(dir_name, label="Generate Info")
+							img_file_name = gr.Textbox(label="File Name")	
+					with gr.Row():	
+							# hiden items
+							img_path = gr.Textbox(dir_name, visible=False)				
+							is_img2img_flag = gr.Checkbox(is_img2img, visible=False)	
+							image_index = gr.Textbox(value=-1, visible=False)						
+							set_index = gr.Button('set_index',  elem_id=id_name(is_img2img,"images_history_set_index"))
+							filenames = gr.State()
+							hide_image = gr.Image(visible=False, type="pil")
+							info1 = gr.Textbox(visible=False)
+							info2 = gr.Textbox(visible=False)
+
+				
+		# turn pages
+		gallery_inputs = [img_path, page_index, image_index]
+		gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image]
+		first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+		next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+		prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)		
+		end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+		page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
 		#page_index.change(page_index_change, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index])
+
+		#other funcitons
+		set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[is_img2img_flag,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
+		delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames, image_index], outputs=gallery_outputs) 
+		hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
+		switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
+		switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
+	
 		
-def create_history_tabs(gr, opts):
+def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
 	with gr.Blocks(analytics_enabled=False) as images_history:
 		with gr.Tabs() as tabs:			
 			with gr.Tab("txt2img history", id="images_history_txt2img"):
 				with gr.Blocks(analytics_enabled=False) as images_history_txt2img:   
-					show_images_history(gr, opts, is_img2img=False) 
+					show_images_history(gr, opts, False, run_pnginfo, switch_dict) 
 			with gr.Tab("img2img history", id="images_history_img2img"):
 				with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-					show_images_history(gr, opts, is_img2img=True)
+					show_images_history(gr, opts, True, run_pnginfo, switch_dict)
 	return images_history
diff --git a/modules/ui.py b/modules/ui.py
index 8762fcf5..21c9236b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1131,8 +1131,15 @@ def create_ui(wrap_gradio_gpu_call):
 
         return f'{changed} settings changed.', opts.dumpjson()    
 
+    #images history
+    images_history_switch_dict = {
+        "fn":modules.generation_parameters_copypaste.connect_paste,
+        "t2i":txt2img_paste_fields,
+        "i2i":img2img_paste_fields
+    }
+    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)    
     
-    images_history = img_his.create_history_tabs(gr, opts)    
+
     with gr.Blocks(analytics_enabled=False) as settings_interface:
         settings_submit = gr.Button(value="Apply settings", variant='primary')
         result = gr.HTML()
-- 
cgit v1.2.3


From 7349088d32b080f64058b6e5de5f0380a71ecd09 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 16:11:14 +0300
Subject: --no-half-vae

---
 modules/devices.py     |  6 +++++-
 modules/processing.py  | 11 +++++++++--
 modules/sd_models.py   |  3 +++
 modules/sd_samplers.py |  4 ++--
 modules/shared.py      |  1 +
 5 files changed, 20 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 0158b11f..03ef58f1 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -36,6 +36,7 @@ errors.run(enable_tf32, "Enabling TF32")
 
 device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
 dtype = torch.float16
+dtype_vae = torch.float16
 
 def randn(seed, shape):
     # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
@@ -59,9 +60,12 @@ def randn_without_seed(shape):
     return torch.randn(shape, device=device)
 
 
-def autocast():
+def autocast(disable=False):
     from modules import shared
 
+    if disable:
+        return contextlib.nullcontext()
+
     if dtype == torch.float32 or shared.cmd_opts.precision == "full":
         return contextlib.nullcontext()
 
diff --git a/modules/processing.py b/modules/processing.py
index 94d2dd62..ec8651ae 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -259,6 +259,13 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     return x
 
 
+def decode_first_stage(model, x):
+    with devices.autocast(disable=x.dtype == devices.dtype_vae):
+        x = model.decode_first_stage(x)
+
+    return x
+
+
 def get_fixed_seed(seed):
     if seed is None or seed == '' or seed == -1:
         return int(random.randrange(4294967294))
@@ -400,7 +407,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
             samples_ddim = samples_ddim.to(devices.dtype)
 
-            x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
+            x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
             del samples_ddim
@@ -533,7 +540,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if self.scale_latent:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
         else:
-            decoded_samples = self.sd_model.decode_first_stage(samples)
+            decoded_samples = decode_first_stage(self.sd_model, samples)
 
             if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
                 decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
diff --git a/modules/sd_models.py b/modules/sd_models.py
index e63d3c29..2cdcd84f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -149,6 +149,7 @@ def load_model_weights(model, checkpoint_info):
         model.half()
 
     devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
+    devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
     vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
     if os.path.exists(vae_file):
@@ -158,6 +159,8 @@ def load_model_weights(model, checkpoint_info):
 
         model.first_stage_model.load_state_dict(vae_dict)
 
+    model.first_stage_model.to(devices.dtype_vae)
+
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 6e743f7e..d168b938 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -7,7 +7,7 @@ import inspect
 import k_diffusion.sampling
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
-from modules import prompt_parser
+from modules import prompt_parser, devices, processing
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -83,7 +83,7 @@ def setup_img2img_steps(p, steps=None):
 
 
 def sample_to_image(samples):
-    x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
+    x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)
diff --git a/modules/shared.py b/modules/shared.py
index 1995a99a..5dfc344c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -25,6 +25,7 @@ parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to director
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
 parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
 parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
+parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-- 
cgit v1.2.3


From 04c745ea4f81518999927fee5f78500560c25e29 Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 22:35:35 +0900
Subject: Custom Width and Height

---
 modules/textual_inversion/dataset.py           |  7 +++----
 modules/textual_inversion/preprocess.py        | 19 ++++++++++---------
 modules/textual_inversion/textual_inversion.py | 11 +++++------
 modules/ui.py                                  | 12 ++++++++----
 4 files changed, 26 insertions(+), 23 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index acc4ce59..bcf772d2 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -15,13 +15,12 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
 
         self.placeholder_token = placeholder_token
 
-        self.size = size
-        self.width = size
-        self.height = size
+        self.width = width
+        self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
         self.dataset = []
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index b3de6fd7..d7efdef2 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -7,8 +7,9 @@ import tqdm
 from modules import shared, images
 
 
-def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption):
-    size = process_size
+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption):
+    width = process_width
+    height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
 
@@ -55,23 +56,23 @@ def preprocess(process_src, process_dst, process_size, process_flip, process_spl
         is_wide = ratio < 1 / 1.35
 
         if process_split and is_tall:
-            img = img.resize((size, size * img.height // img.width))
+            img = img.resize((width, height * img.height // img.width))
 
-            top = img.crop((0, 0, size, size))
+            top = img.crop((0, 0, width, height))
             save_pic(top, index)
 
-            bot = img.crop((0, img.height - size, size, img.height))
+            bot = img.crop((0, img.height - height, width, img.height))
             save_pic(bot, index)
         elif process_split and is_wide:
-            img = img.resize((size * img.width // img.height, size))
+            img = img.resize((width * img.width // img.height, height))
 
-            left = img.crop((0, 0, size, size))
+            left = img.crop((0, 0, width, height))
             save_pic(left, index)
 
-            right = img.crop((img.width - size, 0, img.width, size))
+            right = img.crop((img.width - width, 0, img.width, height))
             save_pic(right, index)
         else:
-            img = images.resize_image(1, img, size, size)
+            img = images.resize_image(1, img, width, height)
             save_pic(img, index)
 
         shared.state.nextjob()
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 769682ea..5965c5a0 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,7 +6,6 @@ import torch
 import tqdm
 import html
 import datetime
-import math
 
 
 from modules import shared, devices, sd_hijack, processing, sd_models
@@ -157,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -183,7 +182,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -227,7 +226,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             loss.backward()
             optimizer.step()
 
-        epoch_num = math.floor(embedding.step / epoch_len)
+        epoch_num = embedding.step // epoch_len
         epoch_step = embedding.step - (epoch_num * epoch_len) + 1
 
         pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
@@ -243,8 +242,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 sd_model=shared.sd_model,
                 prompt=text,
                 steps=20,
-                height=training_size,
-                width=training_size,
+				height=training_height,
+				width=training_width,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
diff --git a/modules/ui.py b/modules/ui.py
index f821fd8d..8c06ad7c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1029,7 +1029,8 @@ def create_ui(wrap_gradio_gpu_call):
 
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
-                    process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1050,7 +1051,8 @@ def create_ui(wrap_gradio_gpu_call):
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
@@ -1095,7 +1097,8 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 process_src,
                 process_dst,
-                process_size,
+                process_width,
+                process_height,
                 process_flip,
                 process_split,
                 process_caption,
@@ -1114,7 +1117,8 @@ def create_ui(wrap_gradio_gpu_call):
                 learn_rate,
                 dataset_directory,
                 log_directory,
-                training_size,
+                training_width,
+                training_height,
                 steps,
                 num_repeats,
                 create_image_every,
-- 
cgit v1.2.3


From 8f1efdc130cf7ff47cb8d3722cdfc0dbeba3069e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 17:03:45 +0300
Subject: --no-half-vae pt2

---
 modules/processing.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index ec8651ae..50ba4fc5 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -405,8 +405,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 # use the image collected previously in sampler loop
                 samples_ddim = shared.state.current_latent
 
-            samples_ddim = samples_ddim.to(devices.dtype)
-
+            samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
-- 
cgit v1.2.3


From ea00c1624bbb0dcb5be07f59c9509061baddf5b1 Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 17:07:46 +0900
Subject: Textual Inversion: Added custom training image size and number of
 repeats per input image in a single epoch

---
 modules/textual_inversion/dataset.py           |  6 +++---
 modules/textual_inversion/preprocess.py        |  4 ++--
 modules/textual_inversion/textual_inversion.py | 15 ++++++++++++---
 modules/ui.py                                  |  8 +++++++-
 4 files changed, 24 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 7c44ea5b..acc4ce59 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -15,13 +15,13 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
+    def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
 
         self.placeholder_token = placeholder_token
 
         self.size = size
-        self.width = width
-        self.height = height
+        self.width = size
+        self.height = size
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
         self.dataset = []
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index f1c002a2..b3de6fd7 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -7,8 +7,8 @@ import tqdm
 from modules import shared, images
 
 
-def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
-    size = 512
+def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption):
+    size = process_size
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index cd9f3498..e34dc2e8 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,6 +6,7 @@ import torch
 import tqdm
 import html
 import datetime
+import math
 
 
 from modules import shared, devices, sd_hijack, processing, sd_models
@@ -156,7 +157,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -182,7 +183,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -200,6 +201,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
     if ititial_step > steps:
         return embedding, filename
 
+    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
+    epoch_len = (tr_img_len * num_repeats) + tr_img_len
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, (x, text) in pbar:
         embedding.step = i + ititial_step
@@ -223,7 +227,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
             loss.backward()
             optimizer.step()
 
-        pbar.set_description(f"loss: {losses.mean():.7f}")
+        epoch_num = math.floor(embedding.step / epoch_len)
+        epoch_step = embedding.step - (epoch_num * epoch_len)
+
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
 
         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
@@ -236,6 +243,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
                 sd_model=shared.sd_model,
                 prompt=text,
                 steps=20,
+                height=training_size,
+                width=training_size,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
diff --git a/modules/ui.py b/modules/ui.py
index 2231a8ed..f821fd8d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1029,6 +1029,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
+                    process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1043,13 +1044,15 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                 with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+                    training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
 
@@ -1092,6 +1095,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 process_src,
                 process_dst,
+                process_size,
                 process_flip,
                 process_split,
                 process_caption,
@@ -1110,7 +1114,9 @@ def create_ui(wrap_gradio_gpu_call):
                 learn_rate,
                 dataset_directory,
                 log_directory,
+                training_size,
                 steps,
+                num_repeats,
                 create_image_every,
                 save_embedding_every,
                 template_file,
-- 
cgit v1.2.3


From 6ad3a53e368d36535de1a4fca73b3bb78fd40654 Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 17:31:33 +0900
Subject: Fixed progress bar output for epoch

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e34dc2e8..769682ea 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -228,7 +228,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             optimizer.step()
 
         epoch_num = math.floor(embedding.step / epoch_len)
-        epoch_step = embedding.step - (epoch_num * epoch_len)
+        epoch_step = embedding.step - (epoch_num * epoch_len) + 1
 
         pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
 
-- 
cgit v1.2.3


From 7a20f914eddfdf09c0ccced157ec108205bc3d0f Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 22:35:35 +0900
Subject: Custom Width and Height

---
 modules/textual_inversion/dataset.py           |  7 +++----
 modules/textual_inversion/preprocess.py        | 19 ++++++++++---------
 modules/textual_inversion/textual_inversion.py | 11 +++++------
 modules/ui.py                                  | 12 ++++++++----
 4 files changed, 26 insertions(+), 23 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index acc4ce59..bcf772d2 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -15,13 +15,12 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
 
         self.placeholder_token = placeholder_token
 
-        self.size = size
-        self.width = size
-        self.height = size
+        self.width = width
+        self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
         self.dataset = []
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index b3de6fd7..d7efdef2 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -7,8 +7,9 @@ import tqdm
 from modules import shared, images
 
 
-def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption):
-    size = process_size
+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption):
+    width = process_width
+    height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
 
@@ -55,23 +56,23 @@ def preprocess(process_src, process_dst, process_size, process_flip, process_spl
         is_wide = ratio < 1 / 1.35
 
         if process_split and is_tall:
-            img = img.resize((size, size * img.height // img.width))
+            img = img.resize((width, height * img.height // img.width))
 
-            top = img.crop((0, 0, size, size))
+            top = img.crop((0, 0, width, height))
             save_pic(top, index)
 
-            bot = img.crop((0, img.height - size, size, img.height))
+            bot = img.crop((0, img.height - height, width, img.height))
             save_pic(bot, index)
         elif process_split and is_wide:
-            img = img.resize((size * img.width // img.height, size))
+            img = img.resize((width * img.width // img.height, height))
 
-            left = img.crop((0, 0, size, size))
+            left = img.crop((0, 0, width, height))
             save_pic(left, index)
 
-            right = img.crop((img.width - size, 0, img.width, size))
+            right = img.crop((img.width - width, 0, img.width, height))
             save_pic(right, index)
         else:
-            img = images.resize_image(1, img, size, size)
+            img = images.resize_image(1, img, width, height)
             save_pic(img, index)
 
         shared.state.nextjob()
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 769682ea..5965c5a0 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,7 +6,6 @@ import torch
 import tqdm
 import html
 import datetime
-import math
 
 
 from modules import shared, devices, sd_hijack, processing, sd_models
@@ -157,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -183,7 +182,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -227,7 +226,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             loss.backward()
             optimizer.step()
 
-        epoch_num = math.floor(embedding.step / epoch_len)
+        epoch_num = embedding.step // epoch_len
         epoch_step = embedding.step - (epoch_num * epoch_len) + 1
 
         pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
@@ -243,8 +242,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 sd_model=shared.sd_model,
                 prompt=text,
                 steps=20,
-                height=training_size,
-                width=training_size,
+				height=training_height,
+				width=training_width,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
diff --git a/modules/ui.py b/modules/ui.py
index f821fd8d..8c06ad7c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1029,7 +1029,8 @@ def create_ui(wrap_gradio_gpu_call):
 
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
-                    process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1050,7 +1051,8 @@ def create_ui(wrap_gradio_gpu_call):
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
@@ -1095,7 +1097,8 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 process_src,
                 process_dst,
-                process_size,
+                process_width,
+                process_height,
                 process_flip,
                 process_split,
                 process_caption,
@@ -1114,7 +1117,8 @@ def create_ui(wrap_gradio_gpu_call):
                 learn_rate,
                 dataset_directory,
                 log_directory,
-                training_size,
+                training_width,
+                training_height,
                 steps,
                 num_repeats,
                 create_image_every,
-- 
cgit v1.2.3


From 707a431100362645e914042bb344d08439f48ac8 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 15:34:49 +0100
Subject: add pixel data footer

---
 modules/textual_inversion/textual_inversion.py | 48 ++++++++++++++++++++++++--
 1 file changed, 46 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 7a24192e..6fb64691 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -12,6 +12,7 @@ from ..images import captionImageOverlay
 import numpy as np
 import base64
 import json
+import zlib
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
@@ -20,7 +21,7 @@ class EmbeddingEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, torch.Tensor):
             return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
-        return json.JSONEncoder.default(self, o)
+        return json.JSONEncoder.default(self, obj)
 
 class EmbeddingDecoder(json.JSONDecoder):
     def __init__(self, *args, **kwargs):
@@ -38,6 +39,45 @@ def embeddingFromB64(data):
     d = base64.b64decode(data)
     return json.loads(d,cls=EmbeddingDecoder)
 
+def appendImageDataFooter(image,data):
+    d = 3
+    data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
+    dnp = np.frombuffer(data_compressed,np.uint8).copy()
+    w = image.size[0]
+    next_size = dnp.shape[0] + (w-(dnp.shape[0]%w))
+    next_size = next_size + ((w*d)-(next_size%(w*d)))
+    dnp.resize(next_size)
+    dnp = dnp.reshape((-1,w,d))
+    print(dnp.shape)
+    im = Image.fromarray(dnp,mode='RGB')
+    background = Image.new('RGB',(image.size[0],image.size[1]+im.size[1]+1),(0,0,0))
+    background.paste(image,(0,0))
+    background.paste(im,(0,image.size[1]+1))
+    return background
+
+def crop_black(img,tol=0):
+    mask = (img>tol).all(2)
+    mask0,mask1 = mask.any(0),mask.any(1)
+    col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax()
+    row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
+    return img[row_start:row_end,col_start:col_end]
+
+def extractImageDataFooter(image):
+    d=3
+    outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) )
+    lastRow = np.where( np.sum(outarr, axis=(1,2))==0)
+    if lastRow[0].shape[0] == 0:
+        print('Image data block not found.')
+        return None
+    lastRow = lastRow[0]
+    
+    lastRow = lastRow.max()
+
+    dataBlock = outarr[lastRow+1::].astype(np.uint8).flatten().tobytes()
+    print(lastRow)
+    data = zlib.decompress(dataBlock)
+    return json.loads(data,cls=EmbeddingDecoder)
+
 class Embedding:
     def __init__(self, vec, name, step=None):
         self.vec = vec
@@ -113,6 +153,9 @@ class EmbeddingDatabase:
                 if 'sd-ti-embedding' in embed_image.text:
                     data = embeddingFromB64(embed_image.text['sd-ti-embedding'])
                     name = data.get('name',name)
+                else:
+                    data = extractImageDataFooter(embed_image)
+                    name = data.get('name',name)
             else:
                 data = torch.load(path, map_location="cpu")
 
@@ -190,7 +233,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -308,6 +351,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 footer_right = '{}'.format(embedding.step)
 
                 captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right)
+                captioned_image = appendImageDataFooter(captioned_image,data)
 
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
             
-- 
cgit v1.2.3


From df6d0d9286279c41c4c67460c3158fa268697524 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 15:43:09 +0100
Subject: convert back to rgb as some hosts add alpha

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 6fb64691..667a7cf2 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -64,7 +64,7 @@ def crop_black(img,tol=0):
 
 def extractImageDataFooter(image):
     d=3
-    outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) )
+    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) )
     lastRow = np.where( np.sum(outarr, axis=(1,2))==0)
     if lastRow[0].shape[0] == 0:
         print('Image data block not found.')
-- 
cgit v1.2.3


From f347ddfd808c56bb1bacdec0c4bedf826ff85cd8 Mon Sep 17 00:00:00 2001
From: RW21 <rinze.watanabe.translation@gmail.com>
Date: Mon, 10 Oct 2022 10:44:11 +0900
Subject: Remove max_batch_count from ui.py

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 8c06ad7c..8ba84911 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -524,7 +524,7 @@ def create_ui(wrap_gradio_gpu_call):
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row():
-                    batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
 
                 cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
@@ -710,7 +710,7 @@ def create_ui(wrap_gradio_gpu_call):
                     tiling = gr.Checkbox(label='Tiling', value=False)
 
                 with gr.Row():
-                    batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
 
                 with gr.Group():
-- 
cgit v1.2.3


From b340439586d844e76782149ca1857c8de35773ec Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Mon, 10 Oct 2022 05:28:06 +0100
Subject: Unlimited Token Works

Unlimited tokens actually work now. Works with textual inversion too. Replaces the previous not-so-much-working implementation.
---
 modules/sd_hijack.py | 69 ++++++++++++++++++++++++++++++++++------------------
 1 file changed, 46 insertions(+), 23 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 437acce4..8d5c77d8 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -43,10 +43,7 @@ def undo_optimizations():
 
 
 def get_target_prompt_token_count(token_count):
-    if token_count < 75:
-        return 75
-
-    return math.ceil(token_count / 10) * 10
+    return math.ceil(max(token_count, 1) / 75) * 75
 
 
 class StableDiffusionModelHijack:
@@ -127,7 +124,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 self.token_mults[ident] = mult
 
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
-        id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
 
         if opts.enable_emphasis:
@@ -154,7 +150,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     i += 1
                 else:
                     emb_len = int(embedding.vec.shape[0])
-                    fixes.append((len(remade_tokens), embedding))
+                    iteration = len(remade_tokens) // 75
+                    fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
                     remade_tokens += [0] * emb_len
                     multipliers += [weight] * emb_len
                     used_custom_terms.append((embedding.name, embedding.checksum()))
@@ -162,10 +159,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         token_count = len(remade_tokens)
         prompt_target_length = get_target_prompt_token_count(token_count)
-        tokens_to_add = prompt_target_length - len(remade_tokens) + 1
+        tokens_to_add = prompt_target_length - len(remade_tokens)
 
-        remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
-        multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
+        remade_tokens = remade_tokens + [id_end] * tokens_to_add
+        multipliers = multipliers + [1.0] * tokens_to_add
 
         return remade_tokens, fixes, multipliers, token_count
 
@@ -260,29 +257,55 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             hijack_fixes.append(fixes)
             batch_multipliers.append(multipliers)
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-
+    
     def forward(self, text):
-
-        if opts.use_old_emphasis_implementation:
+        use_old = opts.use_old_emphasis_implementation
+        if use_old:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
         else:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
 
-        self.hijack.fixes = hijack_fixes
         self.hijack.comments += hijack_comments
 
         if len(used_custom_terms) > 0:
             self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
+        
+        if use_old:
+            self.hijack.fixes = hijack_fixes
+            return self.process_tokens(remade_batch_tokens, batch_multipliers)
+        
+        z = None
+        i = 0
+        while max(map(len, remade_batch_tokens)) != 0:
+            rem_tokens = [x[75:] for x in remade_batch_tokens]
+            rem_multipliers = [x[75:] for x in batch_multipliers]
+            
+            self.hijack.fixes = []
+            for unfiltered in hijack_fixes:
+                fixes = []
+                for fix in unfiltered:
+                    if fix[0] == i:
+                        fixes.append(fix[1])
+                self.hijack.fixes.append(fixes)
+            
+            z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
+            z = z1 if z is None else torch.cat((z, z1), axis=-2)
+            
+            remade_batch_tokens = rem_tokens
+            batch_multipliers = rem_multipliers
+            i += 1
+        
+        return z
+        
+    
+    def process_tokens(self, remade_batch_tokens, batch_multipliers):
+        if not opts.use_old_emphasis_implementation:
+            remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
+            batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
+        
+        tokens = torch.asarray(remade_batch_tokens).to(device)
+        outputs = self.wrapped.transformer(input_ids=tokens)
 
-        target_token_count = get_target_prompt_token_count(token_count) + 2
-
-        position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
-        position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
-
-        remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
-        tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
-
-        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers)
         if opts.CLIP_stop_at_last_layers > 1:
             z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
             z = self.wrapped.transformer.text_model.final_layer_norm(z)
@@ -290,7 +313,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             z = outputs.last_hidden_state
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-        batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
+        batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
         batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
         original_mean = z.mean()
         z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
-- 
cgit v1.2.3


From 460bbae58726c177beddfcddf351f27e205d3fb2 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Mon, 10 Oct 2022 16:09:06 +0100
Subject: Pad beginning of textual inversion embedding

---
 modules/sd_hijack.py | 5 +++++
 1 file changed, 5 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 8d5c77d8..3a60cd63 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -151,6 +151,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 else:
                     emb_len = int(embedding.vec.shape[0])
                     iteration = len(remade_tokens) // 75
+                    if (len(remade_tokens) + emb_len) // 75 != iteration:
+                        rem = (75 * (iteration + 1) - len(remade_tokens))
+                        remade_tokens += [id_end] * rem
+                        multipliers += [1.0] * rem
+                        iteration += 1
                     fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
                     remade_tokens += [0] * emb_len
                     multipliers += [weight] * emb_len
-- 
cgit v1.2.3


From d5c14365fd468dbf89fa12a68bea5b217077273c Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Mon, 10 Oct 2022 16:13:47 +0100
Subject: Add back in output hidden states parameter

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 3a60cd63..3edc0e9d 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -309,7 +309,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
         
         tokens = torch.asarray(remade_batch_tokens).to(device)
-        outputs = self.wrapped.transformer(input_ids=tokens)
+        outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
 
         if opts.CLIP_stop_at_last_layers > 1:
             z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
-- 
cgit v1.2.3


From 9d33baba587637815d818e5e641d8f8b74c4900d Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Mon, 10 Oct 2022 18:46:48 +0300
Subject: Always show previous mask and fix extras_send dest

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 8ba84911..e8039d76 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -961,7 +961,7 @@ def create_ui(wrap_gradio_gpu_call):
         
         extras_send_to_inpaint.click(
             fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_img2img",
+            _js="extract_image_from_gallery_inpaint",
             inputs=[result_images],
             outputs=[init_img_with_mask],
         )
-- 
cgit v1.2.3


From 623251ce2b8d152e242011f62984a8247a14a389 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 10 Oct 2022 17:45:38 +0300
Subject: allow pascal onwards

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 3edc0e9d..827bf304 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -23,7 +23,7 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
-    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6)):
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
-- 
cgit v1.2.3


From 3e7a981194ed9c454e951365846e4eba66fa7095 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 10 Oct 2022 17:51:05 +0300
Subject: remove functorch

---
 modules/sd_hijack_optimizations.py | 2 --
 1 file changed, 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 634fb4b2..18408e62 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -13,8 +13,6 @@ from modules import shared
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
         import xformers.ops
-        import functorch
-        xformers._is_functorch_available = True
         shared.xformers_available = True
     except Exception:
         print("Cannot import xformers", file=sys.stderr)
-- 
cgit v1.2.3


From ece27fe98933eb0eda8ea94dc496dd7554f3a08f Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sun, 9 Oct 2022 18:55:33 +0300
Subject: Add files via upload

---
 modules/swinir_model_arch_v2.py | 1017 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 1017 insertions(+)
 create mode 100644 modules/swinir_model_arch_v2.py

(limited to 'modules')

diff --git a/modules/swinir_model_arch_v2.py b/modules/swinir_model_arch_v2.py
new file mode 100644
index 00000000..0e28ae6e
--- /dev/null
+++ b/modules/swinir_model_arch_v2.py
@@ -0,0 +1,1017 @@
+# -----------------------------------------------------------------------------------
+# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
+# Written by Conde and Choi et al.
+# -----------------------------------------------------------------------------------
+
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
+                 pretrained_window_size=[0, 0]):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.pretrained_window_size = pretrained_window_size
+        self.num_heads = num_heads
+
+        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
+
+        # mlp to generate continuous relative position bias
+        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
+                                     nn.ReLU(inplace=True),
+                                     nn.Linear(512, num_heads, bias=False))
+
+        # get relative_coords_table
+        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
+        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
+        relative_coords_table = torch.stack(
+            torch.meshgrid([relative_coords_h,
+                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
+        if pretrained_window_size[0] > 0:
+            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
+        else:
+            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
+        relative_coords_table *= 8  # normalize to -8, 8
+        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
+            torch.abs(relative_coords_table) + 1.0) / np.log2(8)
+
+        self.register_buffer("relative_coords_table", relative_coords_table)
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(dim))
+            self.v_bias = nn.Parameter(torch.zeros(dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        # cosine attention
+        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
+        logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
+        attn = attn * logit_scale
+
+        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
+        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, ' \
+               f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+        pretrained_window_size (int): Window size in pre-training.
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
+            pretrained_window_size=to_2tuple(pretrained_window_size))
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            attn_mask = self.calculate_mask(self.input_resolution)
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+        
+    def calculate_mask(self, x_size):
+        # calculate attention mask for SW-MSA
+        H, W = x_size
+        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+        return attn_mask        
+
+    def forward(self, x, x_size):
+        H, W = x_size
+        B, L, C = x.shape
+        #assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+        if self.input_resolution == x_size:
+            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+        else:
+            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+            
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+        x = shortcut + self.drop_path(self.norm1(x))
+
+        # FFN
+        x = x + self.drop_path(self.norm2(self.mlp(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(2 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.reduction(x)
+        x = self.norm(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        flops += H * W * self.dim // 2
+        return flops    
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        pretrained_window_size (int): Local window size in pre-training.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 pretrained_window_size=0):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer,
+                                 pretrained_window_size=pretrained_window_size)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, x_size):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, x_size)
+            else:
+                x = blk(x, x_size)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+    def _init_respostnorm(self):
+        for blk in self.blocks:
+            nn.init.constant_(blk.norm1.bias, 0)
+            nn.init.constant_(blk.norm1.weight, 0)
+            nn.init.constant_(blk.norm2.bias, 0)
+            nn.init.constant_(blk.norm2.weight, 0)
+            
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        # assert H == self.img_size[0] and W == self.img_size[1],
+        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops           
+
+class RSTB(nn.Module):
+    """Residual Swin Transformer Block (RSTB).
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        img_size: Input image size.
+        patch_size: Patch size.
+        resi_connection: The convolutional block before residual connection.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 img_size=224, patch_size=4, resi_connection='1conv'):
+        super(RSTB, self).__init__()
+
+        self.dim = dim
+        self.input_resolution = input_resolution
+
+        self.residual_group = BasicLayer(dim=dim,
+                                         input_resolution=input_resolution,
+                                         depth=depth,
+                                         num_heads=num_heads,
+                                         window_size=window_size,
+                                         mlp_ratio=mlp_ratio,
+                                         qkv_bias=qkv_bias, 
+                                         drop=drop, attn_drop=attn_drop,
+                                         drop_path=drop_path,
+                                         norm_layer=norm_layer,
+                                         downsample=downsample,
+                                         use_checkpoint=use_checkpoint)
+
+        if resi_connection == '1conv':
+            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+            norm_layer=None)
+
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+            norm_layer=None)
+
+    def forward(self, x, x_size):
+        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+    def flops(self):
+        flops = 0
+        flops += self.residual_group.flops()
+        H, W = self.input_resolution
+        flops += H * W * self.dim * self.dim * 9
+        flops += self.patch_embed.flops()
+        flops += self.patch_unembed.flops()
+
+        return flops
+
+class PatchUnEmbed(nn.Module):
+    r""" Image to Patch Unembedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+    def forward(self, x, x_size):
+        B, HW, C = x.shape
+        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
+        return x
+
+    def flops(self):
+        flops = 0
+        return flops
+
+
+class Upsample(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample, self).__init__(*m)
+        
+class Upsample_hf(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample_hf, self).__init__(*m)        
+
+
+class UpsampleOneStep(nn.Sequential):
+    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+       Used in lightweight SR to save parameters.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+
+    """
+
+    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+        self.num_feat = num_feat
+        self.input_resolution = input_resolution
+        m = []
+        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+        m.append(nn.PixelShuffle(scale))
+        super(UpsampleOneStep, self).__init__(*m)
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.num_feat * 3 * 9
+        return flops
+    
+    
+
+class Swin2SR(nn.Module):
+    r""" Swin2SR
+        A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 64
+        patch_size (int | tuple(int)): Patch size. Default: 1
+        in_chans (int): Number of input image channels. Default: 3
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 7
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+        img_range: Image range. 1. or 255.
+        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+    """
+
+    def __init__(self, img_size=64, patch_size=1, in_chans=3,
+                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 window_size=7, mlp_ratio=4., qkv_bias=True, 
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+                 **kwargs):
+        super(Swin2SR, self).__init__()
+        num_in_ch = in_chans
+        num_out_ch = in_chans
+        num_feat = 64
+        self.img_range = img_range
+        if in_chans == 3:
+            rgb_mean = (0.4488, 0.4371, 0.4040)
+            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+        else:
+            self.mean = torch.zeros(1, 1, 1, 1)
+        self.upscale = upscale
+        self.upsampler = upsampler
+        self.window_size = window_size
+
+        #####################################################################################################
+        ################################### 1, shallow feature extraction ###################################
+        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+        #####################################################################################################
+        ################################### 2, deep feature extraction ######################################
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = embed_dim
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # merge non-overlapping patches into image
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build Residual Swin Transformer blocks (RSTB)
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = RSTB(dim=embed_dim,
+                         input_resolution=(patches_resolution[0],
+                                           patches_resolution[1]),
+                         depth=depths[i_layer],
+                         num_heads=num_heads[i_layer],
+                         window_size=window_size,
+                         mlp_ratio=self.mlp_ratio,
+                         qkv_bias=qkv_bias, 
+                         drop=drop_rate, attn_drop=attn_drop_rate,
+                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                         norm_layer=norm_layer,
+                         downsample=None,
+                         use_checkpoint=use_checkpoint,
+                         img_size=img_size,
+                         patch_size=patch_size,
+                         resi_connection=resi_connection
+
+                         )
+            self.layers.append(layer)
+            
+        if self.upsampler == 'pixelshuffle_hf':
+            self.layers_hf = nn.ModuleList()
+            for i_layer in range(self.num_layers):
+                layer = RSTB(dim=embed_dim,
+                             input_resolution=(patches_resolution[0],
+                                               patches_resolution[1]),
+                             depth=depths[i_layer],
+                             num_heads=num_heads[i_layer],
+                             window_size=window_size,
+                             mlp_ratio=self.mlp_ratio,
+                             qkv_bias=qkv_bias, 
+                             drop=drop_rate, attn_drop=attn_drop_rate,
+                             drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                             norm_layer=norm_layer,
+                             downsample=None,
+                             use_checkpoint=use_checkpoint,
+                             img_size=img_size,
+                             patch_size=patch_size,
+                             resi_connection=resi_connection
+
+                             )
+                self.layers_hf.append(layer)
+                        
+        self.norm = norm_layer(self.num_features)
+
+        # build the last conv layer in deep feature extraction
+        if resi_connection == '1conv':
+            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+        #####################################################################################################
+        ################################ 3, high quality image reconstruction ################################
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+        elif self.upsampler == 'pixelshuffle_aux':
+            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+            self.conv_before_upsample = nn.Sequential(
+                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))
+            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.conv_after_aux = nn.Sequential(
+                nn.Conv2d(3, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))            
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            
+        elif self.upsampler == 'pixelshuffle_hf':
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.upsample_hf = Upsample_hf(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+            self.conv_before_upsample_hf = nn.Sequential(
+                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))
+            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR (to save parameters)
+            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+                                            (patches_resolution[0], patches_resolution[1]))
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR (less artifacts)
+            assert self.upscale == 4, 'only support x4 now.'
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def check_image_size(self, x):
+        _, _, h, w = x.size()
+        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+        return x
+
+    def forward_features(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x
+    
+    def forward_features_hf(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers_hf:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x    
+
+    def forward(self, x):
+        H, W = x.shape[2:]
+        x = self.check_image_size(x)
+
+        self.mean = self.mean.type_as(x)
+        x = (x - self.mean) * self.img_range
+
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.conv_last(self.upsample(x))
+        elif self.upsampler == 'pixelshuffle_aux':
+            bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
+            bicubic = self.conv_bicubic(bicubic)
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            aux = self.conv_aux(x) # b, 3, LR_H, LR_W
+            x = self.conv_after_aux(aux)
+            x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
+            x = self.conv_last(x)
+            aux = aux / self.img_range + self.mean
+        elif self.upsampler == 'pixelshuffle_hf':
+            # for classical SR with HF
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x_before = self.conv_before_upsample(x)
+            x_out = self.conv_last(self.upsample(x_before))
+            
+            x_hf = self.conv_first_hf(x_before)
+            x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
+            x_hf = self.conv_before_upsample_hf(x_hf)
+            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
+            x = x_out + x_hf
+            x_hf = x_hf / self.img_range + self.mean
+
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.upsample(x)
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.conv_last(self.lrelu(self.conv_hr(x)))
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            x_first = self.conv_first(x)
+            res = self.conv_after_body(self.forward_features(x_first)) + x_first
+            x = x + self.conv_last(res)
+        
+        x = x / self.img_range + self.mean
+        if self.upsampler == "pixelshuffle_aux":
+            return x[:, :, :H*self.upscale, :W*self.upscale], aux
+        
+        elif self.upsampler == "pixelshuffle_hf":
+            x_out = x_out / self.img_range + self.mean
+            return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
+        
+        else:
+            return x[:, :, :H*self.upscale, :W*self.upscale]
+
+    def flops(self):
+        flops = 0
+        H, W = self.patches_resolution
+        flops += H * W * 3 * self.embed_dim * 9
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += H * W * 3 * self.embed_dim * self.embed_dim
+        flops += self.upsample.flops()
+        return flops
+
+
+if __name__ == '__main__':
+    upscale = 4
+    window_size = 8
+    height = (1024 // upscale // window_size + 1) * window_size
+    width = (720 // upscale // window_size + 1) * window_size
+    model = Swin2SR(upscale=2, img_size=(height, width),
+                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+    print(model)
+    print(height, width, model.flops() / 1e9)
+
+    x = torch.randn((1, 3, height, width))
+    x = model(x)
+    print(x.shape)
\ No newline at end of file
-- 
cgit v1.2.3


From ed769977f0d0f201d8e361d365102f18775fc62c Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sun, 9 Oct 2022 18:56:59 +0300
Subject: add swinir v2 support

---
 modules/swinir_model.py | 35 ++++++++++++++++++++++++++++-------
 1 file changed, 28 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index fbd11f84..baa02e3d 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -10,6 +10,7 @@ from tqdm import tqdm
 from modules import modelloader
 from modules.shared import cmd_opts, opts, device
 from modules.swinir_model_arch import SwinIR as net
+from modules.swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
 
 precision_scope = (
@@ -57,22 +58,42 @@ class UpscalerSwinIR(Upscaler):
             filename = path
         if filename is None or not os.path.exists(filename):
             return None
-        model = net(
+        if filename.endswith(".v2.pth"):
+            model = net2(
             upscale=scale,
             in_chans=3,
             img_size=64,
             window_size=8,
             img_range=1.0,
-            depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
-            embed_dim=240,
-            num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+            depths=[6, 6, 6, 6, 6, 6],
+            embed_dim=180,
+            num_heads=[6, 6, 6, 6, 6, 6],
             mlp_ratio=2,
             upsampler="nearest+conv",
-            resi_connection="3conv",
-        )
+            resi_connection="1conv",
+            )
+            params = None
+        else:
+            model = net(
+                upscale=scale,
+                in_chans=3,
+                img_size=64,
+                window_size=8,
+                img_range=1.0,
+                depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
+                embed_dim=240,
+                num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+                mlp_ratio=2,
+                upsampler="nearest+conv",
+                resi_connection="3conv",
+            )
+            params = "params_ema"
 
         pretrained_model = torch.load(filename)
-        model.load_state_dict(pretrained_model["params_ema"], strict=True)
+        if params is not None:
+            model.load_state_dict(pretrained_model[params], strict=True)
+        else:
+            model.load_state_dict(pretrained_model, strict=True)
         if not cmd_opts.no_half:
             model = model.half()
         return model
-- 
cgit v1.2.3


From af62ad4d25dcd0454944368f4925d83101cdedbc Mon Sep 17 00:00:00 2001
From: ssysm <me@theeditorstudio.com>
Date: Mon, 10 Oct 2022 13:25:28 -0400
Subject: change vae loading method

---
 modules/sd_models.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index b0e1d8bd..7a42d924 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -150,9 +150,16 @@ def load_model_weights(model, checkpoint_info):
 
     devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
 
-    vae_file = shared.cmd_opts.vae_path or os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+    vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+    if os.path.exists(vae_file):
+        print(f"Found VAE Weights: {vae_file}")
+    elif shared.cmd_opts.vae_path != None:
+        vae_file = shared.cmd_opts.vae_path
+        print(f'No VAE found for inside the model folder. Using CLI specified : {vae_file}')
+    else:
+        print("No VAE found for inside the model folder. Passing.")
+
     if os.path.exists(vae_file):
-        print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location="cpu")
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
-- 
cgit v1.2.3


From 39919c40dd18f5a14ae21403efea1b0f819756c7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 20:32:37 +0300
Subject: add eta noise seed delta option

---
 modules/processing.py | 6 +++++-
 modules/shared.py     | 1 +
 2 files changed, 6 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 50ba4fc5..698b3069 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -207,7 +207,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     # enables the generation of additional tensors with noise that the sampler will use during its processing.
     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
     # produce the same images as with two batches [100], [101].
-    if p is not None and p.sampler is not None and len(seeds) > 1 and opts.enable_batch_seeds:
+    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
     else:
         sampler_noises = None
@@ -247,6 +247,9 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
         if sampler_noises is not None:
             cnt = p.sampler.number_of_needed_noises(p)
 
+            if opts.eta_noise_seed_delta > 0:
+                torch.manual_seed(seed + opts.eta_noise_seed_delta)
+
             for j in range(cnt):
                 sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
 
@@ -301,6 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
+        "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
     }
 
     generation_params.update(p.extra_generation_params)
diff --git a/modules/shared.py b/modules/shared.py
index 5dfc344c..b1c65ecf 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -260,6 +260,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
 
-- 
cgit v1.2.3


From 727e4d108674dc2813507e2a973a733ef21e8d53 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 20:46:55 +0300
Subject: no to different messages plus fix using != to compare to None

---
 modules/sd_models.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4c06051e..0a55b4c3 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -152,15 +152,12 @@ def load_model_weights(model, checkpoint_info):
     devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
     vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
-    if os.path.exists(vae_file):
-        print(f"Found VAE Weights: {vae_file}")
-    elif shared.cmd_opts.vae_path != None:
+
+    if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
         vae_file = shared.cmd_opts.vae_path
-        print(f'No VAE found for inside the model folder. Using CLI specified : {vae_file}')
-    else:
-        print("No VAE found for inside the model folder. Passing.")
 
     if os.path.exists(vae_file):
+        print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location="cpu")
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
-- 
cgit v1.2.3


From 1d64976dbc5a0f3124567b91fadd5014a9d93c5f Mon Sep 17 00:00:00 2001
From: Justin Maier <Zipp425@gmail.com>
Date: Mon, 10 Oct 2022 12:04:21 -0600
Subject: Simplify crop logic

---
 modules/extras.py | 14 +++-----------
 modules/ui.py     |  4 ++--
 2 files changed, 5 insertions(+), 13 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 83ca7049..b24d7de3 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -73,16 +73,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility,
             crop_info = " (crop)" if upscaling_crop else ""
             info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
 
-            def crop_upscaled_center(image, resize_w, resize_h):
-                left = int(math.ceil((image.width - resize_w) / 2))
-                right = image.width - int(math.floor((image.width - resize_w) / 2))
-                top = int(math.ceil((image.height - resize_h) / 2))
-                bottom = image.height - int(math.floor((image.height - resize_h) / 2))
-
-                image = image.crop((left, top, right, bottom))
-                return image
-
-
         if upscaling_resize != 1.0:
             def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
                 small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
@@ -94,7 +84,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility,
                     upscaler = shared.sd_upscalers[scaler_index]
                     c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
                     if mode == 1 and crop:
-                        c = crop_upscaled_center(c, resize_w, resize_h)
+                        cropped = Image.new("RGB", (resize_w, resize_h))
+                        cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
+                        c = cropped
                     cached_images[key] = c
 
                 return c
diff --git a/modules/ui.py b/modules/ui.py
index 4bb2892b..1aabe18d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -909,8 +909,8 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.TabItem('Scale to'):
                         with gr.Group():
                             with gr.Row():
-                                upscaling_resize_w = gr.Number(label="Width", value=512)
-                                upscaling_resize_h = gr.Number(label="Height", value=512)
+                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
+                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
 
                 with gr.Group():
-- 
cgit v1.2.3


From bc3e183b739913e7be91213a256f038b10eb71e9 Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Tue, 11 Oct 2022 04:30:13 +0900
Subject: Textual Inversion: Preprocess and Training will only pick-up image
 files

---
 modules/textual_inversion/dataset.py           | 3 ++-
 modules/textual_inversion/preprocess.py        | 3 ++-
 modules/textual_inversion/textual_inversion.py | 3 ++-
 3 files changed, 6 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index bcf772d2..d4baf066 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -22,6 +22,7 @@ class PersonalizedBase(Dataset):
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+        self.extns = [".jpg",".jpeg",".png"]
 
         self.dataset = []
 
@@ -32,7 +33,7 @@ class PersonalizedBase(Dataset):
 
         assert data_root, 'dataset directory not specified'
 
-        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
+        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in self.extns]
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
             image = Image.open(path)
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index d7efdef2..b6c78cf8 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -12,12 +12,13 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
     height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
+    extns = [".jpg",".jpeg",".png"]
 
     assert src != dst, 'same directory specified as source and destination'
 
     os.makedirs(dst, exist_ok=True)
 
-    files = os.listdir(src)
+    files = [i for i in os.listdir(src) if os.path.splitext(i.casefold())[1] in extns]
 
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5965c5a0..45397be9 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -161,6 +161,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
+    extns = [".jpg",".jpeg",".png"]
 
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
 
@@ -200,7 +201,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     if ititial_step > steps:
         return embedding, filename
 
-    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
+    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in extns])
     epoch_len = (tr_img_len * num_repeats) + tr_img_len
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-- 
cgit v1.2.3


From f98338faa84ecce503e68d8ba13d5f7bbae52730 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 23:15:48 +0300
Subject: add an option to not add watermark to created images

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index da389f9c..ecd15ef5 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -173,6 +173,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
 
     "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
+    "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
 }))
 
 options_templates.update(options_section(('saving-paths', "Paths for saving"), {
-- 
cgit v1.2.3


From 2536ecbb1790da2af0d61b6a26f38732cba665cd Mon Sep 17 00:00:00 2001
From: Fampai <>
Date: Mon, 10 Oct 2022 17:10:29 -0400
Subject: Refactored learning rate code

---
 modules/textual_inversion/textual_inversion.py | 51 ++++++++++++++++++++++++--
 modules/ui.py                                  |  2 +-
 2 files changed, 48 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5965c5a0..c64a4598 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -189,8 +189,6 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     embedding = hijack.embedding_db.word_embeddings[embedding_name]
     embedding.vec.requires_grad = True
 
-    optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
-
     losses = torch.zeros((32,))
 
     last_saved_file = "<none>"
@@ -203,12 +201,24 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
     epoch_len = (tr_img_len * num_repeats) + tr_img_len
 
+    scheduleIter = iter(LearnSchedule(learn_rate, steps, ititial_step))
+    (learn_rate, end_step) = next(scheduleIter)
+    print(f'Training at rate of {learn_rate} until step {end_step}')
+
+    optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, (x, text) in pbar:
         embedding.step = i + ititial_step
 
-        if embedding.step > steps:
-            break
+        if embedding.step > end_step:
+            try:
+                (learn_rate, end_step) = next(scheduleIter)
+            except:
+                break
+            tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
+            for pg in optimizer.param_groups:
+                pg['lr'] = learn_rate
 
         if shared.state.interrupted:
             break
@@ -277,3 +287,36 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     return embedding, filename
 
+class LearnSchedule:
+    def __init__(self, learn_rate, max_steps, cur_step=0):
+        pairs = learn_rate.split(',')
+        self.rates = []
+        self.it = 0
+        self.maxit = 0
+        for i, pair in enumerate(pairs):
+            tmp = pair.split(':')
+            if len(tmp) == 2:
+                step = int(tmp[1])
+                if step > cur_step:
+                    self.rates.append((float(tmp[0]), min(step, max_steps)))
+                    self.maxit += 1
+                    if step > max_steps:
+                        return
+                elif step == -1:
+                    self.rates.append((float(tmp[0]), max_steps))
+                    self.maxit += 1
+                    return
+            else:
+                self.rates.append((float(tmp[0]), max_steps))
+                self.maxit += 1
+                return
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.it < self.maxit:
+            self.it += 1
+            return self.rates[self.it - 1]
+        else:
+            raise StopIteration
diff --git a/modules/ui.py b/modules/ui.py
index 8c06ad7c..c9e8355b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1047,7 +1047,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                    learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
+                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value = "5.0e-03")
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-- 
cgit v1.2.3


From 907a88b2d0be320575c2129d8d6a1d4f3a68f9eb Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Tue, 11 Oct 2022 06:33:08 +0900
Subject: Added .webp .bmp

---
 modules/textual_inversion/dataset.py           | 2 +-
 modules/textual_inversion/preprocess.py        | 2 +-
 modules/textual_inversion/textual_inversion.py | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index d4baf066..0dc54fb7 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -22,7 +22,7 @@ class PersonalizedBase(Dataset):
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
-        self.extns = [".jpg",".jpeg",".png"]
+        self.extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
         self.dataset = []
 
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index b6c78cf8..8290abe8 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -12,7 +12,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
     height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
-    extns = [".jpg",".jpeg",".png"]
+    extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
     assert src != dst, 'same directory specified as source and destination'
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index a03b299c..33c923d1 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -161,7 +161,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
-    extns = [".jpg",".jpeg",".png"]
+    extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
 
-- 
cgit v1.2.3


From a1a05ad2d13d0b995dbf8ecead6315f17837ef81 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 16:47:58 -0500
Subject: import time missing, added to deepbooru fixxing error on
 get_deepbooru_tags

---
 modules/deepbooru.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index cee4a3b4..12555b2e 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,6 +1,7 @@
 import os.path
 from concurrent.futures import ProcessPoolExecutor
 import multiprocessing
+import time
 
 
 def get_deepbooru_tags(pil_image, threshold=0.5):
-- 
cgit v1.2.3


From b980e7188c671fc55b26557f097076fb5c976ba0 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 16:52:54 -0500
Subject: corrected tag return in get_deepbooru_tags

---
 modules/deepbooru.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 12555b2e..ebdba5e0 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -15,7 +15,6 @@ def get_deepbooru_tags(pil_image, threshold=0.5):
     while shared.deepbooru_process_return["value"] == -1:
         time.sleep(0.2)
     release_process()
-    return ret
 
 
 def deepbooru_process(queue, deepbooru_process_return, threshold):
-- 
cgit v1.2.3


From 315d5a8ed975c88f670bc484f40a23fbf3a77b63 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 23:14:44 +0100
Subject: update data dis[play style

---
 modules/textual_inversion/textual_inversion.py | 88 +++++++++++++++++++-------
 1 file changed, 65 insertions(+), 23 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 667a7cf2..95eebea7 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -39,20 +39,59 @@ def embeddingFromB64(data):
     d = base64.b64decode(data)
     return json.loads(d,cls=EmbeddingDecoder)
 
-def appendImageDataFooter(image,data):
+def xorBlock(block):
+    return np.bitwise_xor(block.astype(np.uint8),
+                          ((np.random.RandomState(0xDEADBEEF).random(block.shape)*255).astype(np.uint8)) & 0x0F )
+
+def styleBlock(block,sequence):
+    im = Image.new('RGB',(block.shape[1],block.shape[0]))
+    draw = ImageDraw.Draw(im)
+    i=0
+    for x in range(-6,im.size[0],8):
+        for yi,y in enumerate(range(-6,im.size[1],8)):
+            offset=0
+            if yi%2==0:
+                offset=4
+            shade = sequence[i%len(sequence)]
+            i+=1
+            draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) )
+
+    fg = np.array(im).astype(np.uint8) & 0xF0
+    return block ^ fg
+
+def insertImageDataEmbed(image,data):
     d = 3
     data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
     dnp = np.frombuffer(data_compressed,np.uint8).copy()
-    w = image.size[0]
-    next_size = dnp.shape[0] + (w-(dnp.shape[0]%w))
-    next_size = next_size + ((w*d)-(next_size%(w*d)))
-    dnp.resize(next_size)
-    dnp = dnp.reshape((-1,w,d))
-    print(dnp.shape)
-    im = Image.fromarray(dnp,mode='RGB')
-    background = Image.new('RGB',(image.size[0],image.size[1]+im.size[1]+1),(0,0,0))
-    background.paste(image,(0,0))
-    background.paste(im,(0,image.size[1]+1))
+    dnphigh = dnp >> 4
+    dnplow  = dnp & 0x0F
+    
+    h = image.size[1]
+    next_size = dnplow.shape[0] + (h-(dnplow.shape[0]%h))
+    next_size = next_size + ((h*d)-(next_size%(h*d)))
+
+    dnplow.resize(next_size)
+    dnplow = dnplow.reshape((h,-1,d))
+
+    dnphigh.resize(next_size)
+    dnphigh = dnphigh.reshape((h,-1,d))
+
+    edgeStyleWeights = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
+    edgeStyleWeights = (np.abs(edgeStyleWeights)/np.max(np.abs(edgeStyleWeights))*255).astype(np.uint8)
+
+    dnplow   = styleBlock(dnplow,sequence=edgeStyleWeights)
+    dnplow   = xorBlock(dnplow)    
+    dnphigh  = styleBlock(dnphigh,sequence=edgeStyleWeights[::-1])
+    dnphigh  = xorBlock(dnphigh)
+
+    imlow  = Image.fromarray(dnplow,mode='RGB')
+    imhigh = Image.fromarray(dnphigh,mode='RGB')
+
+    background = Image.new('RGB',(image.size[0]+imlow.size[0]+imhigh.size[0]+2,image.size[1]),(0,0,0))
+    background.paste(imlow,(0,0))
+    background.paste(image,(imlow.size[0]+1,0))
+    background.paste(imhigh,(imlow.size[0]+1+image.size[0]+1,0))
+
     return background
 
 def crop_black(img,tol=0):
@@ -62,19 +101,22 @@ def crop_black(img,tol=0):
     row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
     return img[row_start:row_end,col_start:col_end]
 
-def extractImageDataFooter(image):
+def extractImageDataEmbed(image):
     d=3
-    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) )
-    lastRow = np.where( np.sum(outarr, axis=(1,2))==0)
-    if lastRow[0].shape[0] == 0:
-        print('Image data block not found.')
+    outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
+    blackCols = np.where( np.sum(outarr, axis=(0,2))==0)
+    if blackCols[0].shape[0] < 2:
+        print('No Image data blocks found.')
         return None
-    lastRow = lastRow[0]
-    
-    lastRow = lastRow.max()
 
-    dataBlock = outarr[lastRow+1::].astype(np.uint8).flatten().tobytes()
-    print(lastRow)
+    dataBlocklower = outarr[:,:blackCols[0].min(),:].astype(np.uint8)
+    dataBlockupper = outarr[:,blackCols[0].max()+1:,:].astype(np.uint8)
+
+    dataBlocklower = xorBlock(dataBlocklower)
+    dataBlockupper = xorBlock(dataBlockupper)
+    
+    dataBlock = (dataBlockupper << 4) | (dataBlocklower)
+    dataBlock = dataBlock.flatten().tobytes()   
     data = zlib.decompress(dataBlock)
     return json.loads(data,cls=EmbeddingDecoder)
 
@@ -154,7 +196,7 @@ class EmbeddingDatabase:
                     data = embeddingFromB64(embed_image.text['sd-ti-embedding'])
                     name = data.get('name',name)
                 else:
-                    data = extractImageDataFooter(embed_image)
+                    data = extractImageDataEmbed(embed_image)
                     name = data.get('name',name)
             else:
                 data = torch.load(path, map_location="cpu")
@@ -351,7 +393,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 footer_right = '{}'.format(embedding.step)
 
                 captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right)
-                captioned_image = appendImageDataFooter(captioned_image,data)
+                captioned_image = insertImageDataEmbed(captioned_image,data)
 
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
             
-- 
cgit v1.2.3


From 767202a4c324f9b49f63ab4dabbb5736fe9df6e5 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 23:20:52 +0100
Subject: add dependency

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 95eebea7..f3cacaa0 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,7 +7,7 @@ import tqdm
 import html
 import datetime
 
-from PIL import Image,PngImagePlugin
+from PIL import Image,PngImagePlugin,ImageDraw
 from ..images import captionImageOverlay
 import numpy as np
 import base64
-- 
cgit v1.2.3


From e0fbe6d27e7b4505766c8cb5a4264e1114cf3721 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 23:26:24 +0100
Subject: colour depth conversion fix

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index f3cacaa0..ae807268 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -103,7 +103,7 @@ def crop_black(img,tol=0):
 
 def extractImageDataEmbed(image):
     d=3
-    outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
+    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
     blackCols = np.where( np.sum(outarr, axis=(0,2))==0)
     if blackCols[0].shape[0] < 2:
         print('No Image data blocks found.')
-- 
cgit v1.2.3


From 76ef3d75f61253516c024553335d9083d9660a8a Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 18:01:49 -0500
Subject: added deepbooru settings (threshold and sort by alpha or likelyhood)

---
 modules/deepbooru.py | 36 +++++++++++++++++++++++++-----------
 modules/shared.py    |  6 ++++++
 2 files changed, 31 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index ebdba5e0..e31e92c0 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -3,31 +3,32 @@ from concurrent.futures import ProcessPoolExecutor
 import multiprocessing
 import time
 
-
-def get_deepbooru_tags(pil_image, threshold=0.5):
+def get_deepbooru_tags(pil_image):
     """
     This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
     """
     from modules import shared  # prevents circular reference
-    create_deepbooru_process(threshold)
+    create_deepbooru_process(shared.opts.deepbooru_threshold, shared.opts.deepbooru_sort_alpha)
     shared.deepbooru_process_return["value"] = -1
     shared.deepbooru_process_queue.put(pil_image)
     while shared.deepbooru_process_return["value"] == -1:
         time.sleep(0.2)
+    tags = shared.deepbooru_process_return["value"]
     release_process()
+    return tags
 
 
-def deepbooru_process(queue, deepbooru_process_return, threshold):
+def deepbooru_process(queue, deepbooru_process_return, threshold, alpha_sort):
     model, tags = get_deepbooru_tags_model()
     while True: # while process is running, keep monitoring queue for new image
         pil_image = queue.get()
         if pil_image == "QUIT":
             break
         else:
-            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold)
+            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort)
 
 
-def create_deepbooru_process(threshold=0.5):
+def create_deepbooru_process(threshold, alpha_sort):
     """
     Creates deepbooru process.  A queue is created to send images into the process.  This enables multiple images
     to be processed in a row without reloading the model or creating a new process.  To return the data, a shared
@@ -40,7 +41,7 @@ def create_deepbooru_process(threshold=0.5):
     shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
     shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
     shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold))
+    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, alpha_sort))
     shared.deepbooru_process.start()
 
 
@@ -80,7 +81,7 @@ def get_deepbooru_tags_model():
     return model, tags
 
 
-def get_deepbooru_tags_from_model(model, tags, pil_image, threshold=0.5):
+def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort):
     import deepdanbooru as dd
     import tensorflow as tf
     import numpy as np
@@ -105,15 +106,28 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold=0.5):
 
     for i, tag in enumerate(tags):
         result_dict[tag] = y[i]
-    result_tags_out = []
+
+    unsorted_tags_in_theshold = []
     result_tags_print = []
     for tag in tags:
         if result_dict[tag] >= threshold:
             if tag.startswith("rating:"):
                 continue
-            result_tags_out.append(tag)
+            unsorted_tags_in_theshold.append((result_dict[tag], tag))
             result_tags_print.append(f'{result_dict[tag]} {tag}')
 
+    # sort tags
+    result_tags_out = []
+    sort_ndx = 0
+    print(alpha_sort)
+    if alpha_sort:
+        sort_ndx = 1
+
+    # sort by reverse by likelihood and normal for alpha
+    unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
+    for weight, tag in unsorted_tags_in_theshold:
+        result_tags_out.append(tag)
+
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
\ No newline at end of file
+    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
diff --git a/modules/shared.py b/modules/shared.py
index 1995a99a..2e307809 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -261,6 +261,12 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
 }))
 
+if cmd_opts.deepdanbooru:
+    options_templates.update(options_section(('deepbooru-params', "DeepBooru parameters"), {
+        "deepbooru_sort_alpha": OptionInfo(True, "Sort Alphabetical", gr.Checkbox),
+        'deepbooru_threshold': OptionInfo(0.5, "Threshold", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    }))
+
 
 class Options:
     data = None
-- 
cgit v1.2.3


From bb932dbf9faf43ba918daa4791873078797b2a48 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 18:37:52 -0500
Subject: added alpha sort and threshold variables to create process method in
 preprocessing

---
 modules/textual_inversion/preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 4a2194da..c0af729b 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -29,7 +29,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
         shared.interrogator.load()
 
     if process_caption_deepbooru:
-        deepbooru.create_deepbooru_process()
+        deepbooru.create_deepbooru_process(opts.deepbooru_threshold, opts.deepbooru_sort_alpha)
 
     def save_pic_with_caption(image, index):
         if process_caption:
-- 
cgit v1.2.3


From 1add3cff84b7e2436d69b1e97ae689281e4a7c33 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Mon, 10 Oct 2022 19:57:43 -0500
Subject: Refresh list of models/ckpts upon hitting restart gradio in the
 settings pane

---
 modules/ui.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index e8039d76..06ff118f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -39,6 +39,7 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
+from modules.sd_models import list_models
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -1290,6 +1291,9 @@ Requested path was: {f}
             shared.state.interrupt()
             settings_interface.gradio_ref.do_restart = True
 
+            # refresh models so that new models/.ckpt's show up on reload
+            list_models()
+
         restart_gradio.click(
             fn=request_restart,
             inputs=[],
-- 
cgit v1.2.3


From 7aa8fcac1e45c3ad9c6a40df0e44a346afcd5032 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 04:17:36 +0100
Subject: use simple lcg in xor

---
 modules/textual_inversion/textual_inversion.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ae807268..13416a08 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -39,9 +39,15 @@ def embeddingFromB64(data):
     d = base64.b64decode(data)
     return json.loads(d,cls=EmbeddingDecoder)
 
+def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
+    while True:
+        seed = (a * seed + c) % m
+        yield seed
+
 def xorBlock(block):
-    return np.bitwise_xor(block.astype(np.uint8),
-                          ((np.random.RandomState(0xDEADBEEF).random(block.shape)*255).astype(np.uint8)) & 0x0F )
+    g = lcg()
+    randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
+    return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) 
 
 def styleBlock(block,sequence):
     im = Image.new('RGB',(block.shape[1],block.shape[0]))
-- 
cgit v1.2.3


From 8b7d3f1bef47bbe048f644ed0d8dd3ad46554045 Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Tue, 11 Oct 2022 02:22:46 -0300
Subject: Make the ctrl+enter shortcut use the generate button on the current
 tab

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index e8039d76..cafda884 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1331,7 +1331,7 @@ Requested path was: {f}
         
         with gr.Tabs() as tabs:
             for interface, label, ifid in interfaces:
-                with gr.TabItem(label, id=ifid):
+                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
                     interface.render()
         
         if os.path.exists(os.path.join(script_path, "notification.mp3")):
-- 
cgit v1.2.3


From 8617396c6df71074c7fd3d39419802026874712a Mon Sep 17 00:00:00 2001
From: Kenneth <kenneth.mccarthy24@yahoo.com>
Date: Mon, 10 Oct 2022 17:23:07 -0600
Subject: Added slider for deepbooru score threshold in settings

---
 modules/shared.py | 1 +
 modules/ui.py     | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index ecd15ef5..e0830e28 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -239,6 +239,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
     "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
+    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
 }))
 
 options_templates.update(options_section(('ui', "User interface"), {
diff --git a/modules/ui.py b/modules/ui.py
index cafda884..ca3151c4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -311,7 +311,7 @@ def interrogate(image):
 
 
 def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image)
+    prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
     return gr_show(True) if prompt is None else prompt
 
 
-- 
cgit v1.2.3


From 5e2627a1a63e4c9f87e6e604ecc24e9936f149de Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Tue, 11 Oct 2022 07:55:28 +0100
Subject: Comma backtrack padding (#2192)

Comma backtrack padding
---
 modules/sd_hijack.py | 19 ++++++++++++++++++-
 modules/shared.py    |  1 +
 2 files changed, 19 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 827bf304..aa4d2cbc 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -107,6 +107,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         self.tokenizer = wrapped.tokenizer
         self.token_mults = {}
 
+        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+
         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
         for text, ident in tokens_with_parens:
             mult = 1.0
@@ -136,6 +138,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         fixes = []
         remade_tokens = []
         multipliers = []
+        last_comma = -1
 
         for tokens, (text, weight) in zip(tokenized, parsed):
             i = 0
@@ -144,6 +147,20 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
                 embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
+                if token == self.comma_token:
+                    last_comma = len(remade_tokens)
+                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
+                    last_comma += 1
+                    reloc_tokens = remade_tokens[last_comma:]
+                    reloc_mults = multipliers[last_comma:]
+
+                    remade_tokens = remade_tokens[:last_comma]
+                    length = len(remade_tokens)
+                    
+                    rem = int(math.ceil(length / 75)) * 75 - length
+                    remade_tokens += [id_end] * rem + reloc_tokens
+                    multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
+                
                 if embedding is None:
                     remade_tokens.append(token)
                     multipliers.append(weight)
@@ -284,7 +301,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         while max(map(len, remade_batch_tokens)) != 0:
             rem_tokens = [x[75:] for x in remade_batch_tokens]
             rem_multipliers = [x[75:] for x in batch_multipliers]
-            
+
             self.hijack.fixes = []
             for unfiltered in hijack_fixes:
                 fixes = []
diff --git a/modules/shared.py b/modules/shared.py
index e0830e28..14b40d70 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -227,6 +227,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
+    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
-- 
cgit v1.2.3


From 948533950c9db5069a874d925fadd50bac00fdb5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 11:09:51 +0300
Subject: replace duplicate code with a function

---
 modules/hypernetwork.py            | 23 ++++++++++++--------
 modules/sd_hijack_optimizations.py | 44 +++++++++++++-------------------------
 2 files changed, 29 insertions(+), 38 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index 498bc9d8..7bbc443e 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -64,21 +64,26 @@ def load_hypernetwork(filename):
         shared.loaded_hypernetwork = None
 
 
+def apply_hypernetwork(hypernetwork, context):
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is None:
+        return context, context
+
+    context_k = hypernetwork_layers[0](context)
+    context_v = hypernetwork_layers[1](context)
+    return context_k, context_v
+
+
 def attention_CrossAttention_forward(self, x, context=None, mask=None):
     h = self.heads
 
     q = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.loaded_hypernetwork
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is not None:
-        k = self.to_k(hypernetwork_layers[0](context))
-        v = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k = self.to_k(context)
-        v = self.to_v(context)
+    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context)
+    k = self.to_k(context_k)
+    v = self.to_v(context_v)
 
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
 
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 18408e62..25cb67a4 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -8,7 +8,8 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange
 
-from modules import shared
+from modules import shared, hypernetwork
+
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
@@ -26,16 +27,10 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     q_in = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.loaded_hypernetwork
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is not None:
-        k_in = self.to_k(hypernetwork_layers[0](context))
-        v_in = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k_in = self.to_k(context)
-        v_in = self.to_v(context)
-    del context, x
+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
+    k_in = self.to_k(context_k)
+    v_in = self.to_v(context_v)
+    del context, context_k, context_v, x
 
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
@@ -59,22 +54,16 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     return self.to_out(r2)
 
 
-# taken from https://github.com/Doggettx/stable-diffusion
+# taken from https://github.com/Doggettx/stable-diffusion and modified
 def split_cross_attention_forward(self, x, context=None, mask=None):
     h = self.heads
 
     q_in = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.loaded_hypernetwork
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is not None:
-        k_in = self.to_k(hypernetwork_layers[0](context))
-        v_in = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k_in = self.to_k(context)
-        v_in = self.to_v(context)
+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
+    k_in = self.to_k(context_k)
+    v_in = self.to_v(context_v)
 
     k_in *= self.scale
 
@@ -130,14 +119,11 @@ def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
     context = default(context, x)
-    hypernetwork = shared.loaded_hypernetwork
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-    if hypernetwork_layers is not None:
-        k_in = self.to_k(hypernetwork_layers[0](context))
-        v_in = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k_in = self.to_k(context)
-        v_in = self.to_v(context)
+
+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
+    k_in = self.to_k(context_k)
+    v_in = self.to_v(context_v)
+
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
-- 
cgit v1.2.3


From b2368a3bce663f19a7209d9cb38617e635ca6e3c Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Tue, 11 Oct 2022 17:32:46 +0900
Subject: Switched to exception handling

---
 modules/textual_inversion/dataset.py           | 10 +++++-----
 modules/textual_inversion/preprocess.py        |  8 +++++---
 modules/textual_inversion/textual_inversion.py | 18 ++++++++----------
 3 files changed, 18 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 0dc54fb7..4d006366 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -22,7 +22,6 @@ class PersonalizedBase(Dataset):
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
-        self.extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
         self.dataset = []
 
@@ -33,12 +32,13 @@ class PersonalizedBase(Dataset):
 
         assert data_root, 'dataset directory not specified'
 
-        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in self.extns]
+        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
-            image = Image.open(path)
-            image = image.convert('RGB')
-            image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
+            try:
+                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
+            except Exception:
+                continue
 
             filename = os.path.basename(path)
             filename_tokens = os.path.splitext(filename)[0]
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 8290abe8..1a672725 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -12,13 +12,12 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
     height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
-    extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
     assert src != dst, 'same directory specified as source and destination'
 
     os.makedirs(dst, exist_ok=True)
 
-    files = [i for i in os.listdir(src) if os.path.splitext(i.casefold())[1] in extns]
+    files = os.listdir(src)
 
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
@@ -47,7 +46,10 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
     for index, imagefile in enumerate(tqdm.tqdm(files)):
         subindex = [0]
         filename = os.path.join(src, imagefile)
-        img = Image.open(filename).convert("RGB")
+        try:
+            img = Image.open(filename).convert("RGB")
+        except Exception:
+            continue
 
         if shared.state.interrupted:
             break
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 33c923d1..91cde04b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -161,7 +161,6 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
-    extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
 
@@ -201,10 +200,6 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     if ititial_step > steps:
         return embedding, filename
 
-    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in extns])
-
-    epoch_len = (tr_img_len * num_repeats) + tr_img_len
-
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, (x, text) in pbar:
         embedding.step = i + ititial_step
@@ -228,10 +223,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             loss.backward()
             optimizer.step()
 
-        epoch_num = embedding.step // epoch_len
-        epoch_step = embedding.step - (epoch_num * epoch_len) + 1
+        epoch_num = embedding.step // len(ds)
+        epoch_step = embedding.step - (epoch_num * len(ds)) + 1
 
-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
 
         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
@@ -243,9 +238,12 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 prompt=text,
-                steps=20,
-				height=training_height,
+                steps=28,
+				height=768,
 				width=training_width,
+                negative_prompt="lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name",
+                cfg_scale=7.0,
+                sampler_index=0,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
-- 
cgit v1.2.3


From 8bacbca0a1ab9aabcb0ad0cbf070e0006991e98a Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Tue, 11 Oct 2022 17:35:09 +0900
Subject: Removed my local edits to checkpoint image generation

---
 modules/textual_inversion/textual_inversion.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 91cde04b..e9ff80c2 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -238,12 +238,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 prompt=text,
-                steps=28,
-				height=768,
+                steps=20,
+				height=training_height,
 				width=training_width,
-                negative_prompt="lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name",
-                cfg_scale=7.0,
-                sampler_index=0,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
-- 
cgit v1.2.3


From 530103b586109c11fd068eb70ef09503ec6a4caf Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 14:53:02 +0300
Subject: fixes related to merge

---
 modules/hypernetwork.py                        | 103 -------------------------
 modules/hypernetwork/hypernetwork.py           |  74 +++++++++++-------
 modules/hypernetwork/ui.py                     |  10 +--
 modules/sd_hijack_optimizations.py             |   3 +-
 modules/shared.py                              |  13 +++-
 modules/textual_inversion/textual_inversion.py |  12 +--
 modules/ui.py                                  |   5 +-
 7 files changed, 73 insertions(+), 147 deletions(-)
 delete mode 100644 modules/hypernetwork.py

(limited to 'modules')

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
deleted file mode 100644
index 7bbc443e..00000000
--- a/modules/hypernetwork.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import glob
-import os
-import sys
-import traceback
-
-import torch
-
-from ldm.util import default
-from modules import devices, shared
-import torch
-from torch import einsum
-from einops import rearrange, repeat
-
-
-class HypernetworkModule(torch.nn.Module):
-    def __init__(self, dim, state_dict):
-        super().__init__()
-
-        self.linear1 = torch.nn.Linear(dim, dim * 2)
-        self.linear2 = torch.nn.Linear(dim * 2, dim)
-
-        self.load_state_dict(state_dict, strict=True)
-        self.to(devices.device)
-
-    def forward(self, x):
-        return x + (self.linear2(self.linear1(x)))
-
-
-class Hypernetwork:
-    filename = None
-    name = None
-
-    def __init__(self, filename):
-        self.filename = filename
-        self.name = os.path.splitext(os.path.basename(filename))[0]
-        self.layers = {}
-
-        state_dict = torch.load(filename, map_location='cpu')
-        for size, sd in state_dict.items():
-            self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
-
-
-def list_hypernetworks(path):
-    res = {}
-    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
-        name = os.path.splitext(os.path.basename(filename))[0]
-        res[name] = filename
-    return res
-
-
-def load_hypernetwork(filename):
-    path = shared.hypernetworks.get(filename, None)
-    if path is not None:
-        print(f"Loading hypernetwork {filename}")
-        try:
-            shared.loaded_hypernetwork = Hypernetwork(path)
-        except Exception:
-            print(f"Error loading hypernetwork {path}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-    else:
-        if shared.loaded_hypernetwork is not None:
-            print(f"Unloading hypernetwork")
-
-        shared.loaded_hypernetwork = None
-
-
-def apply_hypernetwork(hypernetwork, context):
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is None:
-        return context, context
-
-    context_k = hypernetwork_layers[0](context)
-    context_v = hypernetwork_layers[1](context)
-    return context_k, context_v
-
-
-def attention_CrossAttention_forward(self, x, context=None, mask=None):
-    h = self.heads
-
-    q = self.to_q(x)
-    context = default(context, x)
-
-    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context)
-    k = self.to_k(context_k)
-    v = self.to_v(context_v)
-
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
-    if mask is not None:
-        mask = rearrange(mask, 'b ... -> b (...)')
-        max_neg_value = -torch.finfo(sim.dtype).max
-        mask = repeat(mask, 'b j -> (b h) () j', h=h)
-        sim.masked_fill_(~mask, max_neg_value)
-
-    # attention, what we cannot get enough of
-    attn = sim.softmax(dim=-1)
-
-    out = einsum('b i j, b j d -> b i d', attn, v)
-    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-    return self.to_out(out)
diff --git a/modules/hypernetwork/hypernetwork.py b/modules/hypernetwork/hypernetwork.py
index a3d6a47e..aa701bda 100644
--- a/modules/hypernetwork/hypernetwork.py
+++ b/modules/hypernetwork/hypernetwork.py
@@ -26,10 +26,11 @@ class HypernetworkModule(torch.nn.Module):
         if state_dict is not None:
             self.load_state_dict(state_dict, strict=True)
         else:
-            self.linear1.weight.data.fill_(0.0001)
-            self.linear1.bias.data.fill_(0.0001)
-            self.linear2.weight.data.fill_(0.0001)
-            self.linear2.bias.data.fill_(0.0001)
+
+            self.linear1.weight.data.normal_(mean=0.0, std=0.01)
+            self.linear1.bias.data.zero_()
+            self.linear2.weight.data.normal_(mean=0.0, std=0.01)
+            self.linear2.bias.data.zero_()
 
         self.to(devices.device)
 
@@ -92,41 +93,54 @@ class Hypernetwork:
         self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
 
 
-def load_hypernetworks(path):
+def list_hypernetworks(path):
     res = {}
+    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
+        name = os.path.splitext(os.path.basename(filename))[0]
+        res[name] = filename
+    return res
 
-    for filename in glob.iglob(path + '**/*.pt', recursive=True):
+
+def load_hypernetwork(filename):
+    path = shared.hypernetworks.get(filename, None)
+    if path is not None:
+        print(f"Loading hypernetwork {filename}")
         try:
-            hn = Hypernetwork()
-            hn.load(filename)
-            res[hn.name] = hn
+            shared.loaded_hypernetwork = Hypernetwork()
+            shared.loaded_hypernetwork.load(path)
+
         except Exception:
-            print(f"Error loading hypernetwork {filename}", file=sys.stderr)
+            print(f"Error loading hypernetwork {path}", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
+    else:
+        if shared.loaded_hypernetwork is not None:
+            print(f"Unloading hypernetwork")
 
-    return res
+        shared.loaded_hypernetwork = None
 
 
-def attention_CrossAttention_forward(self, x, context=None, mask=None):
-    h = self.heads
+def apply_hypernetwork(hypernetwork, context, layer=None):
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
 
-    q = self.to_q(x)
-    context = default(context, x)
+    if hypernetwork_layers is None:
+        return context, context
 
-    hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None)
+    if layer is not None:
+        layer.hyper_k = hypernetwork_layers[0]
+        layer.hyper_v = hypernetwork_layers[1]
 
-    if hypernetwork_layers is not None:
-        hypernetwork_k, hypernetwork_v = hypernetwork_layers
+    context_k = hypernetwork_layers[0](context)
+    context_v = hypernetwork_layers[1](context)
+    return context_k, context_v
 
-        self.hypernetwork_k = hypernetwork_k
-        self.hypernetwork_v = hypernetwork_v
 
-        context_k = hypernetwork_k(context)
-        context_v = hypernetwork_v(context)
-    else:
-        context_k = context
-        context_v = context
+def attention_CrossAttention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
 
+    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)
     k = self.to_k(context_k)
     v = self.to_v(context_v)
 
@@ -151,7 +165,9 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
 def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
     assert hypernetwork_name, 'embedding not selected'
 
-    shared.hypernetwork = shared.hypernetworks[hypernetwork_name]
+    path = shared.hypernetworks.get(hypernetwork_name, None)
+    shared.loaded_hypernetwork = Hypernetwork()
+    shared.loaded_hypernetwork.load(path)
 
     shared.state.textinfo = "Initializing hypernetwork training..."
     shared.state.job_count = steps
@@ -176,9 +192,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
-    hypernetwork = shared.hypernetworks[hypernetwork_name]
+    hypernetwork = shared.loaded_hypernetwork
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
@@ -194,7 +210,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     if ititial_step > steps:
         return hypernetwork, filename
 
-    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, (x, text) in pbar:
         hypernetwork.step = i + ititial_step
 
diff --git a/modules/hypernetwork/ui.py b/modules/hypernetwork/ui.py
index 525f978c..f6d1d0a3 100644
--- a/modules/hypernetwork/ui.py
+++ b/modules/hypernetwork/ui.py
@@ -6,24 +6,24 @@ import gradio as gr
 import modules.textual_inversion.textual_inversion
 import modules.textual_inversion.preprocess
 from modules import sd_hijack, shared
+from modules.hypernetwork import hypernetwork
 
 
 def create_hypernetwork(name):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
-    hypernetwork = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
-    hypernetwork.save(fn)
+    hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
+    hypernet.save(fn)
 
     shared.reload_hypernetworks()
-    shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None)
 
     return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
 
 
 def train_hypernetwork(*args):
 
-    initial_hypernetwork = shared.hypernetwork
+    initial_hypernetwork = shared.loaded_hypernetwork
 
     try:
         sd_hijack.undo_optimizations()
@@ -38,6 +38,6 @@ Hypernetwork saved to {html.escape(filename)}
     except Exception:
         raise
     finally:
-        shared.hypernetwork = initial_hypernetwork
+        shared.loaded_hypernetwork = initial_hypernetwork
         sd_hijack.apply_optimizations()
 
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 25cb67a4..27e571fc 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -8,7 +8,8 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange
 
-from modules import shared, hypernetwork
+from modules import shared
+from modules.hypernetwork import hypernetwork
 
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
diff --git a/modules/shared.py b/modules/shared.py
index 14b40d70..8753015e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,8 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, hypernetwork
+from modules import sd_samplers
+from modules.hypernetwork import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -29,6 +30,7 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
@@ -82,10 +84,17 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 xformers_available = False
 config_filename = cmd_opts.ui_settings_file
 
-hypernetworks = hypernetwork.list_hypernetworks(os.path.join(models_path, 'hypernetworks'))
+hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
 
+def reload_hypernetworks():
+    global hypernetworks
+
+    hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
+    hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
+
+
 class State:
     skipped = False
     interrupted = False
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5965c5a0..d6977950 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -156,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, preview_image_prompt):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -238,12 +238,14 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
+            preview_text = text if preview_image_prompt == "" else preview_image_prompt
+
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
-                prompt=text,
+                prompt=preview_text,
                 steps=20,
-				height=training_height,
-				width=training_width,
+                height=training_height,
+                width=training_width,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
@@ -254,7 +256,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             shared.state.current_image = image
             image.save(last_saved_image)
 
-            last_saved_image += f", prompt: {text}"
+            last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = embedding.step
 
diff --git a/modules/ui.py b/modules/ui.py
index 10b1ee3a..df653059 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1023,7 +1023,7 @@ def create_ui(wrap_gradio_gpu_call):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            create_embedding = gr.Button(value="Create", variant='primary')
+                            create_embedding = gr.Button(value="Create embedding", variant='primary')
 
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
@@ -1035,7 +1035,7 @@ def create_ui(wrap_gradio_gpu_call):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            create_hypernetwork = gr.Button(value="Create", variant='primary')
+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
 
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
@@ -1147,6 +1147,7 @@ def create_ui(wrap_gradio_gpu_call):
                 create_image_every,
                 save_embedding_every,
                 template_file,
+                preview_image_prompt,
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.3


From 7b1db45e1fda8603d4617affd976066be5e5b821 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 11 Oct 2022 20:17:27 +0800
Subject: images history improvement

---
 modules/images_history.py | 229 ++++++++++++++++++++++++----------------------
 1 file changed, 121 insertions(+), 108 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 0e0a48f3..01d11a01 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,117 +1,130 @@
 import os
 def get_recent_images(dir_name, page_index, step, image_index):
-	print(image_index)
-	page_index = int(page_index)
-	f_list = os.listdir(dir_name)
-	file_list = []
-	for file in f_list:
-		if file[-4:] == ".txt":
-			continue
-		file_list.append(file)	
-	file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
-	num = 48
-	max_page_index = len(file_list) // num + 1	
-	page_index = max_page_index if page_index == -1 else page_index + step
-	page_index = 1 if page_index < 1 else page_index	
-	page_index = max_page_index if page_index > max_page_index else page_index
-	idx_frm = (page_index - 1) * num
-	file_list = file_list[idx_frm:idx_frm + num]
-	print(f"Loading history page {page_index}")	
-	image_index = int(image_index)
-	if image_index < 0 or image_index > len(file_list)  - 1:
-		current_file = None 
-		hide_image = None
-	else:
-		current_file =  file_list[int(image_index)]
-		hide_image = os.path.join(dir_name, current_file)
-	return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image
-def first_page_click(dir_name, page_index, image_index):
-	return get_recent_images(dir_name, 1, 0, image_index)
-def end_page_click(dir_name, page_index, image_index):
-	return get_recent_images(dir_name, -1, 0, image_index)
-def prev_page_click(dir_name, page_index, image_index):
-	return get_recent_images(dir_name, page_index, -1, image_index)
-def next_page_click(dir_name, page_index, image_index):	
-	return get_recent_images(dir_name, page_index, 1, image_index)
-def page_index_change(dir_name, page_index, image_index):	
-	return get_recent_images(dir_name, page_index, 0, image_index)
+    #print(image_index)
+    page_index = int(page_index)
+    f_list = os.listdir(dir_name)
+    file_list = []
+    for file in f_list:
+        if file[-4:] == ".txt":
+            continue
+        file_list.append(file)    
+    file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    num = 48
+    max_page_index = len(file_list) // num + 1    
+    page_index = max_page_index if page_index == -1 else page_index + step
+    page_index = 1 if page_index < 1 else page_index    
+    page_index = max_page_index if page_index > max_page_index else page_index
+    idx_frm = (page_index - 1) * num
+    file_list = file_list[idx_frm:idx_frm + num]
+    #print(f"Loading history page {page_index}")    
+    image_index = int(image_index)
+    if image_index < 0 or image_index > len(file_list)  - 1:
+        current_file = None 
+        hide_image = None
+    else:
+        current_file =  file_list[int(image_index)]
+        hide_image = os.path.join(dir_name, current_file)
+    return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image
+def first_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, 1, 0, image_index)
+def end_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, -1, 0, image_index)
+def prev_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, page_index, -1, image_index)
+def next_page_click(dir_name, page_index, image_index, tabname):    
+    return get_recent_images(dir_name, page_index, 1, image_index)
+def page_index_change(dir_name, page_index, image_index, tabname):    
+    return get_recent_images(dir_name, page_index, 0, image_index)
 
 def show_image_info(num, image_path, filenames):
-	file = filenames[int(num)]
-	return file, num, os.path.join(image_path, file)
-def delete_image(is_img2img, dir_name, name, page_index, filenames, image_index):
-	print("filename", name)
-	path = os.path.join(dir_name, name)		
-	if os.path.exists(path):
-		print(f"Delete file {path}")
-		os.remove(path)	
-	images, page_index, file_list, current_file, hide_image = get_recent_images(dir_name, page_index, 0, image_index)
-	return images, page_index, file_list, current_file, hide_image
+    #print("set img",num)
+    file = filenames[int(num)]
+    return file, num, os.path.join(image_path, file)
+def delete_image(tabname, dir_name, name, page_index, filenames, image_index):
+    #print("filename", name)
+    path = os.path.join(dir_name, name)           
+    if os.path.exists(path):
+        print(f"Delete file {path}")
+        os.remove(path)    
+        new_file_list = []
+        for f in filenames:
+            if f == name:
+                continue
+            new_file_list.append(f)
+    else:
+        print(f"Not exists file {path}")
+        new_file_list = filenames
+    return page_index, new_file_list
 
+def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
+        if tabname == "txt2img":
+            dir_name = opts.outdir_txt2img_samples
+        elif tabname == "img2img":
+            dir_name = opts.outdir_img2img_samples
+        elif tabname == "extras":
+            dir_name = opts.outdir_extras_samples
+        with gr.Row():    
+                renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") 
+                first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page")
+                prev_page = gr.Button('Prev') 
+                page_index = gr.Number(value=1, label="Page Index")
+                next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") 
+                end_page = gr.Button('End')    
+        with gr.Row(elem_id=tabname + "_images_history"):            
+            with gr.Row():     
+                with gr.Column():
+                    history_gallery = gr.Gallery(show_label=False).style(grid=6)
+                with gr.Column():     
+                    with gr.Row():          
+                        delete = gr.Button('Delete')
+                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                    with gr.Row():
+                        with gr.Column():
+                            img_file_info = gr.Textbox(label="Generate Info")
+                            img_file_name = gr.Textbox(label="File Name")    
+                    with gr.Row():    
+                            # hiden items
+                            img_path = gr.Textbox(dir_name, visible=False)                
+                            tabname_box = gr.Textbox(tabname, visible=False)    
+                            image_index = gr.Textbox(value=-1, visible=False)                        
+                            set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
+                            filenames = gr.State()
+                            hide_image = gr.Image(visible=False, type="pil")
+                            info1 = gr.Textbox(visible=False)
+                            info2 = gr.Textbox(visible=False)
 
-def show_images_history(gr, opts, is_img2img, run_pnginfo, switch_dict):
-		def id_name(is_img2img, name):	
-			return ("img2img" if is_img2img else "txt2img") + "_" + name
-		if is_img2img:
-			dir_name = opts.outdir_img2img_samples
-		else:
-			dir_name = opts.outdir_txt2img_samples
-		with gr.Row():	 
-				first_page = gr.Button('First', elem_id=id_name(is_img2img,"images_history_first_page"))
-				prev_page = gr.Button('Prev') 
-				page_index = gr.Number(value=1, label="Page Index")
-				next_page = gr.Button('Next') 
-				end_page = gr.Button('End')	
-		with gr.Row(elem_id=id_name(is_img2img,"images_history")):			
-			with gr.Row():	 
-				with gr.Column():
-					history_gallery = gr.Gallery(show_label=False).style(grid=6)
-				with gr.Column():	 
-					with gr.Row():	      
-						delete = gr.Button('Delete')
-						pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-						pnginfo_send_to_img2img = gr.Button('Send to img2img')
-					with gr.Row():
-						with gr.Column():
-							img_file_info = gr.Textbox(dir_name, label="Generate Info")
-							img_file_name = gr.Textbox(label="File Name")	
-					with gr.Row():	
-							# hiden items
-							img_path = gr.Textbox(dir_name, visible=False)				
-							is_img2img_flag = gr.Checkbox(is_img2img, visible=False)	
-							image_index = gr.Textbox(value=-1, visible=False)						
-							set_index = gr.Button('set_index',  elem_id=id_name(is_img2img,"images_history_set_index"))
-							filenames = gr.State()
-							hide_image = gr.Image(visible=False, type="pil")
-							info1 = gr.Textbox(visible=False)
-							info2 = gr.Textbox(visible=False)
+                
+        # turn pages
+        gallery_inputs = [img_path, page_index, image_index, tabname_box]
+        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image]
 
-				
-		# turn pages
-		gallery_inputs = [img_path, page_index, image_index]
-		gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image]
-		first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-		next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-		prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)		
-		end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-		page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
-		#page_index.change(page_index_change, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index])
+        first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+        next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+        prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)        
+        end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+        page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+        renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+        #page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
 
-		#other funcitons
-		set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[is_img2img_flag,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
-		delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames, image_index], outputs=gallery_outputs) 
-		hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
-		switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
-		switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
-	
-		
+        #other funcitons
+        set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
+        delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) 
+        hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
+        switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
+        switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
+    
+        
 def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
-	with gr.Blocks(analytics_enabled=False) as images_history:
-		with gr.Tabs() as tabs:			
-			with gr.Tab("txt2img history", id="images_history_txt2img"):
-				with gr.Blocks(analytics_enabled=False) as images_history_txt2img:   
-					show_images_history(gr, opts, False, run_pnginfo, switch_dict) 
-			with gr.Tab("img2img history", id="images_history_img2img"):
-				with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-					show_images_history(gr, opts, True, run_pnginfo, switch_dict)
-	return images_history
+    with gr.Blocks(analytics_enabled=False) as images_history:
+        with gr.Tabs() as tabs:            
+            with gr.Tab("txt2img history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:   
+                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict) 
+            with gr.Tab("img2img history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                    show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict)
+            with gr.Tab("extras history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                    show_images_history(gr, opts, "extras", run_pnginfo, switch_dict)
+    return images_history
-- 
cgit v1.2.3


From 92d7a138857b308c97a8d009848f642aeb93d6c8 Mon Sep 17 00:00:00 2001
From: Martin Cairns <Martin.Cairns+github@gmail.com>
Date: Tue, 11 Oct 2022 00:02:44 +0100
Subject: Handle different parameters for DPM fast & adaptive

---
 modules/sd_samplers.py | 25 ++++++++++++++++++-------
 1 file changed, 18 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d168b938..eee52e7d 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -57,7 +57,7 @@ def set_samplers():
     global samplers, samplers_for_img2img
 
     hidden = set(opts.hide_samplers)
-    hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive'])
+    hidden_img2img = set(opts.hide_samplers + ['PLMS'])
 
     samplers = [x for x in all_samplers if x.name not in hidden]
     samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
@@ -365,16 +365,27 @@ class KDiffusionSampler:
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 
-        noise = noise * sigmas[steps - t_enc - 1]
-        xi = x + noise
-
-        extra_params_kwargs = self.initialize(p)
-
         sigma_sched = sigmas[steps - t_enc - 1:]
+        print('check values same', sigmas[steps - t_enc - 1] , sigma_sched[0], sigmas[steps - t_enc - 1] - sigma_sched[0])
+        xi = x + noise * sigma_sched[0]
+        
+        extra_params_kwargs = self.initialize(p)
+        if 'sigma_min' in inspect.signature(self.func).parameters:
+            ## last sigma is zero which is allowed by DPM Fast & Adaptive so taking value before last
+            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
+        if 'sigma_max' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigma_max'] = sigma_sched[0]
+        if 'n' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['n'] = len(sigma_sched) - 1
+        if 'sigma_sched' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigma_sched'] = sigma_sched
+        if 'sigmas' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigmas'] = sigma_sched
 
         self.model_wrap_cfg.init_latent = x
 
-        return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+        return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
         steps = steps or p.steps
-- 
cgit v1.2.3


From 1eae3076078f00ecc5d0fac3c77fffb85cd2eb77 Mon Sep 17 00:00:00 2001
From: Martin Cairns <Martin.Cairns+github@gmail.com>
Date: Tue, 11 Oct 2022 00:04:06 +0100
Subject: Remove debug code for checking that first sigma value is same after
 code cleanup

---
 modules/sd_samplers.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index eee52e7d..32272916 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -366,7 +366,6 @@ class KDiffusionSampler:
             sigmas = self.model_wrap.get_sigmas(steps)
 
         sigma_sched = sigmas[steps - t_enc - 1:]
-        print('check values same', sigmas[steps - t_enc - 1] , sigma_sched[0], sigmas[steps - t_enc - 1] - sigma_sched[0])
         xi = x + noise * sigma_sched[0]
         
         extra_params_kwargs = self.initialize(p)
-- 
cgit v1.2.3


From eacc03b16730bcc5be95cda2d7c966ff1b4a8263 Mon Sep 17 00:00:00 2001
From: Martin Cairns <Martin.Cairns+github@gmail.com>
Date: Tue, 11 Oct 2022 00:36:00 +0100
Subject: Fix typo in comments

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 32272916..20309e06 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -370,7 +370,7 @@ class KDiffusionSampler:
         
         extra_params_kwargs = self.initialize(p)
         if 'sigma_min' in inspect.signature(self.func).parameters:
-            ## last sigma is zero which is allowed by DPM Fast & Adaptive so taking value before last
+            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
             extra_params_kwargs['sigma_min'] = sigma_sched[-2]
         if 'sigma_max' in inspect.signature(self.func).parameters:
             extra_params_kwargs['sigma_max'] = sigma_sched[0]
-- 
cgit v1.2.3


From 87d63bbab5c973ac5cec777ef7304d28f1ab3f24 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 11 Oct 2022 20:37:03 +0800
Subject: images history improvement

---
 modules/images_history.py | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 01d11a01..23f55b30 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -64,12 +64,12 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         elif tabname == "extras":
             dir_name = opts.outdir_extras_samples
         with gr.Row():    
-                renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") 
-                first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page")
-                prev_page = gr.Button('Prev') 
-                page_index = gr.Number(value=1, label="Page Index")
-                next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") 
-                end_page = gr.Button('End')    
+            renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") 
+            first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page")
+            prev_page = gr.Button('Prev') 
+            page_index = gr.Number(value=1, label="Page Index")
+            next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") 
+            end_page = gr.Button('End')    
         with gr.Row(elem_id=tabname + "_images_history"):            
             with gr.Row():     
                 with gr.Column():
@@ -84,15 +84,15 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                             img_file_info = gr.Textbox(label="Generate Info")
                             img_file_name = gr.Textbox(label="File Name")    
                     with gr.Row():    
-                            # hiden items
-                            img_path = gr.Textbox(dir_name, visible=False)                
-                            tabname_box = gr.Textbox(tabname, visible=False)    
-                            image_index = gr.Textbox(value=-1, visible=False)                        
-                            set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
-                            filenames = gr.State()
-                            hide_image = gr.Image(visible=False, type="pil")
-                            info1 = gr.Textbox(visible=False)
-                            info2 = gr.Textbox(visible=False)
+                        # hiden items
+                        img_path = gr.Textbox(dir_name, visible=False)                
+                        tabname_box = gr.Textbox(tabname, visible=False)    
+                        image_index = gr.Textbox(value=-1, visible=False)                        
+                        set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
+                        filenames = gr.State()
+                        hide_image = gr.Image(visible=False, type="pil")
+                        info1 = gr.Textbox(visible=False)
+                        info2 = gr.Textbox(visible=False)
 
                 
         # turn pages
-- 
cgit v1.2.3


From 87b77cad5f3017c952a7dfec0e7904a9df5b72fd Mon Sep 17 00:00:00 2001
From: Ben <110583491+TheLastBen@users.noreply.github.com>
Date: Mon, 10 Oct 2022 19:37:16 +0100
Subject: Layout fix

---
 modules/ui.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index df653059..de4cd7f2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -550,15 +550,15 @@ def create_ui(wrap_gradio_gpu_call):
                         button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
                         open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
 
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+                with gr.Row():
+                    do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
                     
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+                with gr.Row():
+                    download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
-                with gr.Group():
-                    html_info = gr.HTML()
-                    generation_info = gr.Textbox(visible=False)
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -738,15 +738,15 @@ def create_ui(wrap_gradio_gpu_call):
                         button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
                         open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
 
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+                with gr.Row():
+                    do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
                     
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+                with gr.Row():
+                    download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
-                with gr.Group():
-                    html_info = gr.HTML()
-                    generation_info = gr.Textbox(visible=False)
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-- 
cgit v1.2.3


From 861297cefe2bb663f4e09dd4778a4cb93ebe8ff1 Mon Sep 17 00:00:00 2001
From: Ben <110583491+TheLastBen@users.noreply.github.com>
Date: Tue, 11 Oct 2022 08:08:45 +0100
Subject: add a space holder

---
 modules/ui.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index de4cd7f2..fc0f3d3c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -429,7 +429,10 @@ def create_toprow(is_img2img):
 
             with gr.Row():
                 with gr.Column(scale=8):
-                    negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
+                    with gr.Row():
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
+                with gr.Column(scale=1, elem_id="roll_col"):
+                    sh = gr.Button(elem_id="sh", visible=True)                           
 
                 with gr.Column(scale=1, elem_id="style_neg_col"):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
-- 
cgit v1.2.3


From 59925644480b6fd84f6bb84b4df7d4fbc6a0cce8 Mon Sep 17 00:00:00 2001
From: JamnedZ <zaguzinegor@gmail.com>
Date: Tue, 11 Oct 2022 16:40:27 +0700
Subject: Cleaned ngrok integration

---
 modules/ngrok.py  | 15 +++++++++++++++
 modules/shared.py |  1 +
 modules/ui.py     |  5 +++++
 3 files changed, 21 insertions(+)
 create mode 100644 modules/ngrok.py

(limited to 'modules')

diff --git a/modules/ngrok.py b/modules/ngrok.py
new file mode 100644
index 00000000..17e6976f
--- /dev/null
+++ b/modules/ngrok.py
@@ -0,0 +1,15 @@
+from pyngrok import ngrok, conf, exception
+
+
+def connect(token, port):
+    if token == None:
+        token = 'None'
+    conf.get_default().auth_token = token
+    try:
+        public_url = ngrok.connect(port).public_url
+    except exception.PyngrokNgrokError:
+        print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
+              f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
+    else:
+        print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
+               'You can use this link after the launch is complete.')
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index 8753015e..375e3afb 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -38,6 +38,7 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
+parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
diff --git a/modules/ui.py b/modules/ui.py
index fc0f3d3c..f57f32db 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -51,6 +51,11 @@ if not cmd_opts.share and not cmd_opts.listen:
     gradio.utils.version_check = lambda: None
     gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
 
+if cmd_opts.ngrok != None:
+    import modules.ngrok as ngrok
+    print('ngrok authtoken detected, trying to connect...')
+    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860)
+
 
 def gr_show(visible=True):
     return {"visible": visible, "__type__": "update"}
-- 
cgit v1.2.3


From a004d1a855311b0d7ff2976a4e31b0247ad9d1f6 Mon Sep 17 00:00:00 2001
From: JamnedZ <zaguzinegor@gmail.com>
Date: Tue, 11 Oct 2022 16:48:27 +0700
Subject: Added new line at the end of ngrok.py

---
 modules/ngrok.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ngrok.py b/modules/ngrok.py
index 17e6976f..7d03a6df 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -12,4 +12,4 @@ def connect(token, port):
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
     else:
         print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
-               'You can use this link after the launch is complete.')
\ No newline at end of file
+               'You can use this link after the launch is complete.')
-- 
cgit v1.2.3


From 873efeed49bb5197a42da18272115b326c5d68f3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 15:51:22 +0300
Subject: rename hypernetwork dir to hypernetworks to prevent clash with an old
 filename that people who use zip instead of git clone will have

---
 modules/hypernetwork/hypernetwork.py  | 283 ----------------------------------
 modules/hypernetwork/ui.py            |  43 ------
 modules/hypernetworks/hypernetwork.py | 283 ++++++++++++++++++++++++++++++++++
 modules/hypernetworks/ui.py           |  43 ++++++
 modules/sd_hijack.py                  |   2 +-
 modules/sd_hijack_optimizations.py    |   2 +-
 modules/shared.py                     |   2 +-
 modules/ui.py                         |   2 +-
 8 files changed, 330 insertions(+), 330 deletions(-)
 delete mode 100644 modules/hypernetwork/hypernetwork.py
 delete mode 100644 modules/hypernetwork/ui.py
 create mode 100644 modules/hypernetworks/hypernetwork.py
 create mode 100644 modules/hypernetworks/ui.py

(limited to 'modules')

diff --git a/modules/hypernetwork/hypernetwork.py b/modules/hypernetwork/hypernetwork.py
deleted file mode 100644
index aa701bda..00000000
--- a/modules/hypernetwork/hypernetwork.py
+++ /dev/null
@@ -1,283 +0,0 @@
-import datetime
-import glob
-import html
-import os
-import sys
-import traceback
-import tqdm
-
-import torch
-
-from ldm.util import default
-from modules import devices, shared, processing, sd_models
-import torch
-from torch import einsum
-from einops import rearrange, repeat
-import modules.textual_inversion.dataset
-
-
-class HypernetworkModule(torch.nn.Module):
-    def __init__(self, dim, state_dict=None):
-        super().__init__()
-
-        self.linear1 = torch.nn.Linear(dim, dim * 2)
-        self.linear2 = torch.nn.Linear(dim * 2, dim)
-
-        if state_dict is not None:
-            self.load_state_dict(state_dict, strict=True)
-        else:
-
-            self.linear1.weight.data.normal_(mean=0.0, std=0.01)
-            self.linear1.bias.data.zero_()
-            self.linear2.weight.data.normal_(mean=0.0, std=0.01)
-            self.linear2.bias.data.zero_()
-
-        self.to(devices.device)
-
-    def forward(self, x):
-        return x + (self.linear2(self.linear1(x)))
-
-
-class Hypernetwork:
-    filename = None
-    name = None
-
-    def __init__(self, name=None):
-        self.filename = None
-        self.name = name
-        self.layers = {}
-        self.step = 0
-        self.sd_checkpoint = None
-        self.sd_checkpoint_name = None
-
-        for size in [320, 640, 768, 1280]:
-            self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
-
-    def weights(self):
-        res = []
-
-        for k, layers in self.layers.items():
-            for layer in layers:
-                layer.train()
-                res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
-
-        return res
-
-    def save(self, filename):
-        state_dict = {}
-
-        for k, v in self.layers.items():
-            state_dict[k] = (v[0].state_dict(), v[1].state_dict())
-
-        state_dict['step'] = self.step
-        state_dict['name'] = self.name
-        state_dict['sd_checkpoint'] = self.sd_checkpoint
-        state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
-
-        torch.save(state_dict, filename)
-
-    def load(self, filename):
-        self.filename = filename
-        if self.name is None:
-            self.name = os.path.splitext(os.path.basename(filename))[0]
-
-        state_dict = torch.load(filename, map_location='cpu')
-
-        for size, sd in state_dict.items():
-            if type(size) == int:
-                self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
-
-        self.name = state_dict.get('name', self.name)
-        self.step = state_dict.get('step', 0)
-        self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
-        self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
-
-
-def list_hypernetworks(path):
-    res = {}
-    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
-        name = os.path.splitext(os.path.basename(filename))[0]
-        res[name] = filename
-    return res
-
-
-def load_hypernetwork(filename):
-    path = shared.hypernetworks.get(filename, None)
-    if path is not None:
-        print(f"Loading hypernetwork {filename}")
-        try:
-            shared.loaded_hypernetwork = Hypernetwork()
-            shared.loaded_hypernetwork.load(path)
-
-        except Exception:
-            print(f"Error loading hypernetwork {path}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-    else:
-        if shared.loaded_hypernetwork is not None:
-            print(f"Unloading hypernetwork")
-
-        shared.loaded_hypernetwork = None
-
-
-def apply_hypernetwork(hypernetwork, context, layer=None):
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is None:
-        return context, context
-
-    if layer is not None:
-        layer.hyper_k = hypernetwork_layers[0]
-        layer.hyper_v = hypernetwork_layers[1]
-
-    context_k = hypernetwork_layers[0](context)
-    context_v = hypernetwork_layers[1](context)
-    return context_k, context_v
-
-
-def attention_CrossAttention_forward(self, x, context=None, mask=None):
-    h = self.heads
-
-    q = self.to_q(x)
-    context = default(context, x)
-
-    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)
-    k = self.to_k(context_k)
-    v = self.to_v(context_v)
-
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
-    if mask is not None:
-        mask = rearrange(mask, 'b ... -> b (...)')
-        max_neg_value = -torch.finfo(sim.dtype).max
-        mask = repeat(mask, 'b j -> (b h) () j', h=h)
-        sim.masked_fill_(~mask, max_neg_value)
-
-    # attention, what we cannot get enough of
-    attn = sim.softmax(dim=-1)
-
-    out = einsum('b i j, b j d -> b i d', attn, v)
-    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-    return self.to_out(out)
-
-
-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
-    assert hypernetwork_name, 'embedding not selected'
-
-    path = shared.hypernetworks.get(hypernetwork_name, None)
-    shared.loaded_hypernetwork = Hypernetwork()
-    shared.loaded_hypernetwork.load(path)
-
-    shared.state.textinfo = "Initializing hypernetwork training..."
-    shared.state.job_count = steps
-
-    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
-
-    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
-
-    if save_hypernetwork_every > 0:
-        hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
-        os.makedirs(hypernetwork_dir, exist_ok=True)
-    else:
-        hypernetwork_dir = None
-
-    if create_image_every > 0:
-        images_dir = os.path.join(log_directory, "images")
-        os.makedirs(images_dir, exist_ok=True)
-    else:
-        images_dir = None
-
-    cond_model = shared.sd_model.cond_stage_model
-
-    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
-    with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
-
-    hypernetwork = shared.loaded_hypernetwork
-    weights = hypernetwork.weights()
-    for weight in weights:
-        weight.requires_grad = True
-
-    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
-
-    losses = torch.zeros((32,))
-
-    last_saved_file = "<none>"
-    last_saved_image = "<none>"
-
-    ititial_step = hypernetwork.step or 0
-    if ititial_step > steps:
-        return hypernetwork, filename
-
-    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, (x, text) in pbar:
-        hypernetwork.step = i + ititial_step
-
-        if hypernetwork.step > steps:
-            break
-
-        if shared.state.interrupted:
-            break
-
-        with torch.autocast("cuda"):
-            c = cond_model([text])
-
-            x = x.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), c)[0]
-            del x
-
-            losses[hypernetwork.step % losses.shape[0]] = loss.item()
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-        pbar.set_description(f"loss: {losses.mean():.7f}")
-
-        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
-            hypernetwork.save(last_saved_file)
-
-        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-            last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
-
-            preview_text = text if preview_image_prompt == "" else preview_image_prompt
-
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                prompt=preview_text,
-                steps=20,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-            )
-
-            processed = processing.process_images(p)
-            image = processed.images[0]
-
-            shared.state.current_image = image
-            image.save(last_saved_image)
-
-            last_saved_image += f", prompt: {preview_text}"
-
-        shared.state.job_no = hypernetwork.step
-
-        shared.state.textinfo = f"""
-<p>
-Loss: {losses.mean():.7f}<br/>
-Step: {hypernetwork.step}<br/>
-Last prompt: {html.escape(text)}<br/>
-Last saved embedding: {html.escape(last_saved_file)}<br/>
-Last saved image: {html.escape(last_saved_image)}<br/>
-</p>
-"""
-
-    checkpoint = sd_models.select_checkpoint()
-
-    hypernetwork.sd_checkpoint = checkpoint.hash
-    hypernetwork.sd_checkpoint_name = checkpoint.model_name
-    hypernetwork.save(filename)
-
-    return hypernetwork, filename
-
-
diff --git a/modules/hypernetwork/ui.py b/modules/hypernetwork/ui.py
deleted file mode 100644
index f6d1d0a3..00000000
--- a/modules/hypernetwork/ui.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import html
-import os
-
-import gradio as gr
-
-import modules.textual_inversion.textual_inversion
-import modules.textual_inversion.preprocess
-from modules import sd_hijack, shared
-from modules.hypernetwork import hypernetwork
-
-
-def create_hypernetwork(name):
-    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
-    assert not os.path.exists(fn), f"file {fn} already exists"
-
-    hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
-    hypernet.save(fn)
-
-    shared.reload_hypernetworks()
-
-    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
-
-
-def train_hypernetwork(*args):
-
-    initial_hypernetwork = shared.loaded_hypernetwork
-
-    try:
-        sd_hijack.undo_optimizations()
-
-        hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args)
-
-        res = f"""
-Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
-Hypernetwork saved to {html.escape(filename)}
-"""
-        return res, ""
-    except Exception:
-        raise
-    finally:
-        shared.loaded_hypernetwork = initial_hypernetwork
-        sd_hijack.apply_optimizations()
-
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
new file mode 100644
index 00000000..aa701bda
--- /dev/null
+++ b/modules/hypernetworks/hypernetwork.py
@@ -0,0 +1,283 @@
+import datetime
+import glob
+import html
+import os
+import sys
+import traceback
+import tqdm
+
+import torch
+
+from ldm.util import default
+from modules import devices, shared, processing, sd_models
+import torch
+from torch import einsum
+from einops import rearrange, repeat
+import modules.textual_inversion.dataset
+
+
+class HypernetworkModule(torch.nn.Module):
+    def __init__(self, dim, state_dict=None):
+        super().__init__()
+
+        self.linear1 = torch.nn.Linear(dim, dim * 2)
+        self.linear2 = torch.nn.Linear(dim * 2, dim)
+
+        if state_dict is not None:
+            self.load_state_dict(state_dict, strict=True)
+        else:
+
+            self.linear1.weight.data.normal_(mean=0.0, std=0.01)
+            self.linear1.bias.data.zero_()
+            self.linear2.weight.data.normal_(mean=0.0, std=0.01)
+            self.linear2.bias.data.zero_()
+
+        self.to(devices.device)
+
+    def forward(self, x):
+        return x + (self.linear2(self.linear1(x)))
+
+
+class Hypernetwork:
+    filename = None
+    name = None
+
+    def __init__(self, name=None):
+        self.filename = None
+        self.name = name
+        self.layers = {}
+        self.step = 0
+        self.sd_checkpoint = None
+        self.sd_checkpoint_name = None
+
+        for size in [320, 640, 768, 1280]:
+            self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
+
+    def weights(self):
+        res = []
+
+        for k, layers in self.layers.items():
+            for layer in layers:
+                layer.train()
+                res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
+
+        return res
+
+    def save(self, filename):
+        state_dict = {}
+
+        for k, v in self.layers.items():
+            state_dict[k] = (v[0].state_dict(), v[1].state_dict())
+
+        state_dict['step'] = self.step
+        state_dict['name'] = self.name
+        state_dict['sd_checkpoint'] = self.sd_checkpoint
+        state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
+
+        torch.save(state_dict, filename)
+
+    def load(self, filename):
+        self.filename = filename
+        if self.name is None:
+            self.name = os.path.splitext(os.path.basename(filename))[0]
+
+        state_dict = torch.load(filename, map_location='cpu')
+
+        for size, sd in state_dict.items():
+            if type(size) == int:
+                self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
+
+        self.name = state_dict.get('name', self.name)
+        self.step = state_dict.get('step', 0)
+        self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
+        self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
+
+
+def list_hypernetworks(path):
+    res = {}
+    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
+        name = os.path.splitext(os.path.basename(filename))[0]
+        res[name] = filename
+    return res
+
+
+def load_hypernetwork(filename):
+    path = shared.hypernetworks.get(filename, None)
+    if path is not None:
+        print(f"Loading hypernetwork {filename}")
+        try:
+            shared.loaded_hypernetwork = Hypernetwork()
+            shared.loaded_hypernetwork.load(path)
+
+        except Exception:
+            print(f"Error loading hypernetwork {path}", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+    else:
+        if shared.loaded_hypernetwork is not None:
+            print(f"Unloading hypernetwork")
+
+        shared.loaded_hypernetwork = None
+
+
+def apply_hypernetwork(hypernetwork, context, layer=None):
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is None:
+        return context, context
+
+    if layer is not None:
+        layer.hyper_k = hypernetwork_layers[0]
+        layer.hyper_v = hypernetwork_layers[1]
+
+    context_k = hypernetwork_layers[0](context)
+    context_v = hypernetwork_layers[1](context)
+    return context_k, context_v
+
+
+def attention_CrossAttention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+
+    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)
+    k = self.to_k(context_k)
+    v = self.to_v(context_v)
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+    if mask is not None:
+        mask = rearrange(mask, 'b ... -> b (...)')
+        max_neg_value = -torch.finfo(sim.dtype).max
+        mask = repeat(mask, 'b j -> (b h) () j', h=h)
+        sim.masked_fill_(~mask, max_neg_value)
+
+    # attention, what we cannot get enough of
+    attn = sim.softmax(dim=-1)
+
+    out = einsum('b i j, b j d -> b i d', attn, v)
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    return self.to_out(out)
+
+
+def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
+    assert hypernetwork_name, 'embedding not selected'
+
+    path = shared.hypernetworks.get(hypernetwork_name, None)
+    shared.loaded_hypernetwork = Hypernetwork()
+    shared.loaded_hypernetwork.load(path)
+
+    shared.state.textinfo = "Initializing hypernetwork training..."
+    shared.state.job_count = steps
+
+    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+
+    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
+
+    if save_hypernetwork_every > 0:
+        hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
+        os.makedirs(hypernetwork_dir, exist_ok=True)
+    else:
+        hypernetwork_dir = None
+
+    if create_image_every > 0:
+        images_dir = os.path.join(log_directory, "images")
+        os.makedirs(images_dir, exist_ok=True)
+    else:
+        images_dir = None
+
+    cond_model = shared.sd_model.cond_stage_model
+
+    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
+    with torch.autocast("cuda"):
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+
+    hypernetwork = shared.loaded_hypernetwork
+    weights = hypernetwork.weights()
+    for weight in weights:
+        weight.requires_grad = True
+
+    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
+
+    losses = torch.zeros((32,))
+
+    last_saved_file = "<none>"
+    last_saved_image = "<none>"
+
+    ititial_step = hypernetwork.step or 0
+    if ititial_step > steps:
+        return hypernetwork, filename
+
+    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
+    for i, (x, text) in pbar:
+        hypernetwork.step = i + ititial_step
+
+        if hypernetwork.step > steps:
+            break
+
+        if shared.state.interrupted:
+            break
+
+        with torch.autocast("cuda"):
+            c = cond_model([text])
+
+            x = x.to(devices.device)
+            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+            del x
+
+            losses[hypernetwork.step % losses.shape[0]] = loss.item()
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        pbar.set_description(f"loss: {losses.mean():.7f}")
+
+        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
+            hypernetwork.save(last_saved_file)
+
+        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
+            last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
+
+            preview_text = text if preview_image_prompt == "" else preview_image_prompt
+
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                prompt=preview_text,
+                steps=20,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+            )
+
+            processed = processing.process_images(p)
+            image = processed.images[0]
+
+            shared.state.current_image = image
+            image.save(last_saved_image)
+
+            last_saved_image += f", prompt: {preview_text}"
+
+        shared.state.job_no = hypernetwork.step
+
+        shared.state.textinfo = f"""
+<p>
+Loss: {losses.mean():.7f}<br/>
+Step: {hypernetwork.step}<br/>
+Last prompt: {html.escape(text)}<br/>
+Last saved embedding: {html.escape(last_saved_file)}<br/>
+Last saved image: {html.escape(last_saved_image)}<br/>
+</p>
+"""
+
+    checkpoint = sd_models.select_checkpoint()
+
+    hypernetwork.sd_checkpoint = checkpoint.hash
+    hypernetwork.sd_checkpoint_name = checkpoint.model_name
+    hypernetwork.save(filename)
+
+    return hypernetwork, filename
+
+
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
new file mode 100644
index 00000000..811bc31e
--- /dev/null
+++ b/modules/hypernetworks/ui.py
@@ -0,0 +1,43 @@
+import html
+import os
+
+import gradio as gr
+
+import modules.textual_inversion.textual_inversion
+import modules.textual_inversion.preprocess
+from modules import sd_hijack, shared
+from modules.hypernetworks import hypernetwork
+
+
+def create_hypernetwork(name):
+    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
+    assert not os.path.exists(fn), f"file {fn} already exists"
+
+    hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
+    hypernet.save(fn)
+
+    shared.reload_hypernetworks()
+
+    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
+
+
+def train_hypernetwork(*args):
+
+    initial_hypernetwork = shared.loaded_hypernetwork
+
+    try:
+        sd_hijack.undo_optimizations()
+
+        hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args)
+
+        res = f"""
+Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
+Hypernetwork saved to {html.escape(filename)}
+"""
+        return res, ""
+    except Exception:
+        raise
+    finally:
+        shared.loaded_hypernetwork = initial_hypernetwork
+        sd_hijack.apply_optimizations()
+
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index f873049a..f07ec041 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -37,7 +37,7 @@ def apply_optimizations():
 
 
 def undo_optimizations():
-    from modules.hypernetwork import hypernetwork
+    from modules.hypernetworks import hypernetwork
 
     ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 27e571fc..3349b9c3 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -9,7 +9,7 @@ from ldm.util import default
 from einops import rearrange
 
 from modules import shared
-from modules.hypernetwork import hypernetwork
+from modules.hypernetworks import hypernetwork
 
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
diff --git a/modules/shared.py b/modules/shared.py
index 375e3afb..1dc2ccf2 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -14,7 +14,7 @@ import modules.sd_models
 import modules.styles
 import modules.devices as devices
 from modules import sd_samplers
-from modules.hypernetwork import hypernetwork
+from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
diff --git a/modules/ui.py b/modules/ui.py
index f57f32db..42e5d866 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -39,7 +39,7 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
-import modules.hypernetwork.ui
+import modules.hypernetworks.ui
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
-- 
cgit v1.2.3


From b0583be0884cd17dafb408fd79b52b2a0a972563 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 15:54:34 +0300
Subject: more renames

---
 modules/hypernetworks/ui.py | 4 ++--
 modules/ui.py               | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 811bc31e..e7540f41 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -13,7 +13,7 @@ def create_hypernetwork(name):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
-    hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
+    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name)
     hypernet.save(fn)
 
     shared.reload_hypernetworks()
@@ -28,7 +28,7 @@ def train_hypernetwork(*args):
     try:
         sd_hijack.undo_optimizations()
 
-        hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args)
+        hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args)
 
         res = f"""
 Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
diff --git a/modules/ui.py b/modules/ui.py
index 42e5d866..ee333c3b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1111,7 +1111,7 @@ def create_ui(wrap_gradio_gpu_call):
         )
 
         create_hypernetwork.click(
-            fn=modules.hypernetwork.ui.create_hypernetwork,
+            fn=modules.hypernetworks.ui.create_hypernetwork,
             inputs=[
                 new_hypernetwork_name,
             ],
@@ -1164,7 +1164,7 @@ def create_ui(wrap_gradio_gpu_call):
         )
 
         train_hypernetwork.click(
-            fn=wrap_gradio_gpu_call(modules.hypernetwork.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
             inputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.3


From d01a2d01560b31937df1f3433d210c18f97d32fa Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Tue, 11 Oct 2022 08:03:31 -0500
Subject: move list refresh to webui.py and add stdout indicating it's doing so

---
 modules/ui.py | 3 ---
 1 file changed, 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 06ff118f..ae9317a3 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -39,7 +39,6 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
-from modules.sd_models import list_models
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -1291,8 +1290,6 @@ Requested path was: {f}
             shared.state.interrupt()
             settings_interface.gradio_ref.do_restart = True
 
-            # refresh models so that new models/.ckpt's show up on reload
-            list_models()
 
         restart_gradio.click(
             fn=request_restart,
-- 
cgit v1.2.3


From 66b7d7584f0b44ce1316425808c27ca7df38293c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 17:03:00 +0300
Subject: become even stricter with pickles no pickle shall pass thank you
 again, RyotaK

---
 modules/safe.py | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

(limited to 'modules')

diff --git a/modules/safe.py b/modules/safe.py
index 05917463..20be16a5 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -10,6 +10,7 @@ import torch
 import numpy
 import _codecs
 import zipfile
+import re
 
 
 # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
@@ -54,11 +55,27 @@ class RestrictedUnpickler(pickle.Unpickler):
         raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
 
 
+allowed_zip_names = ["archive/data.pkl", "archive/version"]
+allowed_zip_names_re = re.compile(r"^archive/data/\d+$")
+
+
+def check_zip_filenames(filename, names):
+    for name in names:
+        if name in allowed_zip_names:
+            continue
+        if allowed_zip_names_re.match(name):
+            continue
+
+        raise Exception(f"bad file inside {filename}: {name}")
+
+
 def check_pt(filename):
     try:
 
         # new pytorch format is a zip file
         with zipfile.ZipFile(filename) as z:
+            check_zip_filenames(filename, z.namelist())
+
             with z.open('archive/data.pkl') as file:
                 unpickler = RestrictedUnpickler(file)
                 unpickler.load()
-- 
cgit v1.2.3


From c0484f1b986ce7acb0e3596f6089a191279f5442 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 10 Oct 2022 22:48:54 -0400
Subject: Add cross-attention optimization from InvokeAI

* Add cross-attention optimization from InvokeAI (~30% speed improvement on MPS)
* Add command line option for it
* Make it default when CUDA is unavailable
---
 modules/sd_hijack.py               |  5 ++-
 modules/sd_hijack_optimizations.py | 79 ++++++++++++++++++++++++++++++++++++++
 modules/shared.py                  |  5 ++-
 3 files changed, 86 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index f07ec041..5a1b167f 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -30,8 +30,11 @@ def apply_optimizations():
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
+        print("Applying cross attention optimization (InvokeAI).")
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-        print("Applying cross attention optimization.")
+        print("Applying cross attention optimization (Doggettx).")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 3349b9c3..870226c5 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,6 +1,7 @@
 import math
 import sys
 import traceback
+import psutil
 
 import torch
 from torch import einsum
@@ -116,6 +117,84 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
+# -- From https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py (with hypernetworks support added) --
+
+mem_total_gb = psutil.virtual_memory().total // (1 << 30)
+
+def einsum_op_compvis(q, k, v):
+    s = einsum('b i d, b j d -> b i j', q, k)
+    s = s.softmax(dim=-1, dtype=s.dtype)
+    return einsum('b i j, b j d -> b i d', s, v)
+
+def einsum_op_slice_0(q, k, v, slice_size):
+    r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+    for i in range(0, q.shape[0], slice_size):
+        end = i + slice_size
+        r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])
+    return r
+
+def einsum_op_slice_1(q, k, v, slice_size):
+    r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+    for i in range(0, q.shape[1], slice_size):
+        end = i + slice_size
+        r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
+    return r
+
+def einsum_op_mps_v1(q, k, v):
+    if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
+        return einsum_op_compvis(q, k, v)
+    else:
+        slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
+        return einsum_op_slice_1(q, k, v, slice_size)
+
+def einsum_op_mps_v2(q, k, v):
+    if mem_total_gb > 8 and q.shape[1] <= 4096:
+        return einsum_op_compvis(q, k, v)
+    else:
+        return einsum_op_slice_0(q, k, v, 1)
+
+def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
+    size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
+    if size_mb <= max_tensor_mb:
+        return einsum_op_compvis(q, k, v)
+    div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
+    if div <= q.shape[0]:
+        return einsum_op_slice_0(q, k, v, q.shape[0] // div)
+    return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
+
+def einsum_op(q, k, v):
+    if q.device.type == 'mps':
+        if mem_total_gb >= 32:
+            return einsum_op_mps_v1(q, k, v)
+        return einsum_op_mps_v2(q, k, v)
+
+    # Smaller slices are faster due to L2/L3/SLC caches.
+    # Tested on i7 with 8MB L3 cache.
+    return einsum_op_tensor_mem(q, k, v, 32)
+
+def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+
+    hypernetwork = shared.loaded_hypernetwork
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k = self.to_k(hypernetwork_layers[0](context)) * self.scale
+        v = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k = self.to_k(context) * self.scale
+        v = self.to_v(context)
+    del context, x
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+    r = einsum_op(q, k, v)
+    return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
+
+# -- End of code from https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py --
+
 def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
diff --git a/modules/shared.py b/modules/shared.py
index 1dc2ccf2..20b45f23 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -50,9 +50,10 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
 parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
+parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
+parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
-- 
cgit v1.2.3


From 98fd5cde72d5bda1620ab78416c7828fdc3dc10b Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 10 Oct 2022 23:55:48 -0400
Subject: Add check for psutil

---
 modules/sd_hijack.py               | 10 ++++++++--
 modules/sd_hijack_optimizations.py | 19 +++++++++++++++----
 2 files changed, 23 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 5a1b167f..ac70f876 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -10,6 +10,7 @@ from torch.nn.functional import silu
 import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared
 from modules.shared import opts, device, cmd_opts
+from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
@@ -31,8 +32,13 @@ def apply_optimizations():
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
-        print("Applying cross attention optimization (InvokeAI).")
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
+        if not invokeAI_mps_available and shared.device.type == 'mps':
+            print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
+            print("Applying v1 cross attention optimization.")
+            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+        else:
+            print("Applying cross attention optimization (InvokeAI).")
+            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
         print("Applying cross attention optimization (Doggettx).")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 870226c5..2a4ac7e0 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,7 +1,7 @@
 import math
 import sys
 import traceback
-import psutil
+import importlib
 
 import torch
 from torch import einsum
@@ -117,9 +117,20 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
-# -- From https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py (with hypernetworks support added) --
 
-mem_total_gb = psutil.virtual_memory().total // (1 << 30)
+def check_for_psutil():
+    try:
+        spec = importlib.util.find_spec('psutil')
+        return spec is not None
+    except ModuleNotFoundError:
+        return False
+
+invokeAI_mps_available = check_for_psutil()
+
+# -- Taken from https://github.com/invoke-ai/InvokeAI --
+if invokeAI_mps_available:
+    import psutil
+    mem_total_gb = psutil.virtual_memory().total // (1 << 30)
 
 def einsum_op_compvis(q, k, v):
     s = einsum('b i d, b j d -> b i j', q, k)
@@ -193,7 +204,7 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
     r = einsum_op(q, k, v)
     return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
 
-# -- End of code from https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py --
+# -- End of code from https://github.com/invoke-ai/InvokeAI --
 
 def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
-- 
cgit v1.2.3


From 574c8e554a5371eca2cbf344764cb241c6ec4efc Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 11 Oct 2022 03:32:11 -0400
Subject: Add InvokeAI and lstein to credits, add back CUDA support

---
 modules/sd_hijack_optimizations.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 2a4ac7e0..f006427f 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -173,7 +173,20 @@ def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
         return einsum_op_slice_0(q, k, v, q.shape[0] // div)
     return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
 
+def einsum_op_cuda(q, k, v):
+    stats = torch.cuda.memory_stats(q.device)
+    mem_active = stats['active_bytes.all.current']
+    mem_reserved = stats['reserved_bytes.all.current']
+    mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
+    mem_free_torch = mem_reserved - mem_active
+    mem_free_total = mem_free_cuda + mem_free_torch
+    # Divide factor of safety as there's copying and fragmentation
+    return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
+
 def einsum_op(q, k, v):
+    if q.device.type == 'cuda':
+        return einsum_op_cuda(q, k, v)
+
     if q.device.type == 'mps':
         if mem_total_gb >= 32:
             return einsum_op_mps_v1(q, k, v)
-- 
cgit v1.2.3


From 861db783c7acfcb93cf0b5191db3d50f9a9bc531 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 11 Oct 2022 05:13:17 -0400
Subject: Use apply_hypernetwork function

---
 modules/sd_hijack_optimizations.py | 14 ++++----------
 1 file changed, 4 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index f006427f..79405525 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -202,16 +202,10 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
     q = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.loaded_hypernetwork
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is not None:
-        k = self.to_k(hypernetwork_layers[0](context)) * self.scale
-        v = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k = self.to_k(context) * self.scale
-        v = self.to_v(context)
-    del context, x
+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
+    k = self.to_k(context_k) * self.scale
+    v = self.to_v(context_v)
+    del context, context_k, context_v, x
 
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
     r = einsum_op(q, k, v)
-- 
cgit v1.2.3


From d682444ecc99319fbd2b142a12727501e2884ba7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 18:04:47 +0300
Subject: add option to select hypernetwork modules when creating

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 modules/hypernetworks/ui.py           | 4 ++--
 modules/ui.py                         | 2 ++
 3 files changed, 6 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index aa701bda..b081f14e 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -42,7 +42,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None):
+    def __init__(self, name=None, enable_sizes=None):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -50,7 +50,7 @@ class Hypernetwork:
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
 
-        for size in [320, 640, 768, 1280]:
+        for size in enable_sizes or [320, 640, 768, 1280]:
             self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
 
     def weights(self):
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index e7540f41..cdddcce1 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -9,11 +9,11 @@ from modules import sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
 
-def create_hypernetwork(name):
+def create_hypernetwork(name, enable_sizes):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
-    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name)
+    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes])
     hypernet.save(fn)
 
     shared.reload_hypernetworks()
diff --git a/modules/ui.py b/modules/ui.py
index f2d16b12..14b87b92 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1037,6 +1037,7 @@ def create_ui(wrap_gradio_gpu_call):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
 
                     new_hypernetwork_name = gr.Textbox(label="Name")
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1114,6 +1115,7 @@ def create_ui(wrap_gradio_gpu_call):
             fn=modules.hypernetworks.ui.create_hypernetwork,
             inputs=[
                 new_hypernetwork_name,
+                new_hypernetwork_sizes,
             ],
             outputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.3


From ff4ef13dd591ec52f196f344f47537695df95364 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Tue, 11 Oct 2022 10:24:27 -0500
Subject: removed unneeded print

---
 modules/deepbooru.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index e31e92c0..89dcac3c 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -119,7 +119,6 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort)
     # sort tags
     result_tags_out = []
     sort_ndx = 0
-    print(alpha_sort)
     if alpha_sort:
         sort_ndx = 1
 
-- 
cgit v1.2.3


From 6d09b8d1df3a96e1380bb1650f5961781630af96 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 18:33:57 +0300
Subject: produce error when training with medvram/lowvram enabled

---
 modules/hypernetworks/ui.py     | 2 ++
 modules/textual_inversion/ui.py | 3 +++
 2 files changed, 5 insertions(+)

(limited to 'modules')

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index cdddcce1..3541a388 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -25,6 +25,8 @@ def train_hypernetwork(*args):
 
     initial_hypernetwork = shared.loaded_hypernetwork
 
+    assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible'
+
     try:
         sd_hijack.undo_optimizations()
 
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index c57de1f9..70f47343 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -22,6 +22,9 @@ def preprocess(*args):
 
 
 def train_embedding(*args):
+
+    assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible'
+
     try:
         sd_hijack.undo_optimizations()
 
-- 
cgit v1.2.3


From d4ea5f4d8631f778d11efcde397e4a5b8801d43b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 19:03:08 +0300
Subject: add an option to unload models during hypernetwork training to save
 VRAM

---
 modules/hypernetworks/hypernetwork.py          | 25 +++++++++++++++-------
 modules/hypernetworks/ui.py                    |  4 +++-
 modules/shared.py                              |  4 ++++
 modules/textual_inversion/dataset.py           | 29 ++++++++++++++++++--------
 modules/textual_inversion/textual_inversion.py |  2 +-
 5 files changed, 46 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b081f14e..4700e1ec 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -175,6 +175,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
 
     log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
+    unload = shared.opts.unload_models_when_training
 
     if save_hypernetwork_every > 0:
         hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
@@ -188,11 +189,13 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     else:
         images_dir = None
 
-    cond_model = shared.sd_model.cond_stage_model
-
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
+
+    if unload:
+        shared.sd_model.cond_stage_model.to(devices.cpu)
+        shared.sd_model.first_stage_model.to(devices.cpu)
 
     hypernetwork = shared.loaded_hypernetwork
     weights = hypernetwork.weights()
@@ -211,7 +214,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
         return hypernetwork, filename
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, (x, text) in pbar:
+    for i, (x, text, cond) in pbar:
         hypernetwork.step = i + ititial_step
 
         if hypernetwork.step > steps:
@@ -221,11 +224,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([text])
-
+            cond = cond.to(devices.device)
             x = x.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+            loss = shared.sd_model(x.unsqueeze(0), cond)[0]
             del x
+            del cond
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
@@ -244,6 +247,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
             preview_text = text if preview_image_prompt == "" else preview_image_prompt
 
+            optimizer.zero_grad()
+            shared.sd_model.cond_stage_model.to(devices.device)
+            shared.sd_model.first_stage_model.to(devices.device)
+
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 prompt=preview_text,
@@ -255,6 +262,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             processed = processing.process_images(p)
             image = processed.images[0]
 
+            if unload:
+                shared.sd_model.cond_stage_model.to(devices.cpu)
+                shared.sd_model.first_stage_model.to(devices.cpu)
+
             shared.state.current_image = image
             image.save(last_saved_image)
 
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 3541a388..c67facbb 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -5,7 +5,7 @@ import gradio as gr
 
 import modules.textual_inversion.textual_inversion
 import modules.textual_inversion.preprocess
-from modules import sd_hijack, shared
+from modules import sd_hijack, shared, devices
 from modules.hypernetworks import hypernetwork
 
 
@@ -41,5 +41,7 @@ Hypernetwork saved to {html.escape(filename)}
         raise
     finally:
         shared.loaded_hypernetwork = initial_hypernetwork
+        shared.sd_model.cond_stage_model.to(devices.device)
+        shared.sd_model.first_stage_model.to(devices.device)
         sd_hijack.apply_optimizations()
 
diff --git a/modules/shared.py b/modules/shared.py
index 20b45f23..c1092ff7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -228,6 +228,10 @@ options_templates.update(options_section(('system', "System"), {
     "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
 }))
 
+options_templates.update(options_section(('training', "Training"), {
+    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP form VRAM when training"),
+}))
+
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
     "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 4d006366..f61f40d3 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -8,14 +8,14 @@ from torchvision import transforms
 
 import random
 import tqdm
-from modules import devices
+from modules import devices, shared
 import re
 
 re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
 
         self.placeholder_token = placeholder_token
 
@@ -32,6 +32,8 @@ class PersonalizedBase(Dataset):
 
         assert data_root, 'dataset directory not specified'
 
+        cond_model = shared.sd_model.cond_stage_model
+
         self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
@@ -53,7 +55,13 @@ class PersonalizedBase(Dataset):
             init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
             init_latent = init_latent.to(devices.cpu)
 
-            self.dataset.append((init_latent, filename_tokens))
+            if include_cond:
+                text = self.create_text(filename_tokens)
+                cond = cond_model([text]).to(devices.cpu)
+            else:
+                cond = None
+
+            self.dataset.append((init_latent, filename_tokens, cond))
 
         self.length = len(self.dataset) * repeats
 
@@ -64,6 +72,12 @@ class PersonalizedBase(Dataset):
     def shuffle(self):
         self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
 
+    def create_text(self, filename_tokens):
+        text = random.choice(self.lines)
+        text = text.replace("[name]", self.placeholder_token)
+        text = text.replace("[filewords]", ' '.join(filename_tokens))
+        return text
+
     def __len__(self):
         return self.length
 
@@ -72,10 +86,7 @@ class PersonalizedBase(Dataset):
             self.shuffle()
 
         index = self.indexes[i % len(self.indexes)]
-        x, filename_tokens = self.dataset[index]
-
-        text = random.choice(self.lines)
-        text = text.replace("[name]", self.placeholder_token)
-        text = text.replace("[filewords]", ' '.join(filename_tokens))
+        x, filename_tokens, cond = self.dataset[index]
 
-        return x, text
+        text = self.create_text(filename_tokens)
+        return x, text, cond
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index bb05cdc6..35f4bd9e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -201,7 +201,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         return embedding, filename
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, (x, text) in pbar:
+    for i, (x, text, _) in pbar:
         embedding.step = i + ititial_step
 
         if embedding.step > steps:
-- 
cgit v1.2.3


From 6a9ea5b41cf92cd9e980349bb5034439f4e7a58b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 19:22:30 +0300
Subject: prevent extra modules from being saved/loaded with hypernet

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4700e1ec..5608e799 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -50,7 +50,7 @@ class Hypernetwork:
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
 
-        for size in enable_sizes or [320, 640, 768, 1280]:
+        for size in enable_sizes or []:
             self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
 
     def weights(self):
-- 
cgit v1.2.3


From c080f52ceae73b893155eff7de577aaf1a982a2f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 19:37:58 +0100
Subject: move embedding logic to separate file

---
 modules/textual_inversion/image_embedding.py | 234 +++++++++++++++++++++++++++
 1 file changed, 234 insertions(+)
 create mode 100644 modules/textual_inversion/image_embedding.py

(limited to 'modules')

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
new file mode 100644
index 00000000..6ad39602
--- /dev/null
+++ b/modules/textual_inversion/image_embedding.py
@@ -0,0 +1,234 @@
+import base64
+import json
+import numpy as np
+import zlib
+from PIL import Image,PngImagePlugin,ImageDraw,ImageFont
+from fonts.ttf import Roboto
+import torch
+
+class EmbeddingEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, torch.Tensor):
+            return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
+        return json.JSONEncoder.default(self, obj)
+
+class EmbeddingDecoder(json.JSONDecoder):
+    def __init__(self, *args, **kwargs):
+        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
+    def object_hook(self, d):
+        if 'TORCHTENSOR' in d:
+            return torch.from_numpy(np.array(d['TORCHTENSOR']))
+        return d
+
+def embedding_to_b64(data):
+    d = json.dumps(data,cls=EmbeddingEncoder)
+    return base64.b64encode(d.encode())
+
+def embedding_from_b64(data):
+    d = base64.b64decode(data)
+    return json.loads(d,cls=EmbeddingDecoder)
+
+def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
+    while True:
+        seed = (a * seed + c) % m
+        yield seed%255
+
+def xor_block(block):
+    g = lcg()
+    randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
+    return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) 
+
+def style_block(block,sequence):
+    im = Image.new('RGB',(block.shape[1],block.shape[0]))
+    draw = ImageDraw.Draw(im)
+    i=0
+    for x in range(-6,im.size[0],8):
+        for yi,y in enumerate(range(-6,im.size[1],8)):
+            offset=0
+            if yi%2==0:
+                offset=4
+            shade = sequence[i%len(sequence)]
+            i+=1
+            draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) )
+
+    fg = np.array(im).astype(np.uint8) & 0xF0
+
+    return block ^ fg
+
+def insert_image_data_embed(image,data):
+    d = 3
+    data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
+    data_np_ = np.frombuffer(data_compressed,np.uint8).copy()
+    data_np_high = data_np_ >> 4
+    data_np_low  = data_np_ & 0x0F
+    
+    h = image.size[1]
+    next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0]%h))
+    next_size = next_size + ((h*d)-(next_size%(h*d)))
+
+    data_np_low.resize(next_size)
+    data_np_low = data_np_low.reshape((h,-1,d))
+
+    data_np_high.resize(next_size)
+    data_np_high = data_np_high.reshape((h,-1,d))
+
+    edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
+    edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
+
+    data_np_low   = style_block(data_np_low,sequence=edge_style)
+    data_np_low   = xor_block(data_np_low)    
+    data_np_high  = style_block(data_np_high,sequence=edge_style[::-1])
+    data_np_high  = xor_block(data_np_high)
+
+    im_low  = Image.fromarray(data_np_low,mode='RGB')
+    im_high = Image.fromarray(data_np_high,mode='RGB')
+
+    background = Image.new('RGB',(image.size[0]+im_low.size[0]+im_high.size[0]+2,image.size[1]),(0,0,0))
+    background.paste(im_low,(0,0))
+    background.paste(image,(im_low.size[0]+1,0))
+    background.paste(im_high,(im_low.size[0]+1+image.size[0]+1,0))
+
+    return background
+
+def crop_black(img,tol=0):
+    mask = (img>tol).all(2)
+    mask0,mask1 = mask.any(0),mask.any(1)
+    col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax()
+    row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
+    return img[row_start:row_end,col_start:col_end]
+
+def extract_image_data_embed(image):
+    d=3
+    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
+    black_cols = np.where( np.sum(outarr, axis=(0,2))==0)
+    if black_cols[0].shape[0] < 2:
+        print('No Image data blocks found.')
+        return None
+
+    data_block_lower = outarr[:,:black_cols[0].min(),:].astype(np.uint8)
+    data_block_upper = outarr[:,black_cols[0].max()+1:,:].astype(np.uint8)
+
+    data_block_lower = xor_block(data_block_lower)
+    data_block_upper = xor_block(data_block_upper)
+    
+    data_block = (data_block_upper << 4) | (data_block_lower)
+    data_block = data_block.flatten().tobytes()
+
+    data = zlib.decompress(data_block)
+    return json.loads(data,cls=EmbeddingDecoder)
+
+def addCaptionLines(lines,image,initialx,textfont):
+    draw = ImageDraw.Draw(image)
+    hstart =initialx
+    for fill,line in lines:
+        fontsize = 32
+        font = ImageFont.truetype(textfont, fontsize)
+        _,_,w, h = draw.textbbox((0,0),line,font=font)
+        fontsize =  min( int(fontsize * ((image.size[0]-35)/w) ), 28)
+        font = ImageFont.truetype(textfont, fontsize)
+        _,_,w,h = draw.textbbox((0,0),line,font=font)
+        draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill)
+        hstart += h
+    return hstart
+
+def caption_image(image,prelines,postlines,background=(51, 51, 51),font=None):
+    if font is None:
+        try:
+            font = ImageFont.truetype(opts.font or Roboto, fontsize)
+            font = opts.font or Roboto
+        except Exception:
+            font = Roboto
+
+    sample_image = image
+    background = Image.new("RGBA", (sample_image.size[0],sample_image.size[1]+1024), background)
+    hoffset = addCaptionLines(prelines,background,5,font)+16
+    background.paste(sample_image,(0,hoffset))
+    hoffset = hoffset+sample_image.size[1]+8
+    hoffset = addCaptionLines(postlines,background,hoffset,font)
+    background = background.crop((0,0,sample_image.size[0],hoffset+8))
+    return background
+
+def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
+    from math import cos
+
+    image = srcimage.copy()
+
+    if textfont is None:
+        try:
+            textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
+            textfont = opts.font or Roboto
+        except Exception:
+            textfont = Roboto
+
+    factor = 1.5
+    gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0))
+    for y in range(image.size[1]):
+        mag = 1-cos(y/image.size[1]*factor)
+        mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
+        gradient.putpixel((0, y), (0,0,0,int(mag*255)))
+    image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
+
+    draw = ImageDraw.Draw(image)
+    fontsize = 32
+    font = ImageFont.truetype(textfont, fontsize)
+    padding = 10
+
+    _,_,w, h = draw.textbbox((0,0),title,font=font)
+    fontsize =  min( int(fontsize * (((image.size[0]*0.75)-(padding*4))/w) ), 72)
+    font = ImageFont.truetype(textfont, fontsize)
+    _,_,w,h = draw.textbbox((0,0),title,font=font)
+    draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230))
+
+    _,_,w, h = draw.textbbox((0,0),footerLeft,font=font)
+    fontsize_left =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
+    _,_,w, h = draw.textbbox((0,0),footerMid,font=font)
+    fontsize_mid =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
+    _,_,w, h = draw.textbbox((0,0),footerRight,font=font)
+    fontsize_right =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
+
+    font = ImageFont.truetype(textfont, min(fontsize_left,fontsize_mid,fontsize_right))
+
+    draw.text((padding,image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255,255,255,230))
+    draw.text((image.size[0]/2,image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255,255,255,230))
+    draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230))
+
+    return image
+
+if __name__ == '__main__':
+
+    image = Image.new('RGBA',(512,512),(255,255,200,255))
+    caption_image(image,[((255,255,255),'line a'),((255,255,255),'line b')],
+                        [((255,255,255),'line c'),((255,255,255),'line d')])
+
+    image = Image.new('RGBA',(512,512),(255,255,200,255))
+    cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
+
+    test_embed = {'string_to_param':{'*':torch.from_numpy(np.random.random((2, 4096)))}}
+
+    embedded_image = insert_image_data_embed(cap_image, test_embed)
+
+    retrived_embed = extract_image_data_embed(embedded_image)
+
+    assert str(retrived_embed) == str(test_embed)
+
+    embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
+
+    assert embedded_image == embedded_image2
+
+    g = lcg()
+    shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
+
+    reference_random = [253, 242, 127,  44, 157,  27, 239, 133,  38,  79, 167,   4, 177, 
+                         95, 130,  79,  78,  14,  52, 215, 220, 194, 126,  28, 240, 179, 
+                        160, 153, 149,  50, 105,  14,  21, 218, 199,  18,  54, 198, 193, 
+                         38, 128,  19,  53, 195, 124,  75, 205,  12,   6, 145,   0,  28, 
+                         30, 148,   8,  45, 218, 171,  55, 249,  97, 166,  12,  35,   0, 
+                         41, 221, 122, 215, 170,  31, 113, 186,  97, 119,  31,  23, 185, 
+                         66, 140,  30,  41,  37,  63, 137, 109, 216,  55, 159, 145,  82, 
+                         204, 86,  73, 222,  44, 198, 118, 240,  97]
+
+    assert shared_random == reference_random   
+
+    hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
+
+    assert 12731374 == hunna_kay_random_sum
\ No newline at end of file
-- 
cgit v1.2.3


From e5fbf5c755b7c306696546405385d5d2314e555b Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 19:46:33 +0100
Subject: remove embedding related image functions from images

---
 modules/images.py | 77 -------------------------------------------------------
 1 file changed, 77 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index e62eec8e..c0a90676 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -463,80 +463,3 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         txt_fullfn = None
 
     return fullfn, txt_fullfn
-
-def addCaptionLines(lines,image,initialx,textfont):
-    draw = ImageDraw.Draw(image)
-    hstart =initialx
-    for fill,line in lines:
-        fontSize = 32
-        font = ImageFont.truetype(textfont, fontSize)
-        _,_,w, h = draw.textbbox((0,0),line,font=font)
-        fontSize =  min( int(fontSize * ((image.size[0]-35)/w) ), 28)
-        font = ImageFont.truetype(textfont, fontSize)
-        _,_,w,h = draw.textbbox((0,0),line,font=font)
-        draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill)
-        hstart += h
-    return hstart
-
-def captionImge(image,prelines,postlines,background=(51, 51, 51),font=None):
-    if font is None:
-        try:
-            font = ImageFont.truetype(opts.font or Roboto, fontsize)
-            font = opts.font or Roboto
-        except Exception:
-            font = Roboto
-
-    sampleImage = image
-    background = Image.new("RGBA", (sampleImage.size[0],sampleImage.size[1]+1024), background)
-    hoffset = addCaptionLines(prelines,background,5,font)+16
-    background.paste(sampleImage,(0,hoffset))
-    hoffset = hoffset+sampleImage.size[1]+8
-    hoffset = addCaptionLines(postlines,background,hoffset,font)
-    background = background.crop((0,0,sampleImage.size[0],hoffset+8))
-    return background
-
-def captionImageOverlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
-    from math import cos
-
-    image = srcimage.copy()
-
-    if textfont is None:
-        try:
-            textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
-            textfont = opts.font or Roboto
-        except Exception:
-            textfont = Roboto
-
-    factor = 1.5
-    gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0))
-    for y in range(image.size[1]):
-        mag = 1-cos(y/image.size[1]*factor)
-        mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
-        gradient.putpixel((0, y), (0,0,0,int(mag*255)))
-    image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
-
-    draw = ImageDraw.Draw(image)
-    fontSize = 32
-    font = ImageFont.truetype(textfont, fontSize)
-    padding = 10
-
-    _,_,w, h = draw.textbbox((0,0),title,font=font)
-    fontSize =  min( int(fontSize * (((image.size[0]*0.75)-(padding*4))/w) ), 72)
-    font = ImageFont.truetype(textfont, fontSize)
-    _,_,w,h = draw.textbbox((0,0),title,font=font)
-    draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230))
-
-    _,_,w, h = draw.textbbox((0,0),footerLeft,font=font)
-    fontSizeleft =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
-    _,_,w, h = draw.textbbox((0,0),footerMid,font=font)
-    fontSizemid =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
-    _,_,w, h = draw.textbbox((0,0),footerRight,font=font)
-    fontSizeright =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
-
-    font = ImageFont.truetype(textfont, min(fontSizeleft,fontSizemid,fontSizeright))
-
-    draw.text((padding,image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255,255,255,230))
-    draw.text((image.size[0]/2,image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255,255,255,230))
-    draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230))
-
-    return image
-- 
cgit v1.2.3


From 61788c0538415fa9ca1dd1b306519c116b18bd2c Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 19:50:50 +0100
Subject: shift embedding logic out of textual_inversion

---
 modules/textual_inversion/textual_inversion.py | 125 ++-----------------------
 1 file changed, 6 insertions(+), 119 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 8c66aeb5..22b4ae7f 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,124 +7,11 @@ import tqdm
 import html
 import datetime
 
-from PIL import Image,PngImagePlugin,ImageDraw
-from ..images import captionImageOverlay
-import numpy as np
-import base64
-import json
-import zlib
+from PIL import Image,PngImagePlugin
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
-class EmbeddingEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, torch.Tensor):
-            return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
-        return json.JSONEncoder.default(self, obj)
-
-class EmbeddingDecoder(json.JSONDecoder):
-    def __init__(self, *args, **kwargs):
-        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-    def object_hook(self, d):
-        if 'TORCHTENSOR' in d:
-            return torch.from_numpy(np.array(d['TORCHTENSOR']))
-        return d
-
-def embeddingToB64(data):
-    d = json.dumps(data,cls=EmbeddingEncoder)
-    return base64.b64encode(d.encode())
-
-def embeddingFromB64(data):
-    d = base64.b64decode(data)
-    return json.loads(d,cls=EmbeddingDecoder)
-
-def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
-    while True:
-        seed = (a * seed + c) % m
-        yield seed
-
-def xorBlock(block):
-    g = lcg()
-    randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
-    return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) 
-
-def styleBlock(block,sequence):
-    im = Image.new('RGB',(block.shape[1],block.shape[0]))
-    draw = ImageDraw.Draw(im)
-    i=0
-    for x in range(-6,im.size[0],8):
-        for yi,y in enumerate(range(-6,im.size[1],8)):
-            offset=0
-            if yi%2==0:
-                offset=4
-            shade = sequence[i%len(sequence)]
-            i+=1
-            draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) )
-
-    fg = np.array(im).astype(np.uint8) & 0xF0
-    return block ^ fg
-
-def insertImageDataEmbed(image,data):
-    d = 3
-    data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
-    dnp = np.frombuffer(data_compressed,np.uint8).copy()
-    dnphigh = dnp >> 4
-    dnplow  = dnp & 0x0F
-    
-    h = image.size[1]
-    next_size = dnplow.shape[0] + (h-(dnplow.shape[0]%h))
-    next_size = next_size + ((h*d)-(next_size%(h*d)))
-
-    dnplow.resize(next_size)
-    dnplow = dnplow.reshape((h,-1,d))
-
-    dnphigh.resize(next_size)
-    dnphigh = dnphigh.reshape((h,-1,d))
-
-    edgeStyleWeights = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
-    edgeStyleWeights = (np.abs(edgeStyleWeights)/np.max(np.abs(edgeStyleWeights))*255).astype(np.uint8)
-
-    dnplow   = styleBlock(dnplow,sequence=edgeStyleWeights)
-    dnplow   = xorBlock(dnplow)    
-    dnphigh  = styleBlock(dnphigh,sequence=edgeStyleWeights[::-1])
-    dnphigh  = xorBlock(dnphigh)
-
-    imlow  = Image.fromarray(dnplow,mode='RGB')
-    imhigh = Image.fromarray(dnphigh,mode='RGB')
-
-    background = Image.new('RGB',(image.size[0]+imlow.size[0]+imhigh.size[0]+2,image.size[1]),(0,0,0))
-    background.paste(imlow,(0,0))
-    background.paste(image,(imlow.size[0]+1,0))
-    background.paste(imhigh,(imlow.size[0]+1+image.size[0]+1,0))
-
-    return background
-
-def crop_black(img,tol=0):
-    mask = (img>tol).all(2)
-    mask0,mask1 = mask.any(0),mask.any(1)
-    col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax()
-    row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
-    return img[row_start:row_end,col_start:col_end]
-
-def extractImageDataEmbed(image):
-    d=3
-    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
-    blackCols = np.where( np.sum(outarr, axis=(0,2))==0)
-    if blackCols[0].shape[0] < 2:
-        print('No Image data blocks found.')
-        return None
-
-    dataBlocklower = outarr[:,:blackCols[0].min(),:].astype(np.uint8)
-    dataBlockupper = outarr[:,blackCols[0].max()+1:,:].astype(np.uint8)
-
-    dataBlocklower = xorBlock(dataBlocklower)
-    dataBlockupper = xorBlock(dataBlockupper)
-    
-    dataBlock = (dataBlockupper << 4) | (dataBlocklower)
-    dataBlock = dataBlock.flatten().tobytes()   
-    data = zlib.decompress(dataBlock)
-    return json.loads(data,cls=EmbeddingDecoder)
 
 class Embedding:
     def __init__(self, vec, name, step=None):
@@ -199,10 +86,10 @@ class EmbeddingDatabase:
             if filename.upper().endswith('.PNG'):
                 embed_image = Image.open(path)
                 if 'sd-ti-embedding' in embed_image.text:
-                    data = embeddingFromB64(embed_image.text['sd-ti-embedding'])
+                    data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
                     name = data.get('name',name)
                 else:
-                    data = extractImageDataEmbed(embed_image)
+                    data = extract_image_data_embed(embed_image)
                     name = data.get('name',name)
             else:
                 data = torch.load(path, map_location="cpu")
@@ -393,7 +280,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
                 info = PngImagePlugin.PngInfo()
                 data = torch.load(last_saved_file)
-                info.add_text("sd-ti-embedding", embeddingToB64(data))
+                info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
                 title = "<{}>".format(data.get('name','???'))
                 checkpoint = sd_models.select_checkpoint()
@@ -401,8 +288,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 footer_mid = '[{}]'.format(checkpoint.hash)
                 footer_right = '{}'.format(embedding.step)
 
-                captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right)
-                captioned_image = insertImageDataEmbed(captioned_image,data)
+                captioned_image = caption_image_overlay(image,title,footer_left,footer_mid,footer_right)
+                captioned_image = insert_image_data_embed(captioned_image,data)
 
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
             
-- 
cgit v1.2.3


From db71290d2659d3b58ff9b57a82e4721a9eab9229 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 19:55:54 +0100
Subject: remove old caption method

---
 modules/textual_inversion/image_embedding.py | 39 ++--------------------------
 1 file changed, 2 insertions(+), 37 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index 6ad39602..c67028a5 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -117,37 +117,6 @@ def extract_image_data_embed(image):
     data = zlib.decompress(data_block)
     return json.loads(data,cls=EmbeddingDecoder)
 
-def addCaptionLines(lines,image,initialx,textfont):
-    draw = ImageDraw.Draw(image)
-    hstart =initialx
-    for fill,line in lines:
-        fontsize = 32
-        font = ImageFont.truetype(textfont, fontsize)
-        _,_,w, h = draw.textbbox((0,0),line,font=font)
-        fontsize =  min( int(fontsize * ((image.size[0]-35)/w) ), 28)
-        font = ImageFont.truetype(textfont, fontsize)
-        _,_,w,h = draw.textbbox((0,0),line,font=font)
-        draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill)
-        hstart += h
-    return hstart
-
-def caption_image(image,prelines,postlines,background=(51, 51, 51),font=None):
-    if font is None:
-        try:
-            font = ImageFont.truetype(opts.font or Roboto, fontsize)
-            font = opts.font or Roboto
-        except Exception:
-            font = Roboto
-
-    sample_image = image
-    background = Image.new("RGBA", (sample_image.size[0],sample_image.size[1]+1024), background)
-    hoffset = addCaptionLines(prelines,background,5,font)+16
-    background.paste(sample_image,(0,hoffset))
-    hoffset = hoffset+sample_image.size[1]+8
-    hoffset = addCaptionLines(postlines,background,hoffset,font)
-    background = background.crop((0,0,sample_image.size[0],hoffset+8))
-    return background
-
 def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
     from math import cos
 
@@ -195,11 +164,7 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo
     return image
 
 if __name__ == '__main__':
-
-    image = Image.new('RGBA',(512,512),(255,255,200,255))
-    caption_image(image,[((255,255,255),'line a'),((255,255,255),'line b')],
-                        [((255,255,255),'line c'),((255,255,255),'line d')])
-
+    
     image = Image.new('RGBA',(512,512),(255,255,200,255))
     cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
 
@@ -231,4 +196,4 @@ if __name__ == '__main__':
 
     hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
 
-    assert 12731374 == hunna_kay_random_sum
\ No newline at end of file
+    assert 12731374 == hunna_kay_random_sum
-- 
cgit v1.2.3


From d6fcc6b87bc00fcdecea276fe5b7c7945f7a8b14 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 22:03:05 +0300
Subject: apply lr schedule to hypernets

---
 modules/hypernetworks/hypernetwork.py          | 19 ++++++++---
 modules/textual_inversion/learn_schedule.py    | 34 ++++++++++++++++++++
 modules/textual_inversion/textual_inversion.py | 44 +++-----------------------
 modules/ui.py                                  |  2 +-
 4 files changed, 54 insertions(+), 45 deletions(-)
 create mode 100644 modules/textual_inversion/learn_schedule.py

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 5608e799..470659df 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -14,6 +14,7 @@ import torch
 from torch import einsum
 from einops import rearrange, repeat
 import modules.textual_inversion.dataset
+from modules.textual_inversion.learn_schedule import LearnSchedule
 
 
 class HypernetworkModule(torch.nn.Module):
@@ -202,8 +203,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     for weight in weights:
         weight.requires_grad = True
 
-    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
-
     losses = torch.zeros((32,))
 
     last_saved_file = "<none>"
@@ -213,12 +212,24 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     if ititial_step > steps:
         return hypernetwork, filename
 
+    schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
+    (learn_rate, end_step) = next(schedules)
+    print(f'Training at rate of {learn_rate} until step {end_step}')
+
+    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, (x, text, cond) in pbar:
         hypernetwork.step = i + ititial_step
 
-        if hypernetwork.step > steps:
-            break
+        if hypernetwork.step > end_step:
+            try:
+                (learn_rate, end_step) = next(schedules)
+            except Exception:
+                break
+            tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
+            for pg in optimizer.param_groups:
+                pg['lr'] = learn_rate
 
         if shared.state.interrupted:
             break
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
new file mode 100644
index 00000000..db720271
--- /dev/null
+++ b/modules/textual_inversion/learn_schedule.py
@@ -0,0 +1,34 @@
+
+class LearnSchedule:
+    def __init__(self, learn_rate, max_steps, cur_step=0):
+        pairs = learn_rate.split(',')
+        self.rates = []
+        self.it = 0
+        self.maxit = 0
+        for i, pair in enumerate(pairs):
+            tmp = pair.split(':')
+            if len(tmp) == 2:
+                step = int(tmp[1])
+                if step > cur_step:
+                    self.rates.append((float(tmp[0]), min(step, max_steps)))
+                    self.maxit += 1
+                    if step > max_steps:
+                        return
+                elif step == -1:
+                    self.rates.append((float(tmp[0]), max_steps))
+                    self.maxit += 1
+                    return
+            else:
+                self.rates.append((float(tmp[0]), max_steps))
+                self.maxit += 1
+                return
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.it < self.maxit:
+            self.it += 1
+            return self.rates[self.it - 1]
+        else:
+            raise StopIteration
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 47a27faf..7717837d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -10,6 +10,7 @@ import datetime
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
+from modules.textual_inversion.learn_schedule import LearnSchedule
 
 
 class Embedding:
@@ -198,11 +199,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     if ititial_step > steps:
         return embedding, filename
 
-    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
-    epoch_len = (tr_img_len * num_repeats) + tr_img_len
-
-    scheduleIter = iter(LearnSchedule(learn_rate, steps, ititial_step))
-    (learn_rate, end_step) = next(scheduleIter)
+    schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
+    (learn_rate, end_step) = next(schedules)
     print(f'Training at rate of {learn_rate} until step {end_step}')
 
     optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
@@ -213,7 +211,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
         if embedding.step > end_step:
             try:
-                (learn_rate, end_step) = next(scheduleIter)
+                (learn_rate, end_step) = next(schedules)
             except:
                 break
             tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
@@ -288,37 +286,3 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     embedding.save(filename)
 
     return embedding, filename
-
-class LearnSchedule:
-    def __init__(self, learn_rate, max_steps, cur_step=0):
-        pairs = learn_rate.split(',')
-        self.rates = []
-        self.it = 0
-        self.maxit = 0
-        for i, pair in enumerate(pairs):
-            tmp = pair.split(':')
-            if len(tmp) == 2:
-                step = int(tmp[1])
-                if step > cur_step:
-                    self.rates.append((float(tmp[0]), min(step, max_steps)))
-                    self.maxit += 1
-                    if step > max_steps:
-                        return
-                elif step == -1:
-                    self.rates.append((float(tmp[0]), max_steps))
-                    self.maxit += 1
-                    return
-            else:
-                self.rates.append((float(tmp[0]), max_steps))
-                self.maxit += 1
-                return
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self.it < self.maxit:
-            self.it += 1
-            return self.rates[self.it - 1]
-        else:
-            raise StopIteration
diff --git a/modules/ui.py b/modules/ui.py
index 2b688e32..1204eef7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1070,7 +1070,7 @@ def create_ui(wrap_gradio_gpu_call):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
-                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value = "5.0e-03")
+                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-- 
cgit v1.2.3


From aa75d5cfe8c84768b0f5d16f977ddba298677379 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 20:06:13 +0100
Subject: correct conflict resolution typo

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 22b4ae7f..789383ce 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -169,7 +169,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
 
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt)
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
-- 
cgit v1.2.3


From 91d7ee0d097a7ea203d261b570cd2b834837d9e2 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 20:09:10 +0100
Subject: update imports

---
 modules/textual_inversion/textual_inversion.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 789383ce..ff0a62b3 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -12,6 +12,9 @@ from PIL import Image,PngImagePlugin
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
+from modules.textual_inversion.image_embedding import( embedding_to_b64,embedding_from_b64,
+                                                       insert_image_data_embed,extract_image_data_embed,
+                                                       caption_image_overlay )
 
 class Embedding:
     def __init__(self, vec, name, step=None):
-- 
cgit v1.2.3


From 5f3317376bb7952bc5145f05f16c1bbd466efc85 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 20:09:49 +0100
Subject: spacing

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ff0a62b3..485ef46c 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -12,7 +12,7 @@ from PIL import Image,PngImagePlugin
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
-from modules.textual_inversion.image_embedding import( embedding_to_b64,embedding_from_b64,
+from modules.textual_inversion.image_embedding import (embedding_to_b64,embedding_from_b64,
                                                        insert_image_data_embed,extract_image_data_embed,
                                                        caption_image_overlay )
 
-- 
cgit v1.2.3


From 7e6a6e00ad6f3b7ef43c8120db9ecac6e8d6bea5 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 20:20:46 +0100
Subject: Add files via upload

---
 modules/textual_inversion/test_embedding.png | Bin 0 -> 489220 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 modules/textual_inversion/test_embedding.png

(limited to 'modules')

diff --git a/modules/textual_inversion/test_embedding.png b/modules/textual_inversion/test_embedding.png
new file mode 100644
index 00000000..07e2d9af
Binary files /dev/null and b/modules/textual_inversion/test_embedding.png differ
-- 
cgit v1.2.3


From 66ec505975aaa305a217fc27281ce368cbaef281 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 20:21:30 +0100
Subject: add file based test

---
 modules/textual_inversion/image_embedding.py | 8 ++++++++
 1 file changed, 8 insertions(+)

(limited to 'modules')

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index c67028a5..1224fb42 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -164,6 +164,14 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo
     return image
 
 if __name__ == '__main__':
+
+    testEmbed = Image.open('test_embedding.png')
+    
+    data = extract_image_data_embed(testEmbed)
+    assert data is not None
+
+    data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
+    assert data is not None
     
     image = Image.new('RGBA',(512,512),(255,255,200,255))
     cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
-- 
cgit v1.2.3


From 6be32b31d181e42c639dad3451229aa7b9cfd1cf Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 23:07:09 +0300
Subject: reports that training with medvram is possible.

---
 modules/hypernetworks/ui.py     | 2 +-
 modules/textual_inversion/ui.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index c67facbb..dfa599af 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -25,7 +25,7 @@ def train_hypernetwork(*args):
 
     initial_hypernetwork = shared.loaded_hypernetwork
 
-    assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible'
+    assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible'
 
     try:
         sd_hijack.undo_optimizations()
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 70f47343..36881e7a 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -23,7 +23,7 @@ def preprocess(*args):
 
 def train_embedding(*args):
 
-    assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible'
+    assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
 
     try:
         sd_hijack.undo_optimizations()
-- 
cgit v1.2.3


From f53f703aebc801c4204182d52bb1e0bef9808e1f Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Tue, 11 Oct 2022 18:12:12 -0500
Subject: resolved conflicts, moved settings under interrogate section,
 settings only show if deepbooru flag is enabled

---
 modules/deepbooru.py                    |  2 +-
 modules/shared.py                       | 19 +++++++++----------
 modules/textual_inversion/preprocess.py |  2 +-
 modules/ui.py                           |  2 +-
 4 files changed, 12 insertions(+), 13 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 89dcac3c..29529949 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -8,7 +8,7 @@ def get_deepbooru_tags(pil_image):
     This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
     """
     from modules import shared  # prevents circular reference
-    create_deepbooru_process(shared.opts.deepbooru_threshold, shared.opts.deepbooru_sort_alpha)
+    create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, shared.opts.deepbooru_sort_alpha)
     shared.deepbooru_process_return["value"] = -1
     shared.deepbooru_process_queue.put(pil_image)
     while shared.deepbooru_process_return["value"] == -1:
diff --git a/modules/shared.py b/modules/shared.py
index 817203f8..5456c477 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -248,15 +248,20 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-options_templates.update(options_section(('interrogate', "Interrogate Options"), {
+interrogate_option_dictionary = {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
-    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-}))
+    "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)")
+}
+
+if cmd_opts.deepdanbooru:
+    interrogate_option_dictionary["interrogate_deepbooru_score_threshold"] = OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01})
+    interrogate_option_dictionary["deepbooru_sort_alpha"] = OptionInfo(True, "Interrogate: deepbooru sort alphabetically", gr.Checkbox)
+
+options_templates.update(options_section(('interrogate', "Interrogate Options"), interrogate_option_dictionary))
 
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
@@ -282,12 +287,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
-if cmd_opts.deepdanbooru:
-    options_templates.update(options_section(('deepbooru-params', "DeepBooru parameters"), {
-        "deepbooru_sort_alpha": OptionInfo(True, "Sort Alphabetical", gr.Checkbox),
-        'deepbooru_threshold': OptionInfo(0.5, "Threshold", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    }))
-
 
 class Options:
     data = None
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index a96388d6..113cecf1 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -29,7 +29,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
         shared.interrogator.load()
 
     if process_caption_deepbooru:
-        deepbooru.create_deepbooru_process(opts.deepbooru_threshold, opts.deepbooru_sort_alpha)
+        deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, opts.deepbooru_sort_alpha)
 
     def save_pic_with_caption(image, index):
         if process_caption:
diff --git a/modules/ui.py b/modules/ui.py
index 2891fc8c..fa45edca 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -317,7 +317,7 @@ def interrogate(image):
 
 
 def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
+    prompt = get_deepbooru_tags(image)
     return gr_show(True) if prompt is None else prompt
 
 
-- 
cgit v1.2.3


From 65b973ac4e547a325f30a05f852b161421af2041 Mon Sep 17 00:00:00 2001
From: supersteve3d <39339941+supersteve3d@users.noreply.github.com>
Date: Wed, 12 Oct 2022 08:21:52 +0800
Subject: Update shared.py

Correct typo to "Unload VAE and CLIP from VRAM when training" in settings tab.
---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index c1092ff7..46bc740c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -229,7 +229,7 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP form VRAM when training"),
+    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-- 
cgit v1.2.3


From d717eb079cd6b7fa7a4f97c0a10d400bdec753fb Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Tue, 11 Oct 2022 18:02:41 -0700
Subject: Interrogate: add option to include ranks in output

Since the UI also allows users to specify ranks, it can be useful to show people what ranks are being returned by interrogate

This can also give much better results when feeding the interrogate results back into either img2img or txt2img, especially when trying to generate a specific character or scene for which you have a similar concept image

Testing Steps:

Launch Webui with command line arg: --deepdanbooru
Navigate to img2img tab, use interrogate DeepBooru, verify tags appears as before. Use "Interrogate CLIP", verify prompt appears as before
Navigate to Settings tab, enable new option, click "apply settings"
Navigate to img2img, Interrogate DeepBooru again, verify that weights appear and are properly formatted. Note that "Interrogate CLIP" prompt is still unchanged
In my testing, this change has no effect to "Interrogate CLIP", as it seems to generate a sentence-structured caption, and not a set of tags.

(reproduce changes from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2149/commits/6ed4faac46c45ca7353f228aca9b436bbaba7bc7)
---
 modules/deepbooru.py   | 14 +++++++++-----
 modules/interrogate.py |  7 +++++--
 modules/shared.py      |  1 +
 modules/ui.py          |  5 ++---
 4 files changed, 17 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 7e3c0618..32d741e2 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -3,7 +3,7 @@ from concurrent.futures import ProcessPoolExecutor
 from multiprocessing import get_context
 
 
-def _load_tf_and_return_tags(pil_image, threshold):
+def _load_tf_and_return_tags(pil_image, threshold, include_ranks):
     import deepdanbooru as dd
     import tensorflow as tf
     import numpy as np
@@ -52,12 +52,16 @@ def _load_tf_and_return_tags(pil_image, threshold):
         if result_dict[tag] >= threshold:
             if tag.startswith("rating:"):
                 continue
-            result_tags_out.append(tag)
+            tag_formatted = tag.replace('_', ' ').replace(':', ' ')
+            if include_ranks:
+                result_tags_out.append(f'({tag_formatted}:{result_dict[tag]})')
+            else:
+                result_tags_out.append(tag_formatted)
             result_tags_print.append(f'{result_dict[tag]} {tag}')
 
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
+    return ', '.join(result_tags_out)
 
 
 def subprocess_init_no_cuda():
@@ -65,9 +69,9 @@ def subprocess_init_no_cuda():
     os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
 
-def get_deepbooru_tags(pil_image, threshold=0.5):
+def get_deepbooru_tags(pil_image, threshold=0.5, include_ranks=False):
     context = get_context('spawn')
     with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
-        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
+        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, include_ranks)
         ret = f.result()  # will rethrow any exceptions
     return ret
\ No newline at end of file
diff --git a/modules/interrogate.py b/modules/interrogate.py
index 635e266e..af858cc0 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -123,7 +123,7 @@ class InterrogateModels:
 
         return caption[0]
 
-    def interrogate(self, pil_image):
+    def interrogate(self, pil_image, include_ranks=False):
         res = None
 
         try:
@@ -156,7 +156,10 @@ class InterrogateModels:
                 for name, topn, items in self.categories:
                     matches = self.rank(image_features, items, top_count=topn)
                     for match, score in matches:
-                        res += ", " + match
+                        if include_ranks:
+                            res += ", " + match
+                        else:
+                            res += f", ({match}:{score})"
 
         except Exception:
             print(f"Error interrogating", file=sys.stderr)
diff --git a/modules/shared.py b/modules/shared.py
index c1092ff7..3e0bfd72 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -251,6 +251,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
+    "interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
diff --git a/modules/ui.py b/modules/ui.py
index 1204eef7..f4dbe247 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -311,13 +311,12 @@ def apply_styles(prompt, prompt_neg, style1_name, style2_name):
 
 
 def interrogate(image):
-    prompt = shared.interrogator.interrogate(image)
-
+    prompt = shared.interrogator.interrogate(image, include_ranks=opts.interrogate_return_ranks)
     return gr_show(True) if prompt is None else prompt
 
 
 def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
+    prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold, opts.interrogate_return_ranks)
     return gr_show(True) if prompt is None else prompt
 
 
-- 
cgit v1.2.3


From 6ac2ec2b78bc5fabd09cb866dd9a71061d669269 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 07:01:20 +0300
Subject: create dir for hypernetworks

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index c1092ff7..e65e77f8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -86,6 +86,7 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 xformers_available = False
 config_filename = cmd_opts.ui_settings_file
 
+os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
-- 
cgit v1.2.3


From fec2221eeaafb50afd26ba3e109bf6f928011e69 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Tue, 11 Oct 2022 19:29:38 -0700
Subject: Truncate error text to fix service lockup / stall

What:
* Update wrap_gradio_call to add a limit to the maximum amount of text output

Why:
* wrap_gradio_call currently prints out a list of the arguments provided to the failing function.
   * if that function is save_image, this causes the entire image to be printed to stderr
    * If the image is large, this can cause the service to lock up while attempting to print all the text
 * It is easy to generate large images using the x/y plot script
 * it is easy to encounter image save exceptions, including if the output directory does not exist / cannot be written to, or if the file is too big
  * The huge amount of log spam is confusing and not particularly helpful
---
 modules/ui.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 1204eef7..33a49d3b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -181,8 +181,15 @@ def wrap_gradio_call(func, extra_outputs=None):
         try:
             res = list(func(*args, **kwargs))
         except Exception as e:
+            # When printing out our debug argument list, do not print out more than a MB of text
+            max_debug_str_len = 131072 # (1024*1024)/8
+
             print("Error completing request", file=sys.stderr)
-            print("Arguments:", args, kwargs, file=sys.stderr)
+            argStr = f"Arguments: {str(args)} {str(kwargs)}"
+            print(argStr[:max_debug_str_len], file=sys.stderr)
+            if len(argStr) > max_debug_str_len:
+                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
+
             print(traceback.format_exc(), file=sys.stderr)
 
             shared.state.job = ""
-- 
cgit v1.2.3


From 336bd8703c7b4d71f2f096f303599925a30b8167 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 09:00:07 +0300
Subject: just add the deepdanbooru settings unconditionally

---
 modules/shared.py | 13 ++++---------
 1 file changed, 4 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index f150e024..42e99741 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -249,20 +249,15 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-interrogate_option_dictionary = {
+options_templates.update(options_section(('interrogate', "Interrogate Options"), {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)")
-}
-
-if cmd_opts.deepdanbooru:
-    interrogate_option_dictionary["interrogate_deepbooru_score_threshold"] = OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01})
-    interrogate_option_dictionary["deepbooru_sort_alpha"] = OptionInfo(True, "Interrogate: deepbooru sort alphabetically", gr.Checkbox)
-
-options_templates.update(options_section(('interrogate', "Interrogate Options"), interrogate_option_dictionary))
+    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+    "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
+}))
 
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
-- 
cgit v1.2.3


From 57e03cdd244eee4e33ccab7554b3594563a3d0cd Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Wed, 12 Oct 2022 00:54:24 -0400
Subject: Ensure the directory exists before saving to it

The directory for the images saved with the Save button may still not exist, so it needs to be created prior to opening the log.csv file.
---
 modules/ui.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 00bf09ae..cd67b84b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -131,6 +131,8 @@ def save_files(js_data, images, do_make_zip, index):
         images = [images[index]]
         start_index = index
 
+    os.makedirs(opts.outdir_save, exist_ok=True)
+
     with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
         at_start = file.tell() == 0
         writer = csv.writer(file)
-- 
cgit v1.2.3


From 2d006ce16cd95d587533656c3ac4991495e96f23 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Mon, 10 Oct 2022 00:56:36 +0900
Subject: xy_grid: Find hypernetwork by closest name

---
 modules/hypernetworks/hypernetwork.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 470659df..8f2192e2 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -120,6 +120,17 @@ def load_hypernetwork(filename):
         shared.loaded_hypernetwork = None
 
 
+def find_closest_hypernetwork_name(search: str):
+    if not search:
+        return None
+    search = search.lower()
+    applicable = [name for name in shared.hypernetworks if search in name.lower()]
+    if not applicable:
+        return None
+    applicable = sorted(applicable, key=lambda name: len(name))
+    return applicable[0]
+
+
 def apply_hypernetwork(hypernetwork, context, layer=None):
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
 
-- 
cgit v1.2.3


From ee015a1af66a94a75c914659fa0d321e702a0a87 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 11:05:57 +0300
Subject: change textual inversion tab to train remake train interface to use
 tabs

---
 modules/hypernetworks/hypernetwork.py |  2 +-
 modules/ui.py                         | 22 +++++++++-------------
 2 files changed, 10 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8f2192e2..8314450a 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -175,7 +175,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
 
 
 def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
-    assert hypernetwork_name, 'embedding not selected'
+    assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
diff --git a/modules/ui.py b/modules/ui.py
index 4bfdd275..86a2da6c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1035,14 +1035,14 @@ def create_ui(wrap_gradio_gpu_call):
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
-    with gr.Blocks() as textual_inversion_interface:
+    with gr.Blocks() as train_interface:
         with gr.Row().style(equal_height=False):
-            with gr.Column():
-                with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
 
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
+        with gr.Row().style(equal_height=False):
+            with gr.Tabs(elem_id="train_tabs"):
 
+                with gr.Tab(label="Create embedding"):
                     new_embedding_name = gr.Textbox(label="Name")
                     initialization_text = gr.Textbox(label="Initialization text", value="*")
                     nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
@@ -1054,9 +1054,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create embedding", variant='primary')
 
-                with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
-
+                with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
 
@@ -1067,9 +1065,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
 
-                with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
-
+                with gr.Tab(label="Preprocess images"):
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
@@ -1091,7 +1087,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
-                with gr.Group():
+                with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
@@ -1388,7 +1384,7 @@ Requested path was: {f}
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
-        (textual_inversion_interface, "Textual inversion", "ti"),
+        (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
     ]
 
-- 
cgit v1.2.3


From 80f3cf2bb2ce3f00d801cae2c3a8c20a8d4167d8 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Tue, 11 Oct 2022 19:48:53 +0100
Subject: Account when lines are mismatched

---
 modules/sd_hijack.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index ac70f876..2753d4fa 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -321,7 +321,17 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                         fixes.append(fix[1])
                 self.hijack.fixes.append(fixes)
             
-            z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
+            tokens = []
+            multipliers = []
+            for i in range(len(remade_batch_tokens)):
+                if len(remade_batch_tokens[i]) > 0:
+                    tokens.append(remade_batch_tokens[i][:75])
+                    multipliers.append(batch_multipliers[i][:75])
+                else:
+                    tokens.append([self.wrapped.tokenizer.eos_token_id] * 75)
+                    multipliers.append([1.0] * 75)
+
+            z1 = self.process_tokens(tokens, multipliers)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
             
             remade_batch_tokens = rem_tokens
-- 
cgit v1.2.3


From 429442f4a6aab7301efb89d27bef524fe827e81a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 13:38:03 +0300
Subject: fix iterator bug for #2295

---
 modules/sd_hijack.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 2753d4fa..c81722a0 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -323,10 +323,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             
             tokens = []
             multipliers = []
-            for i in range(len(remade_batch_tokens)):
-                if len(remade_batch_tokens[i]) > 0:
-                    tokens.append(remade_batch_tokens[i][:75])
-                    multipliers.append(batch_multipliers[i][:75])
+            for j in range(len(remade_batch_tokens)):
+                if len(remade_batch_tokens[j]) > 0:
+                    tokens.append(remade_batch_tokens[j][:75])
+                    multipliers.append(batch_multipliers[j][:75])
                 else:
                     tokens.append([self.wrapped.tokenizer.eos_token_id] * 75)
                     multipliers.append([1.0] * 75)
-- 
cgit v1.2.3


From 50be33e953be93c40814262c6dbce36e66004528 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 12 Oct 2022 13:13:25 +0100
Subject: formatting

---
 modules/textual_inversion/image_embedding.py | 170 ++++++++++++++-------------
 1 file changed, 91 insertions(+), 79 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index 1224fb42..898ce3b3 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -2,122 +2,134 @@ import base64
 import json
 import numpy as np
 import zlib
-from PIL import Image,PngImagePlugin,ImageDraw,ImageFont
+from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
 from fonts.ttf import Roboto
 import torch
 
+
 class EmbeddingEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, torch.Tensor):
-            return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
+            return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()}
         return json.JSONEncoder.default(self, obj)
 
+
 class EmbeddingDecoder(json.JSONDecoder):
     def __init__(self, *args, **kwargs):
         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
+
     def object_hook(self, d):
         if 'TORCHTENSOR' in d:
             return torch.from_numpy(np.array(d['TORCHTENSOR']))
         return d
 
+
 def embedding_to_b64(data):
-    d = json.dumps(data,cls=EmbeddingEncoder)
+    d = json.dumps(data, cls=EmbeddingEncoder)
     return base64.b64encode(d.encode())
 
+
 def embedding_from_b64(data):
     d = base64.b64decode(data)
-    return json.loads(d,cls=EmbeddingDecoder)
+    return json.loads(d, cls=EmbeddingDecoder)
+
 
 def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
     while True:
         seed = (a * seed + c) % m
-        yield seed%255
+        yield seed % 255
+
 
 def xor_block(block):
     g = lcg()
     randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
-    return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) 
+    return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
 
-def style_block(block,sequence):
-    im = Image.new('RGB',(block.shape[1],block.shape[0]))
+
+def style_block(block, sequence):
+    im = Image.new('RGB', (block.shape[1], block.shape[0]))
     draw = ImageDraw.Draw(im)
-    i=0
-    for x in range(-6,im.size[0],8):
-        for yi,y in enumerate(range(-6,im.size[1],8)):
-            offset=0
-            if yi%2==0:
-                offset=4
-            shade = sequence[i%len(sequence)]
-            i+=1
-            draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) )
+    i = 0
+    for x in range(-6, im.size[0], 8):
+        for yi, y in enumerate(range(-6, im.size[1], 8)):
+            offset = 0
+            if yi % 2 == 0:
+                offset = 4
+            shade = sequence[i % len(sequence)]
+            i += 1
+            draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade))
 
     fg = np.array(im).astype(np.uint8) & 0xF0
 
     return block ^ fg
 
-def insert_image_data_embed(image,data):
+
+def insert_image_data_embed(image, data):
     d = 3
-    data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
-    data_np_ = np.frombuffer(data_compressed,np.uint8).copy()
+    data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9)
+    data_np_ = np.frombuffer(data_compressed, np.uint8).copy()
     data_np_high = data_np_ >> 4
-    data_np_low  = data_np_ & 0x0F
-    
+    data_np_low = data_np_ & 0x0F
+
     h = image.size[1]
-    next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0]%h))
-    next_size = next_size + ((h*d)-(next_size%(h*d)))
+    next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h))
+    next_size = next_size + ((h*d)-(next_size % (h*d)))
 
     data_np_low.resize(next_size)
-    data_np_low = data_np_low.reshape((h,-1,d))
+    data_np_low = data_np_low.reshape((h, -1, d))
 
     data_np_high.resize(next_size)
-    data_np_high = data_np_high.reshape((h,-1,d))
+    data_np_high = data_np_high.reshape((h, -1, d))
 
     edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
     edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
 
-    data_np_low   = style_block(data_np_low,sequence=edge_style)
-    data_np_low   = xor_block(data_np_low)    
-    data_np_high  = style_block(data_np_high,sequence=edge_style[::-1])
-    data_np_high  = xor_block(data_np_high)
+    data_np_low = style_block(data_np_low, sequence=edge_style)
+    data_np_low = xor_block(data_np_low)
+    data_np_high = style_block(data_np_high, sequence=edge_style[::-1])
+    data_np_high = xor_block(data_np_high)
 
-    im_low  = Image.fromarray(data_np_low,mode='RGB')
-    im_high = Image.fromarray(data_np_high,mode='RGB')
+    im_low = Image.fromarray(data_np_low, mode='RGB')
+    im_high = Image.fromarray(data_np_high, mode='RGB')
 
-    background = Image.new('RGB',(image.size[0]+im_low.size[0]+im_high.size[0]+2,image.size[1]),(0,0,0))
-    background.paste(im_low,(0,0))
-    background.paste(image,(im_low.size[0]+1,0))
-    background.paste(im_high,(im_low.size[0]+1+image.size[0]+1,0))
+    background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0))
+    background.paste(im_low, (0, 0))
+    background.paste(image, (im_low.size[0]+1, 0))
+    background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0))
 
     return background
 
-def crop_black(img,tol=0):
-    mask = (img>tol).all(2)
-    mask0,mask1 = mask.any(0),mask.any(1)
-    col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax()
-    row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
-    return img[row_start:row_end,col_start:col_end]
+
+def crop_black(img, tol=0):
+    mask = (img > tol).all(2)
+    mask0, mask1 = mask.any(0), mask.any(1)
+    col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax()
+    row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax()
+    return img[row_start:row_end, col_start:col_end]
+
 
 def extract_image_data_embed(image):
-    d=3
-    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
-    black_cols = np.where( np.sum(outarr, axis=(0,2))==0)
+    d = 3
+    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F
+    black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
     if black_cols[0].shape[0] < 2:
         print('No Image data blocks found.')
         return None
 
-    data_block_lower = outarr[:,:black_cols[0].min(),:].astype(np.uint8)
-    data_block_upper = outarr[:,black_cols[0].max()+1:,:].astype(np.uint8)
+    data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)
+    data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8)
 
     data_block_lower = xor_block(data_block_lower)
     data_block_upper = xor_block(data_block_upper)
-    
+
     data_block = (data_block_upper << 4) | (data_block_lower)
     data_block = data_block.flatten().tobytes()
 
     data = zlib.decompress(data_block)
-    return json.loads(data,cls=EmbeddingDecoder)
+    return json.loads(data, cls=EmbeddingDecoder)
+
 
-def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
+def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
     from math import cos
 
     image = srcimage.copy()
@@ -130,11 +142,11 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo
             textfont = Roboto
 
     factor = 1.5
-    gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0))
+    gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
     for y in range(image.size[1]):
         mag = 1-cos(y/image.size[1]*factor)
-        mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
-        gradient.putpixel((0, y), (0,0,0,int(mag*255)))
+        mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
+        gradient.putpixel((0, y), (0, 0, 0, int(mag*255)))
     image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
 
     draw = ImageDraw.Draw(image)
@@ -142,41 +154,41 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo
     font = ImageFont.truetype(textfont, fontsize)
     padding = 10
 
-    _,_,w, h = draw.textbbox((0,0),title,font=font)
-    fontsize =  min( int(fontsize * (((image.size[0]*0.75)-(padding*4))/w) ), 72)
+    _, _, w, h = draw.textbbox((0, 0), title, font=font)
+    fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
     font = ImageFont.truetype(textfont, fontsize)
-    _,_,w,h = draw.textbbox((0,0),title,font=font)
-    draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230))
+    _, _, w, h = draw.textbbox((0, 0), title, font=font)
+    draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
 
-    _,_,w, h = draw.textbbox((0,0),footerLeft,font=font)
-    fontsize_left =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
-    _,_,w, h = draw.textbbox((0,0),footerMid,font=font)
-    fontsize_mid =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
-    _,_,w, h = draw.textbbox((0,0),footerRight,font=font)
-    fontsize_right =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
+    _, _, w, h = draw.textbbox((0, 0), footerLeft, font=font)
+    fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+    _, _, w, h = draw.textbbox((0, 0), footerMid, font=font)
+    fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+    _, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
+    fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
 
-    font = ImageFont.truetype(textfont, min(fontsize_left,fontsize_mid,fontsize_right))
+    font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))
 
-    draw.text((padding,image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255,255,255,230))
-    draw.text((image.size[0]/2,image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255,255,255,230))
-    draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230))
+    draw.text((padding, image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
+    draw.text((image.size[0]/2, image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))
+    draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230))
 
     return image
 
+
 if __name__ == '__main__':
 
     testEmbed = Image.open('test_embedding.png')
-    
     data = extract_image_data_embed(testEmbed)
     assert data is not None
 
     data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
     assert data is not None
-    
-    image = Image.new('RGBA',(512,512),(255,255,200,255))
+
+    image = Image.new('RGBA', (512, 512), (255, 255, 200, 255))
     cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
 
-    test_embed = {'string_to_param':{'*':torch.from_numpy(np.random.random((2, 4096)))}}
+    test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}}
 
     embedded_image = insert_image_data_embed(cap_image, test_embed)
 
@@ -191,16 +203,16 @@ if __name__ == '__main__':
     g = lcg()
     shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
 
-    reference_random = [253, 242, 127,  44, 157,  27, 239, 133,  38,  79, 167,   4, 177, 
-                         95, 130,  79,  78,  14,  52, 215, 220, 194, 126,  28, 240, 179, 
-                        160, 153, 149,  50, 105,  14,  21, 218, 199,  18,  54, 198, 193, 
-                         38, 128,  19,  53, 195, 124,  75, 205,  12,   6, 145,   0,  28, 
-                         30, 148,   8,  45, 218, 171,  55, 249,  97, 166,  12,  35,   0, 
-                         41, 221, 122, 215, 170,  31, 113, 186,  97, 119,  31,  23, 185, 
-                         66, 140,  30,  41,  37,  63, 137, 109, 216,  55, 159, 145,  82, 
+    reference_random = [253, 242, 127,  44, 157,  27, 239, 133,  38,  79, 167,   4, 177,
+                         95, 130,  79,  78,  14,  52, 215, 220, 194, 126,  28, 240, 179,
+                        160, 153, 149,  50, 105,  14,  21, 218, 199,  18,  54, 198, 193,
+                         38, 128,  19,  53, 195, 124,  75, 205,  12,   6, 145,   0,  28,
+                         30, 148,   8,  45, 218, 171,  55, 249,  97, 166,  12,  35,   0,
+                         41, 221, 122, 215, 170,  31, 113, 186,  97, 119,  31,  23, 185,
+                         66, 140,  30,  41,  37,  63, 137, 109, 216,  55, 159, 145,  82,
                          204, 86,  73, 222,  44, 198, 118, 240,  97]
 
-    assert shared_random == reference_random   
+    assert shared_random == reference_random
 
     hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
 
-- 
cgit v1.2.3


From 10a2de644f8ea4cfade88e85d768da3480f4c9f0 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 12 Oct 2022 13:15:35 +0100
Subject: formatting

---
 modules/textual_inversion/textual_inversion.py | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 485ef46c..b072d745 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,14 +7,14 @@ import tqdm
 import html
 import datetime
 
-from PIL import Image,PngImagePlugin
+from PIL import Image, PngImagePlugin
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
-from modules.textual_inversion.image_embedding import (embedding_to_b64,embedding_from_b64,
-                                                       insert_image_data_embed,extract_image_data_embed,
-                                                       caption_image_overlay )
+from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,
+                                                       insert_image_data_embed, extract_image_data_embed,
+                                                       caption_image_overlay)
 
 class Embedding:
     def __init__(self, vec, name, step=None):
@@ -90,10 +90,10 @@ class EmbeddingDatabase:
                 embed_image = Image.open(path)
                 if 'sd-ti-embedding' in embed_image.text:
                     data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
-                    name = data.get('name',name)
+                    name = data.get('name', name)
                 else:
                     data = extract_image_data_embed(embed_image)
-                    name = data.get('name',name)
+                    name = data.get('name', name)
             else:
                 data = torch.load(path, map_location="cpu")
 
@@ -278,24 +278,24 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             shared.state.current_image = image
 
             if save_image_with_stored_embedding and os.path.exists(last_saved_file):
-                
+
                 last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
 
                 info = PngImagePlugin.PngInfo()
                 data = torch.load(last_saved_file)
                 info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
-                title = "<{}>".format(data.get('name','???'))
+                title = "<{}>".format(data.get('name', '???'))
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
                 footer_right = '{}'.format(embedding.step)
 
-                captioned_image = caption_image_overlay(image,title,footer_left,footer_mid,footer_right)
-                captioned_image = insert_image_data_embed(captioned_image,data)
+                captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
+                captioned_image = insert_image_data_embed(captioned_image, data)
 
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
-            
+
             image.save(last_saved_image)
 
             last_saved_image += f", prompt: {preview_text}"
-- 
cgit v1.2.3


From e05573e1adc1cde1e3bd7eb651a1ab27c446b3d5 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Wed, 12 Oct 2022 20:47:55 +0800
Subject: images history improvement

---
 modules/images_history.py | 67 ++++++++++++++++++++++++++++++-----------------
 1 file changed, 43 insertions(+), 24 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 23f55b30..77f692fe 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,15 +1,29 @@
 import os
-def get_recent_images(dir_name, page_index, step, image_index):
-    #print(image_index)
+import shutil
+def get_recent_images(dir_name, page_index, step, image_index, tabname):
+    print(f"renew page {page_index}")
     page_index = int(page_index)
     f_list = os.listdir(dir_name)
     file_list = []
     for file in f_list:
         if file[-4:] == ".txt":
             continue
-        file_list.append(file)    
+        #subdirectories        
+        if file[-10:].rfind(".") < 0:
+            sub_dir = os.path.join(dir_name, file)
+            if os.path.isfile(sub_dir):
+                continue
+            sub_file_list = os.listdir(sub_dir)
+            for sub_file in sub_file_list:
+                if sub_file[-4:] == ".txt":
+                    continue
+                if os.path.isfile(os.path.join(sub_dir, sub_file) ): 
+                    file_list.append(os.path.join(file, sub_file)) 
+            continue
+        file_list.append(file)        
+
     file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
-    num = 48
+    num = 48 if tabname != "extras" else 12
     max_page_index = len(file_list) // num + 1    
     page_index = max_page_index if page_index == -1 else page_index + step
     page_index = 1 if page_index < 1 else page_index    
@@ -26,26 +40,28 @@ def get_recent_images(dir_name, page_index, step, image_index):
         hide_image = os.path.join(dir_name, current_file)
     return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image
 def first_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, 1, 0, image_index)
+    return get_recent_images(dir_name, 1, 0, image_index, tabname)
 def end_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, -1, 0, image_index)
+    return get_recent_images(dir_name, -1, 0, image_index, tabname)
 def prev_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, -1, image_index)
+    return get_recent_images(dir_name, page_index, -1, image_index, tabname)
 def next_page_click(dir_name, page_index, image_index, tabname):    
-    return get_recent_images(dir_name, page_index, 1, image_index)
+    return get_recent_images(dir_name, page_index, 1, image_index, tabname)
 def page_index_change(dir_name, page_index, image_index, tabname):    
-    return get_recent_images(dir_name, page_index, 0, image_index)
+    return get_recent_images(dir_name, page_index, 0, image_index, tabname)
 
 def show_image_info(num, image_path, filenames):
-    #print("set img",num)
+    print(f"select image {num}")
     file = filenames[int(num)]
     return file, num, os.path.join(image_path, file)
 def delete_image(tabname, dir_name, name, page_index, filenames, image_index):
-    #print("filename", name)
     path = os.path.join(dir_name, name)           
-    if os.path.exists(path):
+    if os.path.exists(path):        
         print(f"Delete file {path}")
-        os.remove(path)    
+        os.remove(path)
+        txt_file = os.path.splitext(path)[0]  + ".txt"
+        if os.path.exists(txt_file):
+        	os.remove(txt_file)
         new_file_list = []
         for f in filenames:
             if f == name:
@@ -64,25 +80,26 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         elif tabname == "extras":
             dir_name = opts.outdir_extras_samples
         with gr.Row():    
-            renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") 
-            first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page")
-            prev_page = gr.Button('Prev') 
+            renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page") 
+            first_page = gr.Button('First Page')
+            prev_page = gr.Button('Prev Page') 
             page_index = gr.Number(value=1, label="Page Index")
-            next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") 
-            end_page = gr.Button('End')    
+            next_page = gr.Button('Next Page') 
+            end_page = gr.Button('End Page')    
         with gr.Row(elem_id=tabname + "_images_history"):            
             with gr.Row():     
-                with gr.Column():
-                    history_gallery = gr.Gallery(show_label=False).style(grid=6)
+                with gr.Column(scale=2):                	
+                    history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
+                    delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")
                 with gr.Column():     
                     with gr.Row():          
-                        delete = gr.Button('Delete')
+                        #pnginfo = gr.Button('PNG info')
                         pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
                         pnginfo_send_to_img2img = gr.Button('Send to img2img')
                     with gr.Row():
                         with gr.Column():
-                            img_file_info = gr.Textbox(label="Generate Info")
-                            img_file_name = gr.Textbox(label="File Name")    
+                            img_file_info = gr.Textbox(label="Generate Info", interactive=False)
+                            img_file_name = gr.Textbox(label="File Name", interactive=False)    
                     with gr.Row():    
                         # hiden items
                         img_path = gr.Textbox(dir_name, visible=False)                
@@ -90,7 +107,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                         image_index = gr.Textbox(value=-1, visible=False)                        
                         set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
                         filenames = gr.State()
-                        hide_image = gr.Image(visible=False, type="pil")
+                        hide_image = gr.Image(type="pil", visible=False)
                         info1 = gr.Textbox(visible=False)
                         info2 = gr.Textbox(visible=False)
 
@@ -111,6 +128,8 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
         delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) 
         hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
+        hide_image.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
+        #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
         switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
         switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
     
-- 
cgit v1.2.3


From a1a94b8b5f342f467aecc53b21b80ed0227ee76a Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 13 Oct 2022 00:19:34 +0800
Subject: images history improvement

---
 modules/images_history.py |  7 ++++---
 modules/ui.py             | 40 +++++++++++++++++++---------------------
 2 files changed, 23 insertions(+), 24 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 2bc4b7ee..1bca0ad9 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -61,7 +61,7 @@ def delete_image(tabname, dir_name, name, page_index, filenames, image_index):
         os.remove(path)
         txt_file = os.path.splitext(path)[0]  + ".txt"
         if os.path.exists(txt_file):
-        	os.remove(txt_file)
+            os.remove(txt_file)
         new_file_list = []
         for f in filenames:
             if f == name:
@@ -88,7 +88,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
             end_page = gr.Button('End Page')    
         with gr.Row(elem_id=tabname + "_images_history"):            
             with gr.Row():     
-                with gr.Column(scale=2):                	
+                with gr.Column(scale=2):                    
                     history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
                     delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")
                 with gr.Column():     
@@ -126,9 +126,10 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
         #other funcitons
         set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
+        img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
         delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) 
         hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
-        hide_image.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
+       
         #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
         switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
         switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
diff --git a/modules/ui.py b/modules/ui.py
index 94297ba6..8cd12b51 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -39,7 +39,7 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
-import modules.hypernetwork.ui
+import modules.hypernetworks.ui
 import modules.images_history as img_his
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
@@ -554,6 +554,7 @@ def create_ui(wrap_gradio_gpu_call):
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
             with gr.Column(variant='panel'):
+
                 with gr.Group():
                     txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
                     txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
@@ -573,9 +574,9 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
-                with gr.Group():
-                    html_info = gr.HTML()
-                    generation_info = gr.Textbox(visible=False)
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -669,7 +670,6 @@ def create_ui(wrap_gradio_gpu_call):
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-        
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
@@ -762,10 +762,10 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
-                with gr.Group():
-                    html_info = gr.HTML()
-                    generation_info = gr.Textbox(visible=False)
-                
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
+
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
@@ -1016,6 +1016,13 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
+    #images history
+    images_history_switch_dict = {
+        "fn":modules.generation_parameters_copypaste.connect_paste,
+        "t2i":txt2img_paste_fields,
+        "i2i":img2img_paste_fields
+    }
+    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) 
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
@@ -1285,16 +1292,7 @@ Requested path was: {f}
 
         opts.save(shared.config_filename)
 
-        return f'{changed} settings changed.', opts.dumpjson()    
-
-    #images history
-    images_history_switch_dict = {
-        "fn":modules.generation_parameters_copypaste.connect_paste,
-        "t2i":txt2img_paste_fields,
-        "i2i":img2img_paste_fields
-    }
-    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)    
-    
+        return f'{changed} settings changed.', opts.dumpjson()
 
     def run_settings_single(value, key):
         if not opts.same_type(value, opts.data_labels[key].default):
@@ -1393,11 +1391,10 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (images_history, "History", "images_history"),
+        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
-
     ]
 
     with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
@@ -1616,3 +1613,4 @@ if 'gradio_routes_templates_response' not in globals():
 
     gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
     gradio.routes.templates.TemplateResponse = template_response
+
-- 
cgit v1.2.3


From a2aa2a68bc7868320b502a78765be597e507ce45 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 13 Oct 2022 00:21:16 +0800
Subject: images history improvement

---
 modules/images_history.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 1bca0ad9..6408973c 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,7 +1,7 @@
 import os
 import shutil
 def get_recent_images(dir_name, page_index, step, image_index, tabname):
-    print(f"renew page {page_index}")
+    #print(f"renew page {page_index}")
     page_index = int(page_index)
     f_list = os.listdir(dir_name)
     file_list = []
@@ -51,7 +51,7 @@ def page_index_change(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, page_index, 0, image_index, tabname)
 
 def show_image_info(num, image_path, filenames):
-    print(f"select image {num}")
+    #print(f"select image {num}")
     file = filenames[int(num)]
     return file, num, os.path.join(image_path, file)
 def delete_image(tabname, dir_name, name, page_index, filenames, image_index):
-- 
cgit v1.2.3


From c3c8eef9fd5a0c8b26319e32ca4a19b56204e6df Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 20:49:47 +0300
Subject: train: change filename processing to be more simple and configurable
 train: make it possible to make text files with prompts train: rework
 scheduler so that there's less repeating code in textual inversion and
 hypernets train: move epochs setting to options

---
 modules/hypernetworks/hypernetwork.py          | 40 +++++++++-------------
 modules/shared.py                              |  3 ++
 modules/textual_inversion/dataset.py           | 47 +++++++++++++++++++-------
 modules/textual_inversion/learn_schedule.py    | 37 +++++++++++++++++++-
 modules/textual_inversion/textual_inversion.py | 35 +++++++------------
 modules/ui.py                                  |  2 --
 6 files changed, 102 insertions(+), 62 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8314450a..b6c06d49 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -14,7 +14,7 @@ import torch
 from torch import einsum
 from einops import rearrange, repeat
 import modules.textual_inversion.dataset
-from modules.textual_inversion.learn_schedule import LearnSchedule
+from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 
 class HypernetworkModule(torch.nn.Module):
@@ -223,31 +223,23 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     if ititial_step > steps:
         return hypernetwork, filename
 
-    schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
-    (learn_rate, end_step) = next(schedules)
-    print(f'Training at rate of {learn_rate} until step {end_step}')
-
-    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, (x, text, cond) in pbar:
+    for i, entry in pbar:
         hypernetwork.step = i + ititial_step
 
-        if hypernetwork.step > end_step:
-            try:
-                (learn_rate, end_step) = next(schedules)
-            except Exception:
-                break
-            tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
-            for pg in optimizer.param_groups:
-                pg['lr'] = learn_rate
+        scheduler.apply(optimizer, hypernetwork.step)
+        if scheduler.finished:
+            break
 
         if shared.state.interrupted:
             break
 
         with torch.autocast("cuda"):
-            cond = cond.to(devices.device)
-            x = x.to(devices.device)
+            cond = entry.cond.to(devices.device)
+            x = entry.latent.to(devices.device)
             loss = shared.sd_model(x.unsqueeze(0), cond)[0]
             del x
             del cond
@@ -267,7 +259,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
 
-            preview_text = text if preview_image_prompt == "" else preview_image_prompt
+            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
 
             optimizer.zero_grad()
             shared.sd_model.cond_stage_model.to(devices.device)
@@ -282,16 +274,16 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             )
 
             processed = processing.process_images(p)
-            image = processed.images[0]
+            image = processed.images[0] if len(processed.images)>0 else None
 
             if unload:
                 shared.sd_model.cond_stage_model.to(devices.cpu)
                 shared.sd_model.first_stage_model.to(devices.cpu)
 
-            shared.state.current_image = image
-            image.save(last_saved_image)
-
-            last_saved_image += f", prompt: {preview_text}"
+            if image is not None:
+                shared.state.current_image = image
+                image.save(last_saved_image)
+                last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
 
@@ -299,7 +291,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {hypernetwork.step}<br/>
-Last prompt: {html.escape(text)}<br/>
+Last prompt: {html.escape(entry.cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
diff --git a/modules/shared.py b/modules/shared.py
index 42e99741..e64e69fc 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -231,6 +231,9 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
+    "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
+    "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
+    "training_image_repeats_per_epoch": OptionInfo(100, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index f61f40d3..67e90afe 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -11,11 +11,21 @@ import tqdm
 from modules import devices, shared
 import re
 
-re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
+re_numbers_at_start = re.compile(r"^[-\d]+\s*")
+
+
+class DatasetEntry:
+    def __init__(self, filename=None, latent=None, filename_text=None):
+        self.filename = filename
+        self.latent = latent
+        self.filename_text = filename_text
+        self.cond = None
+        self.cond_text = None
 
 
 class PersonalizedBase(Dataset):
     def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
+        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None
 
         self.placeholder_token = placeholder_token
 
@@ -42,9 +52,18 @@ class PersonalizedBase(Dataset):
             except Exception:
                 continue
 
+            text_filename = os.path.splitext(path)[0] + ".txt"
             filename = os.path.basename(path)
-            filename_tokens = os.path.splitext(filename)[0]
-            filename_tokens = re_tag.findall(filename_tokens)
+
+            if os.path.exists(text_filename):
+                with open(text_filename, "r", encoding="utf8") as file:
+                    filename_text = file.read()
+            else:
+                filename_text = os.path.splitext(filename)[0]
+                filename_text = re.sub(re_numbers_at_start, '', filename_text)
+                if re_word:
+                    tokens = re_word.findall(filename_text)
+                    filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
 
             npimage = np.array(image).astype(np.uint8)
             npimage = (npimage / 127.5 - 1.0).astype(np.float32)
@@ -55,13 +74,13 @@ class PersonalizedBase(Dataset):
             init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
             init_latent = init_latent.to(devices.cpu)
 
+            entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent)
+
             if include_cond:
-                text = self.create_text(filename_tokens)
-                cond = cond_model([text]).to(devices.cpu)
-            else:
-                cond = None
+                entry.cond_text = self.create_text(filename_text)
+                entry.cond = cond_model([entry.cond_text]).to(devices.cpu)
 
-            self.dataset.append((init_latent, filename_tokens, cond))
+            self.dataset.append(entry)
 
         self.length = len(self.dataset) * repeats
 
@@ -72,10 +91,10 @@ class PersonalizedBase(Dataset):
     def shuffle(self):
         self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
 
-    def create_text(self, filename_tokens):
+    def create_text(self, filename_text):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
-        text = text.replace("[filewords]", ' '.join(filename_tokens))
+        text = text.replace("[filewords]", filename_text)
         return text
 
     def __len__(self):
@@ -86,7 +105,9 @@ class PersonalizedBase(Dataset):
             self.shuffle()
 
         index = self.indexes[i % len(self.indexes)]
-        x, filename_tokens, cond = self.dataset[index]
+        entry = self.dataset[index]
+
+        if entry.cond is None:
+            entry.cond_text = self.create_text(entry.filename_text)
 
-        text = self.create_text(filename_tokens)
-        return x, text, cond
+        return entry
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index db720271..2062726a 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -1,6 +1,12 @@
+import tqdm
 
-class LearnSchedule:
+
+class LearnScheduleIterator:
     def __init__(self, learn_rate, max_steps, cur_step=0):
+        """
+        specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000
+        """
+
         pairs = learn_rate.split(',')
         self.rates = []
         self.it = 0
@@ -32,3 +38,32 @@ class LearnSchedule:
             return self.rates[self.it - 1]
         else:
             raise StopIteration
+
+
+class LearnRateScheduler:
+    def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True):
+        self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step)
+        (self.learn_rate,  self.end_step) = next(self.schedules)
+        self.verbose = verbose
+
+        if self.verbose:
+            print(f'Training at rate of {self.learn_rate} until step {self.end_step}')
+
+        self.finished = False
+
+    def apply(self, optimizer, step_number):
+        if step_number <= self.end_step:
+            return
+
+        try:
+            (self.learn_rate, self.end_step) = next(self.schedules)
+        except Exception:
+            self.finished = True
+            return
+
+        if self.verbose:
+            tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}')
+
+        for pg in optimizer.param_groups:
+            pg['lr'] = self.learn_rate
+
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index c5153e4a..fa0e33a2 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -11,7 +11,7 @@ from PIL import Image, PngImagePlugin
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
-from modules.textual_inversion.learn_schedule import LearnSchedule
+from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,
                                                        insert_image_data_embed, extract_image_data_embed,
@@ -172,8 +172,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -205,7 +204,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -221,32 +220,24 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     if ititial_step > steps:
         return embedding, filename
 
-    schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
-    (learn_rate, end_step) = next(schedules)
-    print(f'Training at rate of {learn_rate} until step {end_step}')
-
-    optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, (x, text, _) in pbar:
+    for i, entry in pbar:
         embedding.step = i + ititial_step
 
-        if embedding.step > end_step:
-            try:
-                (learn_rate, end_step) = next(schedules)
-            except:
-                break
-            tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
-            for pg in optimizer.param_groups:
-                pg['lr'] = learn_rate
+        scheduler.apply(optimizer, embedding.step)
+        if scheduler.finished:
+            break
 
         if shared.state.interrupted:
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([text])
+            c = cond_model([entry.cond_text])
 
-            x = x.to(devices.device)
+            x = entry.latent.to(devices.device)
             loss = shared.sd_model(x.unsqueeze(0), c)[0]
             del x
 
@@ -268,7 +259,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
-            preview_text = text if preview_image_prompt == "" else preview_image_prompt
+            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
 
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
@@ -314,7 +305,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {embedding.step}<br/>
-Last prompt: {html.escape(text)}<br/>
+Last prompt: {html.escape(entry.cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
diff --git a/modules/ui.py b/modules/ui.py
index 2b332267..c42535c8 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1098,7 +1098,6 @@ def create_ui(wrap_gradio_gpu_call):
                     training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
-                    num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
@@ -1176,7 +1175,6 @@ def create_ui(wrap_gradio_gpu_call):
                 training_width,
                 training_height,
                 steps,
-                num_repeats,
                 create_image_every,
                 save_embedding_every,
                 template_file,
-- 
cgit v1.2.3


From 698d303b04e293635bfb49c525409f3bcf671dce Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 21:55:43 +0300
Subject: deepbooru: added option to use spaces or underscores deepbooru: added
 option to quote (\) in tags deepbooru/BLIP: write caption to file instead of
 image filename deepbooru/BLIP: now possible to use both for captions
 deepbooru: process is stopped even if an exception occurs

---
 modules/deepbooru.py                    | 65 ++++++++++++++++++-----
 modules/shared.py                       |  2 +
 modules/textual_inversion/preprocess.py | 92 ++++++++++++++-------------------
 modules/ui.py                           |  7 +--
 4 files changed, 95 insertions(+), 71 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 29529949..419e6a9c 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -2,33 +2,44 @@ import os.path
 from concurrent.futures import ProcessPoolExecutor
 import multiprocessing
 import time
+import re
+
+re_special = re.compile(r'([\\()])')
 
 def get_deepbooru_tags(pil_image):
     """
     This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
     """
     from modules import shared  # prevents circular reference
-    create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, shared.opts.deepbooru_sort_alpha)
-    shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process_queue.put(pil_image)
-    while shared.deepbooru_process_return["value"] == -1:
-        time.sleep(0.2)
-    tags = shared.deepbooru_process_return["value"]
-    release_process()
-    return tags
 
+    try:
+        create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts())
+        return get_tags_from_process(pil_image)
+    finally:
+        release_process()
+
+
+def create_deepbooru_opts():
+    from modules import shared
 
-def deepbooru_process(queue, deepbooru_process_return, threshold, alpha_sort):
+    return {
+        "use_spaces": shared.opts.deepbooru_use_spaces,
+        "use_escape": shared.opts.deepbooru_escape,
+        "alpha_sort": shared.opts.deepbooru_sort_alpha,
+    }
+
+
+def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts):
     model, tags = get_deepbooru_tags_model()
     while True: # while process is running, keep monitoring queue for new image
         pil_image = queue.get()
         if pil_image == "QUIT":
             break
         else:
-            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort)
+            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts)
 
 
-def create_deepbooru_process(threshold, alpha_sort):
+def create_deepbooru_process(threshold, deepbooru_opts):
     """
     Creates deepbooru process.  A queue is created to send images into the process.  This enables multiple images
     to be processed in a row without reloading the model or creating a new process.  To return the data, a shared
@@ -41,10 +52,23 @@ def create_deepbooru_process(threshold, alpha_sort):
     shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
     shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
     shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, alpha_sort))
+    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
     shared.deepbooru_process.start()
 
 
+def get_tags_from_process(image):
+    from modules import shared
+
+    shared.deepbooru_process_return["value"] = -1
+    shared.deepbooru_process_queue.put(image)
+    while shared.deepbooru_process_return["value"] == -1:
+        time.sleep(0.2)
+    caption = shared.deepbooru_process_return["value"]
+    shared.deepbooru_process_return["value"] = -1
+
+    return caption
+
+
 def release_process():
     """
     Stops the deepbooru process to return used memory
@@ -81,10 +105,15 @@ def get_deepbooru_tags_model():
     return model, tags
 
 
-def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort):
+def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts):
     import deepdanbooru as dd
     import tensorflow as tf
     import numpy as np
+
+    alpha_sort = deepbooru_opts['alpha_sort']
+    use_spaces = deepbooru_opts['use_spaces']
+    use_escape = deepbooru_opts['use_escape']
+
     width = model.input_shape[2]
     height = model.input_shape[1]
     image = np.array(pil_image)
@@ -129,4 +158,12 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort)
 
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
+    tags_text = ', '.join(result_tags_out)
+
+    if use_spaces:
+        tags_text = tags_text.replace('_', ' ')
+
+    if use_escape:
+        tags_text = re.sub(re_special, r'\\\1', tags_text)
+
+    return tags_text.replace(':', ' ')
diff --git a/modules/shared.py b/modules/shared.py
index e64e69fc..78b73aae 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -260,6 +260,8 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
     "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
+    "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
+    "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
 }))
 
 options_templates.update(options_section(('ui', "User interface"), {
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 113cecf1..3047bede 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -10,7 +10,28 @@ from modules.shared import opts, cmd_opts
 if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
+
 def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+    try:
+        if process_caption:
+            shared.interrogator.load()
+
+        if process_caption_deepbooru:
+            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts())
+
+        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
+
+    finally:
+
+        if process_caption:
+            shared.interrogator.send_blip_to_ram()
+
+        if process_caption_deepbooru:
+            deepbooru.release_process()
+
+
+
+def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
@@ -25,30 +46,28 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
 
-    if process_caption:
-        shared.interrogator.load()
-
-    if process_caption_deepbooru:
-        deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, opts.deepbooru_sort_alpha)
-
     def save_pic_with_caption(image, index):
+        caption = ""
+
         if process_caption:
-            caption = "-" + shared.interrogator.generate_caption(image)
-            caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
-        elif process_caption_deepbooru:
-            shared.deepbooru_process_return["value"] = -1
-            shared.deepbooru_process_queue.put(image)
-            while shared.deepbooru_process_return["value"] == -1:
-                time.sleep(0.2)
-            caption = "-" + shared.deepbooru_process_return["value"]
-            caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
-            shared.deepbooru_process_return["value"] = -1
-        else:
-            caption = filename
-            caption = os.path.splitext(caption)[0]
-            caption = os.path.basename(caption)
+            caption += shared.interrogator.generate_caption(image)
+
+        if process_caption_deepbooru:
+            if len(caption) > 0:
+                caption += ", "
+            caption += deepbooru.get_tags_from_process(image)
+
+        filename_part = filename
+        filename_part = os.path.splitext(filename_part)[0]
+        filename_part = os.path.basename(filename_part)
+
+        basename = f"{index:05}-{subindex[0]}-{filename_part}"
+        image.save(os.path.join(dst, f"{basename}.png"))
+
+        if len(caption) > 0:
+            with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:
+                file.write(caption)
 
-        image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
         subindex[0] += 1
 
     def save_pic(image, index):
@@ -93,34 +112,3 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             save_pic(img, index)
 
         shared.state.nextjob()
-
-    if process_caption:
-        shared.interrogator.send_blip_to_ram()
-
-    if process_caption_deepbooru:
-        deepbooru.release_process()
-
-
-def sanitize_caption(base_path, original_caption, suffix):
-    operating_system = platform.system().lower()
-    if (operating_system == "windows"):
-        invalid_path_characters = "\\/:*?\"<>|"
-        max_path_length = 259
-    else:
-        invalid_path_characters = "/" #linux/macos
-        max_path_length = 1023
-    caption = original_caption
-    for invalid_character in invalid_path_characters:
-        caption = caption.replace(invalid_character, "")
-    fixed_path_length = len(base_path) + len(suffix) 
-    if fixed_path_length + len(caption) <= max_path_length:
-        return caption
-    caption_tokens = caption.split()
-    new_caption = ""
-    for token in caption_tokens:
-        last_caption = new_caption
-        new_caption = new_caption + token + " "
-        if (len(new_caption) + fixed_path_length - 1  > max_path_length):
-            break
-    print(f"\nPath will be too long. Truncated caption: {original_caption}\nto: {last_caption}", file=sys.stderr)
-    return last_caption.strip()
diff --git a/modules/ui.py b/modules/ui.py
index c42535c8..e07ee0e1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1074,11 +1074,8 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
                         process_split = gr.Checkbox(label='Split oversized images into two')
-                        process_caption = gr.Checkbox(label='Use BLIP caption as filename')
-                        if cmd_opts.deepdanbooru:
-                            process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename')
-                        else:
-                            process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename', visible=False)
+                        process_caption = gr.Checkbox(label='Use BLIP for caption')
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
 
                     with gr.Row():
                         with gr.Column(scale=3):
-- 
cgit v1.2.3


From efefa4862c6c75115d3da9f768348630cc32bdea Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 13:03:00 -0700
Subject: [1/?] [wip] Reintroduce opts.interrogate_return_ranks

looks functionally correct, needs testing

Needs particular testing care around whether the colon usage (:) will break anything in whatever new use cases were introduced by https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2143
---
 modules/deepbooru.py | 25 ++++++++++++++-----------
 1 file changed, 14 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 419e6a9c..2cbf2cab 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -26,6 +26,7 @@ def create_deepbooru_opts():
         "use_spaces": shared.opts.deepbooru_use_spaces,
         "use_escape": shared.opts.deepbooru_escape,
         "alpha_sort": shared.opts.deepbooru_sort_alpha,
+        "include_ranks": shared.opts.interrogate_return_ranks,
     }
 
 
@@ -113,6 +114,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
     alpha_sort = deepbooru_opts['alpha_sort']
     use_spaces = deepbooru_opts['use_spaces']
     use_escape = deepbooru_opts['use_escape']
+    include_ranks = deepbooru_opts['include_ranks']
 
     width = model.input_shape[2]
     height = model.input_shape[1]
@@ -151,19 +153,20 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
     if alpha_sort:
         sort_ndx = 1
 
-    # sort by reverse by likelihood and normal for alpha
+    # sort by reverse by likelihood and normal for alpha, and format tag text as requested
     unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
     for weight, tag in unsorted_tags_in_theshold:
-        result_tags_out.append(tag)
+        # note: tag_outformat will still have a colon if include_ranks is True
+        tag_outformat = tag.replace(':', ' ')
+        if use_spaces:
+            tag_outformat = tag_outformat.replace('_', ' ')
+        if use_escape:
+            tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
+        if include_ranks:
+            use_escape += f":{weight:.3f}"
 
-    print('\n'.join(sorted(result_tags_print, reverse=True)))
-
-    tags_text = ', '.join(result_tags_out)
+        result_tags_out.append(tag_outformat)
 
-    if use_spaces:
-        tags_text = tags_text.replace('_', ' ')
-
-    if use_escape:
-        tags_text = re.sub(re_special, r'\\\1', tags_text)
+    print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return tags_text.replace(':', ' ')
+    return ', '.join(result_tags_out)
-- 
cgit v1.2.3


From f776254b12361b5bae16f6629bcdcb47b450c48d Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 13:08:06 -0700
Subject: [2/?] [wip] ignore OPT_INCLUDE_RANKS for training filenames

---
 modules/deepbooru.py                    | 3 ++-
 modules/textual_inversion/preprocess.py | 4 +++-
 2 files changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 2cbf2cab..fcc05819 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -19,6 +19,7 @@ def get_deepbooru_tags(pil_image):
         release_process()
 
 
+OPT_INCLUDE_RANKS = "include_ranks"
 def create_deepbooru_opts():
     from modules import shared
 
@@ -26,7 +27,7 @@ def create_deepbooru_opts():
         "use_spaces": shared.opts.deepbooru_use_spaces,
         "use_escape": shared.opts.deepbooru_escape,
         "alpha_sort": shared.opts.deepbooru_sort_alpha,
-        "include_ranks": shared.opts.interrogate_return_ranks,
+        OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks,
     }
 
 
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 3047bede..886cf0c3 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -17,7 +17,9 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             shared.interrogator.load()
 
         if process_caption_deepbooru:
-            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts())
+            db_opts = deepbooru.create_deepbooru_opts()
+            db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
+            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
 
         preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
 
-- 
cgit v1.2.3


From 514456101b142b47acf87f6de95bad1a23d73be7 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 13:14:13 -0700
Subject: [3/?] [wip] fix incorrect variable reference

still needs testing
---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index fcc05819..c2004696 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -164,7 +164,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
         if use_escape:
             tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
         if include_ranks:
-            use_escape += f":{weight:.3f}"
+            tag_outformat += f":{weight:.3f}"
 
         result_tags_out.append(tag_outformat)
 
-- 
cgit v1.2.3


From 1cfc2a18981ee56bdb69a2de7b463a11ad05e329 Mon Sep 17 00:00:00 2001
From: Melan <alexleander91@gmail.com>
Date: Wed, 12 Oct 2022 23:36:29 +0200
Subject: Save a csv containing the loss while training

---
 modules/hypernetworks/hypernetwork.py          | 17 ++++++++++++++++-
 modules/textual_inversion/textual_inversion.py | 17 ++++++++++++++++-
 modules/ui.py                                  |  3 +++
 3 files changed, 35 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b6c06d49..6522078f 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -5,6 +5,7 @@ import os
 import sys
 import traceback
 import tqdm
+import csv
 
 import torch
 
@@ -174,7 +175,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     return self.to_out(out)
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
+def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, write_csv_every, template_file, preview_image_prompt):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -256,6 +257,20 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
             hypernetwork.save(last_saved_file)
 
+        print(f"{write_csv_every} > {hypernetwork.step % write_csv_every == 0}, {write_csv_every}")
+        if write_csv_every > 0 and hypernetwork_dir is not None and hypernetwork.step % write_csv_every == 0:
+            write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True
+            
+            with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout:
+
+                csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss"])
+                
+                if write_csv_header:
+                    csv_writer.writeheader()
+
+                csv_writer.writerow({"step": hypernetwork.step, 
+                    "loss": f"{losses.mean():.7f}"})
+
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fa0e33a2..25038a89 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,6 +6,7 @@ import torch
 import tqdm
 import html
 import datetime
+import csv
 
 from PIL import Image, PngImagePlugin
 
@@ -172,7 +173,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, write_csv_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -256,6 +257,20 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
             embedding.save(last_saved_file)
 
+        if write_csv_every > 0 and log_directory is not None and embedding.step % write_csv_every == 0:
+            write_csv_header = False if os.path.exists(os.path.join(log_directory, "textual_inversion_loss.csv")) else True
+
+            with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout:
+
+                csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss"])
+                
+                if write_csv_header:
+                    csv_writer.writeheader()
+
+                csv_writer.writerow({"epoch": epoch_num + 1, 
+                    "epoch_step": epoch_step - 1, 
+                    "loss": f"{losses.mean():.7f}"})
+
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
diff --git a/modules/ui.py b/modules/ui.py
index e07ee0e1..1195c2f1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1096,6 +1096,7 @@ def create_ui(wrap_gradio_gpu_call):
                     training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
+                    write_csv_every = gr.Number(label='Save an csv containing the loss to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
                     preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
@@ -1174,6 +1175,7 @@ def create_ui(wrap_gradio_gpu_call):
                 steps,
                 create_image_every,
                 save_embedding_every,
+                write_csv_every,
                 template_file,
                 save_image_with_stored_embedding,
                 preview_image_prompt,
@@ -1195,6 +1197,7 @@ def create_ui(wrap_gradio_gpu_call):
                 steps,
                 create_image_every,
                 save_embedding_every,
+                write_csv_every,
                 template_file,
                 preview_image_prompt,
             ],
-- 
cgit v1.2.3


From 54e0051bdd7dea7348825c09600ec61ea0771cb8 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Wed, 12 Oct 2022 18:17:26 -0500
Subject: Add drag/drop param loading.

Drop an image or generational text onto the prompt bar, it loads the info for parsing.
---
 modules/images.py | 20 ++++++++++++++++++++
 modules/ui.py     | 30 +++++++++++++++++++++++++++++-
 2 files changed, 49 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index c0a90676..f1155b7f 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -463,3 +463,23 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         txt_fullfn = None
 
     return fullfn, txt_fullfn
+
+
+def image_data(image_path):
+    file, ext = os.path.splitext(image_path.name)
+    data = {}
+    if "png" in ext:
+        image = Image.open(image_path.name, "r")
+        print(f"Image data requested for {image_path.name} {image.format} of {type(image)}")
+        try:
+            data = image.text["parameters"]
+        except Exception as e:
+            print(f"Exception: {e}")
+            pass
+        print(f"Image data: {data}")
+    if "txt" in ext:
+        myfile = open(image_path.name, 'r')
+        data = myfile.read()
+        myfile.close()
+
+    return data, None
diff --git a/modules/ui.py b/modules/ui.py
index 2b332267..dd793c39 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -431,7 +431,6 @@ def create_toprow(is_img2img):
                 with gr.Column(scale=80):
                     with gr.Row():
                         prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)
-
                 with gr.Column(scale=1, elem_id="roll_col"):
                     roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
                     paste = gr.Button(value=paste_symbol, elem_id="paste")
@@ -513,6 +512,7 @@ def create_ui(wrap_gradio_gpu_call):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="file", visible=False)
 
         with gr.Row(elem_id='txt2img_progress_row'):
             with gr.Column(scale=1):
@@ -614,6 +614,18 @@ def create_ui(wrap_gradio_gpu_call):
             txt2img_prompt.submit(**txt2img_args)
             submit.click(**txt2img_args)
 
+            txt_prompt_img.change(
+                fn=modules.images.image_data,
+                # _js = "get_extras_tab_index",
+                inputs=[
+                    txt_prompt_img
+                ],
+                outputs=[
+                    txt2img_prompt,
+                    txt_prompt_img
+                ]
+            )
+
             enable_hr.change(
                 fn=lambda x: gr_show(x),
                 inputs=[enable_hr],
@@ -674,6 +686,9 @@ def create_ui(wrap_gradio_gpu_call):
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
+            img2img_prompt_img = gr.File(label="", elem_id="txt_prompt_image", file_count="single", type="file",
+                                         visible=False)
+
             with gr.Column(scale=1):
                 pass
 
@@ -768,6 +783,18 @@ def create_ui(wrap_gradio_gpu_call):
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
+            img2img_prompt_img.change(
+                fn=modules.images.image_data,
+                # _js = "get_extras_tab_index",
+                inputs=[
+                    txt_prompt_img
+                ],
+                outputs=[
+                    img2img_prompt,
+                    img2img_prompt_img
+                ]
+            )
+
             mask_mode.change(
                 lambda mode, img: {
                     init_img_with_mask: gr_show(mode == 0),
@@ -956,6 +983,7 @@ def create_ui(wrap_gradio_gpu_call):
                 button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''
                 open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
 
+
         submit.click(
             fn=wrap_gradio_gpu_call(modules.extras.run_extras),
             _js="get_extras_tab_index",
-- 
cgit v1.2.3


From 716a9e034f1aff434083363b218bd6043a774fc2 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 13 Oct 2022 12:19:50 +0800
Subject: images history delete a number of images  consecutively next

---
 modules/images_history.py | 44 ++++++++++++++++++++++++--------------------
 1 file changed, 24 insertions(+), 20 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 6408973c..f812ea4e 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -54,23 +54,26 @@ def show_image_info(num, image_path, filenames):
     #print(f"select image {num}")
     file = filenames[int(num)]
     return file, num, os.path.join(image_path, file)
-def delete_image(tabname, dir_name, name, page_index, filenames, image_index):
-    path = os.path.join(dir_name, name)           
-    if os.path.exists(path):        
-        print(f"Delete file {path}")
-        os.remove(path)
-        txt_file = os.path.splitext(path)[0]  + ".txt"
-        if os.path.exists(txt_file):
-            os.remove(txt_file)
-        new_file_list = []
-        for f in filenames:
-            if f == name:
-                continue
-            new_file_list.append(f)
-    else:
-        print(f"Not exists file {path}")
-        new_file_list = filenames
-    return page_index, new_file_list
+def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
+    delete_num = int(delete_num)
+    index = list(filenames).index(name)
+    i = 0
+    new_file_list = []
+    for name in filenames:
+        if i  >= index and i < index + delete_num:            
+            path = os.path.join(dir_name, name)
+            if os.path.exists(path):        
+                print(f"Delete file {path}")
+                os.remove(path)
+                txt_file = os.path.splitext(path)[0]  + ".txt"
+                if os.path.exists(txt_file):
+                    os.remove(txt_file)       
+            else:
+                print(f"Not exists file {path}")
+        else:
+            new_file_list.append(name)
+        i += 1
+    return page_index, new_file_list, 1
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         if tabname == "txt2img":
@@ -90,10 +93,11 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
             with gr.Row():     
                 with gr.Column(scale=2):                    
                     history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
-                    delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")
+                    with gr.Row():
+                        delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")
+                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")                                           
                 with gr.Column():     
                     with gr.Row():          
-                        #pnginfo = gr.Button('PNG info')
                         pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
                         pnginfo_send_to_img2img = gr.Button('Send to img2img')
                     with gr.Row():
@@ -127,7 +131,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         #other funcitons
         set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
         img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-        delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) 
+        delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames, delete_num]) 
         hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
        
         #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
-- 
cgit v1.2.3


From 78592d404acba7db3baf8d78bdc19266906e684a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 07:40:03 +0300
Subject: remove interrogate option I accidentally deleted

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 78b73aae..9bda45c1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -258,6 +258,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
+    "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
     "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
     "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
-- 
cgit v1.2.3


From 04c0e643f2eec68d93a76db171b4d70595808702 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 22:13:53 -0700
Subject: Merge branch 'master' of
 https://github.com/HunterVacui/stable-diffusion-webui

---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index c2004696..f34f3788 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -164,7 +164,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
         if use_escape:
             tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
         if include_ranks:
-            tag_outformat += f":{weight:.3f}"
+            tag_outformat = f"({tag_outformat}:{weight:.3f})"
 
         result_tags_out.append(tag_outformat)
 
-- 
cgit v1.2.3


From e72adc999b3531370eafb9d316924ac497feb445 Mon Sep 17 00:00:00 2001
From: Trung Ngo <codem01@gmail.com>
Date: Sat, 8 Oct 2022 22:57:19 -0500
Subject: Restore last generation params

---
 modules/generation_parameters_copypaste.py | 8 ++++++++
 modules/processing.py                      | 4 ++++
 2 files changed, 12 insertions(+)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index ac1ba7f4..3e75aecc 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -1,5 +1,7 @@
+import os
 import re
 import gradio as gr
+from modules.shared import script_path
 
 re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)"
 re_param = re.compile(re_param_code)
@@ -61,6 +63,12 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
 def connect_paste(button, paste_fields, input_comp, js=None):
     def paste_func(prompt):
+        if not prompt:
+            filename = os.path.join(script_path, "params.txt")
+            if os.path.exists(filename):
+                with open(filename, "r", encoding="utf8") as file:
+                    prompt = file.read()
+
         params = parse_generation_parameters(prompt)
         res = []
 
diff --git a/modules/processing.py b/modules/processing.py
index 698b3069..d5172f00 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -324,6 +324,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     else:
         assert p.prompt is not None
 
+    with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
+        processed = Processed(p, [], p.seed, "")
+        file.write(processed.infotext(p, 0))
+
     devices.torch_gc()
 
     seed = get_fixed_seed(p.seed)
-- 
cgit v1.2.3


From fde7fefa2ea23747f1107e3e46bf60c08a1134f1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 12:26:34 +0300
Subject: update #2336 to prevent reading params.txt when --hide-ui-dir-config
 option is enabled (for servers, since this will let some users access others'
 params)

---
 modules/generation_parameters_copypaste.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 3e75aecc..c27826b6 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -2,6 +2,7 @@ import os
 import re
 import gradio as gr
 from modules.shared import script_path
+from modules import shared
 
 re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)"
 re_param = re.compile(re_param_code)
@@ -63,7 +64,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
 def connect_paste(button, paste_fields, input_comp, js=None):
     def paste_func(prompt):
-        if not prompt:
+        if not prompt and not shared.cmd_opts.hide_ui_dir_config:
             filename = os.path.join(script_path, "params.txt")
             if os.path.exists(filename):
                 with open(filename, "r", encoding="utf8") as file:
-- 
cgit v1.2.3


From aeacbac218c47f61f1d0d3f3b429c9038b8faf0f Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Tue, 11 Oct 2022 19:46:33 -0700
Subject: Fix save error

---
 modules/ui.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index e07ee0e1..4fa405a9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -148,7 +148,10 @@ def save_files(js_data, images, do_make_zip, index):
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
 
-            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+            seed = p.all_seeds[i] if len(p.all_seeds) > 1 else p.seed
+            prompt = p.all_prompts[i] if len(p.all_prompts) > 1 else p.prompt
+            info = p.infotexts[image_index] if len(p.infotexts) > 1 else p.infotexts[0]
+            fullfn, txt_fullfn = save_image(image, path, "", seed=seed, prompt=prompt, extension=extension, info=info, grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
-- 
cgit v1.2.3


From 8711c2fe0135d5c160a57db41cb79ed1942ce7fa Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 16:12:12 -0700
Subject: Fix metadata contents

---
 modules/ui.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 4fa405a9..e07ee0e1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -148,10 +148,7 @@ def save_files(js_data, images, do_make_zip, index):
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
 
-            seed = p.all_seeds[i] if len(p.all_seeds) > 1 else p.seed
-            prompt = p.all_prompts[i] if len(p.all_prompts) > 1 else p.prompt
-            info = p.infotexts[image_index] if len(p.infotexts) > 1 else p.infotexts[0]
-            fullfn, txt_fullfn = save_image(image, path, "", seed=seed, prompt=prompt, extension=extension, info=info, grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
-- 
cgit v1.2.3


From a3f02e4690844715a510b7bc857a0971dd05c4d8 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 16:48:53 -0700
Subject: fix prompt in log.csv

---
 modules/ui.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index e07ee0e1..edb4dab1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -139,6 +139,8 @@ def save_files(js_data, images, do_make_zip, index):
         if at_start:
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
 
+        log_prompt=data["prompt"]
+        log_seed=data["seed"]
         for image_index, filedata in enumerate(images, start_index):
             if filedata.startswith("data:image/png;base64,"):
                 filedata = filedata[len("data:image/png;base64,"):]
@@ -148,7 +150,9 @@ def save_files(js_data, images, do_make_zip, index):
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
 
-            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+            log_seed=p.all_seeds[i]
+            log_prompt=p.all_prompts[i]
+            fullfn, txt_fullfn = save_image(image, path, "", seed=log_seed, prompt=log_prompt, extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
@@ -157,7 +161,7 @@ def save_files(js_data, images, do_make_zip, index):
                 filenames.append(os.path.basename(txt_fullfn))
                 fullfns.append(txt_fullfn)
 
-        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+        writer.writerow([log_prompt, log_seed, data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
 
     # Make Zip
     if do_make_zip:
-- 
cgit v1.2.3


From fed7f0e281a42ea962bbe422e018468bafa6f1e6 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 23:09:30 -0700
Subject: Revert "fix prompt in log.csv"

This reverts commit e4b5d1696429ab78dae9779420ce6ec4cd9c5f67.
---
 modules/ui.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index edb4dab1..e07ee0e1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -139,8 +139,6 @@ def save_files(js_data, images, do_make_zip, index):
         if at_start:
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
 
-        log_prompt=data["prompt"]
-        log_seed=data["seed"]
         for image_index, filedata in enumerate(images, start_index):
             if filedata.startswith("data:image/png;base64,"):
                 filedata = filedata[len("data:image/png;base64,"):]
@@ -150,9 +148,7 @@ def save_files(js_data, images, do_make_zip, index):
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
 
-            log_seed=p.all_seeds[i]
-            log_prompt=p.all_prompts[i]
-            fullfn, txt_fullfn = save_image(image, path, "", seed=log_seed, prompt=log_prompt, extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
@@ -161,7 +157,7 @@ def save_files(js_data, images, do_make_zip, index):
                 filenames.append(os.path.basename(txt_fullfn))
                 fullfns.append(txt_fullfn)
 
-        writer.writerow([log_prompt, log_seed, data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
 
     # Make Zip
     if do_make_zip:
-- 
cgit v1.2.3


From 8636b50aea83f9c743f005722d9f3f8ee9303e00 Mon Sep 17 00:00:00 2001
From: Melan <alexleander91@gmail.com>
Date: Thu, 13 Oct 2022 12:37:58 +0200
Subject: Add learn_rate to csv and removed a left-over debug statement

---
 modules/hypernetworks/hypernetwork.py          | 6 +++---
 modules/textual_inversion/textual_inversion.py | 5 +++--
 2 files changed, 6 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6522078f..2751a8c8 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -257,19 +257,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
             hypernetwork.save(last_saved_file)
 
-        print(f"{write_csv_every} > {hypernetwork.step % write_csv_every == 0}, {write_csv_every}")
         if write_csv_every > 0 and hypernetwork_dir is not None and hypernetwork.step % write_csv_every == 0:
             write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True
             
             with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout:
 
-                csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss"])
+                csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss", "learn_rate"])
                 
                 if write_csv_header:
                     csv_writer.writeheader()
 
                 csv_writer.writerow({"step": hypernetwork.step, 
-                    "loss": f"{losses.mean():.7f}"})
+                    "loss": f"{losses.mean():.7f}",
+                    "learn_rate": scheduler.learn_rate})
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 25038a89..b83df079 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -262,14 +262,15 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
             with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout:
 
-                csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss"])
+                csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss", "learn_rate"])
                 
                 if write_csv_header:
                     csv_writer.writeheader()
 
                 csv_writer.writerow({"epoch": epoch_num + 1, 
                     "epoch_step": epoch_step - 1, 
-                    "loss": f"{losses.mean():.7f}"})
+                    "loss": f"{losses.mean():.7f}",
+                    "learn_rate": scheduler.learn_rate})
 
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
-- 
cgit v1.2.3


From bb7baf6b9cb6b4b9fa09b6f07ef997db32fe6e58 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 16:07:18 +0300
Subject: add option to change what's shown in quicksettings bar

---
 modules/shared.py |  4 ++--
 modules/ui.py     | 16 +++++++++-------
 2 files changed, 11 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 5f6101a4..4d3ed625 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -152,7 +152,6 @@ class OptionInfo:
         self.component_args = component_args
         self.onchange = onchange
         self.section = None
-        self.show_on_main_page = show_on_main_page
 
 
 def options_section(section_identifier, options_dict):
@@ -237,7 +236,7 @@ options_templates.update(options_section(('training', "Training"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
     "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
@@ -250,6 +249,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
+    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
diff --git a/modules/ui.py b/modules/ui.py
index e07ee0e1..a0529860 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1305,6 +1305,9 @@ Requested path was: {f}
         settings_cols = 3
         items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
 
+        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
+        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
+
         quicksettings_list = []
 
         cols_displayed = 0
@@ -1329,7 +1332,7 @@ Requested path was: {f}
 
                     gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
 
-                if item.show_on_main_page:
+                if k in quicksettings_names:
                     quicksettings_list.append((i, k, item))
                     components.append(dummy_component)
                 else:
@@ -1338,7 +1341,11 @@ Requested path was: {f}
                     components.append(component)
                     items_displayed += 1
 
-        request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+        with gr.Row():
+            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+
         request_notifications.click(
             fn=lambda: None,
             inputs=[],
@@ -1346,10 +1353,6 @@ Requested path was: {f}
             _js='function(){}'
         )
 
-        with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-
         def reload_scripts():
             modules.scripts.reload_script_body_only()
 
@@ -1364,7 +1367,6 @@ Requested path was: {f}
             shared.state.interrupt()
             settings_interface.gradio_ref.do_restart = True
 
-
         restart_gradio.click(
             fn=request_restart,
             inputs=[],
-- 
cgit v1.2.3


From a10b0e11fc22cc67b6a3664f2ddd17425d8433a8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 19:22:41 +0300
Subject: options to refresh list of models and hypernetworks

---
 modules/shared.py |  9 +++++----
 modules/ui.py     | 33 +++++++++++++++++++++++++++++----
 2 files changed, 34 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 4d3ed625..d8e3a286 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers
+from modules import sd_samplers, sd_models
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -145,13 +145,14 @@ def realesrgan_models_names():
 
 
 class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False):
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False, refresh=None):
         self.default = default
         self.label = label
         self.component = component
         self.component_args = component_args
         self.onchange = onchange
         self.section = None
+        self.refresh = refresh
 
 
 def options_section(section_identifier, options_dict):
@@ -236,8 +237,8 @@ options_templates.update(options_section(('training', "Training"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
-    "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
+    "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
diff --git a/modules/ui.py b/modules/ui.py
index a0529860..0a58f6be 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -78,6 +78,8 @@ reuse_symbol = '\u267b\ufe0f'  # ♻️
 art_symbol = '\U0001f3a8'  # 🎨
 paste_symbol = '\u2199\ufe0f'  # ↙
 folder_symbol = '\U0001f4c2'  # 📂
+refresh_symbol = '\U0001f504'  # 🔄
+
 
 def plaintext_to_html(text):
     text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
@@ -1210,8 +1212,7 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[],
         )
 
-
-    def create_setting_component(key):
+    def create_setting_component(key, is_quicksettings=False):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
 
@@ -1231,7 +1232,31 @@ def create_ui(wrap_gradio_gpu_call):
         else:
             raise Exception(f'bad options item type: {str(t)} for key {key}')
 
-        return comp(label=info.label, value=fun, **(args or {}))
+        if info.refresh is not None:
+            if is_quicksettings:
+                res = comp(label=info.label, value=fun, **(args or {}))
+                refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_"+key)
+            else:
+                with gr.Row(variant="compact"):
+                    res = comp(label=info.label, value=fun, **(args or {}))
+                    refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_" + key)
+
+            def refresh():
+                info.refresh()
+                refreshed_args = info.component_args() if callable(info.component_args) else info.component_args
+                res.choices = refreshed_args["choices"]
+                return gr.update(**(refreshed_args or {}))
+
+            refresh_button.click(
+                fn=refresh,
+                inputs=[],
+                outputs=[res],
+            )
+        else:
+            res = comp(label=info.label, value=fun, **(args or {}))
+
+
+        return res
 
     components = []
     component_dict = {}
@@ -1401,7 +1426,7 @@ Requested path was: {f}
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings"):
             for i, k, item in quicksettings_list:
-                component = create_setting_component(k)
+                component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
         settings_interface.gradio_ref = demo
-- 
cgit v1.2.3


From 354ef0da3b1f0fa5c113d04b6c79e3908c848d23 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 20:12:37 +0300
Subject: add hypernetwork multipliers

---
 modules/hypernetworks/hypernetwork.py | 8 +++++++-
 modules/shared.py                     | 5 ++++-
 modules/ui.py                         | 5 ++++-
 3 files changed, 15 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b6c06d49..f1248bb7 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -18,6 +18,8 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 
 class HypernetworkModule(torch.nn.Module):
+    multiplier = 1.0
+
     def __init__(self, dim, state_dict=None):
         super().__init__()
 
@@ -36,7 +38,11 @@ class HypernetworkModule(torch.nn.Module):
         self.to(devices.device)
 
     def forward(self, x):
-        return x + (self.linear2(self.linear1(x)))
+        return x + (self.linear2(self.linear1(x))) * self.multiplier
+
+
+def apply_strength(value=None):
+    HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
 
 
 class Hypernetwork:
diff --git a/modules/shared.py b/modules/shared.py
index d8e3a286..5901e605 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -238,7 +238,8 @@ options_templates.update(options_section(('training', "Training"), {
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
-    "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
@@ -348,6 +349,8 @@ class Options:
         item = self.data_labels.get(key)
         item.onchange = func
 
+        func()
+
     def dumpjson(self):
         d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
         return json.dumps(d)
diff --git a/modules/ui.py b/modules/ui.py
index 0a58f6be..673014f2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1244,7 +1244,10 @@ def create_ui(wrap_gradio_gpu_call):
             def refresh():
                 info.refresh()
                 refreshed_args = info.component_args() if callable(info.component_args) else info.component_args
-                res.choices = refreshed_args["choices"]
+
+                for k, v in refreshed_args.items():
+                    setattr(res, k, v)
+
                 return gr.update(**(refreshed_args or {}))
 
             refresh_button.click(
-- 
cgit v1.2.3


From 08b3f7aef15f74f4d2254b1274dd66fcc7940348 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 20:42:27 +0300
Subject: emergency fix for broken send to buttons

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 673014f2..7446439d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1434,7 +1434,7 @@ Requested path was: {f}
 
         settings_interface.gradio_ref = demo
 
-        with gr.Tabs() as tabs:
+        with gr.Tabs(elem_id="tabs") as tabs:
             for interface, label, ifid in interfaces:
                 with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
                     interface.render()
-- 
cgit v1.2.3


From a1489f94283c07824a7a58353c03dc89541bbe49 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Fri, 14 Oct 2022 07:13:38 +0800
Subject: images history fix all known bug

---
 modules/images_history.py | 51 +++++++++++++++++++++++++----------------------
 1 file changed, 27 insertions(+), 24 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index f812ea4e..cdfcffed 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -38,7 +38,7 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname):
     else:
         current_file =  file_list[int(image_index)]
         hide_image = os.path.join(dir_name, current_file)
-    return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image
+    return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image, ""
 def first_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, 1, 0, image_index, tabname)
 def end_page_click(dir_name, page_index, image_index, tabname):
@@ -55,25 +55,28 @@ def show_image_info(num, image_path, filenames):
     file = filenames[int(num)]
     return file, num, os.path.join(image_path, file)
 def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
-    delete_num = int(delete_num)
-    index = list(filenames).index(name)
-    i = 0
-    new_file_list = []
-    for name in filenames:
-        if i  >= index and i < index + delete_num:            
-            path = os.path.join(dir_name, name)
-            if os.path.exists(path):        
-                print(f"Delete file {path}")
-                os.remove(path)
-                txt_file = os.path.splitext(path)[0]  + ".txt"
-                if os.path.exists(txt_file):
-                    os.remove(txt_file)       
+    if name == "":
+        return filenames, delete_num
+    else:
+        delete_num = int(delete_num)
+        index = list(filenames).index(name)
+        i = 0
+        new_file_list = []
+        for name in filenames:
+            if i  >= index and i < index + delete_num:            
+                path = os.path.join(dir_name, name)
+                if os.path.exists(path):        
+                    print(f"Delete file {path}")
+                    os.remove(path)
+                    txt_file = os.path.splitext(path)[0]  + ".txt"
+                    if os.path.exists(txt_file):
+                        os.remove(txt_file)       
+                else:
+                    print(f"Not exists file {path}")
             else:
-                print(f"Not exists file {path}")
-        else:
-            new_file_list.append(name)
-        i += 1
-    return page_index, new_file_list, 1
+                new_file_list.append(name)
+            i += 1
+    return new_file_list, 1
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         if tabname == "txt2img":
@@ -93,9 +96,9 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
             with gr.Row():     
                 with gr.Column(scale=2):                    
                     history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
-                    with gr.Row():
-                        delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")
-                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")                                           
+                    with gr.Row():                        
+                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")   
+                        delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")                                        
                 with gr.Column():     
                     with gr.Row():          
                         pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
@@ -118,7 +121,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                 
         # turn pages
         gallery_inputs = [img_path, page_index, image_index, tabname_box]
-        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image]
+        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image, img_file_name]
 
         first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
         next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
@@ -131,7 +134,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         #other funcitons
         set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
         img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-        delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames, delete_num]) 
+        delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) 
         hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
        
         #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
-- 
cgit v1.2.3


From 4a37c7eedeab579efec03e8dae3f3f9fd4a37b02 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Fri, 14 Oct 2022 11:48:28 +0800
Subject: fix  deep nesting directories problem

---
 modules/images_history.py | 76 ++++++++++++++++++++++++++---------------------
 1 file changed, 42 insertions(+), 34 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index cdfcffed..723f5301 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,44 +1,47 @@
 import os
 import shutil
-def get_recent_images(dir_name, page_index, step, image_index, tabname):
-    #print(f"renew page {page_index}")
-    page_index = int(page_index)
-    f_list = os.listdir(dir_name)
-    file_list = []
+def traverse_all_files(output_dir, image_list, curr_dir=None):
+    curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
+    try:
+        f_list = os.listdir(curr_path)
+    except:
+        if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt":
+            image_list.append(curr_dir)
+        return image_list
     for file in f_list:
+        file = file if curr_dir is None else os.path.join(curr_dir, file)
+        file_path = os.path.join(curr_path, file)
         if file[-4:] == ".txt":
-            continue
-        #subdirectories        
-        if file[-10:].rfind(".") < 0:
-            sub_dir = os.path.join(dir_name, file)
-            if os.path.isfile(sub_dir):
-                continue
-            sub_file_list = os.listdir(sub_dir)
-            for sub_file in sub_file_list:
-                if sub_file[-4:] == ".txt":
-                    continue
-                if os.path.isfile(os.path.join(sub_dir, sub_file) ): 
-                    file_list.append(os.path.join(file, sub_file)) 
-            continue
-        file_list.append(file)        
+            pass
+        elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
+            image_list.append(file)
+        else:
+            image_list = traverse_all_files(output_dir, image_list, file)            
+    return image_list
 
-    file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+        
+def get_recent_images(dir_name, page_index, step, image_index, tabname):
+    page_index = int(page_index)
+    f_list = os.listdir(dir_name)
+    image_list = []
+    image_list = traverse_all_files(dir_name, image_list)
+    image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
     num = 48 if tabname != "extras" else 12
-    max_page_index = len(file_list) // num + 1    
+    max_page_index = len(image_list) // num + 1    
     page_index = max_page_index if page_index == -1 else page_index + step
     page_index = 1 if page_index < 1 else page_index    
     page_index = max_page_index if page_index > max_page_index else page_index
     idx_frm = (page_index - 1) * num
-    file_list = file_list[idx_frm:idx_frm + num]
-    #print(f"Loading history page {page_index}")    
+    image_list = image_list[idx_frm:idx_frm + num]
     image_index = int(image_index)
-    if image_index < 0 or image_index > len(file_list)  - 1:
+    if image_index < 0 or image_index > len(image_list)  - 1:
         current_file = None 
-        hide_image = None
+        hidden = None
     else:
-        current_file =  file_list[int(image_index)]
-        hide_image = os.path.join(dir_name, current_file)
-    return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image, ""
+        current_file =  image_list[int(image_index)]
+        hidden = os.path.join(dir_name, current_file)
+    return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
+
 def first_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, 1, 0, image_index, tabname)
 def end_page_click(dir_name, page_index, image_index, tabname):
@@ -85,6 +88,10 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
             dir_name = opts.outdir_img2img_samples
         elif tabname == "extras":
             dir_name = opts.outdir_extras_samples
+        d = dir_name.split("/")
+        dir_name = d[0]
+        for p in d[1:]:
+            dir_name = os.path.join(dir_name, p)
         with gr.Row():    
             renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page") 
             first_page = gr.Button('First Page')
@@ -109,19 +116,20 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                             img_file_name = gr.Textbox(label="File Name", interactive=False)    
                     with gr.Row():    
                         # hiden items
-                        img_path = gr.Textbox(dir_name, visible=False)                
+
+                        img_path = gr.Textbox(dir_name.rstrip("/") , visible=False)                
                         tabname_box = gr.Textbox(tabname, visible=False)    
                         image_index = gr.Textbox(value=-1, visible=False)                        
                         set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
                         filenames = gr.State()
-                        hide_image = gr.Image(type="pil", visible=False)
+                        hidden = gr.Image(type="pil", visible=False)
                         info1 = gr.Textbox(visible=False)
                         info2 = gr.Textbox(visible=False)
 
                 
         # turn pages
         gallery_inputs = [img_path, page_index, image_index, tabname_box]
-        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image, img_file_name]
+        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
 
         first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
         next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
@@ -132,12 +140,12 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         #page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
 
         #other funcitons
-        set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
+        set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hidden])
         img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
         delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) 
-        hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
+        hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
        
-        #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
+        #pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
         switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
         switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
     
-- 
cgit v1.2.3


From fdecb636855748e03efc40c846a0043800aadfcc Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 09:05:06 +0300
Subject: add an ability to merge three checkpoints

---
 modules/extras.py | 29 +++++++++++++++++++++--------
 modules/ui.py     | 11 +++++++----
 2 files changed, 28 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index b24d7de3..532d869f 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -159,48 +159,61 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half, custom_name):
+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, interp_amount, save_as_half, custom_name):
     # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
-    def weighted_sum(theta0, theta1, alpha):
+    def weighted_sum(theta0, theta1, theta2, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
     # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    def sigmoid(theta0, theta1, alpha):
+    def sigmoid(theta0, theta1, theta2, alpha):
         alpha = alpha * alpha * (3 - (2 * alpha))
         return theta0 + ((theta1 - theta0) * alpha)
 
     # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    def inv_sigmoid(theta0, theta1, alpha):
+    def inv_sigmoid(theta0, theta1, theta2, alpha):
         import math
         alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
         return theta0 + ((theta1 - theta0) * alpha)
 
+    def add_difference(theta0, theta1, theta2, alpha):
+        return theta0 + (theta1 - theta2) * (1.0 - alpha)
+
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
+    teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
 
     print(f"Loading {primary_model_info.filename}...")
     primary_model = torch.load(primary_model_info.filename, map_location='cpu')
+    theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
 
     print(f"Loading {secondary_model_info.filename}...")
     secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
-
-    theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
     theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
 
+    if teritary_model_info is not None:
+        print(f"Loading {teritary_model_info.filename}...")
+        teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
+        theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
+    else:
+        theta_2 = None
+
     theta_funcs = {
         "Weighted Sum": weighted_sum,
         "Sigmoid": sigmoid,
         "Inverse Sigmoid": inv_sigmoid,
+        "Add difference": add_difference,
     }
     theta_func = theta_funcs[interp_method]
 
     print(f"Merging...")
+
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+            theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key] if theta_2 else None, (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
 
+    # I believe this part should be discarded, but I'll leave it for now until I am sure
     for key in theta_1.keys():
         if 'model' in key and key not in theta_0:
             theta_0[key] = theta_1[key]
@@ -219,4 +232,4 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
     sd_models.list_models()
 
     print(f"Checkpoint saved.")
-    return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(3)]
+    return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
diff --git a/modules/ui.py b/modules/ui.py
index 7446439d..220fb80b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1024,11 +1024,12 @@ def create_ui(wrap_gradio_gpu_call):
                 gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
 
                 with gr.Row():
-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")
-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
                 custom_name = gr.Textbox(label="Custom Name (Optional)")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
-                interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation amount (1 - M)', value=0.3)
+                interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid", "Add difference"], value="Weighted Sum", label="Interpolation Method")
                 save_as_half = gr.Checkbox(value=False, label="Save as float16")
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
@@ -1473,6 +1474,7 @@ Requested path was: {f}
             inputs=[
                 primary_model_name,
                 secondary_model_name,
+                tertiary_model_name,
                 interp_method,
                 interp_amount,
                 save_as_half,
@@ -1482,6 +1484,7 @@ Requested path was: {f}
                 submit_result,
                 primary_model_name,
                 secondary_model_name,
+                tertiary_model_name,
                 component_dict['sd_model_checkpoint'],
             ]
         )
-- 
cgit v1.2.3


From bb57f30c2de46cfca5419ad01738a41705f96cc3 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Fri, 14 Oct 2022 10:56:41 +0200
Subject: init

---
 modules/processing.py                          | 17 +++++-
 modules/sd_hijack.py                           | 80 +++++++++++++++++++++++++-
 modules/shared.py                              |  5 ++
 modules/textual_inversion/dataset.py           |  2 +-
 modules/textual_inversion/textual_inversion.py | 35 +++++++----
 modules/txt2img.py                             | 11 +++-
 modules/ui.py                                  | 59 ++++++++++++-------
 7 files changed, 171 insertions(+), 38 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index d5172f00..9a033759 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -316,11 +316,16 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
 
-def process_images(p: StableDiffusionProcessing) -> Processed:
+def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0,
+                   aesthetic_imgs=None,aesthetic_slerp=False) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
+    aesthetic_lr = float(aesthetic_lr)
+    aesthetic_weight = float(aesthetic_weight)
+    aesthetic_steps = int(aesthetic_steps)
+
     if type(p.prompt) == list:
-        assert(len(p.prompt) > 0)
+        assert (len(p.prompt) > 0)
     else:
         assert p.prompt is not None
 
@@ -394,7 +399,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
             #c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
-                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
+                if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
+                    shared.sd_model.cond_stage_model.set_aesthetic_params(0, 0, 0)
+                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt],
+                                                            p.steps)
+                if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
+                    shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight,
+                                                                          aesthetic_steps, aesthetic_imgs,aesthetic_slerp)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index c81722a0..6d5196fe 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -9,11 +9,14 @@ from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared
-from modules.shared import opts, device, cmd_opts
+from modules.shared import opts, device, cmd_opts, aesthetic_embeddings
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
+from transformers import CLIPVisionModel, CLIPModel
+import torch.optim as optim
+import copy
 
 attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
@@ -109,13 +112,29 @@ class StableDiffusionModelHijack:
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
 
+def slerp(low, high, val):
+    low_norm = low/torch.norm(low, dim=1, keepdim=True)
+    high_norm = high/torch.norm(high, dim=1, keepdim=True)
+    omega = torch.acos((low_norm*high_norm).sum(1))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    return res
 
 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
         super().__init__()
         self.wrapped = wrapped
+        self.clipModel = CLIPModel.from_pretrained(
+            self.wrapped.transformer.name_or_path
+        )
+        del self.clipModel.vision_model
         self.hijack: StableDiffusionModelHijack = hijack
         self.tokenizer = wrapped.tokenizer
+        # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval()
+        self.image_embs_name = None
+        self.image_embs = None
+        self.load_image_embs(None)
+
         self.token_mults = {}
 
         self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
@@ -136,6 +155,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
+    def set_aesthetic_params(self, aesthetic_lr, aesthetic_weight, aesthetic_steps, image_embs_name=None,
+                             aesthetic_slerp=True):
+        self.slerp = aesthetic_slerp
+        self.aesthetic_lr = aesthetic_lr
+        self.aesthetic_weight = aesthetic_weight
+        self.aesthetic_steps = aesthetic_steps
+        self.load_image_embs(image_embs_name)
+
+    def load_image_embs(self, image_embs_name):
+        if image_embs_name is None or len(image_embs_name) == 0:
+            image_embs_name = None
+        if image_embs_name is not None and self.image_embs_name != image_embs_name:
+            self.image_embs_name = image_embs_name
+            self.image_embs = torch.load(aesthetic_embeddings[self.image_embs_name], map_location=device)
+            self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
+            self.image_embs.requires_grad_(False)
+
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_end = self.wrapped.tokenizer.eos_token_id
 
@@ -333,7 +369,47 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
             z1 = self.process_tokens(tokens, multipliers)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
-            
+
+            if len(text[
+                       0]) != 0 and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None:
+                if not opts.use_old_emphasis_implementation:
+                    remade_batch_tokens = [
+                        [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in
+                        remade_batch_tokens]
+
+                tokens = torch.asarray(remade_batch_tokens).to(device)
+                with torch.enable_grad():
+                    model = copy.deepcopy(self.clipModel).to(device)
+                    model.requires_grad_(True)
+
+                    # We optimize the model to maximize the similarity
+                    optimizer = optim.Adam(
+                        model.text_model.parameters(), lr=self.aesthetic_lr
+                    )
+
+                    for i in range(self.aesthetic_steps):
+                        text_embs = model.get_text_features(input_ids=tokens)
+                        text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
+                        sim = text_embs @ self.image_embs.T
+                        loss = -sim
+                        optimizer.zero_grad()
+                        loss.mean().backward()
+                        optimizer.step()
+
+                    zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
+                    if opts.CLIP_stop_at_last_layers > 1:
+                        zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
+                        zn = model.text_model.final_layer_norm(zn)
+                    else:
+                        zn = zn.last_hidden_state
+                    model.cpu()
+                    del model
+
+                if self.slerp:
+                    z = slerp(z, zn, self.aesthetic_weight)
+                else:
+                    z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
+
             remade_batch_tokens = rem_tokens
             batch_multipliers = rem_multipliers
             i += 1
diff --git a/modules/shared.py b/modules/shared.py
index 5901e605..cf13a10d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -30,6 +30,8 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(script_path, 'aesthetic_embeddings'),
+                    help="aesthetic_embeddings directory(default: aesthetic_embeddings)")
 parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
@@ -90,6 +92,9 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
+aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
+                        os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
+
 
 def reload_hypernetworks():
     global hypernetworks
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 67e90afe..59b2b021 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -48,7 +48,7 @@ class PersonalizedBase(Dataset):
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
             try:
-                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
+                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.Resampling.BICUBIC)
             except Exception:
                 continue
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fa0e33a2..b12a8e6d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -172,7 +172,15 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
+def batched(dataset, total, n=1):
+    for ndx in range(0, total, n):
+        yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
+
+
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps,
+                    create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding,
+                    preview_image_prompt, batch_size=1,
+                    gradient_accumulation=1):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -204,7 +212,11 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width,
+                                                                height=training_height,
+                                                                repeats=shared.opts.training_image_repeats_per_epoch,
+                                                                placeholder_token=embedding_name, model=shared.sd_model,
+                                                                device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -223,7 +235,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
-    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    pbar = tqdm.tqdm(enumerate(batched(ds, steps - ititial_step, batch_size)), total=steps - ititial_step)
     for i, entry in pbar:
         embedding.step = i + ititial_step
 
@@ -235,17 +247,20 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([entry.cond_text])
+            c = cond_model([e.cond_text for e in entry])
+
+            x = torch.stack([e.latent for e in entry]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
 
-            x = entry.latent.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), c)[0]
             del x
 
             losses[embedding.step % losses.shape[0]] = loss.item()
 
-            optimizer.zero_grad()
             loss.backward()
-            optimizer.step()
+            if ((i + 1) % gradient_accumulation == 0) or (i + 1 == steps - ititial_step):
+                optimizer.step()
+                optimizer.zero_grad()
+
 
         epoch_num = embedding.step // len(ds)
         epoch_step = embedding.step - (epoch_num * len(ds)) + 1
@@ -259,7 +274,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
-            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
+            preview_text = entry[0].cond_text if preview_image_prompt == "" else preview_image_prompt
 
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
@@ -305,7 +320,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {embedding.step}<br/>
-Last prompt: {html.escape(entry.cond_text)}<br/>
+Last prompt: {html.escape(entry[-1].cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
diff --git a/modules/txt2img.py b/modules/txt2img.py
index e985242b..78342024 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,14 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int,
+            restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int,
+            subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool,
+            height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float,
+            aesthetic_lr=0,
+            aesthetic_weight=0, aesthetic_steps=0,
+            aesthetic_imgs=None,
+            aesthetic_slerp=False, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -40,7 +47,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     processed = modules.scripts.scripts_txt2img.run(p, *args)
 
     if processed is None:
-        processed = process_images(p)
+        processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp)
 
     shared.total_tqdm.clear()
 
diff --git a/modules/ui.py b/modules/ui.py
index 220fb80b..d961d126 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -24,7 +24,8 @@ import gradio.routes
 
 from modules import sd_hijack
 from modules.paths import script_path
-from modules.shared import opts, cmd_opts
+from modules.shared import opts, cmd_opts,aesthetic_embeddings
+
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
 import modules.shared as shared
@@ -534,6 +535,14 @@ def create_ui(wrap_gradio_gpu_call):
                     width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
+                with gr.Group():
+                    aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
+                    aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.7)
+                    aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=50)
+
+                    aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
+                    aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
+
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
                     tiling = gr.Checkbox(label='Tiling', value=False)
@@ -586,25 +595,30 @@ def create_ui(wrap_gradio_gpu_call):
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
                 _js="submit",
                 inputs=[
-                    txt2img_prompt,
-                    txt2img_negative_prompt,
-                    txt2img_prompt_style,
-                    txt2img_prompt_style2,
-                    steps,
-                    sampler_index,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    enable_hr,
-                    scale_latent,
-                    denoising_strength,
-                ] + custom_inputs,
+                           txt2img_prompt,
+                           txt2img_negative_prompt,
+                           txt2img_prompt_style,
+                           txt2img_prompt_style2,
+                           steps,
+                           sampler_index,
+                           restore_faces,
+                           tiling,
+                           batch_count,
+                           batch_size,
+                           cfg_scale,
+                           seed,
+                           subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                           height,
+                           width,
+                           enable_hr,
+                           scale_latent,
+                           denoising_strength,
+                           aesthetic_lr,
+                           aesthetic_weight,
+                           aesthetic_steps,
+                           aesthetic_imgs,
+                           aesthetic_slerp
+                       ] + custom_inputs,
                 outputs=[
                     txt2img_gallery,
                     generation_info,
@@ -1097,6 +1111,9 @@ def create_ui(wrap_gradio_gpu_call):
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
                     training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    batch_size = gr.Slider(minimum=1, maximum=64, step=1, label="Batch Size", value=4)
+                    gradient_accumulation = gr.Slider(minimum=1, maximum=256, step=1, label="Gradient accumulation",
+                                                      value=1)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
@@ -1180,6 +1197,8 @@ def create_ui(wrap_gradio_gpu_call):
                 template_file,
                 save_image_with_stored_embedding,
                 preview_image_prompt,
+                batch_size,
+                gradient_accumulation
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.3


From fdef8253a43ca5135923092ca9b85e878d980869 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Fri, 14 Oct 2022 04:42:53 -0400
Subject: Add 'interrogate' and 'all' choices to --use-cpu

* Add 'interrogate' and 'all' choices to --use-cpu
* Change type for --use-cpu argument to str.lower, so that choices are case insensitive
---
 modules/devices.py     |  2 +-
 modules/interrogate.py | 14 +++++++-------
 modules/shared.py      |  6 +++---
 3 files changed, 11 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 03ef58f1..eb422583 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -34,7 +34,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
+device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
 dtype = torch.float16
 dtype_vae = torch.float16
 
diff --git a/modules/interrogate.py b/modules/interrogate.py
index af858cc0..9263d65a 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -55,7 +55,7 @@ class InterrogateModels:
 
         model, preprocess = clip.load(clip_model_name)
         model.eval()
-        model = model.to(shared.device)
+        model = model.to(devices.device_interrogate)
 
         return model, preprocess
 
@@ -65,14 +65,14 @@ class InterrogateModels:
             if not shared.cmd_opts.no_half:
                 self.blip_model = self.blip_model.half()
 
-        self.blip_model = self.blip_model.to(shared.device)
+        self.blip_model = self.blip_model.to(devices.device_interrogate)
 
         if self.clip_model is None:
             self.clip_model, self.clip_preprocess = self.load_clip_model()
             if not shared.cmd_opts.no_half:
                 self.clip_model = self.clip_model.half()
 
-        self.clip_model = self.clip_model.to(shared.device)
+        self.clip_model = self.clip_model.to(devices.device_interrogate)
 
         self.dtype = next(self.clip_model.parameters()).dtype
 
@@ -99,11 +99,11 @@ class InterrogateModels:
             text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
 
         top_count = min(top_count, len(text_array))
-        text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(shared.device)
+        text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate)
         text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
         text_features /= text_features.norm(dim=-1, keepdim=True)
 
-        similarity = torch.zeros((1, len(text_array))).to(shared.device)
+        similarity = torch.zeros((1, len(text_array))).to(devices.device_interrogate)
         for i in range(image_features.shape[0]):
             similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
         similarity /= image_features.shape[0]
@@ -116,7 +116,7 @@ class InterrogateModels:
             transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
             transforms.ToTensor(),
             transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-        ])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+        ])(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
 
         with torch.no_grad():
             caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)
@@ -140,7 +140,7 @@ class InterrogateModels:
 
             res = caption
 
-            clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+            clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
 
             precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
             with torch.no_grad(), precision_scope("cuda"):
diff --git a/modules/shared.py b/modules/shared.py
index 5901e605..b6a5c1a8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -54,7 +54,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en
 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
+parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -76,8 +76,8 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl
 
 cmd_opts = parser.parse_args()
 
-devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
-(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
+(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
 
 device = devices.device
 
-- 
cgit v1.2.3


From 9e5ca5077f43bb3ec1a0ec41b47964cb38d544a6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 16:37:32 +0300
Subject: extra message for unpicking fails

---
 modules/safe.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/safe.py b/modules/safe.py
index 20be16a5..399165a1 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -96,11 +96,18 @@ def load(filename, *args, **kwargs):
         if not shared.cmd_opts.disable_safe_unpickle:
             check_pt(filename)
 
+    except pickle.UnpicklingError:
+        print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+        print(f"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
+        print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
+        return None
+
     except Exception:
         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
         print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
-        print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr)
+        print(f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
         return None
 
     return unsafe_torch_load(filename, *args, **kwargs)
-- 
cgit v1.2.3


From b2261b53ae4ad01b3713bc73ff62ab7b6f479e26 Mon Sep 17 00:00:00 2001
From: Buckzor <bucklogos@yahoo.co.uk>
Date: Thu, 13 Oct 2022 17:07:06 +0100
Subject: Added first_pass_width and height as adjustable inputs to "High Res
 Fix"

---
 modules/processing.py | 6 ++++--
 modules/txt2img.py    | 5 ++++-
 modules/ui.py         | 6 ++++++
 3 files changed, 14 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index d5172f00..abbfdf98 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -506,11 +506,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     firstphase_width_truncated = 0
     firstphase_height_truncated = 0
 
-    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs):
+    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, first_pass_width=512, first_pass_height=512, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.scale_latent = scale_latent
         self.denoising_strength = denoising_strength
+        self.first_pass_width = first_pass_width
+        self.first_pass_height = first_pass_height
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
@@ -519,7 +521,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
-            desired_pixel_count = 512 * 512
+            desired_pixel_count = self.first_pass_width * self.first_pass_height
             actual_pixel_count = self.width * self.height
             scale = math.sqrt(desired_pixel_count / actual_pixel_count)
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index e985242b..85cbece4 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, first_pass_width: int, first_pass_height: int, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -32,6 +32,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         enable_hr=enable_hr,
         scale_latent=scale_latent if enable_hr else None,
         denoising_strength=denoising_strength if enable_hr else None,
+		first_pass_width=first_pass_width if enable_hr else None,
+		first_pass_height=first_pass_height if enable_hr else None,
+
     )
 
     if cmd_opts.enable_console_prompts:
diff --git a/modules/ui.py b/modules/ui.py
index 220fb80b..544419b2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -540,6 +540,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
+                    first_pass_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
+                    first_pass_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
                     scale_latent = gr.Checkbox(label='Scale latent', value=False)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
@@ -604,6 +606,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr,
                     scale_latent,
                     denoising_strength,
+                    first_pass_width,
+                    first_pass_height,
                 ] + custom_inputs,
                 outputs=[
                     txt2img_gallery,
@@ -668,6 +672,8 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 (enable_hr, lambda d: "Denoising strength" in d),
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+                (first_pass_width, "First pass width"),
+                (first_pass_height, "First pass height"),
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-- 
cgit v1.2.3


From 40d1c6e423b4dc52b3bdae43d9e2442960760ced Mon Sep 17 00:00:00 2001
From: Buckzor <bucklogos@yahoo.co.uk>
Date: Thu, 13 Oct 2022 20:04:22 +0100
Subject: Option between stretch and crop for Highres. fix

---
 modules/processing.py | 34 ++++++++++++++++++++++------------
 modules/txt2img.py    |  7 ++++---
 modules/ui.py         | 25 ++++++++++++++++---------
 3 files changed, 42 insertions(+), 24 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index abbfdf98..0246f5dd 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -506,13 +506,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     firstphase_width_truncated = 0
     firstphase_height_truncated = 0
 
-    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, first_pass_width=512, first_pass_height=512, **kwargs):
+    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, crop_scale=False, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.scale_latent = scale_latent
         self.denoising_strength = denoising_strength
-        self.first_pass_width = first_pass_width
-        self.first_pass_height = first_pass_height
+        self.firstphase_width = firstphase_width
+        self.firstphase_height = firstphase_height
+        self.crop_scale = crop_scale
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
@@ -521,14 +522,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
-            desired_pixel_count = self.first_pass_width * self.first_pass_height
-            actual_pixel_count = self.width * self.height
-            scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+            #desired_pixel_count = self.firstphase_width * self.firstphase_height
+            #actual_pixel_count = self.width * self.height
+            #scale = math.sqrt(desired_pixel_count / actual_pixel_count)
 
-            self.firstphase_width = math.ceil(scale * self.width / 64) * 64
-            self.firstphase_height = math.ceil(scale * self.height / 64) * 64
-            self.firstphase_width_truncated = int(scale * self.width)
-            self.firstphase_height_truncated = int(scale * self.height)
+            #self.firstphase_width = math.ceil(scale * self.width / 64) * 64
+            #self.firstphase_height = math.ceil(scale * self.height / 64) * 64
+            #self.firstphase_width_truncated = int(scale * self.width)
+            #self.firstphase_height_truncated = int(scale * self.height)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -541,8 +542,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
-        truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
-        truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
+        truncate_x = 0
+        truncate_y = 0
+
+        if self.crop_scale:
+            if self.width/self.firstphase_width > self.height/self.firstphase_height:
+                #Crop to landscape
+                truncate_y = (self.width -  self.firstphase_width)//2 // opt_f
+
+            elif self.width/self.firstphase_width < self.height/self.firstphase_height:
+                #Crop to portrait
+                truncate_x = (self.height - self.firstphase_height)//2 // opt_f
 
         samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 85cbece4..447ec3d3 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, first_pass_width: int, first_pass_height: int, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, crop_scale: bool, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -32,8 +32,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         enable_hr=enable_hr,
         scale_latent=scale_latent if enable_hr else None,
         denoising_strength=denoising_strength if enable_hr else None,
-		first_pass_width=first_pass_width if enable_hr else None,
-		first_pass_height=first_pass_height if enable_hr else None,
+		firstphase_width=firstphase_width if enable_hr else None,
+		firstphase_height=firstphase_height if enable_hr else None,
+        crop_scale=crop_scale if enable_hr else None,
 
     )
 
diff --git a/modules/ui.py b/modules/ui.py
index 544419b2..f2d81f68 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -540,12 +540,18 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    first_pass_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
-                    first_pass_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
-                    scale_latent = gr.Checkbox(label='Scale latent', value=False)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+                    with gr.Column(scale=1.0):
+                        firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
+                        firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
+                        
+                    with gr.Column(scale=1.0):
+                        with gr.Row():
+                            crop_scale = gr.Checkbox(label='Crop when scaling', value=False)
+                            scale_latent = gr.Checkbox(label='Scale latent', value=False)
+                        with gr.Row():
+                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
-                with gr.Row():
+                with gr.Row(equal_height=True):
                     batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
 
@@ -606,8 +612,9 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr,
                     scale_latent,
                     denoising_strength,
-                    first_pass_width,
-                    first_pass_height,
+                    firstphase_width,
+                    firstphase_height,
+                    crop_scale,
                 ] + custom_inputs,
                 outputs=[
                     txt2img_gallery,
@@ -672,8 +679,8 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 (enable_hr, lambda d: "Denoising strength" in d),
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (first_pass_width, "First pass width"),
-                (first_pass_height, "First pass height"),
+                (firstphase_width, "First pass width"),
+                (firstphase_height, "First pass height"),
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-- 
cgit v1.2.3


From b382de2d77c653c565840ce92d27aa668a1934d7 Mon Sep 17 00:00:00 2001
From: Buckzor <bucklogos@yahoo.co.uk>
Date: Thu, 13 Oct 2022 22:23:22 +0100
Subject: Fixed Scale ratio problem

---
 modules/processing.py | 25 +++++++++++--------------
 1 file changed, 11 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 0246f5dd..d9b0e0e7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -522,15 +522,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
-            #desired_pixel_count = self.firstphase_width * self.firstphase_height
-            #actual_pixel_count = self.width * self.height
-            #scale = math.sqrt(desired_pixel_count / actual_pixel_count)
-
-            #self.firstphase_width = math.ceil(scale * self.width / 64) * 64
-            #self.firstphase_height = math.ceil(scale * self.height / 64) * 64
-            #self.firstphase_width_truncated = int(scale * self.width)
-            #self.firstphase_height_truncated = int(scale * self.height)
-
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
@@ -544,17 +535,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         truncate_x = 0
         truncate_y = 0
+        width_ratio = self.width/self.firstphase_width
+        height_ratio = self.height/self.firstphase_height
 
         if self.crop_scale:
-            if self.width/self.firstphase_width > self.height/self.firstphase_height:
+            if width_ratio > height_ratio:
                 #Crop to landscape
-                truncate_y = (self.width -  self.firstphase_width)//2 // opt_f
+                truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
 
-            elif self.width/self.firstphase_width < self.height/self.firstphase_height:
+            elif width_ratio < height_ratio:
                 #Crop to portrait
-                truncate_x = (self.height - self.firstphase_height)//2 // opt_f
+                truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
+                
+            samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
+
+        
 
-        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
+        
 
         if self.scale_latent:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
-- 
cgit v1.2.3


From e644b5a80beb54b6df4caa63fb19d889dd4ceff6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 17:03:03 +0300
Subject: remove scale latent and no-crop options from hires fix support
 copy-pasting new parameters for hires fix

---
 modules/processing.py | 64 ++++++++++++++++++++++-----------------------------
 modules/txt2img.py    |  9 +++-----
 modules/ui.py         | 19 ++++-----------
 3 files changed, 35 insertions(+), 57 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index d9b0e0e7..100a259f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -506,14 +506,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     firstphase_width_truncated = 0
     firstphase_height_truncated = 0
 
-    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, crop_scale=False, **kwargs):
+    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
-        self.scale_latent = scale_latent
         self.denoising_strength = denoising_strength
         self.firstphase_width = firstphase_width
         self.firstphase_height = firstphase_height
-        self.crop_scale = crop_scale
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
@@ -530,6 +528,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
             return samples
 
+        self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
+
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
@@ -538,46 +538,36 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         width_ratio = self.width/self.firstphase_width
         height_ratio = self.height/self.firstphase_height
 
-        if self.crop_scale:
-            if width_ratio > height_ratio:
-                #Crop to landscape
-                truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
+        if width_ratio > height_ratio:
+            truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
 
-            elif width_ratio < height_ratio:
-                #Crop to portrait
-                truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
+        elif width_ratio < height_ratio:
+            truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
                 
-            samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
-
-        
+        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
 
-        
+        decoded_samples = decode_first_stage(self.sd_model, samples)
 
-        if self.scale_latent:
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
+            decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
         else:
-            decoded_samples = decode_first_stage(self.sd_model, samples)
+            lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
 
-            if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
-                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
-            else:
-                lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
-                batch_images = []
-                for i, x_sample in enumerate(lowres_samples):
-                    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-                    x_sample = x_sample.astype(np.uint8)
-                    image = Image.fromarray(x_sample)
-                    image = images.resize_image(0, image, self.width, self.height)
-                    image = np.array(image).astype(np.float32) / 255.0
-                    image = np.moveaxis(image, 2, 0)
-                    batch_images.append(image)
-
-                decoded_samples = torch.from_numpy(np.array(batch_images))
-                decoded_samples = decoded_samples.to(shared.device)
-                decoded_samples = 2. * decoded_samples - 1.
-
-            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
+            batch_images = []
+            for i, x_sample in enumerate(lowres_samples):
+                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+                x_sample = x_sample.astype(np.uint8)
+                image = Image.fromarray(x_sample)
+                image = images.resize_image(0, image, self.width, self.height)
+                image = np.array(image).astype(np.float32) / 255.0
+                image = np.moveaxis(image, 2, 0)
+                batch_images.append(image)
+
+            decoded_samples = torch.from_numpy(np.array(batch_images))
+            decoded_samples = decoded_samples.to(shared.device)
+            decoded_samples = 2. * decoded_samples - 1.
+
+        samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
         shared.state.nextjob()
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 447ec3d3..2381347f 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, crop_scale: bool, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -30,12 +30,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         restore_faces=restore_faces,
         tiling=tiling,
         enable_hr=enable_hr,
-        scale_latent=scale_latent if enable_hr else None,
         denoising_strength=denoising_strength if enable_hr else None,
-		firstphase_width=firstphase_width if enable_hr else None,
-		firstphase_height=firstphase_height if enable_hr else None,
-        crop_scale=crop_scale if enable_hr else None,
-
+        firstphase_width=firstphase_width if enable_hr else None,
+        firstphase_height=firstphase_height if enable_hr else None,
     )
 
     if cmd_opts.enable_console_prompts:
diff --git a/modules/ui.py b/modules/ui.py
index f2d81f68..d66ddc14 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -540,16 +540,9 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    with gr.Column(scale=1.0):
-                        firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
-                        firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
-                        
-                    with gr.Column(scale=1.0):
-                        with gr.Row():
-                            crop_scale = gr.Checkbox(label='Crop when scaling', value=False)
-                            scale_latent = gr.Checkbox(label='Scale latent', value=False)
-                        with gr.Row():
-                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+                    firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
+                    firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row(equal_height=True):
                     batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
@@ -610,11 +603,9 @@ def create_ui(wrap_gradio_gpu_call):
                     height,
                     width,
                     enable_hr,
-                    scale_latent,
                     denoising_strength,
                     firstphase_width,
                     firstphase_height,
-                    crop_scale,
                 ] + custom_inputs,
                 outputs=[
                     txt2img_gallery,
@@ -679,8 +670,8 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 (enable_hr, lambda d: "Denoising strength" in d),
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (firstphase_width, "First pass width"),
-                (firstphase_height, "First pass height"),
+                (firstphase_width, "First pass size-1"),
+                (firstphase_height, "First pass size-2"),
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-- 
cgit v1.2.3


From 0aec19d7837d8564355fdb286541db7165852e41 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 18:15:03 +0300
Subject: make pasting into img2img prompt work make image params request not
 use temp files

---
 modules/images.py | 36 ++++++++++++++++++------------------
 modules/ui.py     |  9 +++------
 2 files changed, 21 insertions(+), 24 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index f1155b7f..68cdbc93 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -1,4 +1,5 @@
 import datetime
+import io
 import math
 import os
 from collections import namedtuple
@@ -465,21 +466,20 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     return fullfn, txt_fullfn
 
 
-def image_data(image_path):
-    file, ext = os.path.splitext(image_path.name)
-    data = {}
-    if "png" in ext:
-        image = Image.open(image_path.name, "r")
-        print(f"Image data requested for {image_path.name} {image.format} of {type(image)}")
-        try:
-            data = image.text["parameters"]
-        except Exception as e:
-            print(f"Exception: {e}")
-            pass
-        print(f"Image data: {data}")
-    if "txt" in ext:
-        myfile = open(image_path.name, 'r')
-        data = myfile.read()
-        myfile.close()
-
-    return data, None
+def image_data(data):
+    try:
+        image = Image.open(io.BytesIO(data))
+        textinfo = image.text["parameters"]
+        return textinfo, None
+    except Exception:
+        pass
+
+    try:
+        text = data.decode('utf8')
+        assert len(text) < 10000
+        return text, None
+
+    except Exception:
+        pass
+
+    return '', None
diff --git a/modules/ui.py b/modules/ui.py
index 0a3ee887..6266db49 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -514,7 +514,7 @@ def create_ui(wrap_gradio_gpu_call):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
-        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="file", visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
         with gr.Row(elem_id='txt2img_progress_row'):
             with gr.Column(scale=1):
@@ -620,7 +620,6 @@ def create_ui(wrap_gradio_gpu_call):
 
             txt_prompt_img.change(
                 fn=modules.images.image_data,
-                # _js = "get_extras_tab_index",
                 inputs=[
                     txt_prompt_img
                 ],
@@ -692,8 +691,7 @@ def create_ui(wrap_gradio_gpu_call):
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
-            img2img_prompt_img = gr.File(label="", elem_id="txt_prompt_image", file_count="single", type="file",
-                                         visible=False)
+            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
 
             with gr.Column(scale=1):
                 pass
@@ -791,9 +789,8 @@ def create_ui(wrap_gradio_gpu_call):
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-                # _js = "get_extras_tab_index",
                 inputs=[
-                    txt_prompt_img
+                    img2img_prompt_img
                 ],
                 outputs=[
                     img2img_prompt,
-- 
cgit v1.2.3


From 67f447ddcc8a17d11939c3801dca635dc22944c7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 19:30:28 +0300
Subject: possibility to load checkpoint, clip skip, and hypernet from infotext

---
 modules/ui.py | 52 +++++++++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 45 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 6266db49..a37a4e17 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -22,7 +22,7 @@ import gradio as gr
 import gradio.utils
 import gradio.routes
 
-from modules import sd_hijack
+from modules import sd_hijack, sd_models
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts
 if cmd_opts.deepdanbooru:
@@ -507,12 +507,38 @@ def setup_progressbar(progressbar, preview, id_part, textinfo=None):
     )
 
 
+def apply_setting(key, value):
+    if value is None:
+        return gr.update()
+
+    if key == "sd_model_checkpoint":
+        ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+        if ckpt_info is not None:
+            value = ckpt_info.title
+        else:
+            return gr.update()
+
+    comp_args = opts.data_labels[key].component_args
+    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+        return
+
+    valtype = type(opts.data_labels[key].default)
+    oldval = opts.data[key]
+    opts.data[key] = valtype(value) if valtype != type(None) else value
+    if oldval != value and opts.data_labels[key].onchange is not None:
+        opts.data_labels[key].onchange()
+
+    opts.save(shared.config_filename)
+    return value
+
+
 def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
@@ -684,11 +710,10 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
             ]
-            modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -938,7 +963,6 @@ def create_ui(wrap_gradio_gpu_call):
                 (seed_resize_from_h, "Seed resize from-2"),
                 (denoising_strength, "Denoising strength"),
             ]
-            modules.generation_parameters_copypaste.connect_paste(paste, img2img_paste_fields, img2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
@@ -1580,8 +1604,22 @@ Requested path was: {f}
             outputs=[extras_image],
         )
 
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields, generation_info, 'switch_to_txt2img')
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields, generation_info, 'switch_to_img2img_img2img')
+        settings_map = {
+            'sd_hypernetwork': 'Hypernet',
+            'CLIP_stop_at_last_layers': 'Clip skip',
+            'sd_model_checkpoint': 'Model hash',
+        }
+
+        settings_paste_fields = [
+            (component_dict[k], lambda d, k=k, v=v: apply_setting(k, d.get(v, None)))
+            for k, v in settings_map.items()
+        ]
+
+        modules.generation_parameters_copypaste.connect_paste(txt2img_paste, txt2img_paste_fields + settings_paste_fields, txt2img_prompt)
+        modules.generation_parameters_copypaste.connect_paste(img2img_paste, img2img_paste_fields + settings_paste_fields, img2img_prompt)
+
+        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_txt2img')
+        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_img2img_img2img')
 
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
-- 
cgit v1.2.3


From 2fb9891af3bb4c36a6de6b44937e927bda43c10d Mon Sep 17 00:00:00 2001
From: Gugubo <29143981+Gugubo@users.noreply.github.com>
Date: Fri, 14 Oct 2022 14:19:39 +0200
Subject: Change grid row count autodetect to prevent empty spots

Instead of just rounding (sometimes resulting in grids with "empty" spots), find a divisor.
For example: 8 images will now result in a 4x2 grid instead of a 3x3 with one empty spot.
---
 modules/images.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 68cdbc93..90eca37a 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -25,8 +25,9 @@ def image_grid(imgs, batch_size=1, rows=None):
         elif opts.n_rows == 0:
             rows = batch_size
         else:
-            rows = math.sqrt(len(imgs))
-            rows = round(rows)
+            rows = math.floor(math.sqrt(len(imgs)))
+            while len(imgs) % rows != 0:
+                rows -= 1
 
     cols = math.ceil(len(imgs) / rows)
 
-- 
cgit v1.2.3


From 43f926aad1b77a4bb642c1d173adfae1f56cf42d Mon Sep 17 00:00:00 2001
From: Gugubo <29143981+Gugubo@users.noreply.github.com>
Date: Fri, 14 Oct 2022 17:06:51 +0200
Subject: Add option to prevent empty spots in grid (1/2)

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index b6a5c1a8..159f504f 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -175,6 +175,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "grid_format": OptionInfo('png', 'File format for grids'),
     "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
     "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
+    "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
     "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
 
     "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
-- 
cgit v1.2.3


From 5f87dd1ee0960963e3f756c4ebe47652ff57f715 Mon Sep 17 00:00:00 2001
From: Gugubo <29143981+Gugubo@users.noreply.github.com>
Date: Fri, 14 Oct 2022 17:07:24 +0200
Subject: Add option to prevent empty spots in grid (2/2)

---
 modules/images.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 90eca37a..b9589563 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -24,10 +24,13 @@ def image_grid(imgs, batch_size=1, rows=None):
             rows = opts.n_rows
         elif opts.n_rows == 0:
             rows = batch_size
-        else:
+        elif opts.grid_prevent_empty_spots:
             rows = math.floor(math.sqrt(len(imgs)))
             while len(imgs) % rows != 0:
                 rows -= 1
+        else:
+            rows = math.sqrt(len(imgs))
+            rows = round(rows)
 
     cols = math.ceil(len(imgs) / rows)
 
-- 
cgit v1.2.3


From a8eeb2b7ad0c43ad60ac2ba8bd299b9cb265fdd3 Mon Sep 17 00:00:00 2001
From: Ljzd-PRO <63289359+Ljzd-PRO@users.noreply.github.com>
Date: Thu, 13 Oct 2022 02:03:08 +0800
Subject: add `--lowram` parameter

load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server)
---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 159f504f..cd4a4714 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -34,6 +34,7 @@ parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
+parser.add_argument("--lowram", action='store_true', help="load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server)")
 parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
-- 
cgit v1.2.3


From 4a216ded433ded315106e2989c5ff7dec1c49304 Mon Sep 17 00:00:00 2001
From: Ljzd-PRO <63289359+Ljzd-PRO@users.noreply.github.com>
Date: Thu, 13 Oct 2022 02:07:49 +0800
Subject: load models to VRAM when using `--lowram` param

load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server)
---
 modules/sd_models.py | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 0a55b4c3..78a198b9 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -134,7 +134,12 @@ def load_model_weights(model, checkpoint_info):
 
     print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-    pl_sd = torch.load(checkpoint_file, map_location="cpu")
+    if shared.cmd_opts.lowram:
+        print("Load to VRAM if GPU is available (low RAM)")
+        pl_sd = torch.load(checkpoint_file)
+    else:
+        pl_sd = torch.load(checkpoint_file, map_location="cpu")
+
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
 
@@ -158,7 +163,13 @@ def load_model_weights(model, checkpoint_info):
 
     if os.path.exists(vae_file):
         print(f"Loading VAE weights from: {vae_file}")
-        vae_ckpt = torch.load(vae_file, map_location="cpu")
+
+        if shared.cmd_opts.lowram:
+            print("Load to VRAM if GPU is available (low RAM)")
+            vae_ckpt = torch.load(vae_file)
+        else:
+            vae_ckpt = torch.load(vae_file, map_location="cpu")
+
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
         model.first_stage_model.load_state_dict(vae_dict)
-- 
cgit v1.2.3


From bb295f54785ac36dc6aa6f7103a3431464440fc3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 20:03:41 +0300
Subject: rework the code for lowram a bit

---
 modules/sd_models.py | 12 ++----------
 modules/shared.py    |  3 ++-
 2 files changed, 4 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 78a198b9..3a01c93d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -134,11 +134,7 @@ def load_model_weights(model, checkpoint_info):
 
     print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-    if shared.cmd_opts.lowram:
-        print("Load to VRAM if GPU is available (low RAM)")
-        pl_sd = torch.load(checkpoint_file)
-    else:
-        pl_sd = torch.load(checkpoint_file, map_location="cpu")
+    pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
 
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
@@ -164,11 +160,7 @@ def load_model_weights(model, checkpoint_info):
     if os.path.exists(vae_file):
         print(f"Loading VAE weights from: {vae_file}")
 
-        if shared.cmd_opts.lowram:
-            print("Load to VRAM if GPU is available (low RAM)")
-            vae_ckpt = torch.load(vae_file)
-        else:
-            vae_ckpt = torch.load(vae_file, map_location="cpu")
+        vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
 
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
diff --git a/modules/shared.py b/modules/shared.py
index cd4a4714..695d29b6 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -34,7 +34,7 @@ parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
-parser.add_argument("--lowram", action='store_true', help="load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server)")
+parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
 parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
@@ -81,6 +81,7 @@ devices.device, devices.device_interrogate, devices.device_gfpgan, devices.devic
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
 
 device = devices.device
+weight_load_location = None if cmd_opts.lowram else "cpu"
 
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
 parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
-- 
cgit v1.2.3


From c344ba3b325459abbf9b0df2c1b18f7bf99805b2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 20:31:49 +0300
Subject: add option to read generation params for learning previews from
 txt2img

---
 modules/hypernetworks/hypernetwork.py          | 21 ++++++++++++++++-----
 modules/textual_inversion/textual_inversion.py | 25 ++++++++++++++++++-------
 modules/ui.py                                  | 20 +++++++++++++++++---
 3 files changed, 51 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index f1248bb7..e5cb1817 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -180,7 +180,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     return self.to_out(out)
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
+def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -265,20 +265,31 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
 
-            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
-
             optimizer.zero_grad()
             shared.sd_model.cond_stage_model.to(devices.device)
             shared.sd_model.first_stage_model.to(devices.device)
 
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
-                prompt=preview_text,
-                steps=20,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
 
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entry.cond_text
+                p.steps = 20
+
+            preview_text = p.prompt
+
             processed = processing.process_images(p)
             image = processed.images[0] if len(processed.images)>0 else None
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fa0e33a2..3d835358 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -172,7 +172,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -259,18 +259,29 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
-            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
-
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
-                prompt=preview_text,
-                steps=20,
-                height=training_height,
-                width=training_width,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
 
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entry.cond_text
+                p.steps = 20
+                p.width = training_width
+                p.height = training_height
+
+            preview_text = p.prompt
+
             processed = processing.process_images(p)
             image = processed.images[0]
 
diff --git a/modules/ui.py b/modules/ui.py
index 828bfeea..4a04c2cc 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -711,6 +711,18 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
             ]
+
+            txt2img_preview_params = [
+                txt2img_prompt,
+                txt2img_negative_prompt,
+                steps,
+                sampler_index,
+                cfg_scale,
+                seed,
+                width,
+                height,
+            ]
+
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
@@ -1162,7 +1174,7 @@ def create_ui(wrap_gradio_gpu_call):
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
-                    preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
 
                     with gr.Row():
                         interrupt_training = gr.Button(value="Interrupt")
@@ -1240,7 +1252,8 @@ def create_ui(wrap_gradio_gpu_call):
                 save_embedding_every,
                 template_file,
                 save_image_with_stored_embedding,
-                preview_image_prompt,
+                preview_from_txt2img,
+                *txt2img_preview_params,
             ],
             outputs=[
                 ti_output,
@@ -1260,7 +1273,8 @@ def create_ui(wrap_gradio_gpu_call):
                 create_image_every,
                 save_embedding_every,
                 template_file,
-                preview_image_prompt,
+                preview_from_txt2img,
+                *txt2img_preview_params,
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.3


From 2f0e089c7c8e1ad7d2ad658971c6fdec9622e3ab Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 21:20:28 +0300
Subject: should fix the issue with missing layers in chechpoint merger

---
 modules/extras.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 532d869f..2e7b3751 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -209,7 +209,12 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key] if theta_2 else None, (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+            t2 = (theta_2 or {}).get(key)
+            if t2 is None:
+                t2 = torch.zeros_like(theta_0[key])
+
+            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
 
-- 
cgit v1.2.3


From c250cb289c97fe303cef69064bf45899406f6a40 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 22:01:49 +0300
Subject: change checkpoint merger to work in a more obvious way remove sigmoid
 and inverse sigmoid because they just did the same thing as weighed sum only
 with changed multiplier

---
 modules/extras.py | 24 +++++-------------------
 modules/ui.py     |  4 ++--
 2 files changed, 7 insertions(+), 21 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 2e7b3751..f2f5a7b0 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -159,24 +159,12 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, interp_amount, save_as_half, custom_name):
-    # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
     def weighted_sum(theta0, theta1, theta2, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
-    # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    def sigmoid(theta0, theta1, theta2, alpha):
-        alpha = alpha * alpha * (3 - (2 * alpha))
-        return theta0 + ((theta1 - theta0) * alpha)
-
-    # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    def inv_sigmoid(theta0, theta1, theta2, alpha):
-        import math
-        alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
-        return theta0 + ((theta1 - theta0) * alpha)
-
     def add_difference(theta0, theta1, theta2, alpha):
-        return theta0 + (theta1 - theta2) * (1.0 - alpha)
+        return theta0 + (theta1 - theta2) * alpha
 
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
@@ -198,9 +186,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
         theta_2 = None
 
     theta_funcs = {
-        "Weighted Sum": weighted_sum,
-        "Sigmoid": sigmoid,
-        "Inverse Sigmoid": inv_sigmoid,
+        "Weighted sum": weighted_sum,
         "Add difference": add_difference,
     }
     theta_func = theta_funcs[interp_method]
@@ -213,7 +199,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
             if t2 is None:
                 t2 = torch.zeros_like(theta_0[key])
 
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier)
 
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
@@ -227,7 +213,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
-    filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
+    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
     filename = filename if custom_name == '' else (custom_name + '.ckpt')
     output_modelname = os.path.join(ckpt_dir, filename)
 
diff --git a/modules/ui.py b/modules/ui.py
index 4a04c2cc..a08ffc9b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1101,8 +1101,8 @@ def create_ui(wrap_gradio_gpu_call):
                     secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
                     tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
                 custom_name = gr.Textbox(label="Custom Name (Optional)")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation amount (1 - M)', value=0.3)
-                interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid", "Add difference"], value="Weighted Sum", label="Interpolation Method")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
+                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
                 save_as_half = gr.Checkbox(value=False, label="Save as float16")
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
-- 
cgit v1.2.3


From 03d62538aebeff51713619fe808c953bdb70193d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 22:43:55 +0300
Subject: remove duplicate code for log loss, add step, make it read from
 options rather than gradio input

---
 modules/hypernetworks/hypernetwork.py          | 20 ++++--------
 modules/shared.py                              |  3 +-
 modules/textual_inversion/textual_inversion.py | 44 ++++++++++++++++++--------
 modules/ui.py                                  |  3 --
 4 files changed, 38 insertions(+), 32 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index edb8cba1..59c7ac6e 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -15,6 +15,7 @@ import torch
 from torch import einsum
 from einops import rearrange, repeat
 import modules.textual_inversion.dataset
+from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 
@@ -210,7 +211,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -263,19 +264,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
             hypernetwork.save(last_saved_file)
 
-        if write_csv_every > 0 and hypernetwork_dir is not None and hypernetwork.step % write_csv_every == 0:
-            write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True
-            
-            with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout:
-
-                csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss", "learn_rate"])
-                
-                if write_csv_header:
-                    csv_writer.writeheader()
-
-                csv_writer.writerow({"step": hypernetwork.step, 
-                    "loss": f"{losses.mean():.7f}",
-                    "learn_rate": scheduler.learn_rate})
+        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
+            "loss": f"{losses.mean():.7f}",
+            "learn_rate": scheduler.learn_rate
+        })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
diff --git a/modules/shared.py b/modules/shared.py
index 695d29b6..d41a7ab3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -236,7 +236,8 @@ options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-    "training_image_repeats_per_epoch": OptionInfo(100, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
+    "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
+    "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1f5ace6f..da0d77a0 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -173,6 +173,32 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
+def write_loss(log_directory, filename, step, epoch_len, values):
+    if shared.opts.training_write_csv_every == 0:
+        return
+
+    if step % shared.opts.training_write_csv_every != 0:
+        return
+
+    write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
+
+    with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
+        csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())])
+
+        if write_csv_header:
+            csv_writer.writeheader()
+
+        epoch = step // epoch_len
+        epoch_step = step - epoch * epoch_len
+
+        csv_writer.writerow({
+            "step": step + 1,
+            "epoch": epoch + 1,
+            "epoch_step": epoch_step + 1,
+            **values,
+        })
+
+
 def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert embedding_name, 'embedding not selected'
 
@@ -257,20 +283,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
             embedding.save(last_saved_file)
 
-        if write_csv_every > 0 and log_directory is not None and embedding.step % write_csv_every == 0:
-            write_csv_header = False if os.path.exists(os.path.join(log_directory, "textual_inversion_loss.csv")) else True
-
-            with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout:
-
-                csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss", "learn_rate"])
-                
-                if write_csv_header:
-                    csv_writer.writeheader()
-
-                csv_writer.writerow({"epoch": epoch_num + 1, 
-                    "epoch_step": epoch_step - 1, 
-                    "loss": f"{losses.mean():.7f}",
-                    "learn_rate": scheduler.learn_rate})
+        write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
+            "loss": f"{losses.mean():.7f}",
+            "learn_rate": scheduler.learn_rate
+        })
 
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
diff --git a/modules/ui.py b/modules/ui.py
index be4a43a7..a08ffc9b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1172,7 +1172,6 @@ def create_ui(wrap_gradio_gpu_call):
                     training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
-                    write_csv_every = gr.Number(label='Save an csv containing the loss to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
@@ -1251,7 +1250,6 @@ def create_ui(wrap_gradio_gpu_call):
                 steps,
                 create_image_every,
                 save_embedding_every,
-                write_csv_every,
                 template_file,
                 save_image_with_stored_embedding,
                 preview_from_txt2img,
@@ -1274,7 +1272,6 @@ def create_ui(wrap_gradio_gpu_call):
                 steps,
                 create_image_every,
                 save_embedding_every,
-                write_csv_every,
                 template_file,
                 preview_from_txt2img,
                 *txt2img_preview_params,
-- 
cgit v1.2.3


From e21f01f64504bc651da6e85216474bbd35ee010d Mon Sep 17 00:00:00 2001
From: Rae Fu <rraefu@gmail.com>
Date: Thu, 13 Oct 2022 23:00:38 -0600
Subject: add checkpoint cache option to UI for faster model switching

switching time reduced from ~1500ms to ~280ms
---
 modules/sd_models.py | 54 +++++++++++++++++++++++++++++++---------------------
 modules/shared.py    |  1 +
 2 files changed, 33 insertions(+), 22 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 0a55b4c3..f3660d8d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1,4 +1,4 @@
-import glob
+import collections
 import os.path
 import sys
 from collections import namedtuple
@@ -15,6 +15,7 @@ model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
 CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
 checkpoints_list = {}
+checkpoints_loaded = collections.OrderedDict()
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -132,38 +133,46 @@ def load_model_weights(model, checkpoint_info):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
+    if checkpoint_info not in checkpoints_loaded:
+        print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-    pl_sd = torch.load(checkpoint_file, map_location="cpu")
-    if "global_step" in pl_sd:
-        print(f"Global Step: {pl_sd['global_step']}")
+        pl_sd = torch.load(checkpoint_file, map_location="cpu")
+        if "global_step" in pl_sd:
+            print(f"Global Step: {pl_sd['global_step']}")
 
-    sd = get_state_dict_from_checkpoint(pl_sd)
+        sd = get_state_dict_from_checkpoint(pl_sd)
+        model.load_state_dict(sd, strict=False)
 
-    model.load_state_dict(sd, strict=False)
+        if shared.cmd_opts.opt_channelslast:
+            model.to(memory_format=torch.channels_last)
 
-    if shared.cmd_opts.opt_channelslast:
-        model.to(memory_format=torch.channels_last)
+        if not shared.cmd_opts.no_half:
+            model.half()
 
-    if not shared.cmd_opts.no_half:
-        model.half()
+        devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
+        devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
-    devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
-    devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
+        vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
 
-    vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+        if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
+            vae_file = shared.cmd_opts.vae_path
 
-    if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
-        vae_file = shared.cmd_opts.vae_path
+        if os.path.exists(vae_file):
+            print(f"Loading VAE weights from: {vae_file}")
+            vae_ckpt = torch.load(vae_file, map_location="cpu")
+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
-    if os.path.exists(vae_file):
-        print(f"Loading VAE weights from: {vae_file}")
-        vae_ckpt = torch.load(vae_file, map_location="cpu")
-        vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+            model.first_stage_model.load_state_dict(vae_dict)
 
-        model.first_stage_model.load_state_dict(vae_dict)
+        model.first_stage_model.to(devices.dtype_vae)
 
-    model.first_stage_model.to(devices.dtype_vae)
+        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+            checkpoints_loaded.popitem(last=False)  # LRU
+    else:
+        print(f"Loading weights [{sd_model_hash}] from cache")
+        checkpoints_loaded.move_to_end(checkpoint_info)
+        model.load_state_dict(checkpoints_loaded[checkpoint_info])
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
@@ -202,6 +211,7 @@ def reload_model_weights(sd_model, info=None):
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
+        checkpoints_loaded.clear()
         shared.sd_model = load_model()
         return shared.sd_model
 
diff --git a/modules/shared.py b/modules/shared.py
index 5901e605..b2090da1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -238,6 +238,7 @@ options_templates.update(options_section(('training', "Training"), {
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
+    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-- 
cgit v1.2.3


From cd58e44051f658f2efb544203a92837f43786372 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 23:17:28 +0300
Subject: disabling history - i knew it was slow as fuck but i didn't realize
 it would also show galleries on launch

---
 modules/ui.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index a08ffc9b..6d193955 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1089,7 +1089,8 @@ def create_ui(wrap_gradio_gpu_call):
         "t2i":txt2img_paste_fields,
         "i2i":img2img_paste_fields
     }
-    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) 
+
+    #images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
@@ -1486,7 +1487,7 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (images_history, "History", "images_history"),
+        #(images_history, "History", "images_history"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
-- 
cgit v1.2.3


From 368f4cc4c73509c1968cd9defe068d8bf4ff7c4f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 23:19:05 +0300
Subject: set firstpass w/h to 0 by default and rever to old behavior when any
 are 0

---
 modules/processing.py | 49 ++++++++++++++++++++++++++++++-------------------
 modules/ui.py         |  4 ++--
 2 files changed, 32 insertions(+), 21 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 100a259f..a75b9f84 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -501,17 +501,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
-    firstphase_width = 0
-    firstphase_height = 0
-    firstphase_width_truncated = 0
-    firstphase_height_truncated = 0
 
-    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, **kwargs):
+    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.denoising_strength = denoising_strength
         self.firstphase_width = firstphase_width
         self.firstphase_height = firstphase_height
+        self.truncate_x = 0
+        self.truncate_y = 0
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
@@ -520,6 +518,32 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
+            if self.firstphase_width == 0 or self.firstphase_height == 0:
+                desired_pixel_count = 512 * 512
+                actual_pixel_count = self.width * self.height
+                scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+                self.firstphase_width = math.ceil(scale * self.width / 64) * 64
+                self.firstphase_height = math.ceil(scale * self.height / 64) * 64
+                firstphase_width_truncated = int(scale * self.width)
+                firstphase_height_truncated = int(scale * self.height)
+
+            else:
+                self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
+
+                width_ratio = self.width / self.firstphase_width
+                height_ratio = self.height / self.firstphase_height
+
+                if width_ratio > height_ratio:
+                    firstphase_width_truncated = self.firstphase_width
+                    firstphase_height_truncated = self.firstphase_width * self.height / self.width
+                else:
+                    firstphase_width_truncated = self.firstphase_height * self.width / self.height
+                    firstphase_height_truncated = self.firstphase_height
+
+            self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
+            self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
+
+
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
@@ -528,23 +552,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
             return samples
 
-        self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
-
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
-        truncate_x = 0
-        truncate_y = 0
-        width_ratio = self.width/self.firstphase_width
-        height_ratio = self.height/self.firstphase_height
-
-        if width_ratio > height_ratio:
-            truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
-
-        elif width_ratio < height_ratio:
-            truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
-                
-        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
+        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
         decoded_samples = decode_first_stage(self.sd_model, samples)
 
diff --git a/modules/ui.py b/modules/ui.py
index 6d193955..a1d18be9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -567,8 +567,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
-                    firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass height", value=0)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row(equal_height=True):
-- 
cgit v1.2.3


From 4d19f3b7d461fe0f63e7ccff936909b0ce0c6126 Mon Sep 17 00:00:00 2001
From: Melan <alexleander91@gmail.com>
Date: Fri, 14 Oct 2022 22:45:26 +0200
Subject: Raise an assertion error if no training images have been found.

---
 modules/textual_inversion/dataset.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 67e90afe..12e2f43b 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -81,7 +81,8 @@ class PersonalizedBase(Dataset):
                 entry.cond = cond_model([entry.cond_text]).to(devices.cpu)
 
             self.dataset.append(entry)
-
+        
+        assert len(self.dataset) > 1, "No images have been found in the dataset."
         self.length = len(self.dataset) * repeats
 
         self.initial_indexes = np.arange(self.length) % len(self.dataset)
-- 
cgit v1.2.3


From 4dc426509918e90bf4557ecfd1f84031362360c0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 00:21:48 +0300
Subject: rename firstpass w/h to discard old user settings

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index a1d18be9..c5d295ea 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -567,8 +567,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass height", value=0)
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row(equal_height=True):
-- 
cgit v1.2.3


From 4bbe5d62e042e78cfe1dc83492c2398a39a2455c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 00:25:09 +0300
Subject: reformat lines in images_history.py

---
 modules/images_history.py | 182 +++++++++++++++++++++++++---------------------
 1 file changed, 98 insertions(+), 84 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 723f5301..f5ef44fe 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,5 +1,7 @@
 import os
 import shutil
+
+
 def traverse_all_files(output_dir, image_list, curr_dir=None):
     curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
     try:
@@ -16,10 +18,10 @@ def traverse_all_files(output_dir, image_list, curr_dir=None):
         elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
             image_list.append(file)
         else:
-            image_list = traverse_all_files(output_dir, image_list, file)            
+            image_list = traverse_all_files(output_dir, image_list, file)
     return image_list
 
-        
+
 def get_recent_images(dir_name, page_index, step, image_index, tabname):
     page_index = int(page_index)
     f_list = os.listdir(dir_name)
@@ -27,36 +29,48 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname):
     image_list = traverse_all_files(dir_name, image_list)
     image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
     num = 48 if tabname != "extras" else 12
-    max_page_index = len(image_list) // num + 1    
+    max_page_index = len(image_list) // num + 1
     page_index = max_page_index if page_index == -1 else page_index + step
-    page_index = 1 if page_index < 1 else page_index    
+    page_index = 1 if page_index < 1 else page_index
     page_index = max_page_index if page_index > max_page_index else page_index
     idx_frm = (page_index - 1) * num
     image_list = image_list[idx_frm:idx_frm + num]
     image_index = int(image_index)
-    if image_index < 0 or image_index > len(image_list)  - 1:
-        current_file = None 
+    if image_index < 0 or image_index > len(image_list) - 1:
+        current_file = None
         hidden = None
     else:
-        current_file =  image_list[int(image_index)]
+        current_file = image_list[int(image_index)]
         hidden = os.path.join(dir_name, current_file)
     return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
 
+
 def first_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, 1, 0, image_index, tabname)
+
+
 def end_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, -1, 0, image_index, tabname)
+
+
 def prev_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, page_index, -1, image_index, tabname)
-def next_page_click(dir_name, page_index, image_index, tabname):    
+
+
+def next_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, page_index, 1, image_index, tabname)
-def page_index_change(dir_name, page_index, image_index, tabname):    
+
+
+def page_index_change(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, page_index, 0, image_index, tabname)
 
+
 def show_image_info(num, image_path, filenames):
-    #print(f"select image {num}")
+    # print(f"select image {num}")
     file = filenames[int(num)]
     return file, num, os.path.join(image_path, file)
+
+
 def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
     if name == "":
         return filenames, delete_num
@@ -66,14 +80,14 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima
         i = 0
         new_file_list = []
         for name in filenames:
-            if i  >= index and i < index + delete_num:            
+            if i >= index and i < index + delete_num:
                 path = os.path.join(dir_name, name)
-                if os.path.exists(path):        
+                if os.path.exists(path):
                     print(f"Delete file {path}")
                     os.remove(path)
-                    txt_file = os.path.splitext(path)[0]  + ".txt"
+                    txt_file = os.path.splitext(path)[0] + ".txt"
                     if os.path.exists(txt_file):
-                        os.remove(txt_file)       
+                        os.remove(txt_file)
                 else:
                     print(f"Not exists file {path}")
             else:
@@ -81,81 +95,81 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima
             i += 1
     return new_file_list, 1
 
+
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
-        if tabname == "txt2img":
-            dir_name = opts.outdir_txt2img_samples
-        elif tabname == "img2img":
-            dir_name = opts.outdir_img2img_samples
-        elif tabname == "extras":
-            dir_name = opts.outdir_extras_samples
-        d = dir_name.split("/")
-        dir_name = d[0]
-        for p in d[1:]:
-            dir_name = os.path.join(dir_name, p)
-        with gr.Row():    
-            renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page") 
-            first_page = gr.Button('First Page')
-            prev_page = gr.Button('Prev Page') 
-            page_index = gr.Number(value=1, label="Page Index")
-            next_page = gr.Button('Next Page') 
-            end_page = gr.Button('End Page')    
-        with gr.Row(elem_id=tabname + "_images_history"):            
-            with gr.Row():     
-                with gr.Column(scale=2):                    
-                    history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
-                    with gr.Row():                        
-                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")   
-                        delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")                                        
-                with gr.Column():     
-                    with gr.Row():          
-                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
-                    with gr.Row():
-                        with gr.Column():
-                            img_file_info = gr.Textbox(label="Generate Info", interactive=False)
-                            img_file_name = gr.Textbox(label="File Name", interactive=False)    
-                    with gr.Row():    
-                        # hiden items
-
-                        img_path = gr.Textbox(dir_name.rstrip("/") , visible=False)                
-                        tabname_box = gr.Textbox(tabname, visible=False)    
-                        image_index = gr.Textbox(value=-1, visible=False)                        
-                        set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
-                        filenames = gr.State()
-                        hidden = gr.Image(type="pil", visible=False)
-                        info1 = gr.Textbox(visible=False)
-                        info2 = gr.Textbox(visible=False)
-
-                
-        # turn pages
-        gallery_inputs = [img_path, page_index, image_index, tabname_box]
-        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
-
-        first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-        next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-        prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)        
-        end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-        page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-        renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-        #page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
-
-        #other funcitons
-        set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hidden])
-        img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-        delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) 
-        hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-       
-        #pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-        switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
-        switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
-    
-        
+    if tabname == "txt2img":
+        dir_name = opts.outdir_txt2img_samples
+    elif tabname == "img2img":
+        dir_name = opts.outdir_img2img_samples
+    elif tabname == "extras":
+        dir_name = opts.outdir_extras_samples
+    d = dir_name.split("/")
+    dir_name = d[0]
+    for p in d[1:]:
+        dir_name = os.path.join(dir_name, p)
+    with gr.Row():
+        renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
+        first_page = gr.Button('First Page')
+        prev_page = gr.Button('Prev Page')
+        page_index = gr.Number(value=1, label="Page Index")
+        next_page = gr.Button('Next Page')
+        end_page = gr.Button('End Page')
+    with gr.Row(elem_id=tabname + "_images_history"):
+        with gr.Row():
+            with gr.Column(scale=2):
+                history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
+                with gr.Row():
+                    delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
+                    delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
+            with gr.Column():
+                with gr.Row():
+                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                with gr.Row():
+                    with gr.Column():
+                        img_file_info = gr.Textbox(label="Generate Info", interactive=False)
+                        img_file_name = gr.Textbox(label="File Name", interactive=False)
+                with gr.Row():
+                    # hiden items
+
+                    img_path = gr.Textbox(dir_name.rstrip("/"), visible=False)
+                    tabname_box = gr.Textbox(tabname, visible=False)
+                    image_index = gr.Textbox(value=-1, visible=False)
+                    set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False)
+                    filenames = gr.State()
+                    hidden = gr.Image(type="pil", visible=False)
+                    info1 = gr.Textbox(visible=False)
+                    info2 = gr.Textbox(visible=False)
+
+    # turn pages
+    gallery_inputs = [img_path, page_index, image_index, tabname_box]
+    gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
+
+    first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    # page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
+
+    # other funcitons
+    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden])
+    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
+    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
+    hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
+
+    # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
+    switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
+    switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
+
+
 def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
     with gr.Blocks(analytics_enabled=False) as images_history:
         with gr.Tabs() as tabs:
             with gr.Tab("txt2img history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:   
-                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict) 
+                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:
+                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict)
             with gr.Tab("img2img history"):
                 with gr.Blocks(analytics_enabled=False) as images_history_img2img:
                     show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict)
-- 
cgit v1.2.3


From acedbe67d2b8a3af99ca3b9a2f809e7a2db285d1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 00:43:15 +0300
Subject: bring history tab back, make it behave; it's still slow but won't
 fuck anything up until you use it

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index c5d295ea..1bc919c7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1090,7 +1090,7 @@ def create_ui(wrap_gradio_gpu_call):
         "i2i":img2img_paste_fields
     }
 
-    #images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
+    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
@@ -1487,7 +1487,7 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        #(images_history, "History", "images_history"),
+        (images_history, "History", "images_history"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
-- 
cgit v1.2.3


From c7a86f7fe9c0b8967a87e8d709f507d2f44400d8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 09:24:59 +0300
Subject: add option to use batch size for training

---
 modules/hypernetworks/hypernetwork.py          | 33 +++++++++++++++++++-------
 modules/textual_inversion/dataset.py           | 31 ++++++++++++++----------
 modules/textual_inversion/textual_inversion.py | 17 +++++++------
 modules/ui.py                                  |  3 +++
 4 files changed, 54 insertions(+), 30 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 59c7ac6e..a2b3bc0a 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -182,7 +182,21 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     return self.to_out(out)
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def stack_conds(conds):
+    if len(conds) == 1:
+        return torch.stack(conds)
+
+    # same as in reconstruct_multicond_batch
+    token_count = max([x.shape[0] for x in conds])
+    for i in range(len(conds)):
+        if conds[i].shape[0] != token_count:
+            last_vector = conds[i][-1:]
+            last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])
+            conds[i] = torch.vstack([conds[i], last_vector_repeated])
+
+    return torch.stack(conds)
+
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -211,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -235,7 +249,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, entry in pbar:
+    for i, entries in pbar:
         hypernetwork.step = i + ititial_step
 
         scheduler.apply(optimizer, hypernetwork.step)
@@ -246,11 +260,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             break
 
         with torch.autocast("cuda"):
-            cond = entry.cond.to(devices.device)
-            x = entry.latent.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), cond)[0]
+            c = stack_conds([entry.cond for entry in entries]).to(devices.device)
+#            c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
             del x
-            del cond
+            del c
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
@@ -292,7 +307,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
                 p.width = preview_width
                 p.height = preview_height
             else:
-                p.prompt = entry.cond_text
+                p.prompt = entries[0].cond_text
                 p.steps = 20
 
             preview_text = p.prompt
@@ -315,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {hypernetwork.step}<br/>
-Last prompt: {html.escape(entry.cond_text)}<br/>
+Last prompt: {html.escape(entries[0].cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 67e90afe..bd99c0cb 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -24,11 +24,12 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
-        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
+        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
 
         self.placeholder_token = placeholder_token
 
+        self.batch_size = batch_size
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
@@ -78,13 +79,13 @@ class PersonalizedBase(Dataset):
 
             if include_cond:
                 entry.cond_text = self.create_text(filename_text)
-                entry.cond = cond_model([entry.cond_text]).to(devices.cpu)
+                entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
 
             self.dataset.append(entry)
 
-        self.length = len(self.dataset) * repeats
+        self.length = len(self.dataset) * repeats // batch_size
 
-        self.initial_indexes = np.arange(self.length) % len(self.dataset)
+        self.initial_indexes = np.arange(len(self.dataset))
         self.indexes = None
         self.shuffle()
 
@@ -101,13 +102,19 @@ class PersonalizedBase(Dataset):
         return self.length
 
     def __getitem__(self, i):
-        if i % len(self.dataset) == 0:
-            self.shuffle()
+        res = []
 
-        index = self.indexes[i % len(self.indexes)]
-        entry = self.dataset[index]
+        for j in range(self.batch_size):
+            position = i * self.batch_size + j
+            if position % len(self.indexes) == 0:
+                self.shuffle()
 
-        if entry.cond is None:
-            entry.cond_text = self.create_text(entry.filename_text)
+            index = self.indexes[position % len(self.indexes)]
+            entry = self.dataset[index]
 
-        return entry
+            if entry.cond is None:
+                entry.cond_text = self.create_text(entry.filename_text)
+
+            res.append(entry)
+
+        return res
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index da0d77a0..e754747e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -199,7 +199,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
         })
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -231,7 +231,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
 
     hijack = sd_hijack.model_hijack
 
@@ -251,7 +251,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, entry in pbar:
+    for i, entries in pbar:
         embedding.step = i + ititial_step
 
         scheduler.apply(optimizer, embedding.step)
@@ -262,10 +262,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([entry.cond_text])
-
-            x = entry.latent.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+            c = cond_model([entry.cond_text for entry in entries])
+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
             del x
 
             losses[embedding.step % losses.shape[0]] = loss.item()
@@ -307,7 +306,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 p.width = preview_width
                 p.height = preview_height
             else:
-                p.prompt = entry.cond_text
+                p.prompt = entries[0].cond_text
                 p.steps = 20
                 p.width = training_width
                 p.height = training_height
@@ -348,7 +347,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {embedding.step}<br/>
-Last prompt: {html.escape(entry.cond_text)}<br/>
+Last prompt: {html.escape(entries[0].cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
diff --git a/modules/ui.py b/modules/ui.py
index 1bc919c7..45550ea8 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1166,6 +1166,7 @@ def create_ui(wrap_gradio_gpu_call):
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
                     learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
+                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
@@ -1244,6 +1245,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 train_embedding_name,
                 learn_rate,
+                batch_size,
                 dataset_directory,
                 log_directory,
                 training_width,
@@ -1268,6 +1270,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 train_hypernetwork_name,
                 learn_rate,
+                batch_size,
                 dataset_directory,
                 log_directory,
                 steps,
-- 
cgit v1.2.3


From db27b987a97fc8b7894a9dd34bd7641536f9c424 Mon Sep 17 00:00:00 2001
From: aoirusann <yimodo.siki@gmail.com>
Date: Sat, 15 Oct 2022 11:48:13 +0800
Subject: Add hint for `ctrl/alt enter` And duplicate implementations are
 removed

---
 modules/ui.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 45550ea8..baf4c397 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -433,7 +433,10 @@ def create_toprow(is_img2img):
             with gr.Row():
                 with gr.Column(scale=80):
                     with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2, 
+                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
                 with gr.Column(scale=1, elem_id="roll_col"):
                     roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
                     paste = gr.Button(value=paste_symbol, elem_id="paste")
@@ -446,7 +449,10 @@ def create_toprow(is_img2img):
             with gr.Row():
                 with gr.Column(scale=8):
                     with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, 
+                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
                 with gr.Column(scale=1, elem_id="roll_col"):
                     sh = gr.Button(elem_id="sh", visible=True)                           
 
-- 
cgit v1.2.3


From cd28465bf87d911965790513c37e6881e4231523 Mon Sep 17 00:00:00 2001
From: ddPn08 <pyn.g.1125@gmail.com>
Date: Sat, 15 Oct 2022 10:56:02 +0900
Subject: do not force relative paths in image history

---
 modules/images_history.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index f5ef44fe..09c749fe 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -104,7 +104,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     elif tabname == "extras":
         dir_name = opts.outdir_extras_samples
     d = dir_name.split("/")
-    dir_name = d[0]
+    dir_name = "/" if dir_name.startswith("/") else d[0]
     for p in d[1:]:
         dir_name = os.path.join(dir_name, p)
     with gr.Row():
-- 
cgit v1.2.3


From 0da6c1809996f0f696d4047faf4b9c9939e26daa Mon Sep 17 00:00:00 2001
From: ddPn08 <pyn.g.1125@gmail.com>
Date: Sat, 15 Oct 2022 11:22:05 +0900
Subject: use "outdir_samples" if specified

---
 modules/images_history.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 09c749fe..9260df8a 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -97,7 +97,9 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima
 
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
-    if tabname == "txt2img":
+    if opts.outdir_samples != "":
+        dir_name = opts.outdir_samples
+    elif tabname == "txt2img":
         dir_name = opts.outdir_txt2img_samples
     elif tabname == "img2img":
         dir_name = opts.outdir_img2img_samples
-- 
cgit v1.2.3


From a13af34b902bebc5df9509228380206a01f1245b Mon Sep 17 00:00:00 2001
From: githublsx <xiaolinshen@outlook.com>
Date: Thu, 13 Oct 2022 20:05:07 -0700
Subject: Set to -1 when seed input is none

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index a75b9f84..7e2a416d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -140,7 +140,7 @@ class Processed:
         self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
-        self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
+        self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
         self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
 
         self.all_prompts = all_prompts or [self.prompt]
-- 
cgit v1.2.3


From c24df4b486a48c60f48276f7760a9acb4a13e22d Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sat, 15 Oct 2022 03:26:36 +0900
Subject: Disable compiling deepbooru model

This is only necessary when you have to train,
and compiling model produces warning.
---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index f34f3788..4ad334a1 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -102,7 +102,7 @@ def get_deepbooru_tags_model():
 
     tags = dd.project.load_tags_from_project(model_path)
     model = dd.project.load_model_from_project(
-        model_path, compile_model=True
+        model_path, compile_model=False
     )
     return model, tags
 
-- 
cgit v1.2.3


From e8729dd0511f8410db967d9ef192645cfef1be8a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 12:54:23 +0300
Subject: re-apply height hacks to work with new gradio

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index baf4c397..9c7a67dd 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -750,10 +750,10 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
                     with gr.TabItem('img2img', id='img2img'):
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool)
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
 
                     with gr.TabItem('Inpaint', id='inpaint'):
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
 
                         init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
-- 
cgit v1.2.3


From 5967d07d1aa4e2fef031a57b1612b1ab04a3cd78 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 13:11:28 +0300
Subject: fix new gradio failing to preserve image params

---
 modules/ui.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 9c7a67dd..de5ab929 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -7,6 +7,7 @@ import mimetypes
 import os
 import random
 import sys
+import tempfile
 import time
 import traceback
 import platform
@@ -176,6 +177,23 @@ def save_files(js_data, images, do_make_zip, index):
     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
+def save_pil_to_file(pil_image, dir=None):
+    use_metadata = False
+    metadata = PngImagePlugin.PngInfo()
+    for key, value in pil_image.info.items():
+        if isinstance(key, str) and isinstance(value, str):
+            metadata.add_text(key, value)
+            use_metadata = True
+
+    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+    return file_obj
+
+
+# override save to file function so that it also writes PNG info
+gr.processing_utils.save_pil_to_file = save_pil_to_file
+
+
 def wrap_gradio_call(func, extra_outputs=None):
     def f(*args, extra_outputs_array=extra_outputs, **kwargs):
         run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
-- 
cgit v1.2.3


From f7ca63937ac83d32483285c3af09afaa356d6276 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 13:23:12 +0300
Subject: bring back scale latent option in settings

---
 modules/processing.py | 8 ++++----
 modules/shared.py     | 3 ++-
 2 files changed, 6 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 7e2a416d..b9a1660e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -557,11 +557,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
-        decoded_samples = decode_first_stage(self.sd_model, samples)
+        if opts.use_scale_latent_for_hires_fix:
+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
 
-        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
-            decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
         else:
+            decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
 
             batch_images = []
@@ -578,7 +578,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             decoded_samples = decoded_samples.to(shared.device)
             decoded_samples = 2. * decoded_samples - 1.
 
-        samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
+            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
         shared.state.nextjob()
 
diff --git a/modules/shared.py b/modules/shared.py
index aa69bedf..b4141e67 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -218,6 +218,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space iamge when doing hires. fix"),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -256,7 +257,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
-    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -284,6 +284,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-- 
cgit v1.2.3


From d3463bc59a44d62c2de8b357184c49876d84f654 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 14:22:30 +0300
Subject: change styling for top right corner UI made save style button not die
 when you cancel

---
 modules/ui.py | 57 ++++++++++++++++++++++++++++-----------------------------
 1 file changed, 28 insertions(+), 29 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index de5ab929..cab8ab11 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -81,6 +81,8 @@ art_symbol = '\U0001f3a8'  # 🎨
 paste_symbol = '\u2199\ufe0f'  # ↙
 folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+apply_style_symbol = '\U0001f4cb'  # 📋
 
 
 def plaintext_to_html(text):
@@ -322,7 +324,7 @@ def visit(x, func, path=""):
 
 def add_style(name: str, prompt: str, negative_prompt: str):
     if name is None:
-        return [gr_show(), gr_show()]
+        return [gr_show() for x in range(4)]
 
     style = modules.styles.PromptStyle(name, prompt, negative_prompt)
     shared.prompt_styles.styles[style.name] = style
@@ -447,7 +449,7 @@ def create_toprow(is_img2img):
     id_part = "img2img" if is_img2img else "txt2img"
 
     with gr.Row(elem_id="toprow"):
-        with gr.Column(scale=4):
+        with gr.Column(scale=6):
             with gr.Row():
                 with gr.Column(scale=80):
                     with gr.Row():
@@ -455,27 +457,30 @@ def create_toprow(is_img2img):
                             placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
                         )
 
-                with gr.Column(scale=1, elem_id="roll_col"):
-                    roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
-                    paste = gr.Button(value=paste_symbol, elem_id="paste")
-                    token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
-                    token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-
-                with gr.Column(scale=10, elem_id="style_pos_col"):
-                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
-
             with gr.Row():
-                with gr.Column(scale=8):
+                with gr.Column(scale=80):
                     with gr.Row():
                         negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, 
                             placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
                         )
 
-                with gr.Column(scale=1, elem_id="roll_col"):
-                    sh = gr.Button(elem_id="sh", visible=True)                           
+        with gr.Column(scale=1, elem_id="roll_col"):
+            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+            paste = gr.Button(value=paste_symbol, elem_id="paste")
+            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
+            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
 
-                with gr.Column(scale=1, elem_id="style_neg_col"):
-                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
+            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+
+        button_interrogate = None
+        button_deepbooru = None
+        if is_img2img:
+            with gr.Column(scale=1, elem_id="interrogate_col"):
+                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+
+                if cmd_opts.deepdanbooru:
+                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
 
         with gr.Column(scale=1):
             with gr.Row():
@@ -495,20 +500,14 @@ def create_toprow(is_img2img):
                     outputs=[],
                 )
 
-            with gr.Row(scale=1):
-                if is_img2img:
-                    interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-                    if cmd_opts.deepdanbooru:
-                        deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-                    else:
-                        deepbooru = None
-                else:
-                    interrogate = None
-                    deepbooru = None
-                prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
-                save_style = gr.Button('Create style', elem_id="style_create")
+            with gr.Row():
+                with gr.Column(scale=1, elem_id="style_pos_col"):
+                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+
+                with gr.Column(scale=1, elem_id="style_neg_col"):
+                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
-- 
cgit v1.2.3


From 20a1f68c752f8e37492ea00911c97bfc572a6e67 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 15:44:46 +0300
Subject: fix gadio issue with sending files between tabs

---
 modules/ui.py | 8 ++++++++
 1 file changed, 8 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index cab8ab11..c9b53247 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -91,6 +91,14 @@ def plaintext_to_html(text):
 
 
 def image_from_url_text(filedata):
+    if type(filedata) == dict and filedata["is_file"]:
+        filename = filedata["name"]
+        tempdir = os.path.normpath(tempfile.gettempdir())
+        normfn = os.path.normpath(filename)
+        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
+
+        return Image.open(filename)
+
     if type(filedata) == list:
         if len(filedata) == 0:
             return None
-- 
cgit v1.2.3


From 97f0727489ddd3d7ca264c54ed0f63b6847502e2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 15:47:02 +0300
Subject: add First pass size always regardless of whether it was auto chosen
 or specified

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index b9a1660e..941ae089 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -528,7 +528,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 firstphase_height_truncated = int(scale * self.height)
 
             else:
-                self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
 
                 width_ratio = self.width / self.firstphase_width
                 height_ratio = self.height / self.firstphase_height
@@ -540,6 +539,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     firstphase_width_truncated = self.firstphase_height * self.width / self.height
                     firstphase_height_truncated = self.firstphase_height
 
+            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-- 
cgit v1.2.3


From eef3bc649069d6caaef1274f132de28e528bfa7d Mon Sep 17 00:00:00 2001
From: NO_ob <15161159+NO-ob@users.noreply.github.com>
Date: Sat, 15 Oct 2022 11:43:30 +0100
Subject: typo

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index b4141e67..fa30bbb0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -218,7 +218,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
-    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space iamge when doing hires. fix"),
+    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
-- 
cgit v1.2.3


From 37d7ffb415cd8c69b3c0bb5f61844dde0b169f78 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 15:59:37 +0200
Subject: fix to tokens lenght, addend embs generator, add new features to edit
 the embedding before the generation using text

---
 modules/aesthetic_clip.py |  78 ++++++++++++++++++++++++
 modules/processing.py     | 148 +++++++++++++++++++++++++++++++---------------
 modules/sd_hijack.py      | 111 ++++++++++++++++++++++------------
 modules/shared.py         |   4 ++
 modules/txt2img.py        |  10 +++-
 modules/ui.py             |  47 ++++++++++++---
 6 files changed, 302 insertions(+), 96 deletions(-)
 create mode 100644 modules/aesthetic_clip.py

(limited to 'modules')

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
new file mode 100644
index 00000000..f15cfd47
--- /dev/null
+++ b/modules/aesthetic_clip.py
@@ -0,0 +1,78 @@
+import itertools
+import os
+from pathlib import Path
+import html
+import gc
+
+import gradio as gr
+import torch
+from PIL import Image
+from modules import shared
+from modules.shared import device, aesthetic_embeddings
+from transformers import CLIPModel, CLIPProcessor
+
+from tqdm.auto import tqdm
+
+
+def get_all_images_in_folder(folder):
+    return [os.path.join(folder, f) for f in os.listdir(folder) if
+            os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)]
+
+
+def check_is_valid_image_file(filename):
+    return filename.lower().endswith(('.png', '.jpg', '.jpeg'))
+
+
+def batched(dataset, total, n=1):
+    for ndx in range(0, total, n):
+        yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
+
+
+def iter_to_batched(iterable, n=1):
+    it = iter(iterable)
+    while True:
+        chunk = tuple(itertools.islice(it, n))
+        if not chunk:
+            return
+        yield chunk
+
+
+def generate_imgs_embd(name, folder, batch_size):
+    # clipModel = CLIPModel.from_pretrained(
+    #     shared.sd_model.cond_stage_model.clipModel.name_or_path
+    # )
+    model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path).to(device)
+    processor = CLIPProcessor.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path)
+
+    with torch.no_grad():
+        embs = []
+        for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size),
+                          desc=f"Generating embeddings for {name}"):
+            if shared.state.interrupted:
+                break
+            inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device)
+            outputs = model.get_image_features(**inputs).cpu()
+            embs.append(torch.clone(outputs))
+            inputs.to("cpu")
+            del inputs, outputs
+
+        embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True)
+
+        # The generated embedding will be located here
+        path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
+        torch.save(embs, path)
+
+        model = model.cpu()
+        del model
+        del processor
+        del embs
+        gc.collect()
+        torch.cuda.empty_cache()
+        res = f"""
+        Done generating embedding for {name}!
+        Hypernetwork saved to {html.escape(path)}
+        """
+        shared.update_aesthetic_embeddings()
+        return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding",
+                           value=sorted(aesthetic_embeddings.keys())[0] if len(
+                               aesthetic_embeddings) > 0 else None), res, ""
diff --git a/modules/processing.py b/modules/processing.py
index 9a033759..ab68d63a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -20,7 +20,6 @@ import modules.images as images
 import modules.styles
 import logging
 
-
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
 opt_f = 8
@@ -52,8 +51,13 @@ def get_correct_sampler(p):
     elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
         return sd_samplers.samplers_for_img2img
 
+
 class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1,
+                 subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True,
+                 sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512,
+                 restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False,
+                 extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -104,7 +108,8 @@ class StableDiffusionProcessing:
 
 
 class Processed:
-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None,
+                 all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
         self.images = images_list
         self.prompt = p.prompt
         self.negative_prompt = p.negative_prompt
@@ -141,7 +146,8 @@ class Processed:
         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
         self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
-        self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
+        self.subseed = int(
+            self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
 
         self.all_prompts = all_prompts or [self.prompt]
         self.all_seeds = all_seeds or [self.seed]
@@ -181,39 +187,43 @@ class Processed:
 
         return json.dumps(obj)
 
-    def infotext(self,  p: StableDiffusionProcessing, index):
-        return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
+    def infotext(self, p: StableDiffusionProcessing, index):
+        return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[],
+                               position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
 
 
 # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
 def slerp(val, low, high):
-    low_norm = low/torch.norm(low, dim=1, keepdim=True)
-    high_norm = high/torch.norm(high, dim=1, keepdim=True)
-    dot = (low_norm*high_norm).sum(1)
+    low_norm = low / torch.norm(low, dim=1, keepdim=True)
+    high_norm = high / torch.norm(high, dim=1, keepdim=True)
+    dot = (low_norm * high_norm).sum(1)
 
     if dot.mean() > 0.9995:
         return low * val + high * (1 - val)
 
     omega = torch.acos(dot)
     so = torch.sin(omega)
-    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
     return res
 
 
-def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
+def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0,
+                          p=None):
     xs = []
 
     # if we have multiple seeds, this means we are working with batch size>1; this then
     # enables the generation of additional tensors with noise that the sampler will use during its processing.
     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
     # produce the same images as with two batches [100], [101].
-    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
+    if p is not None and p.sampler is not None and (
+            len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
     else:
         sampler_noises = None
 
     for i, seed in enumerate(seeds):
-        noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
+        noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (
+            shape[0], seed_resize_from_h // 8, seed_resize_from_w // 8)
 
         subnoise = None
         if subseeds is not None:
@@ -241,7 +251,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
             dx = max(-dx, 0)
             dy = max(-dy, 0)
 
-            x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
+            x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w]
             noise = x
 
         if sampler_noises is not None:
@@ -293,14 +303,20 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed": all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
-        "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
+        "Model hash": getattr(p, 'sd_model_hash',
+                              None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
+        "Model": (
+            None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(
+                ',', '').replace(':', '')),
+        "Hypernet": (
+            None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(
+                ':', '')),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
-        "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
+        "Seed resize from": (
+            None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
@@ -309,7 +325,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params.update(p.extra_generation_params)
 
-    generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
+    generation_params_text = ", ".join(
+        [k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
 
     negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
 
@@ -317,7 +334,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
 
 def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0,
-                   aesthetic_imgs=None,aesthetic_slerp=False) -> Processed:
+                   aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="",
+                   aesthetic_slerp_angle=0.15,
+                   aesthetic_text_negative=False) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
     aesthetic_lr = float(aesthetic_lr)
@@ -385,7 +404,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
         for n in range(p.n_iter):
             if state.skipped:
                 state.skipped = False
-            
+
             if state.interrupted:
                 break
 
@@ -396,16 +415,19 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
             if (len(prompts) == 0):
                 break
 
-            #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
-            #c = p.sd_model.get_learned_conditioning(prompts)
+            # uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
+            # c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
                 if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
-                    shared.sd_model.cond_stage_model.set_aesthetic_params(0, 0, 0)
+                    shared.sd_model.cond_stage_model.set_aesthetic_params()
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt],
                                                             p.steps)
                 if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
                     shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight,
-                                                                          aesthetic_steps, aesthetic_imgs,aesthetic_slerp)
+                                                                          aesthetic_steps, aesthetic_imgs,
+                                                                          aesthetic_slerp, aesthetic_imgs_text,
+                                                                          aesthetic_slerp_angle,
+                                                                          aesthetic_text_negative)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
@@ -413,13 +435,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                     comments[comment] = 1
 
             if p.n_iter > 1:
-                shared.state.job = f"Batch {n+1} out of {p.n_iter}"
+                shared.state.job = f"Batch {n + 1} out of {p.n_iter}"
 
             with devices.autocast():
-                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
+                                        subseed_strength=p.subseed_strength)
 
             if state.interrupted or state.skipped:
-
                 # if we are interrupted, sample returns just noise
                 # use the image collected previously in sampler loop
                 samples_ddim = shared.state.current_latent
@@ -445,7 +467,9 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
 
                 if p.restore_faces:
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
-                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
+                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i],
+                                          opts.samples_format, info=infotext(n, i), p=p,
+                                          suffix="-before-face-restoration")
 
                     devices.torch_gc()
 
@@ -456,7 +480,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
 
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
-                        images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format,
+                                          info=infotext(n, i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
                 if p.overlay_images is not None and i < len(p.overlay_images):
@@ -474,7 +499,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                     image = image.convert('RGB')
 
                 if opts.samples_save and not p.do_not_save_samples:
-                    images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
+                    images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format,
+                                      info=infotext(n, i), p=p)
 
                 text = infotext(n, i)
                 infotexts.append(text)
@@ -482,7 +508,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                     image.info["parameters"] = text
                 output_images.append(image)
 
-            del x_samples_ddim 
+            del x_samples_ddim
 
             devices.torch_gc()
 
@@ -504,10 +530,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                 index_of_first_image = 1
 
             if opts.grid_save:
-                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
+                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format,
+                                  info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]),
+                     subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds,
+                     index_of_first_image=index_of_first_image, infotexts=infotexts)
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
@@ -543,25 +572,34 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
-            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds,
+                                      subseeds=subseeds, subseed_strength=self.subseed_strength,
+                                      seed_resize_from_h=self.seed_resize_from_h,
+                                      seed_resize_from_w=self.seed_resize_from_w, p=self)
             samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
             return samples
 
-        x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds,
+                                  subseeds=subseeds, subseed_strength=self.subseed_strength,
+                                  seed_resize_from_h=self.seed_resize_from_h,
+                                  seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
         truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
         truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
 
-        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
+        samples = samples[:, :, truncate_y // 2:samples.shape[2] - truncate_y // 2,
+                  truncate_x // 2:samples.shape[3] - truncate_x // 2]
 
         if self.scale_latent:
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f),
+                                                      mode="bilinear")
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
 
             if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
-                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
+                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width),
+                                                                  mode="bilinear")
             else:
                 lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
 
@@ -585,13 +623,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
-        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds,
+                                      subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h,
+                                      seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning,
+                                              steps=self.steps)
 
         return samples
 
@@ -599,7 +640,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs):
+    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4,
+                 inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0,
+                 **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
@@ -607,7 +650,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.denoising_strength: float = denoising_strength
         self.init_latent = None
         self.image_mask = mask
-        #self.image_unblurred_mask = None
+        # self.image_unblurred_mask = None
         self.latent_mask = None
         self.mask_for_overlay = None
         self.mask_blur = mask_blur
@@ -619,7 +662,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.nmask = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index,
+                                                             self.sd_model)
         crop_region = None
 
         if self.image_mask is not None:
@@ -628,7 +672,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if self.inpainting_mask_invert:
                 self.image_mask = ImageOps.invert(self.image_mask)
 
-            #self.image_unblurred_mask = self.image_mask
+            # self.image_unblurred_mask = self.image_mask
 
             if self.mask_blur > 0:
                 self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
@@ -642,7 +686,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
                 mask = mask.crop(crop_region)
                 self.image_mask = images.resize_image(2, mask, self.width, self.height)
-                self.paste_to = (x1, y1, x2-x1, y2-y1)
+                self.paste_to = (x1, y1, x2 - x1, y2 - y1)
             else:
                 self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
                 np_mask = np.array(self.image_mask)
@@ -665,7 +709,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
             if self.image_mask is not None:
                 image_masked = Image.new('RGBa', (image.width, image.height))
-                image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
+                image_masked.paste(image.convert("RGBA").convert("RGBa"),
+                                   mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
 
                 self.overlay_images.append(image_masked.convert('RGBA'))
 
@@ -714,12 +759,17 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
             # this needs to be fixed to be done in sample() using actual seeds for batches
             if self.inpainting_fill == 2:
-                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
+                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:],
+                                                                                        all_seeds[
+                                                                                        0:self.init_latent.shape[
+                                                                                            0]]) * self.nmask
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds,
+                                  subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h,
+                                  seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
 
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 6d5196fe..192883b2 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -14,7 +14,8 @@ from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
-from transformers import CLIPVisionModel, CLIPModel
+from tqdm import trange
+from transformers import CLIPVisionModel, CLIPModel, CLIPTokenizer
 import torch.optim as optim
 import copy
 
@@ -22,21 +23,25 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
 
+
 def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
-    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (
+            6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
+    elif not cmd_opts.disable_opt_split_attention and (
+            cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
         if not invokeAI_mps_available and shared.device.type == 'mps':
-            print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
+            print(
+                "The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
             print("Applying v1 cross attention optimization.")
             ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
         else:
@@ -112,14 +117,16 @@ class StableDiffusionModelHijack:
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
 
+
 def slerp(low, high, val):
-    low_norm = low/torch.norm(low, dim=1, keepdim=True)
-    high_norm = high/torch.norm(high, dim=1, keepdim=True)
-    omega = torch.acos((low_norm*high_norm).sum(1))
+    low_norm = low / torch.norm(low, dim=1, keepdim=True)
+    high_norm = high / torch.norm(high, dim=1, keepdim=True)
+    omega = torch.acos((low_norm * high_norm).sum(1))
     so = torch.sin(omega)
-    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
     return res
 
+
 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
         super().__init__()
@@ -128,6 +135,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             self.wrapped.transformer.name_or_path
         )
         del self.clipModel.vision_model
+        self.tokenizer = CLIPTokenizer.from_pretrained(self.wrapped.transformer.name_or_path)
         self.hijack: StableDiffusionModelHijack = hijack
         self.tokenizer = wrapped.tokenizer
         # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval()
@@ -139,7 +147,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
 
-        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
+        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if
+                              '(' in k or ')' in k or '[' in k or ']' in k]
         for text, ident in tokens_with_parens:
             mult = 1.0
             for c in text:
@@ -155,8 +164,13 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-    def set_aesthetic_params(self, aesthetic_lr, aesthetic_weight, aesthetic_steps, image_embs_name=None,
-                             aesthetic_slerp=True):
+    def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
+                             aesthetic_slerp=True, aesthetic_imgs_text="",
+                             aesthetic_slerp_angle=0.15,
+                             aesthetic_text_negative=False):
+        self.aesthetic_imgs_text = aesthetic_imgs_text
+        self.aesthetic_slerp_angle = aesthetic_slerp_angle
+        self.aesthetic_text_negative = aesthetic_text_negative
         self.slerp = aesthetic_slerp
         self.aesthetic_lr = aesthetic_lr
         self.aesthetic_weight = aesthetic_weight
@@ -180,7 +194,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         else:
             parsed = [[line, 1.0]]
 
-        tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]
+        tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)[
+            "input_ids"]
 
         fixes = []
         remade_tokens = []
@@ -196,18 +211,20 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
                 if token == self.comma_token:
                     last_comma = len(remade_tokens)
-                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
+                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens),
+                                                               1) % 75 == 0 and last_comma != -1 and len(
+                    remade_tokens) - last_comma <= opts.comma_padding_backtrack:
                     last_comma += 1
                     reloc_tokens = remade_tokens[last_comma:]
                     reloc_mults = multipliers[last_comma:]
 
                     remade_tokens = remade_tokens[:last_comma]
                     length = len(remade_tokens)
-                    
+
                     rem = int(math.ceil(length / 75)) * 75 - length
                     remade_tokens += [id_end] * rem + reloc_tokens
                     multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
-                
+
                 if embedding is None:
                     remade_tokens.append(token)
                     multipliers.append(weight)
@@ -248,7 +265,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if line in cache:
                 remade_tokens, fixes, multipliers = cache[line]
             else:
-                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms,
+                                                                                            hijack_comments)
                 token_count = max(current_token_count, token_count)
 
                 cache[line] = (remade_tokens, fixes, multipliers)
@@ -259,7 +277,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
 
-
     def process_text_old(self, text):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
@@ -289,7 +306,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 while i < len(tokens):
                     token = tokens[i]
 
-                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens,
+                                                                                                                i)
 
                     mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
                     if mult_change is not None:
@@ -312,11 +330,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     ovf = remade_tokens[maxlen - 2:]
                     overflowing_words = [vocab.get(int(x), "") for x in ovf]
                     overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
-                    hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+                    hijack_comments.append(
+                        f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
 
                 token_count = len(remade_tokens)
                 remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
-                remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
+                remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
                 cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
 
             multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
@@ -326,23 +345,26 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             hijack_fixes.append(fixes)
             batch_multipliers.append(multipliers)
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-    
+
     def forward(self, text):
         use_old = opts.use_old_emphasis_implementation
         if use_old:
-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
+            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(
+                text)
         else:
-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
+            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(
+                text)
 
         self.hijack.comments += hijack_comments
 
         if len(used_custom_terms) > 0:
-            self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
-        
+            self.hijack.comments.append(
+                "Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
+
         if use_old:
             self.hijack.fixes = hijack_fixes
             return self.process_tokens(remade_batch_tokens, batch_multipliers)
-        
+
         z = None
         i = 0
         while max(map(len, remade_batch_tokens)) != 0:
@@ -356,7 +378,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     if fix[0] == i:
                         fixes.append(fix[1])
                 self.hijack.fixes.append(fixes)
-            
+
             tokens = []
             multipliers = []
             for j in range(len(remade_batch_tokens)):
@@ -378,19 +400,30 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                         remade_batch_tokens]
 
                 tokens = torch.asarray(remade_batch_tokens).to(device)
+
+                model = copy.deepcopy(self.clipModel).to(device)
+                model.requires_grad_(True)
+                if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
+                    text_embs_2 = model.get_text_features(
+                        **self.tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
+                    if self.aesthetic_text_negative:
+                        text_embs_2 = self.image_embs - text_embs_2
+                        text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
+                    img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
+                else:
+                    img_embs = self.image_embs
+
                 with torch.enable_grad():
-                    model = copy.deepcopy(self.clipModel).to(device)
-                    model.requires_grad_(True)
 
                     # We optimize the model to maximize the similarity
                     optimizer = optim.Adam(
                         model.text_model.parameters(), lr=self.aesthetic_lr
                     )
 
-                    for i in range(self.aesthetic_steps):
+                    for i in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
                         text_embs = model.get_text_features(input_ids=tokens)
                         text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
-                        sim = text_embs @ self.image_embs.T
+                        sim = text_embs @ img_embs.T
                         loss = -sim
                         optimizer.zero_grad()
                         loss.mean().backward()
@@ -405,6 +438,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     model.cpu()
                     del model
 
+                zn = torch.concat([zn for i in range(z.shape[1] // 77)], 1)
                 if self.slerp:
                     z = slerp(z, zn, self.aesthetic_weight)
                 else:
@@ -413,15 +447,16 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             remade_batch_tokens = rem_tokens
             batch_multipliers = rem_multipliers
             i += 1
-        
+
         return z
-        
-    
+
     def process_tokens(self, remade_batch_tokens, batch_multipliers):
         if not opts.use_old_emphasis_implementation:
-            remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
+            remade_batch_tokens = [
+                [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in
+                remade_batch_tokens]
             batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
-        
+
         tokens = torch.asarray(remade_batch_tokens).to(device)
         outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
 
@@ -461,8 +496,8 @@ class EmbeddingsWithFixes(torch.nn.Module):
         for fixes, tensor in zip(batch_fixes, inputs_embeds):
             for offset, embedding in fixes:
                 emb = embedding.vec
-                emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
-                tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
+                emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
+                tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
 
             vecs.append(tensor)
 
diff --git a/modules/shared.py b/modules/shared.py
index cf13a10d..7cd608ca 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -95,6 +95,10 @@ loaded_hypernetwork = None
 aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
                         os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
 
+def update_aesthetic_embeddings():
+    global aesthetic_embeddings
+    aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
+                            os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
 
 def reload_hypernetworks():
     global hypernetworks
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 78342024..eedcdfe0 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -13,7 +13,11 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
             aesthetic_lr=0,
             aesthetic_weight=0, aesthetic_steps=0,
             aesthetic_imgs=None,
-            aesthetic_slerp=False, *args):
+            aesthetic_slerp=False,
+            aesthetic_imgs_text="",
+            aesthetic_slerp_angle=0.15,
+            aesthetic_text_negative=False,
+            *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -47,7 +51,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     processed = modules.scripts.scripts_txt2img.run(p, *args)
 
     if processed is None:
-        processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp)
+        processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp,aesthetic_imgs_text,
+                           aesthetic_slerp_angle,
+                           aesthetic_text_negative)
 
     shared.total_tqdm.clear()
 
diff --git a/modules/ui.py b/modules/ui.py
index d961d126..e98e2113 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -41,6 +41,7 @@ from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
+import modules.aesthetic_clip
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -449,7 +450,7 @@ def create_toprow(is_img2img):
                     with gr.Row():
                         negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
                 with gr.Column(scale=1, elem_id="roll_col"):
-                    sh = gr.Button(elem_id="sh", visible=True)                           
+                    sh = gr.Button(elem_id="sh", visible=True)
 
                 with gr.Column(scale=1, elem_id="style_neg_col"):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
@@ -536,9 +537,13 @@ def create_ui(wrap_gradio_gpu_call):
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
                 with gr.Group():
-                    aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
-                    aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.7)
-                    aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=50)
+                    aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001")
+                    aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
+                    aesthetic_steps = gr.Slider(minimum=0, maximum=256, step=1, label="Aesthetic steps", value=5)
+                    with gr.Row():
+                        aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
+                        aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
+                        aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
 
                     aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
                     aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
@@ -617,7 +622,10 @@ def create_ui(wrap_gradio_gpu_call):
                            aesthetic_weight,
                            aesthetic_steps,
                            aesthetic_imgs,
-                           aesthetic_slerp
+                           aesthetic_slerp,
+                           aesthetic_imgs_text,
+                           aesthetic_slerp_angle,
+                           aesthetic_text_negative
                        ] + custom_inputs,
                 outputs=[
                     txt2img_gallery,
@@ -721,7 +729,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                         with gr.Row():
                             inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=1024, step=4, value=32)
 
                     with gr.TabItem('Batch img2img', id='batch'):
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
@@ -1071,6 +1079,17 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create embedding", variant='primary')
 
+                with gr.Tab(label="Create images embedding"):
+                    new_embedding_name_ae = gr.Textbox(label="Name")
+                    process_src_ae = gr.Textbox(label='Source directory')
+                    batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256)
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding_ae = gr.Button(value="Create images embedding", variant='primary')
+
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
@@ -1139,7 +1158,7 @@ def create_ui(wrap_gradio_gpu_call):
             fn=modules.textual_inversion.ui.create_embedding,
             inputs=[
                 new_embedding_name,
-                initialization_text,
+                process_src,
                 nvpt,
             ],
             outputs=[
@@ -1149,6 +1168,20 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
+        create_embedding_ae.click(
+            fn=modules.aesthetic_clip.generate_imgs_embd,
+            inputs=[
+                new_embedding_name_ae,
+                process_src_ae,
+                batch_ae
+            ],
+            outputs=[
+                aesthetic_imgs,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
         create_hypernetwork.click(
             fn=modules.hypernetworks.ui.create_hypernetwork,
             inputs=[
-- 
cgit v1.2.3


From 5fd638f14d75a71a37157ded5d33c716ab9eb8ca Mon Sep 17 00:00:00 2001
From: ruocaled <ruocaled@gmail.com>
Date: Sat, 15 Oct 2022 02:00:46 -0700
Subject: fix download section layout

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index c9b53247..3206113e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -619,7 +619,7 @@ def create_ui(wrap_gradio_gpu_call):
                     txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
                     txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
 
-                with gr.Group():
+                with gr.Column():
                     with gr.Row():
                         save = gr.Button('Save')
                         send_to_img2img = gr.Button('Send to img2img')
@@ -834,7 +834,7 @@ def create_ui(wrap_gradio_gpu_call):
                     img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
                     img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4)
 
-                with gr.Group():
+                with gr.Column():
                     with gr.Row():
                         save = gr.Button('Save')
                         img2img_send_to_img2img = gr.Button('Send to img2img')
-- 
cgit v1.2.3


From 703e6d9e4e161d36b9328eefb5200e1c44fb4afd Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sat, 15 Oct 2022 21:47:08 +0900
Subject: check NaN for hypernetwork tuning

---
 modules/hypernetworks/hypernetwork.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index a2b3bc0a..4905710e 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -272,15 +272,17 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             optimizer.zero_grad()
             loss.backward()
             optimizer.step()
-
-        pbar.set_description(f"loss: {losses.mean():.7f}")
+        mean_loss = losses.mean()
+        if torch.isnan(mean_loss):
+            raise RuntimeError("Loss diverged.")
+        pbar.set_description(f"loss: {mean_loss:.7f}")
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
             hypernetwork.save(last_saved_file)
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
-            "loss": f"{losses.mean():.7f}",
+            "loss": f"{mean_loss:.7f}",
             "learn_rate": scheduler.learn_rate
         })
 
@@ -328,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         shared.state.textinfo = f"""
 <p>
-Loss: {losses.mean():.7f}<br/>
+Loss: {mean_loss:.7f}<br/>
 Step: {hypernetwork.step}<br/>
 Last prompt: {html.escape(entries[0].cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
-- 
cgit v1.2.3


From 9e846083b702a498fdb60accd72f075fa26701d9 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 14 Oct 2022 14:50:25 +0100
Subject: add vector size to embed text

---
 modules/textual_inversion/textual_inversion.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e754747e..6f549d62 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -327,10 +327,16 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
                 title = "<{}>".format(data.get('name', '???'))
+
+                try:
+                    vectorSize = list(data['string_to_param'].values())[0].shape[0]
+                except Exception as e:
+                    vectorSize = '?'
+
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}'.format(embedding.step)
+                footer_right = 'v{} {}s'.format(vectorSize, embedding.step)
 
                 captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                 captioned_image = insert_image_data_embed(captioned_image, data)
-- 
cgit v1.2.3


From 939f16529a72fe48c2ce3ef31bdaba785925a33c Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 14 Oct 2022 14:55:05 +0100
Subject: only save 1 image per embedding

---
 modules/textual_inversion/textual_inversion.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 6f549d62..1d697c90 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -242,6 +242,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
+    embedding_yet_to_be_embedded = False
 
     ititial_step = embedding.step or 0
     if ititial_step > steps:
@@ -281,6 +282,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
             embedding.save(last_saved_file)
+            embedding_yet_to_be_embedded = True
 
         write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
             "loss": f"{losses.mean():.7f}",
@@ -318,7 +320,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
             shared.state.current_image = image
 
-            if save_image_with_stored_embedding and os.path.exists(last_saved_file):
+            if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
 
                 last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
 
@@ -342,6 +344,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 captioned_image = insert_image_data_embed(captioned_image, data)
 
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+                embedding_yet_to_be_embedded = False
 
             image.save(last_saved_image)
 
-- 
cgit v1.2.3


From 9a1dcd78edbf9caf68b9e6286d7b5ca81500e243 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 14 Oct 2022 18:14:02 +0100
Subject: add webp for embed load

---
 modules/textual_inversion/textual_inversion.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1d697c90..c07bffc3 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -96,6 +96,10 @@ class EmbeddingDatabase:
                 else:
                     data = extract_image_data_embed(embed_image)
                     name = data.get('name', name)
+            elif filename.upper().endswith('.WEBP'):
+                embed_image = Image.open(path)
+                data = extract_image_data_embed(embed_image)
+                name = data.get('name', name)
             else:
                 data = torch.load(path, map_location="cpu")
 
-- 
cgit v1.2.3


From ddf6899df0cf87d4da77cb2ce223061f4a5edf18 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 14 Oct 2022 18:23:20 +0100
Subject: generalise to popular lossless formats

---
 modules/textual_inversion/textual_inversion.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index c07bffc3..b99df3b1 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -88,18 +88,14 @@ class EmbeddingDatabase:
 
             data = []
 
-            if filename.upper().endswith('.PNG'):
+            if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
                 embed_image = Image.open(path)
-                if 'sd-ti-embedding' in embed_image.text:
+                if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
                     data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
                     name = data.get('name', name)
                 else:
                     data = extract_image_data_embed(embed_image)
                     name = data.get('name', name)
-            elif filename.upper().endswith('.WEBP'):
-                embed_image = Image.open(path)
-                data = extract_image_data_embed(embed_image)
-                name = data.get('name', name)
             else:
                 data = torch.load(path, map_location="cpu")
 
-- 
cgit v1.2.3


From b6e3b96dab94a00f51725f9cc977eebc6b4072ab Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 15 Oct 2022 15:17:21 +0100
Subject: Change vector size footer label

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index b99df3b1..2ed345b1 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -338,7 +338,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = 'v{} {}s'.format(vectorSize, embedding.step)
+                footer_right = '{}v {}s'.format(vectorSize, embedding.step)
 
                 captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                 captioned_image = insert_image_data_embed(captioned_image, data)
-- 
cgit v1.2.3


From 4387e4fe6479c08f7bc7e42924c3a1093e3a1872 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 18:39:29 +0200
Subject: Update modules/ui.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Víctor Gallego <vicgalle@ucm.es>
---
 modules/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index d0696101..5bb961b2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -599,7 +599,8 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001")
                     aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
-                    aesthetic_steps = gr.Slider(minimum=0, maximum=256, step=1, label="Aesthetic steps", value=5)
+                    aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
+
                     with gr.Row():
                         aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
                         aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
-- 
cgit v1.2.3


From 9b7705e0573bddde26df4575c71f994d73a4d519 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 18:40:34 +0200
Subject: Update modules/aesthetic_clip.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Víctor Gallego <vicgalle@ucm.es>
---
 modules/aesthetic_clip.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
index f15cfd47..bcf2b073 100644
--- a/modules/aesthetic_clip.py
+++ b/modules/aesthetic_clip.py
@@ -70,7 +70,7 @@ def generate_imgs_embd(name, folder, batch_size):
         torch.cuda.empty_cache()
         res = f"""
         Done generating embedding for {name}!
-        Hypernetwork saved to {html.escape(path)}
+        Aesthetic embedding saved to {html.escape(path)}
         """
         shared.update_aesthetic_embeddings()
         return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding",
-- 
cgit v1.2.3


From 0d4f5db235357aeb4c7a8738179ba33aaf5a6b75 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 18:40:58 +0200
Subject: Update modules/ui.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Víctor Gallego <vicgalle@ucm.es>
---
 modules/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 5bb961b2..25eba548 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -597,7 +597,8 @@ def create_ui(wrap_gradio_gpu_call):
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
                 with gr.Group():
-                    aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001")
+                    aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001")
+
                     aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
                     aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
 
-- 
cgit v1.2.3


From ad9bc604a8fadcfebe72be37f66cec51e7e87fb5 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 18:41:18 +0200
Subject: Update modules/ui.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Víctor Gallego <vicgalle@ucm.es>
---
 modules/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 25eba548..3b28b69c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -607,7 +607,8 @@ def create_ui(wrap_gradio_gpu_call):
                         aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
                         aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
 
-                    aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
+                    aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Aesthetic imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
+
                     aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
 
                 with gr.Row():
-- 
cgit v1.2.3


From 3f5c3b981e46c16bb10948d012575b25170efb3b Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 18:41:46 +0200
Subject: Update modules/ui.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Víctor Gallego <vicgalle@ucm.es>
---
 modules/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 3b28b69c..1f6fcdc9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1190,7 +1190,8 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create embedding", variant='primary')
 
-                with gr.Tab(label="Create images embedding"):
+                with gr.Tab(label="Create aesthetic images embedding"):
+
                     new_embedding_name_ae = gr.Textbox(label="Name")
                     process_src_ae = gr.Textbox(label='Source directory')
                     batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256)
-- 
cgit v1.2.3


From 74a9ee70020ffa2746c82300c533de3f7e523f22 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 17:25:35 +0300
Subject: fix saving images compatibility with gradio update

---
 modules/ui.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 3206113e..b867d40f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -154,10 +154,7 @@ def save_files(js_data, images, do_make_zip, index):
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
 
         for image_index, filedata in enumerate(images, start_index):
-            if filedata.startswith("data:image/png;base64,"):
-                filedata = filedata[len("data:image/png;base64,"):]
-
-            image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))
+            image = image_from_url_text(filedata)
 
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
-- 
cgit v1.2.3


From 529afbf4d70165a0dfd19eb9c2ec22416b794a1d Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 15 Oct 2022 19:19:54 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index c81722a0..984b35c4 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -24,7 +24,7 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
-    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
-- 
cgit v1.2.3


From 9a33292ce41b01252cdb8ab6214a11d274e32fa0 Mon Sep 17 00:00:00 2001
From: zhengxiaoyao0716 <1499383852@qq.com>
Date: Sat, 15 Oct 2022 01:04:47 +0800
Subject: reload javascript files when custom script bodies

---
 modules/ui.py | 28 ++++++++++++++++------------
 1 file changed, 16 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index b867d40f..90b8646b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -12,7 +12,7 @@ import time
 import traceback
 import platform
 import subprocess as sp
-from functools import reduce
+from functools import partial, reduce
 
 import numpy as np
 import torch
@@ -1491,6 +1491,7 @@ Requested path was: {f}
 
         def reload_scripts():
             modules.scripts.reload_script_body_only()
+            reload_javascript() # need to refresh the html page
 
         reload_script_bodies.click(
             fn=reload_scripts,
@@ -1738,22 +1739,25 @@ Requested path was: {f}
     return demo
 
 
-with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
-    javascript = f'<script>{jsfile.read()}</script>'
+def load_javascript(raw_response):
+    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
+        javascript = f'<script>{jsfile.read()}</script>'
 
-jsdir = os.path.join(script_path, "javascript")
-for filename in sorted(os.listdir(jsdir)):
-    with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
-        javascript += f"\n<script>{jsfile.read()}</script>"
+    jsdir = os.path.join(script_path, "javascript")
+    for filename in sorted(os.listdir(jsdir)):
+        with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
+            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
 
-
-if 'gradio_routes_templates_response' not in globals():
     def template_response(*args, **kwargs):
-        res = gradio_routes_templates_response(*args, **kwargs)
-        res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
+        res = raw_response(*args, **kwargs)
+        res.body = res.body.replace(
+            b'</head>', f'{javascript}</head>'.encode("utf8"))
         res.init_headers()
         return res
 
-    gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
     gradio.routes.templates.TemplateResponse = template_response
 
+
+reload_javascript = partial(load_javascript,
+                            gradio.routes.templates.TemplateResponse)
+reload_javascript()
-- 
cgit v1.2.3


From 3d21684ee30ca5734126b8d08c05b3a0f513fe75 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sun, 16 Oct 2022 00:01:00 +0200
Subject: Add support to other img format, fixed dropbox update

---
 modules/aesthetic_clip.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
index bcf2b073..68264284 100644
--- a/modules/aesthetic_clip.py
+++ b/modules/aesthetic_clip.py
@@ -8,7 +8,7 @@ import gradio as gr
 import torch
 from PIL import Image
 from modules import shared
-from modules.shared import device, aesthetic_embeddings
+from modules.shared import device
 from transformers import CLIPModel, CLIPProcessor
 
 from tqdm.auto import tqdm
@@ -20,7 +20,7 @@ def get_all_images_in_folder(folder):
 
 
 def check_is_valid_image_file(filename):
-    return filename.lower().endswith(('.png', '.jpg', '.jpeg'))
+    return filename.lower().endswith(('.png', '.jpg', '.jpeg', ".gif", ".tiff", ".webp"))
 
 
 def batched(dataset, total, n=1):
@@ -73,6 +73,6 @@ def generate_imgs_embd(name, folder, batch_size):
         Aesthetic embedding saved to {html.escape(path)}
         """
         shared.update_aesthetic_embeddings()
-        return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding",
-                           value=sorted(aesthetic_embeddings.keys())[0] if len(
-                               aesthetic_embeddings) > 0 else None), res, ""
+        return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
+                           value=sorted(shared.aesthetic_embeddings.keys())[0] if len(
+                               shared.aesthetic_embeddings) > 0 else None), res, ""
-- 
cgit v1.2.3


From 9325c85f780c569d1823e422eaf51b2e497e0d3e Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sun, 16 Oct 2022 00:23:47 +0200
Subject: fixed dropbox update

---
 modules/sd_hijack.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 192883b2..491312b4 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -9,7 +9,7 @@ from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared
-from modules.shared import opts, device, cmd_opts, aesthetic_embeddings
+from modules.shared import opts, device, cmd_opts
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
@@ -182,7 +182,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             image_embs_name = None
         if image_embs_name is not None and self.image_embs_name != image_embs_name:
             self.image_embs_name = image_embs_name
-            self.image_embs = torch.load(aesthetic_embeddings[self.image_embs_name], map_location=device)
+            self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
             self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
             self.image_embs.requires_grad_(False)
 
-- 
cgit v1.2.3


From 763b893f319cee280b86e63025eb55e7c16b02e7 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sun, 16 Oct 2022 10:03:09 +0800
Subject: images history sorting files by date

---
 modules/images_history.py | 261 ++++++++++++++++++++++++++++++++++------------
 1 file changed, 196 insertions(+), 65 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index f5ef44fe..533cf51b 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,33 +1,74 @@
 import os
 import shutil
+import time
+import hashlib
+import gradio
+show_max_dates_num = 3
+system_bak_path = "webui_log_and_bak"
+def is_valid_date(date):
+    try:
+        time.strptime(date, "%Y%m%d")
+        return True
+    except:
+        return False
 
+def reduplicative_file_move(src, dst):
+    def same_name_file(basename, path):
+        name, ext = os.path.splitext(basename)
+        f_list = os.listdir(path)
+        max_num = 0
+        for f in f_list:
+            if len(f) <= len(basename):
+                continue
+            f_ext = f[-len(ext):] if len(ext) > 0 else ""
+            if f[:len(name)] == name and f_ext == ext:                
+                if f[len(name)] == "(" and f[-len(ext)-1] == ")":
+                    number = f[len(name)+1:-len(ext)-1]
+                    if number.isdigit():
+                        if int(number) > max_num:
+                            max_num = int(number)
+        return f"{name}({max_num + 1}){ext}"
+    name = os.path.basename(src)
+    save_name = os.path.join(dst, name)
+    if not os.path.exists(save_name):
+        shutil.move(src, dst)
+    else:
+        name = same_name_file(name, dst)
+        shutil.move(src, os.path.join(dst, name))
 
-def traverse_all_files(output_dir, image_list, curr_dir=None):
-    curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
+def traverse_all_files(curr_path, image_list, all_type=False):
     try:
         f_list = os.listdir(curr_path)
     except:
-        if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt":
-            image_list.append(curr_dir)
+        if all_type or curr_path[-10:].rfind(".") > 0 and curr_path[-4:] != ".txt":
+            image_list.append(curr_path)
         return image_list
     for file in f_list:
-        file = file if curr_dir is None else os.path.join(curr_dir, file)
-        file_path = os.path.join(curr_path, file)
-        if file[-4:] == ".txt":
+        file = os.path.join(curr_path, file)
+        if (not all_type) and file[-4:] == ".txt":
             pass
-        elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
+        elif os.path.isfile(file) and file[-10:].rfind(".") > 0:
             image_list.append(file)
         else:
-            image_list = traverse_all_files(output_dir, image_list, file)
+            image_list = traverse_all_files(file, image_list)
     return image_list
 
-
-def get_recent_images(dir_name, page_index, step, image_index, tabname):
-    page_index = int(page_index)
-    f_list = os.listdir(dir_name)
+def get_recent_images(dir_name, page_index, step, image_index, tabname, date_from, date_to):
+    #print(f"turn_page {page_index}",date_from)
+    if date_from is None or date_from == "":
+        return None, 1, None, ""
     image_list = []
-    image_list = traverse_all_files(dir_name, image_list)
-    image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    date_list = auto_sorting(dir_name)
+    page_index = int(page_index)
+    today = time.strftime("%Y%m%d",time.localtime(time.time()))
+    for date in date_list:
+        if date >= date_from and date <= date_to:
+            path = os.path.join(dir_name, date)
+            if date == today and not os.path.exists(path):
+                continue
+            image_list = traverse_all_files(path, image_list)
+
+    image_list = sorted(image_list, key=lambda file: -os.path.getctime(file))
     num = 48 if tabname != "extras" else 12
     max_page_index = len(image_list) // num + 1
     page_index = max_page_index if page_index == -1 else page_index + step
@@ -38,40 +79,101 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname):
     image_index = int(image_index)
     if image_index < 0 or image_index > len(image_list) - 1:
         current_file = None
-        hidden = None
     else:
-        current_file = image_list[int(image_index)]
-        hidden = os.path.join(dir_name, current_file)
-    return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
+        current_file = image_list[image_index]
+    return image_list, page_index, image_list,  ""
 
+def auto_sorting(dir_name):
+    #print(f"auto sorting")
+    bak_path = os.path.join(dir_name, system_bak_path)
+    if not os.path.exists(bak_path):
+        os.mkdir(bak_path)
+    log_file = None 
+    files_list = []    
+    f_list = os.listdir(dir_name)
+    for file in f_list:   
+        if file == system_bak_path:
+            continue     
+        file_path = os.path.join(dir_name, file)
+        if not is_valid_date(file):
+            if file[-10:].rfind(".") > 0:
+                files_list.append(file_path)
+            else:
+                files_list = traverse_all_files(file_path, files_list, all_type=True)
+
+    for file in files_list:        
+        date_str = time.strftime("%Y%m%d",time.localtime(os.path.getctime(file)))
+        file_path = os.path.dirname(file)
+        hash_path = hashlib.md5(file_path.encode()).hexdigest()
+        path = os.path.join(dir_name, date_str, hash_path)
+        if not os.path.exists(path):
+            os.makedirs(path)
+        if log_file is None:
+            log_file = open(os.path.join(bak_path,"path_mapping.csv"),"a") 
+        log_file.write(f"{hash_path},{file_path}\n")
+        reduplicative_file_move(file, path)
+       
+    date_list = []
+    f_list = os.listdir(dir_name)
+    for f in f_list:
+        if is_valid_date(f):
+            date_list.append(f)
+        elif f == system_bak_path:
+            continue
+        else:
+            reduplicative_file_move(os.path.join(dir_name, f), bak_path)           
+            
+    today = time.strftime("%Y%m%d",time.localtime(time.time()))
+    if today not in date_list:
+        date_list.append(today)
+    return sorted(date_list)
 
-def first_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, 1, 0, image_index, tabname)
 
 
-def end_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, -1, 0, image_index, tabname)
+def archive_images(dir_name):
+    date_list = auto_sorting(dir_name)
+    date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0]
+    return (
+        gradio.update(visible=False), 
+        gradio.update(visible=True), 
+        gradio.Dropdown.update(choices=date_list, value=date_list[-1]),
+        gradio.Dropdown.update(choices=date_list, value=date_from)
+    )
 
+def date_to_change(dir_name, page_index, image_index, tabname, date_from, date_to):
+    #print("date_to", date_to)
+    date_list = auto_sorting(dir_name)
+    date_from_list = [date for date in date_list if date <= date_to]
+    date_from = date_from_list[0] if len(date_from_list) < show_max_dates_num else date_from_list[-show_max_dates_num]
+    image_list, page_index, image_list, _  =get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to)
+    return image_list, page_index, image_list, _, gradio.Dropdown.update(choices=date_from_list, value=date_from)
 
-def prev_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, -1, image_index, tabname)
+def first_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
+    return get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to)
 
 
-def next_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, 1, image_index, tabname)
+def end_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
+    return get_recent_images(dir_name, -1, 0, image_index, tabname, date_from, date_to)
 
 
-def page_index_change(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, 0, image_index, tabname)
+def prev_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
+    return get_recent_images(dir_name, page_index, -1, image_index, tabname, date_from, date_to)
 
 
-def show_image_info(num, image_path, filenames):
-    # print(f"select image {num}")
-    file = filenames[int(num)]
-    return file, num, os.path.join(image_path, file)
+def next_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
+    return get_recent_images(dir_name, page_index, 1, image_index, tabname, date_from, date_to)
+
+
+def page_index_change(dir_name, page_index, image_index, tabname, date_from, date_to):
+    return get_recent_images(dir_name, page_index, 0, image_index, tabname, date_from, date_to)
 
 
-def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
+def show_image_info(tabname_box, num, filenames):
+    # #print(f"select image {num}")
+    file = filenames[int(num)]
+    return file, num, file
+
+def delete_image(delete_num, tabname, name, page_index, filenames, image_index):
     if name == "":
         return filenames, delete_num
     else:
@@ -81,21 +183,19 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima
         new_file_list = []
         for name in filenames:
             if i >= index and i < index + delete_num:
-                path = os.path.join(dir_name, name)
-                if os.path.exists(path):
-                    print(f"Delete file {path}")
-                    os.remove(path)
-                    txt_file = os.path.splitext(path)[0] + ".txt"
+                if os.path.exists(name):
+                    #print(f"Delete file {name}")
+                    os.remove(name)
+                    txt_file = os.path.splitext(name)[0] + ".txt"
                     if os.path.exists(txt_file):
                         os.remove(txt_file)
                 else:
-                    print(f"Not exists file {path}")
+                    #print(f"Not exists file {name}")
             else:
                 new_file_list.append(name)
             i += 1
     return new_file_list, 1
 
-
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     if tabname == "txt2img":
         dir_name = opts.outdir_txt2img_samples
@@ -107,16 +207,32 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     dir_name = d[0]
     for p in d[1:]:
         dir_name = os.path.join(dir_name, p)
-    with gr.Row():
-        renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
-        first_page = gr.Button('First Page')
-        prev_page = gr.Button('Prev Page')
-        page_index = gr.Number(value=1, label="Page Index")
-        next_page = gr.Button('Next Page')
-        end_page = gr.Button('End Page')
-    with gr.Row(elem_id=tabname + "_images_history"):
+
+    f_list = os.listdir(dir_name)
+    sorted_flag = os.path.exists(os.path.join(dir_name, system_bak_path)) or len(f_list) == 0 
+    date_list, date_from, date_to = None, None, None
+    if sorted_flag:
+        #print(sorted_flag)
+        date_list = auto_sorting(dir_name)
+        date_to = date_list[-1]
+        date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0] 
+
+    with gr.Column(visible=sorted_flag) as page_panel:
         with gr.Row():
+            renew_page = gr.Button('Refresh', elem_id=tabname + "_images_history_renew_page", interactive=sorted_flag)
+            first_page = gr.Button('First Page')
+            prev_page = gr.Button('Prev Page')
+            page_index = gr.Number(value=1, label="Page Index")
+            next_page = gr.Button('Next Page')
+            end_page = gr.Button('End Page')
+
+        with gr.Row(elem_id=tabname + "_images_history"):
             with gr.Column(scale=2):
+                with gr.Row():
+                    newest = gr.Button('Newest')
+                    date_to = gr.Dropdown(choices=date_list, value=date_to, label="Date to")
+                    date_from = gr.Dropdown(choices=date_list, value=date_from, label="Date from")                    
+
                 history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
                 with gr.Row():
                     delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
@@ -128,22 +244,31 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                 with gr.Row():
                     with gr.Column():
                         img_file_info = gr.Textbox(label="Generate Info", interactive=False)
-                        img_file_name = gr.Textbox(label="File Name", interactive=False)
-                with gr.Row():
+                        img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
                     # hiden items
+                    with gr.Row(visible=False):
+                        img_path = gr.Textbox(dir_name)
+                        tabname_box = gr.Textbox(tabname)
+                        image_index = gr.Textbox(value=-1)
+                        set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
+                        filenames = gr.State()
+                        hidden = gr.Image(type="pil")
+                        info1 = gr.Textbox()
+                        info2 = gr.Textbox()
+    with gr.Column(visible=not sorted_flag) as init_warning:
+        with gr.Row():
+            gr.Textbox("The system needs to archive the files according to the date. This requires changing the directory structure of the files",
+             label="Waring",
+             css="")
+        with gr.Row():
+            sorted_button = gr.Button('Confirme')
 
-                    img_path = gr.Textbox(dir_name.rstrip("/"), visible=False)
-                    tabname_box = gr.Textbox(tabname, visible=False)
-                    image_index = gr.Textbox(value=-1, visible=False)
-                    set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False)
-                    filenames = gr.State()
-                    hidden = gr.Image(type="pil", visible=False)
-                    info1 = gr.Textbox(visible=False)
-                    info2 = gr.Textbox(visible=False)
-
+                  
+           
+   
     # turn pages
-    gallery_inputs = [img_path, page_index, image_index, tabname_box]
-    gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
+    gallery_inputs = [img_path, page_index, image_index, tabname_box, date_from, date_to]
+    gallery_outputs = [history_gallery, page_index, filenames, img_file_name]
 
     first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
     next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
@@ -154,15 +279,21 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     # page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
 
     # other funcitons
-    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden])
+    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, filenames], outputs=[img_file_name, image_index, hidden])
     img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
+    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
     hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-
+    date_to.change(date_to_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs + [date_from])
     # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
     switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
     switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
 
+    sorted_button.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from])
+    newest.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from])
+   
+    
+    
+
 
 def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
     with gr.Blocks(analytics_enabled=False) as images_history:
-- 
cgit v1.2.3


From 0c5fa9a681672508adadbe1e10fc16d7fe0ed6dd Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 16 Oct 2022 08:51:24 +0300
Subject: do not reload embeddings from disk when doing textual inversion

---
 modules/processing.py                          | 5 +++--
 modules/textual_inversion/textual_inversion.py | 1 +
 2 files changed, 4 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 941ae089..833fed8a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -53,7 +53,7 @@ def get_correct_sampler(p):
         return sd_samplers.samplers_for_img2img
 
 class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None, do_not_reload_embeddings=False):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -80,6 +80,7 @@ class StableDiffusionProcessing:
         self.extra_generation_params: dict = extra_generation_params or {}
         self.overlay_images = overlay_images
         self.eta = eta
+        self.do_not_reload_embeddings = do_not_reload_embeddings
         self.paste_to = None
         self.color_corrections = None
         self.denoising_strength: float = 0
@@ -364,7 +365,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     def infotext(iteration=0, position_in_batch=0):
         return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
 
-    if os.path.exists(cmd_opts.embeddings_dir):
+    if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     infotexts = []
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 2ed345b1..7ec75018 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -296,6 +296,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 sd_model=shared.sd_model,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
+                do_not_reload_embeddings=True,
             )
 
             if preview_from_txt2img:
-- 
cgit v1.2.3


From 2ce27728f6433911274efa67856315d22df56629 Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Sun, 16 Oct 2022 13:50:55 +0900
Subject: added extras batch work from directory

---
 modules/extras.py | 23 ++++++++++++++++++-----
 modules/ui.py     | 12 ++++++++++++
 2 files changed, 30 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index f2f5a7b0..5b52b27d 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -20,26 +20,38 @@ import gradio as gr
 cached_images = {}
 
 
-def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
     devices.torch_gc()
 
     imageArr = []
     # Also keep track of original file names
     imageNameArr = []
-
+    outputs = []
+    
     if extras_mode == 1:
         #convert file to pillow image
         for img in image_folder:
             image = Image.open(img)
             imageArr.append(image)
             imageNameArr.append(os.path.splitext(img.orig_name)[0])
+    elif extras_mode == 2:
+        if input_dir == '':
+            return outputs, "Please select an input directory.", ''
+        image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+        for img in image_list:
+            image = Image.open(img)
+            imageArr.append(image)
+            imageNameArr.append(img)
     else:
         imageArr.append(image)
         imageNameArr.append(None)
 
-    outpath = opts.outdir_samples or opts.outdir_extras_samples
+    if extras_mode == 2 and output_dir != '':
+        outpath = output_dir
+    else:
+        outpath = opts.outdir_samples or opts.outdir_extras_samples
 
-    outputs = []
+    
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
@@ -112,7 +124,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility,
             image.info = existing_pnginfo
             image.info["extras"] = info
 
-        outputs.append(image)
+        if extras_mode != 2 or show_extras_results :
+            outputs.append(image)
 
     devices.torch_gc()
 
diff --git a/modules/ui.py b/modules/ui.py
index b867d40f..08fa72c6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1016,6 +1016,15 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.TabItem('Batch Process'):
                         image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
 
+                    with gr.TabItem('Batch from Directory'):
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs,
+                            placeholder="A directory on the same machine where the server is running."
+                        )
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs,
+                            placeholder="Leave blank to save images to the default path."
+                        )
+                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
+
                 with gr.Tabs(elem_id="extras_resize_mode"):
                     with gr.TabItem('Scale by'):
                         upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
@@ -1060,6 +1069,9 @@ def create_ui(wrap_gradio_gpu_call):
                 dummy_component,
                 extras_image,
                 image_batch,
+                extras_batch_input_dir,
+                extras_batch_output_dir,
+                show_extras_results,
                 gfpgan_visibility,
                 codeformer_visibility,
                 codeformer_weight,
-- 
cgit v1.2.3


From 179e3ca752d0133470fd3ae44153ee0b71450c9f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 16 Oct 2022 09:51:01 +0300
Subject: honor --hide-ui-dir-config option for #2807

---
 modules/extras.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 5b52b27d..0819ed37 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -35,6 +35,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             imageArr.append(image)
             imageNameArr.append(os.path.splitext(img.orig_name)[0])
     elif extras_mode == 2:
+        assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
+
         if input_dir == '':
             return outputs, "Please select an input directory.", ''
         image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
-- 
cgit v1.2.3


From 3395ba493f93214cf037d084d45693a37610bd85 Mon Sep 17 00:00:00 2001
From: ddPn08 <pyn.g.1125@gmail.com>
Date: Sun, 16 Oct 2022 09:24:01 +0900
Subject: Allow specifying the region of ngrok.

---
 modules/ngrok.py  | 8 +++++---
 modules/shared.py | 1 +
 modules/ui.py     | 2 +-
 3 files changed, 7 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/ngrok.py b/modules/ngrok.py
index 7d03a6df..5c5f349a 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -1,12 +1,14 @@
 from pyngrok import ngrok, conf, exception
 
 
-def connect(token, port):
+def connect(token, port, region):
     if token == None:
         token = 'None'
-    conf.get_default().auth_token = token
+    config = conf.PyngrokConfig(
+        auth_token=token, region=region
+    )
     try:
-        public_url = ngrok.connect(port).public_url
+        public_url = ngrok.connect(port, pyngrok_config=config).public_url
     except exception.PyngrokNgrokError:
         print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
diff --git a/modules/shared.py b/modules/shared.py
index fa30bbb0..dcab0af9 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -40,6 +40,7 @@ parser.add_argument("--unload-gfpgan", action='store_true', help="does not do an
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
+parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
diff --git a/modules/ui.py b/modules/ui.py
index 08fa72c6..5c0eaf73 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -56,7 +56,7 @@ if not cmd_opts.share and not cmd_opts.listen:
 if cmd_opts.ngrok != None:
     import modules.ngrok as ngrok
     print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860)
+    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
 
 
 def gr_show(visible=True):
-- 
cgit v1.2.3


From 20bf99052a9d50b5f99d199f4c449ef1ddd6e3cb Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 04:47:03 +0900
Subject: Make style configurable in ui-config.json

---
 modules/ui.py | 7 +++++++
 1 file changed, 7 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 5c0eaf73..78096f27 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -508,9 +508,11 @@ def create_toprow(is_img2img):
             with gr.Row():
                 with gr.Column(scale=1, elem_id="style_pos_col"):
                     prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style.save_to_config = True
 
                 with gr.Column(scale=1, elem_id="style_neg_col"):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style2.save_to_config = True
 
     return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
@@ -1739,6 +1741,11 @@ Requested path was: {f}
         if type(x) == gr.Number:
             apply_field(x, 'value')
 
+        # Since there are many dropdowns that shouldn't be saved,
+        # we only mark dropdowns that should be saved.
+        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
+            apply_field(x, 'value')
+
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
     visit(extras_interface, loadsave, "extras")
-- 
cgit v1.2.3


From b65a3101ce82b42b4ccc525044548e66cc44ae4a Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 04:54:53 +0900
Subject: Use default value when dropdown ui setting is bad

Default value is the first value of selectables.
Particually, None in styles.
---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 78096f27..c8e68bd6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1744,7 +1744,7 @@ Requested path was: {f}
         # Since there are many dropdowns that shouldn't be saved,
         # we only mark dropdowns that should be saved.
         if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
-            apply_field(x, 'value')
+            apply_field(x, 'value', lambda val: val in x.choices)
 
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
-- 
cgit v1.2.3


From 9258a33e3755c76922cd47a03cd59419b6426304 Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 05:09:11 +0900
Subject: Warn when user uses bad ui setting

---
 modules/ui.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index c8e68bd6..10bdf121 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1717,7 +1717,9 @@ Requested path was: {f}
             saved_value = ui_settings.get(key, None)
             if saved_value is None:
                 ui_settings[key] = getattr(obj, field)
-            elif condition is None or condition(saved_value):
+            elif condition and not condition(saved_value):
+                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
+            else:
                 setattr(obj, field, saved_value)
 
         if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
-- 
cgit v1.2.3


From 36a0ba357ab0742c3c4a28437b68fb29a235afbe Mon Sep 17 00:00:00 2001
From: Junpeng Qiu <naminoqiu@gmail.com>
Date: Sat, 15 Oct 2022 21:42:52 -0700
Subject: Added Refresh Button to embedding and hypernetwork names in Train Tab

Problem
everytime I modified pt files in embedding_dir or hypernetwork_dir, I
need to restart webui to have the new files shown in the dropdown of
Train Tab

Solution
refactored create_refresh_button out of create_setting_component so we
can use this method to create button next to gr.Dropdowns of embedding
name and hypernetworks

Extra Modification
hypernetwork pt are now sorted in alphabetic order
---
 modules/ui.py | 45 ++++++++++++++++++++++++++-------------------
 1 file changed, 26 insertions(+), 19 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 10bdf121..ee3d0248 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -568,6 +568,24 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
+    def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+        def refresh():
+            refresh_method()
+            args = refreshed_args() if callable(refreshed_args) else refreshed_args
+
+            for k, v in args.items():
+                setattr(refresh_component, k, v)
+
+            return gr.update(**(args or {}))
+
+        refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
+        refresh_button.click(
+            fn = refresh,
+            inputs = [],
+            outputs = [refresh_component]
+        )
+        return refresh_button
+
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
@@ -1205,8 +1223,12 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
-                    train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                    train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
+                    with gr.Row():
+                        train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+                    with gr.Row():
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
                     learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
                     batch_size = gr.Number(label='Batch size', value=1, precision=0)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
@@ -1357,26 +1379,11 @@ def create_ui(wrap_gradio_gpu_call):
         if info.refresh is not None:
             if is_quicksettings:
                 res = comp(label=info.label, value=fun, **(args or {}))
-                refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_"+key)
+                refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
             else:
                 with gr.Row(variant="compact"):
                     res = comp(label=info.label, value=fun, **(args or {}))
-                    refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_" + key)
-
-            def refresh():
-                info.refresh()
-                refreshed_args = info.component_args() if callable(info.component_args) else info.component_args
-
-                for k, v in refreshed_args.items():
-                    setattr(res, k, v)
-
-                return gr.update(**(refreshed_args or {}))
-
-            refresh_button.click(
-                fn=refresh,
-                inputs=[],
-                outputs=[res],
-            )
+                    refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
         else:
             res = comp(label=info.label, value=fun, **(args or {}))
 
-- 
cgit v1.2.3


From 523140d7805c644700009b8a2483ff4eb4a22304 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sun, 16 Oct 2022 10:23:30 +0200
Subject: ui fix

---
 modules/aesthetic_clip.py |  3 +--
 modules/sd_hijack.py      |  3 +--
 modules/shared.py         |  2 ++
 modules/ui.py             | 24 ++++++++++++++----------
 4 files changed, 18 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
index 68264284..ccb35c73 100644
--- a/modules/aesthetic_clip.py
+++ b/modules/aesthetic_clip.py
@@ -74,5 +74,4 @@ def generate_imgs_embd(name, folder, batch_size):
         """
         shared.update_aesthetic_embeddings()
         return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
-                           value=sorted(shared.aesthetic_embeddings.keys())[0] if len(
-                               shared.aesthetic_embeddings) > 0 else None), res, ""
+                           value="None"), res, ""
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 01fcb78f..2de2eed5 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -392,8 +392,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             z1 = self.process_tokens(tokens, multipliers)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
 
-            if len(text[
-                       0]) != 0 and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None:
+            if self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None:
                 if not opts.use_old_emphasis_implementation:
                     remade_batch_tokens = [
                         [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in
diff --git a/modules/shared.py b/modules/shared.py
index 3c5ffef1..e2c98b2d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -96,11 +96,13 @@ loaded_hypernetwork = None
 
 aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
                         os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
+aesthetic_embeddings = aesthetic_embeddings | {"None": None}
 
 def update_aesthetic_embeddings():
     global aesthetic_embeddings
     aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
                             os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
+    aesthetic_embeddings = aesthetic_embeddings | {"None": None}
 
 def reload_hypernetworks():
     global hypernetworks
diff --git a/modules/ui.py b/modules/ui.py
index 13ba3142..4069f0d2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -594,19 +594,23 @@ def create_ui(wrap_gradio_gpu_call):
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
                 with gr.Group():
-                    aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001")
-
-                    aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
-                    aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
+                    with gr.Accordion("Open for Clip Aesthetic!",open=False):
+                        with gr.Row():
+                            aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
+                            aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
 
-                    with gr.Row():
-                        aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
-                        aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
-                        aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
+                        with gr.Row():
+                            aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001")
+                            aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
+                            aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()),
+                                                         label="Aesthetic imgs embedding",
+                                                         value="None")
 
-                    aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Aesthetic imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
+                        with gr.Row():
+                            aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
+                            aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
+                            aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
 
-                    aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-- 
cgit v1.2.3


From e4f8b5f00dd33b7547cc6b76fbed26bb83b37a64 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sun, 16 Oct 2022 10:28:21 +0200
Subject: ui fix

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 2de2eed5..5d0590af 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -178,7 +178,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         self.load_image_embs(image_embs_name)
 
     def load_image_embs(self, image_embs_name):
-        if image_embs_name is None or len(image_embs_name) == 0:
+        if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
             image_embs_name = None
         if image_embs_name is not None and self.image_embs_name != image_embs_name:
             self.image_embs_name = image_embs_name
-- 
cgit v1.2.3


From f62905fdf928b54aa76765e5cbde8d538d494e49 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sun, 16 Oct 2022 21:22:38 +0800
Subject: images history speed up

---
 modules/images_history.py | 250 ++++++++++++++++++++++++----------------------
 1 file changed, 128 insertions(+), 122 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 7fd75005..ae0b4e40 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -3,8 +3,10 @@ import shutil
 import time
 import hashlib
 import gradio
-show_max_dates_num = 3
+
 system_bak_path = "webui_log_and_bak"
+loads_files_num = 216
+num_of_imgs_per_page = 36
 def is_valid_date(date):
     try:
         time.strptime(date, "%Y%m%d")
@@ -53,38 +55,7 @@ def traverse_all_files(curr_path, image_list, all_type=False):
             image_list = traverse_all_files(file, image_list)
     return image_list
 
-def get_recent_images(dir_name, page_index, step, image_index, tabname, date_from, date_to):
-    #print(f"turn_page {page_index}",date_from)
-    if date_from is None or date_from == "":
-        return None, 1, None, ""
-    image_list = []
-    date_list = auto_sorting(dir_name)
-    page_index = int(page_index)
-    today = time.strftime("%Y%m%d",time.localtime(time.time()))
-    for date in date_list:
-        if date >= date_from and date <= date_to:
-            path = os.path.join(dir_name, date)
-            if date == today and not os.path.exists(path):
-                continue
-            image_list = traverse_all_files(path, image_list)
-
-    image_list = sorted(image_list, key=lambda file: -os.path.getctime(file))
-    num = 48 if tabname != "extras" else 12
-    max_page_index = len(image_list) // num + 1
-    page_index = max_page_index if page_index == -1 else page_index + step
-    page_index = 1 if page_index < 1 else page_index
-    page_index = max_page_index if page_index > max_page_index else page_index
-    idx_frm = (page_index - 1) * num
-    image_list = image_list[idx_frm:idx_frm + num]
-    image_index = int(image_index)
-    if image_index < 0 or image_index > len(image_list) - 1:
-        current_file = None
-    else:
-        current_file = image_list[image_index]
-    return image_list, page_index, image_list,  ""
-
-def auto_sorting(dir_name):
-    #print(f"auto sorting")
+def auto_sorting(dir_name):    
     bak_path = os.path.join(dir_name, system_bak_path)
     if not os.path.exists(bak_path):
         os.mkdir(bak_path)
@@ -126,102 +97,131 @@ def auto_sorting(dir_name):
     today = time.strftime("%Y%m%d",time.localtime(time.time()))
     if today not in date_list:
         date_list.append(today)
-    return sorted(date_list)
+    return sorted(date_list, reverse=True)
 
 
 
-def archive_images(dir_name):
+def archive_images(dir_name, date_to):
     date_list = auto_sorting(dir_name)
-    date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0]
+    today = time.strftime("%Y%m%d",time.localtime(time.time()))
+    date_to = today if date_to is None  or date_to == "" else date_to
+    filenames = []
+    for date in date_list:
+        if date <= date_to:
+            path = os.path.join(dir_name, date)
+            if date == today and not os.path.exists(path):
+                continue
+            filenames = traverse_all_files(path, filenames)
+        if len(filenames) > loads_files_num:            
+            break
+    filenames = sorted(filenames, key=lambda file: -os.path.getctime(file))
+    _, image_list, _, visible_num = get_recent_images(1, 0, filenames)
     return (
         gradio.update(visible=False), 
         gradio.update(visible=True), 
-        gradio.Dropdown.update(choices=date_list, value=date_list[-1]),
-        gradio.Dropdown.update(choices=date_list, value=date_from)
+        gradio.Dropdown.update(choices=date_list, value=date_to),
+        date,
+        filenames,        
+        1,
+        image_list,
+        "",
+        visible_num
     )
+def system_init(dir_name):
+    ret =  [x for x in  archive_images(dir_name, None)]
+    ret += [gradio.update(visible=False)]
+    return ret    
+
+def newest_click(dir_name,  date_to):
+    if date_to == "start":
+         return  True,  False, "start", None, None, 1, None, ""
+    else:
+        return archive_images(dir_name, time.strftime("%Y%m%d",time.localtime(time.time())))
 
-def date_to_change(dir_name, page_index, image_index, tabname, date_from, date_to):
-    #print("date_to", date_to)
-    date_list = auto_sorting(dir_name)
-    date_from_list = [date for date in date_list if date <= date_to]
-    date_from = date_from_list[0] if len(date_from_list) < show_max_dates_num else date_from_list[-show_max_dates_num]
-    image_list, page_index, image_list, _  =get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to)
-    return image_list, page_index, image_list, _, gradio.Dropdown.update(choices=date_from_list, value=date_from)
-
-def first_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
-    return get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to)
-
-
-def end_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
-    return get_recent_images(dir_name, -1, 0, image_index, tabname, date_from, date_to)
-
-
-def prev_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
-    return get_recent_images(dir_name, page_index, -1, image_index, tabname, date_from, date_to)
-
-
-def next_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
-    return get_recent_images(dir_name, page_index, 1, image_index, tabname, date_from, date_to)
-
-
-def page_index_change(dir_name, page_index, image_index, tabname, date_from, date_to):
-    return get_recent_images(dir_name, page_index, 0, image_index, tabname, date_from, date_to)
-
-
-def show_image_info(tabname_box, num, filenames):
-    # #print(f"select image {num}")
-    file = filenames[int(num)]
-    return file, num, file
-
-def delete_image(delete_num, tabname, name, page_index, filenames, image_index):
+def delete_image(delete_num, name, filenames, image_index, visible_num):
     if name == "":
         return filenames, delete_num
     else:
         delete_num = int(delete_num)
+        visible_num = int(visible_num)
+        image_index = int(image_index)
         index = list(filenames).index(name)
         i = 0
         new_file_list = []
         for name in filenames:
             if i >= index and i < index + delete_num:
                 if os.path.exists(name):
-                    #print(f"Delete file {name}")
+                    if visible_num == image_index:
+                        new_file_list.append(name)
+                        continue                    
+                    print(f"Delete file {name}")
                     os.remove(name)
+                    visible_num -= 1
                     txt_file = os.path.splitext(name)[0] + ".txt"
                     if os.path.exists(txt_file):
                         os.remove(txt_file)
                 else:
-                    #print(f"Not exists file {name}")
+                    print(f"Not exists file {name}")
             else:
                 new_file_list.append(name)
             i += 1
-    return new_file_list, 1
+    return new_file_list, 1, visible_num
+
+def get_recent_images(page_index, step, filenames):
+    page_index = int(page_index)
+    max_page_index = len(filenames) // num_of_imgs_per_page + 1
+    page_index = max_page_index if page_index == -1 else page_index + step
+    page_index = 1 if page_index < 1 else page_index
+    page_index = max_page_index if page_index > max_page_index else page_index
+    idx_frm = (page_index - 1) * num_of_imgs_per_page
+    image_list = filenames[idx_frm:idx_frm + num_of_imgs_per_page]
+    length = len(filenames)
+    visible_num = num_of_imgs_per_page if  idx_frm + num_of_imgs_per_page <= length else length % num_of_imgs_per_page 
+    visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num
+    return page_index, image_list,  "", visible_num
+
+def first_page_click(page_index, filenames):
+    return get_recent_images(1, 0, filenames)
+
+def end_page_click(page_index, filenames):
+    return get_recent_images(-1, 0, filenames)
+
+def prev_page_click(page_index, filenames):
+    return get_recent_images(page_index, -1, filenames)
+
+def next_page_click(page_index, filenames):
+    return get_recent_images(page_index, 1, filenames)
+
+def page_index_change(page_index, filenames):
+    return get_recent_images(page_index, 0, filenames)
+
+def show_image_info(tabname_box, num, page_index, filenames):
+    file = filenames[int(num) + int((page_index - 1) * num_of_imgs_per_page)] 
+    return file, num, file
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
-    if opts.outdir_samples != "":
-        dir_name = opts.outdir_samples
-    elif tabname == "txt2img":
+    if tabname == "txt2img":
         dir_name = opts.outdir_txt2img_samples
     elif tabname == "img2img":
         dir_name = opts.outdir_img2img_samples
     elif tabname == "extras":
         dir_name = opts.outdir_extras_samples
+    elif tabname == "saved":
+        dir_name = opts.outdir_save
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name)
     d = dir_name.split("/")
-    dir_name = "/" if dir_name.startswith("/") else d[0]
+    dir_name = d[0]
     for p in d[1:]:
         dir_name = os.path.join(dir_name, p)
 
     f_list = os.listdir(dir_name)
     sorted_flag = os.path.exists(os.path.join(dir_name, system_bak_path)) or len(f_list) == 0 
     date_list, date_from, date_to = None, None, None
-    if sorted_flag:
-        #print(sorted_flag)
-        date_list = auto_sorting(dir_name)
-        date_to = date_list[-1]
-        date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0] 
 
     with gr.Column(visible=sorted_flag) as page_panel:
         with gr.Row():
-            renew_page = gr.Button('Refresh', elem_id=tabname + "_images_history_renew_page", interactive=sorted_flag)
+            #renew_page = gr.Button('Refresh')
             first_page = gr.Button('First Page')
             prev_page = gr.Button('Prev Page')
             page_index = gr.Number(value=1, label="Page Index")
@@ -231,9 +231,9 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         with gr.Row(elem_id=tabname + "_images_history"):
             with gr.Column(scale=2):
                 with gr.Row():
-                    newest = gr.Button('Newest')
-                    date_to = gr.Dropdown(choices=date_list, value=date_to, label="Date to")
-                    date_from = gr.Dropdown(choices=date_list, value=date_from, label="Date from")                    
+                    newest = gr.Button('Refresh', elem_id=tabname + "_images_history_start")                    
+                    date_from = gr.Textbox(label="Date from", interactive=False)  
+                    date_to = gr.Dropdown(value="start" if not sorted_flag else None, label="Date to")                  
 
                 history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
                 with gr.Row():
@@ -247,66 +247,72 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                     with gr.Column():
                         img_file_info = gr.Textbox(label="Generate Info", interactive=False)
                         img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
+                        
                     # hiden items
-                    with gr.Row(visible=False):
+                    with gr.Row(visible=False):   
+                        visible_img_num = gr.Number()                     
                         img_path = gr.Textbox(dir_name)
                         tabname_box = gr.Textbox(tabname)
                         image_index = gr.Textbox(value=-1)
                         set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
                         filenames = gr.State()
+                        all_images_list = gr.State()
                         hidden = gr.Image(type="pil")
                         info1 = gr.Textbox()
                         info2 = gr.Textbox()
+
     with gr.Column(visible=not sorted_flag) as init_warning:
         with gr.Row():
-            gr.Textbox("The system needs to archive the files according to the date. This requires changing the directory structure of the files",
-             label="Waring",
-             css="")
+            warning = gr.Textbox(
+                label="Waring",
+                value=f"The system needs to archive the files according to the date. This requires changing the directory structure of the files.If you have doubts about this operation, you can first back up the files in the '{dir_name}' directory"
+            )
+            warning.style(height=100, width=50)
         with gr.Row():
             sorted_button = gr.Button('Confirme')
 
-                  
-           
+    change_date_output = [init_warning, page_panel, date_to, date_from, filenames, page_index, history_gallery, img_file_name, visible_img_num]           
+    sorted_button.click(system_init, inputs=[img_path], outputs=change_date_output + [sorted_button]) 
+    newest.click(newest_click, inputs=[img_path, date_to], outputs=change_date_output)
+    date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output) 
+    date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    newest.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+
+    delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num])
+    delete.click(fn=None, _js="images_history_delete", inputs=[delete_num, tabname_box, image_index], outputs=None)  
+    
    
     # turn pages
-    gallery_inputs = [img_path, page_index, image_index, tabname_box, date_from, date_to]
-    gallery_outputs = [history_gallery, page_index, filenames, img_file_name]
+    gallery_inputs = [page_index, filenames]
+    gallery_outputs = [page_index, history_gallery, img_file_name, visible_img_num]
+
+    first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
 
-    first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    # page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
+    first_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    next_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    prev_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    end_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    page_index.submit(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
 
     # other funcitons
-    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, filenames], outputs=[img_file_name, image_index, hidden])
-    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
+    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, image_index, hidden])
+    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)   
     hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-    date_to.change(date_to_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs + [date_from])
-    # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
+
     switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
     switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
 
-    sorted_button.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from])
-    newest.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from])
-   
-    
-    
 
 
 def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
     with gr.Blocks(analytics_enabled=False) as images_history:
         with gr.Tabs() as tabs:
-            with gr.Tab("txt2img history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:
-                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict)
-            with gr.Tab("img2img history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-                    show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict)
-            with gr.Tab("extras history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-                    show_images_history(gr, opts, "extras", run_pnginfo, switch_dict)
+            for tab in ["saved", "txt2img", "img2img", "extras"]:
+                with gr.Tab(tab):
+                    with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                        show_images_history(gr, opts, tab, run_pnginfo, switch_dict) 
     return images_history
-- 
cgit v1.2.3


From 91235d8008372862b1f232f7bf99da310a5955e4 Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 20:50:24 +0900
Subject: Fix FileNotFoundError in history tab

Now only traverse images when directory exists
---
 modules/images_history.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 9260df8a..e6284142 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,6 +1,6 @@
 import os
 import shutil
-
+import sys
 
 def traverse_all_files(output_dir, image_list, curr_dir=None):
     curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
@@ -24,10 +24,14 @@ def traverse_all_files(output_dir, image_list, curr_dir=None):
 
 def get_recent_images(dir_name, page_index, step, image_index, tabname):
     page_index = int(page_index)
-    f_list = os.listdir(dir_name)
     image_list = []
-    image_list = traverse_all_files(dir_name, image_list)
-    image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    if not os.path.exists(dir_name):
+        pass
+    elif os.path.isdir(dir_name):
+        image_list = traverse_all_files(dir_name, image_list)
+        image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    else:
+        print(f"ERROR: {dir_name} is not a directory. Check the path in the settings.", file=sys.stderr)
     num = 48 if tabname != "extras" else 12
     max_page_index = len(image_list) // num + 1
     page_index = max_page_index if page_index == -1 else page_index + step
-- 
cgit v1.2.3


From c9836279f58461e04c1dda0a86e718f8bd3f41e4 Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 21:59:05 +0900
Subject: Only make output dir when creating output

---
 modules/processing.py | 6 ------
 modules/ui.py         | 5 ++++-
 2 files changed, 4 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 833fed8a..deb6125e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -334,12 +334,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     seed = get_fixed_seed(p.seed)
     subseed = get_fixed_seed(p.subseed)
 
-    if p.outpath_samples is not None:
-        os.makedirs(p.outpath_samples, exist_ok=True)
-
-    if p.outpath_grids is not None:
-        os.makedirs(p.outpath_grids, exist_ok=True)
-
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
     modules.sd_hijack.model_hijack.clear_comments()
 
diff --git a/modules/ui.py b/modules/ui.py
index ee3d0248..fa73627a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1394,7 +1394,10 @@ def create_ui(wrap_gradio_gpu_call):
     component_dict = {}
 
     def open_folder(f):
-        if not os.path.isdir(f):
+        if not os.path.exists(f):
+            print(f"{f} doesn't exist. After you create an image, the folder will be created.")
+            return
+        elif not os.path.isdir(f):
             print(f"""
 WARNING
 An open_folder request was made with an argument that is not a folder.
-- 
cgit v1.2.3


From adc0ea74e1ee9791f15c3a74bc6c5ad789e10d17 Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 22:03:18 +0900
Subject: Better readablity of logs

---
 modules/images_history.py | 2 +-
 modules/ui.py             | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index e6284142..e06e07bf 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -31,7 +31,7 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname):
         image_list = traverse_all_files(dir_name, image_list)
         image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
     else:
-        print(f"ERROR: {dir_name} is not a directory. Check the path in the settings.", file=sys.stderr)
+        print(f'ERROR: "{dir_name}" is not a directory. Check the path in the settings.', file=sys.stderr)
     num = 48 if tabname != "extras" else 12
     max_page_index = len(image_list) // num + 1
     page_index = max_page_index if page_index == -1 else page_index + step
diff --git a/modules/ui.py b/modules/ui.py
index fa73627a..7b0d5a92 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1395,7 +1395,7 @@ def create_ui(wrap_gradio_gpu_call):
 
     def open_folder(f):
         if not os.path.exists(f):
-            print(f"{f} doesn't exist. After you create an image, the folder will be created.")
+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
             return
         elif not os.path.isdir(f):
             print(f"""
-- 
cgit v1.2.3


From fc220a51cf5bb5bfca83322c16e907a18ec59f6b Mon Sep 17 00:00:00 2001
From: DancingSnow <1121149616@qq.com>
Date: Sun, 16 Oct 2022 10:49:21 +0800
Subject: fix dir_path in some path like `D:/Pic/outputs`

---
 modules/images_history.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index e06e07bf..46b23e56 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -109,10 +109,8 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         dir_name = opts.outdir_img2img_samples
     elif tabname == "extras":
         dir_name = opts.outdir_extras_samples
-    d = dir_name.split("/")
-    dir_name = "/" if dir_name.startswith("/") else d[0]
-    for p in d[1:]:
-        dir_name = os.path.join(dir_name, p)
+    else:
+        return
     with gr.Row():
         renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
         first_page = gr.Button('First Page')
-- 
cgit v1.2.3


From a4de699e3c235d83b5a957d08779cb41cb0781bc Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sun, 16 Oct 2022 22:37:12 +0800
Subject: Images history speed up

---
 modules/images_history.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index ae0b4e40..94bd16a8 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -153,6 +153,7 @@ def delete_image(delete_num, name, filenames, image_index, visible_num):
                 if os.path.exists(name):
                     if visible_num == image_index:
                         new_file_list.append(name)
+                        i += 1
                         continue                    
                     print(f"Delete file {name}")
                     os.remove(name)
@@ -221,7 +222,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
     with gr.Column(visible=sorted_flag) as page_panel:
         with gr.Row():
-            #renew_page = gr.Button('Refresh')
+            renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
             first_page = gr.Button('First Page')
             prev_page = gr.Button('Prev Page')
             page_index = gr.Number(value=1, label="Page Index")
@@ -231,7 +232,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         with gr.Row(elem_id=tabname + "_images_history"):
             with gr.Column(scale=2):
                 with gr.Row():
-                    newest = gr.Button('Refresh', elem_id=tabname + "_images_history_start")                    
+                    newest = gr.Button('Reload', elem_id=tabname + "_images_history_start")                    
                     date_from = gr.Textbox(label="Date from", interactive=False)  
                     date_to = gr.Dropdown(value="start" if not sorted_flag else None, label="Date to")                  
 
@@ -291,12 +292,14 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
     end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
     page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
+    renew_page.click(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
 
     first_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
     next_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
     prev_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
     end_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
     page_index.submit(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    renew_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
 
     # other funcitons
     set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, image_index, hidden])
-- 
cgit v1.2.3


From 9324cdaa3199d65c182858785dd1eca42b192b8e Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sun, 16 Oct 2022 17:53:56 +0200
Subject: ui fix, re organization of the code

---
 modules/aesthetic_clip.py            | 154 +++++++++++++++++++++++++++++++++--
 modules/img2img.py                   |  14 +++-
 modules/processing.py                |  29 ++-----
 modules/sd_hijack.py                 | 102 ++---------------------
 modules/sd_models.py                 |   5 +-
 modules/shared.py                    |  14 +++-
 modules/textual_inversion/dataset.py |   2 +-
 modules/txt2img.py                   |  18 ++--
 modules/ui.py                        |  52 +++++++-----
 9 files changed, 233 insertions(+), 157 deletions(-)

(limited to 'modules')

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
index ccb35c73..34efa931 100644
--- a/modules/aesthetic_clip.py
+++ b/modules/aesthetic_clip.py
@@ -1,3 +1,4 @@
+import copy
 import itertools
 import os
 from pathlib import Path
@@ -7,11 +8,12 @@ import gc
 import gradio as gr
 import torch
 from PIL import Image
-from modules import shared
-from modules.shared import device
-from transformers import CLIPModel, CLIPProcessor
+from torch import optim
 
-from tqdm.auto import tqdm
+from modules import shared
+from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer
+from tqdm.auto import tqdm, trange
+from modules.shared import opts, device
 
 
 def get_all_images_in_folder(folder):
@@ -37,12 +39,39 @@ def iter_to_batched(iterable, n=1):
         yield chunk
 
 
+def create_ui():
+    with gr.Group():
+        with gr.Accordion("Open for Clip Aesthetic!", open=False):
+            with gr.Row():
+                aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight",
+                                             value=0.9)
+                aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
+
+            with gr.Row():
+                aesthetic_lr = gr.Textbox(label='Aesthetic learning rate',
+                                          placeholder="Aesthetic learning rate", value="0.0001")
+                aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
+                aesthetic_imgs = gr.Dropdown(sorted(shared.aesthetic_embeddings.keys()),
+                                             label="Aesthetic imgs embedding",
+                                             value="None")
+
+            with gr.Row():
+                aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs',
+                                                 placeholder="This text is used to rotate the feature space of the imgs embs",
+                                                 value="")
+                aesthetic_slerp_angle = gr.Slider(label='Slerp angle', minimum=0, maximum=1, step=0.01,
+                                                  value=0.1)
+                aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
+
+    return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative
+
+
 def generate_imgs_embd(name, folder, batch_size):
     # clipModel = CLIPModel.from_pretrained(
     #     shared.sd_model.cond_stage_model.clipModel.name_or_path
     # )
-    model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path).to(device)
-    processor = CLIPProcessor.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path)
+    model = shared.clip_model.to(device)
+    processor = CLIPProcessor.from_pretrained(model.name_or_path)
 
     with torch.no_grad():
         embs = []
@@ -63,7 +92,6 @@ def generate_imgs_embd(name, folder, batch_size):
         torch.save(embs, path)
 
         model = model.cpu()
-        del model
         del processor
         del embs
         gc.collect()
@@ -74,4 +102,114 @@ def generate_imgs_embd(name, folder, batch_size):
         """
         shared.update_aesthetic_embeddings()
         return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
-                           value="None"), res, ""
+                                  value="None"), \
+               gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()),
+                                  label="Imgs embedding",
+                                  value="None"), res, ""
+
+
+def slerp(low, high, val):
+    low_norm = low / torch.norm(low, dim=1, keepdim=True)
+    high_norm = high / torch.norm(high, dim=1, keepdim=True)
+    omega = torch.acos((low_norm * high_norm).sum(1))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
+    return res
+
+
+class AestheticCLIP:
+    def __init__(self):
+        self.skip = False
+        self.aesthetic_steps = 0
+        self.aesthetic_weight = 0
+        self.aesthetic_lr = 0
+        self.slerp = False
+        self.aesthetic_text_negative = ""
+        self.aesthetic_slerp_angle = 0
+        self.aesthetic_imgs_text = ""
+
+        self.image_embs_name = None
+        self.image_embs = None
+        self.load_image_embs(None)
+
+    def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
+                             aesthetic_slerp=True, aesthetic_imgs_text="",
+                             aesthetic_slerp_angle=0.15,
+                             aesthetic_text_negative=False):
+        self.aesthetic_imgs_text = aesthetic_imgs_text
+        self.aesthetic_slerp_angle = aesthetic_slerp_angle
+        self.aesthetic_text_negative = aesthetic_text_negative
+        self.slerp = aesthetic_slerp
+        self.aesthetic_lr = aesthetic_lr
+        self.aesthetic_weight = aesthetic_weight
+        self.aesthetic_steps = aesthetic_steps
+        self.load_image_embs(image_embs_name)
+
+    def set_skip(self, skip):
+        self.skip = skip
+
+    def load_image_embs(self, image_embs_name):
+        if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
+            image_embs_name = None
+            self.image_embs_name = None
+        if image_embs_name is not None and self.image_embs_name != image_embs_name:
+            self.image_embs_name = image_embs_name
+            self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
+            self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
+            self.image_embs.requires_grad_(False)
+
+    def __call__(self, z, remade_batch_tokens):
+        if not self.skip and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name is not None:
+            tokenizer = shared.sd_model.cond_stage_model.tokenizer
+            if not opts.use_old_emphasis_implementation:
+                remade_batch_tokens = [
+                    [tokenizer.bos_token_id] + x[:75] + [tokenizer.eos_token_id] for x in
+                    remade_batch_tokens]
+
+            tokens = torch.asarray(remade_batch_tokens).to(device)
+
+            model = copy.deepcopy(shared.clip_model).to(device)
+            model.requires_grad_(True)
+            if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
+                text_embs_2 = model.get_text_features(
+                    **tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
+                if self.aesthetic_text_negative:
+                    text_embs_2 = self.image_embs - text_embs_2
+                    text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
+                img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
+            else:
+                img_embs = self.image_embs
+
+            with torch.enable_grad():
+
+                # We optimize the model to maximize the similarity
+                optimizer = optim.Adam(
+                    model.text_model.parameters(), lr=self.aesthetic_lr
+                )
+
+                for _ in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
+                    text_embs = model.get_text_features(input_ids=tokens)
+                    text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
+                    sim = text_embs @ img_embs.T
+                    loss = -sim
+                    optimizer.zero_grad()
+                    loss.mean().backward()
+                    optimizer.step()
+
+                zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
+                if opts.CLIP_stop_at_last_layers > 1:
+                    zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
+                    zn = model.text_model.final_layer_norm(zn)
+                else:
+                    zn = zn.last_hidden_state
+                model.cpu()
+                del model
+                gc.collect()
+                torch.cuda.empty_cache()
+            zn = torch.concat([zn[77 * i:77 * (i + 1)] for i in range(max(z.shape[1] // 77, 1))], 1)
+            if self.slerp:
+                z = slerp(z, zn, self.aesthetic_weight)
+            else:
+                z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
+
+        return z
diff --git a/modules/img2img.py b/modules/img2img.py
index 24126774..4ed80c4b 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -56,7 +56,14 @@ def process_batch(p, input_dir, output_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str,
+            aesthetic_lr=0,
+            aesthetic_weight=0, aesthetic_steps=0,
+            aesthetic_imgs=None,
+            aesthetic_slerp=False,
+            aesthetic_imgs_text="",
+            aesthetic_slerp_angle=0.15,
+            aesthetic_text_negative=False, *args):
     is_inpaint = mode == 1
     is_batch = mode == 2
 
@@ -109,6 +116,11 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpainting_mask_invert=inpainting_mask_invert,
     )
 
+    shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps),
+                                               aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text,
+                                               aesthetic_slerp_angle,
+                                               aesthetic_text_negative)
+
     if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
 
diff --git a/modules/processing.py b/modules/processing.py
index 1db26c3e..685f9fcd 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -146,7 +146,8 @@ class Processed:
         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
         self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
-        self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
+        self.subseed = int(
+            self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
 
         self.all_prompts = all_prompts or [self.prompt]
         self.all_seeds = all_seeds or [self.seed]
@@ -332,16 +333,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
 
-def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0,
-                   aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="",
-                   aesthetic_slerp_angle=0.15,
-                   aesthetic_text_negative=False) -> Processed:
+def process_images(p: StableDiffusionProcessing) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
-    aesthetic_lr = float(aesthetic_lr)
-    aesthetic_weight = float(aesthetic_weight)
-    aesthetic_steps = int(aesthetic_steps)
-
     if type(p.prompt) == list:
         assert (len(p.prompt) > 0)
     else:
@@ -417,16 +411,10 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
             # uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
             # c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
-                if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
-                    shared.sd_model.cond_stage_model.set_aesthetic_params()
+                shared.aesthetic_clip.set_skip(True)
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt],
                                                             p.steps)
-                if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
-                    shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight,
-                                                                          aesthetic_steps, aesthetic_imgs,
-                                                                          aesthetic_slerp, aesthetic_imgs_text,
-                                                                          aesthetic_slerp_angle,
-                                                                          aesthetic_text_negative)
+                shared.aesthetic_clip.set_skip(False)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
@@ -582,7 +570,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
@@ -600,10 +587,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                                   seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
-        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
+        samples = samples[:, :, self.truncate_y // 2:samples.shape[2] - self.truncate_y // 2,
+                  self.truncate_x // 2:samples.shape[3] - self.truncate_x // 2]
 
         if opts.use_scale_latent_for_hires_fix:
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f),
+                                                      mode="bilinear")
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 5d0590af..227e7670 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -29,8 +29,8 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
-
-    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (
+    6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
@@ -118,33 +118,14 @@ class StableDiffusionModelHijack:
         return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
 
 
-def slerp(low, high, val):
-    low_norm = low / torch.norm(low, dim=1, keepdim=True)
-    high_norm = high / torch.norm(high, dim=1, keepdim=True)
-    omega = torch.acos((low_norm * high_norm).sum(1))
-    so = torch.sin(omega)
-    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
-    return res
-
-
 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
         super().__init__()
         self.wrapped = wrapped
-        self.clipModel = CLIPModel.from_pretrained(
-            self.wrapped.transformer.name_or_path
-        )
-        del self.clipModel.vision_model
-        self.tokenizer = CLIPTokenizer.from_pretrained(self.wrapped.transformer.name_or_path)
-        self.hijack: StableDiffusionModelHijack = hijack
-        self.tokenizer = wrapped.tokenizer
-        # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval()
-        self.image_embs_name = None
-        self.image_embs = None
-        self.load_image_embs(None)
 
         self.token_mults = {}
-
+        self.hijack: StableDiffusionModelHijack = hijack
+        self.tokenizer = wrapped.tokenizer
         self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
 
         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if
@@ -164,28 +145,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-    def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
-                             aesthetic_slerp=True, aesthetic_imgs_text="",
-                             aesthetic_slerp_angle=0.15,
-                             aesthetic_text_negative=False):
-        self.aesthetic_imgs_text = aesthetic_imgs_text
-        self.aesthetic_slerp_angle = aesthetic_slerp_angle
-        self.aesthetic_text_negative = aesthetic_text_negative
-        self.slerp = aesthetic_slerp
-        self.aesthetic_lr = aesthetic_lr
-        self.aesthetic_weight = aesthetic_weight
-        self.aesthetic_steps = aesthetic_steps
-        self.load_image_embs(image_embs_name)
-
-    def load_image_embs(self, image_embs_name):
-        if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
-            image_embs_name = None
-        if image_embs_name is not None and self.image_embs_name != image_embs_name:
-            self.image_embs_name = image_embs_name
-            self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
-            self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
-            self.image_embs.requires_grad_(False)
-
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_end = self.wrapped.tokenizer.eos_token_id
 
@@ -391,58 +350,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
             z1 = self.process_tokens(tokens, multipliers)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
-
-            if self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None:
-                if not opts.use_old_emphasis_implementation:
-                    remade_batch_tokens = [
-                        [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in
-                        remade_batch_tokens]
-
-                tokens = torch.asarray(remade_batch_tokens).to(device)
-
-                model = copy.deepcopy(self.clipModel).to(device)
-                model.requires_grad_(True)
-                if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
-                    text_embs_2 = model.get_text_features(
-                        **self.tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
-                    if self.aesthetic_text_negative:
-                        text_embs_2 = self.image_embs - text_embs_2
-                        text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
-                    img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
-                else:
-                    img_embs = self.image_embs
-
-                with torch.enable_grad():
-
-                    # We optimize the model to maximize the similarity
-                    optimizer = optim.Adam(
-                        model.text_model.parameters(), lr=self.aesthetic_lr
-                    )
-
-                    for i in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
-                        text_embs = model.get_text_features(input_ids=tokens)
-                        text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
-                        sim = text_embs @ img_embs.T
-                        loss = -sim
-                        optimizer.zero_grad()
-                        loss.mean().backward()
-                        optimizer.step()
-
-                    zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
-                    if opts.CLIP_stop_at_last_layers > 1:
-                        zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
-                        zn = model.text_model.final_layer_norm(zn)
-                    else:
-                        zn = zn.last_hidden_state
-                    model.cpu()
-                    del model
-
-                zn = torch.concat([zn for i in range(z.shape[1] // 77)], 1)
-                if self.slerp:
-                    z = slerp(z, zn, self.aesthetic_weight)
-                else:
-                    z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
-
+            z = shared.aesthetic_clip(z, remade_batch_tokens)
             remade_batch_tokens = rem_tokens
             batch_multipliers = rem_multipliers
             i += 1
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 3aa21ec1..8e4ee435 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -20,7 +20,7 @@ checkpoints_loaded = collections.OrderedDict()
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
 
-    from transformers import logging
+    from transformers import logging, CLIPModel
 
     logging.set_verbosity_error()
 except Exception:
@@ -196,6 +196,9 @@ def load_model():
 
     sd_hijack.model_hijack.hijack(sd_model)
 
+    if shared.clip_model is None or shared.clip_model.transformer.name_or_path != sd_model.cond_stage_model.wrapped.transformer.name_or_path:
+        shared.clip_model = CLIPModel.from_pretrained(sd_model.cond_stage_model.wrapped.transformer.name_or_path)
+
     sd_model.eval()
 
     print(f"Model loaded.")
diff --git a/modules/shared.py b/modules/shared.py
index e2c98b2d..e19ca779 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -3,6 +3,7 @@ import datetime
 import json
 import os
 import sys
+from collections import OrderedDict
 
 import gradio as gr
 import tqdm
@@ -94,15 +95,15 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
-aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
-                        os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
-aesthetic_embeddings = aesthetic_embeddings | {"None": None}
+aesthetic_embeddings = {}
 
 def update_aesthetic_embeddings():
     global aesthetic_embeddings
     aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
                             os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
-    aesthetic_embeddings = aesthetic_embeddings | {"None": None}
+    aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings)
+
+update_aesthetic_embeddings()
 
 def reload_hypernetworks():
     global hypernetworks
@@ -381,6 +382,11 @@ sd_upscalers = []
 
 sd_model = None
 
+clip_model = None
+
+from modules.aesthetic_clip import AestheticCLIP
+aesthetic_clip = AestheticCLIP()
+
 progress_print_out = sys.stdout
 
 
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 68ceffe3..23bb4b6a 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -49,7 +49,7 @@ class PersonalizedBase(Dataset):
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
             try:
-                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.Resampling.BICUBIC)
+                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
             except Exception:
                 continue
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 8f394d05..6cbc50fc 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -1,12 +1,17 @@
 import modules.scripts
-from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
+    StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
 import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int,aesthetic_lr=0,
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int,
+            restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int,
+            subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool,
+            height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int,
+            firstphase_height: int, aesthetic_lr=0,
             aesthetic_weight=0, aesthetic_steps=0,
             aesthetic_imgs=None,
             aesthetic_slerp=False,
@@ -41,15 +46,17 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         firstphase_height=firstphase_height if enable_hr else None,
     )
 
+    shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps),
+                                           aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle,
+                                           aesthetic_text_negative)
+
     if cmd_opts.enable_console_prompts:
         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
 
     processed = modules.scripts.scripts_txt2img.run(p, *args)
 
     if processed is None:
-        processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp,aesthetic_imgs_text,
-                           aesthetic_slerp_angle,
-                           aesthetic_text_negative)
+        processed = process_images(p)
 
     shared.total_tqdm.clear()
 
@@ -61,4 +68,3 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         processed.images = []
 
     return processed.images, generation_info_js, plaintext_to_html(processed.info)
-
diff --git a/modules/ui.py b/modules/ui.py
index 4069f0d2..0e5d73f0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -43,7 +43,7 @@ from modules.images import save_image
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
 
-import modules.aesthetic_clip
+import modules.aesthetic_clip as aesthetic_clip
 import modules.images_history as img_his
 
 
@@ -593,23 +593,25 @@ def create_ui(wrap_gradio_gpu_call):
                     width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
-                with gr.Group():
-                    with gr.Accordion("Open for Clip Aesthetic!",open=False):
-                        with gr.Row():
-                            aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
-                            aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
-
-                        with gr.Row():
-                            aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001")
-                            aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
-                            aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()),
-                                                         label="Aesthetic imgs embedding",
-                                                         value="None")
-
-                        with gr.Row():
-                            aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
-                            aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
-                            aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
+                # with gr.Group():
+                #     with gr.Accordion("Open for Clip Aesthetic!",open=False):
+                #         with gr.Row():
+                #             aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
+                #             aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
+                #
+                #         with gr.Row():
+                #             aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001")
+                #             aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
+                #             aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()),
+                #                                          label="Aesthetic imgs embedding",
+                #                                          value="None")
+                #
+                #         with gr.Row():
+                #             aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
+                #             aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
+                #             aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
+
+                aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative = aesthetic_clip.create_ui()
 
 
                 with gr.Row():
@@ -840,6 +842,9 @@ def create_ui(wrap_gradio_gpu_call):
                     width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
+                aesthetic_weight_im, aesthetic_steps_im, aesthetic_lr_im, aesthetic_slerp_im, aesthetic_imgs_im, aesthetic_imgs_text_im, aesthetic_slerp_angle_im, aesthetic_text_negative_im = aesthetic_clip.create_ui()
+
+
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
                     tiling = gr.Checkbox(label='Tiling', value=False)
@@ -944,6 +949,14 @@ def create_ui(wrap_gradio_gpu_call):
                     inpainting_mask_invert,
                     img2img_batch_input_dir,
                     img2img_batch_output_dir,
+                    aesthetic_lr_im,
+                    aesthetic_weight_im,
+                    aesthetic_steps_im,
+                    aesthetic_imgs_im,
+                    aesthetic_slerp_im,
+                    aesthetic_imgs_text_im,
+                    aesthetic_slerp_angle_im,
+                    aesthetic_text_negative_im,
                 ] + custom_inputs,
                 outputs=[
                     img2img_gallery,
@@ -1283,7 +1296,7 @@ def create_ui(wrap_gradio_gpu_call):
         )
 
         create_embedding_ae.click(
-            fn=modules.aesthetic_clip.generate_imgs_embd,
+            fn=aesthetic_clip.generate_imgs_embd,
             inputs=[
                 new_embedding_name_ae,
                 process_src_ae,
@@ -1291,6 +1304,7 @@ def create_ui(wrap_gradio_gpu_call):
             ],
             outputs=[
                 aesthetic_imgs,
+                aesthetic_imgs_im,
                 ti_output,
                 ti_outcome,
             ]
-- 
cgit v1.2.3


From c8045c5ad4f99deb3a19add06e0457de1df62b05 Mon Sep 17 00:00:00 2001
From: SGKoishi <i@sgkoi.dev>
Date: Sun, 16 Oct 2022 10:08:23 -0700
Subject: The hide_ui_dir_config flag also restrict write attempt to path
 settings

---
 modules/shared.py | 10 ++++++++++
 modules/ui.py     |  8 +++++++-
 2 files changed, 17 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index dcab0af9..c2775603 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -77,6 +77,16 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl
 
 
 cmd_opts = parser.parse_args()
+restricted_opts = [
+    "samples_filename_pattern",
+    "outdir_samples",
+    "outdir_txt2img_samples",
+    "outdir_img2img_samples",
+    "outdir_extras_samples",
+    "outdir_grids",
+    "outdir_txt2img_grids",
+    "outdir_save",
+]
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
diff --git a/modules/ui.py b/modules/ui.py
index 7b0d5a92..43dc88fc 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -25,7 +25,7 @@ import gradio.routes
 
 from modules import sd_hijack, sd_models
 from modules.paths import script_path
-from modules.shared import opts, cmd_opts
+from modules.shared import opts, cmd_opts, restricted_opts
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
 import modules.shared as shared
@@ -1430,6 +1430,9 @@ Requested path was: {f}
             if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
                 continue
 
+            if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+                continue
+
             oldval = opts.data.get(key, None)
             opts.data[key] = value
 
@@ -1447,6 +1450,9 @@ Requested path was: {f}
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
+        if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+            return gr.update(value=oldval), opts.dumpjson()
+
         oldval = opts.data.get(key, None)
         opts.data[key] = value
 
-- 
cgit v1.2.3


From 0fd130767102ebcf90e97c6c191ecf199a2d4091 Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sun, 16 Oct 2022 18:44:39 -0400
Subject: improve performance of 3-way merge on machines with not enough ram,
 by only accessing two of the models at a time

---
 modules/extras.py | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 0819ed37..340a45fd 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -175,11 +175,14 @@ def run_pnginfo(image):
 
 
 def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
-    def weighted_sum(theta0, theta1, theta2, alpha):
+    def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
-    def add_difference(theta0, theta1, theta2, alpha):
-        return theta0 + (theta1 - theta2) * alpha
+    def get_difference(theta1, theta2):
+        return theta1 - theta2
+
+    def add_difference(theta0, theta1_2_diff, alpha):
+        return theta0 + (alpha * theta1_2_diff)
 
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
@@ -201,20 +204,24 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
         theta_2 = None
 
     theta_funcs = {
-        "Weighted sum": weighted_sum,
-        "Add difference": add_difference,
+        "Weighted sum": (None, weighted_sum),
+        "Add difference": (get_difference, add_difference),
     }
-    theta_func = theta_funcs[interp_method]
+    theta_func1, theta_func2 = theta_funcs[interp_method]
 
     print(f"Merging...")
 
+    if theta_func1:
+        for key in tqdm.tqdm(theta_1.keys()):
+            if 'model' in key:
+                t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
+                theta_1[key] = theta_func1(theta_1[key], t2)
+    del theta_2, teritary_model
+
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
-            t2 = (theta_2 or {}).get(key)
-            if t2 is None:
-                t2 = torch.zeros_like(theta_0[key])
 
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier)
+            theta_0[key] = theta_func2(theta_0[key], theta_1[key], multiplier)
 
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
-- 
cgit v1.2.3


From 6f7b7a3dcdc471ebe63baa8a7731952557859c5b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 07:56:08 +0300
Subject: only read files with .py extension from  the scripts dir

---
 modules/scripts.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 45230f9a..ac66d448 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -58,6 +58,9 @@ def load_scripts(basedir):
     for filename in sorted(os.listdir(basedir)):
         path = os.path.join(basedir, filename)
 
+        if os.path.splitext(path)[1].lower() != '.py':
+            continue
+
         if not os.path.isfile(path):
             continue
 
-- 
cgit v1.2.3


From 58f3ef77336663bce2321f5b692cf2aeacd3ac1c Mon Sep 17 00:00:00 2001
From: DenkingOfficial <shershnev.andrei@gmail.com>
Date: Mon, 17 Oct 2022 03:10:59 +0500
Subject: Fix CLIP Interrogator and disable ranks for it

---
 modules/interrogate.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 9263d65a..d85d7dcc 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -157,9 +157,9 @@ class InterrogateModels:
                     matches = self.rank(image_features, items, top_count=topn)
                     for match, score in matches:
                         if include_ranks:
-                            res += ", " + match
-                        else:
                             res += f", ({match}:{score})"
+                        else:
+                            res += ", " + match
 
         except Exception:
             print(f"Error interrogating", file=sys.stderr)
-- 
cgit v1.2.3


From 5c94aaf290f8ad7bf4499a91c268ad0791b0432f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 08:28:18 +0300
Subject: fix bug for latest model merge RAM improvement

---
 modules/extras.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 340a45fd..8dbab240 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -201,6 +201,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
         teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
         theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
     else:
+        teritary_model = None
         theta_2 = None
 
     theta_funcs = {
-- 
cgit v1.2.3


From b99d3cf6dd9bc817e51d0d0a6e8eb12c7c0ac6af Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 08:41:02 +0300
Subject: make CLIP interrogate ranks output sane values

---
 modules/interrogate.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index d85d7dcc..64b91eb4 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -123,7 +123,7 @@ class InterrogateModels:
 
         return caption[0]
 
-    def interrogate(self, pil_image, include_ranks=False):
+    def interrogate(self, pil_image):
         res = None
 
         try:
@@ -156,8 +156,8 @@ class InterrogateModels:
                 for name, topn, items in self.categories:
                     matches = self.rank(image_features, items, top_count=topn)
                     for match, score in matches:
-                        if include_ranks:
-                            res += f", ({match}:{score})"
+                        if shared.opts.interrogate_return_ranks:
+                            res += f", ({match}:{score/100:.3f})"
                         else:
                             res += ", " + match
 
-- 
cgit v1.2.3


From 62edfae257e8982cd620d03862c7bdd44159d18f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 16 Oct 2022 20:28:15 +0100
Subject: print list of embeddings on reload

---
 modules/textual_inversion/textual_inversion.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 7ec75018..3be69562 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -137,6 +137,7 @@ class EmbeddingDatabase:
                 continue
 
         print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
+        print("Embeddings:", ', '.join(self.word_embeddings.keys()))
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
-- 
cgit v1.2.3


From 9d702b16f01795c3af900e0ebd70faf4b25200f6 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 17 Oct 2022 16:11:03 +0800
Subject: fix two little bug

---
 modules/images_history.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 23045df1..1ae168ca 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -133,7 +133,7 @@ def archive_images(dir_name, date_to):
             date = sort_array[loads_num][2]
             filenames = [x[1] for x in sort_array]
         else:
-            date =  sort_array[loads_num][2]
+            date =  sort_array[-1][2]
             filenames = [x[1] for x in sort_array]
         filenames = [x[1] for x in sort_array if x[2]>= date]
     _, image_list, _, visible_num = get_recent_images(1, 0, filenames)
@@ -334,6 +334,6 @@ def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict):
                 with gr.Tab(tab):
                     with gr.Blocks(analytics_enabled=False) as images_history_img2img:
                         show_images_history(gr, opts, tab, run_pnginfo, switch_dict) 
-        gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory") #, visible=False)
+        gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False)
     
     return images_history
-- 
cgit v1.2.3


From 60251c9456f5472784862896c2f97e38feb42482 Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 06:58:42 +0000
Subject: initial prototype by borrowing contracts

---
 modules/api/api.py    | 60 +++++++++++++++++++++++++++++++++++++++++++++++++++
 modules/processing.py |  2 +-
 modules/shared.py     |  2 +-
 3 files changed, 62 insertions(+), 2 deletions(-)
 create mode 100644 modules/api/api.py

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
new file mode 100644
index 00000000..9d7c699d
--- /dev/null
+++ b/modules/api/api.py
@@ -0,0 +1,60 @@
+from modules.api.processing import StableDiffusionProcessingAPI
+from modules.processing import StableDiffusionProcessingTxt2Img, process_images
+import modules.shared as shared
+import uvicorn
+from fastapi import FastAPI, Body, APIRouter
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field, Json
+import json
+import io
+import base64
+
+app = FastAPI()
+
+class TextToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: Json
+    info: Json
+
+
+class Api:
+    def __init__(self, txt2img, img2img, run_extras, run_pnginfo):
+        self.router = APIRouter()
+        app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
+
+    def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
+        print(txt2imgreq)
+        p = StableDiffusionProcessingTxt2Img(**vars(txt2imgreq))
+        p.sd_model = shared.sd_model
+        print(p)
+        processed = process_images(p)
+        
+        b64images = []
+        for i in processed.images:
+            buffer = io.BytesIO()
+            i.save(buffer, format="png")
+            b64images.append(base64.b64encode(buffer.getvalue()))
+        
+        response = {
+            "images": b64images,
+            "info": processed.js(),
+            "parameters": json.dumps(vars(txt2imgreq))
+        }
+        
+
+        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
+        
+        
+
+    def img2imgendoint(self):
+        raise NotImplementedError
+
+    def extrasendoint(self):
+        raise NotImplementedError
+
+    def pnginfoendoint(self):
+        raise NotImplementedError
+
+    def launch(self, server_name, port):
+        app.include_router(self.router)
+        uvicorn.run(app, host=server_name, port=port)
\ No newline at end of file
diff --git a/modules/processing.py b/modules/processing.py
index deb6125e..4a7c6ccc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -723,4 +723,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         del x
         devices.torch_gc()
 
-        return samples
+        return samples
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index c2775603..6c6405fd 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -74,7 +74,7 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-
+parser.add_argument("--api", action='store_true', help="use api=True to launch the api instead of the webui")
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.3


From 9e02812afd10582f00a7fbbfa63c8f9188678e26 Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 07:02:08 +0000
Subject: pydantic instrumentation

---
 modules/api/processing.py | 99 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 99 insertions(+)
 create mode 100644 modules/api/processing.py

(limited to 'modules')

diff --git a/modules/api/processing.py b/modules/api/processing.py
new file mode 100644
index 00000000..459a8f49
--- /dev/null
+++ b/modules/api/processing.py
@@ -0,0 +1,99 @@
+from inflection import underscore
+from typing import Any, Dict, Optional
+from pydantic import BaseModel, Field, create_model
+from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+import inspect
+
+
+class ModelDef(BaseModel):
+    """Assistance Class for Pydantic Dynamic Model Generation"""
+
+    field: str
+    field_alias: str
+    field_type: Any
+    field_value: Any
+
+
+class pydanticModelGenerator:
+    """
+    Takes source_data:Dict ( a single instance example of something like a JSON node) and self generates a pythonic data model with Alias to original source field names. This makes it easy to popuate or export to other systems yet handle the data in a pythonic way.
+    Being a pydantic datamodel all the richness of pydantic data validation is available and these models can easily be used in FastAPI and or a ORM
+
+    It does not process full JSON data structures but takes simple JSON document with basic elements
+
+    Provide a model_name, an example of JSON data and a dict of type overrides
+
+    Example:
+
+    source_data = {'Name': '48 Rainbow Rd',
+        'GroupAddressStyle': 'ThreeLevel',
+        'LastModified': '2020-12-21T07:02:51.2400232Z',
+        'ProjectStart': '2020-12-03T07:36:03.324856Z',
+        'Comment': '',
+        'CompletionStatus': 'Editing',
+        'LastUsedPuid': '955',
+        'Guid': '0c85957b-c2ae-4985-9752-b300ab385b36'}
+
+    source_overrides = {'Guid':{'type':uuid.UUID},
+            'LastModified':{'type':datetime },
+            'ProjectStart':{'type':datetime },
+            }
+    source_optionals = {"Comment":True}
+
+    #create Model
+    model_Project=pydanticModelGenerator(
+        model_name="Project",
+        source_data=source_data,
+        overrides=source_overrides,
+        optionals=source_optionals).generate_model()
+
+    #create instance using DynamicModel
+    project_instance=model_Project(**project_info)
+
+    """
+
+    def __init__(
+        self,
+        model_name: str = None,
+        source_data: str = None,
+        params: Dict = {},
+        overrides: Dict = {},
+        optionals: Dict = {},
+    ):
+        def field_type_generator(k, v, overrides, optionals):
+            print(k, v)
+            field_type = str if not overrides.get(k) else overrides[k]["type"]
+            if v is None:
+                field_type = Any
+            else:
+                field_type = type(v)
+            
+            return Optional[field_type]
+        
+        self._model_name = model_name
+        self._json_data = source_data
+        self._model_def = [
+            ModelDef(
+                field=underscore(k),
+                field_alias=k,
+                field_type=field_type_generator(k, v, overrides, optionals),
+                field_value=v
+            )
+            for (k,v) in source_data.items() if k in params
+        ]
+
+    def generate_model(self):
+        """
+        Creates a pydantic BaseModel
+        from the json and overrides provided at initialization
+        """
+        fields = {
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
+        }
+        DynamicModel = create_model(self._model_name, **fields)
+        DynamicModel.__config__.allow_population_by_field_name = True
+        return DynamicModel
+    
+StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing", 
+                                                      StableDiffusionProcessing().__dict__, 
+                                                      inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
\ No newline at end of file
-- 
cgit v1.2.3


From 832b490e5173f78c4d3aa7ca9ca9ac794d140664 Mon Sep 17 00:00:00 2001
From: Jonathan <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 03:18:41 -0400
Subject: Update processing.py

---
 modules/api/processing.py | 41 +++++------------------------------------
 1 file changed, 5 insertions(+), 36 deletions(-)

(limited to 'modules')

diff --git a/modules/api/processing.py b/modules/api/processing.py
index 459a8f49..4c3d0bd0 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -16,46 +16,15 @@ class ModelDef(BaseModel):
 
 class pydanticModelGenerator:
     """
-    Takes source_data:Dict ( a single instance example of something like a JSON node) and self generates a pythonic data model with Alias to original source field names. This makes it easy to popuate or export to other systems yet handle the data in a pythonic way.
-    Being a pydantic datamodel all the richness of pydantic data validation is available and these models can easily be used in FastAPI and or a ORM
-
-    It does not process full JSON data structures but takes simple JSON document with basic elements
-
-    Provide a model_name, an example of JSON data and a dict of type overrides
-
-    Example:
-
-    source_data = {'Name': '48 Rainbow Rd',
-        'GroupAddressStyle': 'ThreeLevel',
-        'LastModified': '2020-12-21T07:02:51.2400232Z',
-        'ProjectStart': '2020-12-03T07:36:03.324856Z',
-        'Comment': '',
-        'CompletionStatus': 'Editing',
-        'LastUsedPuid': '955',
-        'Guid': '0c85957b-c2ae-4985-9752-b300ab385b36'}
-
-    source_overrides = {'Guid':{'type':uuid.UUID},
-            'LastModified':{'type':datetime },
-            'ProjectStart':{'type':datetime },
-            }
-    source_optionals = {"Comment":True}
-
-    #create Model
-    model_Project=pydanticModelGenerator(
-        model_name="Project",
-        source_data=source_data,
-        overrides=source_overrides,
-        optionals=source_optionals).generate_model()
-
-    #create instance using DynamicModel
-    project_instance=model_Project(**project_info)
-
+    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
+    source_data is a snapshot of the default values produced by the class
+    params are the names of the actual keys required by __init__
     """
 
     def __init__(
         self,
         model_name: str = None,
-        source_data: str = None,
+        source_data: {} = {},
         params: Dict = {},
         overrides: Dict = {},
         optionals: Dict = {},
@@ -96,4 +65,4 @@ class pydanticModelGenerator:
     
 StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing", 
                                                       StableDiffusionProcessing().__dict__, 
-                                                      inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
\ No newline at end of file
+                                                      inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
-- 
cgit v1.2.3


From 99013ba68a5fe1bde3621632e5539c03562a3ae8 Mon Sep 17 00:00:00 2001
From: Jonathan <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 03:20:17 -0400
Subject: Update processing.py

---
 modules/api/processing.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/processing.py b/modules/api/processing.py
index 4c3d0bd0..e4df93c5 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -30,7 +30,6 @@ class pydanticModelGenerator:
         optionals: Dict = {},
     ):
         def field_type_generator(k, v, overrides, optionals):
-            print(k, v)
             field_type = str if not overrides.get(k) else overrides[k]["type"]
             if v is None:
                 field_type = Any
-- 
cgit v1.2.3


From 71d42bb44b257f3fb274c3ad5075a195281ff915 Mon Sep 17 00:00:00 2001
From: Jonathan <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 03:22:19 -0400
Subject: Update api.py

---
 modules/api/api.py | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 9d7c699d..4d9619a8 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -23,10 +23,8 @@ class Api:
         app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
-        print(txt2imgreq)
         p = StableDiffusionProcessingTxt2Img(**vars(txt2imgreq))
         p.sd_model = shared.sd_model
-        print(p)
         processed = process_images(p)
         
         b64images = []
@@ -34,13 +32,6 @@ class Api:
             buffer = io.BytesIO()
             i.save(buffer, format="png")
             b64images.append(base64.b64encode(buffer.getvalue()))
-        
-        response = {
-            "images": b64images,
-            "info": processed.js(),
-            "parameters": json.dumps(vars(txt2imgreq))
-        }
-        
 
         return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
         
@@ -57,4 +48,4 @@ class Api:
 
     def launch(self, server_name, port):
         app.include_router(self.router)
-        uvicorn.run(app, host=server_name, port=port)
\ No newline at end of file
+        uvicorn.run(app, host=server_name, port=port)
-- 
cgit v1.2.3


From d42125baf62880854ad06af06c15c23e7e50cca6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 11:50:20 +0300
Subject: add missing requirement for api and fix some typos

---
 modules/api/api.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 4d9619a8..fd09d352 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -18,7 +18,7 @@ class TextToImageResponse(BaseModel):
 
 
 class Api:
-    def __init__(self, txt2img, img2img, run_extras, run_pnginfo):
+    def __init__(self):
         self.router = APIRouter()
         app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
 
-- 
cgit v1.2.3


From 8c6a981d5d9ef30381ac2327460285111550acbc Mon Sep 17 00:00:00 2001
From: Michoko <michoko@hotmail.com>
Date: Mon, 17 Oct 2022 11:05:05 +0200
Subject: Added dark mode switch

Launch the UI in dark mode with the --dark-mode switch
---
 modules/shared.py | 2 +-
 modules/ui.py     | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index c2775603..cbf158e4 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -69,13 +69,13 @@ parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image upload
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
+parser.add_argument("--dark-mode", action='store_true', help="launches the UI in dark mode", default=False)
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 
-
 cmd_opts = parser.parse_args()
 restricted_opts = [
     "samples_filename_pattern",
diff --git a/modules/ui.py b/modules/ui.py
index 43dc88fc..a0cd052e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1783,6 +1783,8 @@ for filename in sorted(os.listdir(jsdir)):
     with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
         javascript += f"\n<script>{jsfile.read()}</script>"
 
+if cmd_opts.dark_mode:
+    javascript += "\n<script>go_dark_mode();</script>\n"
 
 if 'gradio_routes_templates_response' not in globals():
     def template_response(*args, **kwargs):
-- 
cgit v1.2.3


From c408a0b41cfffde184cad35b2d97346342947d83 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 17 Oct 2022 22:28:43 +0800
Subject: fix two bug

---
 modules/images_history.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 1ae168ca..10e5b970 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -181,7 +181,8 @@ def delete_image(delete_num, name, filenames, image_index, visible_num):
     return new_file_list, 1, visible_num
 
 def save_image(file_name):
-    shutil.copy2(file_name, opts.outdir_save)
+    if file_name is not None and os.path.exists(file_name):
+        shutil.copy2(file_name, opts.outdir_save)
 
 def get_recent_images(page_index, step, filenames):
     page_index = int(page_index)
@@ -327,7 +328,6 @@ def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict):
     opts = sys_opts
     loads_files_num = int(opts.images_history_num_per_page)
     num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num)
-    backup_flag = opts.images_history_backup
     with gr.Blocks(analytics_enabled=False) as images_history:
         with gr.Tabs() as tabs:
             for tab in ["txt2img", "img2img", "extras", "saved"]:
-- 
cgit v1.2.3


From 2272cf2f35fafd5cd486bfb4ee89df5bbc625b97 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 17 Oct 2022 23:04:42 +0800
Subject: fix two bug

---
 modules/images_history.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 10e5b970..1c1790a4 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -133,7 +133,7 @@ def archive_images(dir_name, date_to):
             date = sort_array[loads_num][2]
             filenames = [x[1] for x in sort_array]
         else:
-            date =  sort_array[-1][2]
+            date =  None if len(sort_array) == 0 else sort_array[-1][2]
             filenames = [x[1] for x in sort_array]
         filenames = [x[1] for x in sort_array if x[2]>= date]
     _, image_list, _, visible_num = get_recent_images(1, 0, filenames)
-- 
cgit v1.2.3


From 2b5b62e768d892773a7ec1d5e8d8cea23aae1254 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 17 Oct 2022 23:14:03 +0800
Subject: fix two bug

---
 modules/images_history.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 1c1790a4..20324557 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -44,7 +44,7 @@ def traverse_all_files(curr_path, image_list, all_type=False):
         return image_list
     for file in f_list:
         file = os.path.join(curr_path, file)
-        if (not all_type) and file[-4:] == ".txt":
+        if (not all_type) and (file[-4:] == ".txt" or file[-4:] == ".csv"):
             pass
         elif os.path.isfile(file) and file[-10:].rfind(".") > 0:
             image_list.append(file)
@@ -182,7 +182,7 @@ def delete_image(delete_num, name, filenames, image_index, visible_num):
 
 def save_image(file_name):
     if file_name is not None and os.path.exists(file_name):
-        shutil.copy2(file_name, opts.outdir_save)
+        shutil.copy(file_name, opts.outdir_save)
 
 def get_recent_images(page_index, step, filenames):
     page_index = int(page_index)
-- 
cgit v1.2.3


From 665beebc0825a6fad410c8252f27f6f6f0bd900b Mon Sep 17 00:00:00 2001
From: Michoko <michoko@hotmail.com>
Date: Mon, 17 Oct 2022 18:24:24 +0200
Subject: Use of a --theme argument for more flexibility

Added possibility to set the theme (light or dark)
---
 modules/shared.py | 2 +-
 modules/ui.py     | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index cbf158e4..fa084c69 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -69,7 +69,7 @@ parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image upload
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
-parser.add_argument("--dark-mode", action='store_true', help="launches the UI in dark mode", default=False)
+parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
diff --git a/modules/ui.py b/modules/ui.py
index a0cd052e..d41715fa 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1783,8 +1783,8 @@ for filename in sorted(os.listdir(jsdir)):
     with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
         javascript += f"\n<script>{jsfile.read()}</script>"
 
-if cmd_opts.dark_mode:
-    javascript += "\n<script>go_dark_mode();</script>\n"
+if cmd_opts.theme is not None:
+    javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
 
 if 'gradio_routes_templates_response' not in globals():
     def template_response(*args, **kwargs):
-- 
cgit v1.2.3


From 695377a8b9f7de28b880d96487a9ddf7230cff14 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 19:56:23 +0300
Subject: make modelmerger work with ui-config.json

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 43dc88fc..533b1db3 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1767,6 +1767,7 @@ Requested path was: {f}
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
     visit(extras_interface, loadsave, "extras")
+    visit(modelmerger_interface, loadsave, "modelmerger")
 
     if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
         with open(ui_config_file, "w", encoding="utf8") as file:
-- 
cgit v1.2.3


From cf47d13c1e11fcb7169bac7488d2c39e579ee491 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 21:15:32 +0300
Subject: localization support

---
 modules/localization.py | 31 +++++++++++++++++++++++++++++++
 modules/shared.py       |  7 +++++--
 modules/ui.py           | 33 +++++++++++++++++++++++----------
 3 files changed, 59 insertions(+), 12 deletions(-)
 create mode 100644 modules/localization.py

(limited to 'modules')

diff --git a/modules/localization.py b/modules/localization.py
new file mode 100644
index 00000000..b1810cda
--- /dev/null
+++ b/modules/localization.py
@@ -0,0 +1,31 @@
+import json
+import os
+import sys
+import traceback
+
+localizations = {}
+
+
+def list_localizations(dirname):
+    localizations.clear()
+
+    for file in os.listdir(dirname):
+        fn, ext = os.path.splitext(file)
+        if ext.lower() != ".json":
+            continue
+
+        localizations[fn] = os.path.join(dirname, file)
+
+
+def localization_js(current_localization_name):
+    fn = localizations.get(current_localization_name, None)
+    data = {}
+    if fn is not None:
+        try:
+            with open(fn, "r", encoding="utf8") as file:
+                data = json.load(file)
+        except Exception:
+            print(f"Error loading localization from {fn}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    return f"var localization = {json.dumps(data)}\n"
diff --git a/modules/shared.py b/modules/shared.py
index c2775603..2a2b0427 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models
+from modules import sd_samplers, sd_models, localization
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -31,6 +31,7 @@ parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
 parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
+parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
@@ -103,7 +104,6 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
-
 def reload_hypernetworks():
     global hypernetworks
 
@@ -151,6 +151,8 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
 
+localization.list_localizations(cmd_opts.localizations_dir)
+
 
 def realesrgan_models_names():
     import modules.realesrgan_model
@@ -296,6 +298,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
+    'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
diff --git a/modules/ui.py b/modules/ui.py
index 533b1db3..656bab7a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -23,7 +23,7 @@ import gradio as gr
 import gradio.utils
 import gradio.routes
 
-from modules import sd_hijack, sd_models
+from modules import sd_hijack, sd_models, localization
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts, restricted_opts
 if cmd_opts.deepdanbooru:
@@ -1056,10 +1056,10 @@ def create_ui(wrap_gradio_gpu_call):
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
 
                 with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', celem_id="extras_upscaler_2", hoices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
                     extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
 
                 with gr.Group():
@@ -1224,10 +1224,10 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     with gr.Row():
-                        train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
                     with gr.Row():
-                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
                         create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
                     learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
                     batch_size = gr.Number(label='Batch size', value=1, precision=0)
@@ -1376,16 +1376,18 @@ def create_ui(wrap_gradio_gpu_call):
         else:
             raise Exception(f'bad options item type: {str(t)} for key {key}')
 
+        elem_id = "setting_"+key
+
         if info.refresh is not None:
             if is_quicksettings:
-                res = comp(label=info.label, value=fun, **(args or {}))
-                refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+                res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
+                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
             else:
                 with gr.Row(variant="compact"):
-                    res = comp(label=info.label, value=fun, **(args or {}))
-                    refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+                    res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
+                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
         else:
-            res = comp(label=info.label, value=fun, **(args or {}))
+            res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
 
 
         return res
@@ -1509,6 +1511,9 @@ Requested path was: {f}
 
         with gr.Row():
             request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+
+        with gr.Row():
             reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
             restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
 
@@ -1519,6 +1524,13 @@ Requested path was: {f}
             _js='function(){}'
         )
 
+        download_localization.click(
+            fn=lambda: None,
+            inputs=[],
+            outputs=[],
+            _js='download_localization'
+        )
+
         def reload_scripts():
             modules.scripts.reload_script_body_only()
 
@@ -1784,6 +1796,7 @@ for filename in sorted(os.listdir(jsdir)):
     with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
         javascript += f"\n<script>{jsfile.read()}</script>"
 
+javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
 
 if 'gradio_routes_templates_response' not in globals():
     def template_response(*args, **kwargs):
-- 
cgit v1.2.3


From d62ef76614624cda99d842a2900242d5b7923eda Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Tue, 18 Oct 2022 03:09:50 +0800
Subject: Don't eat colons in booru tags

---
 modules/deepbooru.py | 2 --
 1 file changed, 2 deletions(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 4ad334a1..de16b13f 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -157,8 +157,6 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
     # sort by reverse by likelihood and normal for alpha, and format tag text as requested
     unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
     for weight, tag in unsorted_tags_in_theshold:
-        # note: tag_outformat will still have a colon if include_ranks is True
-        tag_outformat = tag.replace(':', ' ')
         if use_spaces:
             tag_outformat = tag_outformat.replace('_', ' ')
         if use_escape:
-- 
cgit v1.2.3


From f80e914ac4aa69a9783b4040813253500b34d925 Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 19:10:36 +0000
Subject: example API working with gradio

---
 modules/api/api.py        |  9 ++++++--
 modules/api/processing.py | 56 ++++++++++++++++++++++++++++++++---------------
 modules/processing.py     | 22 +++++++++++++------
 3 files changed, 60 insertions(+), 27 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index fd09d352..5e86c3bf 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -23,8 +23,13 @@ class Api:
         app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
-        p = StableDiffusionProcessingTxt2Img(**vars(txt2imgreq))
-        p.sd_model = shared.sd_model
+        populate = txt2imgreq.copy(update={ # Override __init__ params
+            "sd_model": shared.sd_model, 
+            "sampler_index": 0,
+            }
+        )
+        p = StableDiffusionProcessingTxt2Img(**vars(populate))
+        # Override object param
         processed = process_images(p)
         
         b64images = []
diff --git a/modules/api/processing.py b/modules/api/processing.py
index e4df93c5..b6798241 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -5,6 +5,24 @@ from modules.processing import StableDiffusionProcessing, Processed, StableDiffu
 import inspect
 
 
+API_NOT_ALLOWED = [
+    "self",
+    "kwargs",
+    "sd_model",
+    "outpath_samples",
+    "outpath_grids",
+    "sampler_index",
+    "do_not_save_samples",
+    "do_not_save_grid",
+    "extra_generation_params",
+    "overlay_images",
+    "do_not_reload_embeddings",
+    "seed_enable_extras",
+    "prompt_for_display",
+    "sampler_noise_scheduler_override",
+    "ddim_discretize"
+]
+
 class ModelDef(BaseModel):
     """Assistance Class for Pydantic Dynamic Model Generation"""
 
@@ -14,7 +32,7 @@ class ModelDef(BaseModel):
     field_value: Any
 
 
-class pydanticModelGenerator:
+class PydanticModelGenerator:
     """
     Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
     source_data is a snapshot of the default values produced by the class
@@ -24,30 +42,33 @@ class pydanticModelGenerator:
     def __init__(
         self,
         model_name: str = None,
-        source_data: {} = {},
-        params: Dict = {},
-        overrides: Dict = {},
-        optionals: Dict = {},
+        class_instance = None
     ):
-        def field_type_generator(k, v, overrides, optionals):
-            field_type = str if not overrides.get(k) else overrides[k]["type"]
-            if v is None:
-                field_type = Any
-            else:
-                field_type = type(v)
+        def field_type_generator(k, v):
+            # field_type = str if not overrides.get(k) else overrides[k]["type"]
+            # print(k, v.annotation, v.default)
+            field_type = v.annotation
             
             return Optional[field_type]
         
+        def merge_class_params(class_):
+            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
+            parameters = {}
+            for classes in all_classes:
+                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
+            return parameters
+            
+                
         self._model_name = model_name
-        self._json_data = source_data
+        self._class_data = merge_class_params(class_instance)
         self._model_def = [
             ModelDef(
                 field=underscore(k),
                 field_alias=k,
-                field_type=field_type_generator(k, v, overrides, optionals),
-                field_value=v
+                field_type=field_type_generator(k, v),
+                field_value=v.default
             )
-            for (k,v) in source_data.items() if k in params
+            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
         ]
 
     def generate_model(self):
@@ -60,8 +81,7 @@ class pydanticModelGenerator:
         }
         DynamicModel = create_model(self._model_name, **fields)
         DynamicModel.__config__.allow_population_by_field_name = True
+        DynamicModel.__config__.allow_mutation = True
         return DynamicModel
     
-StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing", 
-                                                      StableDiffusionProcessing().__dict__, 
-                                                      inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
+StableDiffusionProcessingAPI = PydanticModelGenerator("StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img).generate_model()
diff --git a/modules/processing.py b/modules/processing.py
index 4a7c6ccc..024a4fc3 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -9,6 +9,7 @@ from PIL import Image, ImageFilter, ImageOps
 import random
 import cv2
 from skimage import exposure
+from typing import Any, Dict, List, Optional
 
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram
@@ -51,9 +52,15 @@ def get_correct_sampler(p):
         return sd_samplers.samplers
     elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
         return sd_samplers.samplers_for_img2img
+    elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
+        return sd_samplers.samplers
 
-class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None, do_not_reload_embeddings=False):
+class StableDiffusionProcessing():
+    """
+    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
+    
+    """
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str="", styles: List[str]=None, seed: int=-1, subseed: int=-1, subseed_strength: float=0, seed_resize_from_h: int=-1, seed_resize_from_w: int=-1, seed_enable_extras: bool=True, sampler_index: int=0, batch_size: int=1, n_iter: int=1, steps:int =50, cfg_scale:float=7.0, width:int=512, height:int=512, restore_faces:bool=False, tiling:bool=False, do_not_save_samples:bool=False, do_not_save_grid:bool=False, extra_generation_params: Dict[Any,Any]=None, overlay_images: Any=None, negative_prompt: str=None, eta: float =None, do_not_reload_embeddings: bool=False, denoising_strength: float = 0, ddim_discretize: str = "uniform", s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -86,10 +93,10 @@ class StableDiffusionProcessing:
         self.denoising_strength: float = 0
         self.sampler_noise_scheduler_override = None
         self.ddim_discretize = opts.ddim_discretize
-        self.s_churn = opts.s_churn
-        self.s_tmin = opts.s_tmin
-        self.s_tmax = float('inf')  # not representable as a standard ui option
-        self.s_noise = opts.s_noise
+        self.s_churn = s_churn or opts.s_churn
+        self.s_tmin = s_tmin or opts.s_tmin
+        self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
+        self.s_noise = s_noise or opts.s_noise
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -97,6 +104,7 @@ class StableDiffusionProcessing:
             self.seed_resize_from_h = 0
             self.seed_resize_from_w = 0
 
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -497,7 +505,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
+    def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.denoising_strength = denoising_strength
-- 
cgit v1.2.3


From 2e28c841f438b2090caac2b9a54eb62ddbda837c Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Tue, 18 Oct 2022 03:15:41 +0800
Subject: Oops

---
 modules/deepbooru.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index de16b13f..8914662d 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -157,6 +157,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
     # sort by reverse by likelihood and normal for alpha, and format tag text as requested
     unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
     for weight, tag in unsorted_tags_in_theshold:
+        tag_outformat = tag
         if use_spaces:
             tag_outformat = tag_outformat.replace('_', ' ')
         if use_escape:
-- 
cgit v1.2.3


From f29b16bad19b6332a15b2ef439864d866277fffb Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 20:36:14 +0000
Subject: prevent API from saving

---
 modules/api/api.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 5e86c3bf..ce72c5ee 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -26,6 +26,8 @@ class Api:
         populate = txt2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model, 
             "sampler_index": 0,
+            "do_not_save_samples": True,
+            "do_not_save_grid": True
             }
         )
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
-- 
cgit v1.2.3


From ab3f997c0c4a1423a82623ae1d4d3c66005bb8da Mon Sep 17 00:00:00 2001
From: Jordan Hall <jordan@hall05.co.uk>
Date: Mon, 17 Oct 2022 20:59:44 +0100
Subject: Fix typo in 'choices' when loading upscaler 2 config

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 656bab7a..e4ead347 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1059,7 +1059,7 @@ def create_ui(wrap_gradio_gpu_call):
                     extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', celem_id="extras_upscaler_2", hoices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', celem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
                     extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
 
                 with gr.Group():
-- 
cgit v1.2.3


From d3338bdef18b3049431a0649d55ff22aa18baa68 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 17 Oct 2022 22:46:56 +0100
Subject: extras extend cache key with new upscale to options

---
 modules/extras.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 8dbab240..c908b43e 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -91,7 +91,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
                 small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
                 pixels = tuple(np.array(small).flatten().tolist())
-                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
+                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight, 
+                       resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
 
                 c = cached_images.get(key)
                 if c is None:
-- 
cgit v1.2.3


From c3851a853d99ad35ccedcdd8dbeb6cfbe273439b Mon Sep 17 00:00:00 2001
From: Ryan Voots <simcop2387@simcop2387.info>
Date: Mon, 17 Oct 2022 12:49:33 -0400
Subject: Re-use webui fastapi application rather than requiring one or the
 other, not both.

---
 modules/api/api.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index ce72c5ee..8781cd86 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -2,15 +2,13 @@ from modules.api.processing import StableDiffusionProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, process_images
 import modules.shared as shared
 import uvicorn
-from fastapi import FastAPI, Body, APIRouter
+from fastapi import Body, APIRouter
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field, Json
 import json
 import io
 import base64
 
-app = FastAPI()
-
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
@@ -18,7 +16,7 @@ class TextToImageResponse(BaseModel):
 
 
 class Api:
-    def __init__(self):
+    def __init__(self, app):
         self.router = APIRouter()
         app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
 
-- 
cgit v1.2.3


From 247aeb3aaaf2925c7d68a9cf47c975f3e6d3dd33 Mon Sep 17 00:00:00 2001
From: Ryan Voots <simcop2387@simcop2387.info>
Date: Mon, 17 Oct 2022 12:50:45 -0400
Subject: Put API under /sdapi/ so that routing is simpler in the future.  This
 means that one could allow access to /sdapi/ but not the webui.

---
 modules/api/api.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 8781cd86..14613d8c 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -18,7 +18,7 @@ class TextToImageResponse(BaseModel):
 class Api:
     def __init__(self, app):
         self.router = APIRouter()
-        app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
+        app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
         populate = txt2imgreq.copy(update={ # Override __init__ params
-- 
cgit v1.2.3


From 1df3ff25e6fe2e3f308e45f7a6dd37fb4f1988e6 Mon Sep 17 00:00:00 2001
From: Ryan Voots <simcop2387@simcop2387.info>
Date: Mon, 17 Oct 2022 12:58:34 -0400
Subject: Add --nowebui as a means of disabling the webui and run on the other
 port

---
 modules/shared.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 6c6405fd..8b436970 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -74,7 +74,8 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-parser.add_argument("--api", action='store_true', help="use api=True to launch the api instead of the webui")
+parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
+parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.3


From 7432b6f4d2c3001895fc75411a34afae1810c1a2 Mon Sep 17 00:00:00 2001
From: Mykeehu <halasim@hotmail.hu>
Date: Tue, 18 Oct 2022 07:15:38 +0200
Subject: Fix typo "celem_id" to "elem_id"

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index e4ead347..2a7f64f9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1059,7 +1059,7 @@ def create_ui(wrap_gradio_gpu_call):
                     extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', celem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
                     extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
 
                 with gr.Group():
-- 
cgit v1.2.3


From 8d5d863a9d11850464fdb6b64f34602803c15ccc Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Tue, 18 Oct 2022 06:51:53 +0000
Subject: gradio and FastAPI

---
 modules/api/api.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 14613d8c..ce98cb8c 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -16,9 +16,11 @@ class TextToImageResponse(BaseModel):
 
 
 class Api:
-    def __init__(self, app):
+    def __init__(self, app, queue_lock):
         self.router = APIRouter()
-        app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
+        self.app = app
+        self.queue_lock = queue_lock
+        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
         populate = txt2imgreq.copy(update={ # Override __init__ params
@@ -30,7 +32,8 @@ class Api:
         )
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
         # Override object param
-        processed = process_images(p)
+        with self.queue_lock:
+            processed = process_images(p)
         
         b64images = []
         for i in processed.images:
@@ -52,5 +55,5 @@ class Api:
         raise NotImplementedError
 
     def launch(self, server_name, port):
-        app.include_router(self.router)
-        uvicorn.run(app, host=server_name, port=port)
+        self.app.include_router(self.router)
+        uvicorn.run(self.app, host=server_name, port=port)
-- 
cgit v1.2.3


From 786ed499226177d71e937e0342bcb9d3b1ff260f Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 17 Oct 2022 19:48:39 +0300
Subject: use legacy attnblock

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 984b35c4..2407a461 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -27,7 +27,7 @@ def apply_optimizations():
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-- 
cgit v1.2.3


From 2043c4a231eef838bb15044f502b864b55885037 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 17 Oct 2022 19:49:11 +0300
Subject: delete xformers attnblock

---
 modules/sd_hijack_optimizations.py | 12 ------------
 1 file changed, 12 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 79405525..60da7459 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -292,15 +292,3 @@ def cross_attention_attnblock_forward(self, x):
 
         return h3
     
-def xformers_attnblock_forward(self, x):
-    try:
-        h_ = x
-        h_ = self.norm(h_)
-        q1 = self.q(h_).contiguous()
-        k1 = self.k(h_).contiguous()
-        v = self.v(h_).contiguous()
-        out = xformers.ops.memory_efficient_attention(q1, k1, v)
-        out = self.proj_out(out)
-        return x + out
-    except NotImplementedError:
-        return cross_attention_attnblock_forward(self, x)
-- 
cgit v1.2.3


From 84823275e896bcc1f7cb4ce098ae3c5d05e17b9a Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 17 Oct 2022 22:18:59 +0300
Subject: readd xformers attnblock

---
 modules/sd_hijack_optimizations.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 60da7459..7ebef3f0 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -292,3 +292,18 @@ def cross_attention_attnblock_forward(self, x):
 
         return h3
     
+def xformers_attnblock_forward(self, x):
+    try:
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+        b, c, h, w = q.shape
+        q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+        out = xformers.ops.memory_efficient_attention(q, k, v)
+        out = rearrange(out, 'b (h w) c -> b c h w', h=h)
+        out = self.proj_out(out)
+        return x + out
+    except NotImplementedError:
+        return cross_attention_attnblock_forward(self, x)
-- 
cgit v1.2.3


From 73b5dbf72a93b64445551c74a4c0dc924986081d Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 17 Oct 2022 22:19:18 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 2407a461..984b35c4 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -27,7 +27,7 @@ def apply_optimizations():
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-- 
cgit v1.2.3


From c71008c74156635558bb2e877d1628913f6f781e Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Tue, 18 Oct 2022 00:02:50 +0300
Subject: Update sd_hijack_optimizations.py

---
 modules/sd_hijack_optimizations.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 7ebef3f0..a3345bb9 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -301,6 +301,9 @@ def xformers_attnblock_forward(self, x):
         v = self.v(h_)
         b, c, h, w = q.shape
         q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+        q = q.contiguous()
+        k = k.contiguous()
+        v = v.contiguous()
         out = xformers.ops.memory_efficient_attention(q, k, v)
         out = rearrange(out, 'b (h w) c -> b c h w', h=h)
         out = self.proj_out(out)
-- 
cgit v1.2.3


From 8b02662215917d39f76f86b703a322818d5a8ad4 Mon Sep 17 00:00:00 2001
From: trufty <trufty@gmail.com>
Date: Mon, 17 Oct 2022 10:58:21 -0400
Subject: Disable auto weights swap with config option

---
 modules/shared.py | 1 +
 modules/ui.py     | 4 ++++
 2 files changed, 5 insertions(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 9603d26e..8a1d1881 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -266,6 +266,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
+    "disable_weights_auto_swap": OptionInfo(False, "Disable auto swapping weights to match model hash in prompts"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
diff --git a/modules/ui.py b/modules/ui.py
index 1dae4a65..75eb0b0c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -542,6 +542,10 @@ def apply_setting(key, value):
     if value is None:
         return gr.update()
 
+    # dont allow model to be swapped when model hash exists in prompt
+    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+        return gr.update()
+
     if key == "sd_model_checkpoint":
         ckpt_info = sd_models.get_closet_checkpoint_match(value)
 
-- 
cgit v1.2.3


From d2f459c5cf9f728256775dc1c3380c7e9a7e27fb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 18 Oct 2022 14:22:52 +0300
Subject: clarify the comment for the new option from #2959 and move it to UI
 section.

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 8a1d1881..c0d87168 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -266,7 +266,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
-    "disable_weights_auto_swap": OptionInfo(False, "Disable auto swapping weights to match model hash in prompts"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
@@ -294,6 +293,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
+    "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-- 
cgit v1.2.3


From 97d3ba3941536215ea15431886c7f28300a9d915 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E3=81=B5=E3=81=81?=
 <34892635+fa0311@users.noreply.github.com>
Date: Tue, 18 Oct 2022 17:29:42 +0900
Subject: Add scripts to ui-config,json

---
 modules/scripts.py | 15 +++++++++++++--
 modules/ui.py      |  5 +++++
 2 files changed, 18 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index ac66d448..3402066d 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -96,6 +96,7 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
 class ScriptRunner:
     def __init__(self):
         self.scripts = []
+        self.titles = []
 
     def setup_ui(self, is_img2img):
         for script_class, path in scripts_data:
@@ -107,9 +108,10 @@ class ScriptRunner:
 
             self.scripts.append(script)
 
-        titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
+        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
 
-        dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index")
+        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
+        dropdown.save_to_config = True
         inputs = [dropdown]
 
         for script in self.scripts:
@@ -139,6 +141,15 @@ class ScriptRunner:
 
             return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
 
+        def init_field(title):
+            if title == "None":
+                return
+            script_index = self.titles.index(title)
+            script = self.scripts[script_index]
+            for i in range(script.args_from, script.args_to):
+                inputs[i].visible = True
+
+        dropdown.init_field = init_field
         dropdown.change(
             fn=select_script,
             inputs=[dropdown],
diff --git a/modules/ui.py b/modules/ui.py
index 75eb0b0c..39afbc4e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1753,6 +1753,11 @@ Requested path was: {f}
                 print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
             else:
                 setattr(obj, field, saved_value)
+                if getattr(x, 'init_field', False):
+                    try:
+                        x.init_field(saved_value)
+                    except Exception:
+                        print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
 
         if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
             apply_field(x, 'visible')
-- 
cgit v1.2.3


From de29ec0743fcfb141d8891a3ccbd537ea71bf5b4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E3=81=B5=E3=81=81?=
 <34892635+fa0311@users.noreply.github.com>
Date: Tue, 18 Oct 2022 18:15:00 +0900
Subject: Remove exception handling

---
 modules/ui.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 39afbc4e..b38bfb3f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1754,10 +1754,7 @@ Requested path was: {f}
             else:
                 setattr(obj, field, saved_value)
                 if getattr(x, 'init_field', False):
-                    try:
-                        x.init_field(saved_value)
-                    except Exception:
-                        print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
+                    x.init_field(saved_value)
 
         if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
             apply_field(x, 'visible')
-- 
cgit v1.2.3


From 3003438088502774628656790d83fc8074d51ab4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E3=81=B5=E3=81=81?=
 <34892635+fa0311@users.noreply.github.com>
Date: Tue, 18 Oct 2022 18:51:57 +0900
Subject: Add visible for dropdown

---
 modules/ui.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index b38bfb3f..fb6eb5a0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1737,7 +1737,7 @@ Requested path was: {f}
         print(traceback.format_exc(), file=sys.stderr)
 
     def loadsave(path, x):
-        def apply_field(obj, field, condition=None):
+        def apply_field(obj, field, condition=None, init_field=None):
             key = path + "/" + field
 
             if getattr(obj,'custom_script_source',None) is not None:
@@ -1753,8 +1753,8 @@ Requested path was: {f}
                 print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
             else:
                 setattr(obj, field, saved_value)
-                if getattr(x, 'init_field', False):
-                    x.init_field(saved_value)
+                if init_field is not None:
+                    init_field(saved_value)
 
         if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
             apply_field(x, 'visible')
@@ -1780,7 +1780,8 @@ Requested path was: {f}
         # Since there are many dropdowns that shouldn't be saved,
         # we only mark dropdowns that should be saved.
         if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
-            apply_field(x, 'value', lambda val: val in x.choices)
+            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
+            apply_field(x, 'visible')
 
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
-- 
cgit v1.2.3


From 02622b19191f5f5112db7633c0630e5c7df1b2f7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E3=81=B5=E3=81=81?=
 <34892635+fa0311@users.noreply.github.com>
Date: Tue, 18 Oct 2022 18:52:27 +0900
Subject: update scripts.py

---
 modules/scripts.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 3402066d..1039fa9c 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -142,7 +142,7 @@ class ScriptRunner:
             return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
 
         def init_field(title):
-            if title == "None":
+            if title == 'None':
                 return
             script_index = self.titles.index(title)
             script = self.scripts[script_index]
-- 
cgit v1.2.3


From 4c605c5174a9b211c3a88e9aff5f5be92b53fd92 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 16 Oct 2022 17:24:06 +0100
Subject: add shared option for update check

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index c0d87168..50dc46ae 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -76,6 +76,7 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
+parser.add_argument("--update-check", action='store_true', help="enable http check to confirm that the currently running version is the most recent release.", default=False)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.3


From eb299527b1e5d1f83a14641647fca72e8fb305ac Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 18 Oct 2022 20:14:11 +0800
Subject: Image browser

---
 modules/images_history.py | 227 ++++++++++++++++++++++++++++++----------------
 modules/shared.py         |   7 +-
 modules/ui.py             |   2 +-
 3 files changed, 154 insertions(+), 82 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index 20324557..d56f3a25 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -4,6 +4,7 @@ import time
 import hashlib
 import gradio
 system_bak_path = "webui_log_and_bak"
+browser_tabname = "custom"
 def is_valid_date(date):
     try:
         time.strptime(date, "%Y%m%d")
@@ -99,13 +100,15 @@ def auto_sorting(dir_name):
         date_list.append(today)
     return sorted(date_list, reverse=True)
 
-def archive_images(dir_name, date_to): 
+def archive_images(dir_name, date_to):
+    
     filenames = []   
     loads_num =int(opts.images_history_num_per_page * opts.images_history_pages_num)
+    today = time.strftime("%Y%m%d",time.localtime(time.time()))
+    date_to = today if date_to is None  or date_to == "" else date_to
+    date_to_bak = date_to 
     if opts.images_history_reconstruct_directory:        
-        date_list = auto_sorting(dir_name)
-        today = time.strftime("%Y%m%d",time.localtime(time.time()))
-        date_to = today if date_to is None  or date_to == "" else date_to        
+        date_list = auto_sorting(dir_name)               
         for date in date_list:
             if date <= date_to:
                 path = os.path.join(dir_name, date)
@@ -120,7 +123,7 @@ def archive_images(dir_name, date_to):
         tmparray = [(os.path.getmtime(file), file) for file in filenames ]
         date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400      
         filenames = []
-        date_list = {}
+        date_list = {date_to:None}
         date = time.strftime("%Y%m%d",time.localtime(time.time()))
         for t, f in tmparray:
             date = time.strftime("%Y%m%d",time.localtime(t))
@@ -133,22 +136,29 @@ def archive_images(dir_name, date_to):
             date = sort_array[loads_num][2]
             filenames = [x[1] for x in sort_array]
         else:
-            date =  None if len(sort_array) == 0 else sort_array[-1][2]
+            date =  date_to if len(sort_array) == 0 else sort_array[-1][2]
             filenames = [x[1] for x in sort_array]
-        filenames = [x[1] for x in sort_array if x[2]>= date]
-    _, image_list, _, visible_num = get_recent_images(1, 0, filenames)
+        filenames = [x[1] for x in sort_array if x[2]>= date]   
+    num = len(filenames)  
+    last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000))
+    date = date[:4] + "-" + date[4:6] + "-" + date[6:8]
+    date_to_bak = date_to_bak[:4] + "-" + date_to_bak[4:6] + "-" + date_to_bak[6:8]
+    load_info = f"Loaded {(num + 1) // opts.images_history_pages_num} pades, {num} images, during {date} - {date_to_bak}"
+    _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames)
     return (
         gradio.Dropdown.update(choices=date_list, value=date_to),
-        date,
+        load_info,
         filenames,        
         1,
         image_list,
         "",
-        visible_num
+        "",
+        visible_num, 
+        last_date_from
     )
 
-def newest_click(dir_name,  date_to):
-        return archive_images(dir_name, time.strftime("%Y%m%d",time.localtime(time.time())))
+
+
 
 def delete_image(delete_num, name, filenames, image_index, visible_num):
     if name == "":
@@ -196,7 +206,29 @@ def get_recent_images(page_index, step, filenames):
     length = len(filenames)
     visible_num = num_of_imgs_per_page if  idx_frm + num_of_imgs_per_page <= length else length % num_of_imgs_per_page 
     visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num
-    return page_index, image_list,  "", visible_num
+    return page_index, image_list,  "", "",  visible_num
+
+def newest_click(date_to):
+    if date_to is None:
+        return time.strftime("%Y%m%d",time.localtime(time.time())), []
+    else:
+        return None, []
+def forward_click(last_date_from, date_to_recorder):
+    if len(date_to_recorder) == 0:
+        return None, []
+    if last_date_from == date_to_recorder[-1]:
+        date_to_recorder = date_to_recorder[:-1]
+    if len(date_to_recorder) == 0:
+        return None, []
+    return date_to_recorder[-1], date_to_recorder[:-1]
+
+def backward_click(last_date_from, date_to_recorder):
+    if last_date_from is None or last_date_from == "":
+        return time.strftime("%Y%m%d",time.localtime(time.time())), []
+    if len(date_to_recorder) == 0 or last_date_from != date_to_recorder[-1]:
+        date_to_recorder.append(last_date_from)    
+    return last_date_from, date_to_recorder
+
 
 def first_page_click(page_index, filenames):
     return get_recent_images(1, 0, filenames)
@@ -214,13 +246,33 @@ def page_index_change(page_index, filenames):
     return get_recent_images(page_index, 0, filenames)
 
 def show_image_info(tabname_box, num, page_index, filenames):
-    file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))]     
-    return file, num, file
+    file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))]   
+    tm =   time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file)))
+    return file, tm, num, file
 
 def enable_page_buttons():
     return gradio.update(visible=True)
 
+def change_dir(img_dir, date_to):
+    warning = None
+    try:
+        if os.path.exists(img_dir):
+            try:
+                f = os.listdir(img_dir)                
+            except:
+                warning = f"'{img_dir} is not a directory"
+        else:
+            warning = "The directory is not exist"
+    except:
+        warning = "The format of the directory is incorrect"
+    if warning is None:        
+        today = time.strftime("%Y%m%d",time.localtime(time.time()))
+        return gradio.update(visible=False), gradio.update(visible=True), None,   None if date_to != today else today
+    else:
+        return gradio.update(visible=True), gradio.update(visible=False), warning, date_to
+
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
+    custom_dir = False
     if tabname == "txt2img":
         dir_name = opts.outdir_txt2img_samples
     elif tabname == "img2img":
@@ -229,69 +281,85 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         dir_name = opts.outdir_extras_samples
     elif tabname == "saved":
         dir_name = opts.outdir_save
+    else:
+        custom_dir = True
+        dir_name = None
+
+    if not custom_dir:
+        d = dir_name.split("/")
+        dir_name = d[0]
+        for p in d[1:]:
+            dir_name = os.path.join(dir_name, p)
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name)
 
-    d = dir_name.split("/")
-    dir_name = d[0]
-    for p in d[1:]:
-        dir_name = os.path.join(dir_name, p)
-    if not os.path.exists(dir_name):
-        os.makedirs(dir_name)
-
-    with gr.Column() as page_panel:
-        with gr.Row(visible=False) as turn_page_buttons:
-            renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
-            first_page = gr.Button('First Page')
-            prev_page = gr.Button('Prev Page')
-            page_index = gr.Number(value=1, label="Page Index")
-            next_page = gr.Button('Next Page')
-            end_page = gr.Button('End Page')
-
-        with gr.Row(elem_id=tabname + "_images_history"):
-            with gr.Column(scale=2):
-                with gr.Row():
-                    newest = gr.Button('Reload', elem_id=tabname + "_images_history_start")                    
-                    date_from = gr.Textbox(label="Date from", interactive=False)  
-                    date_to = gr.Dropdown(label="Date to")                  
-
-                history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
-                with gr.Row():
-                    delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
-                    delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
-                    
-            with gr.Column():
-                with gr.Row():
-                    if tabname != "saved":
-                        save_btn = gr.Button('Save')
-                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
-                with gr.Row():
-                    with gr.Column():
-                        img_file_info = gr.Textbox(label="Generate Info", interactive=False)
-                        img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
+    with gr.Column() as page_panel:            
+            with gr.Row():  
+                img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory") 
+            with gr.Row(visible=False) as warning:                 
+                warning_box = gr.Textbox("Message", interactive=False)                        
 
+            with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel:
+                with gr.Column(scale=2):                   
+                    with gr.Row():                    
+                        backward = gr.Button('Backward')         
+                        date_to = gr.Dropdown(label="Date to") 
+                        forward = gr.Button('Forward')  
+                        newest = gr.Button('Reload', elem_id=tabname + "_images_history_start")   
+                    with gr.Row():
+                        load_info = gr.Textbox(show_label=False, interactive=False)  
+                    with gr.Row(visible=False) as turn_page_buttons:
+                        renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
+                        first_page = gr.Button('First Page')
+                        prev_page = gr.Button('Prev Page')
+                        page_index = gr.Number(value=1, label="Page Index")
+                        next_page = gr.Button('Next Page')
+                        end_page = gr.Button('End Page')                 
+
+                    history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=opts.images_history_grid_num)
+                    with gr.Row():
+                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
+                        delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
                         
-                    # hiden items
-                    with gr.Row(visible=False):   
-                        visible_img_num = gr.Number()                     
-                        img_path = gr.Textbox(dir_name)
-                        tabname_box = gr.Textbox(tabname)
-                        image_index = gr.Textbox(value=-1)
-                        set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
-                        filenames = gr.State()
-                        all_images_list = gr.State()
-                        hidden = gr.Image(type="pil")
-                        info1 = gr.Textbox()
-                        info2 = gr.Textbox()
+                with gr.Column():
+                    with gr.Row():
+                        if tabname != "saved":
+                            save_btn = gr.Button('Save')
+                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                    with gr.Row():
+                        with gr.Column():
+                            img_file_info = gr.Textbox(label="Generate Info", interactive=False)
+                            img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
+                            img_file_time= gr.Textbox(value="", label="Create Time", interactive=False)
 
-    
+                            
+                        # hiden items
+                        with gr.Row(): #visible=False):   
+                            visible_img_num = gr.Number()                     
+                            date_to_recorder = gr.State([])
+                            last_date_from = gr.Textbox()
+                            tabname_box = gr.Textbox(tabname)
+                            image_index = gr.Textbox(value=-1)
+                            set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
+                            filenames = gr.State()
+                            all_images_list = gr.State()
+                            hidden = gr.Image(type="pil")
+                            info1 = gr.Textbox()
+                            info2 = gr.Textbox()
+
+    img_path.submit(change_dir, inputs=[img_path, date_to], outputs=[warning, main_panel, warning_box, date_to])
     #change date
-    change_date_output = [date_to, date_from, filenames, page_index, history_gallery, img_file_name, visible_img_num]  
-    newest.click(newest_click, inputs=[img_path, date_to], outputs=change_date_output)
-    date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output)     
-    newest.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])
-    newest.click(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])
+    change_date_output = [date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from]  
+   
+    date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output)   
+    date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])  
+    date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")  
+
+    newest.click(newest_click, inputs=[date_to], outputs=[date_to, date_to_recorder])
+    forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder])
+    backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder])
+
 
     #delete
     delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num])
@@ -301,7 +369,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
     #turn page
     gallery_inputs = [page_index, filenames]
-    gallery_outputs = [page_index, history_gallery, img_file_name, visible_img_num]
+    gallery_outputs = [page_index, history_gallery, img_file_name, img_file_time, visible_img_num]
     first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
     next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
     prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
@@ -317,12 +385,14 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     renew_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
 
     # other funcitons
-    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, image_index, hidden])
+    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, img_file_time, image_index, hidden])
     img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)   
     hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
     switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
     switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
 
+   
+
 def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict):
     global opts;
     opts = sys_opts
@@ -330,10 +400,11 @@ def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict):
     num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num)
     with gr.Blocks(analytics_enabled=False) as images_history:
         with gr.Tabs() as tabs:
-            for tab in ["txt2img", "img2img", "extras", "saved"]:
+            for tab in [browser_tabname, "txt2img", "img2img", "extras", "saved"]:
                 with gr.Tab(tab):
-                    with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                    with gr.Blocks(analytics_enabled=False) :
                         show_images_history(gr, opts, tab, run_pnginfo, switch_dict) 
-        gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False)
+        #gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False)
+        gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_finish_render", visible=False)
     
     return images_history
diff --git a/modules/shared.py b/modules/shared.py
index c2ea4186..1811018d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -309,10 +309,11 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
-options_templates.update(options_section(('images-history', "Images history"), {
-    "images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"),
+options_templates.update(options_section(('images-history', "Images Browser"), {
+    #"images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"),
     "images_history_num_per_page": OptionInfo(36, "Number of pictures displayed on each page"),
-    "images_history_pages_num": OptionInfo(6, "Maximum number of pages per load "),   
+    "images_history_pages_num": OptionInfo(6, "Minimum number of pages per load "),   
+    "images_history_grid_num": OptionInfo(6, "Number of grids in each row"),   
 
 }))
 
diff --git a/modules/ui.py b/modules/ui.py
index 43dc88fc..85abac4d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1548,7 +1548,7 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (images_history, "History", "images_history"),
+        (images_history, "Image Browser", "images_history"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
-- 
cgit v1.2.3


From 433a7525c1f5eb5963340e0cc45d31038ede3f7e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 18 Oct 2022 15:18:02 +0300
Subject: remove shared option for update check (because it is not an argument
 of webui) have launch.py examine both COMMANDLINE_ARGS as well as argv for
 its arguments

---
 modules/shared.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 50dc46ae..c0d87168 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -76,7 +76,6 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-parser.add_argument("--update-check", action='store_true', help="enable http check to confirm that the currently running version is the most recent release.", default=False)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.3


From 2f448d97a9427f9a7bad19cf608561b2878ab2da Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Mon, 17 Oct 2022 23:18:21 +0900
Subject: styles.csv encoding utf8 to utf-8-sig

utf-8-bom for better compatibility for some programs
---
 modules/styles.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/styles.py b/modules/styles.py
index d44dfc1a..3bf5c5b6 100644
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -45,7 +45,7 @@ class StyleDatabase:
         if not os.path.exists(path):
             return
 
-        with open(path, "r", encoding="utf8", newline='') as file:
+        with open(path, "r", encoding="utf-8-sig", newline='') as file:
             reader = csv.DictReader(file)
             for row in reader:
                 # Support loading old CSV format with "name, text"-columns
@@ -79,7 +79,7 @@ class StyleDatabase:
     def save_styles(self, path: str) -> None:
         # Write to temporary file first, so we don't nuke the file if something goes wrong
         fd, temp_path = tempfile.mkstemp(".csv")
-        with os.fdopen(fd, "w", encoding="utf8", newline='') as file:
+        with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
             # _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
             # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
             writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
-- 
cgit v1.2.3


From e20b7e30fe17744acb74ad33c87c0963525ea921 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 18 Oct 2022 15:33:24 +0300
Subject: fix for add difference model merging

---
 modules/extras.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index c908b43e..03f6085e 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -216,8 +216,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
     if theta_func1:
         for key in tqdm.tqdm(theta_1.keys()):
             if 'model' in key:
-                t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
-                theta_1[key] = theta_func1(theta_1[key], t2)
+                if key in theta_2:
+                    t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
+                    theta_1[key] = theta_func1(theta_1[key], t2)
+                else:
+                    theta_1[key] = 0
     del theta_2, teritary_model
 
     for key in tqdm.tqdm(theta_0.keys()):
-- 
cgit v1.2.3


From ec1924ee5789b72c31c65932b549c59ccae0cdd6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 18 Oct 2022 16:05:52 +0300
Subject: additional fix for difference model merging

---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 03f6085e..b853fa5b 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -220,7 +220,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
                     t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
                     theta_1[key] = theta_func1(theta_1[key], t2)
                 else:
-                    theta_1[key] = 0
+                    theta_1[key] = torch.zeros_like(theta_1[key])
     del theta_2, teritary_model
 
     for key in tqdm.tqdm(theta_0.keys()):
-- 
cgit v1.2.3


From b7e78ef692fe912916de6e54f6e2521b000d650c Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 18 Oct 2022 22:21:54 +0800
Subject: Image browser improve

---
 modules/images_history.py | 43 ++++++++++++++++++++++---------------------
 1 file changed, 22 insertions(+), 21 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index d56f3a25..a40cdc0e 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -100,14 +100,15 @@ def auto_sorting(dir_name):
         date_list.append(today)
     return sorted(date_list, reverse=True)
 
-def archive_images(dir_name, date_to):
-    
+def archive_images(dir_name, date_to):    
     filenames = []   
-    loads_num =int(opts.images_history_num_per_page * opts.images_history_pages_num)
+    batch_size =int(opts.images_history_num_per_page * opts.images_history_pages_num)
+    if batch_size <= 0:
+        batch_size = opts.images_history_num_per_page * 6
     today = time.strftime("%Y%m%d",time.localtime(time.time()))
     date_to = today if date_to is None  or date_to == "" else date_to
     date_to_bak = date_to 
-    if opts.images_history_reconstruct_directory:        
+    if False: #opts.images_history_reconstruct_directory:        
         date_list = auto_sorting(dir_name)               
         for date in date_list:
             if date <= date_to:
@@ -115,11 +116,13 @@ def archive_images(dir_name, date_to):
                 if date == today and not os.path.exists(path):
                     continue
                 filenames = traverse_all_files(path, filenames)
-            if len(filenames) > loads_num:            
+            if len(filenames) > batch_size:            
                 break
         filenames = sorted(filenames, key=lambda file: -os.path.getmtime(file))
     else:
-        filenames = traverse_all_files(dir_name, filenames)       
+        filenames = traverse_all_files(dir_name, filenames)  
+        total_num = len(filenames) 
+        batch_count = len(filenames) + 1 //  batch_size + 1
         tmparray = [(os.path.getmtime(file), file) for file in filenames ]
         date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400      
         filenames = []
@@ -132,8 +135,8 @@ def archive_images(dir_name, date_to):
                 filenames.append((t, f ,date))
         date_list = sorted(list(date_list.keys()), reverse=True)
         sort_array = sorted(filenames, key=lambda x:-x[0])
-        if len(sort_array) > loads_num:
-            date = sort_array[loads_num][2]
+        if len(sort_array) > batch_size:
+            date = sort_array[batch_size][2]
             filenames = [x[1] for x in sort_array]
         else:
             date =  date_to if len(sort_array) == 0 else sort_array[-1][2]
@@ -141,9 +144,9 @@ def archive_images(dir_name, date_to):
         filenames = [x[1] for x in sort_array if x[2]>= date]   
     num = len(filenames)  
     last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000))
-    date = date[:4] + "-" + date[4:6] + "-" + date[6:8]
-    date_to_bak = date_to_bak[:4] + "-" + date_to_bak[4:6] + "-" + date_to_bak[6:8]
-    load_info = f"Loaded {(num + 1) // opts.images_history_pages_num} pades, {num} images, during {date} - {date_to_bak}"
+    date = date[:4] + "/" + date[4:6] + "/" + date[6:8]
+    date_to_bak = date_to_bak[:4] + "/" + date_to_bak[4:6] + "/" + date_to_bak[6:8]
+    load_info = f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages"
     _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames)
     return (
         gradio.Dropdown.update(choices=date_list, value=date_to),
@@ -154,12 +157,10 @@ def archive_images(dir_name, date_to):
         "",
         "",
         visible_num, 
-        last_date_from
+        last_date_from, 
+        #gradio.update(visible=batch_count > 1)
     )
 
-
-
-
 def delete_image(delete_num, name, filenames, image_index, visible_num):
     if name == "":
         return filenames, delete_num
@@ -295,16 +296,16 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
     with gr.Column() as page_panel:            
             with gr.Row():  
-                img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory") 
+                img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir) 
             with gr.Row(visible=False) as warning:                 
                 warning_box = gr.Textbox("Message", interactive=False)                        
 
             with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel:
                 with gr.Column(scale=2):                   
-                    with gr.Row():                    
-                        backward = gr.Button('Backward')         
-                        date_to = gr.Dropdown(label="Date to") 
-                        forward = gr.Button('Forward')  
+                    with gr.Row() as batch_panel:                    
+                        forward = gr.Button('Forward')        
+                        date_to = gr.Dropdown(label="Date to")
+                        backward = gr.Button('Backward')                           
                         newest = gr.Button('Reload', elem_id=tabname + "_images_history_start")   
                     with gr.Row():
                         load_info = gr.Textbox(show_label=False, interactive=False)  
@@ -335,7 +336,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
                             
                         # hiden items
-                        with gr.Row(): #visible=False):   
+                        with gr.Row(visible=False):   
                             visible_img_num = gr.Number()                     
                             date_to_recorder = gr.State([])
                             last_date_from = gr.Textbox()
-- 
cgit v1.2.3


From cbf15edbf90a68a08eeab40af5df577ba4ac90b6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 18 Oct 2022 17:23:38 +0300
Subject: remove dependence on TQDM for sampler progress/interrupt
 functionality

---
 modules/processing.py  |   6 ---
 modules/sd_samplers.py | 107 +++++++++++++++++++++++++++----------------------
 2 files changed, 58 insertions(+), 55 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index deb6125e..346eea88 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -402,12 +402,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             with devices.autocast():
                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
 
-            if state.interrupted or state.skipped:
-
-                # if we are interrupted, sample returns just noise
-                # use the image collected previously in sampler loop
-                samples_ddim = shared.state.current_latent
-
             samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 20309e06..b58e810b 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -98,25 +98,8 @@ def store_latent(decoded):
             shared.state.current_image = sample_to_image(decoded)
 
 
-
-def extended_tdqm(sequence, *args, desc=None, **kwargs):
-    state.sampling_steps = len(sequence)
-    state.sampling_step = 0
-
-    seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
-    for x in seq:
-        if state.interrupted or state.skipped:
-            break
-
-        yield x
-
-        state.sampling_step += 1
-        shared.total_tqdm.update()
-
-
-ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
-ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
+class InterruptedException(BaseException):
+    pass
 
 
 class VanillaStableDiffusionSampler:
@@ -128,14 +111,32 @@ class VanillaStableDiffusionSampler:
         self.init_latent = None
         self.sampler_noises = None
         self.step = 0
+        self.stop_at = None
         self.eta = None
         self.default_eta = 0.0
         self.config = None
+        self.last_latent = None
 
     def number_of_needed_noises(self, p):
         return 0
 
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except InterruptedException:
+            return self.last_latent
+
     def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+        if state.interrupted or state.skipped:
+            raise InterruptedException
+
+        if self.stop_at is not None and self.step > self.stop_at:
+            raise InterruptedException
+
+
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
 
@@ -159,11 +160,16 @@ class VanillaStableDiffusionSampler:
         res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
 
         if self.mask is not None:
-            store_latent(self.init_latent * self.mask + self.nmask * res[1])
+            self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
         else:
-            store_latent(res[1])
+            self.last_latent = res[1]
+
+        store_latent(self.last_latent)
 
         self.step += 1
+        state.sampling_step = self.step
+        shared.total_tqdm.update()
+
         return res
 
     def initialize(self, p):
@@ -192,7 +198,7 @@ class VanillaStableDiffusionSampler:
         self.init_latent = x
         self.step = 0
 
-        samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
+        samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
 
         return samples
 
@@ -206,9 +212,9 @@ class VanillaStableDiffusionSampler:
 
         # existing code fails with certain step counts, like 9
         try:
-            samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
+            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
         except Exception:
-            samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
+            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
 
         return samples_ddim
 
@@ -223,6 +229,9 @@ class CFGDenoiser(torch.nn.Module):
         self.step = 0
 
     def forward(self, x, sigma, uncond, cond, cond_scale):
+        if state.interrupted or state.skipped:
+            raise InterruptedException
+
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
 
@@ -268,25 +277,6 @@ class CFGDenoiser(torch.nn.Module):
         return denoised
 
 
-def extended_trange(sampler, count, *args, **kwargs):
-    state.sampling_steps = count
-    state.sampling_step = 0
-
-    seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
-    for x in seq:
-        if state.interrupted or state.skipped:
-            break
-
-        if sampler.stop_at is not None and x > sampler.stop_at:
-            break
-
-        yield x
-
-        state.sampling_step += 1
-        shared.total_tqdm.update()
-
-
 class TorchHijack:
     def __init__(self, kdiff_sampler):
         self.kdiff_sampler = kdiff_sampler
@@ -314,9 +304,28 @@ class KDiffusionSampler:
         self.eta = None
         self.default_eta = 1.0
         self.config = None
+        self.last_latent = None
 
     def callback_state(self, d):
-        store_latent(d["denoised"])
+        step = d['i']
+        latent = d["denoised"]
+        store_latent(latent)
+        self.last_latent = latent
+
+        if self.stop_at is not None and step > self.stop_at:
+            raise InterruptedException
+
+        state.sampling_step = step
+        shared.total_tqdm.update()
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except InterruptedException:
+            return self.last_latent
 
     def number_of_needed_noises(self, p):
         return p.steps
@@ -339,9 +348,6 @@ class KDiffusionSampler:
         self.sampler_noise_index = 0
         self.eta = p.eta or opts.eta_ancestral
 
-        if hasattr(k_diffusion.sampling, 'trange'):
-            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
-
         if self.sampler_noises is not None:
             k_diffusion.sampling.torch = TorchHijack(self)
 
@@ -383,8 +389,9 @@ class KDiffusionSampler:
 
         self.model_wrap_cfg.init_latent = x
 
-        return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
+        return samples
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
         steps = steps or p.steps
@@ -406,6 +413,8 @@ class KDiffusionSampler:
                 extra_params_kwargs['n'] = steps
         else:
             extra_params_kwargs['sigmas'] = sigmas
-        samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
         return samples
 
-- 
cgit v1.2.3


From 6021f7a75f7b5208a2be15cda5526028152f922d Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 00:51:36 +0900
Subject: add options to custom hypernetwork layer structure

---
 modules/hypernetworks/hypernetwork.py | 88 ++++++++++++++++++++++++++---------
 modules/shared.py                     |  4 +-
 2 files changed, 70 insertions(+), 22 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4905710e..cadb9911 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -1,52 +1,98 @@
+import csv
 import datetime
 import glob
 import html
 import os
 import sys
 import traceback
-import tqdm
-import csv
 
+import modules.textual_inversion.dataset
 import torch
-
-from ldm.util import default
-from modules import devices, shared, processing, sd_models
-import torch
-from torch import einsum
+import tqdm
 from einops import rearrange, repeat
-import modules.textual_inversion.dataset
+from ldm.util import default
+from modules import devices, processing, sd_models, shared
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
+from torch import einsum
+
+
+def parse_layer_structure(dim, state_dict):
+    i = 0
+    res = [1]
+    while (key := "linear.{}.weight".format(i)) in state_dict:
+        weight = state_dict[key]
+        res.append(len(weight) // dim)
+        i += 1
+    return res
 
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
+    layer_structure = None
+    add_layer_norm = False
 
     def __init__(self, dim, state_dict=None):
         super().__init__()
+        if (state_dict is None or 'linear.0.weight' not in state_dict) and self.layer_structure is None:
+            layer_structure = (1, 2, 1)
+        else:
+            if self.layer_structure is not None:
+                assert self.layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
+                assert self.layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
+                layer_structure = self.layer_structure
+            else:
+                layer_structure = parse_layer_structure(dim, state_dict)
+
+        linears = []
+        for i in range(len(layer_structure) - 1):
+            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
+            if self.add_layer_norm:
+                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
-        self.linear1 = torch.nn.Linear(dim, dim * 2)
-        self.linear2 = torch.nn.Linear(dim * 2, dim)
+        self.linear = torch.nn.Sequential(*linears)
 
         if state_dict is not None:
-            self.load_state_dict(state_dict, strict=True)
+            try:
+                self.load_state_dict(state_dict)
+            except RuntimeError:
+                self.try_load_previous(state_dict)
         else:
-
-            self.linear1.weight.data.normal_(mean=0.0, std=0.01)
-            self.linear1.bias.data.zero_()
-            self.linear2.weight.data.normal_(mean=0.0, std=0.01)
-            self.linear2.bias.data.zero_()
+            for layer in self.linear:
+                layer.weight.data.normal_(mean = 0.0, std = 0.01)
+                layer.bias.data.zero_()
 
         self.to(devices.device)
 
+    def try_load_previous(self, state_dict):
+        states = self.state_dict()
+        states['linear.0.bias'].copy_(state_dict['linear1.bias'])
+        states['linear.0.weight'].copy_(state_dict['linear1.weight'])
+        states['linear.1.bias'].copy_(state_dict['linear2.bias'])
+        states['linear.1.weight'].copy_(state_dict['linear2.weight'])
+
     def forward(self, x):
-        return x + (self.linear2(self.linear1(x))) * self.multiplier
+        return x + self.linear(x) * self.multiplier
+
+    def trainables(self):
+        res = []
+        for layer in self.linear:
+            res += [layer.weight, layer.bias]
+        return res
 
 
 def apply_strength(value=None):
     HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
 
 
+def apply_layer_structure(value=None):
+    HypernetworkModule.layer_structure = value if value is not None else shared.opts.sd_hypernetwork_layer_structure
+
+
+def apply_layer_norm(value=None):
+    HypernetworkModule.add_layer_norm = value if value is not None else shared.opts.sd_hypernetwork_add_layer_norm
+
+
 class Hypernetwork:
     filename = None
     name = None
@@ -68,7 +114,7 @@ class Hypernetwork:
         for k, layers in self.layers.items():
             for layer in layers:
                 layer.train()
-                res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
+                res += layer.trainables()
 
         return res
 
@@ -226,7 +272,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
-
+        assert ds.length > 1, "Dataset should contain more than 1 images"
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
@@ -261,7 +307,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         with torch.autocast("cuda"):
             c = stack_conds([entry.cond for entry in entries]).to(devices.device)
-#            c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
+            c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
             x = torch.stack([entry.latent for entry in entries]).to(devices.device)
             loss = shared.sd_model(x, c)[0]
             del x
@@ -283,7 +329,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{mean_loss:.7f}",
-            "learn_rate": scheduler.learn_rate
+            "learn_rate": f"{scheduler.learn_rate:.7f}"
         })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
diff --git a/modules/shared.py b/modules/shared.py
index c0d87168..c87ce70e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models, localization
+from modules import sd_models, sd_samplers, localization
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -258,6 +258,8 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork_layer_structure": OptionInfo(None, "Hypernetwork layer structure Default: (1,2,1).", gr.Dropdown, lambda: {"choices": [(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)]}),
+    "sd_hypernetwork_add_layer_norm": OptionInfo(False, "Add layer normalization to hypernetwork architecture."),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
-- 
cgit v1.2.3


From a5611ea5026bd8e12d8e84023384c369d0511dda Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 01:00:01 +0900
Subject: update

---
 modules/hypernetworks/hypernetwork.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index cadb9911..c5835bce 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -1,20 +1,22 @@
-import csv
 import datetime
 import glob
 import html
 import os
 import sys
 import traceback
+import tqdm
+import csv
 
-import modules.textual_inversion.dataset
 import torch
-import tqdm
-from einops import rearrange, repeat
+
 from ldm.util import default
-from modules import devices, processing, sd_models, shared
+from modules import devices, shared, processing, sd_models
+import torch
+from torch import einsum
+from einops import rearrange, repeat
+import modules.textual_inversion.dataset
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
-from torch import einsum
 
 
 def parse_layer_structure(dim, state_dict):
-- 
cgit v1.2.3


From 7f2095c6c8db82a5c9cd7c7177f6ba856a2cc676 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 01:01:22 +0900
Subject: update

---
 modules/shared.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index c87ce70e..6b6d5c41 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_models, sd_samplers, localization
+from modules import sd_samplers, sd_models, localization
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -135,7 +135,7 @@ class State:
         self.job_no += 1
         self.sampling_step = 0
         self.current_image_sampling_step = 0
-        
+
     def get_job_timestamp(self):
         return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
 
-- 
cgit v1.2.3


From e40ba281f1b419cf99552962ea01d87d699840a5 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 01:03:58 +0900
Subject: update

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index c5835bce..082165f4 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -309,7 +309,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         with torch.autocast("cuda"):
             c = stack_conds([entry.cond for entry in entries]).to(devices.device)
-            c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
+            # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
             x = torch.stack([entry.latent for entry in entries]).to(devices.device)
             loss = shared.sd_model(x, c)[0]
             del x
@@ -331,7 +331,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{mean_loss:.7f}",
-            "learn_rate": f"{scheduler.learn_rate:.7f}"
+            "learn_rate": scheduler.learn_rate
         })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-- 
cgit v1.2.3


From e7f4808505f7a6339927c32b9a0c01bc9134bdeb Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Tue, 18 Oct 2022 19:04:56 +0000
Subject: provide sampler by name

---
 modules/api/api.py        | 12 ++++++++++--
 modules/api/processing.py | 16 ++++++++++++++--
 2 files changed, 24 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index ce98cb8c..ff9df0d1 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,14 +1,17 @@
 from modules.api.processing import StableDiffusionProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, process_images
+from modules.sd_samplers import samplers_k_diffusion
 import modules.shared as shared
 import uvicorn
-from fastapi import Body, APIRouter
+from fastapi import Body, APIRouter, HTTPException
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field, Json
 import json
 import io
 import base64
 
+sampler_to_index = lambda name: next(filter(lambda row: name in row[1][2], enumerate(samplers_k_diffusion)), None)
+
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
@@ -23,9 +26,14 @@ class Api:
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
+        sampler_index = sampler_to_index(txt2imgreq.sampler_index)
+        
+        if sampler_index is None:
+            raise HTTPException(status_code=404, detail="Sampler not found") 
+        
         populate = txt2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model, 
-            "sampler_index": 0,
+            "sampler_index": sampler_index[0],
             "do_not_save_samples": True,
             "do_not_save_grid": True
             }
diff --git a/modules/api/processing.py b/modules/api/processing.py
index b6798241..2e6483ee 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -42,7 +42,8 @@ class PydanticModelGenerator:
     def __init__(
         self,
         model_name: str = None,
-        class_instance = None
+        class_instance = None,
+        additional_fields = None,
     ):
         def field_type_generator(k, v):
             # field_type = str if not overrides.get(k) else overrides[k]["type"]
@@ -70,6 +71,13 @@ class PydanticModelGenerator:
             )
             for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
         ]
+        
+        for fields in additional_fields:
+            self._model_def.append(ModelDef(
+                field=underscore(fields["key"]), 
+                field_alias=fields["key"], 
+                field_type=fields["type"],
+                field_value=fields["default"]))
 
     def generate_model(self):
         """
@@ -84,4 +92,8 @@ class PydanticModelGenerator:
         DynamicModel.__config__.allow_mutation = True
         return DynamicModel
     
-StableDiffusionProcessingAPI = PydanticModelGenerator("StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img).generate_model()
+StableDiffusionProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingTxt2Img", 
+    StableDiffusionProcessingTxt2Img,
+    [{"key": "sampler_index", "type": str, "default": "k_euler_a"}]
+).generate_model()
-- 
cgit v1.2.3


From 538bc89c269743e56b07ef2b471d1ce0a39b6776 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Wed, 19 Oct 2022 11:27:51 +0800
Subject: Image browser improved

---
 modules/images_history.py | 135 +++++++++++++++++++++++++---------------------
 modules/shared.py         |   5 ++
 modules/ui.py             |   2 +-
 3 files changed, 80 insertions(+), 62 deletions(-)

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
index a40cdc0e..78fd0543 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -4,7 +4,9 @@ import time
 import hashlib
 import gradio
 system_bak_path = "webui_log_and_bak"
-browser_tabname = "custom"
+custom_tab_name = "custom fold"
+faverate_tab_name = "favorites"
+tabs_list = ["txt2img", "img2img", "extras", faverate_tab_name]
 def is_valid_date(date):
     try:
         time.strptime(date, "%Y%m%d")
@@ -122,7 +124,6 @@ def archive_images(dir_name, date_to):
     else:
         filenames = traverse_all_files(dir_name, filenames)  
         total_num = len(filenames) 
-        batch_count = len(filenames) + 1 //  batch_size + 1
         tmparray = [(os.path.getmtime(file), file) for file in filenames ]
         date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400      
         filenames = []
@@ -146,10 +147,12 @@ def archive_images(dir_name, date_to):
     last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000))
     date = date[:4] + "/" + date[4:6] + "/" + date[6:8]
     date_to_bak = date_to_bak[:4] + "/" + date_to_bak[4:6] + "/" + date_to_bak[6:8]
-    load_info = f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages"
+    load_info = "<div style='color:#999' align='center'>"
+    load_info += f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages"
+    load_info += "</div>"
     _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames)
     return (
-        gradio.Dropdown.update(choices=date_list, value=date_to),
+        date_to,
         load_info,
         filenames,        
         1,
@@ -158,7 +161,7 @@ def archive_images(dir_name, date_to):
         "",
         visible_num, 
         last_date_from, 
-        #gradio.update(visible=batch_count > 1)
+        gradio.update(visible=total_num > num)
     )
 
 def delete_image(delete_num, name, filenames, image_index, visible_num):
@@ -209,7 +212,7 @@ def get_recent_images(page_index, step, filenames):
     visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num
     return page_index, image_list,  "", "",  visible_num
 
-def newest_click(date_to):
+def loac_batch_click(date_to):
     if date_to is None:
         return time.strftime("%Y%m%d",time.localtime(time.time())), []
     else:
@@ -248,7 +251,7 @@ def page_index_change(page_index, filenames):
 
 def show_image_info(tabname_box, num, page_index, filenames):
     file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))]   
-    tm =   time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file)))
+    tm =   "<div style='color:#999' align='right'>" + time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file))) + "</div>"
     return file, tm, num, file
 
 def enable_page_buttons():
@@ -268,9 +271,9 @@ def change_dir(img_dir, date_to):
         warning = "The format of the directory is incorrect"
     if warning is None:        
         today = time.strftime("%Y%m%d",time.localtime(time.time()))
-        return gradio.update(visible=False), gradio.update(visible=True), None,   None if date_to != today else today
+        return gradio.update(visible=False), gradio.update(visible=True), None,   None if date_to != today else today, gradio.update(visible=True), gradio.update(visible=True)
     else:
-        return gradio.update(visible=True), gradio.update(visible=False), warning, date_to
+        return gradio.update(visible=True), gradio.update(visible=False), warning, date_to, gradio.update(visible=False), gradio.update(visible=False)
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     custom_dir = False
@@ -280,7 +283,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         dir_name = opts.outdir_img2img_samples
     elif tabname == "extras":
         dir_name = opts.outdir_extras_samples
-    elif tabname == "saved":
+    elif tabname == faverate_tab_name:
         dir_name = opts.outdir_save
     else:
         custom_dir = True
@@ -295,22 +298,26 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
             os.makedirs(dir_name)
 
     with gr.Column() as page_panel:            
-            with gr.Row():  
-                img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir) 
+            with gr.Row():                
+                with gr.Column(scale=1, visible=not custom_dir) as load_batch_box: 
+                    load_batch = gr.Button('Load', elem_id=tabname + "_images_history_start", full_width=True)    
+                with gr.Column(scale=4): 
+                    with gr.Row():                        
+                        img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir)  
+            with gr.Row():
+                with gr.Column(visible=False, scale=1) as batch_panel: 
+                    with gr.Row():
+                        forward = gr.Button('Prev batch')
+                        backward = gr.Button('Next batch')
+                with gr.Column(scale=3):
+                    load_info =  gr.HTML(visible=not custom_dir)   
             with gr.Row(visible=False) as warning:                 
                 warning_box = gr.Textbox("Message", interactive=False)                        
 
             with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel:
-                with gr.Column(scale=2):                   
-                    with gr.Row() as batch_panel:                    
-                        forward = gr.Button('Forward')        
-                        date_to = gr.Dropdown(label="Date to")
-                        backward = gr.Button('Backward')                           
-                        newest = gr.Button('Reload', elem_id=tabname + "_images_history_start")   
-                    with gr.Row():
-                        load_info = gr.Textbox(show_label=False, interactive=False)  
-                    with gr.Row(visible=False) as turn_page_buttons:
-                        renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
+                with gr.Column(scale=2):                     
+                    with gr.Row(visible=True) as turn_page_buttons:                        
+                        #date_to = gr.Dropdown(label="Date to")                          
                         first_page = gr.Button('First Page')
                         prev_page = gr.Button('Prev Page')
                         page_index = gr.Number(value=1, label="Page Index")
@@ -322,50 +329,54 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                         delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
                         delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
                         
-                with gr.Column():
-                    with gr.Row():
-                        if tabname != "saved":
-                            save_btn = gr.Button('Save')
-                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                with gr.Column():                    
                     with gr.Row():
                         with gr.Column():
-                            img_file_info = gr.Textbox(label="Generate Info", interactive=False)
+                            img_file_info = gr.Textbox(label="Generate Info", interactive=False, lines=6)
+                            gr.HTML("<hr>")                           
                             img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
-                            img_file_time= gr.Textbox(value="", label="Create Time", interactive=False)
-
+                            img_file_time= gr.HTML()
+                    with gr.Row():
+                        if tabname != faverate_tab_name:
+                            save_btn = gr.Button('Collect')
+                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                           
                             
-                        # hiden items
-                        with gr.Row(visible=False):   
-                            visible_img_num = gr.Number()                     
-                            date_to_recorder = gr.State([])
-                            last_date_from = gr.Textbox()
-                            tabname_box = gr.Textbox(tabname)
-                            image_index = gr.Textbox(value=-1)
-                            set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
-                            filenames = gr.State()
-                            all_images_list = gr.State()
-                            hidden = gr.Image(type="pil")
-                            info1 = gr.Textbox()
-                            info2 = gr.Textbox()
-
-    img_path.submit(change_dir, inputs=[img_path, date_to], outputs=[warning, main_panel, warning_box, date_to])
-    #change date
-    change_date_output = [date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from]  
+                    # hiden items
+                    with gr.Row(visible=False): 
+                        renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
+                        batch_date_to = gr.Textbox(label="Date to")  
+                        visible_img_num = gr.Number()                     
+                        date_to_recorder = gr.State([])
+                        last_date_from = gr.Textbox()
+                        tabname_box = gr.Textbox(tabname)
+                        image_index = gr.Textbox(value=-1)
+                        set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
+                        filenames = gr.State()
+                        all_images_list = gr.State()
+                        hidden = gr.Image(type="pil")
+                        info1 = gr.Textbox()
+                        info2 = gr.Textbox()
+
+    img_path.submit(change_dir, inputs=[img_path, batch_date_to], outputs=[warning, main_panel, warning_box, batch_date_to, load_batch_box, load_info])
+
+    #change batch
+    change_date_output = [batch_date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from,  batch_panel]  
    
-    date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output)   
-    date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])  
-    date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")  
+    batch_date_to.change(archive_images, inputs=[img_path, batch_date_to], outputs=change_date_output)   
+    batch_date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])  
+    batch_date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")  
 
-    newest.click(newest_click, inputs=[date_to], outputs=[date_to, date_to_recorder])
-    forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder])
-    backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder])
+    load_batch.click(loac_batch_click, inputs=[batch_date_to], outputs=[batch_date_to, date_to_recorder])
+    forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
+    backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
 
 
     #delete
     delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num])
     delete.click(fn=None, _js="images_history_delete", inputs=[delete_num, tabname_box, image_index], outputs=None) 
-    if tabname != "saved": 
+    if tabname != faverate_tab_name: 
         save_btn.click(save_image, inputs=[img_file_name], outputs=None)     
 
     #turn page
@@ -394,18 +405,20 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
    
 
-def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict):
+def create_history_tabs(gr, sys_opts, cmp_ops, run_pnginfo, switch_dict):
     global opts;
     opts = sys_opts
     loads_files_num = int(opts.images_history_num_per_page)
     num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num)
+    if cmp_ops.browse_all_images:
+        tabs_list.append(custom_tab_name)
     with gr.Blocks(analytics_enabled=False) as images_history:
         with gr.Tabs() as tabs:
-            for tab in [browser_tabname, "txt2img", "img2img", "extras", "saved"]:
+            for tab in tabs_list:
                 with gr.Tab(tab):
                     with gr.Blocks(analytics_enabled=False) :
-                        show_images_history(gr, opts, tab, run_pnginfo, switch_dict) 
-        #gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False)
-        gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_finish_render", visible=False)
-    
+                        show_images_history(gr, opts, tab, run_pnginfo, switch_dict)
+        gradio.Checkbox(opts.images_history_preload, elem_id="images_history_preload", visible=False)         
+        gradio.Textbox(",".join(tabs_list), elem_id="images_history_tabnames_list", visible=False)    
+        
     return images_history
diff --git a/modules/shared.py b/modules/shared.py
index 1811018d..4d735414 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -74,6 +74,10 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
+parser.add_argument("--browse-all-images", action='store_true', help="Allow browsing all images by Image Browser", default=False)
+
+
+cmd_opts = parser.parse_args()
 
 
 cmd_opts = parser.parse_args()
@@ -311,6 +315,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
 
 options_templates.update(options_section(('images-history', "Images Browser"), {
     #"images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"),
+    "images_history_preload": OptionInfo(False, "Preload images at startup"),
     "images_history_num_per_page": OptionInfo(36, "Number of pictures displayed on each page"),
     "images_history_pages_num": OptionInfo(6, "Minimum number of pages per load "),   
     "images_history_grid_num": OptionInfo(6, "Number of grids in each row"),   
diff --git a/modules/ui.py b/modules/ui.py
index 85abac4d..88f46659 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1150,7 +1150,7 @@ def create_ui(wrap_gradio_gpu_call):
         "i2i":img2img_paste_fields
     }
 
-    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
+    images_history = img_his.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
-- 
cgit v1.2.3


From 0f0d6ab8e06898ce066251fc769fe14e77e98ced Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Wed, 19 Oct 2022 05:19:01 +0000
Subject: call sampler by name

---
 modules/api/api.py        | 11 ++++++-----
 modules/api/processing.py |  6 +++---
 2 files changed, 9 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index ff9df0d1..5b0c934e 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,6 +1,7 @@
 from modules.api.processing import StableDiffusionProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, process_images
-from modules.sd_samplers import samplers_k_diffusion
+from modules.sd_samplers import all_samplers
+from modules.extras import run_pnginfo
 import modules.shared as shared
 import uvicorn
 from fastapi import Body, APIRouter, HTTPException
@@ -10,7 +11,7 @@ import json
 import io
 import base64
 
-sampler_to_index = lambda name: next(filter(lambda row: name in row[1][2], enumerate(samplers_k_diffusion)), None)
+sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
@@ -53,13 +54,13 @@ class Api:
         
         
 
-    def img2imgendoint(self):
+    def img2imgapi(self):
         raise NotImplementedError
 
-    def extrasendoint(self):
+    def extrasapi(self):
         raise NotImplementedError
 
-    def pnginfoendoint(self):
+    def pnginfoapi(self):
         raise NotImplementedError
 
     def launch(self, server_name, port):
diff --git a/modules/api/processing.py b/modules/api/processing.py
index 2e6483ee..4c541241 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -1,7 +1,7 @@
 from inflection import underscore
 from typing import Any, Dict, Optional
 from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+from modules.processing import StableDiffusionProcessingTxt2Img
 import inspect
 
 
@@ -95,5 +95,5 @@ class PydanticModelGenerator:
 StableDiffusionProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingTxt2Img", 
     StableDiffusionProcessingTxt2Img,
-    [{"key": "sampler_index", "type": str, "default": "k_euler_a"}]
-).generate_model()
+    [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()
\ No newline at end of file
-- 
cgit v1.2.3


From 10aca1ca3e81e69e08f556a500c3dc603451429b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 19 Oct 2022 08:42:22 +0300
Subject: more careful loading of model weights (eliminates some issues with
 checkpoints that have weird cond_stage_model layer names)

---
 modules/sd_models.py | 28 +++++++++++++++++++++++++---
 1 file changed, 25 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 3aa21ec1..7ad6d474 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -122,11 +122,33 @@ def select_checkpoint():
     return checkpoint_info
 
 
+chckpoint_dict_replacements = {
+    'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
+    'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
+    'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
+}
+
+
+def transform_checkpoint_dict_key(k):
+    for text, replacement in chckpoint_dict_replacements.items():
+        if k.startswith(text):
+            k = replacement + k[len(text):]
+
+    return k
+
+
 def get_state_dict_from_checkpoint(pl_sd):
     if "state_dict" in pl_sd:
-        return pl_sd["state_dict"]
+        pl_sd = pl_sd["state_dict"]
+
+    sd = {}
+    for k, v in pl_sd.items():
+        new_key = transform_checkpoint_dict_key(k)
+
+        if new_key is not None:
+            sd[new_key] = v
 
-    return pl_sd
+    return sd
 
 
 def load_model_weights(model, checkpoint_info):
@@ -141,7 +163,7 @@ def load_model_weights(model, checkpoint_info):
             print(f"Global Step: {pl_sd['global_step']}")
 
         sd = get_state_dict_from_checkpoint(pl_sd)
-        model.load_state_dict(sd, strict=False)
+        missing, extra = model.load_state_dict(sd, strict=False)
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)
-- 
cgit v1.2.3


From da72becb13e4b750fbcb3d158c3f843311ef9938 Mon Sep 17 00:00:00 2001
From: Silent <16026653+s-ilent@users.noreply.github.com>
Date: Wed, 19 Oct 2022 16:14:33 +1030
Subject: Use training width/height when training hypernetworks.

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 modules/ui.py                         | 2 ++
 2 files changed, 4 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4905710e..b8695fc1 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -196,7 +196,7 @@ def stack_conds(conds):
 
     return torch.stack(conds)
 
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -225,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
diff --git a/modules/ui.py b/modules/ui.py
index fb6eb5a0..ca46343f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1341,6 +1341,8 @@ def create_ui(wrap_gradio_gpu_call):
                 batch_size,
                 dataset_directory,
                 log_directory,
+                training_width,
+                training_height,
                 steps,
                 create_image_every,
                 save_embedding_every,
-- 
cgit v1.2.3


From 2fd7935ef4ed296db5dfd8c7fea99244816f8cf0 Mon Sep 17 00:00:00 2001
From: Cheka <carlos93@hotmail.cl>
Date: Tue, 18 Oct 2022 20:28:28 -0300
Subject: Remove wrong self reference in CUDA support for invokeai

---
 modules/sd_hijack_optimizations.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index a3345bb9..98123fbf 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -181,7 +181,7 @@ def einsum_op_cuda(q, k, v):
     mem_free_torch = mem_reserved - mem_active
     mem_free_total = mem_free_cuda + mem_free_torch
     # Divide factor of safety as there's copying and fragmentation
-    return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
+    return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
 
 def einsum_op(q, k, v):
     if q.device.type == 'cuda':
-- 
cgit v1.2.3


From bcfbb33e50a48b237d8d961cc2be038db53774d5 Mon Sep 17 00:00:00 2001
From: Anastasius <anastasiuspernat@gmail.com>
Date: Mon, 17 Oct 2022 13:35:20 -0700
Subject: Added time left estimation

---
 modules/ui.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index ca46343f..9a54aa16 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -261,6 +261,15 @@ def wrap_gradio_call(func, extra_outputs=None):
     return f
 
 
+def calc_time_left(progress):
+    if progress == 0:
+        return "N/A"
+    else:
+        time_since_start = time.time() - shared.state.time_start
+        eta = (time_since_start/progress)
+        return time.strftime('%H:%M:%S', time.gmtime(eta-time_since_start))        
+
+
 def check_progress_call(id_part):
     if shared.state.job_count == 0:
         return "", gr_show(False), gr_show(False), gr_show(False)
@@ -272,11 +281,13 @@ def check_progress_call(id_part):
     if shared.state.sampling_steps > 0:
         progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
 
+    time_left = calc_time_left( progress )
+
     progress = min(progress, 1)
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="width:{progress * 100}%">{str(int(progress*100))+"%" if progress > 0.01 else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:hidden;width:{progress * 100}%">{str(int(progress*100))+"% ETA:"+time_left if progress > 0.01 else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
@@ -308,6 +319,7 @@ def check_progress_call_initial(id_part):
     shared.state.current_latent = None
     shared.state.current_image = None
     shared.state.textinfo = None
+    shared.state.time_start = time.time()
 
     return check_progress_call(id_part)
 
-- 
cgit v1.2.3


From 442dbedc159bb7e9cf94f0c3626f8a409e0a50eb Mon Sep 17 00:00:00 2001
From: Anastasius <anastasiuspernat@gmail.com>
Date: Tue, 18 Oct 2022 10:38:07 -0700
Subject: Estimated time displayed if jobs take more 60 sec

---
 modules/ui.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 9a54aa16..fa54110b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -261,13 +261,17 @@ def wrap_gradio_call(func, extra_outputs=None):
     return f
 
 
-def calc_time_left(progress):
+def calc_time_left(progress, threshold, label, force_display):
     if progress == 0:
-        return "N/A"
+        return ""
     else:
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
-        return time.strftime('%H:%M:%S', time.gmtime(eta-time_since_start))        
+        eta_relative = eta-time_since_start
+        if eta_relative > threshold or force_display:              
+            return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))        
+        else:
+            return ""
 
 
 def check_progress_call(id_part):
@@ -281,13 +285,15 @@ def check_progress_call(id_part):
     if shared.state.sampling_steps > 0:
         progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
 
-    time_left = calc_time_left( progress )
+    time_left = calc_time_left( progress, 60, " ETA:", shared.state.time_left_force_display )
+    if time_left != "":
+        shared.state.time_left_force_display = True
 
     progress = min(progress, 1)
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:hidden;width:{progress * 100}%">{str(int(progress*100))+"% ETA:"+time_left if progress > 0.01 else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:hidden;width:{progress * 100}%">{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
@@ -320,6 +326,7 @@ def check_progress_call_initial(id_part):
     shared.state.current_image = None
     shared.state.textinfo = None
     shared.state.time_start = time.time()
+    shared.state.time_left_force_display = False
 
     return check_progress_call(id_part)
 
-- 
cgit v1.2.3


From 1d4aa376e6111e90888a30ae24d2bcd7f978ec51 Mon Sep 17 00:00:00 2001
From: Anastasius <anastasiuspernat@gmail.com>
Date: Tue, 18 Oct 2022 12:42:39 -0700
Subject: Predictable long operation check for time estimation

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index fa54110b..38ba1138 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -268,7 +268,7 @@ def calc_time_left(progress, threshold, label, force_display):
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
         eta_relative = eta-time_since_start
-        if eta_relative > threshold or force_display:              
+        if (eta_relative > threshold and progress > 0.02) or force_display:           
             return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))        
         else:
             return ""
-- 
cgit v1.2.3


From bb0e7232b301d1706bbd0e09367dece3bb7ac07c Mon Sep 17 00:00:00 2001
From: Ikko Ashimine <eltociear@gmail.com>
Date: Wed, 19 Oct 2022 02:18:56 +0900
Subject: Fix typo in prompt_parser.py

assoicated -> associated
---
 modules/prompt_parser.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 919d5d31..f70872c4 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -275,7 +275,7 @@ re_attention = re.compile(r"""
 
 def parse_prompt_attention(text):
     """
-    Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
+    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
     Accepted tokens are:
       (abc) - increases attention to abc by a multiplier of 1.1
       (abc:3.12) - increases attention to abc by a multiplier of 3.12
-- 
cgit v1.2.3


From f894dd552f68bea27476f1f360ab8e79f3a65b4f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 19 Oct 2022 12:45:30 +0300
Subject: fix for broken checkpoint merger

---
 modules/sd_models.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 7ad6d474..eae22e87 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -148,7 +148,10 @@ def get_state_dict_from_checkpoint(pl_sd):
         if new_key is not None:
             sd[new_key] = v
 
-    return sd
+    pl_sd.clear()
+    pl_sd.update(sd)
+
+    return pl_sd
 
 
 def load_model_weights(model, checkpoint_info):
-- 
cgit v1.2.3


From abeec4b63029c2c4151a78fc395d312113881845 Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Wed, 19 Oct 2022 03:18:26 -0700
Subject: Add auto focal point cropping to Preprocess images

This algorithm plots a bunch of points of interest on the source
image and averages their locations to find a center.

Most points come from OpenCV.  One point comes from an
entropy model. OpenCV points account for 50% of the weight and the
entropy based point is the other 50%.

The center of all weighted points is calculated and a bounding box
is drawn as close to centered over that point as possible.
---
 modules/textual_inversion/preprocess.py | 151 ++++++++++++++++++++++++++++++--
 1 file changed, 146 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 886cf0c3..168bfb09 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,5 +1,7 @@
 import os
-from PIL import Image, ImageOps
+import cv2
+import numpy as np
+from PIL import Image, ImageOps, ImageDraw
 import platform
 import sys
 import tqdm
@@ -11,7 +13,7 @@ if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
 
-def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, process_entropy_focus=False):
     try:
         if process_caption:
             shared.interrogator.load()
@@ -21,7 +23,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
             deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
 
-        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
+        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru, process_entropy_focus)
 
     finally:
 
@@ -33,7 +35,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
 
 
 
-def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, process_entropy_focus=False):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
@@ -93,6 +95,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         is_tall = ratio > 1.35
         is_wide = ratio < 1 / 1.35
 
+        processing_option_ran = False
+
         if process_split and is_tall:
             img = img.resize((width, height * img.height // img.width))
 
@@ -101,6 +105,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
 
             bot = img.crop((0, img.height - height, width, img.height))
             save_pic(bot, index)
+
+            processing_option_ran = True
         elif process_split and is_wide:
             img = img.resize((width * img.width // img.height, height))
 
@@ -109,8 +115,143 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
 
             right = img.crop((img.width - width, 0, img.width, height))
             save_pic(right, index)
-        else:
+            
+            processing_option_ran = True
+
+        if process_entropy_focus and (is_tall or is_wide):
+            if is_tall:
+                img = img.resize((width, height * img.height // img.width))
+            else:
+                img = img.resize((width * img.width // img.height, height))
+
+            x_focal_center, y_focal_center = image_central_focal_point(img, width, height)
+
+            # take the focal point and turn it into crop coordinates that try to center over the focal
+            # point but then get adjusted back into the frame
+            y_half = int(height / 2)
+            x_half = int(width / 2)
+
+            x1 = x_focal_center - x_half
+            if x1 < 0:
+                x1 = 0
+            elif x1 + width > img.width:
+                x1 = img.width - width
+
+            y1 = y_focal_center - y_half
+            if y1 < 0:
+                y1 = 0
+            elif y1 + height > img.height:
+                y1 = img.height - height
+
+            x2 = x1 + width
+            y2 = y1 + height
+
+            crop = [x1, y1, x2, y2]
+
+            focal = img.crop(tuple(crop))
+            save_pic(focal, index)
+
+            processing_option_ran = True
+
+        if not processing_option_ran:
             img = images.resize_image(1, img, width, height)
             save_pic(img, index)
 
         shared.state.nextjob()
+
+
+def image_central_focal_point(im, target_width, target_height):
+    focal_points = []
+
+    focal_points.extend(
+        image_focal_points(im)
+    )
+
+    fp_entropy = image_entropy_point(im, target_width, target_height)
+    fp_entropy['weight'] = len(focal_points) + 1 # about half of the weight to entropy
+
+    focal_points.append(fp_entropy)
+
+    weight = 0.0
+    x = 0.0
+    y = 0.0
+    for focal_point in focal_points:
+        weight += focal_point['weight']
+        x += focal_point['x'] * focal_point['weight']
+        y += focal_point['y'] * focal_point['weight']
+    avg_x = round(x // weight)
+    avg_y = round(y // weight)
+
+    return avg_x, avg_y
+
+
+def image_focal_points(im):
+    grayscale = im.convert("L")
+
+    # naive attempt at preventing focal points from collecting at watermarks near the bottom
+    gd = ImageDraw.Draw(grayscale)
+    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
+
+    np_im = np.array(grayscale)
+
+    points = cv2.goodFeaturesToTrack(
+        np_im,
+        maxCorners=50,
+        qualityLevel=0.04,
+        minDistance=min(grayscale.width, grayscale.height)*0.05,
+        useHarrisDetector=False,
+    )
+
+    if points is None:
+        return []
+
+    focal_points = []
+    for point in points:
+        x, y = point.ravel()
+        focal_points.append({
+            'x': x,
+            'y': y,
+            'weight': 1.0
+        })
+
+    return focal_points
+
+
+def image_entropy_point(im, crop_width, crop_height):
+    img = im.copy()
+    # just make it easier to slide the test crop with images oriented the same way
+    if (img.size[0] < img.size[1]):
+        portrait = True
+        img = img.rotate(90, expand=1)
+
+    e_max = 0
+    crop_current = [0, 0, crop_width, crop_height]
+    crop_best = crop_current
+    while crop_current[2] < img.size[0]:
+        crop = img.crop(tuple(crop_current))
+        e = image_entropy(crop)
+
+        if (e_max < e):
+          e_max = e
+          crop_best = list(crop_current)
+
+        crop_current[0] += 4
+        crop_current[2] += 4
+
+    x_mid = int((crop_best[2] - crop_best[0])/2)
+    y_mid = int((crop_best[3] - crop_best[1])/2)
+
+    return {
+        'x': x_mid,
+        'y': y_mid,
+        'weight': 1.0
+    }
+
+
+def image_entropy(im):
+    # greyscale image entropy
+    band = np.asarray(im.convert("L"))
+    hist, _ = np.histogram(band, bins=range(0, 256))
+    hist = hist[hist > 0]
+    return -np.log2(hist / hist.sum()).sum()
+
-- 
cgit v1.2.3


From 087609ee181a91a523647435ffffa6288a317e2f Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Wed, 19 Oct 2022 03:19:35 -0700
Subject: UI changes for focal point image cropping

---
 modules/ui.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 1ff7eb4f..b6be713b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1234,6 +1234,7 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
                         process_split = gr.Checkbox(label='Split oversized images into two')
+                        process_entropy_focus = gr.Checkbox(label='Create auto focal point crop')
                         process_caption = gr.Checkbox(label='Use BLIP for caption')
                         process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
 
@@ -1318,7 +1319,8 @@ def create_ui(wrap_gradio_gpu_call):
                 process_flip,
                 process_split,
                 process_caption,
-                process_caption_deepbooru
+                process_caption_deepbooru,
+                process_entropy_focus
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.3


From 42fbda83bb9830af18187fddb50c1bedd01da502 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 14:30:33 +0000
Subject: layer options moves into create hnet ui

---
 modules/hypernetworks/hypernetwork.py | 64 +++++++++++++++++------------------
 modules/hypernetworks/ui.py           |  9 +++--
 modules/shared.py                     |  2 --
 modules/ui.py                         |  8 +++--
 4 files changed, 45 insertions(+), 38 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 583ada31..7d519cd9 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -19,37 +19,21 @@ from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 
-def parse_layer_structure(dim, state_dict):
-    i = 0
-    res = [1]
-    while (key := "linear.{}.weight".format(i)) in state_dict:
-        weight = state_dict[key]
-        res.append(len(weight) // dim)
-        i += 1
-    return res
-
-
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
-    layer_structure = None
-    add_layer_norm = False
 
-    def __init__(self, dim, state_dict=None):
+    def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):
         super().__init__()
-        if (state_dict is None or 'linear.0.weight' not in state_dict) and self.layer_structure is None:
-            layer_structure = (1, 2, 1)
+        if layer_structure is not None:
+            assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
+            assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
         else:
-            if self.layer_structure is not None:
-                assert self.layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
-                assert self.layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-                layer_structure = self.layer_structure
-            else:
-                layer_structure = parse_layer_structure(dim, state_dict)
+            layer_structure = parse_layer_structure(dim, state_dict)
 
         linears = []
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
-            if self.add_layer_norm:
+            if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
         self.linear = torch.nn.Sequential(*linears)
@@ -77,38 +61,47 @@ class HypernetworkModule(torch.nn.Module):
         return x + self.linear(x) * self.multiplier
 
     def trainables(self):
-        res = []
+        layer_structure = []
         for layer in self.linear:
-            res += [layer.weight, layer.bias]
-        return res
+            layer_structure += [layer.weight, layer.bias]
+        return layer_structure
 
 
 def apply_strength(value=None):
     HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
 
 
-def apply_layer_structure(value=None):
-    HypernetworkModule.layer_structure = value if value is not None else shared.opts.sd_hypernetwork_layer_structure
+def parse_layer_structure(dim, state_dict):
+    i = 0
+    layer_structure = [1]
 
+    while (key := "linear.{}.weight".format(i)) in state_dict:
+        weight = state_dict[key]
+        layer_structure.append(len(weight) // dim)
+        i += 1
 
-def apply_layer_norm(value=None):
-    HypernetworkModule.add_layer_norm = value if value is not None else shared.opts.sd_hypernetwork_add_layer_norm
+    return layer_structure
 
 
 class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False):
         self.filename = None
         self.name = name
         self.layers = {}
         self.step = 0
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
+        self.layer_structure = layer_structure
+        self.add_layer_norm = add_layer_norm
 
         for size in enable_sizes or []:
-            self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
+            self.layers[size] = (
+                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
+                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
+            )
 
     def weights(self):
         res = []
@@ -128,6 +121,8 @@ class Hypernetwork:
 
         state_dict['step'] = self.step
         state_dict['name'] = self.name
+        state_dict['layer_structure'] = self.layer_structure
+        state_dict['is_layer_norm'] = self.add_layer_norm
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
 
@@ -142,10 +137,15 @@ class Hypernetwork:
 
         for size, sd in state_dict.items():
             if type(size) == int:
-                self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
+                self.layers[size] = (
+                    HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]),
+                    HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]),
+                )
 
         self.name = state_dict.get('name', self.name)
         self.step = state_dict.get('step', 0)
+        self.layer_structure = state_dict.get('layer_structure', None)
+        self.add_layer_norm = state_dict.get('is_layer_norm', False)
         self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
         self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
 
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index dfa599af..7e8ea95e 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -9,11 +9,16 @@ from modules import sd_hijack, shared, devices
 from modules.hypernetworks import hypernetwork
 
 
-def create_hypernetwork(name, enable_sizes):
+def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
-    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes])
+    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
+        name=name,
+        enable_sizes=[int(x) for x in enable_sizes],
+        layer_structure=layer_structure,
+        add_layer_norm=add_layer_norm,
+    )
     hypernet.save(fn)
 
     shared.reload_hypernetworks()
diff --git a/modules/shared.py b/modules/shared.py
index 0540cae9..faede821 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -260,8 +260,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
-    "sd_hypernetwork_layer_structure": OptionInfo(None, "Hypernetwork layer structure Default: (1,2,1).", gr.Dropdown, lambda: {"choices": [(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)]}),
-    "sd_hypernetwork_add_layer_norm": OptionInfo(False, "Add layer normalization to hypernetwork architecture."),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
diff --git a/modules/ui.py b/modules/ui.py
index ca46343f..d9ee462f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -458,14 +458,14 @@ def create_toprow(is_img2img):
             with gr.Row():
                 with gr.Column(scale=80):
                     with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2, 
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
                             placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
                         )
 
             with gr.Row():
                 with gr.Column(scale=80):
                     with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, 
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
                             placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
                         )
 
@@ -1198,6 +1198,8 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+                    new_hypernetwork_layer_structure = gr.Dropdown(label="Hypernetwork layer structure", choices=[(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)])
+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1280,6 +1282,8 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 new_hypernetwork_name,
                 new_hypernetwork_sizes,
+                new_hypernetwork_layer_structure,
+                new_hypernetwork_add_layer_norm,
             ],
             outputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.3


From 3770b8d2fa62066d472a04739c7b84bce8538832 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 15:28:42 +0000
Subject: enable to write layer structure of hn himself

---
 modules/hypernetworks/ui.py | 4 ++++
 modules/ui.py               | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 7e8ea95e..08f75f15 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -1,5 +1,6 @@
 import html
 import os
+import re
 
 import gradio as gr
 
@@ -13,6 +14,9 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
+    if type(layer_structure) == str:
+        layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure)))
+
     hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
         name=name,
         enable_sizes=[int(x) for x in enable_sizes],
diff --git a/modules/ui.py b/modules/ui.py
index d9ee462f..18a2add0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1198,7 +1198,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
-                    new_hypernetwork_layer_structure = gr.Dropdown(label="Hypernetwork layer structure", choices=[(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)])
+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
 
                     with gr.Row():
-- 
cgit v1.2.3


From 019a3a88f07766f2d32c32fbe8e41625f28ecb5e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 19 Oct 2022 17:15:47 +0100
Subject: Update ui.py

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index d2e24880..1573ef82 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1247,7 +1247,7 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                 with gr.Tab(label="Train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images<br/>Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion">wiki</a></p>")
                     with gr.Row():
                         train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-- 
cgit v1.2.3


From c6e9fed5003631c87d548e74d6e359678959a453 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 19 Oct 2022 19:21:16 +0300
Subject: fix for #3086 failing to load any previous hypernet

---
 modules/hypernetworks/hypernetwork.py | 60 ++++++++++++++++-------------------
 1 file changed, 28 insertions(+), 32 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7d519cd9..74300122 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -24,11 +24,10 @@ class HypernetworkModule(torch.nn.Module):
 
     def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):
         super().__init__()
-        if layer_structure is not None:
-            assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
-            assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-        else:
-            layer_structure = parse_layer_structure(dim, state_dict)
+
+        assert layer_structure is not None, "layer_structure mut not be None"
+        assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
+        assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
 
         linears = []
         for i in range(len(layer_structure) - 1):
@@ -39,23 +38,30 @@ class HypernetworkModule(torch.nn.Module):
         self.linear = torch.nn.Sequential(*linears)
 
         if state_dict is not None:
-            try:
-                self.load_state_dict(state_dict)
-            except RuntimeError:
-                self.try_load_previous(state_dict)
+            self.fix_old_state_dict(state_dict)
+            self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                layer.weight.data.normal_(mean = 0.0, std = 0.01)
+                layer.weight.data.normal_(mean=0.0, std=0.01)
                 layer.bias.data.zero_()
 
         self.to(devices.device)
 
-    def try_load_previous(self, state_dict):
-        states = self.state_dict()
-        states['linear.0.bias'].copy_(state_dict['linear1.bias'])
-        states['linear.0.weight'].copy_(state_dict['linear1.weight'])
-        states['linear.1.bias'].copy_(state_dict['linear2.bias'])
-        states['linear.1.weight'].copy_(state_dict['linear2.weight'])
+    def fix_old_state_dict(self, state_dict):
+        changes = {
+            'linear1.bias': 'linear.0.bias',
+            'linear1.weight': 'linear.0.weight',
+            'linear2.bias': 'linear.1.bias',
+            'linear2.weight': 'linear.1.weight',
+        }
+
+        for fr, to in changes.items():
+            x = state_dict.get(fr, None)
+            if x is None:
+                continue
+
+            del state_dict[fr]
+            state_dict[to] = x
 
     def forward(self, x):
         return x + self.linear(x) * self.multiplier
@@ -71,18 +77,6 @@ def apply_strength(value=None):
     HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
 
 
-def parse_layer_structure(dim, state_dict):
-    i = 0
-    layer_structure = [1]
-
-    while (key := "linear.{}.weight".format(i)) in state_dict:
-        weight = state_dict[key]
-        layer_structure.append(len(weight) // dim)
-        i += 1
-
-    return layer_structure
-
-
 class Hypernetwork:
     filename = None
     name = None
@@ -135,17 +129,18 @@ class Hypernetwork:
 
         state_dict = torch.load(filename, map_location='cpu')
 
+        self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
+        self.add_layer_norm = state_dict.get('is_layer_norm', False)
+
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]),
-                    HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm),
                 )
 
         self.name = state_dict.get('name', self.name)
         self.step = state_dict.get('step', 0)
-        self.layer_structure = state_dict.get('layer_structure', None)
-        self.add_layer_norm = state_dict.get('is_layer_norm', False)
         self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
         self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
 
@@ -244,6 +239,7 @@ def stack_conds(conds):
 
     return torch.stack(conds)
 
+
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
-- 
cgit v1.2.3


From 2ce52d32e41fb523d1494f45073fd18496e52d35 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 16:31:12 +0000
Subject: fix for #3086 failing to load any previous hypernet

---
 modules/hypernetworks/hypernetwork.py | 60 ++++++++++++++++-------------------
 1 file changed, 28 insertions(+), 32 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7d519cd9..74300122 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -24,11 +24,10 @@ class HypernetworkModule(torch.nn.Module):
 
     def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):
         super().__init__()
-        if layer_structure is not None:
-            assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
-            assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-        else:
-            layer_structure = parse_layer_structure(dim, state_dict)
+
+        assert layer_structure is not None, "layer_structure mut not be None"
+        assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
+        assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
 
         linears = []
         for i in range(len(layer_structure) - 1):
@@ -39,23 +38,30 @@ class HypernetworkModule(torch.nn.Module):
         self.linear = torch.nn.Sequential(*linears)
 
         if state_dict is not None:
-            try:
-                self.load_state_dict(state_dict)
-            except RuntimeError:
-                self.try_load_previous(state_dict)
+            self.fix_old_state_dict(state_dict)
+            self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                layer.weight.data.normal_(mean = 0.0, std = 0.01)
+                layer.weight.data.normal_(mean=0.0, std=0.01)
                 layer.bias.data.zero_()
 
         self.to(devices.device)
 
-    def try_load_previous(self, state_dict):
-        states = self.state_dict()
-        states['linear.0.bias'].copy_(state_dict['linear1.bias'])
-        states['linear.0.weight'].copy_(state_dict['linear1.weight'])
-        states['linear.1.bias'].copy_(state_dict['linear2.bias'])
-        states['linear.1.weight'].copy_(state_dict['linear2.weight'])
+    def fix_old_state_dict(self, state_dict):
+        changes = {
+            'linear1.bias': 'linear.0.bias',
+            'linear1.weight': 'linear.0.weight',
+            'linear2.bias': 'linear.1.bias',
+            'linear2.weight': 'linear.1.weight',
+        }
+
+        for fr, to in changes.items():
+            x = state_dict.get(fr, None)
+            if x is None:
+                continue
+
+            del state_dict[fr]
+            state_dict[to] = x
 
     def forward(self, x):
         return x + self.linear(x) * self.multiplier
@@ -71,18 +77,6 @@ def apply_strength(value=None):
     HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
 
 
-def parse_layer_structure(dim, state_dict):
-    i = 0
-    layer_structure = [1]
-
-    while (key := "linear.{}.weight".format(i)) in state_dict:
-        weight = state_dict[key]
-        layer_structure.append(len(weight) // dim)
-        i += 1
-
-    return layer_structure
-
-
 class Hypernetwork:
     filename = None
     name = None
@@ -135,17 +129,18 @@ class Hypernetwork:
 
         state_dict = torch.load(filename, map_location='cpu')
 
+        self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
+        self.add_layer_norm = state_dict.get('is_layer_norm', False)
+
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]),
-                    HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm),
                 )
 
         self.name = state_dict.get('name', self.name)
         self.step = state_dict.get('step', 0)
-        self.layer_structure = state_dict.get('layer_structure', None)
-        self.add_layer_norm = state_dict.get('is_layer_norm', False)
         self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
         self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
 
@@ -244,6 +239,7 @@ def stack_conds(conds):
 
     return torch.stack(conds)
 
+
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
-- 
cgit v1.2.3


From 57eb1a64c85d995cacb4fa3832e87405bf6820b9 Mon Sep 17 00:00:00 2001
From: Alexandre Simard <as.simard@outlook.com>
Date: Wed, 19 Oct 2022 12:28:27 -0400
Subject: Update ui.py

---
 modules/ui.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index d2e24880..c9a923ab 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -268,8 +268,13 @@ def calc_time_left(progress, threshold, label, force_display):
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
         eta_relative = eta-time_since_start
-        if (eta_relative > threshold and progress > 0.02) or force_display:           
-            return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))        
+        if (eta_relative > threshold and progress > 0.02) or force_display:
+            if eta_relative > 3600:
+                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
+            elif eta_relative > 60:
+                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))
+            else:
+                return label + time.strftime('%Ss',  time.gmtime(eta_relative))
         else:
             return ""
 
@@ -285,7 +290,7 @@ def check_progress_call(id_part):
     if shared.state.sampling_steps > 0:
         progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
 
-    time_left = calc_time_left( progress, 60, " ETA:", shared.state.time_left_force_display )
+    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
     if time_left != "":
         shared.state.time_left_force_display = True
 
@@ -293,7 +298,7 @@ def check_progress_call(id_part):
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:hidden;width:{progress * 100}%">{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
-- 
cgit v1.2.3


From 1e4809b251d478a102fd980dcfc26e21d6d3730b Mon Sep 17 00:00:00 2001
From: Alexandre Simard <as.simard@outlook.com>
Date: Wed, 19 Oct 2022 12:53:23 -0400
Subject: Added a bit of padding to the left

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index c9a923ab..a2dbd41e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -298,7 +298,7 @@ def check_progress_call(id_part):
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
-- 
cgit v1.2.3


From 5e012e4dfa5dcfeade0394678cf14b70682dba6c Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Wed, 19 Oct 2022 06:17:47 -0700
Subject: Infotext saves more specific hypernet name.

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index ea926fc3..bcb0c32c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.filename.split('\\')[-1].split('.')[0]),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.3


From 46122c4ff6aadc0f96e657f88dbac7bbd9f9bf99 Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Wed, 19 Oct 2022 19:18:52 +0300
Subject: Send empty prompts as valid generation parameter

---
 modules/generation_parameters_copypaste.py | 3 ---
 1 file changed, 3 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index c27826b6..98d24406 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -45,10 +45,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         else:
             prompt += ("" if prompt == "" else "\n") + line
 
-    if len(prompt) > 0:
         res["Prompt"] = prompt
-
-    if len(negative_prompt) > 0:
         res["Negative prompt"] = negative_prompt
 
     for k, v in re_param.findall(lastline):
-- 
cgit v1.2.3


From 14c1c2b9351f16d43ba4e6b6c9062edad44a6bec Mon Sep 17 00:00:00 2001
From: Alexandre Simard <as.simard@outlook.com>
Date: Wed, 19 Oct 2022 13:53:52 -0400
Subject: Show PB texts at same time and earlier

For big tasks (1000+ steps), waiting 1 minute to see ETA is long and this changes it so the number of steps done plays a role in showing the text as well.
---
 modules/ui.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index a2dbd41e..0abd177a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -261,14 +261,14 @@ def wrap_gradio_call(func, extra_outputs=None):
     return f
 
 
-def calc_time_left(progress, threshold, label, force_display):
+def calc_time_left(progress, threshold, label, force_display, showTime):
     if progress == 0:
         return ""
     else:
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
         eta_relative = eta-time_since_start
-        if (eta_relative > threshold and progress > 0.02) or force_display:
+        if (eta_relative > threshold and showTime) or force_display:
             if eta_relative > 3600:
                 return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
             elif eta_relative > 60:
@@ -290,7 +290,10 @@ def check_progress_call(id_part):
     if shared.state.sampling_steps > 0:
         progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
 
-    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
+    # Show progress percentage and time left at the same moment, and base it also on steps done
+    showPBText = progress >= 0.01 or shared.state.sampling_step >= 10
+
+    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display, showPBText )
     if time_left != "":
         shared.state.time_left_force_display = True
 
@@ -298,7 +301,7 @@ def check_progress_call(id_part):
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if showPBText else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
-- 
cgit v1.2.3


From b748b583c0b9f771c1be509175a6913e3f2ad97c Mon Sep 17 00:00:00 2001
From: Mackerel <mackerel@chickatrice.com>
Date: Wed, 19 Oct 2022 14:22:03 -0400
Subject: generation_parameters_copypaste.py: fix indent

---
 modules/generation_parameters_copypaste.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 98d24406..0f041449 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -45,8 +45,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         else:
             prompt += ("" if prompt == "" else "\n") + line
 
-        res["Prompt"] = prompt
-        res["Negative prompt"] = negative_prompt
+    res["Prompt"] = prompt
+    res["Negative prompt"] = negative_prompt
 
     for k, v in re_param.findall(lastline):
         m = re_imagesize.match(v)
-- 
cgit v1.2.3


From eb7ba4b713ac2fb960ecf6365b1de0c89451e583 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 19 Oct 2022 19:50:46 +0100
Subject: update training header text

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 1573ef82..93c0767c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1247,7 +1247,7 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                 with gr.Tab(label="Train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images<br/>Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion">wiki</a></p>")
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images<br/>Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
                     with gr.Row():
                         train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-- 
cgit v1.2.3


From 4d663055ded968831ec97f047dfa8e94036cf1c1 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 19 Oct 2022 20:33:18 +0100
Subject: update ui with extra training options

---
 modules/ui.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 93c0767c..cdb9d335 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1206,6 +1206,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_embedding_name = gr.Textbox(label="Name")
                     initialization_text = gr.Textbox(label="Initialization text", value="*")
                     nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1219,6 +1220,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1247,14 +1249,17 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                 with gr.Tab(label="Train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images<br/>Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
                     with gr.Row():
                         train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
                     with gr.Row():
                         train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
                         create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
-                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
+                    with gr.Row():
+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
+                    
                     batch_size = gr.Number(label='Batch size', value=1, precision=0)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
@@ -1288,6 +1293,7 @@ def create_ui(wrap_gradio_gpu_call):
                 new_embedding_name,
                 initialization_text,
                 nvpt,
+                overwrite_old_embedding,
             ],
             outputs=[
                 train_embedding_name,
@@ -1303,6 +1309,7 @@ def create_ui(wrap_gradio_gpu_call):
                 new_hypernetwork_sizes,
                 new_hypernetwork_layer_structure,
                 new_hypernetwork_add_layer_norm,
+                overwrite_old_hypernetwork,
             ],
             outputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.3


From 41e3877be2c667316515c86037413763eb0ba4da Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Wed, 19 Oct 2022 13:44:59 -0700
Subject: fix entropy point calculation

---
 modules/textual_inversion/preprocess.py | 34 ++++++++++++++++++---------------
 1 file changed, 19 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 168bfb09..7c1a594e 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -196,9 +196,9 @@ def image_focal_points(im):
 
     points = cv2.goodFeaturesToTrack(
         np_im,
-        maxCorners=50,
+        maxCorners=100,
         qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.05,
+        minDistance=min(grayscale.width, grayscale.height)*0.07,
         useHarrisDetector=False,
     )
 
@@ -218,28 +218,32 @@ def image_focal_points(im):
 
 
 def image_entropy_point(im, crop_width, crop_height):
-    img = im.copy()
-    # just make it easier to slide the test crop with images oriented the same way
-    if (img.size[0] < img.size[1]):
-        portrait = True
-        img = img.rotate(90, expand=1)
+    landscape = im.height < im.width
+    portrait = im.height > im.width
+    if landscape:
+      move_idx = [0, 2]
+      move_max = im.size[0]
+    elif portrait:
+      move_idx = [1, 3]
+      move_max = im.size[1]
 
     e_max = 0
     crop_current = [0, 0, crop_width, crop_height]
     crop_best = crop_current
-    while crop_current[2] < img.size[0]:
-        crop = img.crop(tuple(crop_current))
+    while crop_current[move_idx[1]] < move_max:
+        crop = im.crop(tuple(crop_current))
         e = image_entropy(crop)
 
-        if (e_max < e):
+        if (e > e_max):
           e_max = e
           crop_best = list(crop_current)
 
-        crop_current[0] += 4
-        crop_current[2] += 4
+        crop_current[move_idx[0]] += 4
+        crop_current[move_idx[1]] += 4
+
+    x_mid = int(crop_best[0] + crop_width/2)
+    y_mid = int(crop_best[1] + crop_height/2)
 
-    x_mid = int((crop_best[2] - crop_best[0])/2)
-    y_mid = int((crop_best[3] - crop_best[1])/2)
 
     return {
         'x': x_mid,
@@ -250,7 +254,7 @@ def image_entropy_point(im, crop_width, crop_height):
 
 def image_entropy(im):
     # greyscale image entropy
-    band = np.asarray(im.convert("L"))
+    band = np.asarray(im.convert("1"))
     hist, _ = np.histogram(band, bins=range(0, 256))
     hist = hist[hist > 0]
     return -np.log2(hist / hist.sum()).sum()
-- 
cgit v1.2.3


From 8e7097d06a6a261580d34375c9d2a9e4ffc63ffa Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 13:47:45 -0700
Subject: Added support for RunwayML inpainting model

---
 modules/processing.py           |  34 ++++++-
 modules/sd_hijack_inpainting.py | 208 ++++++++++++++++++++++++++++++++++++++++
 modules/sd_models.py            |  16 +++-
 modules/sd_samplers.py          |  50 +++++++---
 4 files changed, 293 insertions(+), 15 deletions(-)
 create mode 100644 modules/sd_hijack_inpainting.py

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index bcb0c32c..a6c308f9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -546,7 +546,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
+            
+            # The "masked-image" in this case will just be all zeros since the entire image is masked.
+            image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device)
+            image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+
+            # Add the fake full 1s mask to the first dimension.
+            image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+            image_conditioning = image_conditioning.to(x.dtype)
+
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=image_conditioning)
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@@ -714,10 +723,31 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
+        if self.image_mask is not None:
+            conditioning_mask = np.array(self.image_mask.convert("L"))
+            conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+            conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+            # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+            conditioning_mask = torch.round(conditioning_mask)
+        else:
+            conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
+
+        # Create another latent image, this time with a masked version of the original input.
+        conditioning_mask = conditioning_mask.to(image.device)
+        conditioning_image = image * (1.0 - conditioning_mask)
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+        # Create the concatenated conditioning tensor to be fed to `c_concat`
+        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
+        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+        self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+        self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
-        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
+        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 
         if self.mask is not None:
             samples = samples * self.nmask + self.init_latent * self.mask
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
new file mode 100644
index 00000000..7e5670d6
--- /dev/null
+++ b/modules/sd_hijack_inpainting.py
@@ -0,0 +1,208 @@
+import torch
+import numpy as np
+
+from tqdm import tqdm
+from einops import rearrange, repeat
+from omegaconf import ListConfig
+
+from types import MethodType
+
+import ldm.models.diffusion.ddpm
+import ldm.models.diffusion.ddim
+
+from ldm.models.diffusion.ddpm import LatentDiffusion
+from ldm.models.diffusion.ddim import DDIMSampler, noise_like
+
+# =================================================================================================
+# Monkey patch DDIMSampler methods from RunwayML repo directly.
+# Adapted from:
+# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
+# =================================================================================================
+@torch.no_grad()
+def sample(
+    self,
+    S,
+    batch_size,
+    shape,
+    conditioning=None,
+    callback=None,
+    normals_sequence=None,
+    img_callback=None,
+    quantize_x0=False,
+    eta=0.,
+    mask=None,
+    x0=None,
+    temperature=1.,
+    noise_dropout=0.,
+    score_corrector=None,
+    corrector_kwargs=None,
+    verbose=True,
+    x_T=None,
+    log_every_t=100,
+    unconditional_guidance_scale=1.,
+    unconditional_conditioning=None,
+    # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+    **kwargs
+    ):
+    if conditioning is not None:
+        if isinstance(conditioning, dict):
+            ctmp = conditioning[list(conditioning.keys())[0]]
+            while isinstance(ctmp, list):
+                ctmp = elf.inpainting_fill == 2:
+                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
+            elif self.inpainting_fill == 3:
+                self.init_latent = self.init_latent * self.mask
+
+        if self.image_mask is not None:
+            conditioning_mask = np.array(self.image_mask.convert("L"))
+            conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+            conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+            # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+            conditioning_mask = torch.round(conditioning_mask)
+        else:
+            conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
+
+        # Create another latent image, this time with a masked version of the original input.
+        conditioning_mask = conditioning_mask.to(image.device)
+        conditioning_image = image * (1.0 - conditioning_mask)
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+        # Create the concatenated conditioning tensor to be fed to `c_concat`
+        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
+        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+        self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+        self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+        x = create_random_tensors([opctmp[0]
+            cbs = ctmp.shape[0]
+            if cbs != batch_size:
+                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+        else:
+            if conditioning.shape[0] != batch_size:
+                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+    # sampling
+    C, H, W = shape
+    size = (batch_size, C, H, W)
+    print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+    samples, intermediates = self.ddim_sampling(conditioning, size,
+                                                callback=callback,
+                                                img_callback=img_callback,
+                                                quantize_denoised=quantize_x0,
+                                                mask=mask, x0=x0,
+                                                ddim_use_original_steps=False,
+                                                noise_dropout=noise_dropout,
+                                                temperature=temperature,
+                                                score_corrector=score_corrector,
+                                                corrector_kwargs=corrector_kwargs,
+                                                x_T=x_T,
+                                                log_every_t=log_every_t,
+                                                unconditional_guidance_scale=unconditional_guidance_scale,
+                                                unconditional_conditioning=unconditional_conditioning,
+                                                )
+    return samples, intermediates
+
+
+@torch.no_grad()
+def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                    unconditional_guidance_scale=1., unconditional_conditioning=None):
+    b, *_, device = *x.shape, x.device
+
+    if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+        e_t = self.model.apply_model(x, t, c)
+    else:
+        x_in = torch.cat([x] * 2)
+        t_in = torch.cat([t] * 2)
+        if isinstance(c, dict):
+            assert isinstance(unconditional_conditioning, dict)
+            c_in = dict()
+            for k in c:
+                if isinstance(c[k], list):
+                    c_in[k] = [
+                        torch.cat([unconditional_conditioning[k][i], c[k][i]])
+                        for i in range(len(c[k]))
+                    ]
+                else:
+                    c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
+        else:
+            c_in = torch.cat([unconditional_conditioning, c])
+        e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+        e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+    if score_corrector is not None:
+        assert self.model.parameterization == "eps"
+        e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+    # select parameters corresponding to the currently considered timestep
+    a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+    a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+    sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+    sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+    # current prediction for x_0
+    pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+    if quantize_denoised:
+        pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+    # direction pointing to x_t
+    dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+    noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+    if noise_dropout > 0.:
+        noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+    x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+    return x_prev, pred_x0
+
+
+# =================================================================================================
+# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
+# Adapted from:
+# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
+# =================================================================================================
+
+@torch.no_grad()
+def get_unconditional_conditioning(self, batch_size, null_label=None):
+    if null_label is not None:
+        xc = null_label
+        if isinstance(xc, ListConfig):
+            xc = list(xc)
+        if isinstance(xc, dict) or isinstance(xc, list):
+            c = self.get_learned_conditioning(xc)
+        else:
+            if hasattr(xc, "to"):
+                xc = xc.to(self.device)
+            c = self.get_learned_conditioning(xc)
+    else:
+        # todo: get null label from cond_stage_model
+        raise NotImplementedError()
+    c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
+    return c
+
+class LatentInpaintDiffusion(LatentDiffusion):
+    def __init__(
+        self,
+        concat_keys=("mask", "masked_image"),
+        masked_image_key="masked_image",
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.masked_image_key = masked_image_key
+        assert self.masked_image_key in concat_keys
+        self.concat_keys = concat_keys
+
+def should_hijack_inpainting(checkpoint_info):
+    return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
+
+def do_inpainting_hijack():
+    ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
+    ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
+    ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
+    ldm.models.diffusion.ddim.DDIMSampler.sample = sample
\ No newline at end of file
diff --git a/modules/sd_models.py b/modules/sd_models.py
index eae22e87..47836d25 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -9,6 +9,7 @@ from ldm.util import instantiate_from_config
 
 from modules import shared, modelloader, devices
 from modules.paths import models_path
+from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
@@ -211,6 +212,19 @@ def load_model():
         print(f"Loading config from: {checkpoint_info.config}")
 
     sd_config = OmegaConf.load(checkpoint_info.config)
+    
+    if should_hijack_inpainting(checkpoint_info):
+        do_inpainting_hijack()
+
+        # Hardcoded config for now...
+        sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
+        sd_config.model.params.use_ema = False
+        sd_config.model.params.conditioning_key = "hybrid"
+        sd_config.model.params.unet_config.params.in_channels = 9
+
+        # Create a "fake" config with a different name so that we know to unload it when switching models.
+        checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
+
     sd_model = instantiate_from_config(sd_config.model)
     load_model_weights(sd_model, checkpoint_info)
 
@@ -234,7 +248,7 @@ def reload_model_weights(sd_model, info=None):
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
-    if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
+    if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
         shared.sd_model = load_model()
         return shared.sd_model
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b58e810b..9d3cf289 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -136,9 +136,15 @@ class VanillaStableDiffusionSampler:
         if self.stop_at is not None and self.step > self.stop_at:
             raise InterruptedException
 
+        # Have to unwrap the inpainting conditioning here to perform pre-preocessing
+        image_conditioning = None
+        if isinstance(cond, dict):
+            image_conditioning = cond["c_concat"][0]
+            cond = cond["c_crossattn"][0]
+            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
 
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
+        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)            
 
         assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
         cond = tensor
@@ -157,6 +163,10 @@ class VanillaStableDiffusionSampler:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)
             x_dec = img_orig * self.mask + self.nmask * x_dec
 
+        if image_conditioning is not None:
+            cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
         res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
 
         if self.mask is not None:
@@ -182,7 +192,7 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None
         self.nmask = p.nmask if hasattr(p, 'nmask') else None
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
 
         self.initialize(p)
@@ -202,7 +212,7 @@ class VanillaStableDiffusionSampler:
 
         return samples
 
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         self.initialize(p)
 
         self.init_latent = None
@@ -210,6 +220,11 @@ class VanillaStableDiffusionSampler:
 
         steps = steps or p.steps
 
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        if image_conditioning is not None:
+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
         # existing code fails with certain step counts, like 9
         try:
             samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
@@ -228,7 +243,7 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None
         self.step = 0
 
-    def forward(self, x, sigma, uncond, cond, cond_scale):
+    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
         if state.interrupted or state.skipped:
             raise InterruptedException
 
@@ -239,28 +254,29 @@ class CFGDenoiser(torch.nn.Module):
         repeats = [len(conds_list[i]) for i in range(batch_size)]
 
         x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
         if tensor.shape[1] == uncond.shape[1]:
             cond_in = torch.cat([tensor, uncond])
 
             if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
+                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
             else:
                 x_out = torch.zeros_like(x_in)
                 for batch_offset in range(0, x_out.shape[0], batch_size):
                     a = batch_offset
                     b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
         else:
             x_out = torch.zeros_like(x_in)
             batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
             for batch_offset in range(0, tensor.shape[0], batch_size):
                 a = batch_offset
                 b = min(a + batch_size, tensor.shape[0])
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
 
-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
 
         denoised_uncond = x_out[-uncond.shape[0]:]
         denoised = torch.clone(denoised_uncond)
@@ -361,7 +377,7 @@ class KDiffusionSampler:
 
         return extra_params_kwargs
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
 
         if p.sampler_noise_scheduler_override:
@@ -389,11 +405,16 @@ class KDiffusionSampler:
 
         self.model_wrap_cfg.init_latent = x
 
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
 
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
         steps = steps or p.steps
 
         if p.sampler_noise_scheduler_override:
@@ -414,7 +435,12 @@ class KDiffusionSampler:
         else:
             extra_params_kwargs['sigmas'] = sigmas
 
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
 
-- 
cgit v1.2.3


From 0719c10bf1b817364a498ee11b90d30d3d527344 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 13:56:26 -0700
Subject: Fixed copying mistake

---
 modules/sd_hijack_inpainting.py | 79 +++++++++++++----------------------------
 1 file changed, 25 insertions(+), 54 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 7e5670d6..d4d28d2e 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -19,63 +19,35 @@ from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 # https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
 # =================================================================================================
 @torch.no_grad()
-def sample(
-    self,
-    S,
-    batch_size,
-    shape,
-    conditioning=None,
-    callback=None,
-    normals_sequence=None,
-    img_callback=None,
-    quantize_x0=False,
-    eta=0.,
-    mask=None,
-    x0=None,
-    temperature=1.,
-    noise_dropout=0.,
-    score_corrector=None,
-    corrector_kwargs=None,
-    verbose=True,
-    x_T=None,
-    log_every_t=100,
-    unconditional_guidance_scale=1.,
-    unconditional_conditioning=None,
-    # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-    **kwargs
-    ):
+def sample(self,
+            S,
+            batch_size,
+            shape,
+            conditioning=None,
+            callback=None,
+            normals_sequence=None,
+            img_callback=None,
+            quantize_x0=False,
+            eta=0.,
+            mask=None,
+            x0=None,
+            temperature=1.,
+            noise_dropout=0.,
+            score_corrector=None,
+            corrector_kwargs=None,
+            verbose=True,
+            x_T=None,
+            log_every_t=100,
+            unconditional_guidance_scale=1.,
+            unconditional_conditioning=None,
+            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+            **kwargs
+            ):
     if conditioning is not None:
         if isinstance(conditioning, dict):
             ctmp = conditioning[list(conditioning.keys())[0]]
             while isinstance(ctmp, list):
-                ctmp = elf.inpainting_fill == 2:
-                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
-            elif self.inpainting_fill == 3:
-                self.init_latent = self.init_latent * self.mask
-
-        if self.image_mask is not None:
-            conditioning_mask = np.array(self.image_mask.convert("L"))
-            conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-            conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
-
-            # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-            conditioning_mask = torch.round(conditioning_mask)
-        else:
-            conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
-
-        # Create another latent image, this time with a masked version of the original input.
-        conditioning_mask = conditioning_mask.to(image.device)
-        conditioning_image = image * (1.0 - conditioning_mask)
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-
-        # Create the concatenated conditioning tensor to be fed to `c_concat`
-        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
-        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-        self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-        self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
-
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        x = create_random_tensors([opctmp[0]
+                ctmp = ctmp[0]
             cbs = ctmp.shape[0]
             if cbs != batch_size:
                 print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
@@ -106,7 +78,6 @@ def sample(
                                                 )
     return samples, intermediates
 
-
 @torch.no_grad()
 def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
                     temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-- 
cgit v1.2.3


From dde9f960727bfe151d418e43685a2881cf580a17 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 14:14:24 -0700
Subject: added support for ddim img2img

---
 modules/sd_samplers.py | 6 ++++++
 1 file changed, 6 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 9d3cf289..d270e4df 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -208,6 +208,12 @@ class VanillaStableDiffusionSampler:
         self.init_latent = x
         self.step = 0
 
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        if image_conditioning is not None:
+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+            
+            
         samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
 
         return samples
-- 
cgit v1.2.3


From c418467c03db916c3e5312e6ac4a67365e196dbd Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 15:09:43 -0700
Subject: Don't compute latent mask if were not using it. Also added support
 for fixed highres_fix generation.

---
 modules/processing.py  | 72 +++++++++++++++++++++++++++++++-------------------
 modules/sd_samplers.py |  4 +++
 2 files changed, 49 insertions(+), 27 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index a6c308f9..684e5833 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -541,12 +541,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
-
-        if not self.enable_hr:
-            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            
+    def create_dummy_mask(self, x):
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
             # The "masked-image" in this case will just be all zeros since the entire image is masked.
             image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device)
             image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
@@ -555,11 +551,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
             image_conditioning = image_conditioning.to(x.dtype)
 
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=image_conditioning)
+        else:
+            # Dummy zero conditioning if we're not using inpainting model.
+            # Still takes up a bit of memory, but no encoder call.
+            image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device)
+
+        return image_conditioning
+
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
+
+        if not self.enable_hr:
+            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
@@ -596,7 +604,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples))
 
         return samples
 
@@ -723,26 +731,36 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        if self.image_mask is not None:
-            conditioning_mask = np.array(self.image_mask.convert("L"))
-            conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-            conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+        conditioning_key = self.sampler.conditioning_key
 
-            # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-            conditioning_mask = torch.round(conditioning_mask)
+        if conditioning_key in {'hybrid', 'concat'}:
+            if self.image_mask is not None:
+                conditioning_mask = np.array(self.image_mask.convert("L"))
+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+                conditioning_mask = torch.round(conditioning_mask)
+            else:
+                conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
+
+            # Create another latent image, this time with a masked version of the original input.
+            conditioning_mask = conditioning_mask.to(image.device)
+            conditioning_image = image * (1.0 - conditioning_mask)
+            conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+            # Create the concatenated conditioning tensor to be fed to `c_concat`
+            conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
+            conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+            self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+            self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
         else:
-            conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
-
-        # Create another latent image, this time with a masked version of the original input.
-        conditioning_mask = conditioning_mask.to(image.device)
-        conditioning_image = image * (1.0 - conditioning_mask)
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-
-        # Create the concatenated conditioning tensor to be fed to `c_concat`
-        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
-        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-        self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-        self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
+            self.image_conditioning = torch.zeros(
+                self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1], 
+                dtype=self.init_latent.dtype, 
+                device=self.init_latent.device
+            )
+
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d270e4df..c21be26e 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -117,6 +117,8 @@ class VanillaStableDiffusionSampler:
         self.config = None
         self.last_latent = None
 
+        self.conditioning_key = sd_model.model.conditioning_key
+
     def number_of_needed_noises(self, p):
         return 0
 
@@ -328,6 +330,8 @@ class KDiffusionSampler:
         self.config = None
         self.last_latent = None
 
+        self.conditioning_key = sd_model.model.conditioning_key
+
     def callback_state(self, d):
         step = d['i']
         latent = d["denoised"]
-- 
cgit v1.2.3


From d6ea5841374a28f3f6deb73abc251c8f0bcb240f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:07:57 +0100
Subject: change html output

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7d519cd9..73c1cb80 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -380,7 +380,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 Loss: {mean_loss:.7f}<br/>
 Step: {hypernetwork.step}<br/>
 Last prompt: {html.escape(entries[0].cond_text)}<br/>
-Last saved embedding: {html.escape(last_saved_file)}<br/>
+Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-- 
cgit v1.2.3


From 166be3919b817cee5e702fd01c34afe9081b952c Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:09:40 +0100
Subject: allow overwrite old hn

---
 modules/hypernetworks/ui.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 08f75f15..f45345ea 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -10,9 +10,10 @@ from modules import sd_hijack, shared, devices
 from modules.hypernetworks import hypernetwork
 
 
-def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False):
+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, add_layer_norm=False):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
-    assert not os.path.exists(fn), f"file {fn} already exists"
+    if not overwrite_old:
+        assert not os.path.exists(fn), f"file {fn} already exists"
 
     if type(layer_structure) == str:
         layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure)))
-- 
cgit v1.2.3


From 0087079c2d487b67b06ffc30f36ce486a74e6318 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:10:59 +0100
Subject: allow overwrite old embedding

---
 modules/textual_inversion/textual_inversion.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 3be69562..5776778b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -153,7 +153,7 @@ class EmbeddingDatabase:
         return None, None
 
 
-def create_embedding(name, num_vectors_per_token, init_text='*'):
+def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     cond_model = shared.sd_model.cond_stage_model
     embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
 
@@ -165,7 +165,8 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
         vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
 
     fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
-    assert not os.path.exists(fn), f"file {fn} already exists"
+    if not overwrite_old:
+        assert not os.path.exists(fn), f"file {fn} already exists"
 
     embedding = Embedding(vec, name)
     embedding.step = 0
-- 
cgit v1.2.3


From 632e8d660293081cadb145d8062e5aff0a4a8f0d Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:19:40 +0100
Subject: split learn rates

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index cdb9d335..d07184ee 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1342,7 +1342,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",
             inputs=[
                 train_embedding_name,
-                learn_rate,
+                embedding_learn_rate,
                 batch_size,
                 dataset_directory,
                 log_directory,
@@ -1367,7 +1367,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",
             inputs=[
                 train_hypernetwork_name,
-                learn_rate,
+                hypernetwork_learn_rate,
                 batch_size,
                 dataset_directory,
                 log_directory,
-- 
cgit v1.2.3


From c3835ec85cbb44fa3c46fa871c622b6fee235c89 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:24:24 +0100
Subject: pass overwrite old flag

---
 modules/textual_inversion/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 36881e7a..e712284d 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -7,8 +7,8 @@ import modules.textual_inversion.preprocess
 from modules import sd_hijack, shared
 
 
-def create_embedding(name, initialization_text, nvpt):
-    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)
+def create_embedding(name, initialization_text, nvpt, overwrite_old):
+    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text)
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
-- 
cgit v1.2.3


From 4d6b9f76a55fd0ac0f72634071032dd9c6efb409 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:27:16 +0100
Subject: reorder create_hypernetwork params

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index d07184ee..322c082b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1307,9 +1307,9 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 new_hypernetwork_name,
                 new_hypernetwork_sizes,
+                overwrite_old_hypernetwork,
                 new_hypernetwork_layer_structure,
                 new_hypernetwork_add_layer_norm,
-                overwrite_old_hypernetwork,
             ],
             outputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.3


From fbcce66601994f6ed370db36d9c238840fed6bd2 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:46:54 +0100
Subject: add existing caption file handling

---
 modules/textual_inversion/preprocess.py | 32 ++++++++++++++++++++++++--------
 1 file changed, 24 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 886cf0c3..5c43fe13 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -48,7 +48,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
 
-    def save_pic_with_caption(image, index):
+    def save_pic_with_caption(image, index, existing_caption=None):
         caption = ""
 
         if process_caption:
@@ -66,17 +66,26 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         basename = f"{index:05}-{subindex[0]}-{filename_part}"
         image.save(os.path.join(dst, f"{basename}.png"))
 
+        if preprocess_txt_action == 'prepend' and existing_caption:
+            caption = existing_caption + ' ' + caption
+        elif preprocess_txt_action == 'append' and existing_caption:
+            caption = caption + ' ' + existing_caption
+        elif preprocess_txt_action == 'copy' and existing_caption:
+            caption = existing_caption
+
+        caption = caption.strip()
+        
         if len(caption) > 0:
             with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:
                 file.write(caption)
 
         subindex[0] += 1
 
-    def save_pic(image, index):
+    def save_pic(image, index, existing_caption=None):
         save_pic_with_caption(image, index)
 
         if process_flip:
-            save_pic_with_caption(ImageOps.mirror(image), index)
+            save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)
 
     for index, imagefile in enumerate(tqdm.tqdm(files)):
         subindex = [0]
@@ -86,6 +95,13 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         except Exception:
             continue
 
+        existing_caption = None
+
+        try:
+            existing_caption = open(os.path.splitext(filename)[0] + '.txt', 'r').read()
+        except Exception as e:
+            print(e)
+
         if shared.state.interrupted:
             break
 
@@ -97,20 +113,20 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
             img = img.resize((width, height * img.height // img.width))
 
             top = img.crop((0, 0, width, height))
-            save_pic(top, index)
+            save_pic(top, index, existing_caption=existing_caption)
 
             bot = img.crop((0, img.height - height, width, img.height))
-            save_pic(bot, index)
+            save_pic(bot, index, existing_caption=existing_caption)
         elif process_split and is_wide:
             img = img.resize((width * img.width // img.height, height))
 
             left = img.crop((0, 0, width, height))
-            save_pic(left, index)
+            save_pic(left, index, existing_caption=existing_caption)
 
             right = img.crop((img.width - width, 0, img.width, height))
-            save_pic(right, index)
+            save_pic(right, index, existing_caption=existing_caption)
         else:
             img = images.resize_image(1, img, width, height)
-            save_pic(img, index)
+            save_pic(img, index, existing_caption=existing_caption)
 
         shared.state.nextjob()
-- 
cgit v1.2.3


From ab353b141df8eee042b0964bcb645015dabf3459 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:48:07 +0100
Subject: link existing txt option

---
 modules/ui.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 322c082b..7f52ac0c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1234,6 +1234,7 @@ def create_ui(wrap_gradio_gpu_call):
                     process_dst = gr.Textbox(label='Destination directory')
                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', choices=['ignore', 'copy', 'prepend', 'append'])
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1326,6 +1327,7 @@ def create_ui(wrap_gradio_gpu_call):
                 process_dst,
                 process_width,
                 process_height,
+                preprocess_txt_action,
                 process_flip,
                 process_split,
                 process_caption,
-- 
cgit v1.2.3


From 9b65c4ecf4f8eb6187ee721918adebe68e9bc631 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:49:23 +0100
Subject: pass preprocess_txt_action param

---
 modules/textual_inversion/preprocess.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 5c43fe13..3713bc89 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -11,7 +11,7 @@ if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
 
-def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False):
     try:
         if process_caption:
             shared.interrogator.load()
@@ -21,7 +21,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
             deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
 
-        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
+        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru)
 
     finally:
 
@@ -33,7 +33,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
 
 
 
-def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
-- 
cgit v1.2.3


From 55d8c6cce6d3aef848b9f194adad2ce53064d8b7 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:53:29 +0100
Subject: default to ignore existing captions

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 7f52ac0c..bd5f1b05 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1234,7 +1234,7 @@ def create_ui(wrap_gradio_gpu_call):
                     process_dst = gr.Textbox(label='Destination directory')
                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', choices=['ignore', 'copy', 'prepend', 'append'])
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
-- 
cgit v1.2.3


From 8b74b9aa9a20e4c5c1f72641f8b9617479eb276b Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Wed, 19 Oct 2022 19:06:14 -0500
Subject: add symbol for clear button and simplify roll_col css selector

---
 modules/ui.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index a2dbd41e..9f6edc5f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -83,6 +83,7 @@ folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 apply_style_symbol = '\U0001f4cb'  # 📋
+trash_prompt_symbol = '\U0001F5D1' # 🗑🗑🗑
 
 
 def plaintext_to_html(text):
@@ -498,6 +499,7 @@ def create_toprow(is_img2img):
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
+            trash_prompt = gr.Button(value=trash_prompt_symbol, elem_id="trash_prompt")
 
             token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
             token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-- 
cgit v1.2.3


From 6f98e89486f55b0e4657e96ce640cf1c4675d187 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Thu, 20 Oct 2022 00:10:45 +0000
Subject: update

---
 modules/hypernetworks/hypernetwork.py | 29 +++++++++++++++--------
 modules/hypernetworks/ui.py           |  3 ++-
 modules/ui.py                         | 43 +++++++++++++++++++----------------
 3 files changed, 44 insertions(+), 31 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 74300122..7d617680 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -22,16 +22,20 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False, activation_func=None):
         super().__init__()
 
-        assert layer_structure is not None, "layer_structure mut not be None"
+        assert layer_structure is not None, "layer_structure must not be None"
         assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
         assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
 
         linears = []
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
+            if activation_func == "relu":
+                linears.append(torch.nn.ReLU())
+            if activation_func == "leakyrelu":
+                linears.append(torch.nn.LeakyReLU())
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -42,8 +46,9 @@ class HypernetworkModule(torch.nn.Module):
             self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                layer.weight.data.normal_(mean=0.0, std=0.01)
-                layer.bias.data.zero_()
+                if not "ReLU" in layer.__str__():
+                    layer.weight.data.normal_(mean=0.0, std=0.01)
+                    layer.bias.data.zero_()
 
         self.to(devices.device)
 
@@ -69,7 +74,8 @@ class HypernetworkModule(torch.nn.Module):
     def trainables(self):
         layer_structure = []
         for layer in self.linear:
-            layer_structure += [layer.weight, layer.bias]
+            if not "ReLU" in layer.__str__():
+                layer_structure += [layer.weight, layer.bias]
         return layer_structure
 
 
@@ -81,7 +87,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False, activation_func=None):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -90,11 +96,12 @@ class Hypernetwork:
         self.sd_checkpoint_name = None
         self.layer_structure = layer_structure
         self.add_layer_norm = add_layer_norm
+        self.activation_func = activation_func
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
-                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
+                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func),
+                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func),
             )
 
     def weights(self):
@@ -117,6 +124,7 @@ class Hypernetwork:
         state_dict['name'] = self.name
         state_dict['layer_structure'] = self.layer_structure
         state_dict['is_layer_norm'] = self.add_layer_norm
+        state_dict['activation_func'] = self.activation_func
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
 
@@ -131,12 +139,13 @@ class Hypernetwork:
 
         self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
+        self.activation_func = state_dict.get('activation_func', None)
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm, self.activation_func),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm, self.activation_func),
                 )
 
         self.name = state_dict.get('name', self.name)
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 08f75f15..83f9547b 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -10,7 +10,7 @@ from modules import sd_hijack, shared, devices
 from modules.hypernetworks import hypernetwork
 
 
-def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False):
+def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False, activation_func=None):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
@@ -22,6 +22,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
         enable_sizes=[int(x) for x in enable_sizes],
         layer_structure=layer_structure,
         add_layer_norm=add_layer_norm,
+        activation_func=activation_func,
     )
     hypernet.save(fn)
 
diff --git a/modules/ui.py b/modules/ui.py
index d2e24880..8751fa9c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -5,43 +5,44 @@ import json
 import math
 import mimetypes
 import os
+import platform
 import random
+import subprocess as sp
 import sys
 import tempfile
 import time
 import traceback
-import platform
-import subprocess as sp
 from functools import partial, reduce
 
+import gradio as gr
+import gradio.routes
+import gradio.utils
 import numpy as np
+import piexif
 import torch
 from PIL import Image, PngImagePlugin
-import piexif
 
-import gradio as gr
-import gradio.utils
-import gradio.routes
-
-from modules import sd_hijack, sd_models, localization
+from modules import localization, sd_hijack, sd_models
 from modules.paths import script_path
-from modules.shared import opts, cmd_opts, restricted_opts
+from modules.shared import cmd_opts, opts, restricted_opts
+
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
-import modules.shared as shared
-from modules.sd_samplers import samplers, samplers_for_img2img
-from modules.sd_hijack import model_hijack
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.images_history as img_his
 import modules.ldsr_model
 import modules.scripts
-import modules.gfpgan_model
-import modules.codeformer_model
+import modules.shared as shared
 import modules.styles
-import modules.generation_parameters_copypaste
+import modules.textual_inversion.ui
 from modules import prompt_parser
 from modules.images import save_image
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-import modules.images_history as img_his
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -268,8 +269,8 @@ def calc_time_left(progress, threshold, label, force_display):
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
         eta_relative = eta-time_since_start
-        if (eta_relative > threshold and progress > 0.02) or force_display:           
-            return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))        
+        if (eta_relative > threshold and progress > 0.02) or force_display:
+            return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
         else:
             return ""
 
@@ -1219,6 +1220,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["relu", "leakyrelu"])
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1303,6 +1305,7 @@ def create_ui(wrap_gradio_gpu_call):
                 new_hypernetwork_sizes,
                 new_hypernetwork_layer_structure,
                 new_hypernetwork_add_layer_norm,
+                new_hypernetwork_activation_func,
             ],
             outputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.3


From ba469343e6a1c6e23e82acf5feb65c6101dacbb2 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Thu, 20 Oct 2022 00:17:04 +0000
Subject: align ui.py imports with upstream

---
 modules/ui.py | 37 ++++++++++++++++++-------------------
 1 file changed, 18 insertions(+), 19 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 987b1d7d..913b23b4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -5,44 +5,43 @@ import json
 import math
 import mimetypes
 import os
-import platform
 import random
-import subprocess as sp
 import sys
 import tempfile
 import time
 import traceback
+import platform
+import subprocess as sp
 from functools import partial, reduce
 
-import gradio as gr
-import gradio.routes
-import gradio.utils
 import numpy as np
-import piexif
 import torch
 from PIL import Image, PngImagePlugin
+import piexif
 
-from modules import localization, sd_hijack, sd_models
-from modules.paths import script_path
-from modules.shared import cmd_opts, opts, restricted_opts
+import gradio as gr
+import gradio.utils
+import gradio.routes
 
+from modules import sd_hijack, sd_models, localization
+from modules.paths import script_path
+from modules.shared import opts, cmd_opts, restricted_opts
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
-
-import modules.codeformer_model
-import modules.generation_parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.images_history as img_his
+import modules.shared as shared
+from modules.sd_samplers import samplers, samplers_for_img2img
+from modules.sd_hijack import model_hijack
 import modules.ldsr_model
 import modules.scripts
-import modules.shared as shared
+import modules.gfpgan_model
+import modules.codeformer_model
 import modules.styles
-import modules.textual_inversion.ui
+import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
-from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
+import modules.textual_inversion.ui
+import modules.hypernetworks.ui
+import modules.images_history as img_his
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
-- 
cgit v1.2.3


From 59ed74438318af893d2cba552b0e28dbc2a9266c Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Wed, 19 Oct 2022 17:19:02 -0700
Subject: face detection algo, configurability, reusability

Try to move the crop in the direction of a face if it is present

More internal configuration options for choosing weights of each of the algorithm's findings

Move logic into its module
---
 modules/textual_inversion/autocrop.py   | 216 ++++++++++++++++++++++++++++++++
 modules/textual_inversion/preprocess.py | 150 +++-------------------
 2 files changed, 230 insertions(+), 136 deletions(-)
 create mode 100644 modules/textual_inversion/autocrop.py

(limited to 'modules')

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
new file mode 100644
index 00000000..f858a958
--- /dev/null
+++ b/modules/textual_inversion/autocrop.py
@@ -0,0 +1,216 @@
+import cv2
+from collections import defaultdict
+from math import log, sqrt
+import numpy as np
+from PIL import Image, ImageDraw
+
+GREEN = "#0F0"
+BLUE = "#00F"
+RED = "#F00"
+
+def crop_image(im, settings):
+  """ Intelligently crop an image to the subject matter """
+  if im.height > im.width:
+      im = im.resize((settings.crop_width, settings.crop_height * im.height // im.width))
+  else:
+      im = im.resize((settings.crop_width * im.width // im.height, settings.crop_height))
+
+  focus = focal_point(im, settings)
+
+  # take the focal point and turn it into crop coordinates that try to center over the focal
+  # point but then get adjusted back into the frame
+  y_half = int(settings.crop_height / 2)
+  x_half = int(settings.crop_width / 2)
+
+  x1 = focus.x - x_half
+  if x1 < 0:
+      x1 = 0
+  elif x1 + settings.crop_width > im.width:
+      x1 = im.width - settings.crop_width
+
+  y1 = focus.y - y_half
+  if y1 < 0:
+      y1 = 0
+  elif y1 + settings.crop_height > im.height:
+      y1 = im.height - settings.crop_height
+
+  x2 = x1 + settings.crop_width
+  y2 = y1 + settings.crop_height
+
+  crop = [x1, y1, x2, y2]
+
+  if settings.annotate_image:
+    d = ImageDraw.Draw(im)
+    rect = list(crop)
+    rect[2] -= 1
+    rect[3] -= 1
+    d.rectangle(rect, outline=GREEN)
+    if settings.destop_view_image:
+      im.show()
+
+  return im.crop(tuple(crop))
+
+def focal_point(im, settings):
+    corner_points = image_corner_points(im, settings)
+    entropy_points = image_entropy_points(im, settings)
+    face_points = image_face_points(im, settings)
+
+    total_points = len(corner_points) + len(entropy_points) + len(face_points)
+
+    corner_weight = settings.corner_points_weight
+    entropy_weight = settings.entropy_points_weight
+    face_weight = settings.face_points_weight
+
+    weight_pref_total = corner_weight + entropy_weight + face_weight
+
+    # weight things
+    pois = []
+    if weight_pref_total == 0 or total_points == 0: 
+      return pois
+
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ]
+    )
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ]
+    )
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
+    )
+
+    if settings.annotate_image:
+      d = ImageDraw.Draw(im)
+
+    average_point = poi_average(pois, settings, im=im)
+
+    if settings.annotate_image:
+      d.ellipse([average_point.x - 25, average_point.y - 25, average_point.x + 25, average_point.y + 25], outline=GREEN)
+      
+    return average_point
+
+
+def image_face_points(im, settings):
+    np_im = np.array(im)
+    gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
+    classifier = cv2.CascadeClassifier(f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml')
+
+    minsize = int(min(im.width, im.height) * 0.15) # at least N percent of the smallest side
+    faces = classifier.detectMultiScale(gray, scaleFactor=1.05,
+      minNeighbors=5, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+
+    if len(faces) == 0:
+      return []
+
+    rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+    if settings.annotate_image:
+      for f in rects:
+        d = ImageDraw.Draw(im)
+        d.rectangle(f, outline=RED)
+    
+    return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2) for r in rects]
+
+
+def image_corner_points(im, settings):
+    grayscale = im.convert("L")
+
+    # naive attempt at preventing focal points from collecting at watermarks near the bottom
+    gd = ImageDraw.Draw(grayscale)
+    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
+
+    np_im = np.array(grayscale)
+
+    points = cv2.goodFeaturesToTrack(
+        np_im,
+        maxCorners=100,
+        qualityLevel=0.04,
+        minDistance=min(grayscale.width, grayscale.height)*0.07,
+        useHarrisDetector=False,
+    )
+
+    if points is None:
+        return []
+
+    focal_points = []
+    for point in points:
+        x, y = point.ravel()
+        focal_points.append(PointOfInterest(x, y))
+
+    return focal_points
+
+
+def image_entropy_points(im, settings):
+    landscape = im.height < im.width
+    portrait = im.height > im.width
+    if landscape:
+      move_idx = [0, 2]
+      move_max = im.size[0]
+    elif portrait:
+      move_idx = [1, 3]
+      move_max = im.size[1]
+    else:
+      return []
+
+    e_max = 0
+    crop_current = [0, 0, settings.crop_width, settings.crop_height]
+    crop_best = crop_current
+    while crop_current[move_idx[1]] < move_max:
+        crop = im.crop(tuple(crop_current))
+        e = image_entropy(crop)
+
+        if (e > e_max):
+          e_max = e
+          crop_best = list(crop_current)
+
+        crop_current[move_idx[0]] += 4
+        crop_current[move_idx[1]] += 4
+
+    x_mid = int(crop_best[0] + settings.crop_width/2)
+    y_mid = int(crop_best[1] + settings.crop_height/2)
+
+    return [PointOfInterest(x_mid, y_mid)]
+
+
+def image_entropy(im):
+    # greyscale image entropy
+    band = np.asarray(im.convert("1"))
+    hist, _ = np.histogram(band, bins=range(0, 256))
+    hist = hist[hist > 0]
+    return -np.log2(hist / hist.sum()).sum()
+
+
+def poi_average(pois, settings, im=None):
+    weight = 0.0
+    x = 0.0
+    y = 0.0
+    for pois in pois:
+        if settings.annotate_image and im is not None:
+          w = 4 * 0.5 * sqrt(pois.weight)
+          d = ImageDraw.Draw(im)
+          d.ellipse([
+            pois.x - w, pois.y - w,
+            pois.x + w, pois.y + w ], fill=BLUE)
+        weight += pois.weight
+        x += pois.x * pois.weight
+        y += pois.y * pois.weight
+    avg_x = round(x / weight)
+    avg_y = round(y / weight)
+
+    return PointOfInterest(avg_x, avg_y)
+
+
+class PointOfInterest:
+  def __init__(self, x, y, weight=1.0):
+    self.x = x
+    self.y = y
+    self.weight = weight
+
+
+class Settings:
+  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False):
+    self.crop_width = crop_width
+    self.crop_height = crop_height
+    self.corner_points_weight = corner_points_weight
+    self.entropy_points_weight = entropy_points_weight
+    self.face_points_weight = entropy_points_weight
+    self.annotate_image = annotate_image
+    self.destop_view_image = False
\ No newline at end of file
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 7c1a594e..0c79f012 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,7 +1,5 @@
 import os
-import cv2
-import numpy as np
-from PIL import Image, ImageOps, ImageDraw
+from PIL import Image, ImageOps
 import platform
 import sys
 import tqdm
@@ -9,6 +7,7 @@ import time
 
 from modules import shared, images
 from modules.shared import opts, cmd_opts
+from modules.textual_inversion import autocrop
 if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
@@ -80,6 +79,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         if process_flip:
             save_pic_with_caption(ImageOps.mirror(image), index)
 
+
     for index, imagefile in enumerate(tqdm.tqdm(files)):
         subindex = [0]
         filename = os.path.join(src, imagefile)
@@ -118,37 +118,16 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
             
             processing_option_ran = True
 
-        if process_entropy_focus and (is_tall or is_wide):
-            if is_tall:
-                img = img.resize((width, height * img.height // img.width))
-            else:
-                img = img.resize((width * img.width // img.height, height))
-
-            x_focal_center, y_focal_center = image_central_focal_point(img, width, height)
-
-            # take the focal point and turn it into crop coordinates that try to center over the focal
-            # point but then get adjusted back into the frame
-            y_half = int(height / 2)
-            x_half = int(width / 2)
-
-            x1 = x_focal_center - x_half
-            if x1 < 0:
-                x1 = 0
-            elif x1 + width > img.width:
-                x1 = img.width - width
-
-            y1 = y_focal_center - y_half
-            if y1 < 0:
-                y1 = 0
-            elif y1 + height > img.height:
-                y1 = img.height - height
-
-            x2 = x1 + width
-            y2 = y1 + height
-
-            crop = [x1, y1, x2, y2]
-
-            focal = img.crop(tuple(crop))
+        if process_entropy_focus and img.height != img.width:
+            autocrop_settings = autocrop.Settings(
+                crop_width = width,
+                crop_height = height,
+                face_points_weight = 0.9,
+                entropy_points_weight = 0.7,
+                corner_points_weight = 0.5,
+                annotate_image = False
+            )
+            focal = autocrop.crop_image(img, autocrop_settings)
             save_pic(focal, index)
 
             processing_option_ran = True
@@ -157,105 +136,4 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
             img = images.resize_image(1, img, width, height)
             save_pic(img, index)
 
-        shared.state.nextjob()
-
-
-def image_central_focal_point(im, target_width, target_height):
-    focal_points = []
-
-    focal_points.extend(
-        image_focal_points(im)
-    )
-
-    fp_entropy = image_entropy_point(im, target_width, target_height)
-    fp_entropy['weight'] = len(focal_points) + 1 # about half of the weight to entropy
-
-    focal_points.append(fp_entropy)
-
-    weight = 0.0
-    x = 0.0
-    y = 0.0
-    for focal_point in focal_points:
-        weight += focal_point['weight']
-        x += focal_point['x'] * focal_point['weight']
-        y += focal_point['y'] * focal_point['weight']
-    avg_x = round(x // weight)
-    avg_y = round(y // weight)
-
-    return avg_x, avg_y
-
-
-def image_focal_points(im):
-    grayscale = im.convert("L")
-
-    # naive attempt at preventing focal points from collecting at watermarks near the bottom
-    gd = ImageDraw.Draw(grayscale)
-    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
-
-    np_im = np.array(grayscale)
-
-    points = cv2.goodFeaturesToTrack(
-        np_im,
-        maxCorners=100,
-        qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.07,
-        useHarrisDetector=False,
-    )
-
-    if points is None:
-        return []
-
-    focal_points = []
-    for point in points:
-        x, y = point.ravel()
-        focal_points.append({
-            'x': x,
-            'y': y,
-            'weight': 1.0
-        })
-
-    return focal_points
-
-
-def image_entropy_point(im, crop_width, crop_height):
-    landscape = im.height < im.width
-    portrait = im.height > im.width
-    if landscape:
-      move_idx = [0, 2]
-      move_max = im.size[0]
-    elif portrait:
-      move_idx = [1, 3]
-      move_max = im.size[1]
-
-    e_max = 0
-    crop_current = [0, 0, crop_width, crop_height]
-    crop_best = crop_current
-    while crop_current[move_idx[1]] < move_max:
-        crop = im.crop(tuple(crop_current))
-        e = image_entropy(crop)
-
-        if (e > e_max):
-          e_max = e
-          crop_best = list(crop_current)
-
-        crop_current[move_idx[0]] += 4
-        crop_current[move_idx[1]] += 4
-
-    x_mid = int(crop_best[0] + crop_width/2)
-    y_mid = int(crop_best[1] + crop_height/2)
-
-
-    return {
-        'x': x_mid,
-        'y': y_mid,
-        'weight': 1.0
-    }
-
-
-def image_entropy(im):
-    # greyscale image entropy
-    band = np.asarray(im.convert("1"))
-    hist, _ = np.histogram(band, bins=range(0, 256))
-    hist = hist[hist > 0]
-    return -np.log2(hist / hist.sum()).sum()
-
+        shared.state.nextjob()
\ No newline at end of file
-- 
cgit v1.2.3


From 858462f719c22ca9f24b94a41699653c34b5f4fb Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 02:57:18 +0100
Subject: do caption copy for both flips

---
 modules/textual_inversion/preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 3713bc89..6bba3852 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -82,7 +82,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
         subindex[0] += 1
 
     def save_pic(image, index, existing_caption=None):
-        save_pic_with_caption(image, index)
+        save_pic_with_caption(image, index, existing_caption=existing_caption)
 
         if process_flip:
             save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)
-- 
cgit v1.2.3


From c6345bd445463b7aa41723d6637e80dfa293a890 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Wed, 19 Oct 2022 21:23:57 -0500
Subject: nerf line length

---
 modules/ui.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 9f6edc5f..cb9a6c6e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -83,7 +83,7 @@ folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 apply_style_symbol = '\U0001f4cb'  # 📋
-trash_prompt_symbol = '\U0001F5D1' # 🗑🗑🗑
+trash_prompt_symbol = '\U0001F5D1' #
 
 
 def plaintext_to_html(text):
@@ -617,7 +617,10 @@ def create_ui(wrap_gradio_gpu_call):
         return refresh_button
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,\
+        txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter,\
+        token_button = create_toprow(is_img2img=False)
+
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
-- 
cgit v1.2.3


From aa7ff2a1972f3865883e10ba28c5414cdebe8e3b Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 21:46:13 -0700
Subject: Fixed non-square highres fix generation

---
 modules/processing.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 684e5833..3caac25e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -541,10 +541,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
 
-    def create_dummy_mask(self, x):
+    def create_dummy_mask(self, x, first_phase: bool = False):
         if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+            height = self.firstphase_height if first_phase else self.height
+            width = self.firstphase_width if first_phase else self.width
+
             # The "masked-image" in this case will just be all zeros since the entire image is masked.
-            image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device)
+            image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
             image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
 
             # Add the fake full 1s mask to the first dimension.
@@ -567,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, first_phase=True))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
-- 
cgit v1.2.3


From 930b4c64f7dbce6918894d53538003e5959fd022 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 20 Oct 2022 08:18:02 +0300
Subject: allow float sizes for hypernet's layer_structure

---
 modules/hypernetworks/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 08f75f15..e0741d08 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -15,7 +15,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
     assert not os.path.exists(fn), f"file {fn} already exists"
 
     if type(layer_structure) == str:
-        layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure)))
+        layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
 
     hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
         name=name,
-- 
cgit v1.2.3


From 158d678f596d7fc304a6ce2f0dc31f8abfe62250 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Thu, 20 Oct 2022 01:08:24 -0500
Subject: clear prompt button now works on both relevant tabs. Device detection
 stuff will be added later.

---
 modules/ui.py | 21 ++++++++++++++++++---
 1 file changed, 18 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index cb9a6c6e..bde546cc 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -424,6 +424,16 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
+# setup button for clearing prompt input boxes on client side of webui
+def connect_trash_prompt(dummy_component, button, is_img2img):
+
+    button.click(
+        fn=lambda: print("Clearing prompt"),
+        _js="trash_prompt",
+        inputs=[],
+        outputs=[],
+    )
+
 def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
     """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
         (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
@@ -540,7 +550,7 @@ def create_toprow(is_img2img):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
                     prompt_style2.save_to_config = True
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button, trash_prompt
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -619,10 +629,11 @@ def create_ui(wrap_gradio_gpu_call):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,\
         txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter,\
-        token_button = create_toprow(is_img2img=False)
+        token_button, trash_prompt_button = create_toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
+        connect_trash_prompt(dummy_component, trash_prompt_button, False)
 
         with gr.Row(elem_id='txt2img_progress_row'):
             with gr.Column(scale=1):
@@ -807,7 +818,11 @@ def create_ui(wrap_gradio_gpu_call):
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit,\
+        img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
+        token_counter, token_button, trash_prompt_button = create_toprow(is_img2img=True)
+
+        connect_trash_prompt(dummy_component,trash_prompt_button, True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-- 
cgit v1.2.3


From 0ddaf8d2028a7251e8c4ad93551a43b5d4700841 Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Thu, 20 Oct 2022 00:34:55 -0700
Subject: improve face detection a lot

---
 modules/textual_inversion/autocrop.py | 99 ++++++++++++++++++++++-------------
 1 file changed, 62 insertions(+), 37 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index f858a958..5a551c25 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -8,12 +8,18 @@ GREEN = "#0F0"
 BLUE = "#00F"
 RED = "#F00"
 
+
 def crop_image(im, settings):
   """ Intelligently crop an image to the subject matter """
   if im.height > im.width:
       im = im.resize((settings.crop_width, settings.crop_height * im.height // im.width))
-  else:
+  elif im.width > im.height:
       im = im.resize((settings.crop_width * im.width // im.height, settings.crop_height))
+  else:
+      im = im.resize((settings.crop_width, settings.crop_height))
+
+  if im.height == im.width:
+    return im
 
   focus = focal_point(im, settings)
 
@@ -78,13 +84,18 @@ def focal_point(im, settings):
       [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
     )
 
-    if settings.annotate_image:
-      d = ImageDraw.Draw(im)
-
-    average_point = poi_average(pois, settings, im=im)
+    average_point = poi_average(pois, settings)
 
     if settings.annotate_image:
-      d.ellipse([average_point.x - 25, average_point.y - 25, average_point.x + 25, average_point.y + 25], outline=GREEN)
+      d = ImageDraw.Draw(im)
+      for f in face_points:
+        d.rectangle(f.bounding(f.size), outline=RED)
+      for f in entropy_points:
+        d.rectangle(f.bounding(30), outline=BLUE)
+      for poi in pois:
+        w = max(4, 4 * 0.5 * sqrt(poi.weight))
+        d.ellipse(poi.bounding(w), fill=BLUE)
+      d.ellipse(average_point.bounding(25), outline=GREEN)
       
     return average_point
 
@@ -92,22 +103,32 @@ def focal_point(im, settings):
 def image_face_points(im, settings):
     np_im = np.array(im)
     gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
-    classifier = cv2.CascadeClassifier(f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml')
-
-    minsize = int(min(im.width, im.height) * 0.15) # at least N percent of the smallest side
-    faces = classifier.detectMultiScale(gray, scaleFactor=1.05,
-      minNeighbors=5, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
 
-    if len(faces) == 0:
-      return []
-
-    rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
-    if settings.annotate_image:
-      for f in rects:
-        d = ImageDraw.Draw(im)
-        d.rectangle(f, outline=RED)
-    
-    return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2) for r in rects]
+    tries = [
+      [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
+    ]
+
+    for t in tries:
+      # print(t[0])
+      classifier = cv2.CascadeClassifier(t[0])
+      minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
+      try:
+        faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
+          minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+      except:
+        continue
+
+      if len(faces) > 0:
+        rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+        return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2])) for r in rects]
+    return []
 
 
 def image_corner_points(im, settings):
@@ -132,8 +153,8 @@ def image_corner_points(im, settings):
 
     focal_points = []
     for point in points:
-        x, y = point.ravel()
-        focal_points.append(PointOfInterest(x, y))
+      x, y = point.ravel()
+      focal_points.append(PointOfInterest(x, y, size=4))
 
     return focal_points
 
@@ -167,31 +188,26 @@ def image_entropy_points(im, settings):
     x_mid = int(crop_best[0] + settings.crop_width/2)
     y_mid = int(crop_best[1] + settings.crop_height/2)
 
-    return [PointOfInterest(x_mid, y_mid)]
+    return [PointOfInterest(x_mid, y_mid, size=25)]
 
 
 def image_entropy(im):
     # greyscale image entropy
-    band = np.asarray(im.convert("1"))
+    # band = np.asarray(im.convert("L"))
+    band = np.asarray(im.convert("1"), dtype=np.uint8)
     hist, _ = np.histogram(band, bins=range(0, 256))
     hist = hist[hist > 0]
     return -np.log2(hist / hist.sum()).sum()
 
 
-def poi_average(pois, settings, im=None):
+def poi_average(pois, settings):
     weight = 0.0
     x = 0.0
     y = 0.0
-    for pois in pois:
-        if settings.annotate_image and im is not None:
-          w = 4 * 0.5 * sqrt(pois.weight)
-          d = ImageDraw.Draw(im)
-          d.ellipse([
-            pois.x - w, pois.y - w,
-            pois.x + w, pois.y + w ], fill=BLUE)
-        weight += pois.weight
-        x += pois.x * pois.weight
-        y += pois.y * pois.weight
+    for poi in pois:
+        weight += poi.weight
+        x += poi.x * poi.weight
+        y += poi.y * poi.weight
     avg_x = round(x / weight)
     avg_y = round(y / weight)
 
@@ -199,10 +215,19 @@ def poi_average(pois, settings, im=None):
 
 
 class PointOfInterest:
-  def __init__(self, x, y, weight=1.0):
+  def __init__(self, x, y, weight=1.0, size=10):
     self.x = x
     self.y = y
     self.weight = weight
+    self.size = size
+
+  def bounding(self, size):
+    return [
+      self.x - size//2,
+      self.y - size//2,
+      self.x + size//2,
+      self.y + size//2
+    ]
 
 
 class Settings:
-- 
cgit v1.2.3


From f8733ad08be08bafb40f4299785590e11f049e96 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Thu, 20 Oct 2022 11:07:37 +0000
Subject: add linear as a act func (option for doin nothing)

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 913b23b4..716f14b8 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1224,7 +1224,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["relu", "leakyrelu"])
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"])
 
                     with gr.Row():
                         with gr.Column(scale=3):
-- 
cgit v1.2.3


From 9681419e422515e42444e0174355b760645a846f Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Thu, 20 Oct 2022 16:53:46 +0900
Subject: train: fixed preprocess image ratio

---
 modules/textual_inversion/preprocess.py | 54 +++++++++++++++++++++------------
 1 file changed, 35 insertions(+), 19 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 886cf0c3..2743bdeb 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,5 +1,6 @@
 import os
 from PIL import Image, ImageOps
+import math
 import platform
 import sys
 import tqdm
@@ -38,6 +39,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
     height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
+    split_threshold = 0.5
+    overlap_ratio = 0.2
 
     assert src != dst, 'same directory specified as source and destination'
 
@@ -78,6 +81,29 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         if process_flip:
             save_pic_with_caption(ImageOps.mirror(image), index)
 
+    def split_pic(image, inverse_xy):
+        if inverse_xy:
+            from_w, from_h = image.height, image.width
+            to_w, to_h = height, width
+        else:
+            from_w, from_h = image.width, image.height
+            to_w, to_h = width, height
+        h = from_h * to_w // from_w
+        if inverse_xy:
+            image = image.resize((h, to_w))
+        else:
+            image = image.resize((to_w, h))
+
+        split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
+        y_step = (h - to_h) / (split_count - 1)
+        for i in range(split_count):
+            y = int(y_step * i)
+            if inverse_xy:
+                splitted = image.crop((y, 0, y + to_h, to_w))
+            else:
+                splitted = image.crop((0, y, to_w, y + to_h))
+            yield splitted
+
     for index, imagefile in enumerate(tqdm.tqdm(files)):
         subindex = [0]
         filename = os.path.join(src, imagefile)
@@ -89,26 +115,16 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         if shared.state.interrupted:
             break
 
-        ratio = img.height / img.width
-        is_tall = ratio > 1.35
-        is_wide = ratio < 1 / 1.35
-
-        if process_split and is_tall:
-            img = img.resize((width, height * img.height // img.width))
-
-            top = img.crop((0, 0, width, height))
-            save_pic(top, index)
-
-            bot = img.crop((0, img.height - height, width, img.height))
-            save_pic(bot, index)
-        elif process_split and is_wide:
-            img = img.resize((width * img.width // img.height, height))
-
-            left = img.crop((0, 0, width, height))
-            save_pic(left, index)
+        if img.height > img.width:
+            ratio = (img.width * height) / (img.height * width)
+            inverse_xy = False
+        else:
+            ratio = (img.height * width) / (img.width * height)
+            inverse_xy = True
 
-            right = img.crop((img.width - width, 0, img.width, height))
-            save_pic(right, index)
+        if process_split and ratio < 1.0 and ratio <= split_threshold:
+            for splitted in split_pic(img, inverse_xy):
+                save_pic(splitted, index)
         else:
             img = images.resize_image(1, img, width, height)
             save_pic(img, index)
-- 
cgit v1.2.3


From 85dd62c4c7635b8e21a75f140d093036069e97a1 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Thu, 20 Oct 2022 22:56:45 +0900
Subject: train: ui: added `Split image threshold` and `Split image overlap
 ratio` to preprocess

---
 modules/textual_inversion/preprocess.py | 10 +++++-----
 modules/ui.py                           | 16 ++++++++++++++--
 2 files changed, 19 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 2743bdeb..c8df8aa0 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -12,7 +12,7 @@ if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
 
-def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2):
     try:
         if process_caption:
             shared.interrogator.load()
@@ -22,7 +22,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
             deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
 
-        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
+        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio)
 
     finally:
 
@@ -34,13 +34,13 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
 
 
 
-def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
-    split_threshold = 0.5
-    overlap_ratio = 0.2
+    split_threshold = max(0.0, min(1.0, split_threshold))
+    overlap_ratio = max(0.0, min(0.9, overlap_ratio))
 
     assert src != dst, 'same directory specified as source and destination'
 
diff --git a/modules/ui.py b/modules/ui.py
index a2dbd41e..bc7f3330 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1240,10 +1240,14 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
-                        process_split = gr.Checkbox(label='Split oversized images into two')
+                        process_split = gr.Checkbox(label='Split oversized images')
                         process_caption = gr.Checkbox(label='Use BLIP for caption')
                         process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
 
+                    with gr.Row(visible=False) as process_split_extra_row:
+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
+
                     with gr.Row():
                         with gr.Column(scale=3):
                             gr.HTML(value="")
@@ -1251,6 +1255,12 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
+                    process_split.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_split],
+                        outputs=[process_split_extra_row],
+                    )
+
                 with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     with gr.Row():
@@ -1327,7 +1337,9 @@ def create_ui(wrap_gradio_gpu_call):
                 process_flip,
                 process_split,
                 process_caption,
-                process_caption_deepbooru
+                process_caption_deepbooru,
+                process_split_threshold,
+                process_overlap_ratio,
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.3


From d8acd34f66ab35a91f10d66330bcc95a83bfcac6 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Thu, 20 Oct 2022 23:43:03 +0900
Subject: generalized some functions and option for ignoring first layer

---
 modules/hypernetworks/hypernetwork.py | 23 +++++++++++++++--------
 1 file changed, 15 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7d617680..3a44b377 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -21,21 +21,27 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
-
+    activation_dict = {"relu": torch.nn.ReLU, "leakyrelu": torch.nn.LeakyReLU, "elu": torch.nn.ELU,
+                       "swish": torch.nn.Hardswish}
+    
     def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False, activation_func=None):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
         assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
         assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-
+        
         linears = []
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
-            if activation_func == "relu":
-                linears.append(torch.nn.ReLU())
-            if activation_func == "leakyrelu":
-                linears.append(torch.nn.LeakyReLU())
+            # if skip_first_layer because first parameters potentially contain negative values
+            if i < 1: continue
+            if activation_func in HypernetworkModule.activation_dict:
+                linears.append(HypernetworkModule.activation_dict[activation_func]())
+            else:
+                print("Invalid key {} encountered as activation function!".format(activation_func))
+            # if use_dropout: 
+            linears.append(torch.nn.Dropout(p=0.3))
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -46,7 +52,7 @@ class HypernetworkModule(torch.nn.Module):
             self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                if not "ReLU" in layer.__str__():
+                if isinstance(layer, torch.nn.Linear):
                     layer.weight.data.normal_(mean=0.0, std=0.01)
                     layer.bias.data.zero_()
 
@@ -298,7 +304,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
+    # if optimizer == "Adam": or else Adam / AdamW / etc...
+    optimizer = torch.optim.Adam(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
-- 
cgit v1.2.3


From a71e0212363979c7cbbb797c9fbd5f8cd03b29d3 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Thu, 20 Oct 2022 23:48:52 +0900
Subject: only linear

---
 modules/hypernetworks/hypernetwork.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3a44b377..905cbeef 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -35,13 +35,13 @@ class HypernetworkModule(torch.nn.Module):
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
             # if skip_first_layer because first parameters potentially contain negative values
-            if i < 1: continue
+            # if i < 1: continue
             if activation_func in HypernetworkModule.activation_dict:
                 linears.append(HypernetworkModule.activation_dict[activation_func]())
             else:
                 print("Invalid key {} encountered as activation function!".format(activation_func))
             # if use_dropout: 
-            linears.append(torch.nn.Dropout(p=0.3))
+            # linears.append(torch.nn.Dropout(p=0.3))
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -80,7 +80,7 @@ class HypernetworkModule(torch.nn.Module):
     def trainables(self):
         layer_structure = []
         for layer in self.linear:
-            if not "ReLU" in layer.__str__():
+            if isinstance(layer, torch.nn.Linear):
                 layer_structure += [layer.weight, layer.bias]
         return layer_structure
 
@@ -304,8 +304,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    # if optimizer == "Adam": or else Adam / AdamW / etc...
-    optimizer = torch.optim.Adam(weights, lr=scheduler.learn_rate)
+    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
+    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
-- 
cgit v1.2.3


From d07cb46f34b3d9fe7a78b102f899ebef352ea56b Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 20 Oct 2022 23:58:52 +0800
Subject: inspiration pull request

---
 modules/inspiration.py | 122 +++++++++++++++++++++++++++++++++++++++++++++++++
 modules/shared.py      |   1 +
 modules/ui.py          |  13 ++++--
 3 files changed, 131 insertions(+), 5 deletions(-)
 create mode 100644 modules/inspiration.py

(limited to 'modules')

diff --git a/modules/inspiration.py b/modules/inspiration.py
new file mode 100644
index 00000000..456bfcb5
--- /dev/null
+++ b/modules/inspiration.py
@@ -0,0 +1,122 @@
+import os
+import random
+import gradio 
+inspiration_path = "inspiration"
+inspiration_system_path = os.path.join(inspiration_path, "system")
+def read_name_list(file):
+    if not os.path.exists(file):
+        return []
+    f = open(file, "r")
+    ret = []
+    line = f.readline()
+    while len(line) > 0:
+        line = line.rstrip("\n")
+        ret.append(line)
+    print(ret)
+    return ret
+
+def save_name_list(file, name): 
+    print(file)
+    f = open(file, "a")
+    f.write(name + "\n")
+
+def get_inspiration_images(source, types):   
+    path = os.path.join(inspiration_path , types) 
+    if source == "Favorites":
+        names = read_name_list(os.path.join(inspiration_system_path, types + "_faverites.txt"))
+        names = random.sample(names, 25)
+    elif source == "Abandoned":
+        names = read_name_list(os.path.join(inspiration_system_path, types + "_abondened.txt"))
+        names = random.sample(names, 25)
+    elif source == "Exclude abandoned":
+        abondened = read_name_list(os.path.join(inspiration_system_path, types + "_abondened.txt"))        
+        all_names = os.listdir(path)
+        names = []
+        while len(names) < 25:
+            name = random.choice(all_names)
+            if name not in abondened:
+                names.append(name)
+    else:
+        names = random.sample(os.listdir(path), 25)
+    names = random.sample(names, 25)
+    image_list = []
+    for a in names:
+        image_path = os.path.join(path, a)
+        images = os.listdir(image_path)        
+        image_list.append(os.path.join(image_path, random.choice(images)))
+    return image_list, names
+
+def select_click(index, types, name_list):
+    name = name_list[int(index)]
+    path = os.path.join(inspiration_path, types, name)
+    images = os.listdir(path)
+    return name, [os.path.join(path, x) for x in images]
+
+def give_up_click(name, types):
+    file = os.path.join(inspiration_system_path, types + "_abandoned.txt")
+    name_list = read_name_list(file)
+    if name not in name_list:
+        save_name_list(file, name)
+   
+def collect_click(name, types):
+    file = os.path.join(inspiration_system_path, types + "_faverites.txt")
+    print(file)
+    name_list = read_name_list(file)
+    print(name_list)
+    if name not in name_list:
+        save_name_list(file, name)
+
+def moveout_click(name, types):
+    file = os.path.join(inspiration_system_path, types + "_faverites.txt")
+    name_list = read_name_list(file)
+    if name not in name_list:
+        save_name_list(file, name)
+
+def source_change(source):
+    if source == "Abandoned" or source == "Favorites":
+        return gradio.Button.update(visible=True, value=f"Move out {source}")
+    else:
+        return gradio.Button.update(visible=False)
+
+def ui(gr, opts):     
+    with gr.Blocks(analytics_enabled=False) as inspiration:
+        flag = os.path.exists(inspiration_path)        
+        if flag:
+            types = os.listdir(inspiration_path)
+            types = [x for x in types if x != "system"]
+            flag = len(types) > 0
+        if not flag:
+            os.mkdir(inspiration_path)
+            gr.HTML("""
+                <div align='center' width="50%>You need get "inspiration" images first. You can create these images by run "Create inspiration images" script in txt2img page, or download zip file from here and unzip these file under fold "inpiration".</div>"
+                """)           
+            return inspiration
+        if not os.path.exists(inspiration_system_path):
+            os.mkdir(inspiration_system_path)
+        gallery, names = get_inspiration_images("Exclude abandoned", types[0])   
+        with gr.Row():
+            with gr.Column(scale=2):                
+                inspiration_gallery = gr.Gallery(gallery, show_label=False, elem_id="inspiration_gallery").style(grid=5, height='auto')
+            with gr.Column(scale=1):
+                types = gr.Dropdown(choices=types, value=types[0], label="Type", visible=len(types) > 1)
+                with gr.Row():                    
+                    source = gr.Dropdown(choices=["All", "Favorites", "Exclude abandoned", "Abandoned"], value="Exclude abandoned", label="Source")
+                    get_inspiration = gr.Button("Get inspiration")
+                name = gr.Textbox(show_label=False, interactive=False)
+                with gr.Row(): 
+                    send_to_txt2img = gr.Button('to txt2img')
+                    send_to_img2img = gr.Button('to img2img')
+                style_gallery = gr.Gallery(show_label=False, elem_id="inspiration_style_gallery").style(grid=2, height='auto')                          
+               
+                collect = gr.Button('Collect')     
+                give_up = gr.Button("Don't show any more")
+                moveout = gr.Button("Move out", visible=False)
+        with gr.Row():
+            select_button = gr.Button('set button', elem_id="inspiration_select_button")
+            name_list = gr.State(names)
+        source.change(source_change, inputs=[source], outputs=[moveout])
+        get_inspiration.click(get_inspiration_images, inputs=[source, types], outputs=[inspiration_gallery, name_list])
+        select_button.click(select_click, _js="inspiration_selected", inputs=[name, types, name_list], outputs=[name, style_gallery])
+        give_up.click(give_up_click, inputs=[name, types], outputs=None)
+        collect.click(collect_click, inputs=[name, types], outputs=None)
+    return inspiration
diff --git a/modules/shared.py b/modules/shared.py
index faede821..ae033710 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -78,6 +78,7 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
+parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
diff --git a/modules/ui.py b/modules/ui.py
index a2dbd41e..6a0a3c3b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -41,7 +41,8 @@ from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
-import modules.images_history as img_his
+import modules.images_history as images_history
+import modules.inspiration as inspiration
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -1082,9 +1083,9 @@ def create_ui(wrap_gradio_gpu_call):
                                 upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
                                 upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-
+                
                 with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers] , value=shared.sd_upscalers[0].name, type="index") 
 
                 with gr.Group():
                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
@@ -1178,7 +1179,8 @@ def create_ui(wrap_gradio_gpu_call):
         "i2i":img2img_paste_fields
     }
 
-    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
+    browser_interface = images_history.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
+    inspiration_interface = inspiration.ui(gr, opts)
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
@@ -1595,7 +1597,8 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (images_history, "History", "images_history"),
+        (browser_interface, "History", "images_history"),
+        (inspiration_interface, "Inspiration", "inspiration"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
-- 
cgit v1.2.3


From 108be15500aac590b4e00420635d7b61fccfa530 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Fri, 21 Oct 2022 01:00:41 +0900
Subject: fix bugs and optimizations

---
 modules/hypernetworks/hypernetwork.py | 105 +++++++++++++++++++---------------
 1 file changed, 59 insertions(+), 46 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 905cbeef..893ba110 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -36,14 +36,14 @@ class HypernetworkModule(torch.nn.Module):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
             # if skip_first_layer because first parameters potentially contain negative values
             # if i < 1: continue
+            if add_layer_norm:
+                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
             if activation_func in HypernetworkModule.activation_dict:
                 linears.append(HypernetworkModule.activation_dict[activation_func]())
             else:
                 print("Invalid key {} encountered as activation function!".format(activation_func))
             # if use_dropout: 
             # linears.append(torch.nn.Dropout(p=0.3))
-            if add_layer_norm:
-                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
         self.linear = torch.nn.Sequential(*linears)
 
@@ -115,11 +115,24 @@ class Hypernetwork:
 
         for k, layers in self.layers.items():
             for layer in layers:
-                layer.train()
                 res += layer.trainables()
 
         return res
 
+    def eval(self):
+        for k, layers in self.layers.items():
+            for layer in layers:
+                layer.eval()
+        for items in self.weights():
+            items.requires_grad = False
+
+    def train(self):
+        for k, layers in self.layers.items():
+            for layer in layers:
+                layer.train()
+        for items in self.weights():
+            items.requires_grad = True
+
     def save(self, filename):
         state_dict = {}
 
@@ -290,10 +303,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     hypernetwork = shared.loaded_hypernetwork
-    weights = hypernetwork.weights()
-    for weight in weights:
-        weight.requires_grad = True
-
     losses = torch.zeros((32,))
 
     last_saved_file = "<none>"
@@ -304,10 +313,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
-    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
+    optimizer = torch.optim.AdamW(hypernetwork.weights(), lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
+    hypernetwork.train()
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
 
@@ -328,8 +337,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
-            optimizer.zero_grad()
+            optimizer.zero_grad(set_to_none=True)
             loss.backward()
+            del loss
             optimizer.step()
         mean_loss = losses.mean()
         if torch.isnan(mean_loss):
@@ -346,44 +356,47 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
+            torch.cuda.empty_cache()
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
+            with torch.no_grad():
+                hypernetwork.eval()
+                shared.sd_model.cond_stage_model.to(devices.device)
+                shared.sd_model.first_stage_model.to(devices.device)
+
+                p = processing.StableDiffusionProcessingTxt2Img(
+                    sd_model=shared.sd_model,
+                    do_not_save_grid=True,
+                    do_not_save_samples=True,
+                )
 
-            optimizer.zero_grad()
-            shared.sd_model.cond_stage_model.to(devices.device)
-            shared.sd_model.first_stage_model.to(devices.device)
-
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-            )
-
-            if preview_from_txt2img:
-                p.prompt = preview_prompt
-                p.negative_prompt = preview_negative_prompt
-                p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
-                p.cfg_scale = preview_cfg_scale
-                p.seed = preview_seed
-                p.width = preview_width
-                p.height = preview_height
-            else:
-                p.prompt = entries[0].cond_text
-                p.steps = 20
-
-            preview_text = p.prompt
-
-            processed = processing.process_images(p)
-            image = processed.images[0] if len(processed.images)>0 else None
-
-            if unload:
-                shared.sd_model.cond_stage_model.to(devices.cpu)
-                shared.sd_model.first_stage_model.to(devices.cpu)
-
-            if image is not None:
-                shared.state.current_image = image
-                image.save(last_saved_image)
-                last_saved_image += f", prompt: {preview_text}"
+                if preview_from_txt2img:
+                    p.prompt = preview_prompt
+                    p.negative_prompt = preview_negative_prompt
+                    p.steps = preview_steps
+                    p.sampler_index = preview_sampler_index
+                    p.cfg_scale = preview_cfg_scale
+                    p.seed = preview_seed
+                    p.width = preview_width
+                    p.height = preview_height
+                else:
+                    p.prompt = entries[0].cond_text
+                    p.steps = 20
+
+                preview_text = p.prompt
+
+                processed = processing.process_images(p)
+                image = processed.images[0] if len(processed.images)>0 else None
+
+                if unload:
+                    shared.sd_model.cond_stage_model.to(devices.cpu)
+                    shared.sd_model.first_stage_model.to(devices.cpu)
+
+                if image is not None:
+                    shared.state.current_image = image
+                    image.save(last_saved_image)
+                    last_saved_image += f", prompt: {preview_text}"
+
+            hypernetwork.train()
 
         shared.state.job_no = hypernetwork.step
 
-- 
cgit v1.2.3


From f89829ec3a0baceb445451ad98d4fb4323e922aa Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Fri, 21 Oct 2022 01:37:11 +0900
Subject: Revert "fix bugs and optimizations"

This reverts commit 108be15500aac590b4e00420635d7b61fccfa530.
---
 modules/hypernetworks/hypernetwork.py | 105 +++++++++++++++-------------------
 1 file changed, 46 insertions(+), 59 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 893ba110..905cbeef 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -36,14 +36,14 @@ class HypernetworkModule(torch.nn.Module):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
             # if skip_first_layer because first parameters potentially contain negative values
             # if i < 1: continue
-            if add_layer_norm:
-                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
             if activation_func in HypernetworkModule.activation_dict:
                 linears.append(HypernetworkModule.activation_dict[activation_func]())
             else:
                 print("Invalid key {} encountered as activation function!".format(activation_func))
             # if use_dropout: 
             # linears.append(torch.nn.Dropout(p=0.3))
+            if add_layer_norm:
+                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
         self.linear = torch.nn.Sequential(*linears)
 
@@ -115,24 +115,11 @@ class Hypernetwork:
 
         for k, layers in self.layers.items():
             for layer in layers:
+                layer.train()
                 res += layer.trainables()
 
         return res
 
-    def eval(self):
-        for k, layers in self.layers.items():
-            for layer in layers:
-                layer.eval()
-        for items in self.weights():
-            items.requires_grad = False
-
-    def train(self):
-        for k, layers in self.layers.items():
-            for layer in layers:
-                layer.train()
-        for items in self.weights():
-            items.requires_grad = True
-
     def save(self, filename):
         state_dict = {}
 
@@ -303,6 +290,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     hypernetwork = shared.loaded_hypernetwork
+    weights = hypernetwork.weights()
+    for weight in weights:
+        weight.requires_grad = True
+
     losses = torch.zeros((32,))
 
     last_saved_file = "<none>"
@@ -313,10 +304,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    optimizer = torch.optim.AdamW(hypernetwork.weights(), lr=scheduler.learn_rate)
+    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
+    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    hypernetwork.train()
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
 
@@ -337,9 +328,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
-            optimizer.zero_grad(set_to_none=True)
+            optimizer.zero_grad()
             loss.backward()
-            del loss
             optimizer.step()
         mean_loss = losses.mean()
         if torch.isnan(mean_loss):
@@ -356,47 +346,44 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-            torch.cuda.empty_cache()
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
-            with torch.no_grad():
-                hypernetwork.eval()
-                shared.sd_model.cond_stage_model.to(devices.device)
-                shared.sd_model.first_stage_model.to(devices.device)
-
-                p = processing.StableDiffusionProcessingTxt2Img(
-                    sd_model=shared.sd_model,
-                    do_not_save_grid=True,
-                    do_not_save_samples=True,
-                )
 
-                if preview_from_txt2img:
-                    p.prompt = preview_prompt
-                    p.negative_prompt = preview_negative_prompt
-                    p.steps = preview_steps
-                    p.sampler_index = preview_sampler_index
-                    p.cfg_scale = preview_cfg_scale
-                    p.seed = preview_seed
-                    p.width = preview_width
-                    p.height = preview_height
-                else:
-                    p.prompt = entries[0].cond_text
-                    p.steps = 20
-
-                preview_text = p.prompt
-
-                processed = processing.process_images(p)
-                image = processed.images[0] if len(processed.images)>0 else None
-
-                if unload:
-                    shared.sd_model.cond_stage_model.to(devices.cpu)
-                    shared.sd_model.first_stage_model.to(devices.cpu)
-
-                if image is not None:
-                    shared.state.current_image = image
-                    image.save(last_saved_image)
-                    last_saved_image += f", prompt: {preview_text}"
-
-            hypernetwork.train()
+            optimizer.zero_grad()
+            shared.sd_model.cond_stage_model.to(devices.device)
+            shared.sd_model.first_stage_model.to(devices.device)
+
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+            )
+
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entries[0].cond_text
+                p.steps = 20
+
+            preview_text = p.prompt
+
+            processed = processing.process_images(p)
+            image = processed.images[0] if len(processed.images)>0 else None
+
+            if unload:
+                shared.sd_model.cond_stage_model.to(devices.cpu)
+                shared.sd_model.first_stage_model.to(devices.cpu)
+
+            if image is not None:
+                shared.state.current_image = image
+                image.save(last_saved_image)
+                last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
 
-- 
cgit v1.2.3


From 92a17a7a4a13fceb3c3e25a2e854b2a7dd6eb5df Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 20 Oct 2022 09:45:03 -0700
Subject: Made dummy latents smaller. Minor code cleanups

---
 modules/processing.py  | 7 ++++---
 modules/sd_samplers.py | 6 ++++--
 2 files changed, 8 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 3caac25e..539cde38 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -557,7 +557,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         else:
             # Dummy zero conditioning if we're not using inpainting model.
             # Still takes up a bit of memory, but no encoder call.
-            image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device)
+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
 
         return image_conditioning
 
@@ -759,8 +760,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
         else:
             self.image_conditioning = torch.zeros(
-                self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1], 
-                dtype=self.init_latent.dtype, 
+                self.init_latent.shape[0], 5, 1, 1,
+                dtype=self.init_latent.dtype,
                 device=self.init_latent.device
             )
 
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index c21be26e..cc682593 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -138,7 +138,7 @@ class VanillaStableDiffusionSampler:
         if self.stop_at is not None and self.step > self.stop_at:
             raise InterruptedException
 
-        # Have to unwrap the inpainting conditioning here to perform pre-preocessing
+        # Have to unwrap the inpainting conditioning here to perform pre-processing
         image_conditioning = None
         if isinstance(cond, dict):
             image_conditioning = cond["c_concat"][0]
@@ -146,7 +146,7 @@ class VanillaStableDiffusionSampler:
             unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
 
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)            
+        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
 
         assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
         cond = tensor
@@ -165,6 +165,8 @@ class VanillaStableDiffusionSampler:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)
             x_dec = img_orig * self.mask + self.nmask * x_dec
 
+        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
+        # Note that they need to be lists because it just concatenates them later.
         if image_conditioning is not None:
             cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-- 
cgit v1.2.3


From d1cb08bfb221cd1b0cfc6078162b4e206ea80a5c Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Thu, 20 Oct 2022 22:49:06 +0300
Subject: fix skip and interrupt for highres. fix option

---
 modules/processing.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index bcb0c32c..6324ca91 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -587,9 +587,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
-
-        return samples
+        return self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) or samples
 
 
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
-- 
cgit v1.2.3


From 708c3a7bd8ce68cbe1aa7c268e5a4b1980affc9f Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 20 Oct 2022 13:28:43 -0700
Subject: Added PLMS hijack and made sure to always replace methods

---
 modules/sd_hijack_inpainting.py | 163 ++++++++++++++++++++++++++++++++++++++--
 modules/sd_models.py            |   3 +-
 2 files changed, 157 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index d4d28d2e..43938071 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -1,16 +1,14 @@
 import torch
-import numpy as np
 
-from tqdm import tqdm
-from einops import rearrange, repeat
+from einops import repeat
 from omegaconf import ListConfig
 
-from types import MethodType
-
 import ldm.models.diffusion.ddpm
 import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
 
 from ldm.models.diffusion.ddpm import LatentDiffusion
+from ldm.models.diffusion.plms import PLMSSampler
 from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 
 # =================================================================================================
@@ -19,7 +17,7 @@ from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 # https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
 # =================================================================================================
 @torch.no_grad()
-def sample(self,
+def sample_ddim(self,
             S,
             batch_size,
             shape,
@@ -132,6 +130,153 @@ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=F
     return x_prev, pred_x0
 
 
+# =================================================================================================
+# Monkey patch PLMSSampler methods.
+# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes.
+# Adapted from:
+# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py
+# =================================================================================================
+@torch.no_grad()
+def sample_plms(self,
+            S,
+            batch_size,
+            shape,
+            conditioning=None,
+            callback=None,
+            normals_sequence=None,
+            img_callback=None,
+            quantize_x0=False,
+            eta=0.,
+            mask=None,
+            x0=None,
+            temperature=1.,
+            noise_dropout=0.,
+            score_corrector=None,
+            corrector_kwargs=None,
+            verbose=True,
+            x_T=None,
+            log_every_t=100,
+            unconditional_guidance_scale=1.,
+            unconditional_conditioning=None,
+            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+            **kwargs
+            ):
+    if conditioning is not None:
+        if isinstance(conditioning, dict):
+            ctmp = conditioning[list(conditioning.keys())[0]]
+            while isinstance(ctmp, list):
+                ctmp = ctmp[0]
+            cbs = ctmp.shape[0]
+            if cbs != batch_size:
+                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+        else:
+            if conditioning.shape[0] != batch_size:
+                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+    # sampling
+    C, H, W = shape
+    size = (batch_size, C, H, W)
+    print(f'Data shape for PLMS sampling is {size}')
+
+    samples, intermediates = self.plms_sampling(conditioning, size,
+                                                callback=callback,
+                                                img_callback=img_callback,
+                                                quantize_denoised=quantize_x0,
+                                                mask=mask, x0=x0,
+                                                ddim_use_original_steps=False,
+                                                noise_dropout=noise_dropout,
+                                                temperature=temperature,
+                                                score_corrector=score_corrector,
+                                                corrector_kwargs=corrector_kwargs,
+                                                x_T=x_T,
+                                                log_every_t=log_every_t,
+                                                unconditional_guidance_scale=unconditional_guidance_scale,
+                                                unconditional_conditioning=unconditional_conditioning,
+                                                )
+    return samples, intermediates
+
+
+@torch.no_grad()
+def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                    unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
+    b, *_, device = *x.shape, x.device
+
+    def get_model_output(x, t):
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            e_t = self.model.apply_model(x, t, c)
+        else:
+            x_in = torch.cat([x] * 2)
+            t_in = torch.cat([t] * 2)
+            
+            if isinstance(c, dict):
+                assert isinstance(unconditional_conditioning, dict)
+                c_in = dict()
+                for k in c:
+                    if isinstance(c[k], list):
+                        c_in[k] = [
+                            torch.cat([unconditional_conditioning[k][i], c[k][i]])
+                            for i in range(len(c[k]))
+                        ]
+                    else:
+                        c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
+            else:
+                c_in = torch.cat([unconditional_conditioning, c])
+
+            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+        if score_corrector is not None:
+            assert self.model.parameterization == "eps"
+            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+        return e_t
+
+    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+    def get_x_prev_and_pred_x0(e_t, index):
+        # select parameters corresponding to the currently considered timestep
+        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+        # current prediction for x_0
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        if quantize_denoised:
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+        return x_prev, pred_x0
+
+    e_t = get_model_output(x, t)
+    if len(old_eps) == 0:
+        # Pseudo Improved Euler (2nd order)
+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+        e_t_next = get_model_output(x_prev, t_next)
+        e_t_prime = (e_t + e_t_next) / 2
+    elif len(old_eps) == 1:
+        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (3 * e_t - old_eps[-1]) / 2
+    elif len(old_eps) == 2:
+        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+    elif len(old_eps) >= 3:
+        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+    return x_prev, pred_x0, e_t
+    
 # =================================================================================================
 # Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
 # Adapted from:
@@ -175,5 +320,9 @@ def should_hijack_inpainting(checkpoint_info):
 def do_inpainting_hijack():
     ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
     ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
+
     ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
-    ldm.models.diffusion.ddim.DDIMSampler.sample = sample
\ No newline at end of file
+    ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
+
+    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
+    ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
\ No newline at end of file
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 47836d25..7072db08 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -214,8 +214,6 @@ def load_model():
     sd_config = OmegaConf.load(checkpoint_info.config)
     
     if should_hijack_inpainting(checkpoint_info):
-        do_inpainting_hijack()
-
         # Hardcoded config for now...
         sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
         sd_config.model.params.use_ema = False
@@ -225,6 +223,7 @@ def load_model():
         # Create a "fake" config with a different name so that we know to unload it when switching models.
         checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
 
+    do_inpainting_hijack()
     sd_model = instantiate_from_config(sd_config.model)
     load_model_weights(sd_model, checkpoint_info)
 
-- 
cgit v1.2.3


From d23a46ceaa76af2847f11172f32c92665c268b1b Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Thu, 20 Oct 2022 23:49:14 +0300
Subject: Different approach to skip/interrupt with highres fix

---
 modules/processing.py  | 4 +++-
 modules/sd_samplers.py | 4 ++++
 2 files changed, 7 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 6324ca91..bcb0c32c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -587,7 +587,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        return self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) or samples
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
+
+        return samples
 
 
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b58e810b..7ff77c01 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -196,6 +196,7 @@ class VanillaStableDiffusionSampler:
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
 
         self.init_latent = x
+        self.last_latent = x
         self.step = 0
 
         samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
@@ -206,6 +207,7 @@ class VanillaStableDiffusionSampler:
         self.initialize(p)
 
         self.init_latent = None
+        self.last_latent = x
         self.step = 0
 
         steps = steps or p.steps
@@ -388,6 +390,7 @@ class KDiffusionSampler:
             extra_params_kwargs['sigmas'] = sigma_sched
 
         self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
 
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
@@ -414,6 +417,7 @@ class KDiffusionSampler:
         else:
             extra_params_kwargs['sigmas'] = sigmas
 
+        self.last_latent = x
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
-- 
cgit v1.2.3


From 49533eed9e3aad19e9868ee140708baec4fd44be Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 20 Oct 2022 16:01:27 -0700
Subject: XY grid correctly re-assignes model when config changes

---
 modules/sd_models.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 7072db08..fea84630 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -204,9 +204,9 @@ def load_model_weights(model, checkpoint_info):
     model.sd_checkpoint_info = checkpoint_info
 
 
-def load_model():
+def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
-    checkpoint_info = select_checkpoint()
+    checkpoint_info = checkpoint_info or select_checkpoint()
 
     if checkpoint_info.config != shared.cmd_opts.config:
         print(f"Loading config from: {checkpoint_info.config}")
@@ -249,7 +249,7 @@ def reload_model_weights(sd_model, info=None):
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
-        shared.sd_model = load_model()
+        shared.sd_model = load_model(checkpoint_info)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-- 
cgit v1.2.3


From a3b047b7c74dc6ca07f40aee778997fc1889d72f Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Thu, 20 Oct 2022 19:28:58 -0500
Subject: add settings option to toggle button visibility

---
 modules/shared.py | 1 +
 modules/ui.py     | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index faede821..7e9c2696 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -300,6 +300,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    "trash_prompt_visible": OptionInfo(True, "Show trash prompt button"),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
diff --git a/modules/ui.py b/modules/ui.py
index bde546cc..13c0b4ca 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -509,7 +509,7 @@ def create_toprow(is_img2img):
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-            trash_prompt = gr.Button(value=trash_prompt_symbol, elem_id="trash_prompt")
+            trash_prompt = gr.Button(value=trash_prompt_symbol, elem_id="trash_prompt", visible=opts.trash_prompt_visible)
 
             token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
             token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-- 
cgit v1.2.3


From 45872181902ada06267e2de601586d512cf5df1a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 09:00:39 +0300
Subject: updated readme and some small stylistic changes to code

---
 modules/processing.py           | 14 ++++++--------
 modules/sd_hijack_inpainting.py |  3 +++
 2 files changed, 9 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 539cde38..21786968 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -540,11 +540,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-
-    def create_dummy_mask(self, x, first_phase: bool = False):
+    def create_dummy_mask(self, x, width=None, height=None):
         if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            height = self.firstphase_height if first_phase else self.height
-            width = self.firstphase_width if first_phase else self.width
+            height = height or self.height
+            width = width or self.width
 
             # The "masked-image" in this case will just be all zeros since the entire image is masked.
             image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
@@ -571,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, first_phase=True))
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
@@ -634,6 +633,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.inpainting_mask_invert = inpainting_mask_invert
         self.mask = None
         self.nmask = None
+        self.image_conditioning = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
@@ -735,9 +735,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        conditioning_key = self.sampler.conditioning_key
-
-        if conditioning_key in {'hybrid', 'concat'}:
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
             if self.image_mask is not None:
                 conditioning_mask = np.array(self.image_mask.convert("L"))
                 conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 43938071..fd92a335 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -301,6 +301,7 @@ def get_unconditional_conditioning(self, batch_size, null_label=None):
     c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
     return c
 
+
 class LatentInpaintDiffusion(LatentDiffusion):
     def __init__(
         self,
@@ -314,9 +315,11 @@ class LatentInpaintDiffusion(LatentDiffusion):
         assert self.masked_image_key in concat_keys
         self.concat_keys = concat_keys
 
+
 def should_hijack_inpainting(checkpoint_info):
     return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
 
+
 def do_inpainting_hijack():
     ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
     ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
-- 
cgit v1.2.3


From 74088c2a06a975092806362aede22f82716cb011 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 20 Oct 2022 08:18:02 +0300
Subject: allow float sizes for hypernet's layer_structure

---
 modules/hypernetworks/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 08f75f15..e0741d08 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -15,7 +15,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
     assert not os.path.exists(fn), f"file {fn} already exists"
 
     if type(layer_structure) == str:
-        layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure)))
+        layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
 
     hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
         name=name,
-- 
cgit v1.2.3


From 60872c5b404114336f9ca0c671ba88fa4a8201c9 Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Thu, 20 Oct 2022 19:10:32 +0900
Subject: Fixed path issue while extras batch processing

---
 modules/extras.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index b853fa5b..f9796624 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -118,10 +118,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         while len(cached_images) > 2:
             del cached_images[next(iter(cached_images.keys()))]
-
-        images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,
-                          forced_filename=image_name if opts.use_original_name_batch else None)
+        
+        if opts.use_original_name_batch and image_name != None:
+            basename = os.path.splitext(os.path.basename(image_name))[0]
+        else:
+            basename = ''
+
+        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.3


From fb5a8cf0d9ed027ea3aa2e5422c946d8e6e72efe Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Thu, 20 Oct 2022 21:31:29 +0900
Subject: Added try except to extras batch from directory

---
 modules/extras.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index f9796624..0d817cf9 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -41,7 +41,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             return outputs, "Please select an input directory.", ''
         image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
         for img in image_list:
-            image = Image.open(img)
+            try:
+                image = Image.open(img)
+            except Exception:
+                continue
             imageArr.append(image)
             imageNameArr.append(img)
     else:
@@ -122,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         if opts.use_original_name_batch and image_name != None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
-            basename = ''
+            basename = None
 
-        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
+        images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.3


From a13c3bed3cec27afe3c015d3d62db36e25b10d1f Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Thu, 20 Oct 2022 21:43:27 +0900
Subject: Fixed path issue while extras batch processing

---
 modules/extras.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 0d817cf9..ac85142c 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -125,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         if opts.use_original_name_batch and image_name != None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
-            basename = None
+            basename = ''
 
-        images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename)
+        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.3


From 9d71eef02e7395e179b8d5e61e6d91ddd8928d2e Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Fri, 21 Oct 2022 09:23:13 +0900
Subject: sort file list in alphabetical ordering in extras

---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index ac85142c..22c5a1c1 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -39,7 +39,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         if input_dir == '':
             return outputs, "Please select an input directory.", ''
-        image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+        image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)]
         for img in image_list:
             try:
                 image = Image.open(img)
-- 
cgit v1.2.3


From c23f666dba2b484d521d2dc4be91cf9e09312647 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 09:47:43 +0300
Subject: a more strict check for activation type and a more reasonable check
 for type of layer in hypernets

---
 modules/hypernetworks/hypernetwork.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7d617680..84e7e350 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -32,10 +32,16 @@ class HypernetworkModule(torch.nn.Module):
         linears = []
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
+
             if activation_func == "relu":
                 linears.append(torch.nn.ReLU())
-            if activation_func == "leakyrelu":
+            elif activation_func == "leakyrelu":
                 linears.append(torch.nn.LeakyReLU())
+            elif activation_func == 'linear' or activation_func is None:
+                pass
+            else:
+                raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
+
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -46,7 +52,7 @@ class HypernetworkModule(torch.nn.Module):
             self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                if not "ReLU" in layer.__str__():
+                if type(layer) == torch.nn.Linear:
                     layer.weight.data.normal_(mean=0.0, std=0.01)
                     layer.bias.data.zero_()
 
@@ -74,7 +80,7 @@ class HypernetworkModule(torch.nn.Module):
     def trainables(self):
         layer_structure = []
         for layer in self.linear:
-            if not "ReLU" in layer.__str__():
+            if type(layer) == torch.nn.Linear:
                 layer_structure += [layer.weight, layer.bias]
         return layer_structure
 
-- 
cgit v1.2.3


From 7157e5d064741fa57ca81a2c6432a651f21ee82f Mon Sep 17 00:00:00 2001
From: Patryk Wychowaniec <pwychowaniec@pm.me>
Date: Thu, 20 Oct 2022 19:22:59 +0200
Subject: interrogate: Fix CLIP-interrogation on CPU

Currently, trying to perform CLIP interrogation on a CPU fails, saying:

```
RuntimeError: "slow_conv2d_cpu" not implemented for 'Half'
```

This merge request fixes this issue by detecting whether the target
device is CPU and, if so, force-enabling `--no-half` and passing
`device="cpu"` to `clip.load()` (which then does some extra tricks to
ensure it works correctly on CPU).
---
 modules/interrogate.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 64b91eb4..65b05d34 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -28,9 +28,11 @@ class InterrogateModels:
     clip_preprocess = None
     categories = None
     dtype = None
+    running_on_cpu = None
 
     def __init__(self, content_dir):
         self.categories = []
+        self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
 
         if os.path.exists(content_dir):
             for filename in os.listdir(content_dir):
@@ -53,7 +55,11 @@ class InterrogateModels:
     def load_clip_model(self):
         import clip
 
-        model, preprocess = clip.load(clip_model_name)
+        if self.running_on_cpu:
+            model, preprocess = clip.load(clip_model_name, device="cpu")
+        else:
+            model, preprocess = clip.load(clip_model_name)
+
         model.eval()
         model = model.to(devices.device_interrogate)
 
@@ -62,14 +68,14 @@ class InterrogateModels:
     def load(self):
         if self.blip_model is None:
             self.blip_model = self.load_blip_model()
-            if not shared.cmd_opts.no_half:
+            if not shared.cmd_opts.no_half and not self.running_on_cpu:
                 self.blip_model = self.blip_model.half()
 
         self.blip_model = self.blip_model.to(devices.device_interrogate)
 
         if self.clip_model is None:
             self.clip_model, self.clip_preprocess = self.load_clip_model()
-            if not shared.cmd_opts.no_half:
+            if not shared.cmd_opts.no_half and not self.running_on_cpu:
                 self.clip_model = self.clip_model.half()
 
         self.clip_model = self.clip_model.to(devices.device_interrogate)
-- 
cgit v1.2.3


From b69c37d25e4ffc56e8f8c247fa2c38b4648cefb7 Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Thu, 20 Oct 2022 22:21:12 +0800
Subject: Allow datasets with only 1 image in TI

---
 modules/textual_inversion/dataset.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 23bb4b6a..5b1c5002 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -83,7 +83,7 @@ class PersonalizedBase(Dataset):
 
             self.dataset.append(entry)
 
-        assert len(self.dataset) > 1, "No images have been found in the dataset."
+        assert len(self.dataset) > 0, "No images have been found in the dataset."
         self.length = len(self.dataset) * repeats // batch_size
 
         self.initial_indexes = np.arange(len(self.dataset))
@@ -91,7 +91,7 @@ class PersonalizedBase(Dataset):
         self.shuffle()
 
     def shuffle(self):
-        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
+        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()]
 
     def create_text(self, filename_text):
         text = random.choice(self.lines)
-- 
cgit v1.2.3


From 5245c7a4935f67b677da0f5a1fc2b74c074aa0e2 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Wed, 19 Oct 2022 12:21:32 -0700
Subject: Issue #2921-Give PNG info to Hypernet previews.

---
 modules/hypernetworks/hypernetwork.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 84e7e350..68c8f26d 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -256,6 +256,9 @@ def stack_conds(conds):
 
 
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+    # images is required here to give training previews their infotext. Importing this at the very top causes a circular dependency.
+    from modules import images
+
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -298,6 +301,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
+    forced_filename = "<none>"
 
     ititial_step = hypernetwork.step or 0
     if ititial_step > steps:
@@ -345,7 +349,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-            last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
+            forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'
+            last_saved_image = os.path.join(images_dir, forced_filename)
 
             optimizer.zero_grad()
             shared.sd_model.cond_stage_model.to(devices.device)
@@ -381,7 +386,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             if image is not None:
                 shared.state.current_image = image
-                image.save(last_saved_image)
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename)
                 last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
-- 
cgit v1.2.3


From 6014fb8afbe05c8d02fffe7a36a2e48128713bd2 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Wed, 19 Oct 2022 12:22:23 -0700
Subject: Do nothing if image file already exists.

---
 modules/images.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index b9589563..550e53ae 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -416,7 +416,11 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
         path = os.path.join(path, dirname)
 
-    os.makedirs(path, exist_ok=True)
+    try:
+        os.makedirs(path, exist_ok=True)
+    except FileExistsError:
+        # If the file already exists, continue and allow said file to be overwritten.
+        pass
 
     if forced_filename is None:
         basecount = get_next_sequence_number(path, basename)
-- 
cgit v1.2.3


From 4ff274e1e35bb642687253ce744d2cfa738ab293 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Wed, 19 Oct 2022 12:32:22 -0700
Subject: Revise comments.

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 modules/images.py                     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 68c8f26d..3f96361c 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -256,7 +256,7 @@ def stack_conds(conds):
 
 
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
-    # images is required here to give training previews their infotext. Importing this at the very top causes a circular dependency.
+    # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
     assert hypernetwork_name, 'hypernetwork not selected'
diff --git a/modules/images.py b/modules/images.py
index 550e53ae..b8834e3c 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -419,7 +419,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     try:
         os.makedirs(path, exist_ok=True)
     except FileExistsError:
-        # If the file already exists, continue and allow said file to be overwritten.
+        # If the file already exists, allow said file to be overwritten.
         pass
 
     if forced_filename is None:
-- 
cgit v1.2.3


From 2273e752fb3e578f1047f6d38b96330b07bf61a9 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Wed, 19 Oct 2022 14:23:48 -0700
Subject: Remove redundant try/except.

---
 modules/images.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index b8834e3c..b9589563 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -416,11 +416,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
         path = os.path.join(path, dirname)
 
-    try:
-        os.makedirs(path, exist_ok=True)
-    except FileExistsError:
-        # If the file already exists, allow said file to be overwritten.
-        pass
+    os.makedirs(path, exist_ok=True)
 
     if forced_filename is None:
         basecount = get_next_sequence_number(path, basename)
-- 
cgit v1.2.3


From 03a1e288c4973dd2dff57a97469b40f146b6fccf Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 10:13:24 +0300
Subject: turns out LayerNorm also has weight and bias and needs to be
 pre-multiplied and trained for hypernets

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3274a802..b1a5d0c7 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -52,7 +52,7 @@ class HypernetworkModule(torch.nn.Module):
             self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                if type(layer) == torch.nn.Linear:
+                if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
                     layer.weight.data.normal_(mean=0.0, std=0.01)
                     layer.bias.data.zero_()
 
@@ -80,7 +80,7 @@ class HypernetworkModule(torch.nn.Module):
     def trainables(self):
         layer_structure = []
         for layer in self.linear:
-            if type(layer) == torch.nn.Linear:
+            if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
                 layer_structure += [layer.weight, layer.bias]
         return layer_structure
 
-- 
cgit v1.2.3


From bf30673f5132c8f28357b31224c54331e788d3e7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 10:19:25 +0300
Subject: Fix Hypernet infotext string split bug for PR #3283

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 21786968..d1deffa9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.filename.split('\\')[-1].split('.')[0]),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else os.path.splitext(os.path.basename(shared.loaded_hypernetwork.filename))[0]),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.3


From df5706409386cc2e88718bd9101045587c39f8bb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 16:10:51 +0300
Subject: do not load aesthetic clip model until it's needed add refresh button
 for aesthetic embeddings add aesthetic params to images' infotext

---
 modules/aesthetic_clip.py                  | 40 +++++++++++++++++++----
 modules/generation_parameters_copypaste.py | 18 +++++++++--
 modules/img2img.py                         |  5 +--
 modules/processing.py                      |  4 +--
 modules/sd_models.py                       |  3 --
 modules/txt2img.py                         |  4 +--
 modules/ui.py                              | 52 ++++++++++++++++++++----------
 7 files changed, 88 insertions(+), 38 deletions(-)

(limited to 'modules')

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
index 34efa931..8c828541 100644
--- a/modules/aesthetic_clip.py
+++ b/modules/aesthetic_clip.py
@@ -40,6 +40,8 @@ def iter_to_batched(iterable, n=1):
 
 
 def create_ui():
+    import modules.ui
+
     with gr.Group():
         with gr.Accordion("Open for Clip Aesthetic!", open=False):
             with gr.Row():
@@ -55,6 +57,8 @@ def create_ui():
                                              label="Aesthetic imgs embedding",
                                              value="None")
 
+                modules.ui.create_refresh_button(aesthetic_imgs, shared.update_aesthetic_embeddings, lambda: {"choices": sorted(shared.aesthetic_embeddings.keys())}, "refresh_aesthetic_embeddings")
+
             with gr.Row():
                 aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs',
                                                  placeholder="This text is used to rotate the feature space of the imgs embs",
@@ -66,11 +70,21 @@ def create_ui():
     return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative
 
 
+aesthetic_clip_model = None
+
+
+def aesthetic_clip():
+    global aesthetic_clip_model
+
+    if aesthetic_clip_model is None or aesthetic_clip_model.name_or_path != shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path:
+        aesthetic_clip_model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path)
+        aesthetic_clip_model.cpu()
+
+    return aesthetic_clip_model
+
+
 def generate_imgs_embd(name, folder, batch_size):
-    # clipModel = CLIPModel.from_pretrained(
-    #     shared.sd_model.cond_stage_model.clipModel.name_or_path
-    # )
-    model = shared.clip_model.to(device)
+    model = aesthetic_clip().to(device)
     processor = CLIPProcessor.from_pretrained(model.name_or_path)
 
     with torch.no_grad():
@@ -91,7 +105,7 @@ def generate_imgs_embd(name, folder, batch_size):
         path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
         torch.save(embs, path)
 
-        model = model.cpu()
+        model.cpu()
         del processor
         del embs
         gc.collect()
@@ -132,7 +146,7 @@ class AestheticCLIP:
         self.image_embs = None
         self.load_image_embs(None)
 
-    def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
+    def set_aesthetic_params(self, p, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
                              aesthetic_slerp=True, aesthetic_imgs_text="",
                              aesthetic_slerp_angle=0.15,
                              aesthetic_text_negative=False):
@@ -145,6 +159,18 @@ class AestheticCLIP:
         self.aesthetic_steps = aesthetic_steps
         self.load_image_embs(image_embs_name)
 
+        if self.image_embs_name is not None:
+            p.extra_generation_params.update({
+                "Aesthetic LR": aesthetic_lr,
+                "Aesthetic weight": aesthetic_weight,
+                "Aesthetic steps": aesthetic_steps,
+                "Aesthetic embedding": self.image_embs_name,
+                "Aesthetic slerp": aesthetic_slerp,
+                "Aesthetic text": aesthetic_imgs_text,
+                "Aesthetic text negative": aesthetic_text_negative,
+                "Aesthetic slerp angle": aesthetic_slerp_angle,
+            })
+
     def set_skip(self, skip):
         self.skip = skip
 
@@ -168,7 +194,7 @@ class AestheticCLIP:
 
             tokens = torch.asarray(remade_batch_tokens).to(device)
 
-            model = copy.deepcopy(shared.clip_model).to(device)
+            model = copy.deepcopy(aesthetic_clip()).to(device)
             model.requires_grad_(True)
             if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
                 text_embs_2 = model.get_text_features(
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 0f041449..f73647da 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -4,13 +4,22 @@ import gradio as gr
 from modules.shared import script_path
 from modules import shared
 
-re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)"
+re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 type_of_gr_update = type(gr.update())
 
 
+def quote(text):
+    if ',' not in str(text):
+        return text
+
+    text = str(text)
+    text = text.replace('\\', '\\\\')
+    text = text.replace('"', '\\"')
+    return f'"{text}"'
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
@@ -83,7 +92,12 @@ def connect_paste(button, paste_fields, input_comp, js=None):
             else:
                 try:
                     valtype = type(output.value)
-                    val = valtype(v)
+
+                    if valtype == bool and v == "False":
+                        val = False
+                    else:
+                        val = valtype(v)
+
                     res.append(gr.update(value=val))
                 except Exception:
                     res.append(gr.update())
diff --git a/modules/img2img.py b/modules/img2img.py
index bc7c66bc..eea5199b 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -109,10 +109,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpainting_mask_invert=inpainting_mask_invert,
     )
 
-    shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps),
-                                               aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text,
-                                               aesthetic_slerp_angle,
-                                               aesthetic_text_negative)
+    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
 
     if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
diff --git a/modules/processing.py b/modules/processing.py
index d1deffa9..f0852cd5 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -12,7 +12,7 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -318,7 +318,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params.update(p.extra_generation_params)
 
-    generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
+    generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
     negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 05a1df28..b1c91b0d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -234,9 +234,6 @@ def load_model(checkpoint_info=None):
 
     sd_hijack.model_hijack.hijack(sd_model)
 
-    if shared.clip_model is None or shared.clip_model.transformer.name_or_path != sd_model.cond_stage_model.wrapped.transformer.name_or_path:
-        shared.clip_model = CLIPModel.from_pretrained(sd_model.cond_stage_model.wrapped.transformer.name_or_path)
-
     sd_model.eval()
 
     print(f"Model loaded.")
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 32ed1d8d..1761cfa2 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -36,9 +36,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         firstphase_height=firstphase_height if enable_hr else None,
     )
 
-    shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps),
-                                           aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle,
-                                           aesthetic_text_negative)
+    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
 
     if cmd_opts.enable_console_prompts:
         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
diff --git a/modules/ui.py b/modules/ui.py
index 381ca925..0d020de6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -597,27 +597,29 @@ def apply_setting(key, value):
     return value
 
 
-def create_ui(wrap_gradio_gpu_call):
-    import modules.img2img
-    import modules.txt2img
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+    def refresh():
+        refresh_method()
+        args = refreshed_args() if callable(refreshed_args) else refreshed_args
 
-    def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
-        def refresh():
-            refresh_method()
-            args = refreshed_args() if callable(refreshed_args) else refreshed_args
+        for k, v in args.items():
+            setattr(refresh_component, k, v)
 
-            for k, v in args.items():
-                setattr(refresh_component, k, v)
+        return gr.update(**(args or {}))
 
-            return gr.update(**(args or {}))
+    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
+    refresh_button.click(
+        fn=refresh,
+        inputs=[],
+        outputs=[refresh_component]
+    )
+    return refresh_button
+
+
+def create_ui(wrap_gradio_gpu_call):
+    import modules.img2img
+    import modules.txt2img
 
-        refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
-        refresh_button.click(
-            fn = refresh,
-            inputs = [],
-            outputs = [refresh_component]
-        )
-        return refresh_button
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
@@ -802,6 +804,14 @@ def create_ui(wrap_gradio_gpu_call):
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
+                (aesthetic_lr, "Aesthetic LR"),
+                (aesthetic_weight, "Aesthetic weight"),
+                (aesthetic_steps, "Aesthetic steps"),
+                (aesthetic_imgs, "Aesthetic embedding"),
+                (aesthetic_slerp, "Aesthetic slerp"),
+                (aesthetic_imgs_text, "Aesthetic text"),
+                (aesthetic_text_negative, "Aesthetic text negative"),
+                (aesthetic_slerp_angle, "Aesthetic slerp angle"),
             ]
 
             txt2img_preview_params = [
@@ -1077,6 +1087,14 @@ def create_ui(wrap_gradio_gpu_call):
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
                 (denoising_strength, "Denoising strength"),
+                (aesthetic_lr_im, "Aesthetic LR"),
+                (aesthetic_weight_im, "Aesthetic weight"),
+                (aesthetic_steps_im, "Aesthetic steps"),
+                (aesthetic_imgs_im, "Aesthetic embedding"),
+                (aesthetic_slerp_im, "Aesthetic slerp"),
+                (aesthetic_imgs_text_im, "Aesthetic text"),
+                (aesthetic_text_negative_im, "Aesthetic text negative"),
+                (aesthetic_slerp_angle_im, "Aesthetic slerp angle"),
             ]
             token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
 
-- 
cgit v1.2.3


From 9286fe53de2eef91f13cc3ad5938ddf67ecc8413 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 16:38:06 +0300
Subject: make aestetic embedding ciompatible with prompts longer than 75
 tokens

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 36198a3c..1f8587d1 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -332,8 +332,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     multipliers.append([1.0] * 75)
 
             z1 = self.process_tokens(tokens, multipliers)
+            z1 = shared.aesthetic_clip(z1, remade_batch_tokens)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
-            z = shared.aesthetic_clip(z, remade_batch_tokens)
 
             remade_batch_tokens = rem_tokens
             batch_multipliers = rem_multipliers
-- 
cgit v1.2.3


From d0ea471b0cdaede163c6e7f6fae8535f5c3cd226 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 21 Oct 2022 14:04:41 +0100
Subject: Use opts in textual_inversion image_embedding.py for dynamic fonts

---
 modules/textual_inversion/image_embedding.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index 898ce3b3..c50b1e7b 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -5,6 +5,7 @@ import zlib
 from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
 from fonts.ttf import Roboto
 import torch
+from modules.shared import opts
 
 
 class EmbeddingEncoder(json.JSONEncoder):
-- 
cgit v1.2.3


From 306e2ff6ab8f4c7e94ab55f4f08ab8f94d73d287 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 21 Oct 2022 14:47:21 +0100
Subject: Update image_embedding.py

---
 modules/textual_inversion/image_embedding.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index c50b1e7b..ea653806 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -134,7 +134,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
     from math import cos
 
     image = srcimage.copy()
-
+    fontsize = 32
     if textfont is None:
         try:
             textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
@@ -151,7 +151,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
     image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
 
     draw = ImageDraw.Draw(image)
-    fontsize = 32
+
     font = ImageFont.truetype(textfont, fontsize)
     padding = 10
 
-- 
cgit v1.2.3


From 51e3dc9ccad157d7161b697a246e26c868d46a7c Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Fri, 21 Oct 2022 02:11:12 -0700
Subject: Sanitize hypernet name input.

---
 modules/hypernetworks/ui.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 266f04f6..e6f50a1f 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -11,6 +11,9 @@ from modules.hypernetworks import hypernetwork
 
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, add_layer_norm=False, activation_func=None):
+    # Remove illegal characters from name.
+    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
+
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     if not overwrite_old:
         assert not os.path.exists(fn), f"file {fn} already exists"
-- 
cgit v1.2.3


From 19818f023cfafc472c6c241cab0b72896a168481 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Fri, 21 Oct 2022 02:14:02 -0700
Subject: Match hypernet name with filename in all cases.

---
 modules/hypernetworks/hypernetwork.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b1a5d0c7..6d392be4 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -340,7 +340,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         pbar.set_description(f"loss: {mean_loss:.7f}")
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
+            temp = hypernetwork.name
+            # Before saving, change name to match current checkpoint.
+            hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
             hypernetwork.save(last_saved_file)
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
@@ -405,6 +408,9 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     hypernetwork.sd_checkpoint = checkpoint.hash
     hypernetwork.sd_checkpoint_name = checkpoint.model_name
+    # Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).
+    hypernetwork.name = hypernetwork_name
+    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt')
     hypernetwork.save(filename)
 
     return hypernetwork, filename
-- 
cgit v1.2.3


From fccad18a59e3c2c33fefbbb1763c6a87a3a68eba Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Fri, 21 Oct 2022 02:17:26 -0700
Subject: Refer to Hypernet's name, sensibly, by its name variable.

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index f0852cd5..ff1ec4c9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else os.path.splitext(os.path.basename(shared.loaded_hypernetwork.filename))[0]),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.3


From 272fa527bbe93143668ffc16838107b7dca35b40 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Fri, 21 Oct 2022 02:41:55 -0700
Subject: Remove unused variable.

---
 modules/hypernetworks/hypernetwork.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6d392be4..47d91ea5 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -340,7 +340,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         pbar.set_description(f"loss: {mean_loss:.7f}")
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
-            temp = hypernetwork.name
             # Before saving, change name to match current checkpoint.
             hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
-- 
cgit v1.2.3


From 02e4d4694dd9254a6ca9f05c2eb7b01ea508abc7 Mon Sep 17 00:00:00 2001
From: Rcmcpe <rcmcpe-cn@outlook.com>
Date: Fri, 21 Oct 2022 15:53:35 +0800
Subject: Change option description of unload_models_when_training

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 5c675b80..41d7f08e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -266,7 +266,7 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-- 
cgit v1.2.3


From 704036ff07b71bf86cadcbbff2bcfeebdd1ed3a6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 17:11:42 +0300
Subject: make aspect ratio overlay work regardless of selected localization

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 0d020de6..85f95792 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -879,8 +879,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-- 
cgit v1.2.3


From ac0aa2b18efeeb9220a5994c8dd54c7cdda7cc40 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 17:35:51 +0300
Subject: loading SD VAE, see PR #3303

---
 modules/sd_models.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index b1c91b0d..d99dbce8 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -155,6 +155,9 @@ def get_state_dict_from_checkpoint(pl_sd):
     return pl_sd
 
 
+vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+
+
 def load_model_weights(model, checkpoint_info):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
@@ -186,7 +189,7 @@ def load_model_weights(model, checkpoint_info):
         if os.path.exists(vae_file):
             print(f"Loading VAE weights from: {vae_file}")
             vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
             model.first_stage_model.load_state_dict(vae_dict)
 
         model.first_stage_model.to(devices.dtype_vae)
-- 
cgit v1.2.3


From f49c08ea566385db339c6628f65c3a121033f67c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 18:46:02 +0300
Subject: prevent error spam when processing images without txt files for
 captions

---
 modules/textual_inversion/preprocess.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 17e4ddc1..33eaddb6 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -122,11 +122,10 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
             continue
 
         existing_caption = None
-
-        try:
-            existing_caption = open(os.path.splitext(filename)[0] + '.txt', 'r').read()
-        except Exception as e:
-            print(e)
+        existing_caption_filename = os.path.splitext(filename)[0] + '.txt'
+        if os.path.exists(existing_caption_filename):
+            with open(existing_caption_filename, 'r', encoding="utf8") as file:
+                existing_caption = file.read()
 
         if shared.state.interrupted:
             break
-- 
cgit v1.2.3


From bb0f1a2cdae3410a41d06ae878f56e29b8154c41 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sat, 22 Oct 2022 01:23:00 +0800
Subject: inspiration finished

---
 modules/inspiration.py | 192 ++++++++++++++++++++++++++++++++-----------------
 modules/shared.py      |   6 ++
 modules/ui.py          |   2 +-
 3 files changed, 133 insertions(+), 67 deletions(-)

(limited to 'modules')

diff --git a/modules/inspiration.py b/modules/inspiration.py
index 456bfcb5..f72ebf3a 100644
--- a/modules/inspiration.py
+++ b/modules/inspiration.py
@@ -1,122 +1,182 @@
 import os
 import random
-import gradio 
-inspiration_path = "inspiration"
-inspiration_system_path = os.path.join(inspiration_path, "system")
-def read_name_list(file):
+import gradio
+from modules.shared import opts
+inspiration_system_path = os.path.join(opts.inspiration_dir, "system")
+def read_name_list(file, types=None, keyword=None):
     if not os.path.exists(file):
         return []
-    f = open(file, "r")
     ret = []
+    f = open(file, "r")    
     line = f.readline()
     while len(line) > 0:
         line = line.rstrip("\n")
-        ret.append(line)
-    print(ret)
+        if types is not None:
+            dirname = os.path.split(line)
+            if dirname[0] in types and keyword in dirname[1]:
+                ret.append(line)
+        else:
+            ret.append(line)
+        line = f.readline()
     return ret
 
 def save_name_list(file, name): 
-    print(file)
-    f = open(file, "a")
-    f.write(name + "\n")
+    with open(file, "a") as f:
+        f.write(name + "\n")
 
-def get_inspiration_images(source, types):   
-    path = os.path.join(inspiration_path , types) 
+def get_types_list():
+    files = os.listdir(opts.inspiration_dir)
+    types = []
+    for x in files:
+        path = os.path.join(opts.inspiration_dir, x)
+        if x[0] == ".":
+            continue
+        if not os.path.isdir(path):
+            continue
+        if path == inspiration_system_path:
+            continue
+        types.append(x)
+    return types
+
+def get_inspiration_images(source, types, keyword):
+    get_num = int(opts.inspiration_rows_num * opts.inspiration_cols_num)
     if source == "Favorites":
-        names = read_name_list(os.path.join(inspiration_system_path, types + "_faverites.txt"))
-        names = random.sample(names, 25)
+        names = read_name_list(os.path.join(inspiration_system_path, "faverites.txt"), types, keyword)
+        names = random.sample(names, get_num) if len(names) > get_num else names
     elif source == "Abandoned":
-        names = read_name_list(os.path.join(inspiration_system_path, types + "_abondened.txt"))
-        names = random.sample(names, 25)
-    elif source == "Exclude abandoned":
-        abondened = read_name_list(os.path.join(inspiration_system_path, types + "_abondened.txt"))        
-        all_names = os.listdir(path)
-        names = []
-        while len(names) < 25:
-            name = random.choice(all_names)
-            if name not in abondened:
-                names.append(name)
+        names = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)
+        print(names)
+        names = random.sample(names, get_num) if len(names) > get_num else names
+    elif source == "Exclude abandoned":        
+        abandoned = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)  
+        all_names = []
+        for tp in types:
+            name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
+            all_names += [os.path.join(tp, x) for x in name_list if keyword in x]
+        
+        if len(all_names) > get_num:
+            names = []
+            while len(names) < get_num:
+                name = random.choice(all_names)
+                if name not in abandoned:
+                    names.append(name)
+        else:
+            names = all_names
     else:
-        names = random.sample(os.listdir(path), 25)
-    names = random.sample(names, 25)
+        all_names = []
+        for tp in types:
+            name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
+            all_names += [os.path.join(tp, x) for x in name_list if keyword in x]
+        names = random.sample(all_names, get_num) if len(all_names) > get_num else all_names
     image_list = []
     for a in names:
-        image_path = os.path.join(path, a)
+        image_path = os.path.join(opts.inspiration_dir, a)
         images = os.listdir(image_path)        
-        image_list.append(os.path.join(image_path, random.choice(images)))
-    return image_list, names
+        image_list.append((os.path.join(image_path, random.choice(images)), a))
+    return image_list, names, ""
 
-def select_click(index, types, name_list):
+def select_click(index, name_list):
     name = name_list[int(index)]
-    path = os.path.join(inspiration_path, types, name)
+    path = os.path.join(opts.inspiration_dir, name)
     images = os.listdir(path)
-    return name, [os.path.join(path, x) for x in images]
+    return name, [os.path.join(path, x) for x in images], ""
 
-def give_up_click(name, types):
-    file = os.path.join(inspiration_system_path, types + "_abandoned.txt")
+def give_up_click(name):
+    file = os.path.join(inspiration_system_path, "abandoned.txt")
     name_list = read_name_list(file)
     if name not in name_list:
         save_name_list(file, name)
+    return "Added to abandoned list"
    
-def collect_click(name, types):
-    file = os.path.join(inspiration_system_path, types + "_faverites.txt")
-    print(file)
+def collect_click(name):
+    file = os.path.join(inspiration_system_path, "faverites.txt")
     name_list = read_name_list(file)
-    print(name_list)
     if name not in name_list:
         save_name_list(file, name)
+    return "Added to faverite list"
 
-def moveout_click(name, types):
-    file = os.path.join(inspiration_system_path, types + "_faverites.txt")
+def moveout_click(name, source):
+    if source == "Abandoned":
+        file = os.path.join(inspiration_system_path, "abandoned.txt")
+    if source == "Favorites":
+        file = os.path.join(inspiration_system_path, "faverites.txt")
+    else:
+        return None
     name_list = read_name_list(file)
-    if name not in name_list:
-        save_name_list(file, name)
+    os.remove(file)
+    with open(file, "a") as f:
+        for a in name_list:
+            if a != name:
+                f.write(a)
+    return "Moved out {name} from {source} list"
 
 def source_change(source):
-    if source == "Abandoned" or source == "Favorites":
-        return gradio.Button.update(visible=True, value=f"Move out {source}")
+    if source in ["Abandoned", "Favorites"]:
+        return gradio.update(visible=True), []
     else:
-        return gradio.Button.update(visible=False)
+        return gradio.update(visible=False), []
+def add_to_prompt(name, prompt):
+    print(name, prompt)
+    name = os.path.basename(name)
+    return prompt + "," + name
 
-def ui(gr, opts):     
+def ui(gr, opts, txt2img_prompt, img2img_prompt):     
     with gr.Blocks(analytics_enabled=False) as inspiration:
-        flag = os.path.exists(inspiration_path)        
+        flag = os.path.exists(opts.inspiration_dir)        
         if flag:
-            types = os.listdir(inspiration_path)
-            types = [x for x in types if x != "system"]
+            types = get_types_list()
             flag = len(types) > 0
-        if not flag:
-            os.mkdir(inspiration_path)
+        else:
+            os.makedirs(opts.inspiration_dir)
+        if not flag:            
             gr.HTML("""
-                <div align='center' width="50%>You need get "inspiration" images first. You can create these images by run "Create inspiration images" script in txt2img page, or download zip file from here and unzip these file under fold "inpiration".</div>"
+                <div align='center' width="50%"><h2>To activate inspiration function, you need get "inspiration" images first. </h2><br>
+                You can create these images by run "Create inspiration images" script in txt2img page, <br> you can get the artists or art styles list from here<br>
+                <a>https://github.com/pharmapsychotic/clip-interrogator/tree/main/data</a><br>
+                download these files, and select these files in the "Create inspiration images" script UI<br>
+                There about 6000 artists and art styles in these files. <br>This takes server hours depending on your GPU type and how many pictures  you generate for each artist/style
+                <br>I suggest at least four images for each<br><br><br>
+                <h2>You can also download generated pictures from here:</h2><br>
+                <a>https://huggingface.co/datasets/yfszzx/inspiration</a><br>
+                unzip the file to the project directory of webui<br>
+                and restart webui, and enjoy the joy of creation!<br></div>
                 """)           
             return inspiration
         if not os.path.exists(inspiration_system_path):
             os.mkdir(inspiration_system_path)
-        gallery, names = get_inspiration_images("Exclude abandoned", types[0])   
         with gr.Row():
             with gr.Column(scale=2):                
-                inspiration_gallery = gr.Gallery(gallery, show_label=False, elem_id="inspiration_gallery").style(grid=5, height='auto')
+                inspiration_gallery = gr.Gallery(show_label=False, elem_id="inspiration_gallery").style(grid=opts.inspiration_cols_num, height='auto')
             with gr.Column(scale=1):
-                types = gr.Dropdown(choices=types, value=types[0], label="Type", visible=len(types) > 1)
+                print(types)
+                types = gr.CheckboxGroup(choices=types, value=types)
+                keyword = gr.Textbox("", label="Key word")                
                 with gr.Row():                    
                     source = gr.Dropdown(choices=["All", "Favorites", "Exclude abandoned", "Abandoned"], value="Exclude abandoned", label="Source")
-                    get_inspiration = gr.Button("Get inspiration")
+                    get_inspiration = gr.Button("Get inspiration", elem_id="inspiration_get_button")
                 name = gr.Textbox(show_label=False, interactive=False)
                 with gr.Row(): 
                     send_to_txt2img = gr.Button('to txt2img')
                     send_to_img2img = gr.Button('to img2img')
-                style_gallery = gr.Gallery(show_label=False, elem_id="inspiration_style_gallery").style(grid=2, height='auto')                          
-               
+                style_gallery = gr.Gallery(show_label=False).style(grid=2, height='auto') 
                 collect = gr.Button('Collect')     
-                give_up = gr.Button("Don't show any more")
+                give_up = gr.Button("Don't show again")
                 moveout = gr.Button("Move out", visible=False)
-        with gr.Row():
+                warning = gr.HTML()
+        with gr.Row(visible=False):
             select_button = gr.Button('set button', elem_id="inspiration_select_button")
-            name_list = gr.State(names)
-        source.change(source_change, inputs=[source], outputs=[moveout])
-        get_inspiration.click(get_inspiration_images, inputs=[source, types], outputs=[inspiration_gallery, name_list])
-        select_button.click(select_click, _js="inspiration_selected", inputs=[name, types, name_list], outputs=[name, style_gallery])
-        give_up.click(give_up_click, inputs=[name, types], outputs=None)
-        collect.click(collect_click, inputs=[name, types], outputs=None)
+            name_list = gr.State()
+        
+        get_inspiration.click(get_inspiration_images, inputs=[source, types, keyword], outputs=[inspiration_gallery, name_list, keyword])
+        source.change(source_change, inputs=[source], outputs=[moveout, style_gallery])
+        source.change(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
+        keyword.submit(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
+        select_button.click(select_click, _js="inspiration_selected", inputs=[name, name_list], outputs=[name, style_gallery, warning])
+        give_up.click(give_up_click, inputs=[name], outputs=[warning])
+        collect.click(collect_click, inputs=[name], outputs=[warning])
+        moveout.click(moveout_click, inputs=[name, source], outputs=[warning])
+        send_to_txt2img.click(add_to_prompt, inputs=[name, txt2img_prompt], outputs=[txt2img_prompt])
+        send_to_img2img.click(add_to_prompt, inputs=[name, img2img_prompt], outputs=[img2img_prompt])
+        send_to_txt2img.click(None, _js='switch_to_txt2img', inputs=None, outputs=None)
+        send_to_img2img.click(None, _js="switch_to_img2img_img2img", inputs=None, outputs=None)
     return inspiration
diff --git a/modules/shared.py b/modules/shared.py
index ae033710..564b1b8d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -316,6 +316,12 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
+options_templates.update(options_section(('inspiration', "Inspiration"), {
+    "inspiration_dir": OptionInfo("inspiration", "Directory of inspiration", component_args=hide_dirs),
+    "inspiration_max_samples": OptionInfo(4, "Maximum number of samples, used to determine which folders to skip when continue running the create script", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
+    "inspiration_rows_num":  OptionInfo(4, "Rows of inspiration interface frame", gr.Slider, {"minimum": 4, "maximum": 16, "step": 1}),
+    "inspiration_cols_num":  OptionInfo(8, "Columns of inspiration interface frame", gr.Slider, {"minimum": 4, "maximum": 16, "step": 1}),
+}))
 
 class Options:
     data = None
diff --git a/modules/ui.py b/modules/ui.py
index 6a0a3c3b..b651eb9c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1180,7 +1180,7 @@ def create_ui(wrap_gradio_gpu_call):
     }
 
     browser_interface = images_history.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
-    inspiration_interface = inspiration.ui(gr, opts)
+    inspiration_interface = inspiration.ui(gr, opts, txt2img_prompt, img2img_prompt)
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
-- 
cgit v1.2.3


From 9ba372de90df81c4f1e992d8b33ae17c6630de95 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 13:55:42 -0500
Subject: initial work on getting prompts cleared on the backend and
 synchronizing token counter

---
 modules/ui.py | 29 +++++++++++++++++++----------
 1 file changed, 19 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index d2cb528e..2748a2e0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -429,15 +429,16 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-# setup button for clearing prompt input boxes on client side of webui
-def connect_trash_prompt(dummy_component, button, is_img2img):
+def clear_prompt(prompt):
+    print(f"type: '{prompt}'")
+    print(prompt)
+
+    # update_token_counter(prompt, steps)
+    return ""
+
+def connect_trash_prompt(prompt, confirmed):
+        if(confirmed): clear_prompt(prompt)
 
-    button.click(
-        fn=lambda: print("Clearing prompt"),
-        _js="trash_prompt",
-        inputs=[],
-        outputs=[],
-    )
 
 def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
     """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
@@ -640,7 +641,16 @@ def create_ui(wrap_gradio_gpu_call):
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
-        connect_trash_prompt(dummy_component, trash_prompt_button, False)
+
+
+        trash_prompt_button.click(
+            # fn=lambda: print("Clearing prompt"),
+            _js="trash_prompt",
+            fn=lambda: clear_prompt(),
+            inputs=[txt2img_prompt, dummy_component],
+            outputs=[txt2img_prompt, dummy_component],
+        )
+
 
         with gr.Row(elem_id='txt2img_progress_row'):
             with gr.Column(scale=1):
@@ -848,7 +858,6 @@ def create_ui(wrap_gradio_gpu_call):
         img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
         token_counter, token_button, trash_prompt_button = create_toprow(is_img2img=True)
 
-        connect_trash_prompt(dummy_component,trash_prompt_button, True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-- 
cgit v1.2.3


From ee0505dd0092ae7073b77aba93a858bda000dc60 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 14:24:14 -0500
Subject: only delete prompt on back end and remove client-side deletion

---
 modules/ui.py | 29 +++++++++++++++--------------
 1 file changed, 15 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 2748a2e0..90c338da 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -429,15 +429,21 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-def clear_prompt(prompt):
-    print(f"type: '{prompt}'")
-    print(prompt)
 
-    # update_token_counter(prompt, steps)
-    return ""
+def connect_trash_prompt(_, confirmed):
+        if(confirmed):
+            # update_token_counter(prompt, steps)
+            return ["", confirmed]
 
-def connect_trash_prompt(prompt, confirmed):
-        if(confirmed): clear_prompt(prompt)
+def trash_prompt_click(button, prompt):
+    dummy_component = gradio.Label(visible=False)
+
+    button.click(
+        _js="trash_prompt",
+        fn=connect_trash_prompt,
+        inputs=[prompt, dummy_component],
+        outputs=[prompt, dummy_component],
+    )
 
 
 def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
@@ -643,13 +649,7 @@ def create_ui(wrap_gradio_gpu_call):
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
 
-        trash_prompt_button.click(
-            # fn=lambda: print("Clearing prompt"),
-            _js="trash_prompt",
-            fn=lambda: clear_prompt(),
-            inputs=[txt2img_prompt, dummy_component],
-            outputs=[txt2img_prompt, dummy_component],
-        )
+        trash_prompt_click(trash_prompt_button, txt2img_prompt)
 
 
         with gr.Row(elem_id='txt2img_progress_row'):
@@ -858,6 +858,7 @@ def create_ui(wrap_gradio_gpu_call):
         img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
         token_counter, token_button, trash_prompt_button = create_toprow(is_img2img=True)
 
+        trash_prompt_click(trash_prompt_button, img2img_prompt)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-- 
cgit v1.2.3


From de70ddaf58fae98c561738a54f574abfa14cd8d1 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 15:00:35 -0500
Subject: update token counter when clearing prompt

---
 modules/ui.py | 17 +++++++----------
 1 file changed, 7 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 90c338da..d3a89bf7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -430,19 +430,16 @@ def create_seed_inputs():
 
 
 
-def connect_trash_prompt(_, confirmed):
+def connect_trash_prompt(_prompt, confirmed, _token_counter):
         if(confirmed):
-            # update_token_counter(prompt, steps)
-            return ["", confirmed]
-
-def trash_prompt_click(button, prompt):
-    dummy_component = gradio.Label(visible=False)
+            return ["", confirmed, update_token_counter("", 1)]
 
+def trash_prompt_click(button, prompt, _dummy_confirmed, token_counter):
     button.click(
         _js="trash_prompt",
         fn=connect_trash_prompt,
-        inputs=[prompt, dummy_component],
-        outputs=[prompt, dummy_component],
+        inputs=[prompt, _dummy_confirmed, token_counter],
+        outputs=[prompt, _dummy_confirmed, token_counter],
     )
 
 
@@ -649,7 +646,6 @@ def create_ui(wrap_gradio_gpu_call):
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
 
-        trash_prompt_click(trash_prompt_button, txt2img_prompt)
 
 
         with gr.Row(elem_id='txt2img_progress_row'):
@@ -720,6 +716,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+            trash_prompt_click(trash_prompt_button, txt2img_prompt, dummy_component, token_counter)
 
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
@@ -858,7 +855,6 @@ def create_ui(wrap_gradio_gpu_call):
         img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
         token_counter, token_button, trash_prompt_button = create_toprow(is_img2img=True)
 
-        trash_prompt_click(trash_prompt_button, img2img_prompt)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -958,6 +954,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+            trash_prompt_click(trash_prompt_button, img2img_prompt, dummy_component, token_counter)
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-- 
cgit v1.2.3


From 9e40520f00d836cfa93187f7f1e81e2a7bd100b9 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 15:13:12 -0500
Subject: refactor internal terminology to use 'clear' instead of 'trash' like
 #2728

---
 modules/shared.py |  2 +-
 modules/ui.py     | 22 +++++++++++-----------
 2 files changed, 12 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 1585d532..ab5a0e9a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -317,7 +317,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
-    "trash_prompt_visible": OptionInfo(True, "Show trash prompt button"),
+    "clear_prompt_visible": OptionInfo(True, "Show clear prompt button"),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
diff --git a/modules/ui.py b/modules/ui.py
index d3a89bf7..31150800 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -88,7 +88,7 @@ folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 apply_style_symbol = '\U0001f4cb'  # 📋
-trash_prompt_symbol = '\U0001F5D1' #
+clear_prompt_symbol = '\U0001F5D1' # 🗑️
 
 
 def plaintext_to_html(text):
@@ -430,14 +430,14 @@ def create_seed_inputs():
 
 
 
-def connect_trash_prompt(_prompt, confirmed, _token_counter):
+def clear_prompt(_prompt, confirmed, _token_counter):
         if(confirmed):
             return ["", confirmed, update_token_counter("", 1)]
 
-def trash_prompt_click(button, prompt, _dummy_confirmed, token_counter):
+def connect_clear_prompt(button, prompt, _dummy_confirmed, token_counter):
     button.click(
-        _js="trash_prompt",
-        fn=connect_trash_prompt,
+        _js="clear_prompt",
+        fn=clear_prompt,
         inputs=[prompt, _dummy_confirmed, token_counter],
         outputs=[prompt, _dummy_confirmed, token_counter],
     )
@@ -518,7 +518,7 @@ def create_toprow(is_img2img):
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-            trash_prompt = gr.Button(value=trash_prompt_symbol, elem_id="trash_prompt", visible=opts.trash_prompt_visible)
+            clear_prompt_button = gr.Button(value=clear_prompt_symbol, elem_id="clear_prompt", visible=opts.clear_prompt_visible)
 
             token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
             token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
@@ -559,7 +559,7 @@ def create_toprow(is_img2img):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
                     prompt_style2.save_to_config = True
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button, trash_prompt
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button, clear_prompt_button
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -640,7 +640,7 @@ def create_ui(wrap_gradio_gpu_call):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,\
         txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter,\
-        token_button, trash_prompt_button = create_toprow(is_img2img=False)
+        token_button, clear_prompt_button = create_toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -716,7 +716,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            trash_prompt_click(trash_prompt_button, txt2img_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button, txt2img_prompt, dummy_component, token_counter)
 
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
@@ -853,7 +853,7 @@ def create_ui(wrap_gradio_gpu_call):
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit,\
         img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
-        token_counter, token_button, trash_prompt_button = create_toprow(is_img2img=True)
+        token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=True)
 
 
         with gr.Row(elem_id='img2img_progress_row'):
@@ -954,7 +954,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            trash_prompt_click(trash_prompt_button, img2img_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button, img2img_prompt, dummy_component, token_counter)
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-- 
cgit v1.2.3


From 0c7cf08b3d27a61bab4cd8b16f8be8ae74879423 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 15:32:26 -0500
Subject: some doc and formatting

---
 modules/ui.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 31150800..b26cf10a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -88,7 +88,7 @@ folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 apply_style_symbol = '\U0001f4cb'  # 📋
-clear_prompt_symbol = '\U0001F5D1' # 🗑️
+clear_prompt_symbol = '\U0001F5D1'  # 🗑️
 
 
 def plaintext_to_html(text):
@@ -429,12 +429,14 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-
 def clear_prompt(_prompt, confirmed, _token_counter):
-        if(confirmed):
-            return ["", confirmed, update_token_counter("", 1)]
+    """Given confirmation from a user on the client-side, go ahead with clearing prompt"""
+    if confirmed:
+        return ["", confirmed, update_token_counter("", 1)]
+
 
 def connect_clear_prompt(button, prompt, _dummy_confirmed, token_counter):
+    """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
     button.click(
         _js="clear_prompt",
         fn=clear_prompt,
@@ -518,7 +520,12 @@ def create_toprow(is_img2img):
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-            clear_prompt_button = gr.Button(value=clear_prompt_symbol, elem_id="clear_prompt", visible=opts.clear_prompt_visible)
+
+            clear_prompt_button = gr.Button(
+                value=clear_prompt_symbol,
+                elem_id="clear_prompt",
+                visible=opts.clear_prompt_visible
+            )
 
             token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
             token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-- 
cgit v1.2.3


From 57eb54b838faa383c10079e1bb5471b7bee6a695 Mon Sep 17 00:00:00 2001
From: Extraltodeus <extraltodeus@gmail.com>
Date: Sat, 22 Oct 2022 00:11:07 +0200
Subject: implement CUDA device selection by ID

---
 modules/devices.py | 21 ++++++++++++++++++---
 1 file changed, 18 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index eb422583..8a159282 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,7 +1,6 @@
+import sys, os, shlex
 import contextlib
-
 import torch
-
 from modules import errors
 
 # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
@@ -9,10 +8,26 @@ has_mps = getattr(torch, 'has_mps', False)
 
 cpu = torch.device("cpu")
 
+def extract_device_id(args, name):
+    for x in range(len(args)):
+        if name in args[x]: return args[x+1]
+    return None
 
 def get_optimal_device():
     if torch.cuda.is_available():
-        return torch.device("cuda")
+        # CUDA device selection support:
+        if "shared" not in sys.modules:
+            commandline_args = os.environ.get('COMMANDLINE_ARGS', "") #re-parse the commandline arguments because using the shared.py module creates an import loop.
+            sys.argv += shlex.split(commandline_args)
+            device_id = extract_device_id(sys.argv, '--device-id')
+        else:
+            device_id = shared.cmd_opts.device_id
+            
+        if device_id is not None:
+            cuda_device = f"cuda:{device_id}"
+            return torch.device(cuda_device)
+        else:
+            return torch.device("cuda")
 
     if has_mps:
         return torch.device("mps")
-- 
cgit v1.2.3


From 29bfacd63cb5c73b9643d94f255cca818fd49d9c Mon Sep 17 00:00:00 2001
From: Extraltodeus <extraltodeus@gmail.com>
Date: Sat, 22 Oct 2022 00:12:46 +0200
Subject: implement CUDA device selection, --device-id arg

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 41d7f08e..03032a47 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -80,6 +80,7 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
+parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.3


From 700340448baa7412c7cc5ff3d1349ac79ee8ed0c Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 17:24:04 -0500
Subject: forgot to clear neg prompt after moving to back. Add tooltip to hints

---
 modules/ui.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index b26cf10a..25aeba3b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -429,19 +429,19 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-def clear_prompt(_prompt, confirmed, _token_counter):
+def clear_prompt(_prompt, _prompt_neg, confirmed, _token_counter):
     """Given confirmation from a user on the client-side, go ahead with clearing prompt"""
     if confirmed:
-        return ["", confirmed, update_token_counter("", 1)]
+        return ["", "", confirmed, update_token_counter("", 1)]
 
 
-def connect_clear_prompt(button, prompt, _dummy_confirmed, token_counter):
+def connect_clear_prompt(button, prompt, prompt_neg, _dummy_confirmed, token_counter):
     """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
     button.click(
         _js="clear_prompt",
         fn=clear_prompt,
-        inputs=[prompt, _dummy_confirmed, token_counter],
-        outputs=[prompt, _dummy_confirmed, token_counter],
+        inputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
+        outputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
     )
 
 
@@ -723,7 +723,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button, txt2img_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button, txt2img_prompt, txt2img_negative_prompt, dummy_component, token_counter)
 
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
@@ -961,7 +961,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button, img2img_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button, img2img_prompt, img2img_negative_prompt, dummy_component, token_counter)
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-- 
cgit v1.2.3


From 40ddb6df61564684263c7442bacf61efe3882b87 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sat, 22 Oct 2022 10:16:22 +0800
Subject: inspiration perfected

---
 modules/inspiration.py | 71 +++++++++++++++++++++++++++-----------------------
 1 file changed, 39 insertions(+), 32 deletions(-)

(limited to 'modules')

diff --git a/modules/inspiration.py b/modules/inspiration.py
index f72ebf3a..319183ab 100644
--- a/modules/inspiration.py
+++ b/modules/inspiration.py
@@ -13,7 +13,7 @@ def read_name_list(file, types=None, keyword=None):
         line = line.rstrip("\n")
         if types is not None:
             dirname = os.path.split(line)
-            if dirname[0] in types and keyword in dirname[1]:
+            if dirname[0] in types and keyword in dirname[1].lower():
                 ret.append(line)
         else:
             ret.append(line)
@@ -21,8 +21,10 @@ def read_name_list(file, types=None, keyword=None):
     return ret
 
 def save_name_list(file, name): 
-    with open(file, "a") as f:
-        f.write(name + "\n")
+    name_list = read_name_list(file)
+    if name not in name_list:
+        with open(file, "a") as f:
+            f.write(name + "\n")
 
 def get_types_list():
     files = os.listdir(opts.inspiration_dir)
@@ -39,20 +41,20 @@ def get_types_list():
     return types
 
 def get_inspiration_images(source, types, keyword):
+    keyword = keyword.strip(" ").lower()
     get_num = int(opts.inspiration_rows_num * opts.inspiration_cols_num)
     if source == "Favorites":
         names = read_name_list(os.path.join(inspiration_system_path, "faverites.txt"), types, keyword)
         names = random.sample(names, get_num) if len(names) > get_num else names
     elif source == "Abandoned":
         names = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)
-        print(names)
         names = random.sample(names, get_num) if len(names) > get_num else names
     elif source == "Exclude abandoned":        
         abandoned = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)  
         all_names = []
         for tp in types:
             name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
-            all_names += [os.path.join(tp, x) for x in name_list if keyword in x]
+            all_names += [os.path.join(tp, x) for x in name_list if keyword in x.lower()]
         
         if len(all_names) > get_num:
             names = []
@@ -66,14 +68,14 @@ def get_inspiration_images(source, types, keyword):
         all_names = []
         for tp in types:
             name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
-            all_names += [os.path.join(tp, x) for x in name_list if keyword in x]
+            all_names += [os.path.join(tp, x) for x in name_list if keyword in x.lower()]
         names = random.sample(all_names, get_num) if len(all_names) > get_num else all_names
     image_list = []
     for a in names:
         image_path = os.path.join(opts.inspiration_dir, a)
         images = os.listdir(image_path)        
         image_list.append((os.path.join(image_path, random.choice(images)), a))
-    return image_list, names, ""
+    return image_list, names
 
 def select_click(index, name_list):
     name = name_list[int(index)]
@@ -83,22 +85,18 @@ def select_click(index, name_list):
 
 def give_up_click(name):
     file = os.path.join(inspiration_system_path, "abandoned.txt")
-    name_list = read_name_list(file)
-    if name not in name_list:
-        save_name_list(file, name)
+    save_name_list(file, name)
     return "Added to abandoned list"
    
 def collect_click(name):
     file = os.path.join(inspiration_system_path, "faverites.txt")
-    name_list = read_name_list(file)
-    if name not in name_list:
-        save_name_list(file, name)
+    save_name_list(file, name)
     return "Added to faverite list"
 
 def moveout_click(name, source):
     if source == "Abandoned":
         file = os.path.join(inspiration_system_path, "abandoned.txt")
-    if source == "Favorites":
+    elif source == "Favorites":
         file = os.path.join(inspiration_system_path, "faverites.txt")
     else:
         return None
@@ -107,8 +105,8 @@ def moveout_click(name, source):
     with open(file, "a") as f:
         for a in name_list:
             if a != name:
-                f.write(a)
-    return "Moved out {name} from {source} list"
+                f.write(a + "\n")
+    return f"Moved out {name} from {source} list"
 
 def source_change(source):
     if source in ["Abandoned", "Favorites"]:
@@ -116,10 +114,12 @@ def source_change(source):
     else:
         return gradio.update(visible=False), []
 def add_to_prompt(name, prompt):
-    print(name, prompt)
     name = os.path.basename(name)
     return prompt + "," + name
 
+def clear_keyword():
+    return ""
+
 def ui(gr, opts, txt2img_prompt, img2img_prompt):     
     with gr.Blocks(analytics_enabled=False) as inspiration:
         flag = os.path.exists(opts.inspiration_dir)        
@@ -132,15 +132,15 @@ def ui(gr, opts, txt2img_prompt, img2img_prompt):
             gr.HTML("""
                 <div align='center' width="50%"><h2>To activate inspiration function, you need get "inspiration" images first. </h2><br>
                 You can create these images by run "Create inspiration images" script in txt2img page, <br> you can get the artists or art styles list from here<br>
-                <a>https://github.com/pharmapsychotic/clip-interrogator/tree/main/data</a><br>
+                <a href="https://github.com/pharmapsychotic/clip-interrogator/tree/main/data">https://github.com/pharmapsychotic/clip-interrogator/tree/main/data</a><br>
                 download these files, and select these files in the "Create inspiration images" script UI<br>
                 There about 6000 artists and art styles in these files. <br>This takes server hours depending on your GPU type and how many pictures  you generate for each artist/style
                 <br>I suggest at least four images for each<br><br><br>
                 <h2>You can also download generated pictures from here:</h2><br>
-                <a>https://huggingface.co/datasets/yfszzx/inspiration</a><br>
+                <a href="https://huggingface.co/datasets/yfszzx/inspiration">https://huggingface.co/datasets/yfszzx/inspiration</a><br>
                 unzip the file to the project directory of webui<br>
                 and restart webui, and enjoy the joy of creation!<br></div>
-                """)           
+                """)      
             return inspiration
         if not os.path.exists(inspiration_system_path):
             os.mkdir(inspiration_system_path)
@@ -148,35 +148,42 @@ def ui(gr, opts, txt2img_prompt, img2img_prompt):
             with gr.Column(scale=2):                
                 inspiration_gallery = gr.Gallery(show_label=False, elem_id="inspiration_gallery").style(grid=opts.inspiration_cols_num, height='auto')
             with gr.Column(scale=1):
-                print(types)
                 types = gr.CheckboxGroup(choices=types, value=types)
-                keyword = gr.Textbox("", label="Key word")                
-                with gr.Row():                    
-                    source = gr.Dropdown(choices=["All", "Favorites", "Exclude abandoned", "Abandoned"], value="Exclude abandoned", label="Source")
-                    get_inspiration = gr.Button("Get inspiration", elem_id="inspiration_get_button")
-                name = gr.Textbox(show_label=False, interactive=False)
                 with gr.Row(): 
+                    source = gr.Dropdown(choices=["All", "Favorites", "Exclude abandoned", "Abandoned"], value="Exclude abandoned", label="Source")
+                    keyword = gr.Textbox("", label="Key word")                                                
+                get_inspiration = gr.Button("Get inspiration", elem_id="inspiration_get_button")   
+                name = gr.Textbox(show_label=False, interactive=False)      
+                with gr.Row():                     
                     send_to_txt2img = gr.Button('to txt2img')
                     send_to_img2img = gr.Button('to img2img')
                 style_gallery = gr.Gallery(show_label=False).style(grid=2, height='auto') 
-                collect = gr.Button('Collect')     
-                give_up = gr.Button("Don't show again")
-                moveout = gr.Button("Move out", visible=False)
                 warning = gr.HTML()
+                with gr.Row():
+                    collect = gr.Button('Collect')     
+                    give_up = gr.Button("Don't show again")
+                moveout = gr.Button("Move out", visible=False)
+                
         with gr.Row(visible=False):
             select_button = gr.Button('set button', elem_id="inspiration_select_button")
             name_list = gr.State()
         
-        get_inspiration.click(get_inspiration_images, inputs=[source, types, keyword], outputs=[inspiration_gallery, name_list, keyword])
-        source.change(source_change, inputs=[source], outputs=[moveout, style_gallery])
-        source.change(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
+        get_inspiration.click(get_inspiration_images, inputs=[source, types, keyword], outputs=[inspiration_gallery, name_list])
         keyword.submit(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
+        source.change(source_change, inputs=[source], outputs=[moveout, style_gallery])
+        source.change(fn=clear_keyword, _js="inspiration_click_get_button", inputs=None, outputs=[keyword])       
+        types.change(fn=clear_keyword, _js="inspiration_click_get_button", inputs=None, outputs=[keyword])  
+
         select_button.click(select_click, _js="inspiration_selected", inputs=[name, name_list], outputs=[name, style_gallery, warning])
         give_up.click(give_up_click, inputs=[name], outputs=[warning])
         collect.click(collect_click, inputs=[name], outputs=[warning])
         moveout.click(moveout_click, inputs=[name, source], outputs=[warning])
+        moveout.click(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
+
         send_to_txt2img.click(add_to_prompt, inputs=[name, txt2img_prompt], outputs=[txt2img_prompt])
         send_to_img2img.click(add_to_prompt, inputs=[name, img2img_prompt], outputs=[img2img_prompt])
+        send_to_txt2img.click(collect_click, inputs=[name], outputs=[warning])
+        send_to_img2img.click(collect_click, inputs=[name], outputs=[warning])
         send_to_txt2img.click(None, _js='switch_to_txt2img', inputs=None, outputs=None)
         send_to_img2img.click(None, _js="switch_to_img2img_img2img", inputs=None, outputs=None)
     return inspiration
-- 
cgit v1.2.3


From d93ea5cdeb2fd3607b7265271ccab2c9bf4c1156 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sat, 22 Oct 2022 10:21:21 +0800
Subject: inspiration perfected

---
 modules/inspiration.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/inspiration.py b/modules/inspiration.py
index 319183ab..94ff139a 100644
--- a/modules/inspiration.py
+++ b/modules/inspiration.py
@@ -73,8 +73,11 @@ def get_inspiration_images(source, types, keyword):
     image_list = []
     for a in names:
         image_path = os.path.join(opts.inspiration_dir, a)
-        images = os.listdir(image_path)        
-        image_list.append((os.path.join(image_path, random.choice(images)), a))
+        images = os.listdir(image_path)
+        if len(images) > 0:        
+            image_list.append((os.path.join(image_path, random.choice(images)), a))
+        else:
+            print(image_path)
     return image_list, names
 
 def select_click(index, name_list):
-- 
cgit v1.2.3


From 67b78f0ea6f196bfdca49932da062631bb40d0b1 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sat, 22 Oct 2022 10:29:23 +0800
Subject: inspiration perfected

---
 modules/inspiration.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/inspiration.py b/modules/inspiration.py
index 94ff139a..29cf8297 100644
--- a/modules/inspiration.py
+++ b/modules/inspiration.py
@@ -160,12 +160,13 @@ def ui(gr, opts, txt2img_prompt, img2img_prompt):
                 with gr.Row():                     
                     send_to_txt2img = gr.Button('to txt2img')
                     send_to_img2img = gr.Button('to img2img')
-                style_gallery = gr.Gallery(show_label=False).style(grid=2, height='auto') 
-                warning = gr.HTML()
-                with gr.Row():
                     collect = gr.Button('Collect')     
                     give_up = gr.Button("Don't show again")
-                moveout = gr.Button("Move out", visible=False)
+                    moveout = gr.Button("Move out", visible=False)
+                warning = gr.HTML()
+                style_gallery = gr.Gallery(show_label=False).style(grid=2, height='auto') 
+                    
+               
                 
         with gr.Row(visible=False):
             select_button = gr.Button('set button', elem_id="inspiration_select_button")
-- 
cgit v1.2.3


From 2b91251637078e04472c91a06a8d9c4db9c1dcf0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 12:23:45 +0300
Subject: removed aesthetic gradients as built-in added support for extensions

---
 modules/aesthetic_clip.py   | 241 --------------------------------------------
 modules/images_history.py   |   2 +-
 modules/img2img.py          |   5 +-
 modules/processing.py       |  35 ++++---
 modules/script_callbacks.py |  42 ++++++++
 modules/scripts.py          | 210 ++++++++++++++++++++++++++++----------
 modules/sd_hijack.py        |   1 -
 modules/sd_models.py        |   7 +-
 modules/shared.py           |  19 ----
 modules/txt2img.py          |   5 +-
 modules/ui.py               |  83 +++------------
 11 files changed, 244 insertions(+), 406 deletions(-)
 delete mode 100644 modules/aesthetic_clip.py
 create mode 100644 modules/script_callbacks.py

(limited to 'modules')

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
deleted file mode 100644
index 8c828541..00000000
--- a/modules/aesthetic_clip.py
+++ /dev/null
@@ -1,241 +0,0 @@
-import copy
-import itertools
-import os
-from pathlib import Path
-import html
-import gc
-
-import gradio as gr
-import torch
-from PIL import Image
-from torch import optim
-
-from modules import shared
-from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer
-from tqdm.auto import tqdm, trange
-from modules.shared import opts, device
-
-
-def get_all_images_in_folder(folder):
-    return [os.path.join(folder, f) for f in os.listdir(folder) if
-            os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)]
-
-
-def check_is_valid_image_file(filename):
-    return filename.lower().endswith(('.png', '.jpg', '.jpeg', ".gif", ".tiff", ".webp"))
-
-
-def batched(dataset, total, n=1):
-    for ndx in range(0, total, n):
-        yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
-
-
-def iter_to_batched(iterable, n=1):
-    it = iter(iterable)
-    while True:
-        chunk = tuple(itertools.islice(it, n))
-        if not chunk:
-            return
-        yield chunk
-
-
-def create_ui():
-    import modules.ui
-
-    with gr.Group():
-        with gr.Accordion("Open for Clip Aesthetic!", open=False):
-            with gr.Row():
-                aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight",
-                                             value=0.9)
-                aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
-
-            with gr.Row():
-                aesthetic_lr = gr.Textbox(label='Aesthetic learning rate',
-                                          placeholder="Aesthetic learning rate", value="0.0001")
-                aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
-                aesthetic_imgs = gr.Dropdown(sorted(shared.aesthetic_embeddings.keys()),
-                                             label="Aesthetic imgs embedding",
-                                             value="None")
-
-                modules.ui.create_refresh_button(aesthetic_imgs, shared.update_aesthetic_embeddings, lambda: {"choices": sorted(shared.aesthetic_embeddings.keys())}, "refresh_aesthetic_embeddings")
-
-            with gr.Row():
-                aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs',
-                                                 placeholder="This text is used to rotate the feature space of the imgs embs",
-                                                 value="")
-                aesthetic_slerp_angle = gr.Slider(label='Slerp angle', minimum=0, maximum=1, step=0.01,
-                                                  value=0.1)
-                aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
-
-    return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative
-
-
-aesthetic_clip_model = None
-
-
-def aesthetic_clip():
-    global aesthetic_clip_model
-
-    if aesthetic_clip_model is None or aesthetic_clip_model.name_or_path != shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path:
-        aesthetic_clip_model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path)
-        aesthetic_clip_model.cpu()
-
-    return aesthetic_clip_model
-
-
-def generate_imgs_embd(name, folder, batch_size):
-    model = aesthetic_clip().to(device)
-    processor = CLIPProcessor.from_pretrained(model.name_or_path)
-
-    with torch.no_grad():
-        embs = []
-        for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size),
-                          desc=f"Generating embeddings for {name}"):
-            if shared.state.interrupted:
-                break
-            inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device)
-            outputs = model.get_image_features(**inputs).cpu()
-            embs.append(torch.clone(outputs))
-            inputs.to("cpu")
-            del inputs, outputs
-
-        embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True)
-
-        # The generated embedding will be located here
-        path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
-        torch.save(embs, path)
-
-        model.cpu()
-        del processor
-        del embs
-        gc.collect()
-        torch.cuda.empty_cache()
-        res = f"""
-        Done generating embedding for {name}!
-        Aesthetic embedding saved to {html.escape(path)}
-        """
-        shared.update_aesthetic_embeddings()
-        return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
-                                  value="None"), \
-               gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()),
-                                  label="Imgs embedding",
-                                  value="None"), res, ""
-
-
-def slerp(low, high, val):
-    low_norm = low / torch.norm(low, dim=1, keepdim=True)
-    high_norm = high / torch.norm(high, dim=1, keepdim=True)
-    omega = torch.acos((low_norm * high_norm).sum(1))
-    so = torch.sin(omega)
-    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
-    return res
-
-
-class AestheticCLIP:
-    def __init__(self):
-        self.skip = False
-        self.aesthetic_steps = 0
-        self.aesthetic_weight = 0
-        self.aesthetic_lr = 0
-        self.slerp = False
-        self.aesthetic_text_negative = ""
-        self.aesthetic_slerp_angle = 0
-        self.aesthetic_imgs_text = ""
-
-        self.image_embs_name = None
-        self.image_embs = None
-        self.load_image_embs(None)
-
-    def set_aesthetic_params(self, p, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
-                             aesthetic_slerp=True, aesthetic_imgs_text="",
-                             aesthetic_slerp_angle=0.15,
-                             aesthetic_text_negative=False):
-        self.aesthetic_imgs_text = aesthetic_imgs_text
-        self.aesthetic_slerp_angle = aesthetic_slerp_angle
-        self.aesthetic_text_negative = aesthetic_text_negative
-        self.slerp = aesthetic_slerp
-        self.aesthetic_lr = aesthetic_lr
-        self.aesthetic_weight = aesthetic_weight
-        self.aesthetic_steps = aesthetic_steps
-        self.load_image_embs(image_embs_name)
-
-        if self.image_embs_name is not None:
-            p.extra_generation_params.update({
-                "Aesthetic LR": aesthetic_lr,
-                "Aesthetic weight": aesthetic_weight,
-                "Aesthetic steps": aesthetic_steps,
-                "Aesthetic embedding": self.image_embs_name,
-                "Aesthetic slerp": aesthetic_slerp,
-                "Aesthetic text": aesthetic_imgs_text,
-                "Aesthetic text negative": aesthetic_text_negative,
-                "Aesthetic slerp angle": aesthetic_slerp_angle,
-            })
-
-    def set_skip(self, skip):
-        self.skip = skip
-
-    def load_image_embs(self, image_embs_name):
-        if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
-            image_embs_name = None
-            self.image_embs_name = None
-        if image_embs_name is not None and self.image_embs_name != image_embs_name:
-            self.image_embs_name = image_embs_name
-            self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
-            self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
-            self.image_embs.requires_grad_(False)
-
-    def __call__(self, z, remade_batch_tokens):
-        if not self.skip and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name is not None:
-            tokenizer = shared.sd_model.cond_stage_model.tokenizer
-            if not opts.use_old_emphasis_implementation:
-                remade_batch_tokens = [
-                    [tokenizer.bos_token_id] + x[:75] + [tokenizer.eos_token_id] for x in
-                    remade_batch_tokens]
-
-            tokens = torch.asarray(remade_batch_tokens).to(device)
-
-            model = copy.deepcopy(aesthetic_clip()).to(device)
-            model.requires_grad_(True)
-            if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
-                text_embs_2 = model.get_text_features(
-                    **tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
-                if self.aesthetic_text_negative:
-                    text_embs_2 = self.image_embs - text_embs_2
-                    text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
-                img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
-            else:
-                img_embs = self.image_embs
-
-            with torch.enable_grad():
-
-                # We optimize the model to maximize the similarity
-                optimizer = optim.Adam(
-                    model.text_model.parameters(), lr=self.aesthetic_lr
-                )
-
-                for _ in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
-                    text_embs = model.get_text_features(input_ids=tokens)
-                    text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
-                    sim = text_embs @ img_embs.T
-                    loss = -sim
-                    optimizer.zero_grad()
-                    loss.mean().backward()
-                    optimizer.step()
-
-                zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
-                if opts.CLIP_stop_at_last_layers > 1:
-                    zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
-                    zn = model.text_model.final_layer_norm(zn)
-                else:
-                    zn = zn.last_hidden_state
-                model.cpu()
-                del model
-                gc.collect()
-                torch.cuda.empty_cache()
-            zn = torch.concat([zn[77 * i:77 * (i + 1)] for i in range(max(z.shape[1] // 77, 1))], 1)
-            if self.slerp:
-                z = slerp(z, zn, self.aesthetic_weight)
-            else:
-                z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
-
-        return z
diff --git a/modules/images_history.py b/modules/images_history.py
index 78fd0543..bc5cf11f 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -310,7 +310,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                         forward = gr.Button('Prev batch')
                         backward = gr.Button('Next batch')
                 with gr.Column(scale=3):
-                    load_info =  gr.HTML(visible=not custom_dir)   
+                    load_info =     gr.HTML(visible=not custom_dir)
             with gr.Row(visible=False) as warning:                 
                 warning_box = gr.Textbox("Message", interactive=False)                        
 
diff --git a/modules/img2img.py b/modules/img2img.py
index eea5199b..8d9f7cf9 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -56,7 +56,7 @@ def process_batch(p, input_dir, output_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args):
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
     is_inpaint = mode == 1
     is_batch = mode == 2
 
@@ -109,7 +109,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpainting_mask_invert=inpainting_mask_invert,
     )
 
-    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
+    p.scripts = modules.scripts.scripts_txt2img
+    p.script_args = args
 
     if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
diff --git a/modules/processing.py b/modules/processing.py
index ff1ec4c9..372489f7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -104,6 +104,12 @@ class StableDiffusionProcessing():
             self.seed_resize_from_h = 0
             self.seed_resize_from_w = 0
 
+        self.scripts = None
+        self.script_args = None
+        self.all_prompts = None
+        self.all_seeds = None
+        self.all_subseeds = None
+
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
@@ -350,32 +356,35 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     shared.prompt_styles.apply_styles(p)
 
     if type(p.prompt) == list:
-        all_prompts = p.prompt
+        p.all_prompts = p.prompt
     else:
-        all_prompts = p.batch_size * p.n_iter * [p.prompt]
+        p.all_prompts = p.batch_size * p.n_iter * [p.prompt]
 
     if type(seed) == list:
-        all_seeds = seed
+        p.all_seeds = seed
     else:
-        all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
+        p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
 
     if type(subseed) == list:
-        all_subseeds = subseed
+        p.all_subseeds = subseed
     else:
-        all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]
+        p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0):
-        return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
+        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
+    if p.scripts is not None:
+        p.scripts.run_alwayson_scripts(p)
+
     infotexts = []
     output_images = []
 
     with torch.no_grad(), p.sd_model.ema_scope():
         with devices.autocast():
-            p.init(all_prompts, all_seeds, all_subseeds)
+            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
 
         if state.job_count == -1:
             state.job_count = p.n_iter
@@ -387,9 +396,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if state.interrupted:
                 break
 
-            prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-            seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
-            subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+            prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
+            subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
             if (len(prompts) == 0):
                 break
@@ -490,10 +499,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 index_of_first_image = 1
 
             if opts.grid_save:
-                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
+                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+    return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
new file mode 100644
index 00000000..866b7acd
--- /dev/null
+++ b/modules/script_callbacks.py
@@ -0,0 +1,42 @@
+
+callbacks_model_loaded = []
+callbacks_ui_tabs = []
+
+
+def clear_callbacks():
+    callbacks_model_loaded.clear()
+    callbacks_ui_tabs.clear()
+
+
+def model_loaded_callback(sd_model):
+    for callback in callbacks_model_loaded:
+        callback(sd_model)
+
+
+def ui_tabs_callback():
+    res = []
+
+    for callback in callbacks_ui_tabs:
+        res += callback() or []
+
+    return res
+
+
+def on_model_loaded(callback):
+    """register a function to be called when the stable diffusion model is created; the model is
+    passed as an argument"""
+    callbacks_model_loaded.append(callback)
+
+
+def on_ui_tabs(callback):
+    """register a function to be called when the UI is creating new tabs.
+    The function must either return a None, which means no new tabs to be added, or a list, where
+    each element is a tuple:
+        (gradio_component, title, elem_id)
+
+    gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
+    title is tab text displayed to user in the UI
+    elem_id is HTML id for the tab
+    """
+    callbacks_ui_tabs.append(callback)
+
diff --git a/modules/scripts.py b/modules/scripts.py
index 1039fa9c..65f25f49 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -1,86 +1,153 @@
 import os
 import sys
 import traceback
+from collections import namedtuple
 
 import modules.ui as ui
 import gradio as gr
 
 from modules.processing import StableDiffusionProcessing
-from modules import shared
+from modules import shared, paths, script_callbacks
+
+AlwaysVisible = object()
+
 
 class Script:
     filename = None
     args_from = None
     args_to = None
+    alwayson = False
+
+    infotext_fields = None
+    """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
+    parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
+    """
 
-    # The title of the script. This is what will be displayed in the dropdown menu.
     def title(self):
+        """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
+
         raise NotImplementedError()
 
-    # How the script is displayed in the UI. See https://gradio.app/docs/#components
-    # for the different UI components you can use and how to create them.
-    # Most UI components can return a value, such as a boolean for a checkbox.
-    # The returned values are passed to the run method as parameters.
     def ui(self, is_img2img):
+        """this function should create gradio UI elements. See https://gradio.app/docs/#components
+        The return value should be an array of all components that are used in processing.
+        Values of those returned componenbts will be passed to run() and process() functions.
+        """
+
         pass
 
-    # Determines when the script should be shown in the dropdown menu via the 
-    # returned value. As an example:
-    # is_img2img is True if the current tab is img2img, and False if it is txt2img.
-    # Thus, return is_img2img to only show the script on the img2img tab.
     def show(self, is_img2img):
+        """
+        is_img2img is True if this function is called for the img2img interface, and Fasle otherwise
+
+        This function should return:
+         - False if the script should not be shown in UI at all
+         - True if the script should be shown in UI if it's scelected in the scripts drowpdown
+         - script.AlwaysVisible if the script should be shown in UI at all times
+         """
+
         return True
 
-    # This is where the additional processing is implemented. The parameters include
-    # self, the model object "p" (a StableDiffusionProcessing class, see
-    # processing.py), and the parameters returned by the ui method.
-    # Custom functions can be defined here, and additional libraries can be imported 
-    # to be used in processing. The return value should be a Processed object, which is
-    # what is returned by the process_images method.
-    def run(self, *args):
+    def run(self, p, *args):
+        """
+        This function is called if the script has been selected in the script dropdown.
+        It must do all processing and return the Processed object with results, same as
+        one returned by processing.process_images.
+
+        Usually the processing is done by calling the processing.process_images function.
+
+        args contains all values returned by components from ui()
+        """
+
         raise NotImplementedError()
 
-    # The description method is currently unused.
-    # To add a description that appears when hovering over the title, amend the "titles" 
-    # dict in script.js to include the script title (returned by title) as a key, and 
-    # your description as the value.
+    def process(self, p, *args):
+        """
+        This function is called before processing begins for AlwaysVisible scripts.
+        scripts. You can modify the processing object (p) here, inject hooks, etc.
+        """
+
+        pass
+
     def describe(self):
+        """unused"""
         return ""
 
 
+current_basedir = paths.script_path
+
+
+def basedir():
+    """returns the base directory for the current script. For scripts in the main scripts directory,
+    this is the main directory (where webui.py resides), and for scripts in extensions directory
+    (ie extensions/aesthetic/script/aesthetic.py), this is extension's directory (extensions/aesthetic)
+    """
+    return current_basedir
+
+
 scripts_data = []
+ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])
+ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir"])
+
+
+def list_scripts(scriptdirname, extension):
+    scripts_list = []
+
+    basedir = os.path.join(paths.script_path, scriptdirname)
+    if os.path.exists(basedir):
+        for filename in sorted(os.listdir(basedir)):
+            scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
+
+    extdir = os.path.join(paths.script_path, "extensions")
+    if os.path.exists(extdir):
+        for dirname in sorted(os.listdir(extdir)):
+            dirpath = os.path.join(extdir, dirname)
+            if not os.path.isdir(dirpath):
+                continue
 
+            for filename in sorted(os.listdir(os.path.join(dirpath, scriptdirname))):
+                scripts_list.append(ScriptFile(dirpath, filename, os.path.join(dirpath, scriptdirname, filename)))
 
-def load_scripts(basedir):
-    if not os.path.exists(basedir):
-        return
+    scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
 
-    for filename in sorted(os.listdir(basedir)):
-        path = os.path.join(basedir, filename)
+    return scripts_list
 
-        if os.path.splitext(path)[1].lower() != '.py':
-            continue
 
-        if not os.path.isfile(path):
-            continue
+def load_scripts():
+    global current_basedir
+    scripts_data.clear()
+    script_callbacks.clear_callbacks()
+
+    scripts_list = list_scripts("scripts", ".py")
+
+    syspath = sys.path
 
+    for scriptfile in sorted(scripts_list):
         try:
-            with open(path, "r", encoding="utf8") as file:
+            if scriptfile.basedir != paths.script_path:
+                sys.path = [scriptfile.basedir] + sys.path
+            current_basedir = scriptfile.basedir
+
+            with open(scriptfile.path, "r", encoding="utf8") as file:
                 text = file.read()
 
             from types import ModuleType
-            compiled = compile(text, path, 'exec')
-            module = ModuleType(filename)
+            compiled = compile(text, scriptfile.path, 'exec')
+            module = ModuleType(scriptfile.filename)
             exec(compiled, module.__dict__)
 
             for key, script_class in module.__dict__.items():
                 if type(script_class) == type and issubclass(script_class, Script):
-                    scripts_data.append((script_class, path))
+                    scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir))
 
         except Exception:
-            print(f"Error loading script: {filename}", file=sys.stderr)
+            print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
+        finally:
+            sys.path = syspath
+            current_basedir = paths.script_path
+
 
 def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
     try:
@@ -96,56 +163,80 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
 class ScriptRunner:
     def __init__(self):
         self.scripts = []
+        self.selectable_scripts = []
+        self.alwayson_scripts = []
         self.titles = []
+        self.infotext_fields = []
 
     def setup_ui(self, is_img2img):
-        for script_class, path in scripts_data:
+        for script_class, path, basedir in scripts_data:
             script = script_class()
             script.filename = path
 
-            if not script.show(is_img2img):
-                continue
+            visibility = script.show(is_img2img)
 
-            self.scripts.append(script)
+            if visibility == AlwaysVisible:
+                self.scripts.append(script)
+                self.alwayson_scripts.append(script)
+                script.alwayson = True
 
-        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
+            elif visibility:
+                self.scripts.append(script)
+                self.selectable_scripts.append(script)
 
-        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
-        dropdown.save_to_config = True
-        inputs = [dropdown]
+        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
+
+        inputs = [None]
+        inputs_alwayson = [True]
 
-        for script in self.scripts:
+        def create_script_ui(script, inputs, inputs_alwayson):
             script.args_from = len(inputs)
             script.args_to = len(inputs)
 
             controls = wrap_call(script.ui, script.filename, "ui", is_img2img)
 
             if controls is None:
-                continue
+                return
 
             for control in controls:
                 control.custom_script_source = os.path.basename(script.filename)
-                control.visible = False
+                if not script.alwayson:
+                    control.visible = False
+
+            if script.infotext_fields is not None:
+                self.infotext_fields += script.infotext_fields
 
             inputs += controls
+            inputs_alwayson += [script.alwayson for _ in controls]
             script.args_to = len(inputs)
 
+        for script in self.alwayson_scripts:
+            with gr.Group():
+                create_script_ui(script, inputs, inputs_alwayson)
+
+        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
+        dropdown.save_to_config = True
+        inputs[0] = dropdown
+
+        for script in self.selectable_scripts:
+            create_script_ui(script, inputs, inputs_alwayson)
+
         def select_script(script_index):
-            if 0 < script_index <= len(self.scripts):
-                script = self.scripts[script_index-1]
+            if 0 < script_index <= len(self.selectable_scripts):
+                script = self.selectable_scripts[script_index-1]
                 args_from = script.args_from
                 args_to = script.args_to
             else:
                 args_from = 0
                 args_to = 0
 
-            return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
+            return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]
 
         def init_field(title):
             if title == 'None':
                 return
             script_index = self.titles.index(title)
-            script = self.scripts[script_index]
+            script = self.selectable_scripts[script_index]
             for i in range(script.args_from, script.args_to):
                 inputs[i].visible = True
 
@@ -164,7 +255,7 @@ class ScriptRunner:
         if script_index == 0:
             return None
 
-        script = self.scripts[script_index-1]
+        script = self.selectable_scripts[script_index-1]
 
         if script is None:
             return None
@@ -176,6 +267,15 @@ class ScriptRunner:
 
         return processed
 
+    def run_alwayson_scripts(self, p):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.process(p, *script_args)
+            except Exception:
+                print(f"Error running alwayson script: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
     def reload_sources(self):
         for si, script in list(enumerate(self.scripts)):
             with open(script.filename, "r", encoding="utf8") as file:
@@ -197,19 +297,21 @@ class ScriptRunner:
                         self.scripts[si].args_from = args_from
                         self.scripts[si].args_to = args_to
 
+
 scripts_txt2img = ScriptRunner()
 scripts_img2img = ScriptRunner()
 
+
 def reload_script_body_only():
     scripts_txt2img.reload_sources()
     scripts_img2img.reload_sources()
 
 
-def reload_scripts(basedir):
+def reload_scripts():
     global scripts_txt2img, scripts_img2img
 
-    scripts_data.clear()
-    load_scripts(basedir)
+    load_scripts()
 
     scripts_txt2img = ScriptRunner()
     scripts_img2img = ScriptRunner()
+
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 1f8587d1..0f10828e 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -332,7 +332,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     multipliers.append([1.0] * 75)
 
             z1 = self.process_tokens(tokens, multipliers)
-            z1 = shared.aesthetic_clip(z1, remade_batch_tokens)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
 
             remade_batch_tokens = rem_tokens
diff --git a/modules/sd_models.py b/modules/sd_models.py
index d99dbce8..f9b3063d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -7,7 +7,7 @@ from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices
+from modules import shared, modelloader, devices, script_callbacks
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
@@ -238,6 +238,9 @@ def load_model(checkpoint_info=None):
     sd_hijack.model_hijack.hijack(sd_model)
 
     sd_model.eval()
+    shared.sd_model = sd_model
+
+    script_callbacks.model_loaded_callback(sd_model)
 
     print(f"Model loaded.")
     return sd_model
@@ -252,7 +255,7 @@ def reload_model_weights(sd_model, info=None):
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
-        shared.sd_model = load_model(checkpoint_info)
+        load_model(checkpoint_info)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
diff --git a/modules/shared.py b/modules/shared.py
index 0dbe360d..7d786f07 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -31,7 +31,6 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(models_path, 'aesthetic_embeddings'), help="aesthetic_embeddings directory(default: aesthetic_embeddings)")
 parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
 parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
@@ -109,21 +108,6 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
-
-os.makedirs(cmd_opts.aesthetic_embeddings_dir, exist_ok=True)
-aesthetic_embeddings = {}
-
-
-def update_aesthetic_embeddings():
-    global aesthetic_embeddings
-    aesthetic_embeddings = {f.replace(".pt", ""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
-                            os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
-    aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings)
-
-
-update_aesthetic_embeddings()
-
-
 def reload_hypernetworks():
     global hypernetworks
 
@@ -415,9 +399,6 @@ sd_model = None
 
 clip_model = None
 
-from modules.aesthetic_clip import AestheticCLIP
-aesthetic_clip = AestheticCLIP()
-
 progress_print_out = sys.stdout
 
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 1761cfa2..c9d5a090 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -7,7 +7,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -36,7 +36,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         firstphase_height=firstphase_height if enable_hr else None,
     )
 
-    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
+    p.scripts = modules.scripts.scripts_txt2img
+    p.script_args = args
 
     if cmd_opts.enable_console_prompts:
         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
diff --git a/modules/ui.py b/modules/ui.py
index 70a9cf10..c977482c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -23,10 +23,10 @@ import gradio as gr
 import gradio.utils
 import gradio.routes
 
-from modules import sd_hijack, sd_models, localization
+from modules import sd_hijack, sd_models, localization, script_callbacks
 from modules.paths import script_path
 
-from modules.shared import opts, cmd_opts, restricted_opts, aesthetic_embeddings
+from modules.shared import opts, cmd_opts, restricted_opts
 
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
@@ -44,7 +44,6 @@ from modules.images import save_image
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
 
-import modules.aesthetic_clip as aesthetic_clip
 import modules.images_history as img_his
 
 
@@ -662,8 +661,6 @@ def create_ui(wrap_gradio_gpu_call):
 
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
 
-                aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative = aesthetic_clip.create_ui()
-
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
@@ -718,14 +715,6 @@ def create_ui(wrap_gradio_gpu_call):
                     denoising_strength,
                     firstphase_width,
                     firstphase_height,
-                    aesthetic_lr,
-                    aesthetic_weight,
-                    aesthetic_steps,
-                    aesthetic_imgs,
-                    aesthetic_slerp,
-                    aesthetic_imgs_text,
-                    aesthetic_slerp_angle,
-                    aesthetic_text_negative
                 ] + custom_inputs,
 
                 outputs=[
@@ -804,14 +793,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
-                (aesthetic_lr, "Aesthetic LR"),
-                (aesthetic_weight, "Aesthetic weight"),
-                (aesthetic_steps, "Aesthetic steps"),
-                (aesthetic_imgs, "Aesthetic embedding"),
-                (aesthetic_slerp, "Aesthetic slerp"),
-                (aesthetic_imgs_text, "Aesthetic text"),
-                (aesthetic_text_negative, "Aesthetic text negative"),
-                (aesthetic_slerp_angle, "Aesthetic slerp angle"),
+                *modules.scripts.scripts_txt2img.infotext_fields
             ]
 
             txt2img_preview_params = [
@@ -896,8 +878,6 @@ def create_ui(wrap_gradio_gpu_call):
 
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
 
-                aesthetic_weight_im, aesthetic_steps_im, aesthetic_lr_im, aesthetic_slerp_im, aesthetic_imgs_im, aesthetic_imgs_text_im, aesthetic_slerp_angle_im, aesthetic_text_negative_im = aesthetic_clip.create_ui()
-
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
 
@@ -988,14 +968,6 @@ def create_ui(wrap_gradio_gpu_call):
                     inpainting_mask_invert,
                     img2img_batch_input_dir,
                     img2img_batch_output_dir,
-                    aesthetic_lr_im,
-                    aesthetic_weight_im,
-                    aesthetic_steps_im,
-                    aesthetic_imgs_im,
-                    aesthetic_slerp_im,
-                    aesthetic_imgs_text_im,
-                    aesthetic_slerp_angle_im,
-                    aesthetic_text_negative_im,
                 ] + custom_inputs,
                 outputs=[
                     img2img_gallery,
@@ -1087,14 +1059,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
                 (denoising_strength, "Denoising strength"),
-                (aesthetic_lr_im, "Aesthetic LR"),
-                (aesthetic_weight_im, "Aesthetic weight"),
-                (aesthetic_steps_im, "Aesthetic steps"),
-                (aesthetic_imgs_im, "Aesthetic embedding"),
-                (aesthetic_slerp_im, "Aesthetic slerp"),
-                (aesthetic_imgs_text_im, "Aesthetic text"),
-                (aesthetic_text_negative_im, "Aesthetic text negative"),
-                (aesthetic_slerp_angle_im, "Aesthetic slerp angle"),
+                *modules.scripts.scripts_img2img.infotext_fields
             ]
             token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
 
@@ -1217,9 +1182,9 @@ def create_ui(wrap_gradio_gpu_call):
         )
     #images history
     images_history_switch_dict = {
-        "fn":modules.generation_parameters_copypaste.connect_paste,
-        "t2i":txt2img_paste_fields,
-        "i2i":img2img_paste_fields
+        "fn": modules.generation_parameters_copypaste.connect_paste,
+        "t2i": txt2img_paste_fields,
+        "i2i": img2img_paste_fields
     }
 
     images_history = img_his.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
@@ -1264,18 +1229,6 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create embedding", variant='primary')
 
-                with gr.Tab(label="Create aesthetic images embedding"):
-
-                    new_embedding_name_ae = gr.Textbox(label="Name")
-                    process_src_ae = gr.Textbox(label='Source directory')
-                    batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256)
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_embedding_ae = gr.Button(value="Create images embedding", variant='primary')
-
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
@@ -1375,21 +1328,6 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
-        create_embedding_ae.click(
-            fn=aesthetic_clip.generate_imgs_embd,
-            inputs=[
-                new_embedding_name_ae,
-                process_src_ae,
-                batch_ae
-            ],
-            outputs=[
-                aesthetic_imgs,
-                aesthetic_imgs_im,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
         create_hypernetwork.click(
             fn=modules.hypernetworks.ui.create_hypernetwork,
             inputs=[
@@ -1580,10 +1518,10 @@ Requested path was: {f}
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
+        oldval = opts.data.get(key, None)
         if cmd_opts.hide_ui_dir_config and key in restricted_opts:
             return gr.update(value=oldval), opts.dumpjson()
 
-        oldval = opts.data.get(key, None)
         opts.data[key] = value
 
         if oldval != value:
@@ -1692,9 +1630,12 @@ Requested path was: {f}
         (images_history, "Image Browser", "images_history"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
-        (settings_interface, "Settings", "settings"),
     ]
 
+    interfaces += script_callbacks.ui_tabs_callback()
+
+    interfaces += [(settings_interface, "Settings", "settings")]
+
     with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
         css = file.read()
 
-- 
cgit v1.2.3


From 6398dc9b1049f242576ca309f95a3fb1e654951c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 13:34:49 +0300
Subject: further support for extensions

---
 modules/scripts.py | 44 +++++++++++++++++++++++++++++++++++---------
 modules/ui.py      | 19 ++++++++++---------
 2 files changed, 45 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 65f25f49..9323af3e 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -102,17 +102,39 @@ def list_scripts(scriptdirname, extension):
     if os.path.exists(extdir):
         for dirname in sorted(os.listdir(extdir)):
             dirpath = os.path.join(extdir, dirname)
-            if not os.path.isdir(dirpath):
+            scriptdirpath = os.path.join(dirpath, scriptdirname)
+
+            if not os.path.isdir(scriptdirpath):
                 continue
 
-            for filename in sorted(os.listdir(os.path.join(dirpath, scriptdirname))):
-                scripts_list.append(ScriptFile(dirpath, filename, os.path.join(dirpath, scriptdirname, filename)))
+            for filename in sorted(os.listdir(scriptdirpath)):
+                scripts_list.append(ScriptFile(dirpath, filename, os.path.join(scriptdirpath, filename)))
 
     scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
 
     return scripts_list
 
 
+def list_files_with_name(filename):
+    res = []
+
+    dirs = [paths.script_path]
+
+    extdir = os.path.join(paths.script_path, "extensions")
+    if os.path.exists(extdir):
+        dirs += [os.path.join(extdir, d) for d in sorted(os.listdir(extdir))]
+
+    for dirpath in dirs:
+        if not os.path.isdir(dirpath):
+            continue
+
+        path = os.path.join(dirpath, filename)
+        if os.path.isfile(filename):
+            res.append(path)
+
+    return res
+
+
 def load_scripts():
     global current_basedir
     scripts_data.clear()
@@ -276,7 +298,7 @@ class ScriptRunner:
                 print(f"Error running alwayson script: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
-    def reload_sources(self):
+    def reload_sources(self, cache):
         for si, script in list(enumerate(self.scripts)):
             with open(script.filename, "r", encoding="utf8") as file:
                 args_from = script.args_from
@@ -286,9 +308,12 @@ class ScriptRunner:
 
                 from types import ModuleType
 
-                compiled = compile(text, filename, 'exec')
-                module = ModuleType(script.filename)
-                exec(compiled, module.__dict__)
+                module = cache.get(filename, None)
+                if module is None:
+                    compiled = compile(text, filename, 'exec')
+                    module = ModuleType(script.filename)
+                    exec(compiled, module.__dict__)
+                    cache[filename] = module
 
                 for key, script_class in module.__dict__.items():
                     if type(script_class) == type and issubclass(script_class, Script):
@@ -303,8 +328,9 @@ scripts_img2img = ScriptRunner()
 
 
 def reload_script_body_only():
-    scripts_txt2img.reload_sources()
-    scripts_img2img.reload_sources()
+    cache = {}
+    scripts_txt2img.reload_sources(cache)
+    scripts_img2img.reload_sources(cache)
 
 
 def reload_scripts():
diff --git a/modules/ui.py b/modules/ui.py
index c977482c..29986124 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1636,13 +1636,15 @@ Requested path was: {f}
 
     interfaces += [(settings_interface, "Settings", "settings")]
 
-    with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
-        css = file.read()
+    css = ""
+
+    for cssfile in modules.scripts.list_files_with_name("style.css"):
+        with open(cssfile, "r", encoding="utf8") as file:
+            css += file.read() + "\n"
 
     if os.path.exists(os.path.join(script_path, "user.css")):
         with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
-            usercss = file.read()
-            css += usercss
+            css += file.read() + "\n"
 
     if not cmd_opts.no_progressbar_hiding:
         css += css_hide_progressbar
@@ -1865,9 +1867,9 @@ def load_javascript(raw_response):
     with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
         javascript = f'<script>{jsfile.read()}</script>'
 
-    jsdir = os.path.join(script_path, "javascript")
-    for filename in sorted(os.listdir(jsdir)):
-        with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
+    scripts_list = modules.scripts.list_scripts("javascript", ".js")
+    for basedir, filename, path in scripts_list:
+        with open(path, "r", encoding="utf8") as jsfile:
             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
 
     if cmd_opts.theme is not None:
@@ -1885,6 +1887,5 @@ def load_javascript(raw_response):
     gradio.routes.templates.TemplateResponse = template_response
 
 
-reload_javascript = partial(load_javascript,
-                            gradio.routes.templates.TemplateResponse)
+reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)
 reload_javascript()
-- 
cgit v1.2.3


From 50b5504401e50b6c94eba41b37fe212b2f27b792 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 14:04:14 +0300
Subject: remove parsing command line from devices.py

---
 modules/devices.py | 14 +++++---------
 modules/lowvram.py |  9 ++++-----
 2 files changed, 9 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 8a159282..dc1f3cdd 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -15,14 +15,10 @@ def extract_device_id(args, name):
 
 def get_optimal_device():
     if torch.cuda.is_available():
-        # CUDA device selection support:
-        if "shared" not in sys.modules:
-            commandline_args = os.environ.get('COMMANDLINE_ARGS', "") #re-parse the commandline arguments because using the shared.py module creates an import loop.
-            sys.argv += shlex.split(commandline_args)
-            device_id = extract_device_id(sys.argv, '--device-id')
-        else:
-            device_id = shared.cmd_opts.device_id
-            
+        from modules import shared
+
+        device_id = shared.cmd_opts.device_id
+
         if device_id is not None:
             cuda_device = f"cuda:{device_id}"
             return torch.device(cuda_device)
@@ -49,7 +45,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
+device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
 
diff --git a/modules/lowvram.py b/modules/lowvram.py
index 7eba1349..f327c3df 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -1,9 +1,8 @@
 import torch
-from modules.devices import get_optimal_device
+from modules import devices
 
 module_in_gpu = None
 cpu = torch.device("cpu")
-device = gpu = get_optimal_device()
 
 
 def send_everything_to_cpu():
@@ -33,7 +32,7 @@ def setup_for_low_vram(sd_model, use_medvram):
         if module_in_gpu is not None:
             module_in_gpu.to(cpu)
 
-        module.to(gpu)
+        module.to(devices.device)
         module_in_gpu = module
 
     # see below for register_forward_pre_hook;
@@ -51,7 +50,7 @@ def setup_for_low_vram(sd_model, use_medvram):
     # send the model to GPU. Then put modules back. the modules will be in CPU.
     stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
     sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None
-    sd_model.to(device)
+    sd_model.to(devices.device)
     sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored
 
     # register hooks for those the first two models
@@ -70,7 +69,7 @@ def setup_for_low_vram(sd_model, use_medvram):
         # so that only one of them is in GPU at a time
         stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
         diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
-        sd_model.model.to(device)
+        sd_model.model.to(devices.device)
         diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
 
         # install hooks for bits of third model
-- 
cgit v1.2.3


From 0e8ca8e7af05be22d7d2c07a47c3c7febe0f0ab6 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Sat, 22 Oct 2022 11:07:00 +0000
Subject: add dropout

---
 modules/hypernetworks/hypernetwork.py | 68 +++++++++++++++++++++--------------
 modules/hypernetworks/ui.py           | 10 +++---
 modules/ui.py                         | 43 +++++++++++-----------
 3 files changed, 70 insertions(+), 51 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 905cbeef..e493f366 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -1,47 +1,60 @@
+import csv
 import datetime
 import glob
 import html
 import os
 import sys
 import traceback
-import tqdm
-import csv
 
+import modules.textual_inversion.dataset
 import torch
-
-from ldm.util import default
-from modules import devices, shared, processing, sd_models
-import torch
-from torch import einsum
+import tqdm
 from einops import rearrange, repeat
-import modules.textual_inversion.dataset
+from ldm.util import default
+from modules import devices, processing, sd_models, shared
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
+from torch import einsum
 
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
-    activation_dict = {"relu": torch.nn.ReLU, "leakyrelu": torch.nn.LeakyReLU, "elu": torch.nn.ELU,
-                       "swish": torch.nn.Hardswish}
-    
-    def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False, activation_func=None):
+    activation_dict = {
+        "relu": torch.nn.ReLU,
+        "leakyrelu": torch.nn.LeakyReLU,
+        "elu": torch.nn.ELU,
+        "swish": torch.nn.Hardswish,
+    }
+
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
         assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
         assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-        
+        assert activation_func not in self.activation_dict.keys() + "linear", f"Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'"
+
         linears = []
         for i in range(len(layer_structure) - 1):
+
+            # Add a fully-connected layer
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
-            # if skip_first_layer because first parameters potentially contain negative values
-            # if i < 1: continue
-            if activation_func in HypernetworkModule.activation_dict:
-                linears.append(HypernetworkModule.activation_dict[activation_func]())
+
+            # Add an activation func
+            if activation_func == "linear":
+                pass
+            elif activation_func in self.activation_dict:
+                linears.append(self.activation_dict[activation_func]())
             else:
-                print("Invalid key {} encountered as activation function!".format(activation_func))
-            # if use_dropout: 
-            # linears.append(torch.nn.Dropout(p=0.3))
+                raise NotImplementedError(
+                    "Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'"
+                )
+
+            # Add dropout
+            if use_dropout:
+                linears.append(torch.nn.Dropout(p=0.3))
+
+            # Add layer normalization
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -93,7 +106,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False, activation_func=None):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -101,13 +114,14 @@ class Hypernetwork:
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
         self.layer_structure = layer_structure
-        self.add_layer_norm = add_layer_norm
         self.activation_func = activation_func
+        self.add_layer_norm = add_layer_norm
+        self.use_dropout = use_dropout
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func),
-                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
             )
 
     def weights(self):
@@ -129,8 +143,9 @@ class Hypernetwork:
         state_dict['step'] = self.step
         state_dict['name'] = self.name
         state_dict['layer_structure'] = self.layer_structure
-        state_dict['is_layer_norm'] = self.add_layer_norm
         state_dict['activation_func'] = self.activation_func
+        state_dict['is_layer_norm'] = self.add_layer_norm
+        state_dict['use_dropout'] = self.use_dropout
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
 
@@ -144,8 +159,9 @@ class Hypernetwork:
         state_dict = torch.load(filename, map_location='cpu')
 
         self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
-        self.add_layer_norm = state_dict.get('is_layer_norm', False)
         self.activation_func = state_dict.get('activation_func', None)
+        self.add_layer_norm = state_dict.get('is_layer_norm', False)
+        self.use_dropout = state_dict.get('use_dropout', False)
 
         for size, sd in state_dict.items():
             if type(size) == int:
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 1a5a27d8..5f6f17b6 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -3,14 +3,13 @@ import os
 import re
 
 import gradio as gr
-
-import modules.textual_inversion.textual_inversion
 import modules.textual_inversion.preprocess
-from modules import sd_hijack, shared, devices
+import modules.textual_inversion.textual_inversion
+from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
 
-def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False, activation_func=None):
+def create_hypernetwork(name, enable_sizes, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
@@ -21,8 +20,9 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
         name=name,
         enable_sizes=[int(x) for x in enable_sizes],
         layer_structure=layer_structure,
-        add_layer_norm=add_layer_norm,
         activation_func=activation_func,
+        add_layer_norm=add_layer_norm,
+        use_dropout=use_dropout,
     )
     hypernet.save(fn)
 
diff --git a/modules/ui.py b/modules/ui.py
index 716f14b8..d4b32c05 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -5,43 +5,44 @@ import json
 import math
 import mimetypes
 import os
+import platform
 import random
+import subprocess as sp
 import sys
 import tempfile
 import time
 import traceback
-import platform
-import subprocess as sp
 from functools import partial, reduce
 
+import gradio as gr
+import gradio.routes
+import gradio.utils
 import numpy as np
+import piexif
 import torch
 from PIL import Image, PngImagePlugin
-import piexif
 
-import gradio as gr
-import gradio.utils
-import gradio.routes
-
-from modules import sd_hijack, sd_models, localization
+from modules import localization, sd_hijack, sd_models
 from modules.paths import script_path
-from modules.shared import opts, cmd_opts, restricted_opts
+from modules.shared import cmd_opts, opts, restricted_opts
+
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
-import modules.shared as shared
-from modules.sd_samplers import samplers, samplers_for_img2img
-from modules.sd_hijack import model_hijack
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.images_history as img_his
 import modules.ldsr_model
 import modules.scripts
-import modules.gfpgan_model
-import modules.codeformer_model
+import modules.shared as shared
 import modules.styles
-import modules.generation_parameters_copypaste
+import modules.textual_inversion.ui
 from modules import prompt_parser
 from modules.images import save_image
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-import modules.images_history as img_his
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -1223,8 +1224,9 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu", "elu", "swish"])
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"])
+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1308,8 +1310,9 @@ def create_ui(wrap_gradio_gpu_call):
                 new_hypernetwork_name,
                 new_hypernetwork_sizes,
                 new_hypernetwork_layer_structure,
-                new_hypernetwork_add_layer_norm,
                 new_hypernetwork_activation_func,
+                new_hypernetwork_add_layer_norm,
+                new_hypernetwork_use_dropout
             ],
             outputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.3


From 1cd3ed7def40198f46d30f74dd37d2906ebdbaa6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 14:28:56 +0300
Subject: fix for extensions without style.css

---
 modules/ui.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 29986124..d8d52db1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1639,6 +1639,9 @@ Requested path was: {f}
     css = ""
 
     for cssfile in modules.scripts.list_files_with_name("style.css"):
+        if not os.path.isfile(cssfile):
+            continue
+
         with open(cssfile, "r", encoding="utf8") as file:
             css += file.read() + "\n"
 
-- 
cgit v1.2.3


From 7fd90128eb6d1820045bfe2c2c1269661023a712 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 14:48:43 +0300
Subject: added a guard for hypernet training that will stop early if weights
 are getting no gradients

---
 modules/hypernetworks/hypernetwork.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 47d91ea5..46039a49 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -310,6 +310,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
     optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
+    steps_without_grad = 0
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
@@ -332,8 +334,17 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
             optimizer.zero_grad()
+            weights[0].grad = None
             loss.backward()
+
+            if weights[0].grad is None:
+                steps_without_grad += 1
+            else:
+                steps_without_grad = 0
+            assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
+
             optimizer.step()
+
         mean_loss = losses.mean()
         if torch.isnan(mean_loss):
             raise RuntimeError("Loss diverged.")
-- 
cgit v1.2.3


From fccba4729db341a299db3343e3264fecd9459a07 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Sat, 22 Oct 2022 12:02:41 +0000
Subject: add an option to avoid dying relu

---
 modules/hypernetworks/hypernetwork.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b7a04038..3132a56c 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -32,7 +32,6 @@ class HypernetworkModule(torch.nn.Module):
         assert layer_structure is not None, "layer_structure must not be None"
         assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
         assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-        assert activation_func not in self.activation_dict.keys() + "linear", f"Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'"
 
         linears = []
         for i in range(len(layer_structure) - 1):
@@ -43,12 +42,13 @@ class HypernetworkModule(torch.nn.Module):
             # Add an activation func
             if activation_func == "linear" or activation_func is None:
                 pass
+            # If ReLU, Skip adding it to the first layer to avoid dying ReLU
+            elif activation_func == "relu" and i < 1:
+                pass
             elif activation_func in self.activation_dict:
                 linears.append(self.activation_dict[activation_func]())
             else:
-                raise RuntimeError(
-                    "Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'"
-                )
+                raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
 
             # Add dropout
             if use_dropout:
@@ -166,8 +166,8 @@ class Hypernetwork:
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm, self.activation_func),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm, self.activation_func),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)
-- 
cgit v1.2.3


From 7912acef725832debef58c4c7bf8ec22fb446c0b Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Sat, 22 Oct 2022 13:00:44 +0000
Subject: small fix

---
 modules/hypernetworks/hypernetwork.py | 12 +++++-------
 modules/ui.py                         |  1 -
 2 files changed, 5 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3132a56c..7d12e0ff 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -42,22 +42,20 @@ class HypernetworkModule(torch.nn.Module):
             # Add an activation func
             if activation_func == "linear" or activation_func is None:
                 pass
-            # If ReLU, Skip adding it to the first layer to avoid dying ReLU
-            elif activation_func == "relu" and i < 1:
-                pass
             elif activation_func in self.activation_dict:
                 linears.append(self.activation_dict[activation_func]())
             else:
                 raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
 
-            # Add dropout
-            if use_dropout:
-                linears.append(torch.nn.Dropout(p=0.3))
-
             # Add layer normalization
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
+            # Add dropout
+            if use_dropout:
+                p = 0.5 if 0 <= i <= len(layer_structure) - 3 else 0.2
+                linears.append(torch.nn.Dropout(p=p))
+
         self.linear = torch.nn.Sequential(*linears)
 
         if state_dict is not None:
diff --git a/modules/ui.py b/modules/ui.py
index cd118552..eca887ca 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1244,7 +1244,6 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
                     overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"])
 
                     with gr.Row():
                         with gr.Column(scale=3):
-- 
cgit v1.2.3


From 6a4fa73a38935a18779ce1809892730fd1572bee Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Sat, 22 Oct 2022 13:44:39 +0000
Subject: small fix

---
 modules/hypernetworks/hypernetwork.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3372aae2..3bc71ee5 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -51,10 +51,9 @@ class HypernetworkModule(torch.nn.Module):
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
-            # Add dropout
-            if use_dropout:
-                p = 0.5 if 0 <= i <= len(layer_structure) - 3 else 0.2
-                linears.append(torch.nn.Dropout(p=p))
+            # Add dropout expect last layer
+            if use_dropout and i < len(layer_structure) - 3:
+                linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
 
-- 
cgit v1.2.3


From d37cfffd537cd29309afbcb192c4f979995c6a34 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 19:18:56 +0300
Subject: added callback for creating new settings in extensions

---
 modules/script_callbacks.py | 11 +++++++++++
 modules/shared.py           | 19 +++++++++++++++++--
 modules/ui.py               |  6 +++++-
 3 files changed, 33 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 866b7acd..1270e50f 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -1,6 +1,7 @@
 
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
+callbacks_ui_settings = []
 
 
 def clear_callbacks():
@@ -22,6 +23,11 @@ def ui_tabs_callback():
     return res
 
 
+def ui_settings_callback():
+    for callback in callbacks_ui_settings:
+        callback()
+
+
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
@@ -40,3 +46,8 @@ def on_ui_tabs(callback):
     """
     callbacks_ui_tabs.append(callback)
 
+
+def on_ui_settings(callback):
+    """register a function to be called before UI  settingsare populated; add your settings
+    by using shared.opts.add_option(shared.OptionInfo(...)) """
+    callbacks_ui_settings.append(callback)
diff --git a/modules/shared.py b/modules/shared.py
index 5d83971e..d9cb65ef 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -165,13 +165,13 @@ def realesrgan_models_names():
 
 
 class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False, refresh=None):
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
         self.default = default
         self.label = label
         self.component = component
         self.component_args = component_args
         self.onchange = onchange
-        self.section = None
+        self.section = section
         self.refresh = refresh
 
 
@@ -327,6 +327,7 @@ options_templates.update(options_section(('images-history', "Images Browser"), {
 
 }))
 
+
 class Options:
     data = None
     data_labels = options_templates
@@ -389,6 +390,20 @@ class Options:
         d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
         return json.dumps(d)
 
+    def add_option(self, key, info):
+        self.data_labels[key] = info
+
+    def reorder(self):
+        """reorder settings so that all items related to section always go together"""
+
+        section_ids = {}
+        settings_items = self.data_labels.items()
+        for k, item in settings_items:
+            if item.section not in section_ids:
+                section_ids[item.section] = len(section_ids)
+
+        self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
+
 
 opts = Options()
 if os.path.exists(config_filename):
diff --git a/modules/ui.py b/modules/ui.py
index d8d52db1..2849b111 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1461,6 +1461,9 @@ def create_ui(wrap_gradio_gpu_call):
     components = []
     component_dict = {}
 
+    script_callbacks.ui_settings_callback()
+    opts.reorder()
+
     def open_folder(f):
         if not os.path.exists(f):
             print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
@@ -1564,7 +1567,8 @@ Requested path was: {f}
 
                     previous_section = item.section
 
-                    gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
+                    elem_id, text = item.section
+                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
 
                 if k in quicksettings_names:
                     quicksettings_list.append((i, k, item))
-- 
cgit v1.2.3


From dbc8ab65f6d496459a76547776b656c96ad1350d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 19:19:17 +0300
Subject: typo

---
 modules/script_callbacks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 1270e50f..5bcccd67 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -48,6 +48,6 @@ def on_ui_tabs(callback):
 
 
 def on_ui_settings(callback):
-    """register a function to be called before UI  settingsare populated; add your settings
+    """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
     callbacks_ui_settings.append(callback)
-- 
cgit v1.2.3


From 72383abacdc6a101704a6f73758ce4d0bb68c9d1 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Sat, 22 Oct 2022 16:50:07 +0200
Subject: Deepdanbooru linux fix

---
 modules/deepbooru.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 8914662d..3c34ab7c 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -50,7 +50,8 @@ def create_deepbooru_process(threshold, deepbooru_opts):
     the tags.
     """
     from modules import shared  # prevents circular reference
-    shared.deepbooru_process_manager = multiprocessing.Manager()
+    context = multiprocessing.get_context("spawn")
+    shared.deepbooru_process_manager = context.Manager()
     shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
     shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
     shared.deepbooru_process_return["value"] = -1
-- 
cgit v1.2.3


From e38625011cd4955da4bc67fe95d1d0f4c0c53899 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Sat, 22 Oct 2022 16:56:52 +0200
Subject: fix part2

---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 3c34ab7c..8bbc90a4 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -55,7 +55,7 @@ def create_deepbooru_process(threshold, deepbooru_opts):
     shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
     shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
     shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
+    shared.deepbooru_process = context.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
     shared.deepbooru_process.start()
 
 
-- 
cgit v1.2.3


From 324c7c732dd9afc3d4c397c354797ae5d655b514 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 20:09:37 +0300
Subject: record First pass size as 0x0 for #3328

---
 modules/processing.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 372489f7..27c669b0 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -524,6 +524,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
+            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
+
             if self.firstphase_width == 0 or self.firstphase_height == 0:
                 desired_pixel_count = 512 * 512
                 actual_pixel_count = self.width * self.height
@@ -545,7 +547,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     firstphase_width_truncated = self.firstphase_height * self.width / self.height
                     firstphase_height_truncated = self.firstphase_height
 
-            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-- 
cgit v1.2.3


From 0df94d3fcf9d1fc47c4d39039352a3d5b3380c1f Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sat, 22 Oct 2022 12:59:21 -0400
Subject: fix aesthetic gradients doing nothing after loading a different model

---
 modules/sd_models.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index f9b3063d..49dc3238 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -236,12 +236,11 @@ def load_model(checkpoint_info=None):
         sd_model.to(shared.device)
 
     sd_hijack.model_hijack.hijack(sd_model)
+    script_callbacks.model_loaded_callback(sd_model)
 
     sd_model.eval()
     shared.sd_model = sd_model
 
-    script_callbacks.model_loaded_callback(sd_model)
-
     print(f"Model loaded.")
     return sd_model
 
@@ -268,6 +267,7 @@ def reload_model_weights(sd_model, info=None):
     load_model_weights(sd_model, checkpoint_info)
 
     sd_hijack.model_hijack.hijack(sd_model)
+    script_callbacks.model_loaded_callback(sd_model)
 
     if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
         sd_model.to(devices.device)
-- 
cgit v1.2.3


From 321bacc6a9eaf4a25f31279f288fa752be507a20 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 20:15:12 +0300
Subject: call model_loaded_callback after setting shared.sd_model in case
 scripts refer to it using that

---
 modules/sd_models.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 49dc3238..e697bb72 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -236,11 +236,12 @@ def load_model(checkpoint_info=None):
         sd_model.to(shared.device)
 
     sd_hijack.model_hijack.hijack(sd_model)
-    script_callbacks.model_loaded_callback(sd_model)
 
     sd_model.eval()
     shared.sd_model = sd_model
 
+    script_callbacks.model_loaded_callback(sd_model)
+
     print(f"Model loaded.")
     return sd_model
 
-- 
cgit v1.2.3


From 24694e5983d0944b901892cb101878e6dec89a20 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 01:57:58 +0900
Subject: Update hypernetwork.py

---
 modules/hypernetworks/hypernetwork.py | 55 ++++++++++++++++++++++++++++-------
 1 file changed, 44 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3bc71ee5..81132be4 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -16,6 +16,7 @@ from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
 
+from statistics import stdev, mean
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
@@ -268,6 +269,32 @@ def stack_conds(conds):
     return torch.stack(conds)
 
 
+def log_statistics(loss_info:dict, key, value):
+    if key not in loss_info:
+        loss_info[key] = [value]
+    else:
+        loss_info[key].append(value)
+        if len(loss_info) > 1024:
+            loss_info.pop(0)
+
+
+def statistics(data):
+    total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"
+    recent_data = data[-32:]
+    recent_information = f"recent 32 loss:{mean(recent_data):.3f}"+u"\u00B1"+f"({stdev(recent_data)/ (len(recent_data)**0.5):.3f})"
+    return total_information, recent_information
+
+
+def report_statistics(loss_info:dict):
+    keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
+    for key in keys:
+        info, recent = statistics(loss_info[key])
+        print("Loss statistics for file " + key)
+        print(info)
+        print(recent)
+
+
+
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
@@ -310,7 +337,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     for weight in weights:
         weight.requires_grad = True
 
-    losses = torch.zeros((32,))
+    size = len(ds.indexes)
+    loss_dict = {}
+    losses = torch.zeros((size,))
+    previous_mean_loss = 0
+    print("Mean loss of {} elements".format(size))
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
@@ -329,7 +360,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
-
+        if loss_dict and i % size == 0:
+            previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)
+            
         scheduler.apply(optimizer, hypernetwork.step)
         if scheduler.finished:
             break
@@ -346,7 +379,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             del c
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
-
+            for entry in entries:
+                log_statistics(loss_dict, entry.filename, loss.item())
+                
             optimizer.zero_grad()
             weights[0].grad = None
             loss.backward()
@@ -359,10 +394,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             optimizer.step()
 
-        mean_loss = losses.mean()
-        if torch.isnan(mean_loss):
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
             raise RuntimeError("Loss diverged.")
-        pbar.set_description(f"loss: {mean_loss:.7f}")
+        pbar.set_description(f"dataset loss: {previous_mean_loss:.7f}")
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
             # Before saving, change name to match current checkpoint.
@@ -371,7 +405,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             hypernetwork.save(last_saved_file)
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
-            "loss": f"{mean_loss:.7f}",
+            "loss": f"{previous_mean_loss:.7f}",
             "learn_rate": scheduler.learn_rate
         })
 
@@ -420,14 +454,15 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         shared.state.textinfo = f"""
 <p>
-Loss: {mean_loss:.7f}<br/>
+Loss: {previous_mean_loss:.7f}<br/>
 Step: {hypernetwork.step}<br/>
 Last prompt: {html.escape(entries[0].cond_text)}<br/>
 Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-
+        
+    report_statistics(loss_dict)
     checkpoint = sd_models.select_checkpoint()
 
     hypernetwork.sd_checkpoint = checkpoint.hash
@@ -438,5 +473,3 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     hypernetwork.save(filename)
 
     return hypernetwork, filename
-
-
-- 
cgit v1.2.3


From 4fdb53c1e9962507fc8336dad9a0fabfe6c418c0 Mon Sep 17 00:00:00 2001
From: Unnoen <Unnoen@users.noreply.github.com>
Date: Wed, 19 Oct 2022 21:38:10 +1100
Subject: Generate grid preview for progress image

---
 modules/sd_samplers.py | 26 +++++++++++++++++++++++++-
 modules/shared.py      |  1 +
 modules/ui.py          |  5 ++++-
 3 files changed, 30 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index f58a29b9..74a480e5 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -7,7 +7,7 @@ import inspect
 import k_diffusion.sampling
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
-from modules import prompt_parser, devices, processing
+from modules import prompt_parser, devices, processing, images
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -89,6 +89,30 @@ def sample_to_image(samples):
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)
 
+def samples_to_image_grid(samples):
+    progress_images = []
+    for i in range(len(samples)):
+        # Decode the samples individually to reduce VRAM usage at the cost of a bit of speed.
+        x_sample = processing.decode_first_stage(shared.sd_model, samples[i:i+1])[0]
+        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+        x_sample = x_sample.astype(np.uint8)
+        progress_images.append(Image.fromarray(x_sample))
+
+    return images.image_grid(progress_images)
+
+def samples_to_image_grid_combined(samples):
+    progress_images = []
+    # Decode all samples at once to increase speed at the cost of VRAM usage.
+    x_samples = processing.decode_first_stage(shared.sd_model, samples)
+    x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
+
+    for x_sample in x_samples:
+        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+        x_sample = x_sample.astype(np.uint8)
+        progress_images.append(Image.fromarray(x_sample))
+
+    return images.image_grid(progress_images)
 
 def store_latent(decoded):
     state.current_latent = decoded
diff --git a/modules/shared.py b/modules/shared.py
index d9cb65ef..95d6e225 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -294,6 +294,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+    "progress_decode_combined": OptionInfo(False, "Decode all progress images at once. (Slighty speeds up progress generation but consumes significantly more VRAM with large batches.)"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
diff --git a/modules/ui.py b/modules/ui.py
index 56c233ab..de0abc7e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -318,7 +318,10 @@ def check_progress_call(id_part):
         if shared.parallel_processing_allowed:
 
             if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:
-                shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
+                if opts.progress_decode_combined:
+                    shared.state.current_image = modules.sd_samplers.samples_to_image_grid_combined(shared.state.current_latent)
+                else:
+                    shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent)
                 shared.state.current_image_sampling_step = shared.state.sampling_step
 
         image = shared.state.current_image
-- 
cgit v1.2.3


From d213d6ca6f90094cb45c11e2f3cb37d25a8d1f94 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 20:48:13 +0300
Subject: removed the option to use 2x more memory when generating previews
 added an option to always only show one image in previews removed duplicate
 code

---
 modules/sd_samplers.py | 35 ++++++++++-------------------------
 modules/shared.py      |  2 +-
 modules/ui.py          |  6 +++---
 3 files changed, 14 insertions(+), 29 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 74a480e5..0b408a70 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -71,6 +71,7 @@ sampler_extra_params = {
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
 }
 
+
 def setup_img2img_steps(p, steps=None):
     if opts.img2img_fix_steps or steps is not None:
         steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
@@ -82,37 +83,21 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc
 
 
-def sample_to_image(samples):
-    x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
+def single_sample_to_image(sample):
+    x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)
 
+
+def sample_to_image(samples):
+    return single_sample_to_image(samples[0])
+
+
 def samples_to_image_grid(samples):
-    progress_images = []
-    for i in range(len(samples)):
-        # Decode the samples individually to reduce VRAM usage at the cost of a bit of speed.
-        x_sample = processing.decode_first_stage(shared.sd_model, samples[i:i+1])[0]
-        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
-        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-        x_sample = x_sample.astype(np.uint8)
-        progress_images.append(Image.fromarray(x_sample))
-
-    return images.image_grid(progress_images)
-
-def samples_to_image_grid_combined(samples):
-    progress_images = []
-    # Decode all samples at once to increase speed at the cost of VRAM usage.
-    x_samples = processing.decode_first_stage(shared.sd_model, samples)
-    x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
-    for x_sample in x_samples:
-        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-        x_sample = x_sample.astype(np.uint8)
-        progress_images.append(Image.fromarray(x_sample))
-
-    return images.image_grid(progress_images)
+    return images.image_grid([single_sample_to_image(sample) for sample in samples])
+
 
 def store_latent(decoded):
     state.current_latent = decoded
diff --git a/modules/shared.py b/modules/shared.py
index 95d6e225..25bfc895 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -294,7 +294,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
-    "progress_decode_combined": OptionInfo(False, "Decode all progress images at once. (Slighty speeds up progress generation but consumes significantly more VRAM with large batches.)"),
+    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
diff --git a/modules/ui.py b/modules/ui.py
index de0abc7e..ffa14cac 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -318,10 +318,10 @@ def check_progress_call(id_part):
         if shared.parallel_processing_allowed:
 
             if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:
-                if opts.progress_decode_combined:
-                    shared.state.current_image = modules.sd_samplers.samples_to_image_grid_combined(shared.state.current_latent)
-                else:
+                if opts.show_progress_grid:
                     shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent)
+                else:
+                    shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
                 shared.state.current_image_sampling_step = shared.state.sampling_step
 
         image = shared.state.current_image
-- 
cgit v1.2.3


From be748e8b086bd9834d08bdd9160649a5e7700af7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 22:05:22 +0300
Subject: add --freeze-settings commandline argument to disable changing
 settings

---
 modules/shared.py |  1 +
 modules/ui.py     | 11 +++++++++--
 2 files changed, 10 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 25bfc895..b55371d3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -64,6 +64,7 @@ parser.add_argument("--port", type=int, help="launch gradio with given server po
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
 parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json'))
 parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
+parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
 parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
diff --git a/modules/ui.py b/modules/ui.py
index ffa14cac..2311572c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -580,6 +580,9 @@ def apply_setting(key, value):
     if value is None:
         return gr.update()
 
+    if shared.cmd_opts.freeze_settings:
+        return gr.update()
+
     # dont allow model to be swapped when model hash exists in prompt
     if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
         return gr.update()
@@ -1501,6 +1504,8 @@ Requested path was: {f}
     def run_settings(*args):
         changed = 0
 
+        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
+
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
             if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
                 return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
@@ -1530,6 +1535,8 @@ Requested path was: {f}
         return f'{changed} settings changed.', opts.dumpjson()
 
     def run_settings_single(value, key):
+        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
+
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
@@ -1582,7 +1589,7 @@ Requested path was: {f}
                     elem_id, text = item.section
                     gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
 
-                if k in quicksettings_names:
+                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
                     quicksettings_list.append((i, k, item))
                     components.append(dummy_component)
                 else:
@@ -1615,7 +1622,7 @@ Requested path was: {f}
 
         def reload_scripts():
             modules.scripts.reload_script_body_only()
-            reload_javascript() # need to refresh the html page
+            reload_javascript()  # need to refresh the html page
 
         reload_script_bodies.click(
             fn=reload_scripts,
-- 
cgit v1.2.3


From ca5a9e79dc28eeaa3a161427a82e34703bf15765 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 22:06:54 +0300
Subject: fix for img2img color correction in a batch #3218

---
 modules/processing.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 27c669b0..b1877b80 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -403,8 +403,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if (len(prompts) == 0):
                 break
 
-            #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
-            #c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
@@ -716,6 +714,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
             if self.overlay_images is not None:
                 self.overlay_images = self.overlay_images * self.batch_size
+
+            if self.color_corrections is not None and len(self.color_corrections) == 1:
+                self.color_corrections = self.color_corrections * self.batch_size
+
         elif len(imgs) <= self.batch_size:
             self.batch_size = len(imgs)
             batch_images = np.array(imgs)
-- 
cgit v1.2.3


From 48dbf99e84045ee7af55bc5b1b86492a240e631e Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 04:17:16 +0900
Subject: Allow tracking real-time loss

Someone had 6000 images in their dataset, and it was shown as 0, which was confusing.
This will allow tracking real time dataset-average loss for registered objects.
---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 81132be4..99fd0f8f 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -360,7 +360,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
-        if loss_dict and i % size == 0:
+        if len(loss_dict) > 0:
             previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)
             
         scheduler.apply(optimizer, hypernetwork.step)
-- 
cgit v1.2.3


From ce42879438bf2dbd76b5b346be656292e42ffb2b Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Sat, 22 Oct 2022 14:53:37 -0500
Subject: fix js func signature and not forget to initialize confirmation var
 to prevent exception upon cancelling confirmation

---
 modules/ui.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 25aeba3b..e58f040e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -429,10 +429,12 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-def clear_prompt(_prompt, _prompt_neg, confirmed, _token_counter):
+def clear_prompt(prompt, _prompt_neg, confirmed, _token_counter):
     """Given confirmation from a user on the client-side, go ahead with clearing prompt"""
     if confirmed:
         return ["", "", confirmed, update_token_counter("", 1)]
+    else:
+        return [prompt, _prompt_neg, confirmed, _token_counter]
 
 
 def connect_clear_prompt(button, prompt, prompt_neg, _dummy_confirmed, token_counter):
-- 
cgit v1.2.3


From 1b4d04737ac513cbd55958bb60a4f85166f3484b Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sat, 22 Oct 2022 20:13:16 -0300
Subject: Remove unused imports

---
 modules/api/api.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 5b0c934e..a5136b4b 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,11 +1,9 @@
 from modules.api.processing import StableDiffusionProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, process_images
 from modules.sd_samplers import all_samplers
-from modules.extras import run_pnginfo
 import modules.shared as shared
 import uvicorn
-from fastapi import Body, APIRouter, HTTPException
-from fastapi.responses import JSONResponse
+from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel, Field, Json
 import json
 import io
@@ -18,7 +16,6 @@ class TextToImageResponse(BaseModel):
     parameters: Json
     info: Json
 
-
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
-- 
cgit v1.2.3


From b02926df1393df311db734af149fb9faf4389cbe Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sat, 22 Oct 2022 20:24:04 -0300
Subject: Moved moodels to their own file and extracted base64 conversion to
 its own function

---
 modules/api/api.py    | 17 ++++++-----------
 modules/api/models.py |  8 ++++++++
 2 files changed, 14 insertions(+), 11 deletions(-)
 create mode 100644 modules/api/models.py

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index a5136b4b..c17d7580 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -4,17 +4,17 @@ from modules.sd_samplers import all_samplers
 import modules.shared as shared
 import uvicorn
 from fastapi import APIRouter, HTTPException
-from pydantic import BaseModel, Field, Json
 import json
 import io
 import base64
+from modules.api.models import *
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
-class TextToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: Json
-    info: Json
+def img_to_base64(img):
+    buffer = io.BytesIO()
+    img.save(buffer, format="png")
+    return base64.b64encode(buffer.getvalue())
 
 class Api:
     def __init__(self, app, queue_lock):
@@ -41,15 +41,10 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = []
-        for i in processed.images:
-            buffer = io.BytesIO()
-            i.save(buffer, format="png")
-            b64images.append(base64.b64encode(buffer.getvalue()))
+        b64images = list(map(img_to_base64, processed.images))
 
         return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
         
-        
 
     def img2imgapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
new file mode 100644
index 00000000..a7d247d8
--- /dev/null
+++ b/modules/api/models.py
@@ -0,0 +1,8 @@
+from pydantic import BaseModel, Field, Json
+
+class TextToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: Json
+    info: Json
+
+    
\ No newline at end of file
-- 
cgit v1.2.3


From 28e26c2bef217ae82eb9e980cceb3f67ef22e109 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sat, 22 Oct 2022 23:13:32 -0300
Subject: Add "extra" single image operation

 - Separate extra modes into 3 endpoints so the user ddoesn't ahve to
handle so many unused parameters.
 - Add response model for codumentation
---
 modules/api/api.py    | 43 ++++++++++++++++++++++++++++++++++++++-----
 modules/api/models.py | 26 +++++++++++++++++++++++++-
 2 files changed, 63 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index c17d7580..3b804373 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -8,20 +8,42 @@ import json
 import io
 import base64
 from modules.api.models import *
+from PIL import Image
+from modules.extras import run_extras
+
+def upscaler_to_index(name: str):
+    try:
+        return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
+    except:
+        raise HTTPException(status_code=400, detail="Upscaler not found")
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
-def img_to_base64(img):
+def img_to_base64(img: str):
     buffer = io.BytesIO()
     img.save(buffer, format="png")
     return base64.b64encode(buffer.getvalue())
 
+def base64_to_bytes(base64Img: str):
+    if "," in base64Img:
+        base64Img = base64Img.split(",")[1]
+    return io.BytesIO(base64.b64decode(base64Img))
+
+def base64_to_images(base64Imgs: list[str]):
+    imgs = []
+    for img in base64Imgs:
+        img = Image.open(base64_to_bytes(img))
+        imgs.append(img)
+    return imgs
+
+
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
-        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
+        self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -45,12 +67,23 @@ class Api:
 
         return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
         
-
     def img2imgapi(self):
         raise NotImplementedError
 
-    def extrasapi(self):
-        raise NotImplementedError
+    def extras_single_image_api(self, req: ExtrasSingleImageRequest):
+        upscaler1Index = upscaler_to_index(req.upscaler_1)
+        upscaler2Index = upscaler_to_index(req.upscaler_2)
+
+        reqDict = vars(req)
+        reqDict.pop('upscaler_1')
+        reqDict.pop('upscaler_2')
+
+        reqDict['image'] = base64_to_images([reqDict['image']])[0]
+
+        with self.queue_lock:
+            result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="")
+
+        return ExtrasSingleImageResponse(image="data:image/png;base64,"+img_to_base64(result[0]), html_info_x=result[1], html_info=result[2])
 
     def pnginfoapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
index a7d247d8..dcf1ab54 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,8 +1,32 @@
 from pydantic import BaseModel, Field, Json
+from typing_extensions import Literal
+from modules.shared import sd_upscalers
 
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
     info: Json
 
-    
\ No newline at end of file
+class ExtrasBaseRequest(BaseModel):
+    resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
+    show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?")
+    gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
+    codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
+    codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
+    upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
+    upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
+    upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
+    upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?")
+    upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
+    upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
+    extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
+
+class ExtraBaseResponse(BaseModel):
+    html_info_x: str
+    html_info: str
+
+class ExtrasSingleImageRequest(ExtrasBaseRequest):
+    image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+
+class ExtrasSingleImageResponse(ExtraBaseResponse):
+    image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
\ No newline at end of file
-- 
cgit v1.2.3


From 1fbfc052eb529d8cf8ce5baf578bcf93d0280c29 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 23 Oct 2022 05:43:34 +0100
Subject: Update hypernetwork.py

---
 modules/hypernetworks/hypernetwork.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 99fd0f8f..98a7b62e 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -288,10 +288,13 @@ def statistics(data):
 def report_statistics(loss_info:dict):
     keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
     for key in keys:
-        info, recent = statistics(loss_info[key])
-        print("Loss statistics for file " + key)
-        print(info)
-        print(recent)
+        try:
+            print("Loss statistics for file " + key)
+            info, recent = statistics(loss_info[key])
+            print(info)
+            print(recent)
+        except Exception as e:
+            print(e)
 
 
 
-- 
cgit v1.2.3


From a7c213d0f5ebb10722629b8490a5863f9ce6c4fa Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Fri, 21 Oct 2022 19:27:40 -0400
Subject: [API][Feature] - Add img2img API endpoint

---
 modules/api/api.py        | 58 +++++++++++++++++++++++++++++++++++++++++++----
 modules/api/processing.py | 11 +++++++--
 modules/processing.py     |  2 +-
 3 files changed, 63 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 5b0c934e..a04f2428 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,5 +1,5 @@
-from modules.api.processing import StableDiffusionProcessingAPI
-from modules.processing import StableDiffusionProcessingTxt2Img, process_images
+from modules.api.processing import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
 from modules.extras import run_pnginfo
 import modules.shared as shared
@@ -10,6 +10,7 @@ from pydantic import BaseModel, Field, Json
 import json
 import io
 import base64
+from PIL import Image
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
@@ -18,6 +19,11 @@ class TextToImageResponse(BaseModel):
     parameters: Json
     info: Json
 
+class ImageToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: Json
+    info: Json
+
 
 class Api:
     def __init__(self, app, queue_lock):
@@ -25,8 +31,9 @@ class Api:
         self.app = app
         self.queue_lock = queue_lock
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
 
-    def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
+    def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
         
         if sampler_index is None:
@@ -54,8 +61,49 @@ class Api:
         
         
 
-    def img2imgapi(self):
-        raise NotImplementedError
+    def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
+        sampler_index = sampler_to_index(img2imgreq.sampler_index)
+        
+        if sampler_index is None:
+            raise HTTPException(status_code=404, detail="Sampler not found") 
+
+
+        init_images = img2imgreq.init_images
+        if init_images is None:
+            raise HTTPException(status_code=404, detail="Init image not found") 
+
+        
+        populate = img2imgreq.copy(update={ # Override __init__ params
+            "sd_model": shared.sd_model, 
+            "sampler_index": sampler_index[0],
+            "do_not_save_samples": True,
+            "do_not_save_grid": True
+            }
+        )
+        p = StableDiffusionProcessingImg2Img(**vars(populate))
+
+        imgs = []
+        for img in init_images:
+            # if has a comma, deal with prefix
+            if "," in img:
+                img = img.split(",")[1]
+            # convert base64 to PIL image
+            img = base64.b64decode(img)
+            img = Image.open(io.BytesIO(img))
+            imgs = [img] * p.batch_size
+
+        p.init_images = imgs
+        # Override object param
+        with self.queue_lock:
+            processed = process_images(p)
+        
+        b64images = []
+        for i in processed.images:
+            buffer = io.BytesIO()
+            i.save(buffer, format="png")
+            b64images.append(base64.b64encode(buffer.getvalue()))
+
+        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info))
 
     def extrasapi(self):
         raise NotImplementedError
diff --git a/modules/api/processing.py b/modules/api/processing.py
index 4c541241..9f1d65c0 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -1,7 +1,8 @@
+from array import array
 from inflection import underscore
 from typing import Any, Dict, Optional
 from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessingTxt2Img
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 import inspect
 
 
@@ -92,8 +93,14 @@ class PydanticModelGenerator:
         DynamicModel.__config__.allow_mutation = True
         return DynamicModel
     
-StableDiffusionProcessingAPI = PydanticModelGenerator(
+StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingTxt2Img", 
     StableDiffusionProcessingTxt2Img,
     [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()
+
+StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingImg2Img", 
+    StableDiffusionProcessingImg2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}]
 ).generate_model()
\ No newline at end of file
diff --git a/modules/processing.py b/modules/processing.py
index b1877b80..1557ed8c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -623,7 +623,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs):
+    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: str=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
-- 
cgit v1.2.3


From 9e1a8b7734a2881451a2efbf80def011ea41ba49 Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Sat, 22 Oct 2022 15:42:00 -0400
Subject: non-implemented mask with any type

---
 modules/api/api.py        | 4 ++++
 modules/api/processing.py | 2 +-
 modules/processing.py     | 2 +-
 3 files changed, 6 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index a04f2428..3df6ff96 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -72,6 +72,10 @@ class Api:
         if init_images is None:
             raise HTTPException(status_code=404, detail="Init image not found") 
 
+        mask = img2imgreq.mask
+        if mask:
+            raise HTTPException(status_code=400, detail="Mask not supported yet") 
+
         
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model, 
diff --git a/modules/api/processing.py b/modules/api/processing.py
index 9f1d65c0..f551fa35 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -102,5 +102,5 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
 StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingImg2Img", 
     StableDiffusionProcessingImg2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}]
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
 ).generate_model()
\ No newline at end of file
diff --git a/modules/processing.py b/modules/processing.py
index 1557ed8c..ff83023c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -623,7 +623,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: str=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
+    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: Any=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
-- 
cgit v1.2.3


From 5dc0739ecdc1ade8fcf4eb77f2a503ef12489f32 Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Sat, 22 Oct 2022 17:10:28 -0400
Subject: working mask

---
 modules/api/api.py | 20 ++++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 3df6ff96..3caa83a4 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -33,6 +33,14 @@ class Api:
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
 
+    def __base64_to_image(self, base64_string):
+        # if has a comma, deal with prefix
+        if "," in base64_string:
+            base64_string = base64_string.split(",")[1]
+        imgdata = base64.b64decode(base64_string)
+        # convert base64 to PIL image
+        return Image.open(io.BytesIO(imgdata))
+
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
         
@@ -74,26 +82,22 @@ class Api:
 
         mask = img2imgreq.mask
         if mask:
-            raise HTTPException(status_code=400, detail="Mask not supported yet") 
+            mask = self.__base64_to_image(mask)
 
         
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model, 
             "sampler_index": sampler_index[0],
             "do_not_save_samples": True,
-            "do_not_save_grid": True
+            "do_not_save_grid": True, 
+            "mask": mask
             }
         )
         p = StableDiffusionProcessingImg2Img(**vars(populate))
 
         imgs = []
         for img in init_images:
-            # if has a comma, deal with prefix
-            if "," in img:
-                img = img.split(",")[1]
-            # convert base64 to PIL image
-            img = base64.b64decode(img)
-            img = Image.open(io.BytesIO(img))
+            img = self.__base64_to_image(img)
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
-- 
cgit v1.2.3


From 1be5933ba21a3badec42b7b2753d626f849b609d Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Sun, 23 Oct 2022 04:11:07 -0700
Subject: auto cropping now works with non square crops

---
 modules/textual_inversion/autocrop.py | 509 ++++++++++++++++++----------------
 1 file changed, 269 insertions(+), 240 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 5a551c25..b2f9241c 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -1,241 +1,270 @@
-import cv2
-from collections import defaultdict
-from math import log, sqrt
-import numpy as np
-from PIL import Image, ImageDraw
-
-GREEN = "#0F0"
-BLUE = "#00F"
-RED = "#F00"
-
-
-def crop_image(im, settings):
-  """ Intelligently crop an image to the subject matter """
-  if im.height > im.width:
-      im = im.resize((settings.crop_width, settings.crop_height * im.height // im.width))
-  elif im.width > im.height:
-      im = im.resize((settings.crop_width * im.width // im.height, settings.crop_height))
-  else:
-      im = im.resize((settings.crop_width, settings.crop_height))
-
-  if im.height == im.width:
-    return im
-
-  focus = focal_point(im, settings)
-
-  # take the focal point and turn it into crop coordinates that try to center over the focal
-  # point but then get adjusted back into the frame
-  y_half = int(settings.crop_height / 2)
-  x_half = int(settings.crop_width / 2)
-
-  x1 = focus.x - x_half
-  if x1 < 0:
-      x1 = 0
-  elif x1 + settings.crop_width > im.width:
-      x1 = im.width - settings.crop_width
-
-  y1 = focus.y - y_half
-  if y1 < 0:
-      y1 = 0
-  elif y1 + settings.crop_height > im.height:
-      y1 = im.height - settings.crop_height
-
-  x2 = x1 + settings.crop_width
-  y2 = y1 + settings.crop_height
-
-  crop = [x1, y1, x2, y2]
-
-  if settings.annotate_image:
-    d = ImageDraw.Draw(im)
-    rect = list(crop)
-    rect[2] -= 1
-    rect[3] -= 1
-    d.rectangle(rect, outline=GREEN)
-    if settings.destop_view_image:
-      im.show()
-
-  return im.crop(tuple(crop))
-
-def focal_point(im, settings):
-    corner_points = image_corner_points(im, settings)
-    entropy_points = image_entropy_points(im, settings)
-    face_points = image_face_points(im, settings)
-
-    total_points = len(corner_points) + len(entropy_points) + len(face_points)
-
-    corner_weight = settings.corner_points_weight
-    entropy_weight = settings.entropy_points_weight
-    face_weight = settings.face_points_weight
-
-    weight_pref_total = corner_weight + entropy_weight + face_weight
-
-    # weight things
-    pois = []
-    if weight_pref_total == 0 or total_points == 0: 
-      return pois
-
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ]
-    )
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ]
-    )
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
-    )
-
-    average_point = poi_average(pois, settings)
-
-    if settings.annotate_image:
-      d = ImageDraw.Draw(im)
-      for f in face_points:
-        d.rectangle(f.bounding(f.size), outline=RED)
-      for f in entropy_points:
-        d.rectangle(f.bounding(30), outline=BLUE)
-      for poi in pois:
-        w = max(4, 4 * 0.5 * sqrt(poi.weight))
-        d.ellipse(poi.bounding(w), fill=BLUE)
-      d.ellipse(average_point.bounding(25), outline=GREEN)
-      
-    return average_point
-
-
-def image_face_points(im, settings):
-    np_im = np.array(im)
-    gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
-
-    tries = [
-      [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
-    ]
-
-    for t in tries:
-      # print(t[0])
-      classifier = cv2.CascadeClassifier(t[0])
-      minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
-      try:
-        faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
-          minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
-      except:
-        continue
-
-      if len(faces) > 0:
-        rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
-        return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2])) for r in rects]
-    return []
-
-
-def image_corner_points(im, settings):
-    grayscale = im.convert("L")
-
-    # naive attempt at preventing focal points from collecting at watermarks near the bottom
-    gd = ImageDraw.Draw(grayscale)
-    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
-
-    np_im = np.array(grayscale)
-
-    points = cv2.goodFeaturesToTrack(
-        np_im,
-        maxCorners=100,
-        qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.07,
-        useHarrisDetector=False,
-    )
-
-    if points is None:
-        return []
-
-    focal_points = []
-    for point in points:
-      x, y = point.ravel()
-      focal_points.append(PointOfInterest(x, y, size=4))
-
-    return focal_points
-
-
-def image_entropy_points(im, settings):
-    landscape = im.height < im.width
-    portrait = im.height > im.width
-    if landscape:
-      move_idx = [0, 2]
-      move_max = im.size[0]
-    elif portrait:
-      move_idx = [1, 3]
-      move_max = im.size[1]
-    else:
-      return []
-
-    e_max = 0
-    crop_current = [0, 0, settings.crop_width, settings.crop_height]
-    crop_best = crop_current
-    while crop_current[move_idx[1]] < move_max:
-        crop = im.crop(tuple(crop_current))
-        e = image_entropy(crop)
-
-        if (e > e_max):
-          e_max = e
-          crop_best = list(crop_current)
-
-        crop_current[move_idx[0]] += 4
-        crop_current[move_idx[1]] += 4
-
-    x_mid = int(crop_best[0] + settings.crop_width/2)
-    y_mid = int(crop_best[1] + settings.crop_height/2)
-
-    return [PointOfInterest(x_mid, y_mid, size=25)]
-
-
-def image_entropy(im):
-    # greyscale image entropy
-    # band = np.asarray(im.convert("L"))
-    band = np.asarray(im.convert("1"), dtype=np.uint8)
-    hist, _ = np.histogram(band, bins=range(0, 256))
-    hist = hist[hist > 0]
-    return -np.log2(hist / hist.sum()).sum()
-
-
-def poi_average(pois, settings):
-    weight = 0.0
-    x = 0.0
-    y = 0.0
-    for poi in pois:
-        weight += poi.weight
-        x += poi.x * poi.weight
-        y += poi.y * poi.weight
-    avg_x = round(x / weight)
-    avg_y = round(y / weight)
-
-    return PointOfInterest(avg_x, avg_y)
-
-
-class PointOfInterest:
-  def __init__(self, x, y, weight=1.0, size=10):
-    self.x = x
-    self.y = y
-    self.weight = weight
-    self.size = size
-
-  def bounding(self, size):
-    return [
-      self.x - size//2,
-      self.y - size//2,
-      self.x + size//2,
-      self.y + size//2
-    ]
-
-
-class Settings:
-  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False):
-    self.crop_width = crop_width
-    self.crop_height = crop_height
-    self.corner_points_weight = corner_points_weight
-    self.entropy_points_weight = entropy_points_weight
-    self.face_points_weight = entropy_points_weight
-    self.annotate_image = annotate_image
+import cv2
+from collections import defaultdict
+from math import log, sqrt
+import numpy as np
+from PIL import Image, ImageDraw
+
+GREEN = "#0F0"
+BLUE = "#00F"
+RED = "#F00"
+
+
+def crop_image(im, settings):
+  """ Intelligently crop an image to the subject matter """
+
+  scale_by = 1
+  if is_landscape(im.width, im.height):
+    scale_by = settings.crop_height / im.height
+  elif is_portrait(im.width, im.height):
+    scale_by = settings.crop_width / im.width
+  elif is_square(im.width, im.height):
+    if is_square(settings.crop_width, settings.crop_height):
+      scale_by = settings.crop_width / im.width
+    elif is_landscape(settings.crop_width, settings.crop_height):
+      scale_by = settings.crop_width / im.width
+    elif is_portrait(settings.crop_width, settings.crop_height):
+      scale_by = settings.crop_height / im.height
+
+  im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
+
+  if im.width == settings.crop_width and im.height == settings.crop_height:
+    if settings.annotate_image:
+      d = ImageDraw.Draw(im)
+      rect = [0, 0, im.width, im.height]
+      rect[2] -= 1
+      rect[3] -= 1
+      d.rectangle(rect, outline=GREEN)
+      if settings.destop_view_image:
+        im.show()
+    return im
+
+  focus = focal_point(im, settings)
+
+  # take the focal point and turn it into crop coordinates that try to center over the focal
+  # point but then get adjusted back into the frame
+  y_half = int(settings.crop_height / 2)
+  x_half = int(settings.crop_width / 2)
+
+  x1 = focus.x - x_half
+  if x1 < 0:
+      x1 = 0
+  elif x1 + settings.crop_width > im.width:
+      x1 = im.width - settings.crop_width
+
+  y1 = focus.y - y_half
+  if y1 < 0:
+      y1 = 0
+  elif y1 + settings.crop_height > im.height:
+      y1 = im.height - settings.crop_height
+
+  x2 = x1 + settings.crop_width
+  y2 = y1 + settings.crop_height
+
+  crop = [x1, y1, x2, y2]
+
+  if settings.annotate_image:
+    d = ImageDraw.Draw(im)
+    rect = list(crop)
+    rect[2] -= 1
+    rect[3] -= 1
+    d.rectangle(rect, outline=GREEN)
+    if settings.destop_view_image:
+      im.show()
+
+  return im.crop(tuple(crop))
+
+def focal_point(im, settings):
+    corner_points = image_corner_points(im, settings)
+    entropy_points = image_entropy_points(im, settings)
+    face_points = image_face_points(im, settings)
+
+    total_points = len(corner_points) + len(entropy_points) + len(face_points)
+
+    corner_weight = settings.corner_points_weight
+    entropy_weight = settings.entropy_points_weight
+    face_weight = settings.face_points_weight
+
+    weight_pref_total = corner_weight + entropy_weight + face_weight
+
+    # weight things
+    pois = []
+    if weight_pref_total == 0 or total_points == 0: 
+      return pois
+
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ]
+    )
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ]
+    )
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
+    )
+
+    average_point = poi_average(pois, settings)
+
+    if settings.annotate_image:
+      d = ImageDraw.Draw(im)
+      for f in face_points:
+        d.rectangle(f.bounding(f.size), outline=RED)
+      for f in entropy_points:
+        d.rectangle(f.bounding(30), outline=BLUE)
+      for poi in pois:
+        w = max(4, 4 * 0.5 * sqrt(poi.weight))
+        d.ellipse(poi.bounding(w), fill=BLUE)
+      d.ellipse(average_point.bounding(25), outline=GREEN)
+      
+    return average_point
+
+
+def image_face_points(im, settings):
+    np_im = np.array(im)
+    gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
+
+    tries = [
+      [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
+    ]
+
+    for t in tries:
+      # print(t[0])
+      classifier = cv2.CascadeClassifier(t[0])
+      minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
+      try:
+        faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
+          minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+      except:
+        continue
+
+      if len(faces) > 0:
+        rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+        return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2])) for r in rects]
+    return []
+
+
+def image_corner_points(im, settings):
+    grayscale = im.convert("L")
+
+    # naive attempt at preventing focal points from collecting at watermarks near the bottom
+    gd = ImageDraw.Draw(grayscale)
+    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
+
+    np_im = np.array(grayscale)
+
+    points = cv2.goodFeaturesToTrack(
+        np_im,
+        maxCorners=100,
+        qualityLevel=0.04,
+        minDistance=min(grayscale.width, grayscale.height)*0.07,
+        useHarrisDetector=False,
+    )
+
+    if points is None:
+        return []
+
+    focal_points = []
+    for point in points:
+      x, y = point.ravel()
+      focal_points.append(PointOfInterest(x, y, size=4))
+
+    return focal_points
+
+
+def image_entropy_points(im, settings):
+    landscape = im.height < im.width
+    portrait = im.height > im.width
+    if landscape:
+      move_idx = [0, 2]
+      move_max = im.size[0]
+    elif portrait:
+      move_idx = [1, 3]
+      move_max = im.size[1]
+    else:
+      return []
+
+    e_max = 0
+    crop_current = [0, 0, settings.crop_width, settings.crop_height]
+    crop_best = crop_current
+    while crop_current[move_idx[1]] < move_max:
+        crop = im.crop(tuple(crop_current))
+        e = image_entropy(crop)
+
+        if (e > e_max):
+          e_max = e
+          crop_best = list(crop_current)
+
+        crop_current[move_idx[0]] += 4
+        crop_current[move_idx[1]] += 4
+
+    x_mid = int(crop_best[0] + settings.crop_width/2)
+    y_mid = int(crop_best[1] + settings.crop_height/2)
+
+    return [PointOfInterest(x_mid, y_mid, size=25)]
+
+
+def image_entropy(im):
+    # greyscale image entropy
+    # band = np.asarray(im.convert("L"))
+    band = np.asarray(im.convert("1"), dtype=np.uint8)
+    hist, _ = np.histogram(band, bins=range(0, 256))
+    hist = hist[hist > 0]
+    return -np.log2(hist / hist.sum()).sum()
+
+
+def poi_average(pois, settings):
+    weight = 0.0
+    x = 0.0
+    y = 0.0
+    for poi in pois:
+        weight += poi.weight
+        x += poi.x * poi.weight
+        y += poi.y * poi.weight
+    avg_x = round(x / weight)
+    avg_y = round(y / weight)
+
+    return PointOfInterest(avg_x, avg_y)
+
+
+def is_landscape(w, h):
+  return w > h
+
+
+def is_portrait(w, h):
+  return h > w
+
+
+def is_square(w, h):
+  return w == h
+
+
+class PointOfInterest:
+  def __init__(self, x, y, weight=1.0, size=10):
+    self.x = x
+    self.y = y
+    self.weight = weight
+    self.size = size
+
+  def bounding(self, size):
+    return [
+      self.x - size//2,
+      self.y - size//2,
+      self.x + size//2,
+      self.y + size//2
+    ]
+
+
+class Settings:
+  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False):
+    self.crop_width = crop_width
+    self.crop_height = crop_height
+    self.corner_points_weight = corner_points_weight
+    self.entropy_points_weight = entropy_points_weight
+    self.face_points_weight = entropy_points_weight
+    self.annotate_image = annotate_image
     self.destop_view_image = False
\ No newline at end of file
-- 
cgit v1.2.3


From 0523704dade0508bf3ae0c8cb799b1ae332d449b Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 12:27:50 -0300
Subject: Update run_extras to use the temp filename

In batch mode run_extras tries to preserve the original file name of the
images. The problem is that this makes no sense since the user only gets
a list of images in the UI, trying to manually save them shows that this
images have random temp names. Also, trying to keep "orig_name" in the
API is a hassle that adds complexity to the consuming UI since the
client has to use (or emulate) an input (type=file) element in a form.
Using the normal file name not only doesn't change the output and
functionality in the original UI but also helps keep the API simple.
---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 22c5a1c1..29ac312e 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -33,7 +33,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         for img in image_folder:
             image = Image.open(img)
             imageArr.append(image)
-            imageNameArr.append(os.path.splitext(img.orig_name)[0])
+            imageNameArr.append(os.path.splitext(img.name)[0])
     elif extras_mode == 2:
         assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
 
-- 
cgit v1.2.3


From 4ff852ffb50859f2eae75375cab94dd790a46886 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 13:07:59 -0300
Subject: Add batch processing "extras" endpoint

---
 modules/api/api.py    | 25 +++++++++++++++++++++++--
 modules/api/models.py | 15 ++++++++++++++-
 2 files changed, 37 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 3b804373..528134a8 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -10,6 +10,7 @@ import base64
 from modules.api.models import *
 from PIL import Image
 from modules.extras import run_extras
+from gradio import processing_utils
 
 def upscaler_to_index(name: str):
     try:
@@ -44,6 +45,7 @@ class Api:
         self.queue_lock = queue_lock
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
+        self.app.add_api_route("/sdapi/v1/extra-batch-image", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -78,12 +80,31 @@ class Api:
         reqDict.pop('upscaler_1')
         reqDict.pop('upscaler_2')
 
-        reqDict['image'] = base64_to_images([reqDict['image']])[0]
+        reqDict['image'] = processing_utils.decode_base64_to_file(reqDict['image'])
 
         with self.queue_lock:
             result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="")
 
-        return ExtrasSingleImageResponse(image="data:image/png;base64,"+img_to_base64(result[0]), html_info_x=result[1], html_info=result[2])
+        return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0]), html_info_x=result[1], html_info=result[2])
+
+    def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
+        upscaler1Index = upscaler_to_index(req.upscaler_1)
+        upscaler2Index = upscaler_to_index(req.upscaler_2)
+
+        reqDict = vars(req)
+        reqDict.pop('upscaler_1')
+        reqDict.pop('upscaler_2')
+
+        reqDict['image_folder'] = list(map(processing_utils.decode_base64_to_file, reqDict['imageList']))
+        reqDict.pop('imageList')
+
+        with self.queue_lock:
+            result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=1, image="", input_dir="", output_dir="")
+
+        return ExtrasBatchImagesResponse(images=list(map(processing_utils.encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
+    
+    def extras_folder_processing_api(self):
+        raise NotImplementedError
 
     def pnginfoapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
index dcf1ab54..bbd0ef53 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -29,4 +29,17 @@ class ExtrasSingleImageRequest(ExtrasBaseRequest):
     image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
 
 class ExtrasSingleImageResponse(ExtraBaseResponse):
-    image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
\ No newline at end of file
+    image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
+
+class SerializableImage(BaseModel):
+  path: str = Field(title="Path", description="The image's path ()")
+
+class ImageItem(BaseModel):
+    data: str = Field(title="image data")
+    name: str = Field(title="filename")
+
+class ExtrasBatchImagesRequest(ExtrasBaseRequest):
+    imageList: list[str] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+
+class ExtrasBatchImagesResponse(ExtraBaseResponse):
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
\ No newline at end of file
-- 
cgit v1.2.3


From e0ca4dfbc10e0af8dfc4185e5e758f33fd2f0d81 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 15:13:37 -0300
Subject: Update endpoints to use gradio's own utils functions

---
 modules/api/api.py    | 75 +++++++++++++++++++++++++--------------------------
 modules/api/models.py |  4 +--
 2 files changed, 38 insertions(+), 41 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 3f490ce2..3acb1f36 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -20,27 +20,27 @@ def upscaler_to_index(name: str):
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
-def img_to_base64(img: str):
-    buffer = io.BytesIO()
-    img.save(buffer, format="png")
-    return base64.b64encode(buffer.getvalue())
-
-def base64_to_bytes(base64Img: str):
-    if "," in base64Img:
-        base64Img = base64Img.split(",")[1]
-    return io.BytesIO(base64.b64decode(base64Img))
-
-def base64_to_images(base64Imgs: list[str]):
-    imgs = []
-    for img in base64Imgs:
-        img = Image.open(base64_to_bytes(img))
-        imgs.append(img)
-    return imgs
+# def img_to_base64(img: str):
+#     buffer = io.BytesIO()
+#     img.save(buffer, format="png")
+#     return base64.b64encode(buffer.getvalue())
+
+# def base64_to_bytes(base64Img: str):
+#     if "," in base64Img:
+#         base64Img = base64Img.split(",")[1]
+#     return io.BytesIO(base64.b64decode(base64Img))
+
+# def base64_to_images(base64Imgs: list[str]):
+#     imgs = []
+#     for img in base64Imgs:
+#         img = Image.open(base64_to_bytes(img))
+#         imgs.append(img)
+#     return imgs
 
 class ImageToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: Json
-    info: Json
+    parameters: dict
+    info: str
 
 
 class Api:
@@ -49,17 +49,17 @@ class Api:
         self.app = app
         self.queue_lock = queue_lock
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
-        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-batch-image", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
-    def __base64_to_image(self, base64_string):
-        # if has a comma, deal with prefix
-        if "," in base64_string:
-            base64_string = base64_string.split(",")[1]
-        imgdata = base64.b64decode(base64_string)
-        # convert base64 to PIL image
-        return Image.open(io.BytesIO(imgdata))
+    # def __base64_to_image(self, base64_string):
+    #     # if has a comma, deal with prefix
+    #     if "," in base64_string:
+    #         base64_string = base64_string.split(",")[1]
+    #     imgdata = base64.b64decode(base64_string)
+    #     # convert base64 to PIL image
+    #     return Image.open(io.BytesIO(imgdata))
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -79,11 +79,9 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = list(map(img_to_base64, processed.images))
-
-        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
-        
+        b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
         
+        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.info)
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
         sampler_index = sampler_to_index(img2imgreq.sampler_index)
@@ -98,7 +96,7 @@ class Api:
 
         mask = img2imgreq.mask
         if mask:
-            mask = self.__base64_to_image(mask)
+            mask = processing_utils.decode_base64_to_image(mask)
 
         
         populate = img2imgreq.copy(update={ # Override __init__ params
@@ -113,7 +111,7 @@ class Api:
 
         imgs = []
         for img in init_images:
-            img = self.__base64_to_image(img)
+            img = processing_utils.decode_base64_to_image(img)
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
@@ -121,13 +119,12 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = []
-        for i in processed.images:
-            buffer = io.BytesIO()
-            i.save(buffer, format="png")
-            b64images.append(base64.b64encode(buffer.getvalue()))
-
-        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info))
+        b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
+        # for i in processed.images:
+        #     buffer = io.BytesIO()
+        #     i.save(buffer, format="png")
+        #     b64images.append(base64.b64encode(buffer.getvalue()))
+        return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.info)
 
     def extras_single_image_api(self, req: ExtrasSingleImageRequest):
         upscaler1Index = upscaler_to_index(req.upscaler_1)
diff --git a/modules/api/models.py b/modules/api/models.py
index bbd0ef53..209f8af5 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -4,8 +4,8 @@ from modules.shared import sd_upscalers
 
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: Json
-    info: Json
+    parameters: str
+    info: str
 
 class ExtrasBaseRequest(BaseModel):
     resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
-- 
cgit v1.2.3


From 866b36d705a338d299aba385788729d60f7d48c8 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 15:35:49 -0300
Subject: Move processing's models into models.py

It didn't make sense to have two differente files for the same and
"models" is a more descriptive name.
---
 modules/api/api.py        |  57 ++++-------------------
 modules/api/models.py     | 112 +++++++++++++++++++++++++++++++++++++++++++++-
 modules/api/processing.py | 106 -------------------------------------------
 3 files changed, 119 insertions(+), 156 deletions(-)
 delete mode 100644 modules/api/processing.py

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 3acb1f36..20e85e82 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,16 +1,11 @@
-from modules.api.processing import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
-from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers
-import modules.shared as shared
 import uvicorn
+from gradio import processing_utils
 from fastapi import APIRouter, HTTPException
-import json
-import io
-import base64
+import modules.shared as shared
 from modules.api.models import *
-from PIL import Image
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+from modules.sd_samplers import all_samplers
 from modules.extras import run_extras
-from gradio import processing_utils
 
 def upscaler_to_index(name: str):
     try:
@@ -20,29 +15,6 @@ def upscaler_to_index(name: str):
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
-# def img_to_base64(img: str):
-#     buffer = io.BytesIO()
-#     img.save(buffer, format="png")
-#     return base64.b64encode(buffer.getvalue())
-
-# def base64_to_bytes(base64Img: str):
-#     if "," in base64Img:
-#         base64Img = base64Img.split(",")[1]
-#     return io.BytesIO(base64.b64decode(base64Img))
-
-# def base64_to_images(base64Imgs: list[str]):
-#     imgs = []
-#     for img in base64Imgs:
-#         img = Image.open(base64_to_bytes(img))
-#         imgs.append(img)
-#     return imgs
-
-class ImageToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: dict
-    info: str
-
-
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
@@ -51,15 +23,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
-        self.app.add_api_route("/sdapi/v1/extra-batch-image", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
-
-    # def __base64_to_image(self, base64_string):
-    #     # if has a comma, deal with prefix
-    #     if "," in base64_string:
-    #         base64_string = base64_string.split(",")[1]
-    #     imgdata = base64.b64decode(base64_string)
-    #     # convert base64 to PIL image
-    #     return Image.open(io.BytesIO(imgdata))
+        self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -81,7 +45,7 @@ class Api:
         
         b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
         
-        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.info)
+        return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.info)
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
         sampler_index = sampler_to_index(img2imgreq.sampler_index)
@@ -120,10 +84,7 @@ class Api:
             processed = process_images(p)
         
         b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
-        # for i in processed.images:
-        #     buffer = io.BytesIO()
-        #     i.save(buffer, format="png")
-        #     b64images.append(base64.b64encode(buffer.getvalue()))
+       
         return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.info)
 
     def extras_single_image_api(self, req: ExtrasSingleImageRequest):
@@ -134,12 +95,12 @@ class Api:
         reqDict.pop('upscaler_1')
         reqDict.pop('upscaler_2')
 
-        reqDict['image'] = processing_utils.decode_base64_to_file(reqDict['image'])
+        reqDict['image'] = processing_utils.decode_base64_to_image(reqDict['image'])
 
         with self.queue_lock:
             result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="")
 
-        return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0]), html_info_x=result[1], html_info=result[2])
+        return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0][0]), html_info_x=result[1], html_info=result[2])
 
     def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
         upscaler1Index = upscaler_to_index(req.upscaler_1)
diff --git a/modules/api/models.py b/modules/api/models.py
index 209f8af5..362e6277 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,10 +1,118 @@
-from pydantic import BaseModel, Field, Json
+import inspect
+from pydantic import BaseModel, Field, Json, create_model
+from typing import Any, Optional
 from typing_extensions import Literal
+from inflection import underscore
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 from modules.shared import sd_upscalers
 
+API_NOT_ALLOWED = [
+    "self",
+    "kwargs",
+    "sd_model",
+    "outpath_samples",
+    "outpath_grids",
+    "sampler_index",
+    "do_not_save_samples",
+    "do_not_save_grid",
+    "extra_generation_params",
+    "overlay_images",
+    "do_not_reload_embeddings",
+    "seed_enable_extras",
+    "prompt_for_display",
+    "sampler_noise_scheduler_override",
+    "ddim_discretize"
+]
+
+class ModelDef(BaseModel):
+    """Assistance Class for Pydantic Dynamic Model Generation"""
+
+    field: str
+    field_alias: str
+    field_type: Any
+    field_value: Any
+
+
+class PydanticModelGenerator:
+    """
+    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
+    source_data is a snapshot of the default values produced by the class
+    params are the names of the actual keys required by __init__
+    """
+
+    def __init__(
+        self,
+        model_name: str = None,
+        class_instance = None,
+        additional_fields = None,
+    ):
+        def field_type_generator(k, v):
+            # field_type = str if not overrides.get(k) else overrides[k]["type"]
+            # print(k, v.annotation, v.default)
+            field_type = v.annotation
+            
+            return Optional[field_type]
+        
+        def merge_class_params(class_):
+            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
+            parameters = {}
+            for classes in all_classes:
+                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
+            return parameters
+            
+                
+        self._model_name = model_name
+        self._class_data = merge_class_params(class_instance)
+        self._model_def = [
+            ModelDef(
+                field=underscore(k),
+                field_alias=k,
+                field_type=field_type_generator(k, v),
+                field_value=v.default
+            )
+            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
+        ]
+        
+        for fields in additional_fields:
+            self._model_def.append(ModelDef(
+                field=underscore(fields["key"]), 
+                field_alias=fields["key"], 
+                field_type=fields["type"],
+                field_value=fields["default"]))
+
+    def generate_model(self):
+        """
+        Creates a pydantic BaseModel
+        from the json and overrides provided at initialization
+        """
+        fields = {
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
+        }
+        DynamicModel = create_model(self._model_name, **fields)
+        DynamicModel.__config__.allow_population_by_field_name = True
+        DynamicModel.__config__.allow_mutation = True
+        return DynamicModel
+    
+StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingTxt2Img", 
+    StableDiffusionProcessingTxt2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()
+
+StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingImg2Img", 
+    StableDiffusionProcessingImg2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
+).generate_model()
+
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: str
+    parameters: dict
+    info: str
+
+class ImageToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: dict
     info: str
 
 class ExtrasBaseRequest(BaseModel):
diff --git a/modules/api/processing.py b/modules/api/processing.py
deleted file mode 100644
index f551fa35..00000000
--- a/modules/api/processing.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from array import array
-from inflection import underscore
-from typing import Any, Dict, Optional
-from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
-import inspect
-
-
-API_NOT_ALLOWED = [
-    "self",
-    "kwargs",
-    "sd_model",
-    "outpath_samples",
-    "outpath_grids",
-    "sampler_index",
-    "do_not_save_samples",
-    "do_not_save_grid",
-    "extra_generation_params",
-    "overlay_images",
-    "do_not_reload_embeddings",
-    "seed_enable_extras",
-    "prompt_for_display",
-    "sampler_noise_scheduler_override",
-    "ddim_discretize"
-]
-
-class ModelDef(BaseModel):
-    """Assistance Class for Pydantic Dynamic Model Generation"""
-
-    field: str
-    field_alias: str
-    field_type: Any
-    field_value: Any
-
-
-class PydanticModelGenerator:
-    """
-    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
-    source_data is a snapshot of the default values produced by the class
-    params are the names of the actual keys required by __init__
-    """
-
-    def __init__(
-        self,
-        model_name: str = None,
-        class_instance = None,
-        additional_fields = None,
-    ):
-        def field_type_generator(k, v):
-            # field_type = str if not overrides.get(k) else overrides[k]["type"]
-            # print(k, v.annotation, v.default)
-            field_type = v.annotation
-            
-            return Optional[field_type]
-        
-        def merge_class_params(class_):
-            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
-            parameters = {}
-            for classes in all_classes:
-                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
-            return parameters
-            
-                
-        self._model_name = model_name
-        self._class_data = merge_class_params(class_instance)
-        self._model_def = [
-            ModelDef(
-                field=underscore(k),
-                field_alias=k,
-                field_type=field_type_generator(k, v),
-                field_value=v.default
-            )
-            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
-        ]
-        
-        for fields in additional_fields:
-            self._model_def.append(ModelDef(
-                field=underscore(fields["key"]), 
-                field_alias=fields["key"], 
-                field_type=fields["type"],
-                field_value=fields["default"]))
-
-    def generate_model(self):
-        """
-        Creates a pydantic BaseModel
-        from the json and overrides provided at initialization
-        """
-        fields = {
-            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
-        }
-        DynamicModel = create_model(self._model_name, **fields)
-        DynamicModel.__config__.allow_population_by_field_name = True
-        DynamicModel.__config__.allow_mutation = True
-        return DynamicModel
-    
-StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingTxt2Img", 
-    StableDiffusionProcessingTxt2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}]
-).generate_model()
-
-StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingImg2Img", 
-    StableDiffusionProcessingImg2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
-).generate_model()
\ No newline at end of file
-- 
cgit v1.2.3


From 1e625624ba6ab3dfc167f0a5226780bb9b50fb58 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 16:01:16 -0300
Subject: Add folder processing endpoint

Also minor refactor
---
 modules/api/api.py    | 56 +++++++++++++++++++++++++++------------------------
 modules/api/models.py |  6 +++++-
 2 files changed, 35 insertions(+), 27 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 20e85e82..7b4fbe29 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,5 +1,5 @@
 import uvicorn
-from gradio import processing_utils
+from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, HTTPException
 import modules.shared as shared
 from modules.api.models import *
@@ -11,10 +11,18 @@ def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
     except:
-        raise HTTPException(status_code=400, detail="Upscaler not found")
+        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
+def setUpscalers(req: dict):
+    reqDict = vars(req)
+    reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
+    reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2)
+    reqDict.pop('upscaler_1')
+    reqDict.pop('upscaler_2')
+    return reqDict
+
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
@@ -24,6 +32,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
+        self.app.add_api_route("/sdapi/v1/extra-folder-images", self.extras_folder_processing_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -43,7 +52,7 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
+        b64images = list(map(encode_pil_to_base64, processed.images))
         
         return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.info)
 
@@ -60,7 +69,7 @@ class Api:
 
         mask = img2imgreq.mask
         if mask:
-            mask = processing_utils.decode_base64_to_image(mask)
+            mask = decode_base64_to_image(mask)
 
         
         populate = img2imgreq.copy(update={ # Override __init__ params
@@ -75,7 +84,7 @@ class Api:
 
         imgs = []
         for img in init_images:
-            img = processing_utils.decode_base64_to_image(img)
+            img = decode_base64_to_image(img)
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
@@ -83,43 +92,38 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
+        b64images = list(map(encode_pil_to_base64, processed.images))
        
         return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.info)
 
     def extras_single_image_api(self, req: ExtrasSingleImageRequest):
-        upscaler1Index = upscaler_to_index(req.upscaler_1)
-        upscaler2Index = upscaler_to_index(req.upscaler_2)
-
-        reqDict = vars(req)
-        reqDict.pop('upscaler_1')
-        reqDict.pop('upscaler_2')
+        reqDict = setUpscalers(req)
 
-        reqDict['image'] = processing_utils.decode_base64_to_image(reqDict['image'])
+        reqDict['image'] = decode_base64_to_image(reqDict['image'])
 
         with self.queue_lock:
-            result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="")
+            result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", **reqDict)
 
-        return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0][0]), html_info_x=result[1], html_info=result[2])
+        return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info_x=result[1], html_info=result[2])
 
     def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
-        upscaler1Index = upscaler_to_index(req.upscaler_1)
-        upscaler2Index = upscaler_to_index(req.upscaler_2)
+        reqDict = setUpscalers(req)
 
-        reqDict = vars(req)
-        reqDict.pop('upscaler_1')
-        reqDict.pop('upscaler_2')
-
-        reqDict['image_folder'] = list(map(processing_utils.decode_base64_to_file, reqDict['imageList']))
+        reqDict['image_folder'] = list(map(decode_base64_to_file, reqDict['imageList']))
         reqDict.pop('imageList')
 
         with self.queue_lock:
-            result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=1, image="", input_dir="", output_dir="")
+            result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict)
 
-        return ExtrasBatchImagesResponse(images=list(map(processing_utils.encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
+        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
     
-    def extras_folder_processing_api(self):
-        raise NotImplementedError
+    def extras_folder_processing_api(self, req:ExtrasFoldersRequest):
+        reqDict = setUpscalers(req)
+
+        with self.queue_lock:
+            result = run_extras(extras_mode=2, image=None, image_folder=None, **reqDict)
+
+        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
 
     def pnginfoapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
index 362e6277..6f096807 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -150,4 +150,8 @@ class ExtrasBatchImagesRequest(ExtrasBaseRequest):
     imageList: list[str] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
\ No newline at end of file
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
+
+class ExtrasFoldersRequest(ExtrasBaseRequest):
+    input_dir: str = Field(title="Input directory", description="Directory path from where to take the images")
+    output_dir: str = Field(title="Output directory", description="Directory path to put the processsed images into")
-- 
cgit v1.2.3


From 90f02c75220d187e075203a4e3b450bfba392c4d Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 16:03:30 -0300
Subject: Remove unused field and class

---
 modules/api/api.py    | 6 +++---
 modules/api/models.py | 6 +-----
 2 files changed, 4 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 7b4fbe29..799e3701 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -104,7 +104,7 @@ class Api:
         with self.queue_lock:
             result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", **reqDict)
 
-        return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info_x=result[1], html_info=result[2])
+        return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
 
     def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
         reqDict = setUpscalers(req)
@@ -115,7 +115,7 @@ class Api:
         with self.queue_lock:
             result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict)
 
-        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
+        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
     
     def extras_folder_processing_api(self, req:ExtrasFoldersRequest):
         reqDict = setUpscalers(req)
@@ -123,7 +123,7 @@ class Api:
         with self.queue_lock:
             result = run_extras(extras_mode=2, image=None, image_folder=None, **reqDict)
 
-        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
+        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
 
     def pnginfoapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
index 6f096807..e461d397 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -130,8 +130,7 @@ class ExtrasBaseRequest(BaseModel):
     extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
 
 class ExtraBaseResponse(BaseModel):
-    html_info_x: str
-    html_info: str
+    html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
 
 class ExtrasSingleImageRequest(ExtrasBaseRequest):
     image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
@@ -139,9 +138,6 @@ class ExtrasSingleImageRequest(ExtrasBaseRequest):
 class ExtrasSingleImageResponse(ExtraBaseResponse):
     image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
 
-class SerializableImage(BaseModel):
-  path: str = Field(title="Path", description="The image's path ()")
-
 class ImageItem(BaseModel):
     data: str = Field(title="image data")
     name: str = Field(title="filename")
-- 
cgit v1.2.3


From 124e44cf1eed1edc68954f63a2a9bc428aabbcec Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 24 Oct 2022 09:51:56 +0800
Subject: remove browser to extension

---
 modules/images_history.py   | 424 --------------------------------------------
 modules/inspiration.py      | 193 --------------------
 modules/script_callbacks.py |   2 -
 modules/shared.py           |  15 --
 modules/ui.py               |  20 +--
 5 files changed, 4 insertions(+), 650 deletions(-)
 delete mode 100644 modules/images_history.py
 delete mode 100644 modules/inspiration.py

(limited to 'modules')

diff --git a/modules/images_history.py b/modules/images_history.py
deleted file mode 100644
index bc5cf11f..00000000
--- a/modules/images_history.py
+++ /dev/null
@@ -1,424 +0,0 @@
-import os
-import shutil
-import time
-import hashlib
-import gradio
-system_bak_path = "webui_log_and_bak"
-custom_tab_name = "custom fold"
-faverate_tab_name = "favorites"
-tabs_list = ["txt2img", "img2img", "extras", faverate_tab_name]
-def is_valid_date(date):
-    try:
-        time.strptime(date, "%Y%m%d")
-        return True
-    except:
-        return False
-
-def reduplicative_file_move(src, dst):
-    def same_name_file(basename, path):
-        name, ext = os.path.splitext(basename)
-        f_list = os.listdir(path)
-        max_num = 0
-        for f in f_list:
-            if len(f) <= len(basename):
-                continue
-            f_ext = f[-len(ext):] if len(ext) > 0 else ""
-            if f[:len(name)] == name and f_ext == ext:                
-                if f[len(name)] == "(" and f[-len(ext)-1] == ")":
-                    number = f[len(name)+1:-len(ext)-1]
-                    if number.isdigit():
-                        if int(number) > max_num:
-                            max_num = int(number)
-        return f"{name}({max_num + 1}){ext}"
-    name = os.path.basename(src)
-    save_name = os.path.join(dst, name)
-    if not os.path.exists(save_name):
-        shutil.move(src, dst)
-    else:
-        name = same_name_file(name, dst)
-        shutil.move(src, os.path.join(dst, name))
-
-def traverse_all_files(curr_path, image_list, all_type=False):
-    try:
-        f_list = os.listdir(curr_path)
-    except:
-        if all_type or (curr_path[-10:].rfind(".") > 0 and curr_path[-4:] != ".txt" and curr_path[-4:] != ".csv"):
-            image_list.append(curr_path)
-        return image_list
-    for file in f_list:
-        file = os.path.join(curr_path, file)
-        if (not all_type) and (file[-4:] == ".txt" or file[-4:] == ".csv"):
-            pass
-        elif os.path.isfile(file) and file[-10:].rfind(".") > 0:
-            image_list.append(file)
-        else:
-            image_list = traverse_all_files(file, image_list)
-    return image_list
-
-def auto_sorting(dir_name):    
-    bak_path = os.path.join(dir_name, system_bak_path)
-    if not os.path.exists(bak_path):
-        os.mkdir(bak_path)
-    log_file = None 
-    files_list = []    
-    f_list = os.listdir(dir_name)
-    for file in f_list:   
-        if file == system_bak_path:
-            continue     
-        file_path = os.path.join(dir_name, file)
-        if not is_valid_date(file):
-            if file[-10:].rfind(".") > 0:
-                files_list.append(file_path)
-            else:
-                files_list = traverse_all_files(file_path, files_list, all_type=True)
-
-    for file in files_list:        
-        date_str = time.strftime("%Y%m%d",time.localtime(os.path.getmtime(file)))
-        file_path = os.path.dirname(file)
-        hash_path = hashlib.md5(file_path.encode()).hexdigest()
-        path = os.path.join(dir_name, date_str, hash_path)
-        if not os.path.exists(path):
-            os.makedirs(path)
-        if log_file is None:
-            log_file = open(os.path.join(bak_path,"path_mapping.csv"),"a") 
-        log_file.write(f"{hash_path},{file_path}\n")
-        reduplicative_file_move(file, path)
-       
-    date_list = []
-    f_list = os.listdir(dir_name)
-    for f in f_list:
-        if is_valid_date(f):
-            date_list.append(f)
-        elif f == system_bak_path:
-            continue
-        else:
-            try:
-                reduplicative_file_move(os.path.join(dir_name, f), bak_path)  
-            except:
-                pass         
-            
-    today = time.strftime("%Y%m%d",time.localtime(time.time()))
-    if today not in date_list:
-        date_list.append(today)
-    return sorted(date_list, reverse=True)
-
-def archive_images(dir_name, date_to):    
-    filenames = []   
-    batch_size =int(opts.images_history_num_per_page * opts.images_history_pages_num)
-    if batch_size <= 0:
-        batch_size = opts.images_history_num_per_page * 6
-    today = time.strftime("%Y%m%d",time.localtime(time.time()))
-    date_to = today if date_to is None  or date_to == "" else date_to
-    date_to_bak = date_to 
-    if False: #opts.images_history_reconstruct_directory:        
-        date_list = auto_sorting(dir_name)               
-        for date in date_list:
-            if date <= date_to:
-                path = os.path.join(dir_name, date)
-                if date == today and not os.path.exists(path):
-                    continue
-                filenames = traverse_all_files(path, filenames)
-            if len(filenames) > batch_size:            
-                break
-        filenames = sorted(filenames, key=lambda file: -os.path.getmtime(file))
-    else:
-        filenames = traverse_all_files(dir_name, filenames)  
-        total_num = len(filenames) 
-        tmparray = [(os.path.getmtime(file), file) for file in filenames ]
-        date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400      
-        filenames = []
-        date_list = {date_to:None}
-        date = time.strftime("%Y%m%d",time.localtime(time.time()))
-        for t, f in tmparray:
-            date = time.strftime("%Y%m%d",time.localtime(t))
-            date_list[date] = None
-            if t <= date_stamp:
-                filenames.append((t, f ,date))
-        date_list = sorted(list(date_list.keys()), reverse=True)
-        sort_array = sorted(filenames, key=lambda x:-x[0])
-        if len(sort_array) > batch_size:
-            date = sort_array[batch_size][2]
-            filenames = [x[1] for x in sort_array]
-        else:
-            date =  date_to if len(sort_array) == 0 else sort_array[-1][2]
-            filenames = [x[1] for x in sort_array]
-        filenames = [x[1] for x in sort_array if x[2]>= date]   
-    num = len(filenames)  
-    last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000))
-    date = date[:4] + "/" + date[4:6] + "/" + date[6:8]
-    date_to_bak = date_to_bak[:4] + "/" + date_to_bak[4:6] + "/" + date_to_bak[6:8]
-    load_info = "<div style='color:#999' align='center'>"
-    load_info += f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages"
-    load_info += "</div>"
-    _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames)
-    return (
-        date_to,
-        load_info,
-        filenames,        
-        1,
-        image_list,
-        "",
-        "",
-        visible_num, 
-        last_date_from, 
-        gradio.update(visible=total_num > num)
-    )
-
-def delete_image(delete_num, name, filenames, image_index, visible_num):
-    if name == "":
-        return filenames, delete_num
-    else:
-        delete_num = int(delete_num)
-        visible_num = int(visible_num)
-        image_index = int(image_index)
-        index = list(filenames).index(name)
-        i = 0
-        new_file_list = []
-        for name in filenames:
-            if i >= index and i < index + delete_num:
-                if os.path.exists(name):
-                    if visible_num == image_index:
-                        new_file_list.append(name)
-                        i += 1
-                        continue                    
-                    print(f"Delete file {name}")
-                    os.remove(name)
-                    visible_num -= 1
-                    txt_file = os.path.splitext(name)[0] + ".txt"
-                    if os.path.exists(txt_file):
-                        os.remove(txt_file)
-                else:
-                    print(f"Not exists file {name}")
-            else:
-                new_file_list.append(name)
-            i += 1
-    return new_file_list, 1, visible_num
-
-def save_image(file_name):
-    if file_name is not None and os.path.exists(file_name):
-        shutil.copy(file_name, opts.outdir_save)
-
-def get_recent_images(page_index, step, filenames):
-    page_index = int(page_index)
-    num_of_imgs_per_page = int(opts.images_history_num_per_page)
-    max_page_index = len(filenames) // num_of_imgs_per_page + 1
-    page_index = max_page_index if page_index == -1 else page_index + step
-    page_index = 1 if page_index < 1 else page_index
-    page_index = max_page_index if page_index > max_page_index else page_index
-    idx_frm = (page_index - 1) * num_of_imgs_per_page
-    image_list = filenames[idx_frm:idx_frm + num_of_imgs_per_page]
-    length = len(filenames)
-    visible_num = num_of_imgs_per_page if  idx_frm + num_of_imgs_per_page <= length else length % num_of_imgs_per_page 
-    visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num
-    return page_index, image_list,  "", "",  visible_num
-
-def loac_batch_click(date_to):
-    if date_to is None:
-        return time.strftime("%Y%m%d",time.localtime(time.time())), []
-    else:
-        return None, []
-def forward_click(last_date_from, date_to_recorder):
-    if len(date_to_recorder) == 0:
-        return None, []
-    if last_date_from == date_to_recorder[-1]:
-        date_to_recorder = date_to_recorder[:-1]
-    if len(date_to_recorder) == 0:
-        return None, []
-    return date_to_recorder[-1], date_to_recorder[:-1]
-
-def backward_click(last_date_from, date_to_recorder):
-    if last_date_from is None or last_date_from == "":
-        return time.strftime("%Y%m%d",time.localtime(time.time())), []
-    if len(date_to_recorder) == 0 or last_date_from != date_to_recorder[-1]:
-        date_to_recorder.append(last_date_from)    
-    return last_date_from, date_to_recorder
-
-
-def first_page_click(page_index, filenames):
-    return get_recent_images(1, 0, filenames)
-
-def end_page_click(page_index, filenames):
-    return get_recent_images(-1, 0, filenames)
-
-def prev_page_click(page_index, filenames):
-    return get_recent_images(page_index, -1, filenames)
-
-def next_page_click(page_index, filenames):
-    return get_recent_images(page_index, 1, filenames)
-
-def page_index_change(page_index, filenames):
-    return get_recent_images(page_index, 0, filenames)
-
-def show_image_info(tabname_box, num, page_index, filenames):
-    file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))]   
-    tm =   "<div style='color:#999' align='right'>" + time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file))) + "</div>"
-    return file, tm, num, file
-
-def enable_page_buttons():
-    return gradio.update(visible=True)
-
-def change_dir(img_dir, date_to):
-    warning = None
-    try:
-        if os.path.exists(img_dir):
-            try:
-                f = os.listdir(img_dir)                
-            except:
-                warning = f"'{img_dir} is not a directory"
-        else:
-            warning = "The directory is not exist"
-    except:
-        warning = "The format of the directory is incorrect"
-    if warning is None:        
-        today = time.strftime("%Y%m%d",time.localtime(time.time()))
-        return gradio.update(visible=False), gradio.update(visible=True), None,   None if date_to != today else today, gradio.update(visible=True), gradio.update(visible=True)
-    else:
-        return gradio.update(visible=True), gradio.update(visible=False), warning, date_to, gradio.update(visible=False), gradio.update(visible=False)
-
-def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
-    custom_dir = False
-    if tabname == "txt2img":
-        dir_name = opts.outdir_txt2img_samples
-    elif tabname == "img2img":
-        dir_name = opts.outdir_img2img_samples
-    elif tabname == "extras":
-        dir_name = opts.outdir_extras_samples
-    elif tabname == faverate_tab_name:
-        dir_name = opts.outdir_save
-    else:
-        custom_dir = True
-        dir_name = None
-
-    if not custom_dir:
-        d = dir_name.split("/")
-        dir_name = d[0]
-        for p in d[1:]:
-            dir_name = os.path.join(dir_name, p)
-        if not os.path.exists(dir_name):
-            os.makedirs(dir_name)
-
-    with gr.Column() as page_panel:            
-            with gr.Row():                
-                with gr.Column(scale=1, visible=not custom_dir) as load_batch_box: 
-                    load_batch = gr.Button('Load', elem_id=tabname + "_images_history_start", full_width=True)    
-                with gr.Column(scale=4): 
-                    with gr.Row():                        
-                        img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir)  
-            with gr.Row():
-                with gr.Column(visible=False, scale=1) as batch_panel: 
-                    with gr.Row():
-                        forward = gr.Button('Prev batch')
-                        backward = gr.Button('Next batch')
-                with gr.Column(scale=3):
-                    load_info =     gr.HTML(visible=not custom_dir)
-            with gr.Row(visible=False) as warning:                 
-                warning_box = gr.Textbox("Message", interactive=False)                        
-
-            with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel:
-                with gr.Column(scale=2):                     
-                    with gr.Row(visible=True) as turn_page_buttons:                        
-                        #date_to = gr.Dropdown(label="Date to")                          
-                        first_page = gr.Button('First Page')
-                        prev_page = gr.Button('Prev Page')
-                        page_index = gr.Number(value=1, label="Page Index")
-                        next_page = gr.Button('Next Page')
-                        end_page = gr.Button('End Page')                 
-
-                    history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=opts.images_history_grid_num)
-                    with gr.Row():
-                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
-                        delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
-                        
-                with gr.Column():                    
-                    with gr.Row():
-                        with gr.Column():
-                            img_file_info = gr.Textbox(label="Generate Info", interactive=False, lines=6)
-                            gr.HTML("<hr>")                           
-                            img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
-                            img_file_time= gr.HTML()
-                    with gr.Row():
-                        if tabname != faverate_tab_name:
-                            save_btn = gr.Button('Collect')
-                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
-                           
-                            
-                    # hiden items
-                    with gr.Row(visible=False): 
-                        renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
-                        batch_date_to = gr.Textbox(label="Date to")  
-                        visible_img_num = gr.Number()                     
-                        date_to_recorder = gr.State([])
-                        last_date_from = gr.Textbox()
-                        tabname_box = gr.Textbox(tabname)
-                        image_index = gr.Textbox(value=-1)
-                        set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
-                        filenames = gr.State()
-                        all_images_list = gr.State()
-                        hidden = gr.Image(type="pil")
-                        info1 = gr.Textbox()
-                        info2 = gr.Textbox()
-
-    img_path.submit(change_dir, inputs=[img_path, batch_date_to], outputs=[warning, main_panel, warning_box, batch_date_to, load_batch_box, load_info])
-
-    #change batch
-    change_date_output = [batch_date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from,  batch_panel]  
-   
-    batch_date_to.change(archive_images, inputs=[img_path, batch_date_to], outputs=change_date_output)   
-    batch_date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])  
-    batch_date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")  
-
-    load_batch.click(loac_batch_click, inputs=[batch_date_to], outputs=[batch_date_to, date_to_recorder])
-    forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
-    backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
-
-
-    #delete
-    delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num])
-    delete.click(fn=None, _js="images_history_delete", inputs=[delete_num, tabname_box, image_index], outputs=None) 
-    if tabname != faverate_tab_name: 
-        save_btn.click(save_image, inputs=[img_file_name], outputs=None)     
-
-    #turn page
-    gallery_inputs = [page_index, filenames]
-    gallery_outputs = [page_index, history_gallery, img_file_name, img_file_time, visible_img_num]
-    first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-    next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-    prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-    end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-    page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
-    renew_page.click(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
-
-    first_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    next_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    prev_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    end_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    page_index.submit(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    renew_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-
-    # other funcitons
-    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, img_file_time, image_index, hidden])
-    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)   
-    hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-    switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
-    switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
-
-   
-
-def create_history_tabs(gr, sys_opts, cmp_ops, run_pnginfo, switch_dict):
-    global opts;
-    opts = sys_opts
-    loads_files_num = int(opts.images_history_num_per_page)
-    num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num)
-    if cmp_ops.browse_all_images:
-        tabs_list.append(custom_tab_name)
-    with gr.Blocks(analytics_enabled=False) as images_history:
-        with gr.Tabs() as tabs:
-            for tab in tabs_list:
-                with gr.Tab(tab):
-                    with gr.Blocks(analytics_enabled=False) :
-                        show_images_history(gr, opts, tab, run_pnginfo, switch_dict)
-        gradio.Checkbox(opts.images_history_preload, elem_id="images_history_preload", visible=False)         
-        gradio.Textbox(",".join(tabs_list), elem_id="images_history_tabnames_list", visible=False)    
-        
-    return images_history
diff --git a/modules/inspiration.py b/modules/inspiration.py
deleted file mode 100644
index 29cf8297..00000000
--- a/modules/inspiration.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import os
-import random
-import gradio
-from modules.shared import opts
-inspiration_system_path = os.path.join(opts.inspiration_dir, "system")
-def read_name_list(file, types=None, keyword=None):
-    if not os.path.exists(file):
-        return []
-    ret = []
-    f = open(file, "r")    
-    line = f.readline()
-    while len(line) > 0:
-        line = line.rstrip("\n")
-        if types is not None:
-            dirname = os.path.split(line)
-            if dirname[0] in types and keyword in dirname[1].lower():
-                ret.append(line)
-        else:
-            ret.append(line)
-        line = f.readline()
-    return ret
-
-def save_name_list(file, name): 
-    name_list = read_name_list(file)
-    if name not in name_list:
-        with open(file, "a") as f:
-            f.write(name + "\n")
-
-def get_types_list():
-    files = os.listdir(opts.inspiration_dir)
-    types = []
-    for x in files:
-        path = os.path.join(opts.inspiration_dir, x)
-        if x[0] == ".":
-            continue
-        if not os.path.isdir(path):
-            continue
-        if path == inspiration_system_path:
-            continue
-        types.append(x)
-    return types
-
-def get_inspiration_images(source, types, keyword):
-    keyword = keyword.strip(" ").lower()
-    get_num = int(opts.inspiration_rows_num * opts.inspiration_cols_num)
-    if source == "Favorites":
-        names = read_name_list(os.path.join(inspiration_system_path, "faverites.txt"), types, keyword)
-        names = random.sample(names, get_num) if len(names) > get_num else names
-    elif source == "Abandoned":
-        names = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)
-        names = random.sample(names, get_num) if len(names) > get_num else names
-    elif source == "Exclude abandoned":        
-        abandoned = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)  
-        all_names = []
-        for tp in types:
-            name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
-            all_names += [os.path.join(tp, x) for x in name_list if keyword in x.lower()]
-        
-        if len(all_names) > get_num:
-            names = []
-            while len(names) < get_num:
-                name = random.choice(all_names)
-                if name not in abandoned:
-                    names.append(name)
-        else:
-            names = all_names
-    else:
-        all_names = []
-        for tp in types:
-            name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
-            all_names += [os.path.join(tp, x) for x in name_list if keyword in x.lower()]
-        names = random.sample(all_names, get_num) if len(all_names) > get_num else all_names
-    image_list = []
-    for a in names:
-        image_path = os.path.join(opts.inspiration_dir, a)
-        images = os.listdir(image_path)
-        if len(images) > 0:        
-            image_list.append((os.path.join(image_path, random.choice(images)), a))
-        else:
-            print(image_path)
-    return image_list, names
-
-def select_click(index, name_list):
-    name = name_list[int(index)]
-    path = os.path.join(opts.inspiration_dir, name)
-    images = os.listdir(path)
-    return name, [os.path.join(path, x) for x in images], ""
-
-def give_up_click(name):
-    file = os.path.join(inspiration_system_path, "abandoned.txt")
-    save_name_list(file, name)
-    return "Added to abandoned list"
-   
-def collect_click(name):
-    file = os.path.join(inspiration_system_path, "faverites.txt")
-    save_name_list(file, name)
-    return "Added to faverite list"
-
-def moveout_click(name, source):
-    if source == "Abandoned":
-        file = os.path.join(inspiration_system_path, "abandoned.txt")
-    elif source == "Favorites":
-        file = os.path.join(inspiration_system_path, "faverites.txt")
-    else:
-        return None
-    name_list = read_name_list(file)
-    os.remove(file)
-    with open(file, "a") as f:
-        for a in name_list:
-            if a != name:
-                f.write(a + "\n")
-    return f"Moved out {name} from {source} list"
-
-def source_change(source):
-    if source in ["Abandoned", "Favorites"]:
-        return gradio.update(visible=True), []
-    else:
-        return gradio.update(visible=False), []
-def add_to_prompt(name, prompt):
-    name = os.path.basename(name)
-    return prompt + "," + name
-
-def clear_keyword():
-    return ""
-
-def ui(gr, opts, txt2img_prompt, img2img_prompt):     
-    with gr.Blocks(analytics_enabled=False) as inspiration:
-        flag = os.path.exists(opts.inspiration_dir)        
-        if flag:
-            types = get_types_list()
-            flag = len(types) > 0
-        else:
-            os.makedirs(opts.inspiration_dir)
-        if not flag:            
-            gr.HTML("""
-                <div align='center' width="50%"><h2>To activate inspiration function, you need get "inspiration" images first. </h2><br>
-                You can create these images by run "Create inspiration images" script in txt2img page, <br> you can get the artists or art styles list from here<br>
-                <a href="https://github.com/pharmapsychotic/clip-interrogator/tree/main/data">https://github.com/pharmapsychotic/clip-interrogator/tree/main/data</a><br>
-                download these files, and select these files in the "Create inspiration images" script UI<br>
-                There about 6000 artists and art styles in these files. <br>This takes server hours depending on your GPU type and how many pictures  you generate for each artist/style
-                <br>I suggest at least four images for each<br><br><br>
-                <h2>You can also download generated pictures from here:</h2><br>
-                <a href="https://huggingface.co/datasets/yfszzx/inspiration">https://huggingface.co/datasets/yfszzx/inspiration</a><br>
-                unzip the file to the project directory of webui<br>
-                and restart webui, and enjoy the joy of creation!<br></div>
-                """)      
-            return inspiration
-        if not os.path.exists(inspiration_system_path):
-            os.mkdir(inspiration_system_path)
-        with gr.Row():
-            with gr.Column(scale=2):                
-                inspiration_gallery = gr.Gallery(show_label=False, elem_id="inspiration_gallery").style(grid=opts.inspiration_cols_num, height='auto')
-            with gr.Column(scale=1):
-                types = gr.CheckboxGroup(choices=types, value=types)
-                with gr.Row(): 
-                    source = gr.Dropdown(choices=["All", "Favorites", "Exclude abandoned", "Abandoned"], value="Exclude abandoned", label="Source")
-                    keyword = gr.Textbox("", label="Key word")                                                
-                get_inspiration = gr.Button("Get inspiration", elem_id="inspiration_get_button")   
-                name = gr.Textbox(show_label=False, interactive=False)      
-                with gr.Row():                     
-                    send_to_txt2img = gr.Button('to txt2img')
-                    send_to_img2img = gr.Button('to img2img')
-                    collect = gr.Button('Collect')     
-                    give_up = gr.Button("Don't show again")
-                    moveout = gr.Button("Move out", visible=False)
-                warning = gr.HTML()
-                style_gallery = gr.Gallery(show_label=False).style(grid=2, height='auto') 
-                    
-               
-                
-        with gr.Row(visible=False):
-            select_button = gr.Button('set button', elem_id="inspiration_select_button")
-            name_list = gr.State()
-        
-        get_inspiration.click(get_inspiration_images, inputs=[source, types, keyword], outputs=[inspiration_gallery, name_list])
-        keyword.submit(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
-        source.change(source_change, inputs=[source], outputs=[moveout, style_gallery])
-        source.change(fn=clear_keyword, _js="inspiration_click_get_button", inputs=None, outputs=[keyword])       
-        types.change(fn=clear_keyword, _js="inspiration_click_get_button", inputs=None, outputs=[keyword])  
-
-        select_button.click(select_click, _js="inspiration_selected", inputs=[name, name_list], outputs=[name, style_gallery, warning])
-        give_up.click(give_up_click, inputs=[name], outputs=[warning])
-        collect.click(collect_click, inputs=[name], outputs=[warning])
-        moveout.click(moveout_click, inputs=[name, source], outputs=[warning])
-        moveout.click(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
-
-        send_to_txt2img.click(add_to_prompt, inputs=[name, txt2img_prompt], outputs=[txt2img_prompt])
-        send_to_img2img.click(add_to_prompt, inputs=[name, img2img_prompt], outputs=[img2img_prompt])
-        send_to_txt2img.click(collect_click, inputs=[name], outputs=[warning])
-        send_to_img2img.click(collect_click, inputs=[name], outputs=[warning])
-        send_to_txt2img.click(None, _js='switch_to_txt2img', inputs=None, outputs=None)
-        send_to_img2img.click(None, _js="switch_to_img2img_img2img", inputs=None, outputs=None)
-    return inspiration
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 5bcccd67..66666a56 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -1,4 +1,3 @@
-
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
@@ -16,7 +15,6 @@ def model_loaded_callback(sd_model):
 
 def ui_tabs_callback():
     res = []
-
     for callback in callbacks_ui_tabs:
         res += callback() or []
 
diff --git a/modules/shared.py b/modules/shared.py
index 0aaaadac..5dfd7927 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -321,21 +321,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
-options_templates.update(options_section(('inspiration', "Inspiration"), {
-    "inspiration_dir": OptionInfo("inspiration", "Directory of inspiration", component_args=hide_dirs),
-    "inspiration_max_samples": OptionInfo(4, "Maximum number of samples, used to determine which folders to skip when continue running the create script", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
-    "inspiration_rows_num":  OptionInfo(4, "Rows of inspiration interface frame", gr.Slider, {"minimum": 4, "maximum": 16, "step": 1}),
-    "inspiration_cols_num":  OptionInfo(8, "Columns of inspiration interface frame", gr.Slider, {"minimum": 4, "maximum": 16, "step": 1}),
-}))
-
-options_templates.update(options_section(('images-history', "Images Browser"), {
-    #"images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"),
-    "images_history_preload": OptionInfo(False, "Preload images at startup"),
-    "images_history_num_per_page": OptionInfo(36, "Number of pictures displayed on each page"),
-    "images_history_pages_num": OptionInfo(6, "Minimum number of pages per load "),   
-    "images_history_grid_num": OptionInfo(6, "Number of grids in each row"),   
-
-}))
 
 class Options:
     data = None
diff --git a/modules/ui.py b/modules/ui.py
index a73175f5..fa42712e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -49,14 +49,12 @@ from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
-import modules.images_history as images_history
-import modules.inspiration as inspiration
-
-
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
+txt2img_paste_fields = []
+img2img_paste_fields = []
 
 
 if not cmd_opts.share and not cmd_opts.listen:
@@ -1193,16 +1191,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
-    #images history
-    images_history_switch_dict = {
-        "fn": modules.generation_parameters_copypaste.connect_paste,
-        "t2i": txt2img_paste_fields,
-        "i2i": img2img_paste_fields
-    }
-
-    browser_interface = images_history.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
-    inspiration_interface = inspiration.ui(gr, opts, txt2img_prompt, img2img_prompt)
-
+   
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
@@ -1651,8 +1640,6 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (inspiration_interface, "Inspiration", "inspiration"),
-        (browser_interface , "Image Browser", "images_history"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
     ]
@@ -1896,6 +1883,7 @@ def load_javascript(raw_response):
         javascript = f'<script>{jsfile.read()}</script>'
 
     scripts_list = modules.scripts.list_scripts("javascript", ".js")
+    scripts_list += modules.scripts.list_scripts("scripts", ".js")
     for basedir, filename, path in scripts_list:
         with open(path, "r", encoding="utf8") as jsfile:
             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-- 
cgit v1.2.3


From cef1b89aa2e6c7647db7e93a4cd4ec020da3f2da Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 24 Oct 2022 10:10:33 +0800
Subject: remove browser to extension

---
 modules/script_callbacks.py | 2 ++
 modules/shared.py           | 1 -
 modules/ui.py               | 2 +-
 3 files changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 66666a56..f46d3d9a 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -1,3 +1,4 @@
+
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
@@ -15,6 +16,7 @@ def model_loaded_callback(sd_model):
 
 def ui_tabs_callback():
     res = []
+    
     for callback in callbacks_ui_tabs:
         res += callback() or []
 
diff --git a/modules/shared.py b/modules/shared.py
index 5dfd7927..6541e679 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -82,7 +82,6 @@ parser.add_argument("--api", action='store_true', help="use api=True to launch t
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-parser.add_argument("--browse-all-images", action='store_true', help="Allow browsing all images by Image Browser", default=False)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
diff --git a/modules/ui.py b/modules/ui.py
index fa42712e..a32f7259 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1104,7 +1104,7 @@ def create_ui(wrap_gradio_gpu_call):
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
                 
                 with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers] , value=shared.sd_upscalers[0].name, type="index") 
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") 
 
                 with gr.Group():
                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-- 
cgit v1.2.3


From a889c93f23f1e80d0dac4e5ddbc3a26207e8cdf1 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 24 Oct 2022 11:13:16 +0800
Subject: paste_fields add to public

---
 modules/ui.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index a32f7259..a73b9ff0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -784,6 +784,7 @@ def create_ui(wrap_gradio_gpu_call):
                 ]
             )
 
+            global txt2img_paste_fields 
             txt2img_paste_fields = [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
@@ -1054,6 +1055,7 @@ def create_ui(wrap_gradio_gpu_call):
                     outputs=[prompt, negative_prompt, style1, style2],
                 )
 
+            global img2img_paste_fields
             img2img_paste_fields = [
                 (img2img_prompt, "Prompt"),
                 (img2img_negative_prompt, "Negative prompt"),
-- 
cgit v1.2.3


From 974196932583b96b6b76632052fc0d7e70820bf3 Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sun, 23 Oct 2022 22:38:42 +0300
Subject: Save properly processed image before color correction

---
 modules/processing.py | 33 ++++++++++++++++++---------------
 1 file changed, 18 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index ff83023c..15b639e1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -46,6 +46,20 @@ def apply_color_correction(correction, image):
 
     return image
 
+def apply_overlay(overlay_exists, overlay, paste_loc, image):
+    if overlay_exists:
+        if paste_loc is not None:
+            x, y, w, h = paste_loc
+            base_image = Image.new('RGBA', (overlay.width, overlay.height))
+            image = images.resize_image(1, image, w, h)
+            base_image.paste(image, (x, y))
+            image = base_image
+
+        image = image.convert('RGBA')
+        image.alpha_composite(overlay)
+        image = image.convert('RGB')
+
+    return image
 
 def get_correct_sampler(p):
     if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
@@ -446,25 +460,14 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                     devices.torch_gc()
 
                 image = Image.fromarray(x_sample)
-
+                
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
-                        images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        image_without_cc = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
+                        images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
-                if p.overlay_images is not None and i < len(p.overlay_images):
-                    overlay = p.overlay_images[i]
-
-                    if p.paste_to is not None:
-                        x, y, w, h = p.paste_to
-                        base_image = Image.new('RGBA', (overlay.width, overlay.height))
-                        image = images.resize_image(1, image, w, h)
-                        base_image.paste(image, (x, y))
-                        image = base_image
-
-                    image = image.convert('RGBA')
-                    image.alpha_composite(overlay)
-                    image = image.convert('RGB')
+                image = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
 
                 if opts.samples_save and not p.do_not_save_samples:
                     images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
-- 
cgit v1.2.3


From f2cc3f32d5bc8538e95edec54d7dc1b9efdf769a Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sun, 23 Oct 2022 22:44:46 +0300
Subject: fix whitespaces

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 15b639e1..2a332514 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -460,7 +460,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                     devices.torch_gc()
 
                 image = Image.fromarray(x_sample)
-                
+
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
                         image_without_cc = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
-- 
cgit v1.2.3


From b297cc3324979ec78d69b2d11dd18030dfad7bcc Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 20:06:42 +0900
Subject: Hypernetworks - fix KeyError in statistics caching

Statistics logging has changed to {filename : list[losses]}, so it has to use loss_info[key].pop()
---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 98a7b62e..33827210 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -274,8 +274,8 @@ def log_statistics(loss_info:dict, key, value):
         loss_info[key] = [value]
     else:
         loss_info[key].append(value)
-        if len(loss_info) > 1024:
-            loss_info.pop(0)
+        if len(loss_info[key]) > 1024:
+            loss_info[key].pop(0)
 
 
 def statistics(data):
-- 
cgit v1.2.3


From 40b56c9289bf9458ae5ef3c1990ccea851c6c3e2 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 21:07:07 +0900
Subject: cleanup some code

---
 modules/hypernetworks/hypernetwork.py | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 33827210..4072bf54 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -16,6 +16,7 @@ from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
 
+from collections import defaultdict, deque
 from statistics import stdev, mean
 
 class HypernetworkModule(torch.nn.Module):
@@ -269,15 +270,6 @@ def stack_conds(conds):
     return torch.stack(conds)
 
 
-def log_statistics(loss_info:dict, key, value):
-    if key not in loss_info:
-        loss_info[key] = [value]
-    else:
-        loss_info[key].append(value)
-        if len(loss_info[key]) > 1024:
-            loss_info[key].pop(0)
-
-
 def statistics(data):
     total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"
     recent_data = data[-32:]
@@ -341,7 +333,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         weight.requires_grad = True
 
     size = len(ds.indexes)
-    loss_dict = {}
+    loss_dict = defaultdict(lambda : deque(maxlen = 1024))
     losses = torch.zeros((size,))
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
@@ -383,7 +375,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
             for entry in entries:
-                log_statistics(loss_dict, entry.filename, loss.item())
+                loss_dict[entry.filename].append(loss.item())
                 
             optimizer.zero_grad()
             weights[0].grad = None
-- 
cgit v1.2.3


From 348f89c8d40397c1875cff4a7331018785f9c3b8 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 21:29:53 +0900
Subject: statistics for pbar

---
 modules/hypernetworks/hypernetwork.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4072bf54..48b56029 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -335,6 +335,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     size = len(ds.indexes)
     loss_dict = defaultdict(lambda : deque(maxlen = 1024))
     losses = torch.zeros((size,))
+    previous_mean_losses = [0]
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
 
@@ -356,7 +357,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
         if len(loss_dict) > 0:
-            previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)
+            previous_mean_losses = [i[-1] for i in loss_dict.values()]
+            previous_mean_loss = mean(previous_mean_losses)
             
         scheduler.apply(optimizer, hypernetwork.step)
         if scheduler.finished:
@@ -391,7 +393,13 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
             raise RuntimeError("Loss diverged.")
-        pbar.set_description(f"dataset loss: {previous_mean_loss:.7f}")
+        
+        if len(previous_mean_losses) > 1:
+            std = stdev(previous_mean_losses)
+        else:
+            std = 0
+        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
+        pbar.set_description(dataset_loss_info)
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
             # Before saving, change name to match current checkpoint.
-- 
cgit v1.2.3


From 0d2e1dac407a0e2f5b148d314715f0457b2525b7 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 21:41:39 +0900
Subject: convert deque -> list

I don't feel this being efficient
---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 48b56029..fb510fa7 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -282,7 +282,7 @@ def report_statistics(loss_info:dict):
     for key in keys:
         try:
             print("Loss statistics for file " + key)
-            info, recent = statistics(loss_info[key])
+            info, recent = statistics(list(loss_info[key]))
             print(info)
             print(recent)
         except Exception as e:
-- 
cgit v1.2.3


From e9a410b5357612f63528015c5533c2185dcff92e Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 21:47:39 +0900
Subject: check length for variance

---
 modules/hypernetworks/hypernetwork.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index fb510fa7..d647ea55 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -271,9 +271,17 @@ def stack_conds(conds):
 
 
 def statistics(data):
-    total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"
+    if len(data) < 2:
+        std = 0
+    else:
+        std = stdev(data)
+    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
     recent_data = data[-32:]
-    recent_information = f"recent 32 loss:{mean(recent_data):.3f}"+u"\u00B1"+f"({stdev(recent_data)/ (len(recent_data)**0.5):.3f})"
+    if len(recent_data) < 2:
+        std = 0
+    else:
+        std = stdev(recent_data)
+    recent_information = f"recent 32 loss:{mean(recent_data):.3f}" + u"\u00B1" + f"({std / (len(recent_data) ** 0.5):.3f})"
     return total_information, recent_information
 
 
-- 
cgit v1.2.3


From 6cbb04f7a5e675cf1f6dfc247aa9c9e8df7dc5ce Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 09:15:26 +0300
Subject: fix #3517 breaking txt2img

---
 modules/processing.py | 33 +++++++++++++++++++--------------
 1 file changed, 19 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 2a332514..c61bbfbd 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -46,18 +46,23 @@ def apply_color_correction(correction, image):
 
     return image
 
-def apply_overlay(overlay_exists, overlay, paste_loc, image):
-    if overlay_exists:
-        if paste_loc is not None:
-            x, y, w, h = paste_loc
-            base_image = Image.new('RGBA', (overlay.width, overlay.height))
-            image = images.resize_image(1, image, w, h)
-            base_image.paste(image, (x, y))
-            image = base_image
-
-        image = image.convert('RGBA')
-        image.alpha_composite(overlay)
-        image = image.convert('RGB')
+
+def apply_overlay(image, paste_loc, index, overlays):
+    if overlays is None or index >= len(overlays):
+        return image
+
+    overlay = overlays[index]
+
+    if paste_loc is not None:
+        x, y, w, h = paste_loc
+        base_image = Image.new('RGBA', (overlay.width, overlay.height))
+        image = images.resize_image(1, image, w, h)
+        base_image.paste(image, (x, y))
+        image = base_image
+
+    image = image.convert('RGBA')
+    image.alpha_composite(overlay)
+    image = image.convert('RGB')
 
     return image
 
@@ -463,11 +468,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
-                        image_without_cc = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
+                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
                         images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
-                image = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
+                image = apply_overlay(image, p.paste_to, i, p.overlay_images)
 
                 if opts.samples_save and not p.do_not_save_samples:
                     images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
-- 
cgit v1.2.3


From 734986dde3231416813f827242c111da212b2ccb Mon Sep 17 00:00:00 2001
From: Trung Ngo <codem01@gmail.com>
Date: Mon, 24 Oct 2022 01:17:09 -0500
Subject: add callback after image is saved

---
 modules/images.py           |  3 ++-
 modules/script_callbacks.py | 12 +++++++++++-
 2 files changed, 13 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index b9589563..01c60f89 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -12,7 +12,7 @@ from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
 from fonts.ttf import Roboto
 import string
 
-from modules import sd_samplers, shared
+from modules import sd_samplers, shared, script_callbacks
 from modules.shared import opts, cmd_opts
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
@@ -467,6 +467,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     else:
         txt_fullfn = None
 
+    script_callbacks.image_saved_callback(image, p, fullfn, txt_fullfn)
     return fullfn, txt_fullfn
 
 
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 5bcccd67..5836e4b9 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -2,11 +2,12 @@
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
-
+callbacks_image_saved = []
 
 def clear_callbacks():
     callbacks_model_loaded.clear()
     callbacks_ui_tabs.clear()
+    callbacks_image_saved.clear()
 
 
 def model_loaded_callback(sd_model):
@@ -28,6 +29,10 @@ def ui_settings_callback():
         callback()
 
 
+def image_saved_callback(image, p, fullfn, txt_fullfn):
+    for callback in callbacks_image_saved:
+        callback(image, p, fullfn, txt_fullfn)
+
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
@@ -51,3 +56,8 @@ def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
     callbacks_ui_settings.append(callback)
+
+
+def on_save_imaged(callback):
+    """register a function to call after modules.images.save_image is called returning same values, original image and p """
+    callbacks_image_saved.append(callback)
-- 
cgit v1.2.3


From 876a96f0f9843382ebc8984db3de5d8af0e9ce4c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 09:39:46 +0300
Subject: remove erroneous dir in the extension directory remove loading .js
 files from scripts dir (they go into javascript) load scripts after models,
 for scripts that depend on loaded models

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index a73b9ff0..03528968 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1885,7 +1885,7 @@ def load_javascript(raw_response):
         javascript = f'<script>{jsfile.read()}</script>'
 
     scripts_list = modules.scripts.list_scripts("javascript", ".js")
-    scripts_list += modules.scripts.list_scripts("scripts", ".js")
+    
     for basedir, filename, path in scripts_list:
         with open(path, "r", encoding="utf8") as jsfile:
             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-- 
cgit v1.2.3


From 3be6b29d81408d2adb741bff5b11c80214aa621e Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Mon, 24 Oct 2022 15:14:34 +0900
Subject: indent=4 config.json

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 6541e679..d6ddfe59 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -348,7 +348,7 @@ class Options:
 
     def save(self, filename):
         with open(filename, "w", encoding="utf8") as file:
-            json.dump(self.data, file)
+            json.dump(self.data, file, indent=4)
 
     def same_type(self, x, y):
         if x is None or y is None:
-- 
cgit v1.2.3


From c5d90628a4058bf49c2fdabf620a24db73407f31 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 17:16:55 +0900
Subject: move "file_decoration" initialize section

into "if forced_filename is None:"
no need to initialize it if it's not going to be used
---
 modules/images.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index b9589563..50a59cff 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -386,18 +386,6 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         txt_fullfn (`str` or None):
             If a text file is saved for this image, this will be its full path. Otherwise None.
     '''
-    if short_filename or prompt is None or seed is None:
-        file_decoration = ""
-    elif opts.save_to_dirs:
-        file_decoration = opts.samples_filename_pattern or "[seed]"
-    else:
-        file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
-
-    if file_decoration != "":
-        file_decoration = "-" + file_decoration.lower()
-
-    file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix
-
     if extension == 'png' and opts.enable_pnginfo and info is not None:
         pnginfo = PngImagePlugin.PngInfo()
 
@@ -419,6 +407,18 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     os.makedirs(path, exist_ok=True)
 
     if forced_filename is None:
+        if short_filename or prompt is None or seed is None:
+            file_decoration = ""
+        elif opts.save_to_dirs:
+            file_decoration = opts.samples_filename_pattern or "[seed]"
+        else:
+            file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
+
+        if file_decoration != "":
+            file_decoration = "-" + file_decoration.lower()
+
+        file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix
+
         basecount = get_next_sequence_number(path, basename)
         fullfn = "a.png"
         fullfn_without_extension = "a"
-- 
cgit v1.2.3


From 7d4a4db9ea7543c079f4a4a702c2945f4b66cd11 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 17:48:59 +0900
Subject: modify unnecessary sting assignment as it's going to get overwritten

---
 modules/images.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 50a59cff..cc5066b1 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -420,8 +420,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix
 
         basecount = get_next_sequence_number(path, basename)
-        fullfn = "a.png"
-        fullfn_without_extension = "a"
+        fullfn = None
+        fullfn_without_extension = None
         for i in range(500):
             fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
             fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
-- 
cgit v1.2.3


From 37dd6deafb831a809eaf7ae8d232937a8c7998e7 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 21:11:15 +0900
Subject: filename pattern [datetime], extended customizable Format and Time
 Zone

format:
[datetime]
[datetime<Format>]
[datetime<Format><Time Zone>]
---
 modules/images.py | 54 +++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 53 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index cc5066b1..fb0b373d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -1,4 +1,5 @@
 import datetime
+import pytz
 import io
 import math
 import os
@@ -302,7 +303,7 @@ def apply_filename_pattern(x, p, seed, prompt):
 
     x = x.replace("[model_hash]", getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash))
     x = x.replace("[date]", datetime.date.today().isoformat())
-    x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
+    x = replace_datetime(x)
     x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp))
 
     # Apply [prompt] at last. Because it may contain any replacement word.^M
@@ -353,6 +354,57 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
+def replace_datetime(input_str: str):
+    """
+    Formats sub_string of input_str with formatted datetime with time zone support.
+    accepts sub_string format: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
+    case insensitive
+
+    e.g.
+    input: "___[Datetime<%Y_%m_%d %H-%M-%S><Asia/Tokyo>]___"
+    return: "___2022_10_22 20-40-14___"
+
+    handles invalid Formats and Time Zones
+
+    time format reference:
+    https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes
+
+    valid time zones
+    print(pytz.all_timezones)
+    https://pytz.sourceforge.net/
+    """
+    default_time_format = '%Y%m%d%H%M%S'
+    time_now = datetime.datetime.now()
+
+    # match all datetime to be replace
+    match_itr = re.finditer(r'\[datetime(?:<([^>]*)>(?:<([^>]*)>)?)?]', input_str, re.IGNORECASE)
+    for match in reversed(list(match_itr)):
+        # extract format
+        time_format = match.group(1)
+        if time_format == '':
+            # if time_format is blank use default YYYYMMDDHHMMSS
+            time_format = default_time_format
+
+        # extract timezone
+        try:
+            time_zone = pytz.timezone(match.group(2))
+        except pytz.exceptions.UnknownTimeZoneError as _:
+            # if no time_zone or invalid, use system time
+            time_zone = None
+
+        # generate time string
+        time_zone_time = time_now.astimezone(time_zone)
+        try:
+            formatted_time = time_zone_time.strftime(time_format)
+
+        except (ValueError, TypeError) as _:
+            # if format error then use default_time_format
+            formatted_time = time_zone_time.strftime(default_time_format)
+
+        input_str = input_str[:match.start()] + formatted_time + input_str[match.end():]
+    return input_str
+
+
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
     '''Save an image.
 
-- 
cgit v1.2.3


From 480d8e764611d7779bf8e0245640a6b84af626f9 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 22:26:57 +0900
Subject: replace "srt.replace()" in apply_filename_pattern() with equivalent
 re.sub()

the file_decoration passed into apply_filename_pattern() is formatted to lowercase to increase compatibility
the use of case sensitive srt.replace()

but because the newly implemented "time format" is case sensitive
the lowercasing the file_decoration will cause time format to be broken

in order to resolve this issue
I decided to replace every srt.replace() and in if "str" in x to regular expression (case insensitive) equivalent
---
 modules/images.py | 35 +++++++++++++++++------------------
 1 file changed, 17 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index fb0b373d..55d8ab5e 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -291,25 +291,24 @@ def apply_filename_pattern(x, p, seed, prompt):
     max_prompt_words = opts.directories_max_prompt_words
 
     if seed is not None:
-        x = x.replace("[seed]", str(seed))
+        x = re.sub(r'\[seed]', str(seed), x, flags=re.IGNORECASE)
 
     if p is not None:
-        x = x.replace("[steps]", str(p.steps))
-        x = x.replace("[cfg]", str(p.cfg_scale))
-        x = x.replace("[width]", str(p.width))
-        x = x.replace("[height]", str(p.height))
-        x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
-        x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
-
-    x = x.replace("[model_hash]", getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash))
-    x = x.replace("[date]", datetime.date.today().isoformat())
+        x = re.sub(r'\[steps]', str(p.steps), x, flags=re.IGNORECASE)
+        x = re.sub(r'\[cfg]', str(p.cfg_scale), x, flags=re.IGNORECASE)
+        x = re.sub(r'\[width]', str(p.width), x, flags=re.IGNORECASE)
+        x = re.sub(r'\[height]', str(p.height), x, flags=re.IGNORECASE)
+        x = re.sub(r'\[styles]', sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False), x, flags=re.IGNORECASE)
+        x = re.sub(r'\[sampler]', sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False), x, flags=re.IGNORECASE)
+
+    x = re.sub(r'\[model_hash]', getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash), x, flags=re.IGNORECASE)
+    x = re.sub(r'\[date]', datetime.date.today().isoformat(), x, flags=re.IGNORECASE)
     x = replace_datetime(x)
-    x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp))
-
+    x = re.sub(r'\[job_timestamp]', getattr(p, "job_timestamp", shared.state.job_timestamp), x, flags=re.IGNORECASE)
     # Apply [prompt] at last. Because it may contain any replacement word.^M
     if prompt is not None:
-        x = x.replace("[prompt]", sanitize_filename_part(prompt))
-        if "[prompt_no_styles]" in x:
+        x = re.sub(r'\[prompt]', sanitize_filename_part(prompt), x, flags=re.IGNORECASE)
+        if re.search(r'\[prompt_no_styles]', x, re.IGNORECASE):
             prompt_no_style = prompt
             for style in shared.prompt_styles.get_style_prompts(p.styles):
                 if len(style) > 0:
@@ -317,14 +316,14 @@ def apply_filename_pattern(x, p, seed, prompt):
                     for part in style_parts:
                         prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
             prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
-            x = x.replace("[prompt_no_styles]", sanitize_filename_part(prompt_no_style, replace_spaces=False))
+            x = re.sub(r'\[prompt_no_styles]', sanitize_filename_part(prompt_no_style, replace_spaces=False), x, flags=re.IGNORECASE)
 
-        x = x.replace("[prompt_spaces]", sanitize_filename_part(prompt, replace_spaces=False))
-        if "[prompt_words]" in x:
+        x = re.sub(r'\[prompt_spaces]', sanitize_filename_part(prompt, replace_spaces=False), x, flags=re.IGNORECASE)
+        if re.search(r'\[prompt_words]', x, re.IGNORECASE):
             words = [x for x in re_nonletters.split(prompt or "") if len(x) > 0]
             if len(words) == 0:
                 words = ["empty"]
-            x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))
+            x = re.sub(r'\[prompt_words]', sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False), x, flags=re.IGNORECASE)
 
     if cmd_opts.hide_ui_dir_config:
         x = re.sub(r'^[\\/]+|\.{2,}[\\/]+|[\\/]+\.{2,}', '', x)
-- 
cgit v1.2.3


From 00952fb4a8b86b75402cc8276e00f3daadf1f459 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 22:31:02 +0900
Subject: add sanitize_filename() to datetime

---
 modules/images.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 55d8ab5e..898b065b 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -400,6 +400,7 @@ def replace_datetime(input_str: str):
             # if format error then use default_time_format
             formatted_time = time_zone_time.strftime(default_time_format)
 
+        formatted_time = sanitize_filename_part(formatted_time, replace_spaces=False)
         input_str = input_str[:match.start()] + formatted_time + input_str[match.end():]
     return input_str
 
-- 
cgit v1.2.3


From 8f6af4ed651cdb4456e2f3474d05dd3a18086ac2 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 22:32:26 +0900
Subject: remove lowercasing file_decoration as it is not needed anymore

---
 modules/images.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 898b065b..321439e3 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -467,7 +467,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
 
         if file_decoration != "":
-            file_decoration = "-" + file_decoration.lower()
+            file_decoration = "-" + file_decoration
 
         file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix
 
-- 
cgit v1.2.3


From 5a981310e68253c77f9fe3144d247cfd531f9e48 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sun, 23 Oct 2022 18:09:21 +0900
Subject: replace_datetime() can now accept a datetime parameter

---
 modules/images.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 321439e3..ff00fc52 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -303,7 +303,7 @@ def apply_filename_pattern(x, p, seed, prompt):
 
     x = re.sub(r'\[model_hash]', getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash), x, flags=re.IGNORECASE)
     x = re.sub(r'\[date]', datetime.date.today().isoformat(), x, flags=re.IGNORECASE)
-    x = replace_datetime(x)
+    x = replace_datetime(x, datetime.datetime.now())
     x = re.sub(r'\[job_timestamp]', getattr(p, "job_timestamp", shared.state.job_timestamp), x, flags=re.IGNORECASE)
     # Apply [prompt] at last. Because it may contain any replacement word.^M
     if prompt is not None:
@@ -353,8 +353,14 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
-def replace_datetime(input_str: str):
+def replace_datetime(input_str: str, time_datetime: datetime.datetime = None):
     """
+    Args:
+        input_str (`str`):
+            the String to be Formatted
+        time_datetime (`datetime.datetime`)
+            the time to be used, if None, use datetime.datetime.now()
+
     Formats sub_string of input_str with formatted datetime with time zone support.
     accepts sub_string format: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
     case insensitive
@@ -373,8 +379,8 @@ def replace_datetime(input_str: str):
     https://pytz.sourceforge.net/
     """
     default_time_format = '%Y%m%d%H%M%S'
-    time_now = datetime.datetime.now()
-
+    if time_datetime is None:
+        time_datetime = datetime.datetime.now()
     # match all datetime to be replace
     match_itr = re.finditer(r'\[datetime(?:<([^>]*)>(?:<([^>]*)>)?)?]', input_str, re.IGNORECASE)
     for match in reversed(list(match_itr)):
@@ -392,7 +398,7 @@ def replace_datetime(input_str: str):
             time_zone = None
 
         # generate time string
-        time_zone_time = time_now.astimezone(time_zone)
+        time_zone_time = time_datetime.astimezone(time_zone)
         try:
             formatted_time = time_zone_time.strftime(time_format)
 
-- 
cgit v1.2.3


From eb007e5884c23fbc38d7e9d1dd3669625270ca27 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sun, 23 Oct 2022 18:17:39 +0900
Subject: use the same datetime object for [date] and [datetime]

---
 modules/images.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index ff00fc52..a9b1330d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -302,8 +302,9 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = re.sub(r'\[sampler]', sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False), x, flags=re.IGNORECASE)
 
     x = re.sub(r'\[model_hash]', getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash), x, flags=re.IGNORECASE)
-    x = re.sub(r'\[date]', datetime.date.today().isoformat(), x, flags=re.IGNORECASE)
-    x = replace_datetime(x, datetime.datetime.now())
+    current_time = datetime.datetime.now()
+    x = re.sub(r'\[date]', current_time.strftime('%Y-%m-%d'), x, flags=re.IGNORECASE)
+    x = replace_datetime(x, current_time)  # replace [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
     x = re.sub(r'\[job_timestamp]', getattr(p, "job_timestamp", shared.state.job_timestamp), x, flags=re.IGNORECASE)
     # Apply [prompt] at last. Because it may contain any replacement word.^M
     if prompt is not None:
-- 
cgit v1.2.3


From 994aaadf0861366b9e6f219e1a3c25a233fbb63c Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 24 Oct 2022 16:44:36 +0800
Subject: a strange bug

---
 modules/ui.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index a73b9ff0..8c6dc026 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -55,6 +55,7 @@ mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
 txt2img_paste_fields = []
 img2img_paste_fields = []
+init_img_components = {}
 
 
 if not cmd_opts.share and not cmd_opts.listen:
@@ -1174,6 +1175,9 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[init_img_with_mask],
         )
 
+    global  init_img_components
+    init_img_components = {"img2img":init_img, "inpaint":init_img_with_mask, "extras":extras_image}
+
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
-- 
cgit v1.2.3


From 8da1bd48bf9d0411cd9ba87b8d9220743cb5807e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 14:03:58 +0300
Subject: add an option to skip adding number to filenames when saving. rework
 filename pattern function go through the pattern once and not calculate any
 of replacements until they are actually encountered in the pattern.

---
 modules/images.py | 250 ++++++++++++++++++++++++++++--------------------------
 modules/shared.py |   8 +-
 2 files changed, 135 insertions(+), 123 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index a9b1330d..848ede75 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -1,4 +1,7 @@
 import datetime
+import sys
+import traceback
+
 import pytz
 import io
 import math
@@ -274,10 +277,15 @@ invalid_filename_chars = '<>:"/\\|?*\n'
 invalid_filename_prefix = ' '
 invalid_filename_postfix = ' .'
 re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
+re_pattern = re.compile(r"([^\[\]]+|\[([^]]+)]|[\[\]]*)")
+re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
 max_filename_part_length = 128
 
 
 def sanitize_filename_part(text, replace_spaces=True):
+    if text is None:
+        return None
+
     if replace_spaces:
         text = text.replace(' ', '_')
 
@@ -287,49 +295,103 @@ def sanitize_filename_part(text, replace_spaces=True):
     return text
 
 
-def apply_filename_pattern(x, p, seed, prompt):
-    max_prompt_words = opts.directories_max_prompt_words
-
-    if seed is not None:
-        x = re.sub(r'\[seed]', str(seed), x, flags=re.IGNORECASE)
-
-    if p is not None:
-        x = re.sub(r'\[steps]', str(p.steps), x, flags=re.IGNORECASE)
-        x = re.sub(r'\[cfg]', str(p.cfg_scale), x, flags=re.IGNORECASE)
-        x = re.sub(r'\[width]', str(p.width), x, flags=re.IGNORECASE)
-        x = re.sub(r'\[height]', str(p.height), x, flags=re.IGNORECASE)
-        x = re.sub(r'\[styles]', sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False), x, flags=re.IGNORECASE)
-        x = re.sub(r'\[sampler]', sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False), x, flags=re.IGNORECASE)
-
-    x = re.sub(r'\[model_hash]', getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash), x, flags=re.IGNORECASE)
-    current_time = datetime.datetime.now()
-    x = re.sub(r'\[date]', current_time.strftime('%Y-%m-%d'), x, flags=re.IGNORECASE)
-    x = replace_datetime(x, current_time)  # replace [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
-    x = re.sub(r'\[job_timestamp]', getattr(p, "job_timestamp", shared.state.job_timestamp), x, flags=re.IGNORECASE)
-    # Apply [prompt] at last. Because it may contain any replacement word.^M
-    if prompt is not None:
-        x = re.sub(r'\[prompt]', sanitize_filename_part(prompt), x, flags=re.IGNORECASE)
-        if re.search(r'\[prompt_no_styles]', x, re.IGNORECASE):
-            prompt_no_style = prompt
-            for style in shared.prompt_styles.get_style_prompts(p.styles):
-                if len(style) > 0:
-                    style_parts = [y for y in style.split("{prompt}")]
-                    for part in style_parts:
-                        prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
-            prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
-            x = re.sub(r'\[prompt_no_styles]', sanitize_filename_part(prompt_no_style, replace_spaces=False), x, flags=re.IGNORECASE)
-
-        x = re.sub(r'\[prompt_spaces]', sanitize_filename_part(prompt, replace_spaces=False), x, flags=re.IGNORECASE)
-        if re.search(r'\[prompt_words]', x, re.IGNORECASE):
-            words = [x for x in re_nonletters.split(prompt or "") if len(x) > 0]
-            if len(words) == 0:
-                words = ["empty"]
-            x = re.sub(r'\[prompt_words]', sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False), x, flags=re.IGNORECASE)
-
-    if cmd_opts.hide_ui_dir_config:
-        x = re.sub(r'^[\\/]+|\.{2,}[\\/]+|[\\/]+\.{2,}', '', x)
-
-    return x
+class FilenameGenerator:
+    replacements = {
+        'seed': lambda self: self.seed if self.seed is not None else '',
+        'steps': lambda self:  self.p and self.p.steps,
+        'cfg': lambda self: self.p and self.p.cfg_scale,
+        'width': lambda self: self.p and self.p.width,
+        'height': lambda self: self.p and self.p.height,
+        'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
+        'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
+        'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
+        'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
+        'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
+        'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
+        'prompt': lambda self: sanitize_filename_part(self.prompt),
+        'prompt_no_styles': lambda self: self.prompt_no_style(),
+        'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
+        'prompt_words': lambda self: self.prompt_words(),
+    }
+    default_time_format = '%Y%m%d%H%M%S'
+
+    def __init__(self, p, seed, prompt):
+        self.p = p
+        self.seed = seed
+        self.prompt = prompt
+
+    def prompt_no_style(self):
+        if self.p is None or self.prompt is None:
+            return None
+
+        prompt_no_style = self.prompt
+        for style in shared.prompt_styles.get_style_prompts(self.p.styles):
+            if len(style) > 0:
+                for part in style.split("{prompt}"):
+                    prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
+
+                prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
+
+        return sanitize_filename_part(prompt_no_style, replace_spaces=False)
+
+    def prompt_words(self):
+        words = [x for x in re_nonletters.split(self.prompt or "") if len(x) > 0]
+        if len(words) == 0:
+            words = ["empty"]
+        return sanitize_filename_part(" ".join(words[0:opts.directories_max_prompt_words]), replace_spaces=False)
+
+    def datetime(self, *args):
+        time_datetime = datetime.datetime.now()
+
+        time_format = args[0] if len(args) > 0 else self.default_time_format
+        time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
+
+        time_zone_time = time_datetime.astimezone(time_zone)
+        try:
+            formatted_time = time_zone_time.strftime(time_format)
+        except (ValueError, TypeError) as _:
+            formatted_time = time_zone_time.strftime(self.default_time_format)
+
+        return sanitize_filename_part(formatted_time, replace_spaces=False)
+
+    def apply(self, x):
+        res = ''
+
+        for m in re_pattern.finditer(x):
+            text, pattern = m.groups()
+
+            if pattern is None:
+                res += text
+                continue
+
+            pattern_args = []
+            while True:
+                m = re_pattern_arg.match(pattern)
+                if m is None:
+                    break
+
+                pattern, arg = m.groups()
+                pattern_args.insert(0, arg)
+
+            fun = self.replacements.get(pattern.lower())
+            if fun is not None:
+                try:
+                    replacement = fun(self, *pattern_args)
+                except Exception:
+                    replacement = None
+                    print(f"Error adding [{pattern}] to filename", file=sys.stderr)
+                    print(traceback.format_exc(), file=sys.stderr)
+
+                if replacement is None:
+                    res += f'[{pattern}]'
+                else:
+                    res += str(replacement)
+
+                continue
+
+            res += f'[{pattern}]'
+
+        return res
 
 
 def get_next_sequence_number(path, basename):
@@ -354,66 +416,8 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
-def replace_datetime(input_str: str, time_datetime: datetime.datetime = None):
-    """
-    Args:
-        input_str (`str`):
-            the String to be Formatted
-        time_datetime (`datetime.datetime`)
-            the time to be used, if None, use datetime.datetime.now()
-
-    Formats sub_string of input_str with formatted datetime with time zone support.
-    accepts sub_string format: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
-    case insensitive
-
-    e.g.
-    input: "___[Datetime<%Y_%m_%d %H-%M-%S><Asia/Tokyo>]___"
-    return: "___2022_10_22 20-40-14___"
-
-    handles invalid Formats and Time Zones
-
-    time format reference:
-    https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes
-
-    valid time zones
-    print(pytz.all_timezones)
-    https://pytz.sourceforge.net/
-    """
-    default_time_format = '%Y%m%d%H%M%S'
-    if time_datetime is None:
-        time_datetime = datetime.datetime.now()
-    # match all datetime to be replace
-    match_itr = re.finditer(r'\[datetime(?:<([^>]*)>(?:<([^>]*)>)?)?]', input_str, re.IGNORECASE)
-    for match in reversed(list(match_itr)):
-        # extract format
-        time_format = match.group(1)
-        if time_format == '':
-            # if time_format is blank use default YYYYMMDDHHMMSS
-            time_format = default_time_format
-
-        # extract timezone
-        try:
-            time_zone = pytz.timezone(match.group(2))
-        except pytz.exceptions.UnknownTimeZoneError as _:
-            # if no time_zone or invalid, use system time
-            time_zone = None
-
-        # generate time string
-        time_zone_time = time_datetime.astimezone(time_zone)
-        try:
-            formatted_time = time_zone_time.strftime(time_format)
-
-        except (ValueError, TypeError) as _:
-            # if format error then use default_time_format
-            formatted_time = time_zone_time.strftime(default_time_format)
-
-        formatted_time = sanitize_filename_part(formatted_time, replace_spaces=False)
-        input_str = input_str[:match.start()] + formatted_time + input_str[match.end():]
-    return input_str
-
-
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
-    '''Save an image.
+    """Save an image.
 
     Args:
         image (`PIL.Image`):
@@ -444,7 +448,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             The full path of the saved imaged.
         txt_fullfn (`str` or None):
             If a text file is saved for this image, this will be its full path. Otherwise None.
-    '''
+    """
+    namegen = FilenameGenerator(p, seed, prompt)
+
     if extension == 'png' and opts.enable_pnginfo and info is not None:
         pnginfo = PngImagePlugin.PngInfo()
 
@@ -460,33 +466,37 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
     if save_to_dirs:
-        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
+        dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')
         path = os.path.join(path, dirname)
 
     os.makedirs(path, exist_ok=True)
 
     if forced_filename is None:
-        if short_filename or prompt is None or seed is None:
+        if short_filename or seed is None:
             file_decoration = ""
-        elif opts.save_to_dirs:
-            file_decoration = opts.samples_filename_pattern or "[seed]"
         else:
-            file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
+            file_decoration = opts.samples_filename_pattern or "[seed]"
+
+        add_number = opts.save_images_add_number or file_decoration == ''
 
-        if file_decoration != "":
+        if file_decoration != "" and add_number:
             file_decoration = "-" + file_decoration
 
-        file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix
-
-        basecount = get_next_sequence_number(path, basename)
-        fullfn = None
-        fullfn_without_extension = None
-        for i in range(500):
-            fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
-            fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
-            fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
-            if not os.path.exists(fullfn):
-                break
+        file_decoration = namegen.apply(file_decoration) + suffix
+
+        if add_number:
+            basecount = get_next_sequence_number(path, basename)
+            fullfn = None
+            fullfn_without_extension = None
+            for i in range(500):
+                fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
+                fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
+                fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
+                if not os.path.exists(fullfn):
+                    break
+        else:
+            fullfn = os.path.join(path, f"{file_decoration}.{extension}")
+            fullfn_without_extension = os.path.join(path, file_decoration)
     else:
         fullfn = os.path.join(path, f"{forced_filename}.{extension}")
         fullfn_without_extension = os.path.join(path, forced_filename)
diff --git a/modules/shared.py b/modules/shared.py
index d6ddfe59..76cbb1bd 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -86,6 +86,7 @@ parser.add_argument("--device-id", type=str, help="Select the default CUDA devic
 cmd_opts = parser.parse_args()
 restricted_opts = [
     "samples_filename_pattern",
+    "directories_filename_pattern",
     "outdir_samples",
     "outdir_txt2img_samples",
     "outdir_img2img_samples",
@@ -190,7 +191,8 @@ options_templates = {}
 options_templates.update(options_section(('saving-images', "Saving images/grids"), {
     "samples_save": OptionInfo(True, "Always save all generated images"),
     "samples_format": OptionInfo('png', 'File format for images'),
-    "samples_filename_pattern": OptionInfo("", "Images filename pattern"),
+    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
+    "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
 
     "grid_save": OptionInfo(True, "Always save all generated image grids"),
     "grid_format": OptionInfo('png', 'File format for grids'),
@@ -225,8 +227,8 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
     "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
     "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
     "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
-    "directories_filename_pattern": OptionInfo("", "Directory name pattern"),
-    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
+    "directories_filename_pattern": OptionInfo("", "Directory name pattern", component_args=hide_dirs),
+    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
 }))
 
 options_templates.update(options_section(('upscaling', "Upscaling"), {
-- 
cgit v1.2.3


From 2c05e06ea770b013de55636f15aae0b8e60e0590 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 14:11:14 +0300
Subject: rename api/processing to api/models for #3511

---
 modules/api/api.py        |   2 +-
 modules/api/models.py     | 106 ++++++++++++++++++++++++++++++++++++++++++++++
 modules/api/processing.py | 106 ----------------------------------------------
 3 files changed, 107 insertions(+), 107 deletions(-)
 create mode 100644 modules/api/models.py
 delete mode 100644 modules/api/processing.py

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 3caa83a4..a860a964 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,4 +1,4 @@
-from modules.api.processing import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
+from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
 from modules.extras import run_pnginfo
diff --git a/modules/api/models.py b/modules/api/models.py
new file mode 100644
index 00000000..f551fa35
--- /dev/null
+++ b/modules/api/models.py
@@ -0,0 +1,106 @@
+from array import array
+from inflection import underscore
+from typing import Any, Dict, Optional
+from pydantic import BaseModel, Field, create_model
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
+import inspect
+
+
+API_NOT_ALLOWED = [
+    "self",
+    "kwargs",
+    "sd_model",
+    "outpath_samples",
+    "outpath_grids",
+    "sampler_index",
+    "do_not_save_samples",
+    "do_not_save_grid",
+    "extra_generation_params",
+    "overlay_images",
+    "do_not_reload_embeddings",
+    "seed_enable_extras",
+    "prompt_for_display",
+    "sampler_noise_scheduler_override",
+    "ddim_discretize"
+]
+
+class ModelDef(BaseModel):
+    """Assistance Class for Pydantic Dynamic Model Generation"""
+
+    field: str
+    field_alias: str
+    field_type: Any
+    field_value: Any
+
+
+class PydanticModelGenerator:
+    """
+    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
+    source_data is a snapshot of the default values produced by the class
+    params are the names of the actual keys required by __init__
+    """
+
+    def __init__(
+        self,
+        model_name: str = None,
+        class_instance = None,
+        additional_fields = None,
+    ):
+        def field_type_generator(k, v):
+            # field_type = str if not overrides.get(k) else overrides[k]["type"]
+            # print(k, v.annotation, v.default)
+            field_type = v.annotation
+            
+            return Optional[field_type]
+        
+        def merge_class_params(class_):
+            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
+            parameters = {}
+            for classes in all_classes:
+                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
+            return parameters
+            
+                
+        self._model_name = model_name
+        self._class_data = merge_class_params(class_instance)
+        self._model_def = [
+            ModelDef(
+                field=underscore(k),
+                field_alias=k,
+                field_type=field_type_generator(k, v),
+                field_value=v.default
+            )
+            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
+        ]
+        
+        for fields in additional_fields:
+            self._model_def.append(ModelDef(
+                field=underscore(fields["key"]), 
+                field_alias=fields["key"], 
+                field_type=fields["type"],
+                field_value=fields["default"]))
+
+    def generate_model(self):
+        """
+        Creates a pydantic BaseModel
+        from the json and overrides provided at initialization
+        """
+        fields = {
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
+        }
+        DynamicModel = create_model(self._model_name, **fields)
+        DynamicModel.__config__.allow_population_by_field_name = True
+        DynamicModel.__config__.allow_mutation = True
+        return DynamicModel
+    
+StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingTxt2Img", 
+    StableDiffusionProcessingTxt2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()
+
+StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingImg2Img", 
+    StableDiffusionProcessingImg2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
+).generate_model()
\ No newline at end of file
diff --git a/modules/api/processing.py b/modules/api/processing.py
deleted file mode 100644
index f551fa35..00000000
--- a/modules/api/processing.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from array import array
-from inflection import underscore
-from typing import Any, Dict, Optional
-from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
-import inspect
-
-
-API_NOT_ALLOWED = [
-    "self",
-    "kwargs",
-    "sd_model",
-    "outpath_samples",
-    "outpath_grids",
-    "sampler_index",
-    "do_not_save_samples",
-    "do_not_save_grid",
-    "extra_generation_params",
-    "overlay_images",
-    "do_not_reload_embeddings",
-    "seed_enable_extras",
-    "prompt_for_display",
-    "sampler_noise_scheduler_override",
-    "ddim_discretize"
-]
-
-class ModelDef(BaseModel):
-    """Assistance Class for Pydantic Dynamic Model Generation"""
-
-    field: str
-    field_alias: str
-    field_type: Any
-    field_value: Any
-
-
-class PydanticModelGenerator:
-    """
-    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
-    source_data is a snapshot of the default values produced by the class
-    params are the names of the actual keys required by __init__
-    """
-
-    def __init__(
-        self,
-        model_name: str = None,
-        class_instance = None,
-        additional_fields = None,
-    ):
-        def field_type_generator(k, v):
-            # field_type = str if not overrides.get(k) else overrides[k]["type"]
-            # print(k, v.annotation, v.default)
-            field_type = v.annotation
-            
-            return Optional[field_type]
-        
-        def merge_class_params(class_):
-            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
-            parameters = {}
-            for classes in all_classes:
-                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
-            return parameters
-            
-                
-        self._model_name = model_name
-        self._class_data = merge_class_params(class_instance)
-        self._model_def = [
-            ModelDef(
-                field=underscore(k),
-                field_alias=k,
-                field_type=field_type_generator(k, v),
-                field_value=v.default
-            )
-            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
-        ]
-        
-        for fields in additional_fields:
-            self._model_def.append(ModelDef(
-                field=underscore(fields["key"]), 
-                field_alias=fields["key"], 
-                field_type=fields["type"],
-                field_value=fields["default"]))
-
-    def generate_model(self):
-        """
-        Creates a pydantic BaseModel
-        from the json and overrides provided at initialization
-        """
-        fields = {
-            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
-        }
-        DynamicModel = create_model(self._model_name, **fields)
-        DynamicModel.__config__.allow_population_by_field_name = True
-        DynamicModel.__config__.allow_mutation = True
-        return DynamicModel
-    
-StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingTxt2Img", 
-    StableDiffusionProcessingTxt2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}]
-).generate_model()
-
-StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingImg2Img", 
-    StableDiffusionProcessingImg2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
-).generate_model()
\ No newline at end of file
-- 
cgit v1.2.3


From 595dca85af9e26b5d76cd64659a5bdd9da4f2b89 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Mon, 24 Oct 2022 08:32:18 -0300
Subject: Reverse run_extras change

Update serialization on the batch images endpoint
---
 modules/api/api.py    | 7 ++++++-
 modules/api/models.py | 8 ++++----
 modules/extras.py     | 2 +-
 3 files changed, 11 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 799e3701..67b783de 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -109,7 +109,12 @@ class Api:
     def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
         reqDict = setUpscalers(req)
 
-        reqDict['image_folder'] = list(map(decode_base64_to_file, reqDict['imageList']))
+        def prepareFiles(file):
+            file = decode_base64_to_file(file.data, file_path=file.name)
+            file.orig_name = file.name
+            return file
+
+        reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
         reqDict.pop('imageList')
 
         with self.queue_lock:
diff --git a/modules/api/models.py b/modules/api/models.py
index e461d397..fca2f991 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -138,12 +138,12 @@ class ExtrasSingleImageRequest(ExtrasBaseRequest):
 class ExtrasSingleImageResponse(ExtraBaseResponse):
     image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
 
-class ImageItem(BaseModel):
-    data: str = Field(title="image data")
-    name: str = Field(title="filename")
+class FileData(BaseModel):
+    data: str = Field(title="File data", description="Base64 representation of the file")
+    name: str = Field(title="File name")
 
 class ExtrasBatchImagesRequest(ExtrasBaseRequest):
-    imageList: list[str] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+    imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
     images: list[str] = Field(title="Images", description="The generated images in base64 format.")
diff --git a/modules/extras.py b/modules/extras.py
index 29ac312e..22c5a1c1 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -33,7 +33,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         for img in image_folder:
             image = Image.open(img)
             imageArr.append(image)
-            imageNameArr.append(os.path.splitext(img.name)[0])
+            imageNameArr.append(os.path.splitext(img.orig_name)[0])
     elif extras_mode == 2:
         assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
 
-- 
cgit v1.2.3


From 0c0028a9d3be4fc98bbdf975a6e0daa663bbed2d Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Mon, 24 Oct 2022 21:13:36 +0900
Subject: UnknownTimeZoneError

---
 modules/images.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 848ede75..9a8fe3ed 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -344,7 +344,10 @@ class FilenameGenerator:
         time_datetime = datetime.datetime.now()
 
         time_format = args[0] if len(args) > 0 else self.default_time_format
-        time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
+        try:
+            time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
+        except pytz.exceptions.UnknownTimeZoneError as _:
+            time_zone = None
 
         time_zone_time = time_datetime.astimezone(time_zone)
         try:
-- 
cgit v1.2.3


From b38370275275bf6e11575000f39c50c6e90b1f7a Mon Sep 17 00:00:00 2001
From: ritosonn <ritosonn@gmail.com>
Date: Fri, 21 Oct 2022 23:46:32 +0900
Subject: fix #3145 #3093

---
 modules/sd_samplers.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 0b408a70..3670b57d 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -228,7 +228,7 @@ class VanillaStableDiffusionSampler:
             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
             
             
-        samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
+        samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
 
         return samples
 
@@ -429,7 +429,7 @@ class KDiffusionSampler:
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
 
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
             'cond': conditioning, 
             'image_cond': image_conditioning, 
             'uncond': unconditional_conditioning, 
-- 
cgit v1.2.3


From 77a320f406a76425176b8ca4c034c362b6734713 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 17:23:51 +0300
Subject: do not stop execution when script's callback misbehaves and report
 which script it was

---
 modules/script_callbacks.py | 45 ++++++++++++++++++++++++++++++++++++---------
 1 file changed, 36 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index f46d3d9a..5e9a6d4b 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -1,4 +1,15 @@
+import sys
+import traceback
+from collections import namedtuple
+import inspect
 
+
+def report_exception(c, job):
+    print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
+    print(traceback.format_exc(), file=sys.stderr)
+
+
+ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
@@ -10,28 +21,44 @@ def clear_callbacks():
 
 
 def model_loaded_callback(sd_model):
-    for callback in callbacks_model_loaded:
-        callback(sd_model)
+    for c in callbacks_model_loaded:
+        try:
+            c.callback(sd_model)
+        except Exception:
+            report_exception(c, 'model_loaded_callback')
 
 
 def ui_tabs_callback():
     res = []
     
-    for callback in callbacks_ui_tabs:
-        res += callback() or []
+    for c in callbacks_ui_tabs:
+        try:
+            res += c.callback() or []
+        except Exception:
+            report_exception(c, 'ui_tabs_callback')
 
     return res
 
 
 def ui_settings_callback():
-    for callback in callbacks_ui_settings:
-        callback()
+    for c in callbacks_ui_settings:
+        try:
+            c.callback()
+        except Exception:
+            report_exception(c, 'ui_settings_callback')
+
+
+def add_callback(callbacks, fun):
+    stack = [x for x in inspect.stack() if x.filename != __file__]
+    filename = stack[0].filename if len(stack) > 0 else 'unknown file'
+
+    callbacks.append(ScriptCallback(filename, fun))
 
 
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
-    callbacks_model_loaded.append(callback)
+    add_callback(callbacks_model_loaded, callback)
 
 
 def on_ui_tabs(callback):
@@ -44,10 +71,10 @@ def on_ui_tabs(callback):
     title is tab text displayed to user in the UI
     elem_id is HTML id for the tab
     """
-    callbacks_ui_tabs.append(callback)
+    add_callback(callbacks_ui_tabs, callback)
 
 
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
-    callbacks_ui_settings.append(callback)
+    add_callback(callbacks_ui_settings, callback)
-- 
cgit v1.2.3


From 4c24347e45776d505937856ab280548d9298f0a8 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 24 Oct 2022 23:04:50 -0400
Subject: Remove BSRGAN from --use-cpu, add SwinIR

---
 modules/devices.py      |  2 +-
 modules/shared.py       |  6 +++---
 modules/swinir_model.py | 12 ++++++------
 3 files changed, 10 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index dc1f3cdd..033a42d5 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -45,7 +45,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = None
+device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
 
diff --git a/modules/shared.py b/modules/shared.py
index 76cbb1bd..308fccce 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -58,7 +58,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en
 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -96,8 +96,8 @@ restricted_opts = [
     "outdir_save",
 ]
 
-devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
-(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
+(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
 
 device = devices.device
 weight_load_location = None if cmd_opts.lowram else "cpu"
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index baa02e3d..facd262d 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -7,8 +7,8 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 from tqdm import tqdm
 
-from modules import modelloader
-from modules.shared import cmd_opts, opts, device
+from modules import modelloader, devices
+from modules.shared import cmd_opts, opts
 from modules.swinir_model_arch import SwinIR as net
 from modules.swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
@@ -42,7 +42,7 @@ class UpscalerSwinIR(Upscaler):
         model = self.load_model(model_file)
         if model is None:
             return img
-        model = model.to(device)
+        model = model.to(devices.device_swinir)
         img = upscale(img, model)
         try:
             torch.cuda.empty_cache()
@@ -111,7 +111,7 @@ def upscale(
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(device)
+    img = img.unsqueeze(0).to(devices.device_swinir)
     with torch.no_grad(), precision_scope("cuda"):
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old
@@ -139,8 +139,8 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
     stride = tile - tile_overlap
     h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
     w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
-    E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
-    W = torch.zeros_like(E, dtype=torch.half, device=device)
+    E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=devices.device_swinir).type_as(img)
+    W = torch.zeros_like(E, dtype=torch.half, device=devices.device_swinir)
 
     with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
         for h_idx in h_idx_list:
-- 
cgit v1.2.3


From faed465a0b1a7d19669568738c93e04907c10415 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 25 Oct 2022 02:01:57 -0400
Subject: MPS Upscalers Fix

Get ESRGAN, SCUNet, and SwinIR working correctly on MPS by ensuring memory is contiguous for tensor views before sending to MPS device.
---
 modules/devices.py      | 4 ++++
 modules/esrgan_model.py | 2 +-
 modules/scunet_model.py | 3 +--
 modules/swinir_model.py | 2 +-
 4 files changed, 7 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 033a42d5..7511e1dc 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -81,3 +81,7 @@ def autocast(disable=False):
         return contextlib.nullcontext()
 
     return torch.autocast("cuda")
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+def mps_contiguous(input_tensor, device): return input_tensor.contiguous() if device.type == 'mps' else input_tensor
+def mps_contiguous_to(input_tensor, device): return mps_contiguous(input_tensor, device).to(device)
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index a49e2258..a13cf6ac 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -190,7 +190,7 @@ def upscale_without_tiling(model, img):
     img = img[:, :, ::-1]
     img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(devices.device_esrgan)
+    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan)
     with torch.no_grad():
         output = model(img)
     output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index 36a996bf..59532274 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -54,9 +54,8 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         img = img[:, :, ::-1]
         img = np.moveaxis(img, 2, 0) / 255
         img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(device)
+        img = devices.mps_contiguous_to(img.unsqueeze(0), device)
 
-        img = img.to(device)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index facd262d..4253b66d 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -111,7 +111,7 @@ def upscale(
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(devices.device_swinir)
+    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_swinir)
     with torch.no_grad(), precision_scope("cuda"):
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old
-- 
cgit v1.2.3


From 91c1e1e6a92061b99c92a5b1d548535907d2ad96 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Tue, 25 Oct 2022 00:46:28 +0900
Subject: fix default filename pattern

---
 modules/images.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 0e044af2..286de2ae 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -477,8 +477,10 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     if forced_filename is None:
         if short_filename or seed is None:
             file_decoration = ""
-        else:
+        elif opts.save_to_dirs:
             file_decoration = opts.samples_filename_pattern or "[seed]"
+        else:
+            file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
 
         add_number = opts.save_images_add_number or file_decoration == ''
 
-- 
cgit v1.2.3


From ff305acd51cc71c5eea8aee0f537a26a6d1ba2a1 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 25 Oct 2022 15:33:43 +0800
Subject: some rights for extensions

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 76cbb1bd..7b1fadf2 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -82,6 +82,7 @@ parser.add_argument("--api", action='store_true', help="use api=True to launch t
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
+parser.add_argument("--administrator", type=str, help="Administrator rights", default=None)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.3


From 3e15f8e0f5cc87507f77546d92435670644dbd18 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 25 Oct 2022 12:16:17 +0300
Subject: update callbacks code for #3549

---
 modules/script_callbacks.py | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 9933fa38..dc520abc 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -49,6 +49,14 @@ def ui_settings_callback():
             report_exception(c, 'ui_settings_callback')
 
 
+def image_saved_callback(image, p, fullfn, txt_fullfn):
+    for c in callbacks_image_saved:
+        try:
+            c.callback(image, p, fullfn, txt_fullfn)
+        except Exception:
+            report_exception(c, 'image_saved_callback')
+
+
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -56,9 +64,6 @@ def add_callback(callbacks, fun):
     callbacks.append(ScriptCallback(filename, fun))
 
 
-def image_saved_callback(image, p, fullfn, txt_fullfn):
-    for callback in callbacks_image_saved:
-        callback(image, p, fullfn, txt_fullfn)
 
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
@@ -82,9 +87,14 @@ def on_ui_tabs(callback):
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
-    callbacks_ui_settings.append(callback)
+    add_callback(callbacks_ui_settings, callback)
 
 
 def on_save_imaged(callback):
-    """register a function to call after modules.images.save_image is called returning same values, original image and p """
-    callbacks_image_saved.append(callback)
+    """register a function to be called after modules.images.save_image is called.
+    The callback is called with three arguments:
+        - p - procesing object (or a dummy object with same fields if the image is saved using save button)
+        - fullfn - image filename
+        - txt_fullfn - text file with parameters; may be None
+    """
+    add_callback(callbacks_image_saved, callback)
-- 
cgit v1.2.3


From 9ba439b53313ef78984dd8e39f25b34501188ee2 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 25 Oct 2022 18:48:07 +0800
Subject: need some rights for extensions

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 7b1fadf2..b5975707 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -82,7 +82,7 @@ parser.add_argument("--api", action='store_true', help="use api=True to launch t
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-parser.add_argument("--administrator", type=str, help="Administrator rights", default=None)
+parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.3


From f9549d1cbb3f1d7d1f0fb70375a06e31f9c5dd9d Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Tue, 25 Oct 2022 11:14:12 -0700
Subject: Added option to use unmasked conditioning image.

---
 modules/processing.py | 6 +++++-
 modules/shared.py     | 1 +
 2 files changed, 6 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index c61bbfbd..96f56b0d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -768,7 +768,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
             # Create another latent image, this time with a masked version of the original input.
             conditioning_mask = conditioning_mask.to(image.device)
-            conditioning_image = image * (1.0 - conditioning_mask)
+
+            conditioning_image = image
+            if shared.opts.inpainting_mask_image:
+                conditioning_image = conditioning_image * (1.0 - conditioning_mask)                
+            
             conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
 
             # Create the concatenated conditioning tensor to be fed to `c_concat`
diff --git a/modules/shared.py b/modules/shared.py
index 308fccce..1d0ff1a1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -320,6 +320,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
+    "inpainting_mask_image": OptionInfo(True, "Mask original image for conditioning used by inpainting model."),
 }))
 
 
-- 
cgit v1.2.3


From 605d27687f433c0fefb9025aace7dc94f0ebd454 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Tue, 25 Oct 2022 12:20:54 -0700
Subject: Added conditioning image masking to xy_grid. Use `True` and `False`
 to select values.

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 96f56b0d..23ee5e02 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -770,7 +770,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             conditioning_mask = conditioning_mask.to(image.device)
 
             conditioning_image = image
-            if shared.opts.inpainting_mask_image:
+            if getattr(self, "inpainting_mask_image", shared.opts.inpainting_mask_image):
                 conditioning_image = conditioning_image * (1.0 - conditioning_mask)                
             
             conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-- 
cgit v1.2.3


From 3e6c2420c1177e9e79f2b566a5a7795b7416e34a Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Tue, 25 Oct 2022 13:10:58 -0700
Subject: improve debug markers, fix algo weighting

---
 modules/textual_inversion/autocrop.py | 207 +++++++++++++++++++++-------------
 1 file changed, 129 insertions(+), 78 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index b2f9241c..caaf18c8 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -1,4 +1,5 @@
 import cv2
+import os
 from collections import defaultdict
 from math import log, sqrt
 import numpy as np
@@ -26,19 +27,9 @@ def crop_image(im, settings):
       scale_by = settings.crop_height / im.height
 
   im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
+  im_debug = im.copy()
 
-  if im.width == settings.crop_width and im.height == settings.crop_height:
-    if settings.annotate_image:
-      d = ImageDraw.Draw(im)
-      rect = [0, 0, im.width, im.height]
-      rect[2] -= 1
-      rect[3] -= 1
-      d.rectangle(rect, outline=GREEN)
-      if settings.destop_view_image:
-        im.show()
-    return im
-
-  focus = focal_point(im, settings)
+  focus = focal_point(im_debug, settings)
 
   # take the focal point and turn it into crop coordinates that try to center over the focal
   # point but then get adjusted back into the frame
@@ -62,89 +53,143 @@ def crop_image(im, settings):
 
   crop = [x1, y1, x2, y2]
 
+  results = []
+
+  results.append(im.crop(tuple(crop)))
+
   if settings.annotate_image:
-    d = ImageDraw.Draw(im)
+    d = ImageDraw.Draw(im_debug)
     rect = list(crop)
     rect[2] -= 1
     rect[3] -= 1
     d.rectangle(rect, outline=GREEN)
+    results.append(im_debug)
     if settings.destop_view_image:
-      im.show()
+      im_debug.show()
 
-  return im.crop(tuple(crop))
+  return results
 
 def focal_point(im, settings):
     corner_points = image_corner_points(im, settings)
     entropy_points = image_entropy_points(im, settings)
     face_points = image_face_points(im, settings)
 
-    total_points = len(corner_points) + len(entropy_points) + len(face_points)
-
-    corner_weight = settings.corner_points_weight
-    entropy_weight = settings.entropy_points_weight
-    face_weight = settings.face_points_weight
-
-    weight_pref_total = corner_weight + entropy_weight + face_weight
-
-    # weight things
     pois = []
-    if weight_pref_total == 0 or total_points == 0: 
-      return pois
 
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ]
-    )
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ]
-    )
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
-    )
+    weight_pref_total = 0
+    if len(corner_points) > 0:
+      weight_pref_total += settings.corner_points_weight
+    if len(entropy_points) > 0:
+      weight_pref_total += settings.entropy_points_weight
+    if len(face_points) > 0:
+      weight_pref_total += settings.face_points_weight
+
+    corner_centroid = None
+    if len(corner_points) > 0:
+      corner_centroid = centroid(corner_points)
+      corner_centroid.weight = settings.corner_points_weight / weight_pref_total 
+      pois.append(corner_centroid)
+
+    entropy_centroid = None
+    if len(entropy_points) > 0:
+      entropy_centroid = centroid(entropy_points)
+      entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
+      pois.append(entropy_centroid)
+
+    face_centroid = None
+    if len(face_points) > 0:
+      face_centroid = centroid(face_points)
+      face_centroid.weight = settings.face_points_weight / weight_pref_total 
+      pois.append(face_centroid)
 
     average_point = poi_average(pois, settings)
 
     if settings.annotate_image:
       d = ImageDraw.Draw(im)
-      for f in face_points:
-        d.rectangle(f.bounding(f.size), outline=RED)
-      for f in entropy_points:
-        d.rectangle(f.bounding(30), outline=BLUE)
-      for poi in pois:
-        w = max(4, 4 * 0.5 * sqrt(poi.weight))
-        d.ellipse(poi.bounding(w), fill=BLUE)
-      d.ellipse(average_point.bounding(25), outline=GREEN)
+      max_size = min(im.width, im.height) * 0.07
+      if corner_centroid is not None:
+        color = BLUE
+        box = corner_centroid.bounding(max_size * corner_centroid.weight)
+        d.text((box[0], box[1]-15), "Edge: %.02f" % corner_centroid.weight, fill=color)
+        d.ellipse(box, outline=color)
+        if len(corner_points) > 1:
+          for f in corner_points:
+            d.rectangle(f.bounding(4), outline=color)
+      if entropy_centroid is not None:
+        color = "#ff0"
+        box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
+        d.text((box[0], box[1]-15), "Entropy: %.02f" % entropy_centroid.weight, fill=color)
+        d.ellipse(box, outline=color)
+        if len(entropy_points) > 1:
+          for f in entropy_points:
+            d.rectangle(f.bounding(4), outline=color)
+      if face_centroid is not None:
+        color = RED
+        box = face_centroid.bounding(max_size * face_centroid.weight)
+        d.text((box[0], box[1]-15), "Face: %.02f" % face_centroid.weight, fill=color)
+        d.ellipse(box, outline=color)
+        if len(face_points) > 1:
+          for f in face_points:
+            d.rectangle(f.bounding(4), outline=color)
+
+      d.ellipse(average_point.bounding(max_size), outline=GREEN)
       
     return average_point
 
 
 def image_face_points(im, settings):
-    np_im = np.array(im)
-    gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
-
-    tries = [
-      [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
-    ]
-
-    for t in tries:
-      # print(t[0])
-      classifier = cv2.CascadeClassifier(t[0])
-      minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
-      try:
-        faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
-          minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
-      except:
-        continue
-
-      if len(faces) > 0:
-        rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
-        return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2])) for r in rects]
+    if settings.dnn_model_path is not None:
+      detector = cv2.FaceDetectorYN.create(
+          settings.dnn_model_path,
+          "",
+          (im.width, im.height),
+          0.8, # score threshold
+          0.3, # nms threshold
+          5000 # keep top k before nms
+      )
+      faces = detector.detect(np.array(im))
+      results = []
+      if faces[1] is not None:
+        for face in faces[1]:
+          x = face[0]
+          y = face[1]
+          w = face[2]
+          h = face[3]
+          results.append(
+            PointOfInterest(
+              int(x + (w * 0.5)), # face focus left/right is center
+              int(y + (h * 0)), # face focus up/down is close to the top of the head
+              size = w,
+              weight = 1/len(faces[1])
+            )
+          )
+      return results
+    else:
+      np_im = np.array(im)
+      gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
+
+      tries = [
+        [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
+        [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
+      ]
+      for t in tries:
+        classifier = cv2.CascadeClassifier(t[0])
+        minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
+        try:
+          faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
+            minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+        except:
+          continue
+
+        if len(faces) > 0:
+          rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+          return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
     return []
 
 
@@ -161,7 +206,7 @@ def image_corner_points(im, settings):
         np_im,
         maxCorners=100,
         qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.07,
+        minDistance=min(grayscale.width, grayscale.height)*0.03,
         useHarrisDetector=False,
     )
 
@@ -171,7 +216,7 @@ def image_corner_points(im, settings):
     focal_points = []
     for point in points:
       x, y = point.ravel()
-      focal_points.append(PointOfInterest(x, y, size=4))
+      focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
 
     return focal_points
 
@@ -205,17 +250,22 @@ def image_entropy_points(im, settings):
     x_mid = int(crop_best[0] + settings.crop_width/2)
     y_mid = int(crop_best[1] + settings.crop_height/2)
 
-    return [PointOfInterest(x_mid, y_mid, size=25)]
+    return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
 
 
 def image_entropy(im):
     # greyscale image entropy
-    # band = np.asarray(im.convert("L"))
-    band = np.asarray(im.convert("1"), dtype=np.uint8)
+    band = np.asarray(im.convert("L"))
+    # band = np.asarray(im.convert("1"), dtype=np.uint8)
     hist, _ = np.histogram(band, bins=range(0, 256))
     hist = hist[hist > 0]
     return -np.log2(hist / hist.sum()).sum()
 
+def centroid(pois):
+  x = [poi.x for poi in pois]
+  y = [poi.y for poi in pois]
+  return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois))
+
 
 def poi_average(pois, settings):
     weight = 0.0
@@ -260,11 +310,12 @@ class PointOfInterest:
 
 
 class Settings:
-  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False):
+  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
     self.crop_width = crop_width
     self.crop_height = crop_height
     self.corner_points_weight = corner_points_weight
     self.entropy_points_weight = entropy_points_weight
-    self.face_points_weight = entropy_points_weight
+    self.face_points_weight = face_points_weight
     self.annotate_image = annotate_image
-    self.destop_view_image = False
\ No newline at end of file
+    self.destop_view_image = False
+    self.dnn_model_path = dnn_model_path
\ No newline at end of file
-- 
cgit v1.2.3


From 8b4f32779f28010fc8077e8fcfb85a3205b36bc2 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Tue, 25 Oct 2022 13:15:08 -0700
Subject: Switch to a continous blend for cond. image.

---
 modules/processing.py | 9 ++++++---
 modules/shared.py     | 2 +-
 2 files changed, 7 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 23ee5e02..02292bdc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -769,9 +769,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             # Create another latent image, this time with a masked version of the original input.
             conditioning_mask = conditioning_mask.to(image.device)
 
-            conditioning_image = image
-            if getattr(self, "inpainting_mask_image", shared.opts.inpainting_mask_image):
-                conditioning_image = conditioning_image * (1.0 - conditioning_mask)                
+            # Smoothly interpolate between the masked and unmasked latent conditioning image.
+            conditioning_image = torch.lerp(
+                image,
+                image * (1.0 - conditioning_mask),
+                getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
+            )
             
             conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
 
diff --git a/modules/shared.py b/modules/shared.py
index 1d0ff1a1..e0ffb824 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -320,7 +320,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
-    "inpainting_mask_image": OptionInfo(True, "Mask original image for conditioning used by inpainting model."),
+    "inpainting_mask_weight": OptionInfo(1.0, "Blend betweeen an unmasked and masked conditioning image for inpainting models.", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
 }))
 
 
-- 
cgit v1.2.3


From db8ed5fe5cd6e967d12d43d96b7f83083e58626c Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Tue, 25 Oct 2022 15:22:29 -0700
Subject: Focal crop UI elements

---
 modules/textual_inversion/preprocess.py | 26 +++++++++++++-------------
 modules/ui.py                           | 20 ++++++++++++++++++--
 2 files changed, 31 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index a8c17c6f..1e4d4de8 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -13,7 +13,7 @@ if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
 
-def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_entropy_focus=False):
+def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):
     try:
         if process_caption:
             shared.interrogator.load()
@@ -23,7 +23,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
             db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
             deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
 
-        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_entropy_focus)
+        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug)
 
     finally:
 
@@ -35,7 +35,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
 
 
 
-def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_entropy_focus=False):
+def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
@@ -139,27 +139,27 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
             ratio = (img.height * width) / (img.width * height)
             inverse_xy = True
 
-        processing_option_ran = False
+        process_default_resize = True
 
         if process_split and ratio < 1.0 and ratio <= split_threshold:
             for splitted in split_pic(img, inverse_xy):
                 save_pic(splitted, index, existing_caption=existing_caption)
-            processing_option_ran = True
+            process_default_resize = False
 
         if process_entropy_focus and img.height != img.width:
             autocrop_settings = autocrop.Settings(
                 crop_width = width,
                 crop_height = height,
-                face_points_weight = 0.9,
-                entropy_points_weight = 0.7,
-                corner_points_weight = 0.5,
-                annotate_image = False
+                face_points_weight = process_focal_crop_face_weight,
+                entropy_points_weight = process_focal_crop_entropy_weight,
+                corner_points_weight = process_focal_crop_edges_weight,
+                annotate_image = process_focal_crop_debug
             )
-            focal = autocrop.crop_image(img, autocrop_settings)
-            save_pic(focal, index, existing_caption=existing_caption)
-            processing_option_ran = True
+            for focal in autocrop.crop_image(img, autocrop_settings):
+                save_pic(focal, index, existing_caption=existing_caption)
+            process_default_resize = False
 
-        if not processing_option_ran:
+        if process_default_resize:
             img = images.resize_image(1, img, width, height)
             save_pic(img, index, existing_caption=existing_caption)
 
diff --git a/modules/ui.py b/modules/ui.py
index 028eb4e5..95b9c703 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1260,7 +1260,7 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
                         process_split = gr.Checkbox(label='Split oversized images')
-                        process_entropy_focus = gr.Checkbox(label='Create auto focal point crop')
+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
                         process_caption = gr.Checkbox(label='Use BLIP for caption')
                         process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
 
@@ -1268,6 +1268,12 @@ def create_ui(wrap_gradio_gpu_call):
                         process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
                         process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
 
+                    with gr.Row(visible=False) as process_focal_crop_row:
+                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.3, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
+
                     with gr.Row():
                         with gr.Column(scale=3):
                             gr.HTML(value="")
@@ -1281,6 +1287,12 @@ def create_ui(wrap_gradio_gpu_call):
                         outputs=[process_split_extra_row],
                     )
 
+                    process_focal_crop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_focal_crop],
+                        outputs=[process_focal_crop_row],
+                    )
+
                 with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
                     with gr.Row():
@@ -1368,7 +1380,11 @@ def create_ui(wrap_gradio_gpu_call):
                 process_caption_deepbooru,
                 process_split_threshold,
                 process_overlap_ratio,
-                process_entropy_focus,
+                process_focal_crop,
+                process_focal_crop_face_weight,
+                process_focal_crop_entropy_weight,
+                process_focal_crop_edges_weight,
+                process_focal_crop_debug,
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.3


From 54f0c1482427a5b3f2248b97be55878e742cbcb1 Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Tue, 25 Oct 2022 16:14:13 -0700
Subject: download better face detection module dynamically

---
 modules/textual_inversion/autocrop.py   | 20 ++++++++++++++++++++
 modules/textual_inversion/preprocess.py | 13 +++++++++++--
 2 files changed, 31 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index caaf18c8..01a92b12 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -1,4 +1,5 @@
 import cv2
+import requests
 import os
 from collections import defaultdict
 from math import log, sqrt
@@ -293,6 +294,25 @@ def is_square(w, h):
   return w == h
 
 
+def download_and_cache_models(dirname):
+  download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
+  model_file_name = 'face_detection_yunet.onnx'
+
+  if not os.path.exists(dirname):
+    os.makedirs(dirname)
+
+  cache_file = os.path.join(dirname, model_file_name)
+  if not os.path.exists(cache_file):
+    print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
+    response = requests.get(download_url)
+    with open(cache_file, "wb") as f:
+      f.write(response.content)
+
+  if os.path.exists(cache_file):
+    return cache_file
+  return None
+
+
 class PointOfInterest:
   def __init__(self, x, y, weight=1.0, size=10):
     self.x = x
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 1e4d4de8..e13b1894 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -7,6 +7,7 @@ import tqdm
 import time
 
 from modules import shared, images
+from modules.paths import models_path
 from modules.shared import opts, cmd_opts
 from modules.textual_inversion import autocrop
 if cmd_opts.deepdanbooru:
@@ -146,14 +147,22 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
                 save_pic(splitted, index, existing_caption=existing_caption)
             process_default_resize = False
 
-        if process_entropy_focus and img.height != img.width:
+        if process_focal_crop and img.height != img.width:
+
+            dnn_model_path = None
+            try:
+                dnn_model_path = autocrop.download_and_cache_models(os.path.join(models_path, "opencv"))
+            except Exception as e:
+                print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
+
             autocrop_settings = autocrop.Settings(
                 crop_width = width,
                 crop_height = height,
                 face_points_weight = process_focal_crop_face_weight,
                 entropy_points_weight = process_focal_crop_entropy_weight,
                 corner_points_weight = process_focal_crop_edges_weight,
-                annotate_image = process_focal_crop_debug
+                annotate_image = process_focal_crop_debug,
+                dnn_model_path = dnn_model_path,
             )
             for focal in autocrop.crop_image(img, autocrop_settings):
                 save_pic(focal, index, existing_caption=existing_caption)
-- 
cgit v1.2.3


From df0c5ea29d7f0c682ac81f184f3e482a6450d018 Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Tue, 25 Oct 2022 17:06:59 -0700
Subject: update default weights

---
 modules/textual_inversion/autocrop.py | 16 ++++++++--------
 modules/ui.py                         |  2 +-
 2 files changed, 9 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 01a92b12..9859974a 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -71,9 +71,9 @@ def crop_image(im, settings):
   return results
 
 def focal_point(im, settings):
-    corner_points = image_corner_points(im, settings)
-    entropy_points = image_entropy_points(im, settings)
-    face_points = image_face_points(im, settings)
+    corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
+    entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
+    face_points = image_face_points(im, settings) if settings.face_points_weight > 0 else []
 
     pois = []
 
@@ -144,7 +144,7 @@ def image_face_points(im, settings):
           settings.dnn_model_path,
           "",
           (im.width, im.height),
-          0.8, # score threshold
+          0.9, # score threshold
           0.3, # nms threshold
           5000 # keep top k before nms
       )
@@ -159,7 +159,7 @@ def image_face_points(im, settings):
           results.append(
             PointOfInterest(
               int(x + (w * 0.5)), # face focus left/right is center
-              int(y + (h * 0)), # face focus up/down is close to the top of the head
+              int(y + (h * 0.33)), # face focus up/down is close to the top of the head
               size = w,
               weight = 1/len(faces[1])
             )
@@ -207,7 +207,7 @@ def image_corner_points(im, settings):
         np_im,
         maxCorners=100,
         qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.03,
+        minDistance=min(grayscale.width, grayscale.height)*0.06,
         useHarrisDetector=False,
     )
 
@@ -256,8 +256,8 @@ def image_entropy_points(im, settings):
 
 def image_entropy(im):
     # greyscale image entropy
-    band = np.asarray(im.convert("L"))
-    # band = np.asarray(im.convert("1"), dtype=np.uint8)
+    # band = np.asarray(im.convert("L"))
+    band = np.asarray(im.convert("1"), dtype=np.uint8)
     hist, _ = np.histogram(band, bins=range(0, 256))
     hist = hist[hist > 0]
     return -np.log2(hist / hist.sum()).sum()
diff --git a/modules/ui.py b/modules/ui.py
index 95b9c703..095200a8 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1270,7 +1270,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.Row(visible=False) as process_focal_crop_row:
                         process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.3, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
                         process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
                         process_focal_crop_debug = gr.Checkbox(label='Create debug image')
 
-- 
cgit v1.2.3


From 2f4c91894d4c0a055c1069b2fda0e4da8fcda188 Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 12:10:30 +0800
Subject: Remove activation from final layer of HNs

---
 modules/hypernetworks/hypernetwork.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index d647ea55..54346b64 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -41,8 +41,8 @@ class HypernetworkModule(torch.nn.Module):
             # Add a fully-connected layer
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
 
-            # Add an activation func
-            if activation_func == "linear" or activation_func is None:
+            # Add an activation func except last layer
+            if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 3:
                 pass
             elif activation_func in self.activation_dict:
                 linears.append(self.activation_dict[activation_func]())
@@ -53,7 +53,7 @@ class HypernetworkModule(torch.nn.Module):
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
-            # Add dropout expect last layer
+            # Add dropout except last layer
             if use_dropout and i < len(layer_structure) - 3:
                 linears.append(torch.nn.Dropout(p=0.3))
 
-- 
cgit v1.2.3


From c702d4d0df21790199d199818f25c449213ffe0f Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 13:43:04 +0800
Subject: Fix off-by-one

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 54346b64..3ce85bb5 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -42,7 +42,7 @@ class HypernetworkModule(torch.nn.Module):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
 
             # Add an activation func except last layer
-            if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 3:
+            if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 2:
                 pass
             elif activation_func in self.activation_dict:
                 linears.append(self.activation_dict[activation_func]())
@@ -54,7 +54,7 @@ class HypernetworkModule(torch.nn.Module):
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
             # Add dropout except last layer
-            if use_dropout and i < len(layer_structure) - 3:
+            if use_dropout and i < len(layer_structure) - 2:
                 linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
-- 
cgit v1.2.3


From de096d0ce752c96e45508dcc7b9e84f7dbe10cca Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Tue, 25 Oct 2022 14:48:49 +0900
Subject: Weight initialization and More activation func

add weight init

add weight init option in create_hypernetwork

fstringify hypernet info

save weight initialization info for further debugging

fill bias with zero for He/Xavier

initialize LayerNorm with Normal

fix loading weight_init
---
 modules/hypernetworks/hypernetwork.py | 47 ++++++++++++++++++++++++++++-------
 modules/hypernetworks/ui.py           |  4 ++-
 modules/ui.py                         |  4 ++-
 3 files changed, 44 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index d647ea55..afbcdff8 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -5,6 +5,7 @@ import html
 import os
 import sys
 import traceback
+import inspect
 
 import modules.textual_inversion.dataset
 import torch
@@ -15,10 +16,12 @@ from modules import devices, processing, sd_models, shared
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
+from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
 
 from collections import defaultdict, deque
 from statistics import stdev, mean
 
+
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
     activation_dict = {
@@ -26,9 +29,12 @@ class HypernetworkModule(torch.nn.Module):
         "leakyrelu": torch.nn.LeakyReLU,
         "elu": torch.nn.ELU,
         "swish": torch.nn.Hardswish,
+        "tanh": torch.nn.Tanh,
+        "sigmoid": torch.nn.Sigmoid,
     }
+    activation_dict.update({cls_name: cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -65,9 +71,24 @@ class HypernetworkModule(torch.nn.Module):
         else:
             for layer in self.linear:
                 if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
-                    layer.weight.data.normal_(mean=0.0, std=0.01)
-                    layer.bias.data.zero_()
-
+                    w, b = layer.weight.data, layer.bias.data
+                    if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:
+                        normal_(w, mean=0.0, std=0.01)
+                        normal_(b, mean=0.0, std=0.005)
+                    elif weight_init == 'XavierUniform':
+                        xavier_uniform_(w)
+                        zeros_(b)
+                    elif weight_init == 'XavierNormal':
+                        xavier_normal_(w)
+                        zeros_(b)
+                    elif weight_init == 'KaimingUniform':
+                        kaiming_uniform_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
+                        zeros_(b)
+                    elif weight_init == 'KaimingNormal':
+                        kaiming_normal_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
+                        zeros_(b)
+                    else:
+                        raise KeyError(f"Key {weight_init} is not defined as initialization!")
         self.to(devices.device)
 
     def fix_old_state_dict(self, state_dict):
@@ -105,7 +126,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -114,13 +135,14 @@ class Hypernetwork:
         self.sd_checkpoint_name = None
         self.layer_structure = layer_structure
         self.activation_func = activation_func
+        self.weight_init = weight_init
         self.add_layer_norm = add_layer_norm
         self.use_dropout = use_dropout
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
             )
 
     def weights(self):
@@ -144,6 +166,7 @@ class Hypernetwork:
         state_dict['layer_structure'] = self.layer_structure
         state_dict['activation_func'] = self.activation_func
         state_dict['is_layer_norm'] = self.add_layer_norm
+        state_dict['weight_initialization'] = self.weight_init
         state_dict['use_dropout'] = self.use_dropout
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
@@ -158,15 +181,21 @@ class Hypernetwork:
         state_dict = torch.load(filename, map_location='cpu')
 
         self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
+        print(self.layer_structure)
         self.activation_func = state_dict.get('activation_func', None)
+        print(f"Activation function is {self.activation_func}")
+        self.weight_init = state_dict.get('weight_initialization', 'Normal')
+        print(f"Weight initialization is {self.weight_init}")
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
+        print(f"Layer norm is set to {self.add_layer_norm}")
         self.use_dropout = state_dict.get('use_dropout', False)
+        print(f"Dropout usage is set to {self.use_dropout}" )
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 2b472d87..2c6c0470 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -8,8 +8,9 @@ import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
+keys = list(hypernetwork.HypernetworkModule.activation_dict.keys())
 
-def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
     # Remove illegal characters from name.
     name = "".join( x for x in name if (x.isalnum() or x in "._- "))
 
@@ -25,6 +26,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
         enable_sizes=[int(x) for x in enable_sizes],
         layer_structure=layer_structure,
         activation_func=activation_func,
+        weight_init=weight_init,
         add_layer_norm=add_layer_norm,
         use_dropout=use_dropout,
     )
diff --git a/modules/ui.py b/modules/ui.py
index 03528968..8e343258 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1238,7 +1238,8 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu", "elu", "swish"])
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
                     overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
@@ -1342,6 +1343,7 @@ def create_ui(wrap_gradio_gpu_call):
                 overwrite_old_hypernetwork,
                 new_hypernetwork_layer_structure,
                 new_hypernetwork_activation_func,
+                new_hypernetwork_initialization_option,
                 new_hypernetwork_add_layer_norm,
                 new_hypernetwork_use_dropout
             ],
-- 
cgit v1.2.3


From 7207e3bf49ed000464d288cd67e02f0ba8614dc3 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Tue, 25 Oct 2022 15:24:59 +0900
Subject: remove duplicate keys and lowercase

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index afbcdff8..842b6447 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -32,7 +32,7 @@ class HypernetworkModule(torch.nn.Module):
         "tanh": torch.nn.Tanh,
         "sigmoid": torch.nn.Sigmoid,
     }
-    activation_dict.update({cls_name: cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
+    activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
     def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
         super().__init__()
-- 
cgit v1.2.3


From cbb857b675cf0f169b21515c29da492b513cc8c4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 26 Oct 2022 09:44:02 +0300
Subject: enable creating embedding with --medvram

---
 modules/textual_inversion/textual_inversion.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 529ed3e2..647ffe3e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -157,6 +157,9 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     cond_model = shared.sd_model.cond_stage_model
     embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
 
+    with devices.autocast():
+        cond_model([""])  # will send cond model to GPU if lowvram/medvram is active
+
     ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
     embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
     vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
-- 
cgit v1.2.3


From db9ab1a46b5ad4d36ecce76dfee04b7164249829 Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Mon, 24 Oct 2022 11:16:07 -0400
Subject: [Bugfix][API] - Fix API response for colab users

---
 modules/api/api.py    | 17 +++++++++++++----
 modules/api/models.py | 10 ++++++----
 2 files changed, 19 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index a860a964..ba890243 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -7,6 +7,7 @@ import uvicorn
 from fastapi import Body, APIRouter, HTTPException
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field, Json
+from typing import List
 import json
 import io
 import base64
@@ -15,12 +16,12 @@ from PIL import Image
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
 class TextToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
     info: Json
 
 class ImageToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
     info: Json
 
@@ -41,6 +42,9 @@ class Api:
         # convert base64 to PIL image
         return Image.open(io.BytesIO(imgdata))
 
+    def __processed_info_to_json(self, processed):
+        return json.dumps(processed.info)
+
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
         
@@ -65,7 +69,7 @@ class Api:
             i.save(buffer, format="png")
             b64images.append(base64.b64encode(buffer.getvalue()))
 
-        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
+        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js())
         
         
 
@@ -111,7 +115,12 @@ class Api:
             i.save(buffer, format="png")
             b64images.append(base64.b64encode(buffer.getvalue()))
 
-        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info))
+        if (not img2imgreq.include_init_images):
+            # remove img2imgreq.init_images and img2imgreq.mask
+            img2imgreq.init_images = None
+            img2imgreq.mask = None
+
+        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js())
 
     def extrasapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
index f551fa35..c6d43606 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -31,6 +31,7 @@ class ModelDef(BaseModel):
     field_alias: str
     field_type: Any
     field_value: Any
+    field_exclude: bool = False
 
 
 class PydanticModelGenerator:
@@ -68,7 +69,7 @@ class PydanticModelGenerator:
                 field=underscore(k),
                 field_alias=k,
                 field_type=field_type_generator(k, v),
-                field_value=v.default
+                field_value=v.default,
             )
             for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
         ]
@@ -78,7 +79,8 @@ class PydanticModelGenerator:
                 field=underscore(fields["key"]), 
                 field_alias=fields["key"], 
                 field_type=fields["type"],
-                field_value=fields["default"]))
+                field_value=fields["default"],
+                field_exclude=fields["exclude"] if "exclude" in fields else False))
 
     def generate_model(self):
         """
@@ -86,7 +88,7 @@ class PydanticModelGenerator:
         from the json and overrides provided at initialization
         """
         fields = {
-            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def
         }
         DynamicModel = create_model(self._model_name, **fields)
         DynamicModel.__config__.allow_population_by_field_name = True
@@ -102,5 +104,5 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
 StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingImg2Img", 
     StableDiffusionProcessingImg2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
 ).generate_model()
\ No newline at end of file
-- 
cgit v1.2.3


From b46c64c6e5b40d69521e4d50e2d35f6a35468129 Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Mon, 24 Oct 2022 12:18:54 -0400
Subject: clean

---
 modules/api/api.py    | 4 ----
 modules/api/models.py | 2 +-
 2 files changed, 1 insertion(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index ba890243..6e9d6097 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -42,9 +42,6 @@ class Api:
         # convert base64 to PIL image
         return Image.open(io.BytesIO(imgdata))
 
-    def __processed_info_to_json(self, processed):
-        return json.dumps(processed.info)
-
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
         
@@ -116,7 +113,6 @@ class Api:
             b64images.append(base64.b64encode(buffer.getvalue()))
 
         if (not img2imgreq.include_init_images):
-            # remove img2imgreq.init_images and img2imgreq.mask
             img2imgreq.init_images = None
             img2imgreq.mask = None
 
diff --git a/modules/api/models.py b/modules/api/models.py
index c6d43606..079e33d9 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -69,7 +69,7 @@ class PydanticModelGenerator:
                 field=underscore(k),
                 field_alias=k,
                 field_type=field_type_generator(k, v),
-                field_value=v.default,
+                field_value=v.default
             )
             for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
         ]
-- 
cgit v1.2.3


From 146856f66d7e06a762f5ef5bf61a226057de6757 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Tue, 25 Oct 2022 06:21:31 +0900
Subject: images: allow nested bracket in filename pattern

---
 modules/images.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 286de2ae..ed448a8a 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -277,7 +277,7 @@ invalid_filename_chars = '<>:"/\\|?*\n'
 invalid_filename_prefix = ' '
 invalid_filename_postfix = ' .'
 re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
-re_pattern = re.compile(r"([^\[\]]+|\[([^]]+)]|[\[\]]*)")
+re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
 re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
 max_filename_part_length = 128
 
@@ -362,9 +362,9 @@ class FilenameGenerator:
 
         for m in re_pattern.finditer(x):
             text, pattern = m.groups()
+            res += text
 
             if pattern is None:
-                res += text
                 continue
 
             pattern_args = []
@@ -385,12 +385,9 @@ class FilenameGenerator:
                     print(f"Error adding [{pattern}] to filename", file=sys.stderr)
                     print(traceback.format_exc(), file=sys.stderr)
 
-                if replacement is None:
-                    res += f'[{pattern}]'
-                else:
+                if replacement is not None:
                     res += str(replacement)
-
-                continue
+                    continue
 
             res += f'[{pattern}]'
 
-- 
cgit v1.2.3


From 877d94f97ca5491d8779440769b191e0dcd32c8e Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 14:50:58 +0800
Subject: Back compatibility

---
 modules/hypernetworks/hypernetwork.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3ce85bb5..dd317085 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -28,7 +28,7 @@ class HypernetworkModule(torch.nn.Module):
         "swish": torch.nn.Hardswish,
     }
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -42,7 +42,7 @@ class HypernetworkModule(torch.nn.Module):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
 
             # Add an activation func except last layer
-            if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 2:
+            if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output):
                 pass
             elif activation_func in self.activation_dict:
                 linears.append(self.activation_dict[activation_func]())
@@ -105,7 +105,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -116,11 +116,12 @@ class Hypernetwork:
         self.activation_func = activation_func
         self.add_layer_norm = add_layer_norm
         self.use_dropout = use_dropout
+        self.activate_output = activate_output
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
             )
 
     def weights(self):
@@ -147,6 +148,7 @@ class Hypernetwork:
         state_dict['use_dropout'] = self.use_dropout
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
+        state_dict['activate_output'] = self.activate_output
 
         torch.save(state_dict, filename)
 
@@ -161,12 +163,13 @@ class Hypernetwork:
         self.activation_func = state_dict.get('activation_func', None)
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
         self.use_dropout = state_dict.get('use_dropout', False)
+        self.activate_output = state_dict.get('activate_output', True)
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
                 )
 
         self.name = state_dict.get('name', self.name)
-- 
cgit v1.2.3


From 757264c453eca533ee1c9ea7e9d9b45a009367d7 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Tue, 25 Oct 2022 23:39:21 +0900
Subject: default_time_format if format is blank

---
 modules/images.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index ed448a8a..bfc2ba06 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -343,7 +343,7 @@ class FilenameGenerator:
     def datetime(self, *args):
         time_datetime = datetime.datetime.now()
 
-        time_format = args[0] if len(args) > 0 else self.default_time_format
+        time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
         try:
             time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
         except pytz.exceptions.UnknownTimeZoneError as _:
-- 
cgit v1.2.3


From 9d82c351ac36d1511f5f65b24443c60250ee3e9e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 26 Oct 2022 09:56:25 +0300
Subject: fix typo in on_save_imaged/on_image_saved; hope no extension is using
 it yet

---
 modules/script_callbacks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index dc520abc..6803d57b 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -90,7 +90,7 @@ def on_ui_settings(callback):
     add_callback(callbacks_ui_settings, callback)
 
 
-def on_save_imaged(callback):
+def on_image_saved(callback):
     """register a function to be called after modules.images.save_image is called.
     The callback is called with three arguments:
         - p - procesing object (or a dummy object with same fields if the image is saved using save button)
-- 
cgit v1.2.3


From 91bb35b1e6842b30ce7553009c8ecea3643de8d2 Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 15:00:03 +0800
Subject: Merge fix

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index eab8b32f..bd171793 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -190,7 +190,7 @@ class Hypernetwork:
         print(f"Weight initialization is {self.weight_init}")
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
         print(f"Layer norm is set to {self.add_layer_norm}")
-        self.use_dropout = state_dict.get('use_dropout', False
+        self.use_dropout = state_dict.get('use_dropout', False)
         print(f"Dropout usage is set to {self.use_dropout}" )
         self.activate_output = state_dict.get('activate_output', True)
 
-- 
cgit v1.2.3


From cb49800c08a9f6619733250401952e5571dc12f8 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Tue, 25 Oct 2022 01:39:59 -0700
Subject: img2img, use smartphone photos' EXIF orientation

---
 modules/img2img.py | 8 ++++++++
 1 file changed, 8 insertions(+)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index 8d9f7cf9..9c0cf23e 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -39,6 +39,8 @@ def process_batch(p, input_dir, output_dir, args):
             break
 
         img = Image.open(image)
+        # Use the EXIF orientation of photos taken by smartphones.
+        img = ImageOps.exif_transpose(img) 
         p.init_images = [img] * p.batch_size
 
         proc = modules.scripts.scripts_img2img.run(p, *args)
@@ -61,19 +63,25 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     is_batch = mode == 2
 
     if is_inpaint:
+        # Drawn mask
         if mask_mode == 0:
             image = init_img_with_mask['image']
             mask = init_img_with_mask['mask']
             alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
             mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
             image = image.convert('RGB')
+        # Uploaded mask
         else:
             image = init_img_inpaint
             mask = init_mask_inpaint
+    # No mask
     else:
         image = init_img
         mask = None
 
+    # Use the EXIF orientation of photos taken by smartphones.
+    image = ImageOps.exif_transpose(image) 
+
     assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
 
     p = StableDiffusionProcessingImg2Img(
-- 
cgit v1.2.3


From a524d137d0a89bb19a6676dc9b8fbb5d1b580678 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Mon, 24 Oct 2022 23:48:05 -0700
Subject: patch bug (SeverianVoid's comment on 5245c7a)

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 842b6447..8113b35b 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -487,7 +487,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             if image is not None:
                 shared.state.current_image = image
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename)
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                 last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
-- 
cgit v1.2.3


From c2dc9bfa89070b8e1d857f8773a790b752f1b709 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Mon, 24 Oct 2022 23:22:58 -0700
Subject: Implement PR #3189 but for embeddings.

---
 modules/textual_inversion/textual_inversion.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 647ffe3e..22c7b54b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -10,7 +10,7 @@ import csv
 
 from PIL import Image, PngImagePlugin
 
-from modules import shared, devices, sd_hijack, processing, sd_models
+from modules import shared, devices, sd_hijack, processing, sd_models, images
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -247,6 +247,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
+    forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
 
     ititial_step = embedding.step or 0
@@ -296,8 +297,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         })
 
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
-            last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
-
+            forced_filename = f'{embedding_name}-{embedding.step}'
+            last_saved_image = os.path.join(images_dir, forced_filename)
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 do_not_save_grid=True,
@@ -353,8 +354,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
                 embedding_yet_to_be_embedded = False
 
-            image.save(last_saved_image)
-
+            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename)
             last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = embedding.step
-- 
cgit v1.2.3


From 4875a6c217df5cc06ee2bf11fb645b172c7156a8 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Mon, 24 Oct 2022 23:38:07 -0700
Subject: Implement PR #3309 but for embeddings.

---
 modules/textual_inversion/textual_inversion.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 22c7b54b..4921bd01 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -167,6 +167,8 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     for i in range(num_vectors_per_token):
         vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
 
+    # Remove illegal characters from name.
+    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
     fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
     if not overwrite_old:
         assert not os.path.exists(fn), f"file {fn} already exists"
@@ -287,7 +289,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
 
         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
-            last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
+            # Before saving, change name to match current checkpoint.
+            embedding.name = f'{embedding_name}-{embedding.step}'
+            last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
             embedding.save(last_saved_file)
             embedding_yet_to_be_embedded = True
 
@@ -374,6 +378,9 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     embedding.sd_checkpoint = checkpoint.hash
     embedding.sd_checkpoint_name = checkpoint.model_name
     embedding.cached_checksum = None
+    # Before saving for the last time, change name back to base name (as opposed to the save_embedding_every step-suffixed naming convention).
+    embedding.name = embedding_name
+    filename = os.path.join(shared.cmd_opts.embedding_dir, f'{embedding.name}.pt')
     embedding.save(filename)
 
     return embedding, filename
-- 
cgit v1.2.3


From f4e14642173a04723200b131deb417c6c79cab17 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Tue, 25 Oct 2022 00:04:25 -0700
Subject: Implement PR #3625 but for embeddings.

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 4921bd01..4fcebe74 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -358,7 +358,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
                 embedding_yet_to_be_embedded = False
 
-            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename)
+            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
             last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = embedding.step
-- 
cgit v1.2.3


From b6a8bb123bd519736306417399f6441e504f1e8b Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 15:15:19 +0800
Subject: Fix merge

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index bd171793..2997cead 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -60,7 +60,7 @@ class HypernetworkModule(torch.nn.Module):
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
             # Add dropout except last layer
-            if use_dropout and i < len(layer_structure) - 2:
+            if use_dropout and i < len(layer_structure) - 3:
                 linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
@@ -126,7 +126,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False)
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False):
         self.filename = None
         self.name = name
         self.layers = {}
-- 
cgit v1.2.3


From 1e428238db4e399b7a06ad5251cb16eef23a014d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 26 Oct 2022 11:47:07 +0300
Subject: add override_settings to API as an alternative to #3629

---
 modules/processing.py | 25 ++++++++++++++++++++-----
 modules/shared.py     |  4 ++--
 2 files changed, 22 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index c61bbfbd..4efba946 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -77,9 +77,8 @@ def get_correct_sampler(p):
 class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
-    
     """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str="", styles: List[str]=None, seed: int=-1, subseed: int=-1, subseed_strength: float=0, seed_resize_from_h: int=-1, seed_resize_from_w: int=-1, seed_enable_extras: bool=True, sampler_index: int=0, batch_size: int=1, n_iter: int=1, steps:int =50, cfg_scale:float=7.0, width:int=512, height:int=512, restore_faces:bool=False, tiling:bool=False, do_not_save_samples:bool=False, do_not_save_grid:bool=False, extra_generation_params: Dict[Any,Any]=None, overlay_images: Any=None, negative_prompt: str=None, eta: float =None, do_not_reload_embeddings: bool=False, denoising_strength: float = 0, ddim_discretize: str = "uniform", s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_index: int = 0, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -109,13 +108,14 @@ class StableDiffusionProcessing():
         self.do_not_reload_embeddings = do_not_reload_embeddings
         self.paste_to = None
         self.color_corrections = None
-        self.denoising_strength: float = 0
+        self.denoising_strength: float = denoising_strength
         self.sampler_noise_scheduler_override = None
-        self.ddim_discretize = opts.ddim_discretize
+        self.ddim_discretize = ddim_discretize or opts.ddim_discretize
         self.s_churn = s_churn or opts.s_churn
         self.s_tmin = s_tmin or opts.s_tmin
         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
         self.s_noise = s_noise or opts.s_noise
+        self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -129,7 +129,6 @@ class StableDiffusionProcessing():
         self.all_seeds = None
         self.all_subseeds = None
 
-
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -351,6 +350,22 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
 
 def process_images(p: StableDiffusionProcessing) -> Processed:
+    stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
+
+    try:
+        for k, v in p.override_settings.items():
+            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model, hypernet impossible
+
+        res = process_images_inner(p)
+
+    finally:
+        for k, v in stored_opts.items():
+            opts.data[k] = v
+
+    return res
+
+
+def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
     if type(p.prompt) == list:
diff --git a/modules/shared.py b/modules/shared.py
index 308fccce..1a9b8289 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -84,7 +84,7 @@ parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load mod
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
 
 cmd_opts = parser.parse_args()
-restricted_opts = [
+restricted_opts = {
     "samples_filename_pattern",
     "directories_filename_pattern",
     "outdir_samples",
@@ -94,7 +94,7 @@ restricted_opts = [
     "outdir_grids",
     "outdir_txt2img_grids",
     "outdir_save",
-]
+}
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
-- 
cgit v1.2.3


From 0cd74602531a40f72d1a75b471a8a9166135d333 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 26 Oct 2022 13:12:44 +0300
Subject: add script callback for before image save and change callback for
 after image save to use a class with parameters

---
 modules/images.py           | 42 ++++++++++++++++++++++-----------------
 modules/script_callbacks.py | 48 +++++++++++++++++++++++++++++++++++++--------
 2 files changed, 64 insertions(+), 26 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index bfc2ba06..7870b5b7 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -451,17 +451,6 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     """
     namegen = FilenameGenerator(p, seed, prompt)
 
-    if extension == 'png' and opts.enable_pnginfo and info is not None:
-        pnginfo = PngImagePlugin.PngInfo()
-
-        if existing_info is not None:
-            for k, v in existing_info.items():
-                pnginfo.add_text(k, str(v))
-
-        pnginfo.add_text(pnginfo_section_name, info)
-    else:
-        pnginfo = None
-
     if save_to_dirs is None:
         save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
@@ -489,19 +478,27 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         if add_number:
             basecount = get_next_sequence_number(path, basename)
             fullfn = None
-            fullfn_without_extension = None
             for i in range(500):
                 fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
                 fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
-                fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
                 if not os.path.exists(fullfn):
                     break
         else:
             fullfn = os.path.join(path, f"{file_decoration}.{extension}")
-            fullfn_without_extension = os.path.join(path, file_decoration)
     else:
         fullfn = os.path.join(path, f"{forced_filename}.{extension}")
-        fullfn_without_extension = os.path.join(path, forced_filename)
+
+    pnginfo = existing_info or {}
+    if info is not None:
+        pnginfo[pnginfo_section_name] = info
+
+    params = script_callbacks.ImageSaveParams(image, p, fullfn, pnginfo)
+    script_callbacks.before_image_saved_callback(params)
+
+    image = params.image
+    fullfn = params.filename
+    info = params.pnginfo.get(pnginfo_section_name, None)
+    fullfn_without_extension, extension = os.path.splitext(params.filename)
 
     def exif_bytes():
         return piexif.dump({
@@ -510,12 +507,20 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             },
         })
 
-    if extension.lower() in ("jpg", "jpeg", "webp"):
+    if extension.lower() == '.png':
+        pnginfo_data = PngImagePlugin.PngInfo()
+        for k, v in params.pnginfo.items():
+            pnginfo_data.add_text(k, str(v))
+
+        image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
+
+    elif extension.lower() in (".jpg", ".jpeg", ".webp"):
         image.save(fullfn, quality=opts.jpeg_quality)
+
         if opts.enable_pnginfo and info is not None:
             piexif.insert(exif_bytes(), fullfn)
     else:
-        image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
+        image.save(fullfn, quality=opts.jpeg_quality)
 
     target_side_length = 4000
     oversize = image.width > target_side_length or image.height > target_side_length
@@ -538,7 +543,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     else:
         txt_fullfn = None
 
-    script_callbacks.image_saved_callback(image, p, fullfn, txt_fullfn)
+    script_callbacks.image_saved_callback(params)
+
     return fullfn, txt_fullfn
 
 
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 6803d57b..6ea58d61 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -9,15 +9,34 @@ def report_exception(c, job):
     print(traceback.format_exc(), file=sys.stderr)
 
 
+class ImageSaveParams:
+    def __init__(self, image, p, filename, pnginfo):
+        self.image = image
+        """the PIL image itself"""
+
+        self.p = p
+        """p object with processing parameters; either StableDiffusionProcessing or an object with same fields"""
+
+        self.filename = filename
+        """name of file that the image would be saved to"""
+
+        self.pnginfo = pnginfo
+        """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
+
+
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
+callbacks_before_image_saved = []
 callbacks_image_saved = []
 
+
 def clear_callbacks():
     callbacks_model_loaded.clear()
     callbacks_ui_tabs.clear()
+    callbacks_ui_settings.clear()
+    callbacks_before_image_saved.clear()
     callbacks_image_saved.clear()
 
 
@@ -49,10 +68,18 @@ def ui_settings_callback():
             report_exception(c, 'ui_settings_callback')
 
 
-def image_saved_callback(image, p, fullfn, txt_fullfn):
+def before_image_saved_callback(params: ImageSaveParams):
     for c in callbacks_image_saved:
         try:
-            c.callback(image, p, fullfn, txt_fullfn)
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'before_image_saved_callback')
+
+
+def image_saved_callback(params: ImageSaveParams):
+    for c in callbacks_image_saved:
+        try:
+            c.callback(params)
         except Exception:
             report_exception(c, 'image_saved_callback')
 
@@ -64,7 +91,6 @@ def add_callback(callbacks, fun):
     callbacks.append(ScriptCallback(filename, fun))
 
 
-
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
@@ -90,11 +116,17 @@ def on_ui_settings(callback):
     add_callback(callbacks_ui_settings, callback)
 
 
+def on_before_image_saved(callback):
+    """register a function to be called before an image is saved to a file.
+    The callback is called with one argument:
+        - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
+    """
+    add_callback(callbacks_before_image_saved, callback)
+
+
 def on_image_saved(callback):
-    """register a function to be called after modules.images.save_image is called.
-    The callback is called with three arguments:
-        - p - procesing object (or a dummy object with same fields if the image is saved using save button)
-        - fullfn - image filename
-        - txt_fullfn - text file with parameters; may be None
+    """register a function to be called after an image is saved to a file.
+    The callback is called with one argument:
+        - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
     """
     add_callback(callbacks_image_saved, callback)
-- 
cgit v1.2.3


From 7bd8581e461623932ffbd5762ee931ee51f798db Mon Sep 17 00:00:00 2001
From: Sihan Wang <31711261+shwang95@users.noreply.github.com>
Date: Wed, 26 Oct 2022 20:32:55 +0800
Subject: Fix error caused by EXIF transpose when using custom scripts

Some custom scripts read image directly and no need to select image in UI, this will cause error.
---
 modules/img2img.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index 9c0cf23e..86a19f37 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -80,7 +80,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         mask = None
 
     # Use the EXIF orientation of photos taken by smartphones.
-    image = ImageOps.exif_transpose(image) 
+    if image is not None:
+        image = ImageOps.exif_transpose(image) 
 
     assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
 
-- 
cgit v1.2.3


From b2e0d8ba789b345145436f6e960a3f0a896a6643 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Wed, 26 Oct 2022 09:54:26 -0300
Subject: Remove folder endpoint

---
 modules/api/api.py    | 9 ---------
 modules/api/models.py | 6 +-----
 2 files changed, 1 insertion(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index ca289d9f..49c213ea 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -32,7 +32,6 @@ class Api:
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
-        self.app.add_api_route("/sdapi/v1/extra-folder-images", self.extras_folder_processing_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -126,14 +125,6 @@ class Api:
 
         return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
     
-    def extras_folder_processing_api(self, req:ExtrasFoldersRequest):
-        reqDict = setUpscalers(req)
-
-        with self.queue_lock:
-            result = run_extras(extras_mode=2, image=None, image_folder=None, **reqDict)
-
-        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
-
     def pnginfoapi(self):
         raise NotImplementedError
 
diff --git a/modules/api/models.py b/modules/api/models.py
index 00406368..dd122321 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -148,8 +148,4 @@ class ExtrasBatchImagesRequest(ExtrasBaseRequest):
     imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
-
-class ExtrasFoldersRequest(ExtrasBaseRequest):
-    input_dir: str = Field(title="Input directory", description="Directory path from where to take the images")
-    output_dir: str = Field(title="Output directory", description="Directory path to put the processsed images into")
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
\ No newline at end of file
-- 
cgit v1.2.3


From 737eb28faca8be2bb996ee0930ec77d1f7ebd939 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 26 Oct 2022 14:45:33 +0100
Subject: typo: cmd_opts.embedding_dir to cmd_opts.embeddings_dir

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 4fcebe74..ff002d3e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -380,7 +380,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     embedding.cached_checksum = None
     # Before saving for the last time, change name back to base name (as opposed to the save_embedding_every step-suffixed naming convention).
     embedding.name = embedding_name
-    filename = os.path.join(shared.cmd_opts.embedding_dir, f'{embedding.name}.pt')
+    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding.name}.pt')
     embedding.save(filename)
 
     return embedding, filename
-- 
cgit v1.2.3


From fddb4883f4a408b3464076465e1b0949ebe0fc30 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Wed, 26 Oct 2022 22:33:45 +0800
Subject: prototype progress api

---
 modules/api/api.py | 89 +++++++++++++++++++++++++++++++++++++++++++++---------
 modules/shared.py  | 13 ++++++++
 2 files changed, 88 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 6e9d6097..c038f674 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,8 +1,11 @@
+import time
+
 from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
 from modules.extras import run_pnginfo
 import modules.shared as shared
+from modules import devices
 import uvicorn
 from fastapi import Body, APIRouter, HTTPException
 from fastapi.responses import JSONResponse
@@ -25,6 +28,37 @@ class ImageToImageResponse(BaseModel):
     parameters: Json
     info: Json
 
+class ProgressResponse(BaseModel):
+    progress: float
+    eta_relative: float
+    state: Json
+
+# copy from wrap_gradio_gpu_call of webui.py
+# because queue lock will be acquired in api handlers
+# and time start needs to be set
+# the function has been modified into two parts
+
+def before_gpu_call():
+    devices.torch_gc()
+
+    shared.state.sampling_step = 0
+    shared.state.job_count = -1
+    shared.state.job_no = 0
+    shared.state.job_timestamp = shared.state.get_job_timestamp()
+    shared.state.current_latent = None
+    shared.state.current_image = None
+    shared.state.current_image_sampling_step = 0
+    shared.state.skipped = False
+    shared.state.interrupted = False
+    shared.state.textinfo = None
+    shared.state.time_start = time.time()
+
+
+def after_gpu_call():
+    shared.state.job = ""
+    shared.state.job_count = 0
+
+    devices.torch_gc()
 
 class Api:
     def __init__(self, app, queue_lock):
@@ -33,6 +67,7 @@ class Api:
         self.queue_lock = queue_lock
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"])
 
     def __base64_to_image(self, base64_string):
         # if has a comma, deal with prefix
@@ -44,12 +79,12 @@ class Api:
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
-        
+
         if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found") 
-        
+            raise HTTPException(status_code=404, detail="Sampler not found")
+
         populate = txt2imgreq.copy(update={ # Override __init__ params
-            "sd_model": shared.sd_model, 
+            "sd_model": shared.sd_model,
             "sampler_index": sampler_index[0],
             "do_not_save_samples": True,
             "do_not_save_grid": True
@@ -57,9 +92,11 @@ class Api:
         )
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
         # Override object param
+        before_gpu_call()
         with self.queue_lock:
             processed = process_images(p)
-        
+        after_gpu_call()
+
         b64images = []
         for i in processed.images:
             buffer = io.BytesIO()
@@ -67,30 +104,30 @@ class Api:
             b64images.append(base64.b64encode(buffer.getvalue()))
 
         return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js())
-        
-        
+
+
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
         sampler_index = sampler_to_index(img2imgreq.sampler_index)
-        
+
         if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found") 
+            raise HTTPException(status_code=404, detail="Sampler not found")
 
 
         init_images = img2imgreq.init_images
         if init_images is None:
-            raise HTTPException(status_code=404, detail="Init image not found") 
+            raise HTTPException(status_code=404, detail="Init image not found")
 
         mask = img2imgreq.mask
         if mask:
             mask = self.__base64_to_image(mask)
 
-        
+
         populate = img2imgreq.copy(update={ # Override __init__ params
-            "sd_model": shared.sd_model, 
+            "sd_model": shared.sd_model,
             "sampler_index": sampler_index[0],
             "do_not_save_samples": True,
-            "do_not_save_grid": True, 
+            "do_not_save_grid": True,
             "mask": mask
             }
         )
@@ -103,9 +140,11 @@ class Api:
 
         p.init_images = imgs
         # Override object param
+        before_gpu_call()
         with self.queue_lock:
             processed = process_images(p)
-        
+        after_gpu_call()
+
         b64images = []
         for i in processed.images:
             buffer = io.BytesIO()
@@ -118,6 +157,28 @@ class Api:
 
         return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js())
 
+    def progressapi(self):
+        # copy from check_progress_call of ui.py
+
+        if shared.state.job_count == 0:
+            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.js())
+
+        # avoid dividing zero
+        progress = 0.01
+
+        if shared.state.job_count > 0:
+            progress += shared.state.job_no / shared.state.job_count
+        if shared.state.sampling_steps > 0:
+            progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
+
+        time_since_start = time.time() - shared.state.time_start
+        eta = (time_since_start/progress)
+        eta_relative = eta-time_since_start
+
+        progress = min(progress, 1)
+
+        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.js())
+
     def extrasapi(self):
         raise NotImplementedError
 
diff --git a/modules/shared.py b/modules/shared.py
index 1a9b8289..00f61898 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -146,6 +146,19 @@ class State:
     def get_job_timestamp(self):
         return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
 
+    def js(self):
+        obj = {
+            "skipped": self.skipped,
+            "interrupted": self.skipped,
+            "job": self.job,
+            "job_count": self.job_count,
+            "job_no": self.job_no,
+            "sampling_step": self.sampling_step,
+            "sampling_steps": self.sampling_steps,
+        }
+
+        return json.dumps(obj)
+
 
 state = State()
 
-- 
cgit v1.2.3


From 0dd8480281ffa3e58439a3ce059c02d9f3baa5c7 Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Wed, 26 Oct 2022 11:08:44 -0400
Subject: fix: Correct before image saved callback

---
 modules/script_callbacks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 6ea58d61..cedbe7bd 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -69,7 +69,7 @@ def ui_settings_callback():
 
 
 def before_image_saved_callback(params: ImageSaveParams):
-    for c in callbacks_image_saved:
+    for c in callbacks_before_image_saved:
         try:
             c.callback(params)
         except Exception:
-- 
cgit v1.2.3


From 3de036514138d7cdcba9729c975f1683a8e06b16 Mon Sep 17 00:00:00 2001
From: xmodar <modar.alfadly@gmail.com>
Date: Wed, 26 Oct 2022 23:56:11 +0300
Subject: Add id access to scripts list in the css

---
 modules/scripts.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 9323af3e..a7f36012 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -236,7 +236,7 @@ class ScriptRunner:
             with gr.Group():
                 create_script_ui(script, inputs, inputs_alwayson)
 
-        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
+        dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
         dropdown.save_to_config = True
         inputs[0] = dropdown
 
-- 
cgit v1.2.3


From 4a4647e0dfc812783db7fa993d486b031f098ef8 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 27 Oct 2022 13:36:11 +0800
Subject: create send to buttons in one module

---
 modules/generation_parameters_copypaste.py |  86 +++++++-
 modules/shared.py                          |   1 +
 modules/ui.py                              | 344 +++++++++--------------------
 3 files changed, 184 insertions(+), 247 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index f73647da..2b80737a 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -3,13 +3,16 @@ import re
 import gradio as gr
 from modules.shared import script_path
 from modules import shared
+import tempfile
+from PIL import Image, PngImagePlugin
 
 re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 type_of_gr_update = type(gr.update())
-
+paste_fields = {}
+bind_list = []
 
 def quote(text):
     if ',' not in str(text):
@@ -20,6 +23,81 @@ def quote(text):
     text = text.replace('"', '\\"')
     return f'"{text}"'
 
+def image_from_url_text(filedata):
+    if type(filedata) == dict and filedata["is_file"]:
+        filename = filedata["name"]
+        tempdir = os.path.normpath(tempfile.gettempdir())
+        normfn = os.path.normpath(filename)
+        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
+
+        return Image.open(filename)
+
+    if type(filedata) == list:
+        if len(filedata) == 0:
+            return None
+
+        filedata = filedata[0]
+
+    if filedata.startswith("data:image/png;base64,"):
+        filedata = filedata[len("data:image/png;base64,"):]
+
+    filedata = base64.decodebytes(filedata.encode('utf-8'))
+    image = Image.open(io.BytesIO(filedata))
+    return image
+
+def add_paste_fields(tabname, init_img, fields):
+    paste_fields[tabname] = {"init_img":init_img, "fields": fields}
+
+def create_buttons(tabs_list):
+    buttons = {}
+    for tab in tabs_list:
+        buttons[tab] = gr.Button(f"Send to {tab}")
+    return buttons
+
+#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab 
+def bind_buttons(buttons, send_image, send_generate_info):
+    bind_list.append([buttons, send_image, send_generate_info])
+
+def run_bind():
+    for buttons, send_image, send_generate_info in bind_list:
+        for tab in buttons:
+            button = buttons[tab]
+            if send_image and paste_fields[tab]["init_img"]:
+                if type(send_image) == gr.Gallery:
+                    button.click(
+                        fn=lambda x: image_from_url_text(x),
+                        _js="extract_image_from_gallery",
+                        inputs=[send_image],
+                        outputs=[paste_fields[tab]["init_img"]],
+                    )
+                else:
+                    button.click(
+                        fn=lambda x:x,
+                        inputs=[send_image],
+                        outputs=[paste_fields[tab]["init_img"]],
+                    )
+            
+            if send_generate_info and paste_fields[tab]["fields"] is not None:
+                paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2']
+                if shared.opts.send_seed:
+                    paste_field_names += ["Seed"]
+                if send_generate_info in paste_fields:                    
+                    button.click(
+                        fn=lambda *x:x,
+                        inputs=[field for field,name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
+                        outputs=[field for field,name in paste_fields[tab]["fields"] if name in paste_field_names],
+                    )
+
+                else:
+                    connect_paste(button, [(field, name) for field, name in paste_fields[tab]["fields"]  if name in paste_field_names], send_generate_info)
+
+            button.click(
+                fn=None,
+                _js=f"switch_to_{tab}",
+                inputs=None,
+                outputs=None,
+            )
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
@@ -67,8 +145,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
     return res
 
-
-def connect_paste(button, paste_fields, input_comp, js=None):
+def connect_paste(button, paste_fields, input_comp):
     def paste_func(prompt):
         if not prompt and not shared.cmd_opts.hide_ui_dir_config:
             filename = os.path.join(script_path, "params.txt")
@@ -106,7 +183,8 @@ def connect_paste(button, paste_fields, input_comp, js=None):
 
     button.click(
         fn=paste_func,
-        _js=js,
         inputs=[input_comp],
         outputs=[x[0] for x in paste_fields],
     )
+
+
diff --git a/modules/shared.py b/modules/shared.py
index f8b13b06..3ade2afa 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -279,6 +279,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
+    "send_seed": OptionInfo(False, "Send seed when sending prompt or image to other interface"),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
diff --git a/modules/ui.py b/modules/ui.py
index 3e5b84d2..ccba14b6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -35,7 +35,7 @@ if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
 
 import modules.codeformer_model
-import modules.generation_parameters_copypaste
+import modules.generation_parameters_copypaste as parameters_copypaste
 import modules.gfpgan_model
 import modules.hypernetworks.ui
 import modules.ldsr_model
@@ -49,14 +49,11 @@ from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
-txt2img_paste_fields = []
-img2img_paste_fields = []
-init_img_components = {}
-
 
 if not cmd_opts.share and not cmd_opts.listen:
     # fix gradio phoning home
@@ -99,37 +96,11 @@ def plaintext_to_html(text):
     text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
     return text
 
-
-def image_from_url_text(filedata):
-    if type(filedata) == dict and filedata["is_file"]:
-        filename = filedata["name"]
-        tempdir = os.path.normpath(tempfile.gettempdir())
-        normfn = os.path.normpath(filename)
-        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
-
-        return Image.open(filename)
-
-    if type(filedata) == list:
-        if len(filedata) == 0:
-            return None
-
-        filedata = filedata[0]
-
-    if filedata.startswith("data:image/png;base64,"):
-        filedata = filedata[len("data:image/png;base64,"):]
-
-    filedata = base64.decodebytes(filedata.encode('utf-8'))
-    image = Image.open(io.BytesIO(filedata))
-    return image
-
-
 def send_gradio_gallery_to_image(x):
     if len(x) == 0:
         return None
-
     return image_from_url_text(x[0])
 
-
 def save_files(js_data, images, do_make_zip, index):
     import csv
     filenames = []
@@ -193,7 +164,6 @@ def save_files(js_data, images, do_make_zip, index):
 
     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
-
 def save_pil_to_file(pil_image, dir=None):
     use_metadata = False
     metadata = PngImagePlugin.PngInfo()
@@ -626,6 +596,83 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
     )
     return refresh_button
 
+def create_output_panel(tabname, outdir):    
+    def open_folder(f):
+        if not os.path.exists(f):
+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+            return
+        elif not os.path.isdir(f):
+            print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+            return
+
+        if not shared.cmd_opts.hide_ui_dir_config:
+            path = os.path.normpath(f)
+            if platform.system() == "Windows":
+                os.startfile(path)
+            elif platform.system() == "Darwin":
+                sp.Popen(["open", path])
+            else:
+                sp.Popen(["xdg-open", path])
+    
+    with gr.Column(variant='panel'):        
+            with gr.Group():                
+                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+               
+            generation_info = None
+            with gr.Column():
+                with gr.Row():
+                    if tabname != "extras":
+                        save = gr.Button('Save')
+
+                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+                    open_folder = gr.Button(folder_symbol, elem_id=button_id)
+
+                open_folder.click(
+                    fn=lambda: open_folder(opts.outdir_samples or outdir),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                if tabname != "extras":           
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
+
+                        save.click(
+                            fn=wrap_gradio_call(save_files),
+                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+                            inputs=[
+                                generation_info,
+                                result_gallery,
+                                do_make_zip,
+                                html_info,
+                            ],
+                            outputs=[
+                                download_files,
+                                html_info,
+                                html_info,
+                                html_info,
+                            ]
+                        )
+                else:
+                    html_info_x = gr.HTML()
+                    html_info = gr.HTML()
+                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+                    
+            
 
 def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
@@ -676,31 +723,10 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
-            with gr.Column(variant='panel'):
-
-                with gr.Group():
-                    txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
-                    txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
-
-                with gr.Column():
-                    with gr.Row():
-                        save = gr.Button('Save')
-                        send_to_img2img = gr.Button('Send to img2img')
-                        send_to_inpaint = gr.Button('Send to inpaint')
-                        send_to_extras = gr.Button('Send to extras')
-                        button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                        open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
-
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
+     
 
+            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)  
+                
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
@@ -755,24 +781,7 @@ def create_ui(wrap_gradio_gpu_call):
                 fn=lambda x: gr_show(x),
                 inputs=[enable_hr],
                 outputs=[hr_options],
-            )
-
-            save.click(
-                fn=wrap_gradio_call(save_files),
-                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                inputs=[
-                    generation_info,
-                    txt2img_gallery,
-                    do_make_zip,
-                    html_info,
-                ],
-                outputs=[
-                    download_files,
-                    html_info,
-                    html_info,
-                    html_info,
-                ]
-            )
+            )            
 
             roll.click(
                 fn=roll_artist,
@@ -785,8 +794,7 @@ def create_ui(wrap_gradio_gpu_call):
                 ]
             )
 
-            global txt2img_paste_fields 
-            txt2img_paste_fields = [
+            parameters_copypaste.add_paste_fields("txt2img", None, [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
                 (steps, "Steps"),
@@ -807,7 +815,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
                 *modules.scripts.scripts_txt2img.infotext_fields
-            ]
+            ])
 
             txt2img_preview_params = [
                 txt2img_prompt,
@@ -894,30 +902,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
 
-            with gr.Column(variant='panel'):
-
-                with gr.Group():
-                    img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
-                    img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4)
-
-                with gr.Column():
-                    with gr.Row():
-                        save = gr.Button('Save')
-                        img2img_send_to_img2img = gr.Button('Send to img2img')
-                        img2img_send_to_inpaint = gr.Button('Send to inpaint')
-                        img2img_send_to_extras = gr.Button('Send to extras')
-                        button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                        open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
-
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
+            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)                 
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -1004,24 +989,8 @@ def create_ui(wrap_gradio_gpu_call):
                     fn=interrogate_deepbooru,
                     inputs=[init_img],
                     outputs=[img2img_prompt],
-                )
-
-            save.click(
-                fn=wrap_gradio_call(save_files),
-                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                inputs=[
-                    generation_info,
-                    img2img_gallery,
-                    do_make_zip,
-                    html_info,
-                ],
-                outputs=[
-                    download_files,
-                    html_info,
-                    html_info,
-                    html_info,
-                ]
             )
+           
 
             roll.click(
                 fn=roll_artist,
@@ -1056,7 +1025,8 @@ def create_ui(wrap_gradio_gpu_call):
                     outputs=[prompt, negative_prompt, style1, style2],
                 )
 
-            global img2img_paste_fields
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+
             img2img_paste_fields = [
                 (img2img_prompt, "Prompt"),
                 (img2img_negative_prompt, "Negative prompt"),
@@ -1075,7 +1045,9 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 *modules.scripts.scripts_img2img.infotext_fields
             ]
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields) 
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
@@ -1122,15 +1094,8 @@ def create_ui(wrap_gradio_gpu_call):
 
                 submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
 
-            with gr.Column(variant='panel'):
-                result_images = gr.Gallery(label="Result", show_label=False)
-                html_info_x = gr.HTML()
-                html_info = gr.HTML()
-                extras_send_to_img2img = gr.Button('Send to img2img')
-                extras_send_to_inpaint = gr.Button('Send to inpaint')
-                button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''
-                open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
 
+            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
             fn=wrap_gradio_gpu_call(modules.extras.run_extras),
@@ -1160,23 +1125,8 @@ def create_ui(wrap_gradio_gpu_call):
                 html_info,
             ]
         )
+        parameters_copypaste.add_paste_fields("extras", extras_image, None) 
 
-        extras_send_to_img2img.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_img2img",
-            inputs=[result_images],
-            outputs=[init_img],
-        )
-
-        extras_send_to_inpaint.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_inpaint",
-            inputs=[result_images],
-            outputs=[init_img_with_mask],
-        )
-
-    global  init_img_components
-    init_img_components = {"img2img":init_img, "inpaint":init_img_with_mask, "extras":extras_image}
 
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
         with gr.Row().style(equal_height=False):
@@ -1187,11 +1137,10 @@ def create_ui(wrap_gradio_gpu_call):
                 html = gr.HTML()
                 generation_info = gr.Textbox(visible=False)
                 html2 = gr.HTML()
-
                 with gr.Row():
-                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
-
+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+                parameters_copypaste.bind_buttons(buttons, image, generation_info)                
+                
         image.change(
             fn=wrap_gradio_call(modules.extras.run_pnginfo),
             inputs=[image],
@@ -1475,28 +1424,6 @@ def create_ui(wrap_gradio_gpu_call):
     script_callbacks.ui_settings_callback()
     opts.reorder()
 
-    def open_folder(f):
-        if not os.path.exists(f):
-            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
-            return
-        elif not os.path.isdir(f):
-            print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
-            return
-
-        if not shared.cmd_opts.hide_ui_dir_config:
-            path = os.path.normpath(f)
-            if platform.system() == "Windows":
-                os.startfile(path)
-            elif platform.system() == "Darwin":
-                sp.Popen(["open", path])
-            else:
-                sp.Popen(["xdg-open", path])
-
     def run_settings(*args):
         changed = 0
 
@@ -1641,6 +1568,8 @@ Requested path was: {f}
         if column is not None:
             column.__exit__()
 
+        parameters_copypaste.run_bind()
+
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
         (img2img_interface, "img2img", "img2img"),
@@ -1731,85 +1660,14 @@ Requested path was: {f}
                 component_dict['sd_model_checkpoint'],
             ]
         )
-        paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2']
-        txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names]
-        img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names]
-        send_to_img2img.click(
-            fn=lambda img, *args: (image_from_url_text(img),*args),
-            _js="(gallery, ...args) => [extract_image_from_gallery_img2img(gallery), ...args]",
-            inputs=[txt2img_gallery] + txt2img_fields,
-            outputs=[init_img] + img2img_fields,
-        )
-
-        send_to_inpaint.click(
-            fn=lambda x, *args: (image_from_url_text(x), *args),
-            _js="(gallery, ...args) => [extract_image_from_gallery_inpaint(gallery), ...args]",
-            inputs=[txt2img_gallery] + txt2img_fields,
-            outputs=[init_img_with_mask] + img2img_fields,
-        )
-
-        img2img_send_to_img2img.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_img2img",
-            inputs=[img2img_gallery],
-            outputs=[init_img],
-        )
-
-        img2img_send_to_inpaint.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_inpaint",
-            inputs=[img2img_gallery],
-            outputs=[init_img_with_mask],
-        )
-
-        send_to_extras.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_extras",
-            inputs=[txt2img_gallery],
-            outputs=[extras_image],
-        )
-
-        open_txt2img_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_txt2img_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        open_img2img_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_img2img_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        open_extras_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_extras_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        img2img_send_to_extras.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_extras",
-            inputs=[img2img_gallery],
-            outputs=[extras_image],
-        )
+ 
 
         settings_map = {
             'sd_hypernetwork': 'Hypernet',
             'CLIP_stop_at_last_layers': 'Clip skip',
             'sd_model_checkpoint': 'Model hash',
         }
-
-        settings_paste_fields = [
-            (component_dict[k], lambda d, k=k, v=v: apply_setting(k, d.get(v, None)))
-            for k, v in settings_map.items()
-        ]
-
-        modules.generation_parameters_copypaste.connect_paste(txt2img_paste, txt2img_paste_fields + settings_paste_fields, txt2img_prompt)
-        modules.generation_parameters_copypaste.connect_paste(img2img_paste, img2img_paste_fields + settings_paste_fields, img2img_prompt)
-
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_txt2img')
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_img2img_img2img')
+       
 
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
-- 
cgit v1.2.3


From 85fcccc105aa50f1d78de559233eaa9f384608b5 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Wed, 26 Oct 2022 22:24:33 +0900
Subject: Squashed commit of fixing dropout silently

fix dropouts for future hypernetworks

add kwargs for Hypernetwork class

hypernet UI for gradio input

add recommended options

remove as options

revert adding options in ui
---
 modules/hypernetworks/hypernetwork.py | 25 +++++++++++++++++--------
 modules/ui.py                         |  4 ++--
 2 files changed, 19 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 2997cead..dd921153 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -34,7 +34,8 @@ class HypernetworkModule(torch.nn.Module):
     }
     activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False, activate_output=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
+                 add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -60,7 +61,7 @@ class HypernetworkModule(torch.nn.Module):
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
             # Add dropout except last layer
-            if use_dropout and i < len(layer_structure) - 3:
+            if 'last_layer_dropout' in kwargs and kwargs['last_layer_dropout'] and use_dropout and i < len(layer_structure) - 2:
                 linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
@@ -126,7 +127,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -139,11 +140,14 @@ class Hypernetwork:
         self.add_layer_norm = add_layer_norm
         self.use_dropout = use_dropout
         self.activate_output = activate_output
+        self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
             )
 
     def weights(self):
@@ -172,7 +176,8 @@ class Hypernetwork:
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
         state_dict['activate_output'] = self.activate_output
-
+        state_dict['last_layer_dropout'] = self.last_layer_dropout
+        
         torch.save(state_dict, filename)
 
     def load(self, filename):
@@ -193,12 +198,16 @@ class Hypernetwork:
         self.use_dropout = state_dict.get('use_dropout', False)
         print(f"Dropout usage is set to {self.use_dropout}" )
         self.activate_output = state_dict.get('activate_output', True)
+        print(f"Activate last layer is set to {self.activate_output}")
+        self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)
diff --git a/modules/ui.py b/modules/ui.py
index 0a63e357..55cbe859 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1238,8 +1238,8 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Normal is default, for experiments, relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
                     overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
-- 
cgit v1.2.3


From cc56df996e95c2c82295ab7b9928da2544791220 Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 23:51:51 +0800
Subject: Fix dropout logic

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index dd921153..b17598fe 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -35,7 +35,7 @@ class HypernetworkModule(torch.nn.Module):
     activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
     def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
-                 add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
+                 add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=True):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -61,7 +61,7 @@ class HypernetworkModule(torch.nn.Module):
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
             # Add dropout except last layer
-            if 'last_layer_dropout' in kwargs and kwargs['last_layer_dropout'] and use_dropout and i < len(layer_structure) - 2:
+            if use_dropout and (i < len(layer_structure) - 3 or last_layer_dropout and i < len(layer_structure) - 2):
                 linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
-- 
cgit v1.2.3


From 029d7c75436558f1e884bb127caed73caaecb83a Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Thu, 27 Oct 2022 14:44:53 +0900
Subject: Revert unresolved changes in Bias initialization

it should be zeros_ or parameterized in future properly.
---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b17598fe..25427a37 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -75,7 +75,7 @@ class HypernetworkModule(torch.nn.Module):
                     w, b = layer.weight.data, layer.bias.data
                     if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:
                         normal_(w, mean=0.0, std=0.01)
-                        normal_(b, mean=0.0, std=0.005)
+                        normal_(b, mean=0.0, std=0)
                     elif weight_init == 'XavierUniform':
                         xavier_uniform_(w)
                         zeros_(b)
-- 
cgit v1.2.3


From 462e6ba6675bd14c0f82e465423a0eedfff82372 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Thu, 27 Oct 2022 15:40:24 +0900
Subject: Disable unavailable or duplicate options

---
 modules/hypernetworks/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 2c6c0470..c2d4b51c 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -8,7 +8,8 @@ import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
-keys = list(hypernetwork.HypernetworkModule.activation_dict.keys())
+not_available = ["hardswish", "multiheadattention"]
+keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
     # Remove illegal characters from name.
-- 
cgit v1.2.3


From e0cbf53f451f45ea73dafab654eb6596cbd67ec2 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 27 Oct 2022 18:00:51 +0800
Subject: create send to buttons by extensions

---
 modules/ui.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index ccba14b6..922a2163 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1568,7 +1568,8 @@ def create_ui(wrap_gradio_gpu_call):
         if column is not None:
             column.__exit__()
 
-        parameters_copypaste.run_bind()
+
+        
 
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
@@ -1581,7 +1582,7 @@ def create_ui(wrap_gradio_gpu_call):
 
     interfaces += script_callbacks.ui_tabs_callback()
 
-    interfaces += [(settings_interface, "Settings", "settings")]
+    interfaces += [(settings_interface, "Settings", "settings")]       
 
     css = ""
 
@@ -1667,7 +1668,8 @@ def create_ui(wrap_gradio_gpu_call):
             'CLIP_stop_at_last_layers': 'Clip skip',
             'sd_model_checkpoint': 'Model hash',
         }
-       
+
+        parameters_copypaste.run_bind()       
 
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
-- 
cgit v1.2.3


From c4b5ca5778340b21288d84dfb8fe1d5773c886a8 Mon Sep 17 00:00:00 2001
From: Yuta Hayashibe <yuta@hayashibe.jp>
Date: Thu, 27 Oct 2022 22:00:28 +0900
Subject: Truncate too long filename

---
 modules/images.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 7870b5b7..42363ed3 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -416,6 +416,14 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
+def truncate_fullpath(full_path, encoding='utf-8'):
+    dir_name, full_name = os.path.split(full_path)
+    file_name, file_ext = os.path.splitext(full_name)
+    max_length = os.statvfs(dir_name).f_namemax
+    file_name_truncated = file_name.encode(encoding)[:max_length - len(file_ext)].decode(encoding, 'ignore')
+    return os.path.join(dir_name , file_name_truncated + file_ext)
+
+
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
     """Save an image.
 
@@ -456,7 +464,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
     if save_to_dirs:
         dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')
-        path = os.path.join(path, dirname)
+        path = truncate_fullpath(os.path.join(path, dirname))
 
     os.makedirs(path, exist_ok=True)
 
@@ -480,13 +488,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             fullfn = None
             for i in range(500):
                 fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
-                fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
+                fullfn = truncate_fullpath(os.path.join(path, f"{fn}{file_decoration}.{extension}"))
                 if not os.path.exists(fullfn):
                     break
         else:
-            fullfn = os.path.join(path, f"{file_decoration}.{extension}")
+            fullfn = truncate_fullpath(os.path.join(path, f"{file_decoration}.{extension}"))
     else:
-        fullfn = os.path.join(path, f"{forced_filename}.{extension}")
+        fullfn = truncate_fullpath(os.path.join(path, f"{forced_filename}.{extension}"))
 
     pnginfo = existing_info or {}
     if info is not None:
-- 
cgit v1.2.3


From 0995e879cea8ce871489ea8e393bb0eba6edc09c Mon Sep 17 00:00:00 2001
From: Florian Horn <florianins@gmail.com>
Date: Thu, 27 Oct 2022 16:20:01 +0200
Subject: added save button and shortcut (s) to Modal View

---
 modules/ui.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 0a63e357..1332e265 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -630,7 +630,7 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
-
+    
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
@@ -683,7 +683,8 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Column():
                     with gr.Row():
-                        save = gr.Button('Save')
+                        saveButtonId = 'save_txt2img'
+                        save = gr.Button('Save', elem_id=saveButtonId)
                         send_to_img2img = gr.Button('Send to img2img')
                         send_to_inpaint = gr.Button('Send to inpaint')
                         send_to_extras = gr.Button('Send to extras')
@@ -901,7 +902,8 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Column():
                     with gr.Row():
-                        save = gr.Button('Save')
+                        saveButtonId = 'save_img2img'
+                        save = gr.Button('Save', elem_id=saveButtonId)
                         img2img_send_to_img2img = gr.Button('Send to img2img')
                         img2img_send_to_inpaint = gr.Button('Send to inpaint')
                         img2img_send_to_extras = gr.Button('Send to extras')
-- 
cgit v1.2.3


From 268159cfe3231743c554a1a9bf15d090c758f920 Mon Sep 17 00:00:00 2001
From: Florian Horn <florianins@gmail.com>
Date: Thu, 27 Oct 2022 16:32:10 +0200
Subject: fixed indentation

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 1332e265..d49b10b2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -630,7 +630,7 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
-    
+
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
-- 
cgit v1.2.3


From a0a7024c679056dd66beb1832e52041b10143130 Mon Sep 17 00:00:00 2001
From: FlameLaw <116745066+FlameLaw@users.noreply.github.com>
Date: Fri, 28 Oct 2022 02:13:48 +0900
Subject: Fix random dataset shuffle on TI

---
 modules/textual_inversion/dataset.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 5b1c5002..8bb00d27 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -86,12 +86,12 @@ class PersonalizedBase(Dataset):
         assert len(self.dataset) > 0, "No images have been found in the dataset."
         self.length = len(self.dataset) * repeats // batch_size
 
-        self.initial_indexes = np.arange(len(self.dataset))
+        self.dataset_length = len(self.dataset)
         self.indexes = None
         self.shuffle()
 
     def shuffle(self):
-        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()]
+        self.indexes = np.random.permutation(self.dataset_length)
 
     def create_text(self, filename_text):
         text = random.choice(self.lines)
-- 
cgit v1.2.3


From 26a3fd2fe9314330336fb0e28d1e9ca7d2abe10e Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 27 Oct 2022 11:27:59 -0700
Subject: Highres fix works with unmasked latent. Also refactor the mask
 creation to make it more accesible.

---
 modules/processing.py | 134 ++++++++++++++++++++++++++++----------------------
 1 file changed, 76 insertions(+), 58 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index f72185ac..548eec29 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -129,6 +129,73 @@ class StableDiffusionProcessing():
         self.all_seeds = None
         self.all_subseeds = None
 
+    def txt2img_image_conditioning(self, x, width=None, height=None):
+        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
+            # Dummy zero conditioning if we're not using inpainting model.
+            # Still takes up a bit of memory, but no encoder call.
+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+            return torch.zeros(
+                x.shape[0], 5, 1, 1, 
+                dtype=x.dtype, 
+                device=x.device
+            )
+
+        height = height or self.height
+        width = width or self.width
+
+        # The "masked-image" in this case will just be all zeros since the entire image is masked.
+        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+
+        # Add the fake full 1s mask to the first dimension.
+        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+        image_conditioning = image_conditioning.to(x.dtype)            
+
+        return image_conditioning
+
+    def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
+        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
+            # Dummy zero conditioning if we're not using inpainting model.
+            return torch.zeros(
+                latent_image.shape[0], 5, 1, 1,
+                dtype=latent_image.dtype,
+                device=latent_image.device
+            )
+
+        # Handle the different mask inputs
+        if image_mask is not None:
+            if torch.is_tensor(image_mask):
+                conditioning_mask = image_mask
+            else:
+                conditioning_mask = np.array(image_mask.convert("L"))
+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+                conditioning_mask = torch.round(conditioning_mask)
+        else:
+            conditioning_mask = torch.ones(1, 1, *source_image.shape[-2:])
+
+        # Create another latent image, this time with a masked version of the original input.
+        # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
+        conditioning_mask = conditioning_mask.to(source_image.device)
+        conditioning_image = torch.lerp(
+            source_image,
+            source_image * (1.0 - conditioning_mask),
+            getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
+        )
+        
+        # Encode the new masked image using first stage of network.
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+        # Create the concatenated conditioning tensor to be fed to `c_concat`
+        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
+        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+        image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+        image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
+        return image_conditioning
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -571,37 +638,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-    def create_dummy_mask(self, x, width=None, height=None):
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            height = height or self.height
-            width = width or self.width
-
-            # The "masked-image" in this case will just be all zeros since the entire image is masked.
-            image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-            image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
-
-            # Add the fake full 1s mask to the first dimension.
-            image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-            image_conditioning = image_conditioning.to(x.dtype)
-
-        else:
-            # Dummy zero conditioning if we're not using inpainting model.
-            # Still takes up a bit of memory, but no encoder call.
-            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
-
-        return image_conditioning
-
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height))
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
@@ -638,7 +684,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples))
+        image_conditioning = self.img2img_image_conditioning(
+            decoded_samples, 
+            samples, 
+            decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
+        )
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
 
         return samples
 
@@ -770,40 +821,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            if self.image_mask is not None:
-                conditioning_mask = np.array(self.image_mask.convert("L"))
-                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
-
-                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-                conditioning_mask = torch.round(conditioning_mask)
-            else:
-                conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
-
-            # Create another latent image, this time with a masked version of the original input.
-            conditioning_mask = conditioning_mask.to(image.device)
-
-            # Smoothly interpolate between the masked and unmasked latent conditioning image.
-            conditioning_image = torch.lerp(
-                image,
-                image * (1.0 - conditioning_mask),
-                getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
-            )
-            
-            conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-
-            # Create the concatenated conditioning tensor to be fed to `c_concat`
-            conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
-            conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-            self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-            self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
-        else:
-            self.image_conditioning = torch.zeros(
-                self.init_latent.shape[0], 5, 1, 1,
-                dtype=self.init_latent.dtype,
-                device=self.init_latent.device
-            )
+        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
 
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-- 
cgit v1.2.3


From a38496c1deef12f56f74f8abce2034bef8bdaccb Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 27 Oct 2022 11:31:31 -0700
Subject: Moved mask weight config to SD section

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index d47378e8..9c2fa0d4 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -267,6 +267,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
+    "inpainting_mask_weight": OptionInfo(1.0, "Strength of img2img conditioning mask for inpainting models.", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
@@ -320,7 +321,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
-    "inpainting_mask_weight": OptionInfo(1.0, "Blend betweeen an unmasked and masked conditioning image for inpainting models.", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
 }))
 
 
-- 
cgit v1.2.3


From b68c7c437eda2840a304539dd2acd0b0894e920c Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 27 Oct 2022 11:45:35 -0700
Subject: Updated name and hover text.

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 9c2fa0d4..7c428d90 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -267,7 +267,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
-    "inpainting_mask_weight": OptionInfo(1.0, "Strength of img2img conditioning mask for inpainting models.", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
-- 
cgit v1.2.3


From bdc90837987ed8919dd611fd01553b0c170ded5c Mon Sep 17 00:00:00 2001
From: Roy Shilkrot <roy.shil@gmail.com>
Date: Thu, 27 Oct 2022 15:20:15 -0400
Subject: Add a barebones interrogate API

---
 modules/api/api.py    | 25 ++++++++++++++++++++++++-
 modules/api/models.py | 13 ++++++++++++-
 2 files changed, 36 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 6e9d6097..eabdb7b8 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,4 +1,4 @@
-from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
+from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI, InterrogateAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
 from modules.extras import run_pnginfo
@@ -25,6 +25,11 @@ class ImageToImageResponse(BaseModel):
     parameters: Json
     info: Json
 
+class InterrogateResponse(BaseModel):
+    caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
+    parameters: Json
+    info: Json
+
 
 class Api:
     def __init__(self, app, queue_lock):
@@ -33,6 +38,7 @@ class Api:
         self.queue_lock = queue_lock
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
 
     def __base64_to_image(self, base64_string):
         # if has a comma, deal with prefix
@@ -118,6 +124,23 @@ class Api:
 
         return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js())
 
+    def interrogateapi(self, interrogatereq: InterrogateAPI):
+        image_b64 = interrogatereq.image
+        if image_b64 is None:
+            raise HTTPException(status_code=404, detail="Image not found") 
+
+        populate = interrogatereq.copy(update={ # Override __init__ params
+            }
+        )
+
+        img = self.__base64_to_image(image_b64)
+
+        # Override object param
+        with self.queue_lock:
+            processed = shared.interrogator.interrogate(img)
+        
+        return InterrogateResponse(caption=processed, parameters=json.dumps(vars(interrogatereq)), info=None)
+
     def extrasapi(self):
         raise NotImplementedError
 
diff --git a/modules/api/models.py b/modules/api/models.py
index 079e33d9..8be64749 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -63,7 +63,12 @@ class PydanticModelGenerator:
             
                 
         self._model_name = model_name
-        self._class_data = merge_class_params(class_instance)
+
+        if class_instance is not None:
+            self._class_data = merge_class_params(class_instance)
+        else:
+            self._class_data = {}            
+
         self._model_def = [
             ModelDef(
                 field=underscore(k),
@@ -105,4 +110,10 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingImg2Img", 
     StableDiffusionProcessingImg2Img,
     [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
+).generate_model()
+
+InterrogateAPI = PydanticModelGenerator(
+    "Interrogate", 
+    None,
+    [{"key": "image", "type": str, "default": None}]
 ).generate_model()
\ No newline at end of file
-- 
cgit v1.2.3


From b50ff4f4e4d4d6bf31e222832d3fe4cfde4703c9 Mon Sep 17 00:00:00 2001
From: Josh Watzman <github@jwatzman.org>
Date: Thu, 27 Oct 2022 21:59:16 +0100
Subject: Reduce peak memory usage when changing models

A few tweaks to reduce peak memory usage, the biggest being that if we
aren't using the checkpoint cache, we shouldn't duplicate the model
state dict just to immediately throw it away.

On my machine with 16GB of RAM, this change means I can typically change
models, whereas before it would typically OOM.
---
 modules/sd_models.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index e697bb72..203e99a8 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -170,7 +170,9 @@ def load_model_weights(model, checkpoint_info):
             print(f"Global Step: {pl_sd['global_step']}")
 
         sd = get_state_dict_from_checkpoint(pl_sd)
-        missing, extra = model.load_state_dict(sd, strict=False)
+        del pl_sd
+        model.load_state_dict(sd, strict=False)
+        del sd
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)
@@ -194,9 +196,10 @@ def load_model_weights(model, checkpoint_info):
 
         model.first_stage_model.to(devices.dtype_vae)
 
-        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
-            checkpoints_loaded.popitem(last=False)  # LRU
+        if shared.opts.sd_checkpoint_cache > 0:
+            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+                checkpoints_loaded.popitem(last=False)  # LRU
     else:
         print(f"Loading weights [{sd_model_hash}] from cache")
         checkpoints_loaded.move_to_end(checkpoint_info)
-- 
cgit v1.2.3


From 5d5dc64064d8ca399a76fe44dbb62bdef6c4b7c4 Mon Sep 17 00:00:00 2001
From: Antonio <antonio.hernandez.uk@gmail.com>
Date: Fri, 28 Oct 2022 05:49:39 +0200
Subject: Natural sorting for dropdown checkpoint list

Example:

Before					After

11.ckpt					11.ckpt
ab.ckpt					ab.ckpt
ade_pablo_step_1000.ckpt	ade_pablo_step_500.ckpt
ade_pablo_step_500.ckpt	ade_pablo_step_1000.ckpt
ade_step_1000.ckpt		ade_step_500.ckpt
ade_step_1500.ckpt		ade_step_1000.ckpt
ade_step_2000.ckpt		ade_step_1500.ckpt
ade_step_2500.ckpt		ade_step_2000.ckpt
ade_step_3000.ckpt		ade_step_2500.ckpt
ade_step_500.ckpt			ade_step_3000.ckpt
atp_step_5500.ckpt			atp_step_5500.ckpt
model1.ckpt				model1.ckpt
model10.ckpt				model10.ckpt
model1000.ckpt			model33.ckpt
model33.ckpt				model50.ckpt
model400.ckpt			model400.ckpt
model50.ckpt				model1000.ckpt
moo44.ckpt				moo44.ckpt
v1-4-pruned-emaonly.ckpt	v1-4-pruned-emaonly.ckpt
v1-5-pruned-emaonly.ckpt	v1-5-pruned-emaonly.ckpt
v1-5-pruned.ckpt			v1-5-pruned.ckpt
v1-5-vae.ckpt				v1-5-vae.ckpt
---
 modules/sd_models.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index e697bb72..64d5ee0d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -3,6 +3,7 @@ import os.path
 import sys
 from collections import namedtuple
 import torch
+import re
 from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
@@ -35,8 +36,10 @@ def setup_model():
     list_models()
 
 
-def checkpoint_tiles():
-    return sorted([x.title for x in checkpoints_list.values()])
+def checkpoint_tiles(): 
+    convert = lambda name: int(name) if name.isdigit() else name.lower() 
+    alphanumeric_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)] 
+    return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
 
 
 def list_models():
-- 
cgit v1.2.3


From b2a8b263b2f09bd772f75502c5a83656580f34ec Mon Sep 17 00:00:00 2001
From: benkyoujouzu <benkyoujouzu@gmail.com>
Date: Thu, 27 Oct 2022 13:00:47 +0800
Subject: Add missing support for linear activation in hypernetwork

---
 modules/hypernetworks/hypernetwork.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8113b35b..87cf3cf3 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -25,6 +25,7 @@ from statistics import stdev, mean
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
     activation_dict = {
+        "linear": torch.nn.Identity,
         "relu": torch.nn.ReLU,
         "leakyrelu": torch.nn.LeakyReLU,
         "elu": torch.nn.ELU,
-- 
cgit v1.2.3


From 9e465c8aa5616df4c6723bee007ffd3910404f12 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Thu, 27 Oct 2022 23:03:34 -0700
Subject: Add strength to textinfo.

---
 modules/processing.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 4efba946..93066522 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -329,6 +329,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
         "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
+        "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.3


From d4a069a23cb19104b4e58a33d0d1670fadaefb7a Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Thu, 27 Oct 2022 23:16:27 -0700
Subject: Read hypernet strength from PNG info.

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 0a63e357..62a2f4f3 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1812,6 +1812,7 @@ Requested path was: {f}
 
         settings_map = {
             'sd_hypernetwork': 'Hypernet',
+            'sd_hypernetwork_strength': 'Hypernetwork strength',
             'CLIP_stop_at_last_layers': 'Clip skip',
             'sd_model_checkpoint': 'Model hash',
         }
-- 
cgit v1.2.3


From c0677b33161f04c3ed1a7a78f4c7288fb95787b7 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Thu, 27 Oct 2022 23:31:45 -0700
Subject: Explicitly state when Hypernet is none.

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 93066522..74a0cd64 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -328,7 +328,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
+        "Hypernet": ("None" if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
         "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
-- 
cgit v1.2.3


From db5a354c489bfd1c95e0bbf9af12ab8b5d6fe170 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Fri, 28 Oct 2022 01:41:57 -0700
Subject: Always ignore "None.pt" in the hypernet directory.

---
 modules/hypernetworks/hypernetwork.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8113b35b..cd920df5 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -208,13 +208,16 @@ def list_hypernetworks(path):
     res = {}
     for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
         name = os.path.splitext(os.path.basename(filename))[0]
-        res[name] = filename
+        # Prevent a hypothetical "None.pt" from being listed.
+        if name != "None":
+            res[name] = filename
     return res
 
 
 def load_hypernetwork(filename):
     path = shared.hypernetworks.get(filename, None)
-    if path is not None:
+    # Prevent any file named "None.pt" from being loaded.
+    if path is not None and filename != "None":
         print(f"Loading hypernetwork {filename}")
         try:
             shared.loaded_hypernetwork = Hypernetwork()
-- 
cgit v1.2.3


From 9ceef81f77ecce89f0c8f412c4d849210d852e82 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Fri, 28 Oct 2022 20:48:08 +0700
Subject: Fix log off by 1

---
 modules/hypernetworks/hypernetwork.py          | 12 +++++++-----
 modules/textual_inversion/learn_schedule.py    |  2 +-
 modules/textual_inversion/textual_inversion.py | 24 ++++++++++++------------
 3 files changed, 20 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8113b35b..a0297997 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -428,7 +428,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             optimizer.step()
 
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
+        steps_done = hypernetwork.step + 1
+
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
             raise RuntimeError("Loss diverged.")
         
         if len(previous_mean_losses) > 1:
@@ -438,9 +440,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
         pbar.set_description(dataset_loss_info)
 
-        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
+        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
             # Before saving, change name to match current checkpoint.
-            hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
+            hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
             hypernetwork.save(last_saved_file)
 
@@ -449,8 +451,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             "learn_rate": scheduler.learn_rate
         })
 
-        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-            forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{hypernetwork_name}-{steps_done}'
             last_saved_image = os.path.join(images_dir, forced_filename)
 
             optimizer.zero_grad()
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index 2062726a..3a736065 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -52,7 +52,7 @@ class LearnRateScheduler:
         self.finished = False
 
     def apply(self, optimizer, step_number):
-        if step_number <= self.end_step:
+        if step_number < self.end_step:
             return
 
         try:
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ff002d3e..17dfb223 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -184,9 +184,8 @@ def write_loss(log_directory, filename, step, epoch_len, values):
     if shared.opts.training_write_csv_every == 0:
         return
 
-    if step % shared.opts.training_write_csv_every != 0:
+    if (step + 1) % shared.opts.training_write_csv_every != 0:
         return
-
     write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
 
     with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
@@ -196,11 +195,11 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             csv_writer.writeheader()
 
         epoch = step // epoch_len
-        epoch_step = step - epoch * epoch_len
+        epoch_step = step % epoch_len 
 
         csv_writer.writerow({
             "step": step + 1,
-            "epoch": epoch + 1,
+            "epoch": epoch,
             "epoch_step": epoch_step + 1,
             **values,
         })
@@ -282,15 +281,16 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             loss.backward()
             optimizer.step()
 
+        steps_done = embedding.step + 1
 
         epoch_num = embedding.step // len(ds)
-        epoch_step = embedding.step - (epoch_num * len(ds)) + 1
+        epoch_step = embedding.step % len(ds)
 
-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
 
-        if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
+        if embedding_dir is not None and steps_done % save_embedding_every == 0:
             # Before saving, change name to match current checkpoint.
-            embedding.name = f'{embedding_name}-{embedding.step}'
+            embedding.name = f'{embedding_name}-{steps_done}'
             last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
             embedding.save(last_saved_file)
             embedding_yet_to_be_embedded = True
@@ -300,8 +300,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             "learn_rate": scheduler.learn_rate
         })
 
-        if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
-            forced_filename = f'{embedding_name}-{embedding.step}'
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{embedding_name}-{steps_done}'
             last_saved_image = os.path.join(images_dir, forced_filename)
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
@@ -334,7 +334,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
             if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
 
-                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
+                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
 
                 info = PngImagePlugin.PngInfo()
                 data = torch.load(last_saved_file)
@@ -350,7 +350,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}v {}s'.format(vectorSize, embedding.step)
+                footer_right = '{}v {}s'.format(vectorSize, steps_done)
 
                 captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                 captioned_image = insert_image_data_embed(captioned_image, data)
-- 
cgit v1.2.3


From 26d08193848568b06105a1ee7b76f338ebf0f0ee Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Fri, 28 Oct 2022 13:24:11 -0500
Subject: extras: Add option to run upscaling before face fixing

Face restoration can look much better if ran after upscaling, as it
allows the restoration to fix upscaling artifacts. This patch adds
an option to choose which order to run upscaling/face fixing in.
---
 modules/extras.py | 145 +++++++++++++++++++++++++++++++++++-------------------
 modules/ui.py     |   4 ++
 2 files changed, 99 insertions(+), 50 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 22c5a1c1..79047f3a 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -7,6 +7,10 @@ from PIL import Image
 import torch
 import tqdm
 
+from typing import Callable, List, Tuple
+from functools import partial
+from dataclasses import dataclass
+
 from modules import processing, shared, images, devices, sd_models
 from modules.shared import opts
 import modules.gfpgan_model
@@ -20,7 +24,7 @@ import gradio as gr
 cached_images = {}
 
 
-def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool ):
     devices.torch_gc()
 
     imageArr = []
@@ -56,68 +60,109 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     else:
         outpath = opts.outdir_samples or opts.outdir_extras_samples
 
-    
-    for image, image_name in zip(imageArr, imageNameArr):
-        if image is None:
-            return outputs, "Please select an input image.", ''
-        existing_pnginfo = image.info or {}
 
-        image = image.convert("RGB")
-        info = ""
+    # Extra operation definitions
+    def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
+        res = Image.fromarray(restored_img)
+
+        if gfpgan_visibility < 1.0:
+            res = Image.blend(image, res, gfpgan_visibility)
+
+        info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
+        return (res, info)
 
-        if gfpgan_visibility > 0:
-            restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
-            res = Image.fromarray(restored_img)
+    def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
+        res = Image.fromarray(restored_img)
 
-            if gfpgan_visibility < 1.0:
-                res = Image.blend(image, res, gfpgan_visibility)
+        if codeformer_visibility < 1.0:
+            res = Image.blend(image, res, codeformer_visibility)
 
-            info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
-            image = res
+        info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
+        return (res, info)
 
-        if codeformer_visibility > 0:
-            restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
-            res = Image.fromarray(restored_img)
 
-            if codeformer_visibility < 1.0:
-                res = Image.blend(image, res, codeformer_visibility)
+    def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
+        small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
+        pixels = tuple(np.array(small).flatten().tolist())
+        key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight,
+                resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
 
-            info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
-            image = res
+        c = cached_images.get(key)
+        if c is None:
+            upscaler = shared.sd_upscalers[scaler_index]
+            c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+            if mode == 1 and crop:
+                cropped = Image.new("RGB", (resize_w, resize_h))
+                cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
+                c = cropped
+            cached_images[key] = c
+        return c
 
+
+    def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        # Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
+        nonlocal upscaling_resize
         if resize_mode == 1:
             upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
             crop_info = " (crop)" if upscaling_crop else ""
             info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
+        return (image, info)
+
+    @dataclass
+    class UpscaleParams:
+        upscaler_idx: int
+        blend_alpha: float
+
+    def run_upscalers_blend( params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        blended_result: Image.Image = None
+        for upscaler in params:
+            res = upscale(image, upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
+            info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
+            if blended_result is None:
+                blended_result = res
+            else:
+                blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)
+        return (blended_result, info)
+
+    # Build a list of operations to run
+    facefix_ops: List[Callable] = []
+    if gfpgan_visibility > 0:
+        facefix_ops.append(run_gfpgan)
+    if codeformer_visibility > 0:
+        facefix_ops.append(run_codeformer)
+
+    upscale_ops: List[Callable] = []
+    if resize_mode == 1:
+        upscale_ops.append(run_prepare_crop)
+
+    if upscaling_resize != 0:
+        step_params: List[UpscaleParams] = []
+        step_params.append( UpscaleParams( upscaler_idx=extras_upscaler_1, blend_alpha=1.0 ))
+        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
+            step_params.append( UpscaleParams( upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility ) )
+
+        upscale_ops.append( partial(run_upscalers_blend, step_params) )
+
+
+    extras_ops: List[Callable] = []
+    if upscale_first:
+        extras_ops = upscale_ops + facefix_ops
+    else:
+        extras_ops = facefix_ops + upscale_ops
+
+
+    for image, image_name in zip(imageArr, imageNameArr):
+        if image is None:
+            return outputs, "Please select an input image.", ''
+        existing_pnginfo = image.info or {}
 
-        if upscaling_resize != 1.0:
-            def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
-                small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
-                pixels = tuple(np.array(small).flatten().tolist())
-                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight, 
-                       resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
-
-                c = cached_images.get(key)
-                if c is None:
-                    upscaler = shared.sd_upscalers[scaler_index]
-                    c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
-                    if mode == 1 and crop:
-                        cropped = Image.new("RGB", (resize_w, resize_h))
-                        cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
-                        c = cropped
-                    cached_images[key] = c
-
-                return c
-
-            info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
-            res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-
-            if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-                res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
-                res = Image.blend(res, res2, extras_upscaler_2_visibility)
-
-            image = res
+        image = image.convert("RGB")
+        info = ""
+        # Run each operation on each image
+        for op in extras_ops:
+            image, info = op(image, info)
 
         while len(cached_images) > 2:
             del cached_images[next(iter(cached_images.keys()))]
diff --git a/modules/ui.py b/modules/ui.py
index 0a63e357..16b6ac49 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1119,6 +1119,9 @@ def create_ui(wrap_gradio_gpu_call):
                     codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
                     codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
 
+                with gr.Group():
+                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
+
                 submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
 
             with gr.Column(variant='panel'):
@@ -1152,6 +1155,7 @@ def create_ui(wrap_gradio_gpu_call):
                 extras_upscaler_1,
                 extras_upscaler_2,
                 extras_upscaler_2_visibility,
+                upscale_before_face_fix,
             ],
             outputs=[
                 result_images,
-- 
cgit v1.2.3


From bde4731f1d3ddf30c46f86c9f6e71e6c0644089d Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Fri, 28 Oct 2022 14:30:04 -0500
Subject: extras: Rework image cache

Bit of a refactor to the image cache to make it easier to extend.
Also takes into account the entire image instead of just a cropped portion.
---
 modules/extras.py | 52 ++++++++++++++++++++++++++++++++--------------------
 1 file changed, 32 insertions(+), 20 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 79047f3a..cffe0381 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -7,7 +7,7 @@ from PIL import Image
 import torch
 import tqdm
 
-from typing import Callable, List, Tuple
+from typing import Callable, Dict, List, Tuple
 from functools import partial
 from dataclasses import dataclass
 
@@ -21,7 +21,18 @@ import piexif.helper
 import gradio as gr
 
 
-cached_images = {}
+@dataclass(frozen=True)
+class CacheKey:
+    image_hash: int
+    info_hash: int
+    args_hash: int
+
+@dataclass
+class CacheEntry:
+    image: Image.Image
+    info: str
+
+cached_images: Dict[CacheKey, CacheEntry] = {}
 
 
 def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool ):
@@ -84,22 +95,13 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
 
     def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
-        small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
-        pixels = tuple(np.array(small).flatten().tolist())
-        key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight,
-                resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
-
-        c = cached_images.get(key)
-        if c is None:
-            upscaler = shared.sd_upscalers[scaler_index]
-            c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
-            if mode == 1 and crop:
-                cropped = Image.new("RGB", (resize_w, resize_h))
-                cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
-                c = cropped
-            cached_images[key] = c
-        return c
-
+        upscaler = shared.sd_upscalers[scaler_index]
+        res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+        if mode == 1 and crop:
+            cropped = Image.new("RGB", (resize_w, resize_h))
+            cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))
+            res = cropped
+        return res
 
     def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
         # Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
@@ -118,8 +120,18 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     def run_upscalers_blend( params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
         blended_result: Image.Image = None
         for upscaler in params:
-            res = upscale(image, upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-            info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
+            upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
+            cache_key = CacheKey( image_hash = hash(np.array(image.getdata()).tobytes()),
+                                  info_hash = hash(info),
+                                  args_hash = hash(upscale_args) )
+            cached_entry = cached_images.get(cache_key)
+            if cached_entry is None:
+                res = upscale(image, *upscale_args)
+                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
+                cached_images[cache_key] = CacheEntry(image=res, info=info)
+            else:
+                res, info = cached_entry.image, cached_entry.info
+
             if blended_result is None:
                 blended_result = res
             else:
-- 
cgit v1.2.3


From 1f1b327959b546b5e6f995905a1699c5fe4a0c35 Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Fri, 28 Oct 2022 16:11:16 -0500
Subject: extras: Make image cache LRU

This changes the extras image cache into a Least-Recently-Used
cache. This allows more experimentation with different upscalers
without missing the cache.

Max cache size is increased to 5 and is cleared on source image
update.
---
 modules/extras.py | 67 +++++++++++++++++++++++++++++++------------------------
 modules/ui.py     |  5 +++++
 2 files changed, 43 insertions(+), 29 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index cffe0381..72cc6d1d 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -1,3 +1,4 @@
+from __future__ import annotations
 import math
 import os
 
@@ -7,7 +8,7 @@ from PIL import Image
 import torch
 import tqdm
 
-from typing import Callable, Dict, List, Tuple
+from typing import Callable, List, OrderedDict, Tuple
 from functools import partial
 from dataclasses import dataclass
 
@@ -21,18 +22,34 @@ import piexif.helper
 import gradio as gr
 
 
-@dataclass(frozen=True)
-class CacheKey:
-    image_hash: int
-    info_hash: int
-    args_hash: int
+class LruCache(OrderedDict):
+    @dataclass(frozen=True)
+    class Key:
+        image_hash: int
+        info_hash: int
+        args_hash: int
 
-@dataclass
-class CacheEntry:
-    image: Image.Image
-    info: str
+    @dataclass
+    class Value:
+        image: Image.Image
+        info: str
+
+    def __init__(self, max_size:int = 5, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._max_size = max_size
+
+    def get(self, key: LruCache.Key) -> LruCache.Value:
+        ret = super().get(key)
+        if ret is not None:
+            self.move_to_end(key) # Move to end of eviction list
+        return ret
+
+    def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
+        self[key] = value
+        while len(self) > self._max_size:
+            self.popitem(last=False)
 
-cached_images: Dict[CacheKey, CacheEntry] = {}
+cached_images: LruCache = LruCache(max_size = 5)
 
 
 def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool ):
@@ -121,14 +138,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         blended_result: Image.Image = None
         for upscaler in params:
             upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-            cache_key = CacheKey( image_hash = hash(np.array(image.getdata()).tobytes()),
+            cache_key = LruCache.Key( image_hash = hash(np.array(image.getdata()).tobytes()),
                                   info_hash = hash(info),
-                                  args_hash = hash(upscale_args) )
+                                  args_hash = hash(upscale_args + (upscaler.blend_alpha,)) )
             cached_entry = cached_images.get(cache_key)
             if cached_entry is None:
                 res = upscale(image, *upscale_args)
                 info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
-                cached_images[cache_key] = CacheEntry(image=res, info=info)
+                cached_images.put(cache_key, LruCache.Value(image=res, info=info))
             else:
                 res, info = cached_entry.image, cached_entry.info
 
@@ -140,14 +157,11 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     # Build a list of operations to run
     facefix_ops: List[Callable] = []
-    if gfpgan_visibility > 0:
-        facefix_ops.append(run_gfpgan)
-    if codeformer_visibility > 0:
-        facefix_ops.append(run_codeformer)
+    facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []
+    facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []
 
     upscale_ops: List[Callable] = []
-    if resize_mode == 1:
-        upscale_ops.append(run_prepare_crop)
+    upscale_ops += [run_prepare_crop] if resize_mode == 1 else []
 
     if upscaling_resize != 0:
         step_params: List[UpscaleParams] = []
@@ -157,12 +171,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         upscale_ops.append( partial(run_upscalers_blend, step_params) )
 
-
-    extras_ops: List[Callable] = []
-    if upscale_first:
-        extras_ops = upscale_ops + facefix_ops
-    else:
-        extras_ops = facefix_ops + upscale_ops
+    extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
 
 
     for image, image_name in zip(imageArr, imageNameArr):
@@ -176,9 +185,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         for op in extras_ops:
             image, info = op(image, info)
 
-        while len(cached_images) > 2:
-            del cached_images[next(iter(cached_images.keys()))]
-        
         if opts.use_original_name_batch and image_name != None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
@@ -198,6 +204,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     return outputs, plaintext_to_html(info), ''
 
+def clear_cache():
+    cached_images.clear()
+
 
 def run_pnginfo(image):
     if image is None:
diff --git a/modules/ui.py b/modules/ui.py
index 16b6ac49..b7c36c55 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1178,6 +1178,11 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[init_img_with_mask],
         )
 
+        extras_image.change(
+            fn=modules.extras.clear_cache,
+            inputs=[], outputs=[]
+        )
+
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
-- 
cgit v1.2.3


From 5732c0282d529ef2e0591c76e16959e97240dad8 Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Fri, 28 Oct 2022 16:36:25 -0500
Subject: extras-tweaks: autoformat changed lines

---
 modules/extras.py | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 72cc6d1d..50026a25 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -34,14 +34,14 @@ class LruCache(OrderedDict):
         image: Image.Image
         info: str
 
-    def __init__(self, max_size:int = 5, *args, **kwargs):
+    def __init__(self, max_size: int = 5, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._max_size = max_size
 
     def get(self, key: LruCache.Key) -> LruCache.Value:
         ret = super().get(key)
         if ret is not None:
-            self.move_to_end(key) # Move to end of eviction list
+            self.move_to_end(key)  # Move to end of eviction list
         return ret
 
     def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
@@ -49,10 +49,11 @@ class LruCache(OrderedDict):
         while len(self) > self._max_size:
             self.popitem(last=False)
 
-cached_images: LruCache = LruCache(max_size = 5)
 
+cached_images: LruCache = LruCache(max_size=5)
 
-def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool ):
+
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool):
     devices.torch_gc()
 
     imageArr = []
@@ -88,8 +89,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     else:
         outpath = opts.outdir_samples or opts.outdir_extras_samples
 
-
     # Extra operation definitions
+
     def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
         restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
         res = Image.fromarray(restored_img)
@@ -110,7 +111,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
         return (res, info)
 
-
     def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
         upscaler = shared.sd_upscalers[scaler_index]
         res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
@@ -134,13 +134,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         upscaler_idx: int
         blend_alpha: float
 
-    def run_upscalers_blend( params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+    def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
         blended_result: Image.Image = None
         for upscaler in params:
-            upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-            cache_key = LruCache.Key( image_hash = hash(np.array(image.getdata()).tobytes()),
-                                  info_hash = hash(info),
-                                  args_hash = hash(upscale_args + (upscaler.blend_alpha,)) )
+            upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
+                            upscaling_resize_w, upscaling_resize_h, upscaling_crop)
+            cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
+                                     info_hash=hash(info),
+                                     args_hash=hash(upscale_args + (upscaler.blend_alpha,)))
             cached_entry = cached_images.get(cache_key)
             if cached_entry is None:
                 res = upscale(image, *upscale_args)
@@ -165,15 +166,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     if upscaling_resize != 0:
         step_params: List[UpscaleParams] = []
-        step_params.append( UpscaleParams( upscaler_idx=extras_upscaler_1, blend_alpha=1.0 ))
+        step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))
         if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-            step_params.append( UpscaleParams( upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility ) )
+            step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))
 
-        upscale_ops.append( partial(run_upscalers_blend, step_params) )
+        upscale_ops.append(partial(run_upscalers_blend, step_params))
 
     extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
 
-
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
-- 
cgit v1.2.3


From d8b366146748555a18b595af400c8cb222ea0ec9 Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Fri, 28 Oct 2022 16:55:02 -0500
Subject: extras: upscaler blending should not be considered in cache key

---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 50026a25..681d8d5a 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -141,7 +141,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
                             upscaling_resize_w, upscaling_resize_h, upscaling_crop)
             cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
                                      info_hash=hash(info),
-                                     args_hash=hash(upscale_args + (upscaler.blend_alpha,)))
+                                     args_hash=hash(upscale_args))
             cached_entry = cached_images.get(cache_key)
             if cached_entry is None:
                 res = upscale(image, *upscale_args)
-- 
cgit v1.2.3


From 539c0f51e436beeb0ca2b8b8d52b24f4b59ad56a Mon Sep 17 00:00:00 2001
From: Yaiol <38218161+Yaiol@users.noreply.github.com>
Date: Sat, 29 Oct 2022 01:07:01 +0200
Subject: Update images.py

Filename tags [height] and [width] are wrongly referencing to process size instead of resulting image size. Making all upscale files named wrongly.
---
 modules/images.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 7870b5b7..a0728553 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -300,8 +300,8 @@ class FilenameGenerator:
         'seed': lambda self: self.seed if self.seed is not None else '',
         'steps': lambda self:  self.p and self.p.steps,
         'cfg': lambda self: self.p and self.p.cfg_scale,
-        'width': lambda self: self.p and self.p.width,
-        'height': lambda self: self.p and self.p.height,
+        'width': lambda self: self.image.width,
+        'height': lambda self: self.image.height,
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
@@ -315,10 +315,11 @@ class FilenameGenerator:
     }
     default_time_format = '%Y%m%d%H%M%S'
 
-    def __init__(self, p, seed, prompt):
+    def __init__(self, p, seed, prompt, image):
         self.p = p
         self.seed = seed
         self.prompt = prompt
+        self.image = image
 
     def prompt_no_style(self):
         if self.p is None or self.prompt is None:
@@ -449,7 +450,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         txt_fullfn (`str` or None):
             If a text file is saved for this image, this will be its full path. Otherwise None.
     """
-    namegen = FilenameGenerator(p, seed, prompt)
+    namegen = FilenameGenerator(p, seed, prompt, image)
 
     if save_to_dirs is None:
         save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
-- 
cgit v1.2.3


From f361e804ebaa5af4a10711ece2522869fb64a4c6 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sat, 29 Oct 2022 08:36:50 +0900
Subject: Re enable linear

---
 modules/hypernetworks/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index c2d4b51c..aad09ffc 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
 not_available = ["hardswish", "multiheadattention"]
-keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
     # Remove illegal characters from name.
-- 
cgit v1.2.3


From bce5adcd6de1ad608df8d813a92e1167b7a1d3f2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 07:37:06 +0300
Subject: change default hypernet activation function to linear

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 0a63e357..2541970d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1238,7 +1238,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
                     new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
-- 
cgit v1.2.3


From a1e5e0d7669def010ecf31d801d6f0667bcf8061 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 08:11:03 +0300
Subject: skip filenames starting with . for img2img and extras batch modes

---
 modules/extras.py  | 2 +-
 modules/img2img.py | 2 +-
 modules/shared.py  | 5 +++++
 3 files changed, 7 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 681d8d5a..4d51088b 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -72,7 +72,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         if input_dir == '':
             return outputs, "Please select an input directory.", ''
-        image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)]
+        image_list = shared.listfiles(input_dir)
         for img in image_list:
             try:
                 image = Image.open(img)
diff --git a/modules/img2img.py b/modules/img2img.py
index 9c0cf23e..efda26e1 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -19,7 +19,7 @@ import modules.scripts
 def process_batch(p, input_dir, output_dir, args):
     processing.fix_seed(p)
 
-    images = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+    images = shared.listfiles(input_dir)
 
     print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
 
diff --git a/modules/shared.py b/modules/shared.py
index 7c428d90..7e634423 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -450,3 +450,8 @@ total_tqdm = TotalTQDM()
 
 mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
 mem_mon.start()
+
+
+def listfiles(dirname):
+    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
+    return [file for file in filenames if os.path.isfile(file)]
-- 
cgit v1.2.3


From 2d220afb24bd9812d5124814f670ec2a1ff5b0fe Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 08:26:12 +0300
Subject: fix open folder button not working

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 922a2163..20cc10cf 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -631,9 +631,9 @@ Requested path was: {f}
 
                     buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
                     button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                    open_folder = gr.Button(folder_symbol, elem_id=button_id)
+                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
 
-                open_folder.click(
+                open_folder_button.click(
                     fn=lambda: open_folder(opts.outdir_samples or outdir),
                     inputs=[],
                     outputs=[],
-- 
cgit v1.2.3


From a33d0a9a65189be35038be5765f2b4d1b19bab0f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 08:28:48 +0300
Subject: remove weird spaces added to ui.py over time

---
 modules/ui.py | 51 +++++++++++++++++++++++++--------------------------
 1 file changed, 25 insertions(+), 26 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 20cc10cf..46657dd4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -596,7 +596,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
     )
     return refresh_button
 
-def create_output_panel(tabname, outdir):    
+def create_output_panel(tabname, outdir):
     def open_folder(f):
         if not os.path.exists(f):
             print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
@@ -618,11 +618,11 @@ Requested path was: {f}
                 sp.Popen(["open", path])
             else:
                 sp.Popen(["xdg-open", path])
-    
-    with gr.Column(variant='panel'):        
-            with gr.Group():                
+
+    with gr.Column(variant='panel'):
+            with gr.Group():
                 result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
-               
+
             generation_info = None
             with gr.Column():
                 with gr.Row():
@@ -639,7 +639,7 @@ Requested path was: {f}
                     outputs=[],
                 )
 
-                if tabname != "extras":           
+                if tabname != "extras":
                     with gr.Row():
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
 
@@ -671,8 +671,7 @@ Requested path was: {f}
                     html_info = gr.HTML()
                 parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
                 return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
-                    
-            
+
 
 def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
@@ -723,10 +722,10 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
-     
 
-            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)  
-                
+
+            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
@@ -781,7 +780,7 @@ def create_ui(wrap_gradio_gpu_call):
                 fn=lambda x: gr_show(x),
                 inputs=[enable_hr],
                 outputs=[hr_options],
-            )            
+            )
 
             roll.click(
                 fn=roll_artist,
@@ -902,7 +901,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
 
-            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)                 
+            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -990,7 +989,7 @@ def create_ui(wrap_gradio_gpu_call):
                     inputs=[init_img],
                     outputs=[img2img_prompt],
             )
-           
+
 
             roll.click(
                 fn=roll_artist,
@@ -1045,7 +1044,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 *modules.scripts.scripts_img2img.infotext_fields
             ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields) 
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
             parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
 
 
@@ -1077,9 +1076,9 @@ def create_ui(wrap_gradio_gpu_call):
                                 upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
                                 upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-                
+
                 with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") 
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
@@ -1125,7 +1124,7 @@ def create_ui(wrap_gradio_gpu_call):
                 html_info,
             ]
         )
-        parameters_copypaste.add_paste_fields("extras", extras_image, None) 
+        parameters_copypaste.add_paste_fields("extras", extras_image, None)
 
 
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
@@ -1139,14 +1138,14 @@ def create_ui(wrap_gradio_gpu_call):
                 html2 = gr.HTML()
                 with gr.Row():
                     buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-                parameters_copypaste.bind_buttons(buttons, image, generation_info)                
-                
+                parameters_copypaste.bind_buttons(buttons, image, generation_info)
+
         image.change(
             fn=wrap_gradio_call(modules.extras.run_pnginfo),
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
-   
+
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
@@ -1569,7 +1568,7 @@ def create_ui(wrap_gradio_gpu_call):
             column.__exit__()
 
 
-        
+
 
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
@@ -1582,7 +1581,7 @@ def create_ui(wrap_gradio_gpu_call):
 
     interfaces += script_callbacks.ui_tabs_callback()
 
-    interfaces += [(settings_interface, "Settings", "settings")]       
+    interfaces += [(settings_interface, "Settings", "settings")]
 
     css = ""
 
@@ -1661,7 +1660,7 @@ def create_ui(wrap_gradio_gpu_call):
                 component_dict['sd_model_checkpoint'],
             ]
         )
- 
+
 
         settings_map = {
             'sd_hypernetwork': 'Hypernet',
@@ -1669,7 +1668,7 @@ def create_ui(wrap_gradio_gpu_call):
             'sd_model_checkpoint': 'Model hash',
         }
 
-        parameters_copypaste.run_bind()       
+        parameters_copypaste.run_bind()
 
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
@@ -1749,7 +1748,7 @@ def load_javascript(raw_response):
         javascript = f'<script>{jsfile.read()}</script>'
 
     scripts_list = modules.scripts.list_scripts("javascript", ".js")
-    
+
     for basedir, filename, path in scripts_list:
         with open(path, "r", encoding="utf8") as jsfile:
             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-- 
cgit v1.2.3


From 3c207ca68483b3406faf519bde2743b578dac222 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 08:42:34 +0300
Subject: add needed imports fr new code in copypaste.py

---
 modules/generation_parameters_copypaste.py | 9 +++++++++
 modules/ui.py                              | 7 -------
 2 files changed, 9 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 2b80737a..224a17ea 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -1,3 +1,5 @@
+import base64
+import io
 import os
 import re
 import gradio as gr
@@ -14,6 +16,7 @@ type_of_gr_update = type(gr.update())
 paste_fields = {}
 bind_list = []
 
+
 def quote(text):
     if ',' not in str(text):
         return text
@@ -23,6 +26,7 @@ def quote(text):
     text = text.replace('"', '\\"')
     return f'"{text}"'
 
+
 def image_from_url_text(filedata):
     if type(filedata) == dict and filedata["is_file"]:
         filename = filedata["name"]
@@ -45,19 +49,23 @@ def image_from_url_text(filedata):
     image = Image.open(io.BytesIO(filedata))
     return image
 
+
 def add_paste_fields(tabname, init_img, fields):
     paste_fields[tabname] = {"init_img":init_img, "fields": fields}
 
+
 def create_buttons(tabs_list):
     buttons = {}
     for tab in tabs_list:
         buttons[tab] = gr.Button(f"Send to {tab}")
     return buttons
 
+
 #if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab 
 def bind_buttons(buttons, send_image, send_generate_info):
     bind_list.append([buttons, send_image, send_generate_info])
 
+
 def run_bind():
     for buttons, send_image, send_generate_info in bind_list:
         for tab in buttons:
@@ -98,6 +106,7 @@ def run_bind():
                 outputs=None,
             )
 
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
diff --git a/modules/ui.py b/modules/ui.py
index 46657dd4..280910d0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1,6 +1,4 @@
-import base64
 import html
-import io
 import json
 import math
 import mimetypes
@@ -18,13 +16,8 @@ import gradio as gr
 import gradio.routes
 import gradio.utils
 import numpy as np
-import piexif
-import torch
 from PIL import Image, PngImagePlugin
 
-import gradio as gr
-import gradio.utils
-import gradio.routes
 
 from modules import sd_hijack, sd_models, localization, script_callbacks
 from modules.paths import script_path
-- 
cgit v1.2.3


From 2922d8144f677ea3c37189a2b2e3c3d3ff6d3916 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 09:01:04 +0300
Subject: make existing image browser extension not break

---
 modules/generation_parameters_copypaste.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 224a17ea..d590e9ee 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -51,7 +51,14 @@ def image_from_url_text(filedata):
 
 
 def add_paste_fields(tabname, init_img, fields):
-    paste_fields[tabname] = {"init_img":init_img, "fields": fields}
+    paste_fields[tabname] = {"init_img": init_img, "fields": fields}
+
+    # backwards compatibility for existing extensions
+    import modules.ui
+    if tabname == 'txt2img':
+        modules.ui.txt2img_paste_fields = fields
+    elif tabname == 'img2img':
+        modules.ui.img2img_paste_fields = fields
 
 
 def create_buttons(tabs_list):
@@ -61,7 +68,7 @@ def create_buttons(tabs_list):
     return buttons
 
 
-#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab 
+#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab
 def bind_buttons(buttons, send_image, send_generate_info):
     bind_list.append([buttons, send_image, send_generate_info])
 
@@ -84,12 +91,12 @@ def run_bind():
                         inputs=[send_image],
                         outputs=[paste_fields[tab]["init_img"]],
                     )
-            
+
             if send_generate_info and paste_fields[tab]["fields"] is not None:
                 paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2']
                 if shared.opts.send_seed:
                     paste_field_names += ["Seed"]
-                if send_generate_info in paste_fields:                    
+                if send_generate_info in paste_fields:
                     button.click(
                         fn=lambda *x:x,
                         inputs=[field for field,name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
@@ -154,7 +161,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
     return res
 
-def connect_paste(button, paste_fields, input_comp):
+
+def connect_paste(button, paste_fields, input_comp, jsfunc=None):
     def paste_func(prompt):
         if not prompt and not shared.cmd_opts.hide_ui_dir_config:
             filename = os.path.join(script_path, "params.txt")
@@ -192,6 +200,7 @@ def connect_paste(button, paste_fields, input_comp):
 
     button.click(
         fn=paste_func,
+        _js=jsfunc,
         inputs=[input_comp],
         outputs=[x[0] for x in paste_fields],
     )
-- 
cgit v1.2.3


From 28e6d4a54ea1fa1e34ad1ea0742ab2003ed7fa7f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 09:13:36 +0300
Subject: add element ids for save buttons for #3798

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 66b743f5..3c34eca0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -620,7 +620,7 @@ Requested path was: {f}
             with gr.Column():
                 with gr.Row():
                     if tabname != "extras":
-                        save = gr.Button('Save')
+                        save = gr.Button('Save', elem_id=f'save_{tabname}')
 
                     buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
                     button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-- 
cgit v1.2.3


From beb6fc29798d82f1b08a34cf5dd79e4ab29d4cd0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 09:57:22 +0300
Subject: move send seed option to UI section and make it false by default

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 5d1ceb85..fb84afd8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -280,7 +280,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
-    "send_seed": OptionInfo(False, "Send seed when sending prompt or image to other interface"),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -306,6 +305,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
     "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
+    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-- 
cgit v1.2.3


From 2c4d20388425a5e40b93eef3722e42e8d375fbb4 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Sat, 29 Oct 2022 00:36:51 -0700
Subject: Revert "Explicitly state when Hypernet is none."

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 377c0978..04fdda7c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -395,7 +395,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": ("None" if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
         "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
-- 
cgit v1.2.3


From 35c45df28b303a05d56a13cb56d4046f08cf8c25 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 10:56:19 +0300
Subject: =?UTF-8?q?fix=20broken=20=E2=86=99=20button,=20fix=20field=20past?=
 =?UTF-8?q?e=20ignoring=20most=20of=20useful=20fields=20for=20for=20#3768?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 modules/generation_parameters_copypaste.py | 36 ++++++++++++++++++-------
 modules/ui.py                              | 43 +++++++++++-------------------
 2 files changed, 41 insertions(+), 38 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index d590e9ee..bbaad42e 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -6,7 +6,7 @@ import gradio as gr
 from modules.shared import script_path
 from modules import shared
 import tempfile
-from PIL import Image, PngImagePlugin
+from PIL import Image
 
 re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
@@ -61,6 +61,24 @@ def add_paste_fields(tabname, init_img, fields):
         modules.ui.img2img_paste_fields = fields
 
 
+def integrate_settings_paste_fields(component_dict):
+    from modules import ui
+
+    settings_map = {
+        'sd_hypernetwork': 'Hypernet',
+        'CLIP_stop_at_last_layers': 'Clip skip',
+        'sd_model_checkpoint': 'Model hash',
+    }
+    settings_paste_fields = [
+        (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))
+        for k, v in settings_map.items()
+    ]
+
+    for tabname, info in paste_fields.items():
+        if info["fields"] is not None:
+            info["fields"] += settings_paste_fields
+
+
 def create_buttons(tabs_list):
     buttons = {}
     for tab in tabs_list:
@@ -87,24 +105,22 @@ def run_bind():
                     )
                 else:
                     button.click(
-                        fn=lambda x:x,
+                        fn=lambda x: x,
                         inputs=[send_image],
                         outputs=[paste_fields[tab]["init_img"]],
                     )
 
             if send_generate_info and paste_fields[tab]["fields"] is not None:
-                paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2']
-                if shared.opts.send_seed:
-                    paste_field_names += ["Seed"]
                 if send_generate_info in paste_fields:
+                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2'] + (["Seed"] if shared.opts.send_seed else [])
+
                     button.click(
-                        fn=lambda *x:x,
-                        inputs=[field for field,name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
-                        outputs=[field for field,name in paste_fields[tab]["fields"] if name in paste_field_names],
+                        fn=lambda *x: x,
+                        inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
+                        outputs=[field for field, name in paste_fields[tab]["fields"] if name in paste_field_names],
                     )
-
                 else:
-                    connect_paste(button, [(field, name) for field, name in paste_fields[tab]["fields"]  if name in paste_field_names], send_generate_info)
+                    connect_paste(button, paste_fields[tab]["fields"], send_generate_info)
 
             button.click(
                 fn=None,
diff --git a/modules/ui.py b/modules/ui.py
index 3c34eca0..5055ca64 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -589,6 +589,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
     )
     return refresh_button
 
+
 def create_output_panel(tabname, outdir):
     def open_folder(f):
         if not os.path.exists(f):
@@ -716,6 +717,7 @@ def create_ui(wrap_gradio_gpu_call):
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
             txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -784,7 +786,7 @@ def create_ui(wrap_gradio_gpu_call):
                 ]
             )
 
-            parameters_copypaste.add_paste_fields("txt2img", None, [
+            txt2img_paste_fields = [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
                 (steps, "Steps"),
@@ -805,7 +807,8 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
                 *modules.scripts.scripts_txt2img.infotext_fields
-            ])
+            ]
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
 
             txt2img_preview_params = [
                 txt2img_prompt,
@@ -893,6 +896,7 @@ def create_ui(wrap_gradio_gpu_call):
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
 
             img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
+            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -1038,7 +1042,6 @@ def create_ui(wrap_gradio_gpu_call):
             parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
             parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
 
-
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
@@ -1050,12 +1053,8 @@ def create_ui(wrap_gradio_gpu_call):
                         image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
 
                     with gr.TabItem('Batch from Directory'):
-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs,
-                            placeholder="A directory on the same machine where the server is running."
-                        )
-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs,
-                            placeholder="Leave blank to save images to the default path."
-                        )
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
                         show_extras_results = gr.Checkbox(label='Show result images', value=True)
 
                 with gr.Tabs(elem_id="extras_resize_mode"):
@@ -1087,7 +1086,6 @@ def create_ui(wrap_gradio_gpu_call):
 
                 submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
 
-
             result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
@@ -1121,7 +1119,6 @@ def create_ui(wrap_gradio_gpu_call):
         )
         parameters_copypaste.add_paste_fields("extras", extras_image, None)
 
-
         extras_image.change(
             fn=modules.extras.clear_cache,
             inputs=[], outputs=[]
@@ -1587,9 +1584,6 @@ def create_ui(wrap_gradio_gpu_call):
         if column is not None:
             column.__exit__()
 
-
-
-
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
         (img2img_interface, "img2img", "img2img"),
@@ -1599,10 +1593,6 @@ def create_ui(wrap_gradio_gpu_call):
         (train_interface, "Train", "ti"),
     ]
 
-    interfaces += script_callbacks.ui_tabs_callback()
-
-    interfaces += [(settings_interface, "Settings", "settings")]
-
     css = ""
 
     for cssfile in modules.scripts.list_files_with_name("style.css"):
@@ -1619,6 +1609,9 @@ def create_ui(wrap_gradio_gpu_call):
     if not cmd_opts.no_progressbar_hiding:
         css += css_hide_progressbar
 
+    interfaces += script_callbacks.ui_tabs_callback()
+    interfaces += [(settings_interface, "Settings", "settings")]
+
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings"):
             for i, k, item in quicksettings_list:
@@ -1627,6 +1620,9 @@ def create_ui(wrap_gradio_gpu_call):
 
         settings_interface.gradio_ref = demo
 
+        parameters_copypaste.integrate_settings_paste_fields(component_dict)
+        parameters_copypaste.run_bind()
+
         with gr.Tabs(elem_id="tabs") as tabs:
             for interface, label, ifid in interfaces:
                 with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
@@ -1681,15 +1677,6 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
-
-        settings_map = {
-            'sd_hypernetwork': 'Hypernet',
-            'CLIP_stop_at_last_layers': 'Clip skip',
-            'sd_model_checkpoint': 'Model hash',
-        }
-
-        parameters_copypaste.run_bind()
-
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
     settings_count = len(ui_settings)
@@ -1708,7 +1695,7 @@ def create_ui(wrap_gradio_gpu_call):
         def apply_field(obj, field, condition=None, init_field=None):
             key = path + "/" + field
 
-            if getattr(obj,'custom_script_source',None) is not None:
+            if getattr(obj, 'custom_script_source', None) is not None:
               key = 'customscript/' + obj.custom_script_source + '/' + key
 
             if getattr(obj, 'do_not_save_to_config', False):
-- 
cgit v1.2.3


From a5f3adbdd7d9b8245f7782216ac48913660e6bb5 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 29 Oct 2022 15:37:24 +0700
Subject: Allow trailing comma in learning rate

---
 modules/textual_inversion/learn_schedule.py | 33 +++++++++++++++++------------
 1 file changed, 20 insertions(+), 13 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index 3a736065..76e611b6 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -11,23 +11,30 @@ class LearnScheduleIterator:
         self.rates = []
         self.it = 0
         self.maxit = 0
-        for i, pair in enumerate(pairs):
-            tmp = pair.split(':')
-            if len(tmp) == 2:
-                step = int(tmp[1])
-                if step > cur_step:
-                    self.rates.append((float(tmp[0]), min(step, max_steps)))
-                    self.maxit += 1
-                    if step > max_steps:
+        try:
+            for i, pair in enumerate(pairs):
+                if not pair.strip():
+                    continue
+                tmp = pair.split(':')
+                if len(tmp) == 2:
+                    step = int(tmp[1])
+                    if step > cur_step:
+                        self.rates.append((float(tmp[0]), min(step, max_steps)))
+                        self.maxit += 1
+                        if step > max_steps:
+                            return
+                    elif step == -1:
+                        self.rates.append((float(tmp[0]), max_steps))
+                        self.maxit += 1
                         return
-                elif step == -1:
+                else:
                     self.rates.append((float(tmp[0]), max_steps))
                     self.maxit += 1
                     return
-            else:
-                self.rates.append((float(tmp[0]), max_steps))
-                self.maxit += 1
-                return
+            assert self.rates
+        except (ValueError, AssertionError):
+            raise Exception("Invalid learning rate schedule")
+
 
     def __iter__(self):
         return self
-- 
cgit v1.2.3


From ef4c94e1cfe66299227aa95a28c2380d21cb1600 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 29 Oct 2022 15:42:51 +0700
Subject: Improve lr schedule error message

---
 modules/textual_inversion/learn_schedule.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index 76e611b6..dd0c0ad1 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -4,7 +4,7 @@ import tqdm
 class LearnScheduleIterator:
     def __init__(self, learn_rate, max_steps, cur_step=0):
         """
-        specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000
+        specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000
         """
 
         pairs = learn_rate.split(',')
@@ -33,7 +33,7 @@ class LearnScheduleIterator:
                     return
             assert self.rates
         except (ValueError, AssertionError):
-            raise Exception("Invalid learning rate schedule")
+            raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.')
 
 
     def __iter__(self):
-- 
cgit v1.2.3


From ab27c111d06ec920791c73eea25ad9a61671852e Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 29 Oct 2022 18:09:17 +0700
Subject: Add input validations before loading dataset for training

---
 modules/hypernetworks/hypernetwork.py          | 38 +++++++++++---------
 modules/textual_inversion/textual_inversion.py | 48 +++++++++++++++++++-------
 2 files changed, 58 insertions(+), 28 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 2e84583b..38f35c58 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -332,7 +332,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
-    assert hypernetwork_name, 'hypernetwork not selected'
+    save_hypernetwork_every = save_hypernetwork_every or 0
+    create_image_every = create_image_every or 0
+    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
@@ -358,39 +360,43 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:
         images_dir = None
 
+    hypernetwork = shared.loaded_hypernetwork
+
+    ititial_step = hypernetwork.step or 0
+    if ititial_step > steps:
+        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        return hypernetwork, filename
+
+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+    
+    # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
 
-    hypernetwork = shared.loaded_hypernetwork
-    weights = hypernetwork.weights()
-    for weight in weights:
-        weight.requires_grad = True
-
     size = len(ds.indexes)
     loss_dict = defaultdict(lambda : deque(maxlen = 1024))
     losses = torch.zeros((size,))
     previous_mean_losses = [0]
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
-
-    last_saved_file = "<none>"
-    last_saved_image = "<none>"
-    forced_filename = "<none>"
-
-    ititial_step = hypernetwork.step or 0
-    if ititial_step > steps:
-        return hypernetwork, filename
-
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+    
+    weights = hypernetwork.weights()
+    for weight in weights:
+        weight.requires_grad = True
     # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
     optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     steps_without_grad = 0
 
+    last_saved_file = "<none>"
+    last_saved_image = "<none>"
+    forced_filename = "<none>"
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 17dfb223..44f06443 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -204,9 +204,30 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             **values,
         })
 
+def validate_train_inputs(model_name, learn_rate, batch_size, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
+    assert model_name, f"{name} not selected"
+    assert learn_rate, "Learning rate is empty or 0"
+    assert isinstance(batch_size, int), "Batch size must be integer"
+    assert batch_size > 0, "Batch size must be positive"
+    assert data_root, "Dataset directory is empty"
+    assert os.path.isdir(data_root), "Dataset directory doesn't exist"
+    assert os.listdir(data_root), "Dataset directory is empty"
+    assert template_file, "Prompt template file is empty"
+    assert os.path.isfile(template_file), "Prompt template file doesn't exist"
+    assert steps, "Max steps is empty or 0"
+    assert isinstance(steps, int), "Max steps must be integer"
+    assert steps > 0 , "Max steps must be positive"
+    assert isinstance(save_model_every, int), "Save {name} must be integer"
+    assert save_model_every >= 0 , "Save {name} must be positive or 0"
+    assert isinstance(create_image_every, int), "Create image must be integer"
+    assert create_image_every >= 0 , "Create image must be positive or 0"
+    if save_model_every or create_image_every:
+        assert log_directory, "Log directory is empty"
 
 def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
-    assert embedding_name, 'embedding not selected'
+    save_embedding_every = save_embedding_every or 0
+    create_image_every = create_image_every or 0
+    validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
 
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
@@ -232,17 +253,27 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         os.makedirs(images_embeds_dir, exist_ok=True)
     else:
         images_embeds_dir = None
-        
+
     cond_model = shared.sd_model.cond_stage_model
 
+    hijack = sd_hijack.model_hijack
+
+    embedding = hijack.embedding_db.word_embeddings[embedding_name]
+
+    ititial_step = embedding.step or 0
+    if ititial_step > steps:
+        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        return embedding, filename
+
+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+
+    # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
 
-    hijack = sd_hijack.model_hijack
-
-    embedding = hijack.embedding_db.word_embeddings[embedding_name]
     embedding.vec.requires_grad = True
+    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
     losses = torch.zeros((32,))
 
@@ -251,13 +282,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
 
-    ititial_step = embedding.step or 0
-    if ititial_step > steps:
-        return embedding, filename
-
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
-
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, entries in pbar:
         embedding.step = i + ititial_step
-- 
cgit v1.2.3


From 3ce2bfdf95bd5f26d0f6e250e67338ada91980d1 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 29 Oct 2022 19:43:21 +0700
Subject: Add cleanup after training

---
 modules/hypernetworks/hypernetwork.py          | 201 +++++++++++++------------
 modules/textual_inversion/textual_inversion.py | 185 ++++++++++++-----------
 2 files changed, 200 insertions(+), 186 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 38f35c58..170d5ea4 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -398,110 +398,112 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     forced_filename = "<none>"
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, entries in pbar:
-        hypernetwork.step = i + ititial_step
-        if len(loss_dict) > 0:
-            previous_mean_losses = [i[-1] for i in loss_dict.values()]
-            previous_mean_loss = mean(previous_mean_losses)
-            
-        scheduler.apply(optimizer, hypernetwork.step)
-        if scheduler.finished:
-            break
-
-        if shared.state.interrupted:
-            break
-
-        with torch.autocast("cuda"):
-            c = stack_conds([entry.cond for entry in entries]).to(devices.device)
-            # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
-            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-            loss = shared.sd_model(x, c)[0]
-            del x
-            del c
-
-            losses[hypernetwork.step % losses.shape[0]] = loss.item()
-            for entry in entries:
-                loss_dict[entry.filename].append(loss.item())
-                
-            optimizer.zero_grad()
-            weights[0].grad = None
-            loss.backward()
 
-            if weights[0].grad is None:
-                steps_without_grad += 1
+    try:
+        for i, entries in pbar:
+            hypernetwork.step = i + ititial_step
+            if len(loss_dict) > 0:
+                previous_mean_losses = [i[-1] for i in loss_dict.values()]
+                previous_mean_loss = mean(previous_mean_losses)
+                
+            scheduler.apply(optimizer, hypernetwork.step)
+            if scheduler.finished:
+                break
+
+            if shared.state.interrupted:
+                break
+
+            with torch.autocast("cuda"):
+                c = stack_conds([entry.cond for entry in entries]).to(devices.device)
+                # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
+                x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+                loss = shared.sd_model(x, c)[0]
+                del x
+                del c
+
+                losses[hypernetwork.step % losses.shape[0]] = loss.item()
+                for entry in entries:
+                    loss_dict[entry.filename].append(loss.item())
+                    
+                optimizer.zero_grad()
+                weights[0].grad = None
+                loss.backward()
+
+                if weights[0].grad is None:
+                    steps_without_grad += 1
+                else:
+                    steps_without_grad = 0
+                assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
+
+                optimizer.step()
+
+            steps_done = hypernetwork.step + 1
+
+            if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
+                raise RuntimeError("Loss diverged.")
+            
+            if len(previous_mean_losses) > 1:
+                std = stdev(previous_mean_losses)
             else:
-                steps_without_grad = 0
-            assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
-
-            optimizer.step()
-
-        steps_done = hypernetwork.step + 1
-
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
-            raise RuntimeError("Loss diverged.")
-        
-        if len(previous_mean_losses) > 1:
-            std = stdev(previous_mean_losses)
-        else:
-            std = 0
-        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
-        pbar.set_description(dataset_loss_info)
-
-        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
-            # Before saving, change name to match current checkpoint.
-            hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
-            hypernetwork.save(last_saved_file)
-
-        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
-            "loss": f"{previous_mean_loss:.7f}",
-            "learn_rate": scheduler.learn_rate
-        })
-
-        if images_dir is not None and steps_done % create_image_every == 0:
-            forced_filename = f'{hypernetwork_name}-{steps_done}'
-            last_saved_image = os.path.join(images_dir, forced_filename)
-
-            optimizer.zero_grad()
-            shared.sd_model.cond_stage_model.to(devices.device)
-            shared.sd_model.first_stage_model.to(devices.device)
-
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-            )
+                std = 0
+            dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
+            pbar.set_description(dataset_loss_info)
+
+            if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
+                # Before saving, change name to match current checkpoint.
+                hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
+                last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
+                hypernetwork.save(last_saved_file)
+
+            textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
+                "loss": f"{previous_mean_loss:.7f}",
+                "learn_rate": scheduler.learn_rate
+            })
+
+            if images_dir is not None and steps_done % create_image_every == 0:
+                forced_filename = f'{hypernetwork_name}-{steps_done}'
+                last_saved_image = os.path.join(images_dir, forced_filename)
+
+                optimizer.zero_grad()
+                shared.sd_model.cond_stage_model.to(devices.device)
+                shared.sd_model.first_stage_model.to(devices.device)
+
+                p = processing.StableDiffusionProcessingTxt2Img(
+                    sd_model=shared.sd_model,
+                    do_not_save_grid=True,
+                    do_not_save_samples=True,
+                )
 
-            if preview_from_txt2img:
-                p.prompt = preview_prompt
-                p.negative_prompt = preview_negative_prompt
-                p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
-                p.cfg_scale = preview_cfg_scale
-                p.seed = preview_seed
-                p.width = preview_width
-                p.height = preview_height
-            else:
-                p.prompt = entries[0].cond_text
-                p.steps = 20
+                if preview_from_txt2img:
+                    p.prompt = preview_prompt
+                    p.negative_prompt = preview_negative_prompt
+                    p.steps = preview_steps
+                    p.sampler_index = preview_sampler_index
+                    p.cfg_scale = preview_cfg_scale
+                    p.seed = preview_seed
+                    p.width = preview_width
+                    p.height = preview_height
+                else:
+                    p.prompt = entries[0].cond_text
+                    p.steps = 20
 
-            preview_text = p.prompt
+                preview_text = p.prompt
 
-            processed = processing.process_images(p)
-            image = processed.images[0] if len(processed.images)>0 else None
+                processed = processing.process_images(p)
+                image = processed.images[0] if len(processed.images)>0 else None
 
-            if unload:
-                shared.sd_model.cond_stage_model.to(devices.cpu)
-                shared.sd_model.first_stage_model.to(devices.cpu)
+                if unload:
+                    shared.sd_model.cond_stage_model.to(devices.cpu)
+                    shared.sd_model.first_stage_model.to(devices.cpu)
 
-            if image is not None:
-                shared.state.current_image = image
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-                last_saved_image += f", prompt: {preview_text}"
+                if image is not None:
+                    shared.state.current_image = image
+                    last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                    last_saved_image += f", prompt: {preview_text}"
 
-        shared.state.job_no = hypernetwork.step
+            shared.state.job_no = hypernetwork.step
 
-        shared.state.textinfo = f"""
+            shared.state.textinfo = f"""
 <p>
 Loss: {previous_mean_loss:.7f}<br/>
 Step: {hypernetwork.step}<br/>
@@ -510,7 +512,14 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-        
+    finally:
+        if weights:
+            for weight in weights:
+                weight.requires_grad = False
+        if unload:
+            shared.sd_model.cond_stage_model.to(devices.device)
+            shared.sd_model.first_stage_model.to(devices.device)
+
     report_statistics(loss_dict)
     checkpoint = sd_models.select_checkpoint()
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 44f06443..fd7f0897 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -283,111 +283,113 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     embedding_yet_to_be_embedded = False
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, entries in pbar:
-        embedding.step = i + ititial_step
 
-        scheduler.apply(optimizer, embedding.step)
-        if scheduler.finished:
-            break
-
-        if shared.state.interrupted:
-            break
-
-        with torch.autocast("cuda"):
-            c = cond_model([entry.cond_text for entry in entries])
-            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-            loss = shared.sd_model(x, c)[0]
-            del x
-
-            losses[embedding.step % losses.shape[0]] = loss.item()
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-        steps_done = embedding.step + 1
-
-        epoch_num = embedding.step // len(ds)
-        epoch_step = embedding.step % len(ds)
-
-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
-
-        if embedding_dir is not None and steps_done % save_embedding_every == 0:
-            # Before saving, change name to match current checkpoint.
-            embedding.name = f'{embedding_name}-{steps_done}'
-            last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
-            embedding.save(last_saved_file)
-            embedding_yet_to_be_embedded = True
-
-        write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
-            "loss": f"{losses.mean():.7f}",
-            "learn_rate": scheduler.learn_rate
-        })
-
-        if images_dir is not None and steps_done % create_image_every == 0:
-            forced_filename = f'{embedding_name}-{steps_done}'
-            last_saved_image = os.path.join(images_dir, forced_filename)
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-                do_not_reload_embeddings=True,
-            )
-
-            if preview_from_txt2img:
-                p.prompt = preview_prompt
-                p.negative_prompt = preview_negative_prompt
-                p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
-                p.cfg_scale = preview_cfg_scale
-                p.seed = preview_seed
-                p.width = preview_width
-                p.height = preview_height
-            else:
-                p.prompt = entries[0].cond_text
-                p.steps = 20
-                p.width = training_width
-                p.height = training_height
+    try:
+        for i, entries in pbar:
+            embedding.step = i + ititial_step
+
+            scheduler.apply(optimizer, embedding.step)
+            if scheduler.finished:
+                break
+
+            if shared.state.interrupted:
+                break
+
+            with torch.autocast("cuda"):
+                c = cond_model([entry.cond_text for entry in entries])
+                x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+                loss = shared.sd_model(x, c)[0]
+                del x
+
+                losses[embedding.step % losses.shape[0]] = loss.item()
+
+                optimizer.zero_grad()
+                loss.backward()
+                optimizer.step()
+
+            steps_done = embedding.step + 1
+
+            epoch_num = embedding.step // len(ds)
+            epoch_step = embedding.step % len(ds)
+
+            pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
+
+            if embedding_dir is not None and steps_done % save_embedding_every == 0:
+                # Before saving, change name to match current checkpoint.
+                embedding.name = f'{embedding_name}-{steps_done}'
+                last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
+                embedding.save(last_saved_file)
+                embedding_yet_to_be_embedded = True
+
+            write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
+                "loss": f"{losses.mean():.7f}",
+                "learn_rate": scheduler.learn_rate
+            })
+
+            if images_dir is not None and steps_done % create_image_every == 0:
+                forced_filename = f'{embedding_name}-{steps_done}'
+                last_saved_image = os.path.join(images_dir, forced_filename)
+                p = processing.StableDiffusionProcessingTxt2Img(
+                    sd_model=shared.sd_model,
+                    do_not_save_grid=True,
+                    do_not_save_samples=True,
+                    do_not_reload_embeddings=True,
+                )
+
+                if preview_from_txt2img:
+                    p.prompt = preview_prompt
+                    p.negative_prompt = preview_negative_prompt
+                    p.steps = preview_steps
+                    p.sampler_index = preview_sampler_index
+                    p.cfg_scale = preview_cfg_scale
+                    p.seed = preview_seed
+                    p.width = preview_width
+                    p.height = preview_height
+                else:
+                    p.prompt = entries[0].cond_text
+                    p.steps = 20
+                    p.width = training_width
+                    p.height = training_height
 
-            preview_text = p.prompt
+                preview_text = p.prompt
 
-            processed = processing.process_images(p)
-            image = processed.images[0]
+                processed = processing.process_images(p)
+                image = processed.images[0]
 
-            shared.state.current_image = image
+                shared.state.current_image = image
 
-            if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
+                if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
 
-                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
+                    last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
 
-                info = PngImagePlugin.PngInfo()
-                data = torch.load(last_saved_file)
-                info.add_text("sd-ti-embedding", embedding_to_b64(data))
+                    info = PngImagePlugin.PngInfo()
+                    data = torch.load(last_saved_file)
+                    info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
-                title = "<{}>".format(data.get('name', '???'))
+                    title = "<{}>".format(data.get('name', '???'))
 
-                try:
-                    vectorSize = list(data['string_to_param'].values())[0].shape[0]
-                except Exception as e:
-                    vectorSize = '?'
+                    try:
+                        vectorSize = list(data['string_to_param'].values())[0].shape[0]
+                    except Exception as e:
+                        vectorSize = '?'
 
-                checkpoint = sd_models.select_checkpoint()
-                footer_left = checkpoint.model_name
-                footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}v {}s'.format(vectorSize, steps_done)
+                    checkpoint = sd_models.select_checkpoint()
+                    footer_left = checkpoint.model_name
+                    footer_mid = '[{}]'.format(checkpoint.hash)
+                    footer_right = '{}v {}s'.format(vectorSize, steps_done)
 
-                captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
-                captioned_image = insert_image_data_embed(captioned_image, data)
+                    captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
+                    captioned_image = insert_image_data_embed(captioned_image, data)
 
-                captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
-                embedding_yet_to_be_embedded = False
+                    captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+                    embedding_yet_to_be_embedded = False
 
-            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-            last_saved_image += f", prompt: {preview_text}"
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                last_saved_image += f", prompt: {preview_text}"
 
-        shared.state.job_no = embedding.step
+            shared.state.job_no = embedding.step
 
-        shared.state.textinfo = f"""
+            shared.state.textinfo = f"""
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {embedding.step}<br/>
@@ -396,6 +398,9 @@ Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
+    finally:
+        if embedding and embedding.vec is not None:
+            embedding.vec.requires_grad = False
 
     checkpoint = sd_models.select_checkpoint()
 
-- 
cgit v1.2.3


From a27d19de2eff633b6a39f9f4a5c0f2d6abb81bb5 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 29 Oct 2022 19:44:05 +0700
Subject: Additional assert on dataset

---
 modules/textual_inversion/dataset.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 8bb00d27..ad726577 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -42,6 +42,8 @@ class PersonalizedBase(Dataset):
         self.lines = lines
 
         assert data_root, 'dataset directory not specified'
+        assert os.path.isdir(data_root), "Dataset directory doesn't exist"
+        assert os.listdir(data_root), "Dataset directory is empty"
 
         cond_model = shared.sd_model.cond_stage_model
 
-- 
cgit v1.2.3


From de1dc0d279a877d5d9f512befe30a7d7e5cf3881 Mon Sep 17 00:00:00 2001
From: Martin Cairns <4314538+MartinCairnsSQL@users.noreply.github.com>
Date: Sat, 29 Oct 2022 15:23:19 +0100
Subject: Add adjust_steps_if_invalid to find next valid step for ddim uniform
 sampler

---
 modules/sd_samplers.py | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 3670b57d..aca014e8 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -1,5 +1,6 @@
 from collections import namedtuple
 import numpy as np
+from math import floor
 import torch
 import tqdm
 from PIL import Image
@@ -205,17 +206,22 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None
         self.nmask = p.nmask if hasattr(p, 'nmask') else None
 
+
+    def adjust_steps_if_invalid(self, p, num_steps):
+        if self.config.name == 'DDIM' and p.ddim_discretize == 'uniform':
+            valid_step = 999 / (1000 // num_steps)
+            if valid_step == floor(valid_step):
+                return int(valid_step) + 1
+        
+        return num_steps
+
+
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
-
+        steps = self.adjust_steps_if_invalid(p, steps)
         self.initialize(p)
 
-        # existing code fails with certain step counts, like 9
-        try:
-            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-        except Exception:
-            self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-
+        self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
 
         self.init_latent = x
@@ -239,18 +245,14 @@ class VanillaStableDiffusionSampler:
         self.last_latent = x
         self.step = 0
 
-        steps = steps or p.steps
+        steps = self.adjust_steps_if_invalid(p, steps or p.steps)
 
         # Wrap the conditioning models with additional image conditioning for inpainting model
         if image_conditioning is not None:
             conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
 
-        # existing code fails with certain step counts, like 9
-        try:
-            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
-        except Exception:
-            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
+        samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
 
         return samples_ddim
 
-- 
cgit v1.2.3


From 44ab954fabb9c1273366ebdca47f8da394d61aab Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Sat, 29 Oct 2022 10:02:56 -0700
Subject: Fix latent upscale highres fix #3888

---
 modules/processing.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 548eec29..f18b7db2 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -653,6 +653,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         if opts.use_scale_latent_for_hires_fix:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            image_conditioning = self.txt2img_image_conditioning(samples)
 
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
@@ -674,6 +675,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
+            image_conditioning = self.img2img_image_conditioning(
+                decoded_samples, 
+                samples, 
+                decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
+            )
+
         shared.state.nextjob()
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -684,11 +691,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        image_conditioning = self.img2img_image_conditioning(
-            decoded_samples, 
-            samples, 
-            decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
-        )
         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
 
         return samples
-- 
cgit v1.2.3


From ab05a74ead9fabb45dd099990e34061c7eb02ca3 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 00:32:02 +0700
Subject: Revert "Add cleanup after training"

This reverts commit 3ce2bfdf95bd5f26d0f6e250e67338ada91980d1.
---
 modules/hypernetworks/hypernetwork.py          | 201 ++++++++++++-------------
 modules/textual_inversion/textual_inversion.py | 185 +++++++++++------------
 2 files changed, 186 insertions(+), 200 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 170d5ea4..38f35c58 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -398,112 +398,110 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     forced_filename = "<none>"
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-
-    try:
-        for i, entries in pbar:
-            hypernetwork.step = i + ititial_step
-            if len(loss_dict) > 0:
-                previous_mean_losses = [i[-1] for i in loss_dict.values()]
-                previous_mean_loss = mean(previous_mean_losses)
-                
-            scheduler.apply(optimizer, hypernetwork.step)
-            if scheduler.finished:
-                break
-
-            if shared.state.interrupted:
-                break
-
-            with torch.autocast("cuda"):
-                c = stack_conds([entry.cond for entry in entries]).to(devices.device)
-                # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
-                x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-                loss = shared.sd_model(x, c)[0]
-                del x
-                del c
-
-                losses[hypernetwork.step % losses.shape[0]] = loss.item()
-                for entry in entries:
-                    loss_dict[entry.filename].append(loss.item())
-                    
-                optimizer.zero_grad()
-                weights[0].grad = None
-                loss.backward()
-
-                if weights[0].grad is None:
-                    steps_without_grad += 1
-                else:
-                    steps_without_grad = 0
-                assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
-
-                optimizer.step()
-
-            steps_done = hypernetwork.step + 1
-
-            if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
-                raise RuntimeError("Loss diverged.")
+    for i, entries in pbar:
+        hypernetwork.step = i + ititial_step
+        if len(loss_dict) > 0:
+            previous_mean_losses = [i[-1] for i in loss_dict.values()]
+            previous_mean_loss = mean(previous_mean_losses)
             
-            if len(previous_mean_losses) > 1:
-                std = stdev(previous_mean_losses)
+        scheduler.apply(optimizer, hypernetwork.step)
+        if scheduler.finished:
+            break
+
+        if shared.state.interrupted:
+            break
+
+        with torch.autocast("cuda"):
+            c = stack_conds([entry.cond for entry in entries]).to(devices.device)
+            # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
+            del x
+            del c
+
+            losses[hypernetwork.step % losses.shape[0]] = loss.item()
+            for entry in entries:
+                loss_dict[entry.filename].append(loss.item())
+                
+            optimizer.zero_grad()
+            weights[0].grad = None
+            loss.backward()
+
+            if weights[0].grad is None:
+                steps_without_grad += 1
             else:
-                std = 0
-            dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
-            pbar.set_description(dataset_loss_info)
-
-            if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
-                # Before saving, change name to match current checkpoint.
-                hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
-                last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
-                hypernetwork.save(last_saved_file)
-
-            textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
-                "loss": f"{previous_mean_loss:.7f}",
-                "learn_rate": scheduler.learn_rate
-            })
-
-            if images_dir is not None and steps_done % create_image_every == 0:
-                forced_filename = f'{hypernetwork_name}-{steps_done}'
-                last_saved_image = os.path.join(images_dir, forced_filename)
-
-                optimizer.zero_grad()
-                shared.sd_model.cond_stage_model.to(devices.device)
-                shared.sd_model.first_stage_model.to(devices.device)
-
-                p = processing.StableDiffusionProcessingTxt2Img(
-                    sd_model=shared.sd_model,
-                    do_not_save_grid=True,
-                    do_not_save_samples=True,
-                )
+                steps_without_grad = 0
+            assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
 
-                if preview_from_txt2img:
-                    p.prompt = preview_prompt
-                    p.negative_prompt = preview_negative_prompt
-                    p.steps = preview_steps
-                    p.sampler_index = preview_sampler_index
-                    p.cfg_scale = preview_cfg_scale
-                    p.seed = preview_seed
-                    p.width = preview_width
-                    p.height = preview_height
-                else:
-                    p.prompt = entries[0].cond_text
-                    p.steps = 20
+            optimizer.step()
 
-                preview_text = p.prompt
+        steps_done = hypernetwork.step + 1
 
-                processed = processing.process_images(p)
-                image = processed.images[0] if len(processed.images)>0 else None
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
+            raise RuntimeError("Loss diverged.")
+        
+        if len(previous_mean_losses) > 1:
+            std = stdev(previous_mean_losses)
+        else:
+            std = 0
+        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
+        pbar.set_description(dataset_loss_info)
+
+        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
+            # Before saving, change name to match current checkpoint.
+            hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
+            hypernetwork.save(last_saved_file)
+
+        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
+            "loss": f"{previous_mean_loss:.7f}",
+            "learn_rate": scheduler.learn_rate
+        })
+
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{hypernetwork_name}-{steps_done}'
+            last_saved_image = os.path.join(images_dir, forced_filename)
+
+            optimizer.zero_grad()
+            shared.sd_model.cond_stage_model.to(devices.device)
+            shared.sd_model.first_stage_model.to(devices.device)
 
-                if unload:
-                    shared.sd_model.cond_stage_model.to(devices.cpu)
-                    shared.sd_model.first_stage_model.to(devices.cpu)
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+            )
 
-                if image is not None:
-                    shared.state.current_image = image
-                    last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-                    last_saved_image += f", prompt: {preview_text}"
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entries[0].cond_text
+                p.steps = 20
+
+            preview_text = p.prompt
+
+            processed = processing.process_images(p)
+            image = processed.images[0] if len(processed.images)>0 else None
+
+            if unload:
+                shared.sd_model.cond_stage_model.to(devices.cpu)
+                shared.sd_model.first_stage_model.to(devices.cpu)
 
-            shared.state.job_no = hypernetwork.step
+            if image is not None:
+                shared.state.current_image = image
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                last_saved_image += f", prompt: {preview_text}"
 
-            shared.state.textinfo = f"""
+        shared.state.job_no = hypernetwork.step
+
+        shared.state.textinfo = f"""
 <p>
 Loss: {previous_mean_loss:.7f}<br/>
 Step: {hypernetwork.step}<br/>
@@ -512,14 +510,7 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-    finally:
-        if weights:
-            for weight in weights:
-                weight.requires_grad = False
-        if unload:
-            shared.sd_model.cond_stage_model.to(devices.device)
-            shared.sd_model.first_stage_model.to(devices.device)
-
+        
     report_statistics(loss_dict)
     checkpoint = sd_models.select_checkpoint()
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fd7f0897..44f06443 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -283,113 +283,111 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     embedding_yet_to_be_embedded = False
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    for i, entries in pbar:
+        embedding.step = i + ititial_step
 
-    try:
-        for i, entries in pbar:
-            embedding.step = i + ititial_step
-
-            scheduler.apply(optimizer, embedding.step)
-            if scheduler.finished:
-                break
-
-            if shared.state.interrupted:
-                break
-
-            with torch.autocast("cuda"):
-                c = cond_model([entry.cond_text for entry in entries])
-                x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-                loss = shared.sd_model(x, c)[0]
-                del x
-
-                losses[embedding.step % losses.shape[0]] = loss.item()
-
-                optimizer.zero_grad()
-                loss.backward()
-                optimizer.step()
-
-            steps_done = embedding.step + 1
-
-            epoch_num = embedding.step // len(ds)
-            epoch_step = embedding.step % len(ds)
-
-            pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
-
-            if embedding_dir is not None and steps_done % save_embedding_every == 0:
-                # Before saving, change name to match current checkpoint.
-                embedding.name = f'{embedding_name}-{steps_done}'
-                last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
-                embedding.save(last_saved_file)
-                embedding_yet_to_be_embedded = True
-
-            write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
-                "loss": f"{losses.mean():.7f}",
-                "learn_rate": scheduler.learn_rate
-            })
-
-            if images_dir is not None and steps_done % create_image_every == 0:
-                forced_filename = f'{embedding_name}-{steps_done}'
-                last_saved_image = os.path.join(images_dir, forced_filename)
-                p = processing.StableDiffusionProcessingTxt2Img(
-                    sd_model=shared.sd_model,
-                    do_not_save_grid=True,
-                    do_not_save_samples=True,
-                    do_not_reload_embeddings=True,
-                )
-
-                if preview_from_txt2img:
-                    p.prompt = preview_prompt
-                    p.negative_prompt = preview_negative_prompt
-                    p.steps = preview_steps
-                    p.sampler_index = preview_sampler_index
-                    p.cfg_scale = preview_cfg_scale
-                    p.seed = preview_seed
-                    p.width = preview_width
-                    p.height = preview_height
-                else:
-                    p.prompt = entries[0].cond_text
-                    p.steps = 20
-                    p.width = training_width
-                    p.height = training_height
+        scheduler.apply(optimizer, embedding.step)
+        if scheduler.finished:
+            break
+
+        if shared.state.interrupted:
+            break
+
+        with torch.autocast("cuda"):
+            c = cond_model([entry.cond_text for entry in entries])
+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
+            del x
+
+            losses[embedding.step % losses.shape[0]] = loss.item()
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        steps_done = embedding.step + 1
+
+        epoch_num = embedding.step // len(ds)
+        epoch_step = embedding.step % len(ds)
+
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
+
+        if embedding_dir is not None and steps_done % save_embedding_every == 0:
+            # Before saving, change name to match current checkpoint.
+            embedding.name = f'{embedding_name}-{steps_done}'
+            last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
+            embedding.save(last_saved_file)
+            embedding_yet_to_be_embedded = True
+
+        write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
+            "loss": f"{losses.mean():.7f}",
+            "learn_rate": scheduler.learn_rate
+        })
+
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{embedding_name}-{steps_done}'
+            last_saved_image = os.path.join(images_dir, forced_filename)
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+                do_not_reload_embeddings=True,
+            )
+
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entries[0].cond_text
+                p.steps = 20
+                p.width = training_width
+                p.height = training_height
 
-                preview_text = p.prompt
+            preview_text = p.prompt
 
-                processed = processing.process_images(p)
-                image = processed.images[0]
+            processed = processing.process_images(p)
+            image = processed.images[0]
 
-                shared.state.current_image = image
+            shared.state.current_image = image
 
-                if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
+            if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
 
-                    last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
+                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
 
-                    info = PngImagePlugin.PngInfo()
-                    data = torch.load(last_saved_file)
-                    info.add_text("sd-ti-embedding", embedding_to_b64(data))
+                info = PngImagePlugin.PngInfo()
+                data = torch.load(last_saved_file)
+                info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
-                    title = "<{}>".format(data.get('name', '???'))
+                title = "<{}>".format(data.get('name', '???'))
 
-                    try:
-                        vectorSize = list(data['string_to_param'].values())[0].shape[0]
-                    except Exception as e:
-                        vectorSize = '?'
+                try:
+                    vectorSize = list(data['string_to_param'].values())[0].shape[0]
+                except Exception as e:
+                    vectorSize = '?'
 
-                    checkpoint = sd_models.select_checkpoint()
-                    footer_left = checkpoint.model_name
-                    footer_mid = '[{}]'.format(checkpoint.hash)
-                    footer_right = '{}v {}s'.format(vectorSize, steps_done)
+                checkpoint = sd_models.select_checkpoint()
+                footer_left = checkpoint.model_name
+                footer_mid = '[{}]'.format(checkpoint.hash)
+                footer_right = '{}v {}s'.format(vectorSize, steps_done)
 
-                    captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
-                    captioned_image = insert_image_data_embed(captioned_image, data)
+                captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
+                captioned_image = insert_image_data_embed(captioned_image, data)
 
-                    captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
-                    embedding_yet_to_be_embedded = False
+                captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+                embedding_yet_to_be_embedded = False
 
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-                last_saved_image += f", prompt: {preview_text}"
+            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+            last_saved_image += f", prompt: {preview_text}"
 
-            shared.state.job_no = embedding.step
+        shared.state.job_no = embedding.step
 
-            shared.state.textinfo = f"""
+        shared.state.textinfo = f"""
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {embedding.step}<br/>
@@ -398,9 +396,6 @@ Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-    finally:
-        if embedding and embedding.vec is not None:
-            embedding.vec.requires_grad = False
 
     checkpoint = sd_models.select_checkpoint()
 
-- 
cgit v1.2.3


From 6e2ce4e735db64afcd0fe637327ca4ec78335706 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Sat, 29 Oct 2022 10:35:51 -0700
Subject: Added image conditioning to latent upscale. Only comuted  if the mask
 weight is not 1.0 to avoid extra memory. Also includes some code cleanup.

---
 modules/processing.py | 29 +++++++++++------------------
 1 file changed, 11 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index f18b7db2..ee0e9e34 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -134,11 +134,7 @@ class StableDiffusionProcessing():
             # Dummy zero conditioning if we're not using inpainting model.
             # Still takes up a bit of memory, but no encoder call.
             # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-            return torch.zeros(
-                x.shape[0], 5, 1, 1, 
-                dtype=x.dtype, 
-                device=x.device
-            )
+            return x.new_zeros(x.shape[0], 5, 1, 1)
 
         height = height or self.height
         width = width or self.width
@@ -156,11 +152,7 @@ class StableDiffusionProcessing():
     def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
         if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
             # Dummy zero conditioning if we're not using inpainting model.
-            return torch.zeros(
-                latent_image.shape[0], 5, 1, 1,
-                dtype=latent_image.dtype,
-                device=latent_image.device
-            )
+            return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
 
         # Handle the different mask inputs
         if image_mask is not None:
@@ -174,11 +166,10 @@ class StableDiffusionProcessing():
                 # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
                 conditioning_mask = torch.round(conditioning_mask)
         else:
-            conditioning_mask = torch.ones(1, 1, *source_image.shape[-2:])
+            conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
 
         # Create another latent image, this time with a masked version of the original input.
         # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
-        conditioning_mask = conditioning_mask.to(source_image.device)
         conditioning_image = torch.lerp(
             source_image,
             source_image * (1.0 - conditioning_mask),
@@ -653,7 +644,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         if opts.use_scale_latent_for_hires_fix:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
-            image_conditioning = self.txt2img_image_conditioning(samples)
+            
+            # Avoid making the inpainting conditioning unless necessary as 
+            # this does need some extra compute to decode / encode the image again.
+            if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
+                image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
+            else:
+                image_conditioning = self.txt2img_image_conditioning(samples)
 
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
@@ -675,11 +672,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
-            image_conditioning = self.img2img_image_conditioning(
-                decoded_samples, 
-                samples, 
-                decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
-            )
+            image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
 
         shared.state.nextjob()
 
-- 
cgit v1.2.3


From a07f054c86f33360ff620d6a3fffdee366ab2d99 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 00:49:29 +0700
Subject: Add missing info on hypernetwork/embedding model log

Mentioned here: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/1528#discussioncomment-3991513

Also group the saving into one
---
 modules/hypernetworks/hypernetwork.py          | 31 +++++++++++++-------
 modules/textual_inversion/textual_inversion.py | 39 +++++++++++++++++---------
 2 files changed, 47 insertions(+), 23 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 38f35c58..86daf825 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -361,6 +361,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         images_dir = None
 
     hypernetwork = shared.loaded_hypernetwork
+    checkpoint = sd_models.select_checkpoint()
 
     ititial_step = hypernetwork.step or 0
     if ititial_step > steps:
@@ -449,9 +450,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
             # Before saving, change name to match current checkpoint.
-            hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
-            hypernetwork.save(last_saved_file)
+            hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+            save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{previous_mean_loss:.7f}",
@@ -512,13 +513,23 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 """
         
     report_statistics(loss_dict)
-    checkpoint = sd_models.select_checkpoint()
 
-    hypernetwork.sd_checkpoint = checkpoint.hash
-    hypernetwork.sd_checkpoint_name = checkpoint.model_name
-    # Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).
-    hypernetwork.name = hypernetwork_name
-    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt')
-    hypernetwork.save(filename)
+    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+    save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
 
     return hypernetwork, filename
+
+def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
+    old_hypernetwork_name = hypernetwork.name
+    old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
+    old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None
+    try:
+        hypernetwork.sd_checkpoint = checkpoint.hash
+        hypernetwork.sd_checkpoint_name = checkpoint.model_name
+        hypernetwork.name = hypernetwork_name
+        hypernetwork.save(filename)
+    except:
+        hypernetwork.sd_checkpoint = old_sd_checkpoint
+        hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
+        hypernetwork.name = old_hypernetwork_name
+        raise
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 44f06443..ee9917ce 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -119,7 +119,7 @@ class EmbeddingDatabase:
             vec = emb.detach().to(devices.device, dtype=torch.float32)
             embedding = Embedding(vec, name)
             embedding.step = data.get('step', None)
-            embedding.sd_checkpoint = data.get('hash', None)
+            embedding.sd_checkpoint = data.get('sd_checkpoint', None)
             embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
             self.register_embedding(embedding, shared.sd_model)
 
@@ -259,6 +259,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     hijack = sd_hijack.model_hijack
 
     embedding = hijack.embedding_db.word_embeddings[embedding_name]
+    checkpoint = sd_models.select_checkpoint()
 
     ititial_step = embedding.step or 0
     if ititial_step > steps:
@@ -314,9 +315,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
         if embedding_dir is not None and steps_done % save_embedding_every == 0:
             # Before saving, change name to match current checkpoint.
-            embedding.name = f'{embedding_name}-{steps_done}'
-            last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
-            embedding.save(last_saved_file)
+            embedding_name_every = f'{embedding_name}-{steps_done}'
+            last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
+            save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
             embedding_yet_to_be_embedded = True
 
         write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
@@ -397,14 +398,26 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
 
-    checkpoint = sd_models.select_checkpoint()
-
-    embedding.sd_checkpoint = checkpoint.hash
-    embedding.sd_checkpoint_name = checkpoint.model_name
-    embedding.cached_checksum = None
-    # Before saving for the last time, change name back to base name (as opposed to the save_embedding_every step-suffixed naming convention).
-    embedding.name = embedding_name
-    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding.name}.pt')
-    embedding.save(filename)
+    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+    save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
 
     return embedding, filename
+
+def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True):
+    old_embedding_name = embedding.name
+    old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
+    old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
+    old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None
+    try:
+        embedding.sd_checkpoint = checkpoint.hash
+        embedding.sd_checkpoint_name = checkpoint.model_name
+        if remove_cached_checksum:
+            embedding.cached_checksum = None
+        embedding.name = embedding_name
+        embedding.save(filename)
+    except:
+        embedding.sd_checkpoint = old_sd_checkpoint
+        embedding.sd_checkpoint_name = old_sd_checkpoint_name
+        embedding.name = old_embedding_name
+        embedding.cached_checksum = old_cached_checksum
+        raise
-- 
cgit v1.2.3


From 3d58510f214c645ce5cdb261aa47df6573b239e9 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 00:54:59 +0700
Subject: Fix dataset still being loaded even when training will be skipped

---
 modules/hypernetworks/hypernetwork.py          | 2 +-
 modules/textual_inversion/textual_inversion.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 86daf825..07acadc9 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -364,7 +364,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     checkpoint = sd_models.select_checkpoint()
 
     ititial_step = hypernetwork.step or 0
-    if ititial_step > steps:
+    if ititial_step >= steps:
         shared.state.textinfo = f"Model has already been trained beyond specified max steps"
         return hypernetwork, filename
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ee9917ce..e0babb46 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -262,7 +262,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     checkpoint = sd_models.select_checkpoint()
 
     ititial_step = embedding.step or 0
-    if ititial_step > steps:
+    if ititial_step >= steps:
         shared.state.textinfo = f"Model has already been trained beyond specified max steps"
         return embedding, filename
 
-- 
cgit v1.2.3


From 4609b83cd496013a05e77c42af031d89f07785a9 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sat, 29 Oct 2022 16:09:19 -0300
Subject: Add PNG Info endpoint

---
 modules/api/api.py    | 12 +++++++++---
 modules/api/models.py |  9 ++++++++-
 2 files changed, 17 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 49c213ea..8fcd068d 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -5,7 +5,7 @@ import modules.shared as shared
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
-from modules.extras import run_extras
+from modules.extras import run_extras, run_pnginfo
 
 def upscaler_to_index(name: str):
     try:
@@ -32,6 +32,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
+        self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -125,8 +126,13 @@ class Api:
 
         return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
     
-    def pnginfoapi(self):
-        raise NotImplementedError
+    def pnginfoapi(self, req:PNGInfoRequest):
+        if(not req.image.strip()):
+            return PNGInfoResponse(info="")
+
+        result = run_pnginfo(decode_base64_to_image(req.image.strip()))
+
+        return PNGInfoResponse(info=result[1])
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)
diff --git a/modules/api/models.py b/modules/api/models.py
index dd122321..58e8e58b 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,4 +1,5 @@
 import inspect
+from click import prompt
 from pydantic import BaseModel, Field, create_model
 from typing import Any, Optional
 from typing_extensions import Literal
@@ -148,4 +149,10 @@ class ExtrasBatchImagesRequest(ExtrasBaseRequest):
     imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
\ No newline at end of file
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
+
+class PNGInfoRequest(BaseModel):
+    image: str = Field(title="Image", description="The base64 encoded PNG image")
+
+class PNGInfoResponse(BaseModel):
+    info: str = Field(title="Image info", description="A string with all the info the image had")
\ No newline at end of file
-- 
cgit v1.2.3


From 83a1f44ae26cb89492064bb8be0321b14a75efe4 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sat, 29 Oct 2022 16:10:00 -0300
Subject: Fix space

---
 modules/api/api.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 8fcd068d..d0f488ca 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -126,7 +126,7 @@ class Api:
 
         return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
     
-    def pnginfoapi(self, req:PNGInfoRequest):
+    def pnginfoapi(self, req: PNGInfoRequest):
         if(not req.image.strip()):
             return PNGInfoResponse(info="")
 
-- 
cgit v1.2.3


From 9bb6b6509aff8c1e6546d5a798ef9e9922758dc4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 22:20:02 +0300
Subject: add postprocess call for scripts

---
 modules/processing.py | 12 +++++++++---
 modules/scripts.py    | 24 +++++++++++++++++++++---
 2 files changed, 30 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 548eec29..50343846 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -478,7 +478,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     if p.scripts is not None:
-        p.scripts.run_alwayson_scripts(p)
+        p.scripts.process(p)
 
     infotexts = []
     output_images = []
@@ -501,7 +501,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
             subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
-            if (len(prompts) == 0):
+            if len(prompts) == 0:
                 break
 
             with devices.autocast():
@@ -590,7 +590,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+
+    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+
+    if p.scripts is not None:
+        p.scripts.postprocess(p, res)
+
+    return res
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
diff --git a/modules/scripts.py b/modules/scripts.py
index a7f36012..96e44bfd 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -64,7 +64,16 @@ class Script:
     def process(self, p, *args):
         """
         This function is called before processing begins for AlwaysVisible scripts.
-        scripts. You can modify the processing object (p) here, inject hooks, etc.
+        You can modify the processing object (p) here, inject hooks, etc.
+        args contains all values returned by components from ui()
+        """
+
+        pass
+
+    def postprocess(self, p, processed, *args):
+        """
+        This function is called after processing ends for AlwaysVisible scripts.
+        args contains all values returned by components from ui()
         """
 
         pass
@@ -289,13 +298,22 @@ class ScriptRunner:
 
         return processed
 
-    def run_alwayson_scripts(self, p):
+    def process(self, p):
         for script in self.alwayson_scripts:
             try:
                 script_args = p.script_args[script.args_from:script.args_to]
                 script.process(p, *script_args)
             except Exception:
-                print(f"Error running alwayson script: {script.filename}", file=sys.stderr)
+                print(f"Error running process: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
+    def postprocess(self, p, processed):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess(p, processed, *script_args)
+            except Exception:
+                print(f"Error running postprocess: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
     def reload_sources(self, cache):
-- 
cgit v1.2.3


From f62db4d5c753bc32d2ae166606ce41f4c5fa5c43 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 03:55:43 +0800
Subject: fix progress response model

---
 modules/api/api.py    | 30 ------------------------------
 modules/api/models.py |  8 ++++----
 2 files changed, 4 insertions(+), 34 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index e93cddcb..7e8522a2 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,33 +1,3 @@
-# import time
-
-# from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
-# from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-# from modules.sd_samplers import all_samplers
-# from modules.extras import run_pnginfo
-# import modules.shared as shared
-# from modules import devices
-# import uvicorn
-# from fastapi import Body, APIRouter, HTTPException
-# from fastapi.responses import JSONResponse
-# from pydantic import BaseModel, Field, Json
-# from typing import List
-# import json
-# import io
-# import base64
-# from PIL import Image
-
-# sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
-
-# class TextToImageResponse(BaseModel):
-#     images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-#     parameters: Json
-#     info: Json
-
-# class ImageToImageResponse(BaseModel):
-#     images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-#     parameters: Json
-#     info: Json
-
 import time
 import uvicorn
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
diff --git a/modules/api/models.py b/modules/api/models.py
index 8d4abc39..e1762fb9 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,6 +1,6 @@
 import inspect
 from click import prompt
-from pydantic import BaseModel, Field, create_model
+from pydantic import BaseModel, Field, Json, create_model
 from typing import Any, Optional
 from typing_extensions import Literal
 from inflection import underscore
@@ -158,6 +158,6 @@ class PNGInfoResponse(BaseModel):
     info: str = Field(title="Image info", description="A string with all the info the image had")
 
 class ProgressResponse(BaseModel):
-    progress: float
-    eta_relative: float
-    state: dict
+    progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
+    eta_relative: float = Field(title="ETA in secs")
+    state: Json
-- 
cgit v1.2.3


From e9c6c2a51f972fd7cd88ea740ade4ac3d8108b67 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 04:02:56 +0800
Subject: add description for state field

---
 modules/api/models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index e1762fb9..709ab5a6 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -160,4 +160,4 @@ class PNGInfoResponse(BaseModel):
 class ProgressResponse(BaseModel):
     progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
     eta_relative: float = Field(title="ETA in secs")
-    state: Json
+    state: Json = Field(title="State", description="The current state snapshot")
-- 
cgit v1.2.3


From 88f46a5bec610cf03641f18becbe3deda541e982 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 05:04:29 +0800
Subject: update progress response model

---
 modules/api/api.py    | 6 +++---
 modules/api/models.py | 4 ++--
 modules/shared.py     | 4 ++--
 3 files changed, 7 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 7e8522a2..5912d289 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -61,7 +61,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
         self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
-        self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"])
+        self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -171,7 +171,7 @@ class Api:
         # copy from check_progress_call of ui.py
 
         if shared.state.job_count == 0:
-            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.js())
+            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict())
 
         # avoid dividing zero
         progress = 0.01
@@ -187,7 +187,7 @@ class Api:
 
         progress = min(progress, 1)
 
-        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.js())
+        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict())
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)
diff --git a/modules/api/models.py b/modules/api/models.py
index 709ab5a6..0ab85ec5 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,6 +1,6 @@
 import inspect
 from click import prompt
-from pydantic import BaseModel, Field, Json, create_model
+from pydantic import BaseModel, Field, create_model
 from typing import Any, Optional
 from typing_extensions import Literal
 from inflection import underscore
@@ -160,4 +160,4 @@ class PNGInfoResponse(BaseModel):
 class ProgressResponse(BaseModel):
     progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
     eta_relative: float = Field(title="ETA in secs")
-    state: Json = Field(title="State", description="The current state snapshot")
+    state: dict = Field(title="State", description="The current state snapshot")
diff --git a/modules/shared.py b/modules/shared.py
index 0f4c035d..f7b0990c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -147,7 +147,7 @@ class State:
     def get_job_timestamp(self):
         return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
 
-    def js(self):
+    def dict(self):
         obj = {
             "skipped": self.skipped,
             "interrupted": self.skipped,
@@ -158,7 +158,7 @@ class State:
             "sampling_steps": self.sampling_steps,
         }
 
-        return json.dumps(obj)
+        return obj
 
 
 state = State()
-- 
cgit v1.2.3


From 39f55c3c35873bc7dd9792cb2155746a1c3d4292 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Sat, 29 Oct 2022 14:13:02 -0700
Subject: Re-add explicit device move

---
 modules/processing.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index ee0e9e34..d07e3db9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -170,6 +170,7 @@ class StableDiffusionProcessing():
 
         # Create another latent image, this time with a masked version of the original input.
         # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
+        conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)
         conditioning_image = torch.lerp(
             source_image,
             source_image * (1.0 - conditioning_mask),
-- 
cgit v1.2.3


From 9f104b53c425e248595e5b6481336d2a339e015e Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 05:19:17 +0800
Subject: preview current image when opts.show_progress_every_n_steps is
 enabled

---
 modules/api/api.py    | 8 ++++++--
 modules/api/models.py | 1 +
 2 files changed, 7 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 5912d289..e960bb7b 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,7 +1,7 @@
 import time
 import uvicorn
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException
 import modules.shared as shared
 from modules import devices
 from modules.api.models import *
@@ -187,7 +187,11 @@ class Api:
 
         progress = min(progress, 1)
 
-        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict())
+        current_image = None
+        if shared.state.current_image:
+            current_image = encode_pil_to_base64(shared.state.current_image)
+
+        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)
diff --git a/modules/api/models.py b/modules/api/models.py
index 0ab85ec5..c8bc719a 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -161,3 +161,4 @@ class ProgressResponse(BaseModel):
     progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
     eta_relative: float = Field(title="ETA in secs")
     state: dict = Field(title="State", description="The current state snapshot")
+    current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
-- 
cgit v1.2.3


From 66d038f6a41507af2243ff1f6618a745a092c290 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Sat, 29 Oct 2022 15:00:08 -0700
Subject: Read hypernet strength from PNG info.

---
 modules/generation_parameters_copypaste.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index bbaad42e..59c6d7da 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -66,6 +66,7 @@ def integrate_settings_paste_fields(component_dict):
 
     settings_map = {
         'sd_hypernetwork': 'Hypernet',
+        'sd_hypernetwork_strength': 'Hypernetwork strength',
         'CLIP_stop_at_last_layers': 'Clip skip',
         'sd_model_checkpoint': 'Model hash',
     }
-- 
cgit v1.2.3


From 9f4f894d74b57c3d02ebccaa59f9c22fca2b6c90 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 06:03:32 +0800
Subject: allow skip current image in progress api

---
 modules/api/api.py    | 4 ++--
 modules/api/models.py | 3 +++
 2 files changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index e960bb7b..5c5b210f 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -167,7 +167,7 @@ class Api:
 
         return PNGInfoResponse(info=result[1])
 
-    def progressapi(self):
+    def progressapi(self, req: ProgressRequest = Depends()):
         # copy from check_progress_call of ui.py
 
         if shared.state.job_count == 0:
@@ -188,7 +188,7 @@ class Api:
         progress = min(progress, 1)
 
         current_image = None
-        if shared.state.current_image:
+        if shared.state.current_image and not req.skip_current_image:
             current_image = encode_pil_to_base64(shared.state.current_image)
 
         return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
diff --git a/modules/api/models.py b/modules/api/models.py
index c8bc719a..9ee42a17 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -157,6 +157,9 @@ class PNGInfoRequest(BaseModel):
 class PNGInfoResponse(BaseModel):
     info: str = Field(title="Image info", description="A string with all the info the image had")
 
+class ProgressRequest(BaseModel):
+    skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
+
 class ProgressResponse(BaseModel):
     progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
     eta_relative: float = Field(title="ETA in secs")
-- 
cgit v1.2.3


From 05a657dd357eaca6940c4775daa946bd33f1167d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 30 Oct 2022 07:36:56 +0300
Subject: fix broken hires fix

---
 modules/processing.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 50343846..947ce6fa 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -686,15 +686,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
+        image_conditioning = self.txt2img_image_conditioning(x)
+
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()
 
-        image_conditioning = self.img2img_image_conditioning(
-            decoded_samples, 
-            samples, 
-            decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
-        )
         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
 
         return samples
-- 
cgit v1.2.3


From 61836bd544fc8f4ef62f311c9d5964fbdaeb3f4c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 30 Oct 2022 08:48:53 +0300
Subject: shorten Hypernetwork strength in infotext and omit it when it's the
 default value.

---
 modules/generation_parameters_copypaste.py | 2 +-
 modules/processing.py                      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 59c6d7da..df70c728 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -66,7 +66,7 @@ def integrate_settings_paste_fields(component_dict):
 
     settings_map = {
         'sd_hypernetwork': 'Hypernet',
-        'sd_hypernetwork_strength': 'Hypernetwork strength',
+        'sd_hypernetwork_strength': 'Hypernet strength',
         'CLIP_stop_at_last_layers': 'Clip skip',
         'sd_model_checkpoint': 'Model hash',
     }
diff --git a/modules/processing.py b/modules/processing.py
index ecaa78e2..b1df4918 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -396,7 +396,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
         "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
-        "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength),
+        "Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.3


From 149784202cca8612b43629c601ee27cfda64e623 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 30 Oct 2022 09:10:22 +0300
Subject: rework #3722 to not introduce duplicate code

---
 modules/api/api.py | 43 +++++++++++++------------------------------
 modules/shared.py  | 22 +++++++++++++++++++---
 2 files changed, 32 insertions(+), 33 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 5c5b210f..6c06d449 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -9,31 +9,6 @@ from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusion
 from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
 
-# copy from wrap_gradio_gpu_call of webui.py
-# because queue lock will be acquired in api handlers
-# and time start needs to be set
-# the function has been modified into two parts
-
-def before_gpu_call():
-    devices.torch_gc()
-
-    shared.state.sampling_step = 0
-    shared.state.job_count = -1
-    shared.state.job_no = 0
-    shared.state.job_timestamp = shared.state.get_job_timestamp()
-    shared.state.current_latent = None
-    shared.state.current_image = None
-    shared.state.current_image_sampling_step = 0
-    shared.state.skipped = False
-    shared.state.interrupted = False
-    shared.state.textinfo = None
-    shared.state.time_start = time.time()
-
-def after_gpu_call():
-    shared.state.job = ""
-    shared.state.job_count = 0
-
-    devices.torch_gc()
 
 def upscaler_to_index(name: str):
     try:
@@ -41,8 +16,10 @@ def upscaler_to_index(name: str):
     except:
         raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
 
+
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
+
 def setUpscalers(req: dict):
     reqDict = vars(req)
     reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
@@ -51,6 +28,7 @@ def setUpscalers(req: dict):
     reqDict.pop('upscaler_2')
     return reqDict
 
+
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
@@ -78,10 +56,13 @@ class Api:
         )
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
         # Override object param
-        before_gpu_call()
+
+        shared.state.begin()
+
         with self.queue_lock:
             processed = process_images(p)
-        after_gpu_call()
+
+        shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
@@ -119,11 +100,13 @@ class Api:
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
-        # Override object param
-        before_gpu_call()
+
+        shared.state.begin()
+
         with self.queue_lock:
             processed = process_images(p)
-        after_gpu_call()
+
+        shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
diff --git a/modules/shared.py b/modules/shared.py
index f7b0990c..e4f163c1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -144,9 +144,6 @@ class State:
         self.sampling_step = 0
         self.current_image_sampling_step = 0
 
-    def get_job_timestamp(self):
-        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
-
     def dict(self):
         obj = {
             "skipped": self.skipped,
@@ -160,6 +157,25 @@ class State:
 
         return obj
 
+    def begin(self):
+        self.sampling_step = 0
+        self.job_count = -1
+        self.job_no = 0
+        self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+        self.current_latent = None
+        self.current_image = None
+        self.current_image_sampling_step = 0
+        self.skipped = False
+        self.interrupted = False
+        self.textinfo = None
+
+        devices.torch_gc()
+
+    def end(self):
+        self.job = ""
+        self.job_count = 0
+
+        devices.torch_gc()
 
 state = State()
 
-- 
cgit v1.2.3


From 71571e3f055237d71ba2d47756846ad1d73be00c Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Sun, 30 Oct 2022 00:35:40 -0700
Subject: Replaced master branch fix with updated fix.

---
 modules/processing.py | 2 --
 1 file changed, 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 3dd44d3a..512c484f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -688,8 +688,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
-        image_conditioning = self.txt2img_image_conditioning(x)
-
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()
-- 
cgit v1.2.3


From be27fd4690b1eb6c74da1e31c9696a0f1901fbba Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 17:01:01 +0800
Subject: fix broken progress api by previous rework

---
 modules/shared.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index e4f163c1..2c7d28a5 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -4,6 +4,7 @@ import json
 import os
 import sys
 from collections import OrderedDict
+import time
 
 import gradio as gr
 import tqdm
@@ -132,6 +133,7 @@ class State:
     current_image = None
     current_image_sampling_step = 0
     textinfo = None
+    time_start = None
 
     def skip(self):
         self.skipped = True
@@ -168,6 +170,7 @@ class State:
         self.skipped = False
         self.interrupted = False
         self.textinfo = None
+        self.time_start = time.time()
 
         devices.torch_gc()
 
-- 
cgit v1.2.3


From 1a4ff2de6a835cd8cc1590bbc1a8dedb5ad37e5b Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 17:02:47 +0800
Subject: fix current image in progress api when parallel processing enabled

---
 modules/api/api.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 6c06d449..97497f3f 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -3,10 +3,9 @@ import uvicorn
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, Depends, HTTPException
 import modules.shared as shared
-from modules import devices
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers
+from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid
 from modules.extras import run_extras, run_pnginfo
 
 
@@ -170,6 +169,16 @@ class Api:
 
         progress = min(progress, 1)
 
+        # copy from check_progress_call of ui.py
+
+        if shared.parallel_processing_allowed:
+            if shared.state.sampling_step - shared.state.current_image_sampling_step >= shared.opts.show_progress_every_n_steps and shared.state.current_latent is not None:
+                if shared.opts.show_progress_grid:
+                    shared.state.current_image = samples_to_image_grid(shared.state.current_latent)
+                else:
+                    shared.state.current_image = sample_to_image(shared.state.current_latent)
+                shared.state.current_image_sampling_step = shared.state.sampling_step
+
         current_image = None
         if shared.state.current_image and not req.skip_current_image:
             current_image = encode_pil_to_base64(shared.state.current_image)
-- 
cgit v1.2.3


From 34c86c12b0a9d650d4e7c5be478bca34ad8ed048 Mon Sep 17 00:00:00 2001
From: Martin Cairns <4314538+MartinCairnsSQL@users.noreply.github.com>
Date: Sun, 30 Oct 2022 11:04:27 +0000
Subject: Include PLMS in adjust steps as it also can fail in the same way

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index aca014e8..8772db56 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -208,7 +208,7 @@ class VanillaStableDiffusionSampler:
 
 
     def adjust_steps_if_invalid(self, p, num_steps):
-        if self.config.name == 'DDIM' and p.ddim_discretize == 'uniform':
+        if  (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
             valid_step = 999 / (1000 // num_steps)
             if valid_step == floor(valid_step):
                 return int(valid_step) + 1
-- 
cgit v1.2.3


From 4b8a192f680101de247dca79e48974b53bf961fe Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sat, 29 Oct 2022 16:36:43 +0900
Subject: add optimizer save option to shared.opts

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index e4f163c1..065b893d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -286,6 +286,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-- 
cgit v1.2.3


From 20194fd9752a280306fb66b57b258609b0918c46 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sat, 29 Oct 2022 16:56:42 +0900
Subject: We have duplicate linear now

---
 modules/hypernetworks/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index aad09ffc..c2d4b51c 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
 not_available = ["hardswish", "multiheadattention"]
-keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
     # Remove illegal characters from name.
-- 
cgit v1.2.3


From 9d96d7d0a0aa0a966a9aefd24342345eb65952ed Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Sun, 30 Oct 2022 20:39:04 +0900
Subject: resolve conflicts

---
 modules/hypernetworks/hypernetwork.py | 44 ++++++++++++++++++++++++++++++-----
 1 file changed, 38 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index a11e01d6..8f74cdea 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -21,6 +21,7 @@ from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_norm
 from collections import defaultdict, deque
 from statistics import stdev, mean
 
+optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
@@ -139,6 +140,8 @@ class Hypernetwork:
         self.weight_init = weight_init
         self.add_layer_norm = add_layer_norm
         self.use_dropout = use_dropout
+        self.optimizer_name = None
+        self.optimizer_state_dict = None
 
         for size in enable_sizes or []:
             self.layers[size] = (
@@ -171,6 +174,10 @@ class Hypernetwork:
         state_dict['use_dropout'] = self.use_dropout
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
+        if self.optimizer_name is not None:
+            state_dict['optimizer_name'] = self.optimizer_name
+        if self.optimizer_state_dict:
+            state_dict['optimizer_state_dict'] = self.optimizer_state_dict
 
         torch.save(state_dict, filename)
 
@@ -190,7 +197,14 @@ class Hypernetwork:
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
         print(f"Layer norm is set to {self.add_layer_norm}")
         self.use_dropout = state_dict.get('use_dropout', False)
-        print(f"Dropout usage is set to {self.use_dropout}" )
+        print(f"Dropout usage is set to {self.use_dropout}")
+        self.optimizer_name = state_dict.get('optimizer_name', 'AdamW')
+        print(f"Optimizer name is {self.optimizer_name}")
+        self.optimizer_state_dict = state_dict.get('optimizer_state_dict', None)
+        if self.optimizer_state_dict:
+            print("Loaded existing optimizer from checkpoint")
+        else:
+            print("No saved optimizer exists in checkpoint")
 
         for size, sd in state_dict.items():
             if type(size) == int:
@@ -392,8 +406,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
-    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
-    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
+    # Here we use optimizer from saved HN, or we can specify as UI option.
+    if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict:
+        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
+    else:
+        print(f"Optimizer type {optimizer_name} is not defined!")
+        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
+        optimizer_name = 'AdamW'
+    if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
+        try:
+            optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
+        except RuntimeError as e:
+            print("Cannot resume from saved optimizer!")
+            print(e)
 
     steps_without_grad = 0
 
@@ -455,8 +480,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             # Before saving, change name to match current checkpoint.
             hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+            hypernetwork.optimizer_name = optimizer_name
+            if shared.opts.save_optimizer_state:
+                hypernetwork.optimizer_state_dict = optimizer.state_dict()
             save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
-
+            hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{previous_mean_loss:.7f}",
             "learn_rate": scheduler.learn_rate
@@ -514,14 +542,18 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-        
     report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+    hypernetwork.optimizer_name = optimizer_name
+    if shared.opts.save_optimizer_state:
+        hypernetwork.optimizer_state_dict = optimizer.state_dict()
     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
-
+    del optimizer
+    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
     return hypernetwork, filename
 
+
 def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
     old_hypernetwork_name = hypernetwork.name
     old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
-- 
cgit v1.2.3


From c9bb33dd43dbb9479ff1b70351df14508c89ac60 Mon Sep 17 00:00:00 2001
From: victorca25 <victorca25@users.noreply.github.com>
Date: Sun, 30 Oct 2022 12:52:50 +0100
Subject: add resrgan 8x, allow use 1x and up to 8x extra models, move BSRGAN
 model, add nearest

---
 modules/esrgan_model.py | 17 +++++++++++++----
 modules/modelloader.py  |  3 +++
 modules/ui.py           |  2 +-
 modules/upscaler.py     | 17 ++++++++++++++++-
 4 files changed, 33 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index a13cf6ac..c61669b4 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -50,6 +50,7 @@ def mod2normal(state_dict):
 def resrgan2normal(state_dict, nb=23):
     # this code is copied from https://github.com/victorca25/iNNfer
     if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
+        re8x = 0
         crt_net = {}
         items = []
         for k, v in state_dict.items():
@@ -75,10 +76,18 @@ def resrgan2normal(state_dict, nb=23):
         crt_net['model.3.bias'] = state_dict['conv_up1.bias']
         crt_net['model.6.weight'] = state_dict['conv_up2.weight']
         crt_net['model.6.bias'] = state_dict['conv_up2.bias']
-        crt_net['model.8.weight'] = state_dict['conv_hr.weight']
-        crt_net['model.8.bias'] = state_dict['conv_hr.bias']
-        crt_net['model.10.weight'] = state_dict['conv_last.weight']
-        crt_net['model.10.bias'] = state_dict['conv_last.bias']
+
+        if 'conv_up3.weight' in state_dict:
+            # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
+            re8x = 3
+            crt_net['model.9.weight'] = state_dict['conv_up3.weight']
+            crt_net['model.9.bias'] = state_dict['conv_up3.bias']
+
+        crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
+        crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
+        crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
+        crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
+
         state_dict = crt_net
     return state_dict
 
diff --git a/modules/modelloader.py b/modules/modelloader.py
index b0f2f33d..e4a6f8ac 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -85,6 +85,9 @@ def cleanup_models():
     src_path = os.path.join(root_path, "ESRGAN")
     dest_path = os.path.join(models_path, "ESRGAN")
     move_files(src_path, dest_path)
+    src_path = os.path.join(models_path, "BSRGAN")
+    dest_path = os.path.join(models_path, "ESRGAN")
+    move_files(src_path, dest_path, ".pth")
     src_path = os.path.join(root_path, "gfpgan")
     dest_path = os.path.join(models_path, "GFPGAN")
     move_files(src_path, dest_path)
diff --git a/modules/ui.py b/modules/ui.py
index 5055ca64..47610f5c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1059,7 +1059,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tabs(elem_id="extras_resize_mode"):
                     with gr.TabItem('Scale by'):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
                     with gr.TabItem('Scale to'):
                         with gr.Group():
                             with gr.Row():
diff --git a/modules/upscaler.py b/modules/upscaler.py
index 6ab2fb40..83fde7ca 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -10,6 +10,7 @@ import modules.shared
 from modules import modelloader, shared
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST)
 from modules.paths import models_path
 
 
@@ -57,7 +58,7 @@ class Upscaler:
         dest_w = img.width * scale
         dest_h = img.height * scale
         for i in range(3):
-            if img.width >= dest_w and img.height >= dest_h:
+            if img.width > dest_w and img.height > dest_h:
                 break
             img = self.do_upscale(img, selected_model)
         if img.width != dest_w or img.height != dest_h:
@@ -120,3 +121,17 @@ class UpscalerLanczos(Upscaler):
         self.name = "Lanczos"
         self.scalers = [UpscalerData("Lanczos", None, self)]
 
+
+class UpscalerNearest(Upscaler):
+    scalers = []
+
+    def do_upscale(self, img, selected_model=None):
+        return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=NEAREST)
+
+    def load_model(self, _):
+        pass
+
+    def __init__(self, dirname=None):
+        super().__init__(False)
+        self.name = "Nearest"
+        self.scalers = [UpscalerData("Nearest", None, self)]
\ No newline at end of file
-- 
cgit v1.2.3


From 423f22228306ae72d0480e25add9777c3c5d8fdf Mon Sep 17 00:00:00 2001
From: Maiko Tan <maiko.tan.coding@gmail.com>
Date: Sun, 30 Oct 2022 22:46:43 +0800
Subject: feat: add app started callback

---
 modules/script_callbacks.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 6ea58d61..f5509629 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -3,6 +3,8 @@ import traceback
 from collections import namedtuple
 import inspect
 
+from fastapi import FastAPI
+from gradio import Blocks
 
 def report_exception(c, job):
     print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
@@ -25,6 +27,7 @@ class ImageSaveParams:
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
+callbacks_app_started = []
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
@@ -40,6 +43,14 @@ def clear_callbacks():
     callbacks_image_saved.clear()
 
 
+def app_started_callback(demo: Blocks, app: FastAPI):
+    for c in callbacks_app_started:
+        try:
+            c.callback(demo, app)
+        except Exception:
+            report_exception(c, 'app_started_callback')
+
+
 def model_loaded_callback(sd_model):
     for c in callbacks_model_loaded:
         try:
@@ -91,6 +102,10 @@ def add_callback(callbacks, fun):
     callbacks.append(ScriptCallback(filename, fun))
 
 
+def on_app_started(callback):
+    add_callback(callbacks_app_started, callback)
+
+
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
-- 
cgit v1.2.3


From cb31abcf58ea1f64266e6d821937eed058c35f4d Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 21:54:31 +0700
Subject: Settings to select VAE

---
 modules/sd_models.py |  31 +++++--------
 modules/sd_vae.py    | 121 +++++++++++++++++++++++++++++++++++++++++++++++++++
 modules/shared.py    |   8 ++--
 3 files changed, 136 insertions(+), 24 deletions(-)
 create mode 100644 modules/sd_vae.py

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index f86dc3ed..91ad4b5e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -8,7 +8,7 @@ from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices, script_callbacks
+from modules import shared, modelloader, devices, script_callbacks, sd_vae
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
@@ -160,12 +160,11 @@ def get_state_dict_from_checkpoint(pl_sd):
 
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 
-
-def load_model_weights(model, checkpoint_info):
+def load_model_weights(model, checkpoint_info, force=False):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    if checkpoint_info not in checkpoints_loaded:
+    if force or checkpoint_info not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
         pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
@@ -186,17 +185,7 @@ def load_model_weights(model, checkpoint_info):
         devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
         devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
-        vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
-
-        if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
-            vae_file = shared.cmd_opts.vae_path
-
-        if os.path.exists(vae_file):
-            print(f"Loading VAE weights from: {vae_file}")
-            vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-            model.first_stage_model.load_state_dict(vae_dict)
-
+        sd_vae.load_vae(model, checkpoint_file)
         model.first_stage_model.to(devices.dtype_vae)
 
         if shared.opts.sd_checkpoint_cache > 0:
@@ -213,7 +202,7 @@ def load_model_weights(model, checkpoint_info):
     model.sd_checkpoint_info = checkpoint_info
 
 
-def load_model(checkpoint_info=None):
+def load_model(checkpoint_info=None, force=False):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
 
@@ -234,7 +223,7 @@ def load_model(checkpoint_info=None):
 
     do_inpainting_hijack()
     sd_model = instantiate_from_config(sd_config.model)
-    load_model_weights(sd_model, checkpoint_info)
+    load_model_weights(sd_model, checkpoint_info, force=force)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
@@ -252,16 +241,16 @@ def load_model(checkpoint_info=None):
     return sd_model
 
 
-def reload_model_weights(sd_model, info=None):
+def reload_model_weights(sd_model, info=None, force=False):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
 
-    if sd_model.sd_model_checkpoint == checkpoint_info.filename:
+    if sd_model.sd_model_checkpoint == checkpoint_info.filename and not force:
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
-        load_model(checkpoint_info)
+        load_model(checkpoint_info, force=force)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -271,7 +260,7 @@ def reload_model_weights(sd_model, info=None):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info)
+    load_model_weights(sd_model, checkpoint_info, force=force)
 
     sd_hijack.model_hijack.hijack(sd_model)
     script_callbacks.model_loaded_callback(sd_model)
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
new file mode 100644
index 00000000..82764e55
--- /dev/null
+++ b/modules/sd_vae.py
@@ -0,0 +1,121 @@
+import torch
+import os
+from collections import namedtuple
+from modules import shared, devices
+from modules.paths import models_path
+import glob
+
+model_dir = "Stable-diffusion"
+model_path = os.path.abspath(os.path.join(models_path, model_dir))
+vae_dir = "VAE"
+vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
+
+vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+default_vae_dict = {"auto": "auto", "None": "None"}
+default_vae_list = ["auto", "None"]
+default_vae_values = [default_vae_dict[x] for x in default_vae_list]
+vae_dict = dict(default_vae_dict)
+vae_list = list(default_vae_list)
+first_load = True
+
+def get_filename(filepath):
+    return os.path.splitext(os.path.basename(filepath))[0]
+
+def refresh_vae_list(vae_path=vae_path, model_path=model_path):
+    global vae_dict, vae_list
+    res = {}
+    candidates = [
+        *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
+        *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True)
+    ]
+    if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
+        candidates.append(shared.cmd_opts.vae_path)
+    for filepath in candidates:
+        name = get_filename(filepath)
+        res[name] = filepath
+    vae_list.clear()
+    vae_list.extend(default_vae_list)
+    vae_list.extend(list(res.keys()))
+    vae_dict.clear()
+    vae_dict.update(default_vae_dict)
+    vae_dict.update(res)
+    return vae_list
+
+def load_vae(model, checkpoint_file, vae_file="auto"):
+    global first_load, vae_dict, vae_list
+    # save_settings = False
+
+    # if vae_file argument is provided, it takes priority
+    if vae_file and vae_file not in default_vae_list:
+        if not os.path.isfile(vae_file):
+            vae_file = "auto"
+            # save_settings = True
+            print("VAE provided as function argument doesn't exist")
+    # for the first load, if vae-path is provided, it takes priority and failure is reported
+    if first_load and shared.cmd_opts.vae_path is not None:
+        if os.path.isfile(shared.cmd_opts.vae_path):
+            vae_file = shared.cmd_opts.vae_path
+            # save_settings = True
+            # print("Using VAE provided as command line argument")
+        else:
+            print("VAE provided as command line argument doesn't exist")
+    # else, we load from settings
+    if vae_file == "auto" and shared.opts.sd_vae is not None:
+        # if saved VAE settings isn't recognized, fallback to auto
+        vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
+        # if VAE selected but not found, fallback to auto
+        if vae_file not in default_vae_values and not os.path.isfile(vae_file):
+            vae_file = "auto"
+            print("Selected VAE doesn't exist")
+    # vae-path cmd arg takes priority for auto
+    if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
+        if os.path.isfile(shared.cmd_opts.vae_path):
+            vae_file = shared.cmd_opts.vae_path
+            print("Using VAE provided as command line argument")
+    # if still not found, try look for ".vae.pt" beside model
+    model_path = os.path.splitext(checkpoint_file)[0]
+    if vae_file == "auto":
+        vae_file_try = model_path + ".vae.pt"
+        if os.path.isfile(vae_file_try):
+            vae_file = vae_file_try
+            print("Using VAE found beside selected model")
+    # if still not found, try look for ".vae.ckpt" beside model
+    if vae_file == "auto":
+        vae_file_try = model_path + ".vae.ckpt"
+        if os.path.isfile(vae_file_try):
+            vae_file = vae_file_try
+            print("Using VAE found beside selected model")
+    # No more fallbacks for auto
+    if vae_file == "auto":
+        vae_file = None
+    # Last check, just because
+    if vae_file and not os.path.exists(vae_file):
+        vae_file = None
+
+    if vae_file:
+        print(f"Loading VAE weights from: {vae_file}")
+        vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
+        vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
+        model.first_stage_model.load_state_dict(vae_dict_1)
+
+    # If vae used is not in dict, update it
+    # It will be removed on refresh though
+    if vae_file is not None:
+        vae_opt = get_filename(vae_file)
+        if vae_opt not in vae_dict:
+            vae_dict[vae_opt] = vae_file
+            vae_list.append(vae_opt)
+
+    """
+    # Save current VAE to VAE settings, maybe? will it work?
+    if save_settings:
+        if vae_file is None:
+            vae_opt = "None"
+
+        # shared.opts.sd_vae = vae_opt
+    """
+
+    first_load = False
+    model.first_stage_model.to(devices.dtype_vae)
diff --git a/modules/shared.py b/modules/shared.py
index e4f163c1..06440ac4 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -14,7 +14,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models, localization
+from modules import sd_samplers, sd_models, localization, sd_vae
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -295,6 +295,7 @@ options_templates.update(options_section(('training', "Training"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
@@ -407,11 +408,12 @@ class Options:
         if bad_settings > 0:
             print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
 
-    def onchange(self, key, func):
+    def onchange(self, key, func, call=True):
         item = self.data_labels.get(key)
         item.onchange = func
 
-        func()
+        if call:
+            func()
 
     def dumpjson(self):
         d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
-- 
cgit v1.2.3


From e1b2ea6e0012ecc988385fc523d8fb50ea5d6be5 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 22:11:45 +0700
Subject: Change VAE search order and thus priority

---
 modules/sd_vae.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 82764e55..0767b925 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -25,10 +25,10 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     global vae_dict, vae_list
     res = {}
     candidates = [
-        *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
         *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
-        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
+        *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
         *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True)
+        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
     ]
     if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
         candidates.append(shared.cmd_opts.vae_path)
-- 
cgit v1.2.3


From d587586d3be2de061238defb8a556f03743287f6 Mon Sep 17 00:00:00 2001
From: mawr <mawr@mail.ru>
Date: Mon, 31 Oct 2022 00:14:07 +0300
Subject: Added "--clip-models-path" switch to avoid using default
 "~/.cache/clip" and enable to run under unprivileged user without homedir

---
 modules/interrogate.py | 4 ++--
 modules/shared.py      | 1 +
 2 files changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 65b05d34..9769aa34 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -56,9 +56,9 @@ class InterrogateModels:
         import clip
 
         if self.running_on_cpu:
-            model, preprocess = clip.load(clip_model_name, device="cpu")
+            model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.cmd_opts.clip_models_path)
         else:
-            model, preprocess = clip.load(clip_model_name)
+            model, preprocess = clip.load(clip_model_name, download_root=shared.cmd_opts.clip_models_path)
 
         model.eval()
         model = model.to(devices.device_interrogate)
diff --git a/modules/shared.py b/modules/shared.py
index e4f163c1..36212031 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -51,6 +51,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
 parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
+parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
 parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
-- 
cgit v1.2.3


From d9e4e4d7a09d4aee8ce249a3c8e91ce165b10fa5 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Sun, 30 Oct 2022 15:33:02 -0700
Subject: Fix non-square full resolution inpainting.

---
 modules/masking.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/masking.py b/modules/masking.py
index fd8d9241..a5c4d2da 100644
--- a/modules/masking.py
+++ b/modules/masking.py
@@ -49,7 +49,7 @@ def expand_crop_region(crop_region, processing_width, processing_height, image_w
     ratio_processing = processing_width / processing_height
 
     if ratio_crop_region > ratio_processing:
-        desired_height = (x2 - x1) * ratio_processing
+        desired_height = (x2 - x1) / ratio_processing
         desired_height_diff = int(desired_height - (y2-y1))
         y1 -= desired_height_diff//2
         y2 += desired_height_diff - desired_height_diff//2
-- 
cgit v1.2.3


From 21fba39c609859a60616420afda3b34a89e00761 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 30 Oct 2022 23:45:52 +0000
Subject: Add callbacks and param objects

---
 modules/script_callbacks.py | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 6ea58d61..a206ea59 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -24,12 +24,22 @@ class ImageSaveParams:
         """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
 
 
+class CGFDenoiserParams:
+    def __init__(self, x_in, image_cond_in, sigma_in, sampling_step, total_sampling_steps):
+        self.x_in = x_in
+        self.image_cond_in = image_cond_in
+        self.sigma_in = sigma_in
+        self.sampling_step = sampling_step
+        self.total_sampling_steps = total_sampling_steps
+
+
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
 callbacks_before_image_saved = []
 callbacks_image_saved = []
+callbacks_cfg_denoiser = []
 
 
 def clear_callbacks():
@@ -84,6 +94,14 @@ def image_saved_callback(params: ImageSaveParams):
             report_exception(c, 'image_saved_callback')
 
 
+def cfg_denoiser_callback(params: CGFDenoiserParams):
+    for c in callbacks_cfg_denoiser:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'cfg_denoiser_callback')
+
+
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -130,3 +148,12 @@ def on_image_saved(callback):
         - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
     """
     add_callback(callbacks_image_saved, callback)
+
+
+def on_cfg_denoiser(callback):
+    """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
+    The callback is called with one argument:
+        - params: CGFDenoiserParams - parameters to be passed to the inner model and sampling state details.
+    """
+    add_callback(callbacks_cfg_denoiser, callback)
+
-- 
cgit v1.2.3


From 8906be85ac91310b37dccddc44f23631eb7a15f5 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 30 Oct 2022 23:47:08 +0000
Subject: add callback cleardown

---
 modules/script_callbacks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index a206ea59..b0b8dc47 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -48,7 +48,7 @@ def clear_callbacks():
     callbacks_ui_settings.clear()
     callbacks_before_image_saved.clear()
     callbacks_image_saved.clear()
-
+    callbacks_cfg_denoiser.clear()
 
 def model_loaded_callback(sd_model):
     for c in callbacks_model_loaded:
-- 
cgit v1.2.3


From 8ae0ea9deaa5a09d1e0aa8b2f8e97c38d71cdbda Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 30 Oct 2022 23:48:33 +0000
Subject: Add callback to sd_samplers

---
 modules/sd_samplers.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 3670b57d..30cb5c4b 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -11,6 +11,7 @@ from modules import prompt_parser, devices, processing, images
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
+from modules.script_callbacks import CGFDenoiserParams, cfg_denoiser_callback
 
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
@@ -278,6 +279,8 @@ class CFGDenoiser(torch.nn.Module):
         image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
+        cfg_denoiser_callback(CGFDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps))
+
         if tensor.shape[1] == uncond.shape[1]:
             cond_in = torch.cat([tensor, uncond])
 
-- 
cgit v1.2.3


From 081df45da47feadfb055552c0ad5c6e6ecdd9f28 Mon Sep 17 00:00:00 2001
From: Maiko Sinkyaet Tan <maiko.tan.coding@gmail.com>
Date: Mon, 31 Oct 2022 08:47:43 +0800
Subject: docs: add python doc (?)

not sure if this available...
---
 modules/script_callbacks.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index f5509629..d8aa6f00 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -103,6 +103,8 @@ def add_callback(callbacks, fun):
 
 
 def on_app_started(callback):
+    """register a function to be called when the webui started, the gradio `Block` component and
+    fastapi `FastAPI` object are passed as the arguments"""
     add_callback(callbacks_app_started, callback)
 
 
-- 
cgit v1.2.3


From adaa699e3888e1396162083d65c63cd6774cc6b0 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 18:08:40 +0800
Subject: prototype interrupt api

---
 modules/api/api.py | 6 ++++++
 1 file changed, 6 insertions(+)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 6c06d449..e702c9c0 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -40,6 +40,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
         self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
         self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
+        self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -176,6 +177,11 @@ class Api:
 
         return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
 
+    def interruptapi(self):
+        shared.state.interrupt()
+
+        return {}
+
     def launch(self, server_name, port):
         self.app.include_router(self.router)
         uvicorn.run(self.app, host=server_name, port=port)
-- 
cgit v1.2.3


From 29f758afe9c09834a1cd2dac3f5dd6fbf7dfeaa7 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 31 Oct 2022 02:39:55 +0000
Subject: Extend extras image cache with upscale_first arg

---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 4d51088b..8e2ab35c 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -141,7 +141,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
                             upscaling_resize_w, upscaling_resize_h, upscaling_crop)
             cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
                                      info_hash=hash(info),
-                                     args_hash=hash(upscale_args))
+                                     args_hash=hash((upscale_args, upscale_first)))
             cached_entry = cached_images.get(cache_key)
             if cached_entry is None:
                 res = upscale(image, *upscale_args)
-- 
cgit v1.2.3


From b96d0c4e9ecec3c856b9b4ec795dbd0d34fcac51 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Mon, 31 Oct 2022 14:42:28 +0700
Subject: Fix typo from previous commit

---
 modules/sd_vae.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 0767b925..2ce44d5f 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -27,8 +27,8 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     candidates = [
         *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
         *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
-        *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True)
-        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True)
     ]
     if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
         candidates.append(shared.cmd_opts.vae_path)
-- 
cgit v1.2.3


From 726769da35970f4c100fa7edf11850f9dc059c41 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Mon, 31 Oct 2022 15:19:34 +0700
Subject: Checkpoint cache by combination key of checkpoint and vae

---
 modules/sd_models.py | 27 ++++++++++++++++-----------
 modules/sd_vae.py    |  8 +++++++-
 2 files changed, 23 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 91ad4b5e..850f7b7b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -160,11 +160,15 @@ def get_state_dict_from_checkpoint(pl_sd):
 
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 
-def load_model_weights(model, checkpoint_info, force=False):
+def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    if force or checkpoint_info not in checkpoints_loaded:
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+
+    checkpoint_key = (checkpoint_info, vae_file)
+
+    if checkpoint_key not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
         pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
@@ -185,24 +189,25 @@ def load_model_weights(model, checkpoint_info, force=False):
         devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
         devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
-        sd_vae.load_vae(model, checkpoint_file)
+        sd_vae.load_vae(model, vae_file)
         model.first_stage_model.to(devices.dtype_vae)
 
         if shared.opts.sd_checkpoint_cache > 0:
-            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+            checkpoints_loaded[checkpoint_key] = model.state_dict().copy()
             while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
                 checkpoints_loaded.popitem(last=False)  # LRU
     else:
-        print(f"Loading weights [{sd_model_hash}] from cache")
-        checkpoints_loaded.move_to_end(checkpoint_info)
-        model.load_state_dict(checkpoints_loaded[checkpoint_info])
+        vae_name = sd_vae.get_filename(vae_file)
+        print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")
+        checkpoints_loaded.move_to_end(checkpoint_key)
+        model.load_state_dict(checkpoints_loaded[checkpoint_key])
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
 
-def load_model(checkpoint_info=None, force=False):
+def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
 
@@ -223,7 +228,7 @@ def load_model(checkpoint_info=None, force=False):
 
     do_inpainting_hijack()
     sd_model = instantiate_from_config(sd_config.model)
-    load_model_weights(sd_model, checkpoint_info, force=force)
+    load_model_weights(sd_model, checkpoint_info)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
@@ -250,7 +255,7 @@ def reload_model_weights(sd_model, info=None, force=False):
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
-        load_model(checkpoint_info, force=force)
+        load_model(checkpoint_info)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -260,7 +265,7 @@ def reload_model_weights(sd_model, info=None, force=False):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info, force=force)
+    load_model_weights(sd_model, checkpoint_info)
 
     sd_hijack.model_hijack.hijack(sd_model)
     script_callbacks.model_loaded_callback(sd_model)
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 2ce44d5f..e9239326 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -43,7 +43,7 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     vae_dict.update(res)
     return vae_list
 
-def load_vae(model, checkpoint_file, vae_file="auto"):
+def resolve_vae(checkpoint_file, vae_file="auto"):
     global first_load, vae_dict, vae_list
     # save_settings = False
 
@@ -94,6 +94,12 @@ def load_vae(model, checkpoint_file, vae_file="auto"):
     if vae_file and not os.path.exists(vae_file):
         vae_file = None
 
+    return vae_file
+
+def load_vae(model, vae_file):
+    global first_load, vae_dict, vae_list
+    # save_settings = False
+
     if vae_file:
         print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-- 
cgit v1.2.3


From 36966e3200943dbf890b5338cfa939df552d3c47 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Mon, 31 Oct 2022 15:38:58 +0700
Subject: Fix #4035

---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index f86dc3ed..a29c8c1a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -201,7 +201,7 @@ def load_model_weights(model, checkpoint_info):
 
         if shared.opts.sd_checkpoint_cache > 0:
             checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1:
                 checkpoints_loaded.popitem(last=False)  # LRU
     else:
         print(f"Loading weights [{sd_model_hash}] from cache")
-- 
cgit v1.2.3


From bf7a699845675eefdabb9cfa40c55398976274ae Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Mon, 31 Oct 2022 16:27:27 +0700
Subject: Fix #4035 for real now

---
 modules/sd_models.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index a29c8c1a..b2dd005a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -165,6 +165,9 @@ def load_model_weights(model, checkpoint_info):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
+    if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
+        checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
+
     if checkpoint_info not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
@@ -198,16 +201,14 @@ def load_model_weights(model, checkpoint_info):
             model.first_stage_model.load_state_dict(vae_dict)
 
         model.first_stage_model.to(devices.dtype_vae)
-
-        if shared.opts.sd_checkpoint_cache > 0:
-            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1:
-                checkpoints_loaded.popitem(last=False)  # LRU
     else:
         print(f"Loading weights [{sd_model_hash}] from cache")
-        checkpoints_loaded.move_to_end(checkpoint_info)
         model.load_state_dict(checkpoints_loaded[checkpoint_info])
 
+    if shared.opts.sd_checkpoint_cache > 0:
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+            checkpoints_loaded.popitem(last=False)  # LRU
+
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
-- 
cgit v1.2.3


From 006756f9cd6258eae418e9209cfc13f940ec53e1 Mon Sep 17 00:00:00 2001
From: Fampai <>
Date: Mon, 31 Oct 2022 07:26:08 -0400
Subject: Added TI training optimizations

option to use xattention optimizations when training
option to unload vae when training
---
 modules/shared.py                              | 3 ++-
 modules/textual_inversion/textual_inversion.py | 9 +++++++++
 modules/textual_inversion/ui.py                | 7 +++++--
 3 files changed, 16 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index fb84afd8..4c3d0ce7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -256,11 +256,12 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
     "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
+    "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 17dfb223..b0a1d26b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -214,6 +214,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
 
     log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
+    unload = shared.opts.unload_models_when_training
 
     if save_embedding_every > 0:
         embedding_dir = os.path.join(log_directory, "embeddings")
@@ -238,6 +239,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+    if unload:
+        shared.sd_model.first_stage_model.to(devices.cpu)
 
     hijack = sd_hijack.model_hijack
 
@@ -303,6 +306,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         if images_dir is not None and steps_done % create_image_every == 0:
             forced_filename = f'{embedding_name}-{steps_done}'
             last_saved_image = os.path.join(images_dir, forced_filename)
+
+            shared.sd_model.first_stage_model.to(devices.device)
+
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 do_not_save_grid=True,
@@ -330,6 +336,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             processed = processing.process_images(p)
             image = processed.images[0]
 
+            if unload:
+                shared.sd_model.first_stage_model.to(devices.cpu)
+
             shared.state.current_image = image
 
             if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index e712284d..d679e6f4 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -25,8 +25,10 @@ def train_embedding(*args):
 
     assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
 
+    apply_optimizations = shared.opts.training_xattention_optimizations
     try:
-        sd_hijack.undo_optimizations()
+        if not apply_optimizations:
+            sd_hijack.undo_optimizations()
 
         embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
 
@@ -38,5 +40,6 @@ Embedding saved to {html.escape(filename)}
     except Exception:
         raise
     finally:
-        sd_hijack.apply_optimizations()
+        if not apply_optimizations:
+            sd_hijack.apply_optimizations()
 
-- 
cgit v1.2.3


From 890e68aaf75ae80d5eb2fa95b4bf1adf78b96881 Mon Sep 17 00:00:00 2001
From: Fampai <>
Date: Mon, 31 Oct 2022 10:07:12 -0400
Subject: Fixed minor bug

when unloading vae during TI training, generating images after
training will error out
---
 modules/textual_inversion/textual_inversion.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 54a734f1..0aeb0459 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -409,6 +409,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
     save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+    shared.sd_model.first_stage_model.to(devices.device)
 
     return embedding, filename
 
-- 
cgit v1.2.3


From 910a097ae2ed78a62101951f1b87137f9e1baaea Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 31 Oct 2022 17:36:45 +0300
Subject: add initial version of the extensions tab fix broken Restart Gradio
 button

---
 modules/extensions.py                      |  83 +++++++++++++++
 modules/generation_parameters_copypaste.py |   5 +
 modules/scripts.py                         |  21 +---
 modules/shared.py                          |  10 +-
 modules/ui.py                              |  16 ++-
 modules/ui_extensions.py                   | 162 +++++++++++++++++++++++++++++
 6 files changed, 274 insertions(+), 23 deletions(-)
 create mode 100644 modules/extensions.py
 create mode 100644 modules/ui_extensions.py

(limited to 'modules')

diff --git a/modules/extensions.py b/modules/extensions.py
new file mode 100644
index 00000000..8d6ae848
--- /dev/null
+++ b/modules/extensions.py
@@ -0,0 +1,83 @@
+import os
+import sys
+import traceback
+
+import git
+
+from modules import paths, shared
+
+
+extensions = []
+extensions_dir = os.path.join(paths.script_path, "extensions")
+
+
+def active():
+    return [x for x in extensions if x.enabled]
+
+
+class Extension:
+    def __init__(self, name, path, enabled=True):
+        self.name = name
+        self.path = path
+        self.enabled = enabled
+        self.status = ''
+        self.can_update = False
+
+        repo = None
+        try:
+            if os.path.exists(os.path.join(path, ".git")):
+                repo = git.Repo(path)
+        except Exception:
+            print(f"Error reading github repository info from {path}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+        if repo is None or repo.bare:
+            self.remote = None
+        else:
+            self.remote = next(repo.remote().urls, None)
+            self.status = 'unknown'
+
+    def list_files(self, subdir, extension):
+        from modules import scripts
+
+        dirpath = os.path.join(self.path, subdir)
+        if not os.path.isdir(dirpath):
+            return []
+
+        res = []
+        for filename in sorted(os.listdir(dirpath)):
+            res.append(scripts.ScriptFile(dirpath, filename, os.path.join(dirpath, filename)))
+
+        res = [x for x in res if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
+
+        return res
+
+    def check_updates(self):
+        repo = git.Repo(self.path)
+        for fetch in repo.remote().fetch("--dry-run"):
+            if fetch.flags != fetch.HEAD_UPTODATE:
+                self.can_update = True
+                self.status = "behind"
+                return
+
+        self.can_update = False
+        self.status = "latest"
+
+    def pull(self):
+        repo = git.Repo(self.path)
+        repo.remotes.origin.pull()
+
+
+def list_extensions():
+    extensions.clear()
+
+    if not os.path.isdir(extensions_dir):
+        return
+
+    for dirname in sorted(os.listdir(extensions_dir)):
+        path = os.path.join(extensions_dir, dirname)
+        if not os.path.isdir(path):
+            continue
+
+        extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions)
+        extensions.append(extension)
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index df70c728..985ec95e 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -17,6 +17,11 @@ paste_fields = {}
 bind_list = []
 
 
+def reset():
+    paste_fields.clear()
+    bind_list.clear()
+
+
 def quote(text):
     if ',' not in str(text):
         return text
diff --git a/modules/scripts.py b/modules/scripts.py
index 96e44bfd..533db45c 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -7,7 +7,7 @@ import modules.ui as ui
 import gradio as gr
 
 from modules.processing import StableDiffusionProcessing
-from modules import shared, paths, script_callbacks
+from modules import shared, paths, script_callbacks, extensions
 
 AlwaysVisible = object()
 
@@ -107,17 +107,8 @@ def list_scripts(scriptdirname, extension):
         for filename in sorted(os.listdir(basedir)):
             scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
 
-    extdir = os.path.join(paths.script_path, "extensions")
-    if os.path.exists(extdir):
-        for dirname in sorted(os.listdir(extdir)):
-            dirpath = os.path.join(extdir, dirname)
-            scriptdirpath = os.path.join(dirpath, scriptdirname)
-
-            if not os.path.isdir(scriptdirpath):
-                continue
-
-            for filename in sorted(os.listdir(scriptdirpath)):
-                scripts_list.append(ScriptFile(dirpath, filename, os.path.join(scriptdirpath, filename)))
+    for ext in extensions.active():
+        scripts_list += ext.list_files(scriptdirname, extension)
 
     scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
 
@@ -127,11 +118,7 @@ def list_scripts(scriptdirname, extension):
 def list_files_with_name(filename):
     res = []
 
-    dirs = [paths.script_path]
-
-    extdir = os.path.join(paths.script_path, "extensions")
-    if os.path.exists(extdir):
-        dirs += [os.path.join(extdir, d) for d in sorted(os.listdir(extdir))]
+    dirs = [paths.script_path] + [ext.path for ext in extensions.active()]
 
     for dirpath in dirs:
         if not os.path.isdir(dirpath):
diff --git a/modules/shared.py b/modules/shared.py
index e4f163c1..cce87081 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -132,6 +132,7 @@ class State:
     current_image = None
     current_image_sampling_step = 0
     textinfo = None
+    need_restart = False
 
     def skip(self):
         self.skipped = True
@@ -354,6 +355,12 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
+options_templates.update(options_section((None, "Hidden options"), {
+    "disabled_extensions": OptionInfo([], "Disable those extensions"),
+}))
+
+options_templates.update()
+
 
 class Options:
     data = None
@@ -365,8 +372,9 @@ class Options:
 
     def __setattr__(self, key, value):
         if self.data is not None:
-            if key in self.data:
+            if key in self.data or key in self.data_labels:
                 self.data[key] = value
+                return
 
         return super(Options, self).__setattr__(key, value)
 
diff --git a/modules/ui.py b/modules/ui.py
index 5055ca64..2c15abb7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -19,7 +19,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 
 
-from modules import sd_hijack, sd_models, localization, script_callbacks
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
 from modules.paths import script_path
 
 from modules.shared import opts, cmd_opts, restricted_opts
@@ -671,6 +671,7 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
+    parameters_copypaste.reset()
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
@@ -1511,8 +1512,9 @@ def create_ui(wrap_gradio_gpu_call):
         column = None
         with gr.Row(elem_id="settings").style(equal_height=False):
             for i, (k, item) in enumerate(opts.data_labels.items()):
+                section_must_be_skipped = item.section[0] is None
 
-                if previous_section != item.section:
+                if previous_section != item.section and not section_must_be_skipped:
                     if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
                         if column is not None:
                             column.__exit__()
@@ -1531,6 +1533,8 @@ def create_ui(wrap_gradio_gpu_call):
                 if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
                     quicksettings_list.append((i, k, item))
                     components.append(dummy_component)
+                elif section_must_be_skipped:
+                    components.append(dummy_component)
                 else:
                     component = create_setting_component(k)
                     component_dict[k] = component
@@ -1572,9 +1576,10 @@ def create_ui(wrap_gradio_gpu_call):
 
         def request_restart():
             shared.state.interrupt()
-            settings_interface.gradio_ref.do_restart = True
+            shared.state.need_restart = True
 
         restart_gradio.click(
+
             fn=request_restart,
             inputs=[],
             outputs=[],
@@ -1612,14 +1617,15 @@ def create_ui(wrap_gradio_gpu_call):
     interfaces += script_callbacks.ui_tabs_callback()
     interfaces += [(settings_interface, "Settings", "settings")]
 
+    extensions_interface = ui_extensions.create_ui()
+    interfaces += [(extensions_interface, "Extensions", "extensions")]
+
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings"):
             for i, k, item in quicksettings_list:
                 component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
-        settings_interface.gradio_ref = demo
-
         parameters_copypaste.integrate_settings_paste_fields(component_dict)
         parameters_copypaste.run_bind()
 
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
new file mode 100644
index 00000000..b7d747dc
--- /dev/null
+++ b/modules/ui_extensions.py
@@ -0,0 +1,162 @@
+import json
+import os.path
+import shutil
+import sys
+import time
+import traceback
+
+import git
+
+import gradio as gr
+import html
+
+from modules import extensions, shared, paths
+
+
+def apply_and_restart(disable_list, update_list):
+    disabled = json.loads(disable_list)
+    assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
+
+    update = json.loads(update_list)
+    assert type(update) == list, f"wrong update_list data for apply_and_restart: {update_list}"
+
+    update = set(update)
+
+    for ext in extensions.extensions:
+        if ext.name not in update:
+            continue
+
+        try:
+            ext.pull()
+        except Exception:
+            print(f"Error pulling updates for {ext.name}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    shared.opts.disabled_extensions = disabled
+    shared.opts.save(shared.config_filename)
+
+    shared.state.interrupt()
+    shared.state.need_restart = True
+
+
+def check_updates():
+    for ext in extensions.extensions:
+        if ext.remote is None:
+            continue
+
+        try:
+            ext.check_updates()
+        except Exception:
+            print(f"Error checking updates for {ext.name}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    return extension_table()
+
+
+def extension_table():
+    code = f"""<!-- {time.time()} -->
+    <table id="extensions">
+        <thead>
+            <tr>
+                <th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
+                <th>URL</th>
+                <th><abbr title="Use checkbox to mark the extension for update; it will be updated when you click apply button">Update</abbr></th>
+            </tr>
+        </thead>
+        <tbody>
+    """
+
+    for ext in extensions.extensions:
+        if ext.can_update:
+            ext_status = f"""<label><input class="gr-check-radio gr-checkbox" name="update_{html.escape(ext.name)}" checked="checked" type="checkbox">{html.escape(ext.status)}</label>"""
+        else:
+            ext_status = ext.status
+
+        code += f"""
+            <tr>
+                <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
+                <td><a href="{html.escape(ext.remote or '')}">{html.escape(ext.remote or '')}</a></td>
+                <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
+            </tr>
+    """
+
+    code += """
+        </tbody>
+    </table>
+    """
+
+    return code
+
+
+def install_extension_from_url(dirname, url):
+    assert url, 'No URL specified'
+
+    if dirname is None or dirname == "":
+        *parts, last_part = url.split('/')
+        last_part = last_part.replace(".git", "")
+
+        dirname = last_part
+
+    target_dir = os.path.join(extensions.extensions_dir, dirname)
+    assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
+
+    assert len([x for x in extensions.extensions if x.remote == url]) == 0, 'Extension with this URL is already installed'
+
+    tmpdir = os.path.join(paths.script_path, "tmp", dirname)
+
+    try:
+        shutil.rmtree(tmpdir, True)
+
+        repo = git.Repo.clone_from(url, tmpdir)
+        repo.remote().fetch()
+
+        os.rename(tmpdir, target_dir)
+
+        extensions.list_extensions()
+        return [extension_table(), html.escape(f"Installed into {target_dir}. Use Installed tab to restart.")]
+    finally:
+        shutil.rmtree(tmpdir, True)
+
+
+def create_ui():
+    import modules.ui
+
+    with gr.Blocks(analytics_enabled=False) as ui:
+        with gr.Tabs(elem_id="tabs_extensions") as tabs:
+            with gr.TabItem("Installed"):
+                extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False)
+                extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False)
+
+                with gr.Row():
+                    apply = gr.Button(value="Apply and restart UI", variant="primary")
+                    check = gr.Button(value="Check for updates")
+
+                extensions_table = gr.HTML(lambda: extension_table())
+
+                apply.click(
+                    fn=apply_and_restart,
+                    _js="extensions_apply",
+                    inputs=[extensions_disabled_list, extensions_update_list],
+                    outputs=[],
+                )
+
+                check.click(
+                    fn=check_updates,
+                    _js="extensions_check",
+                    inputs=[],
+                    outputs=[extensions_table],
+                )
+
+            with gr.TabItem("Install from URL"):
+                install_url = gr.Text(label="URL for extension's git repository")
+                install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
+                intall_button = gr.Button(value="Install", variant="primary")
+                intall_result = gr.HTML(elem_id="extension_install_result")
+
+                intall_button.click(
+                    fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
+                    inputs=[install_dirname, install_url],
+                    outputs=[extensions_table, intall_result],
+                )
+
+    return ui
-- 
cgit v1.2.3


From dc7425a56e7a014cbfa3b3d44ad2321e519fe378 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 31 Oct 2022 18:33:44 +0300
Subject: disable access to extension stuff for non-local servers

---
 modules/shared.py        |  5 ++++-
 modules/ui_extensions.py | 10 ++++++++++
 2 files changed, 14 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index cce87081..a27c654e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -40,7 +40,7 @@ parser.add_argument("--lowram", action='store_true', help="load stable diffusion
 parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
-parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
+parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
 parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
@@ -97,6 +97,9 @@ restricted_opts = {
     "outdir_save",
 }
 
+if cmd_opts.share or cmd_opts.listen:
+    cmd_opts.disable_extension_access = True
+
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
 
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index b7d747dc..e74b7d68 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -13,7 +13,13 @@ import html
 from modules import extensions, shared, paths
 
 
+def check_access():
+    assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
+
+
 def apply_and_restart(disable_list, update_list):
+    check_access()
+
     disabled = json.loads(disable_list)
     assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
 
@@ -40,6 +46,8 @@ def apply_and_restart(disable_list, update_list):
 
 
 def check_updates():
+    check_access()
+
     for ext in extensions.extensions:
         if ext.remote is None:
             continue
@@ -89,6 +97,8 @@ def extension_table():
 
 
 def install_extension_from_url(dirname, url):
+    check_access()
+
     assert url, 'No URL specified'
 
     if dirname is None or dirname == "":
-- 
cgit v1.2.3


From 58cc03edd0fe8c7e64297bcfe51111caaafabfd7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 31 Oct 2022 18:40:47 +0300
Subject: fix scripts I broke with the extension tab changes

---
 modules/extensions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extensions.py b/modules/extensions.py
index 8d6ae848..897af96e 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -46,7 +46,7 @@ class Extension:
 
         res = []
         for filename in sorted(os.listdir(dirpath)):
-            res.append(scripts.ScriptFile(dirpath, filename, os.path.join(dirpath, filename)))
+            res.append(scripts.ScriptFile(self.path, filename, os.path.join(dirpath, filename)))
 
         res = [x for x in res if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
 
-- 
cgit v1.2.3


From 9e22a357545c0395c81dd800c72fa18f350545ec Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 31 Oct 2022 18:45:50 +0300
Subject: fix the error with extension tab not working because of the previous
 commit

---
 modules/shared.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index a27c654e..c83fb9f5 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -97,8 +97,7 @@ restricted_opts = {
     "outdir_save",
 }
 
-if cmd_opts.share or cmd_opts.listen:
-    cmd_opts.disable_extension_access = True
+cmd_opts.disable_extension_access = cmd_opts.share or cmd_opts.listen
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
-- 
cgit v1.2.3


From df6a7ebfe8cc4da23861e3e2583693bb7808d573 Mon Sep 17 00:00:00 2001
From: Roy Shilkrot <roy.shil@gmail.com>
Date: Mon, 31 Oct 2022 11:50:33 -0400
Subject: revert things to master

---
 modules/api/api.py    | 2 --
 modules/api/models.py | 6 +-----
 2 files changed, 1 insertion(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index c510a833..6a903e4c 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -117,8 +117,6 @@ class Api:
 
         return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
 
-    def extrasapi(self):
-        raise NotImplementedError
     def extras_single_image_api(self, req: ExtrasSingleImageRequest):
         reqDict = setUpscalers(req)
 
diff --git a/modules/api/models.py b/modules/api/models.py
index 035a7179..82ab29b8 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -64,11 +64,7 @@ class PydanticModelGenerator:
 
 
         self._model_name = model_name
-
-        if class_instance is not None:
-            self._class_data = merge_class_params(class_instance)
-        else:
-            self._class_data = {}            
+        self._class_data = merge_class_params(class_instance)
 
         self._model_def = [
             ModelDef(
-- 
cgit v1.2.3


From 3f3d14afd5abd07d3843370dc1c28be299dbdbab Mon Sep 17 00:00:00 2001
From: Roy Shilkrot <roy.shil@gmail.com>
Date: Mon, 31 Oct 2022 11:51:21 -0400
Subject: nix unused thing

---
 modules/api/api.py | 4 ----
 1 file changed, 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 6a903e4c..536e3f16 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -182,10 +182,6 @@ class Api:
         if image_b64 is None:
             raise HTTPException(status_code=404, detail="Image not found") 
 
-        populate = interrogatereq.copy(update={ # Override __init__ params
-            }
-        )
-
         img = self.__base64_to_image(image_b64)
 
         # Override object param
-- 
cgit v1.2.3


From 8792be50078c2e89ac2fa8ff4e4c1ca82f140d45 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Mon, 31 Oct 2022 17:29:04 -0700
Subject: Add PNG info to pngs only if option is enabled.

---
 modules/images.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index a0728553..ae705cbd 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -510,8 +510,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
     if extension.lower() == '.png':
         pnginfo_data = PngImagePlugin.PngInfo()
-        for k, v in params.pnginfo.items():
-            pnginfo_data.add_text(k, str(v))
+        if opts.enable_pnginfo:
+            for k, v in params.pnginfo.items():
+                pnginfo_data.add_text(k, str(v))
 
         image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
 
-- 
cgit v1.2.3


From 525c1edf431d9c9efc1349be8657f0301299e3bc Mon Sep 17 00:00:00 2001
From: k_sugawara <k_sugawara@radiu5.co.jp>
Date: Tue, 1 Nov 2022 09:40:54 +0900
Subject: make save dir if save dir is not exists

---
 modules/img2img.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index efda26e1..35c5df9b 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -55,6 +55,7 @@ def process_batch(p, input_dir, output_dir, args):
                 filename = f"{left}-{n}{right}"
 
             if not save_normally:
+                os.makedirs(output_dir, exist_ok=True)
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-- 
cgit v1.2.3


From 5b0f624bdc1335313258f59a37607e699e800c22 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 1 Nov 2022 09:59:00 +0300
Subject: Added Available tab to extensions UI.

---
 modules/ui_extensions.py | 112 +++++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 104 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index e74b7d68..ab807722 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -13,6 +13,9 @@ import html
 from modules import extensions, shared, paths
 
 
+available_extensions = {"extensions": []}
+
+
 def check_access():
     assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
 
@@ -96,6 +99,14 @@ def extension_table():
     return code
 
 
+def normalize_git_url(url):
+    if url is None:
+        return ""
+
+    url = url.replace(".git", "")
+    return url
+
+
 def install_extension_from_url(dirname, url):
     check_access()
 
@@ -103,14 +114,15 @@ def install_extension_from_url(dirname, url):
 
     if dirname is None or dirname == "":
         *parts, last_part = url.split('/')
-        last_part = last_part.replace(".git", "")
+        last_part = normalize_git_url(last_part)
 
         dirname = last_part
 
     target_dir = os.path.join(extensions.extensions_dir, dirname)
     assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
 
-    assert len([x for x in extensions.extensions if x.remote == url]) == 0, 'Extension with this URL is already installed'
+    normalized_url = normalize_git_url(url)
+    assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
 
     tmpdir = os.path.join(paths.script_path, "tmp", dirname)
 
@@ -128,18 +140,80 @@ def install_extension_from_url(dirname, url):
         shutil.rmtree(tmpdir, True)
 
 
+def install_extension_from_index(url):
+    ext_table, message = install_extension_from_url(None, url)
+
+    return refresh_available_extensions_from_data(), ext_table, message
+
+
+def refresh_available_extensions(url):
+    global available_extensions
+
+    import urllib.request
+    with urllib.request.urlopen(url) as response:
+        text = response.read()
+
+    available_extensions = json.loads(text)
+
+    return url, refresh_available_extensions_from_data(), ''
+
+
+def refresh_available_extensions_from_data():
+    extlist = available_extensions["extensions"]
+    installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
+
+    code = f"""<!-- {time.time()} -->
+    <table id="available_extensions">
+        <thead>
+            <tr>
+                <th>Extension</th>
+                <th>Description</th>
+                <th>Action</th>
+            </tr>
+        </thead>
+        <tbody>
+    """
+
+    for ext in extlist:
+        name = ext.get("name", "noname")
+        url = ext.get("url", None)
+        description = ext.get("description", "")
+
+        if url is None:
+            continue
+
+        existing = installed_extension_urls.get(normalize_git_url(url), None)
+
+        install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
+
+        code += f"""
+            <tr>
+                <td><a href="{html.escape(url)}">{html.escape(name)}</a></td>
+                <td>{html.escape(description)}</td>
+                <td>{install_code}</td>
+            </tr>
+    """
+
+    code += """
+        </tbody>
+    </table>
+    """
+
+    return code
+
+
 def create_ui():
     import modules.ui
 
     with gr.Blocks(analytics_enabled=False) as ui:
         with gr.Tabs(elem_id="tabs_extensions") as tabs:
             with gr.TabItem("Installed"):
-                extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False)
-                extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False)
 
                 with gr.Row():
                     apply = gr.Button(value="Apply and restart UI", variant="primary")
                     check = gr.Button(value="Check for updates")
+                    extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
+                    extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
 
                 extensions_table = gr.HTML(lambda: extension_table())
 
@@ -157,16 +231,38 @@ def create_ui():
                     outputs=[extensions_table],
                 )
 
+            with gr.TabItem("Available"):
+                with gr.Row():
+                    refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
+                    available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/wiki/AUTOMATIC1111/stable-diffusion-webui/Extensions-index.md", label="Extension index URL").style(container=False)
+                    extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
+                    install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
+
+                install_result = gr.HTML()
+                available_extensions_table = gr.HTML()
+
+                refresh_available_extensions_button.click(
+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update()]),
+                    inputs=[available_extensions_index],
+                    outputs=[available_extensions_index, available_extensions_table, install_result],
+                )
+
+                install_extension_button.click(
+                    fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
+                    inputs=[extension_to_install],
+                    outputs=[available_extensions_table, extensions_table, install_result],
+                )
+
             with gr.TabItem("Install from URL"):
                 install_url = gr.Text(label="URL for extension's git repository")
                 install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
-                intall_button = gr.Button(value="Install", variant="primary")
-                intall_result = gr.HTML(elem_id="extension_install_result")
+                install_button = gr.Button(value="Install", variant="primary")
+                install_result = gr.HTML(elem_id="extension_install_result")
 
-                intall_button.click(
+                install_button.click(
                     fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
                     inputs=[install_dirname, install_url],
-                    outputs=[extensions_table, intall_result],
+                    outputs=[extensions_table, install_result],
                 )
 
     return ui
-- 
cgit v1.2.3


From af758e97fa2c4c853042f121af4e974be01e6696 Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Tue, 1 Nov 2022 04:01:49 -0300
Subject: Unload sd_model before loading the other

---
 modules/lowvram.py    | 21 +++++++++++++--------
 modules/processing.py |  3 +++
 modules/sd_hijack.py  |  4 ++++
 modules/sd_models.py  | 14 +++++++++++++-
 4 files changed, 33 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/lowvram.py b/modules/lowvram.py
index f327c3df..a4652cb1 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -38,13 +38,18 @@ def setup_for_low_vram(sd_model, use_medvram):
     # see below for register_forward_pre_hook;
     # first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
     # useless here, and we just replace those methods
-    def first_stage_model_encode_wrap(self, encoder, x):
-        send_me_to_gpu(self, None)
-        return encoder(x)
 
-    def first_stage_model_decode_wrap(self, decoder, z):
-        send_me_to_gpu(self, None)
-        return decoder(z)
+    first_stage_model = sd_model.first_stage_model
+    first_stage_model_encode = sd_model.first_stage_model.encode
+    first_stage_model_decode = sd_model.first_stage_model.decode
+
+    def first_stage_model_encode_wrap(x):
+        send_me_to_gpu(first_stage_model, None)
+        return first_stage_model_encode(x)
+
+    def first_stage_model_decode_wrap(z):
+        send_me_to_gpu(first_stage_model, None)
+        return first_stage_model_decode(z)
 
     # remove three big modules, cond, first_stage, and unet from the model and then
     # send the model to GPU. Then put modules back. the modules will be in CPU.
@@ -56,8 +61,8 @@ def setup_for_low_vram(sd_model, use_medvram):
     # register hooks for those the first two models
     sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
     sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
-    sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x)
-    sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z)
+    sd_model.first_stage_model.encode = first_stage_model_encode_wrap
+    sd_model.first_stage_model.decode = first_stage_model_decode_wrap
     parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
     if use_medvram:
diff --git a/modules/processing.py b/modules/processing.py
index b1df4918..57d3a523 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -597,6 +597,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
 
+    p.sd_model = None
+    p.sampler = None
+
     return res
 
 
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 0f10828e..bc49d235 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -94,6 +94,10 @@ class StableDiffusionModelHijack:
         if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
             model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
 
+        self.layers = None
+        self.circular_enabled = False
+        self.clip = None
+
     def apply_circular(self, enable):
         if self.circular_enabled == enable:
             return
diff --git a/modules/sd_models.py b/modules/sd_models.py
index f86dc3ed..90007da3 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1,6 +1,7 @@
 import collections
 import os.path
 import sys
+import gc
 from collections import namedtuple
 import torch
 import re
@@ -220,6 +221,12 @@ def load_model(checkpoint_info=None):
     if checkpoint_info.config != shared.cmd_opts.config:
         print(f"Loading config from: {checkpoint_info.config}")
 
+    if shared.sd_model:
+        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+        shared.sd_model = None
+        gc.collect()
+        devices.torch_gc()
+
     sd_config = OmegaConf.load(checkpoint_info.config)
     
     if should_hijack_inpainting(checkpoint_info):
@@ -233,6 +240,7 @@ def load_model(checkpoint_info=None):
         checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
 
     do_inpainting_hijack()
+
     sd_model = instantiate_from_config(sd_config.model)
     load_model_weights(sd_model, checkpoint_info)
 
@@ -252,14 +260,18 @@ def load_model(checkpoint_info=None):
     return sd_model
 
 
-def reload_model_weights(sd_model, info=None):
+def reload_model_weights(sd_model=None, info=None):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
 
+    if not sd_model:
+        sd_model = shared.sd_model
+
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+        del sd_model
         checkpoints_loaded.clear()
         load_model(checkpoint_info)
         return shared.sd_model
-- 
cgit v1.2.3


From d35bf649456da2558cbb6f2ea16fa1606022b7e7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 1 Nov 2022 14:19:24 +0300
Subject: make launch.py run installers for extensions that have ones add some
 more classes to safety module for an extension

---
 modules/safe.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/safe.py b/modules/safe.py
index 399165a1..348a24fc 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -32,7 +32,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             return getattr(collections, name)
         if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
             return getattr(torch._utils, name)
-        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
+        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage']:
             return getattr(torch, name)
         if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
             return getattr(torch.nn.modules.container, name)
-- 
cgit v1.2.3


From 467cae167a3066ffa2b2a5e6f16dd42642219aba Mon Sep 17 00:00:00 2001
From: TinkTheBoush <TinkTheBoush@github.com>
Date: Tue, 1 Nov 2022 23:29:12 +0900
Subject: append_tag_shuffle

---
 modules/hypernetworks/hypernetwork.py          |  4 ++--
 modules/textual_inversion/dataset.py           | 10 ++++++++--
 modules/textual_inversion/textual_inversion.py |  4 ++--
 modules/ui.py                                  |  3 +++
 4 files changed, 15 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index a11e01d6..7630fb81 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -331,7 +331,7 @@ def report_statistics(loss_info:dict):
 
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, shuffle_tags, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
@@ -376,7 +376,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, shuffle_tags=shuffle_tags, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index ad726577..e9d97cc1 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -24,7 +24,7 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", shuffle_tags=True, model=None, device=None, template_file=None, include_cond=False, batch_size=1):
         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
 
         self.placeholder_token = placeholder_token
@@ -33,6 +33,7 @@ class PersonalizedBase(Dataset):
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+        self.shuffle_tags = shuffle_tags
 
         self.dataset = []
 
@@ -98,7 +99,12 @@ class PersonalizedBase(Dataset):
     def create_text(self, filename_text):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
-        text = text.replace("[filewords]", filename_text)
+        if self.tag_shuffle:
+            tags = filename_text.split(',')
+            random.shuffle(tags)
+            text = text.replace("[filewords]", ','.join(tags))
+        else:
+            text = text.replace("[filewords]", filename_text)
         return text
 
     def __len__(self):
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e0babb46..64700e23 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -224,7 +224,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, data_root, templat
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 
-def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, shuffle_tags, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
     validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
@@ -271,7 +271,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, shuffle_tags=shuffle_tags, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
 
     embedding.vec.requires_grad = True
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
diff --git a/modules/ui.py b/modules/ui.py
index 2c15abb7..ad383979 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1267,6 +1267,7 @@ def create_ui(wrap_gradio_gpu_call):
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+                    shuffle_tags = gr.Checkbox(label='Shuffleing tags by "," when create texts', value=True)
 
                     with gr.Row():
                         interrupt_training = gr.Button(value="Interrupt")
@@ -1361,6 +1362,7 @@ def create_ui(wrap_gradio_gpu_call):
                 template_file,
                 save_image_with_stored_embedding,
                 preview_from_txt2img,
+                shuffle_tags,
                 *txt2img_preview_params,
             ],
             outputs=[
@@ -1385,6 +1387,7 @@ def create_ui(wrap_gradio_gpu_call):
                 save_embedding_every,
                 template_file,
                 preview_from_txt2img,
+                shuffle_tags,
                 *txt2img_preview_params,
             ],
             outputs=[
-- 
cgit v1.2.3


From bc607686065b8c7751d1af7c05b960378fa256de Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Tue, 1 Nov 2022 23:26:55 +0800
Subject: Enable override_settings to take effect for hypernetworks

---
 modules/processing.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 57d3a523..86d015af 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -422,13 +422,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     try:
         for k, v in p.override_settings.items():
-            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model, hypernet impossible
+            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model impossible
+            if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet since it is relatively fast to load on-change, while SD models are not
 
         res = process_images_inner(p)
 
-    finally:
+    finally:  # restore opts to original state
         for k, v in stored_opts.items():
             opts.data[k] = v
+            if k == 'sd_hypernetwork': shared.reload_hypernetworks()
 
     return res
 
-- 
cgit v1.2.3


From 198a1ffcfc963a3d74674fad560e87dbebf7949f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 1 Nov 2022 19:13:59 +0300
Subject: fix API returning extra stuff in base64 encoded iamges for #3972

---
 modules/api/api.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index e702c9c0..bb87d795 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,6 +1,8 @@
+import base64
+import io
 import time
 import uvicorn
-from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
+from gradio.processing_utils import decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, Depends, HTTPException
 import modules.shared as shared
 from modules import devices
@@ -29,6 +31,12 @@ def setUpscalers(req: dict):
     return reqDict
 
 
+def encode_pil_to_base64(image):
+    buffer = io.BytesIO()
+    image.save(buffer, format="png")
+    return base64.b64encode(buffer.getvalue())
+
+
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
-- 
cgit v1.2.3


From 401350cd59555439570ba5bc95f0ac5698e372e4 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Tue, 1 Nov 2022 14:03:56 -0500
Subject: clear on the client-side again

---
 modules/ui.py | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 447722cd..f43e79ab 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -400,19 +400,12 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-def clear_prompt(prompt, _prompt_neg, confirmed, _token_counter):
-    """Given confirmation from a user on the client-side, go ahead with clearing prompt"""
-    if confirmed:
-        return ["", "", confirmed, update_token_counter("", 1)]
-    else:
-        return [prompt, _prompt_neg, confirmed, _token_counter]
-
 
 def connect_clear_prompt(button, prompt, prompt_neg, _dummy_confirmed, token_counter):
     """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
     button.click(
         _js="clear_prompt",
-        fn=clear_prompt,
+        fn=None,
         inputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
         outputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
     )
-- 
cgit v1.2.3


From 1dd5d6bafad7575f347056a29636cbab71c1c468 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Tue, 1 Nov 2022 14:33:55 -0500
Subject: clean py func defs

---
 modules/ui.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index f43e79ab..8a1f3887 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -401,13 +401,13 @@ def create_seed_inputs():
 
 
 
-def connect_clear_prompt(button, prompt, prompt_neg, _dummy_confirmed, token_counter):
+def connect_clear_prompt(button):
     """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
     button.click(
         _js="clear_prompt",
         fn=None,
-        inputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
-        outputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
+        inputs=[],
+        outputs=[],
     )
 
 
@@ -746,7 +746,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button, txt2img_prompt, txt2img_negative_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button)
 
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
@@ -929,7 +929,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button, img2img_prompt, img2img_negative_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button)
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-- 
cgit v1.2.3


From 86d35526a13a0e2432ab71d1d40b191615d3e343 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Tue, 1 Nov 2022 14:53:40 -0500
Subject: make line evil again

---
 modules/ui.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 8a1f3887..bd67c1bd 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -692,9 +692,7 @@ def create_ui(wrap_gradio_gpu_call):
     parameters_copypaste.reset()
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,\
-        txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter,\
-        token_button, clear_prompt_button = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -850,9 +848,7 @@ def create_ui(wrap_gradio_gpu_call):
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit,\
-        img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
-        token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=True)
 
 
         with gr.Row(elem_id='img2img_progress_row'):
-- 
cgit v1.2.3


From cffc240a7327ae60671ff533469fc4ed4bf605de Mon Sep 17 00:00:00 2001
From: Nerogar <nerogar@arcor.de>
Date: Sun, 23 Oct 2022 14:05:25 +0200
Subject: fixed textual inversion training with inpainting models

---
 modules/textual_inversion/textual_inversion.py | 27 +++++++++++++++++++++++++-
 1 file changed, 26 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 0aeb0459..2630c7c9 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -224,6 +224,26 @@ def validate_train_inputs(model_name, learn_rate, batch_size, data_root, templat
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 
+def create_dummy_mask(x, width=None, height=None):
+    if shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}:
+
+        # The "masked-image" in this case will just be all zeros since the entire image is masked.
+        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+        image_conditioning = shared.sd_model.get_first_stage_encoding(shared.sd_model.encode_first_stage(image_conditioning))
+
+        # Add the fake full 1s mask to the first dimension.
+        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+        image_conditioning = image_conditioning.to(x.dtype)
+
+    else:
+        # Dummy zero conditioning if we're not using inpainting model.
+        # Still takes up a bit of memory, but no encoder call.
+        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+        image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+
+    return image_conditioning
+
+
 def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
@@ -286,6 +306,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
 
+    img_c = None
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, entries in pbar:
         embedding.step = i + ititial_step
@@ -299,8 +320,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
         with torch.autocast("cuda"):
             c = cond_model([entry.cond_text for entry in entries])
+            if img_c is None:
+                img_c = create_dummy_mask(c, training_width, training_height)
+
             x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-            loss = shared.sd_model(x, c)[0]
+            cond = {"c_concat": [img_c], "c_crossattn": [c]}
+            loss = shared.sd_model(x, cond)[0]
             del x
 
             losses[embedding.step % losses.shape[0]] = loss.item()
-- 
cgit v1.2.3


From cd88e21dc5d5cfdfbd408454acd259b7db9d0ec8 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 2 Nov 2022 00:34:58 +0000
Subject: Class Name typo and add descriptions to fields.

---
 modules/script_callbacks.py | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index b0b8dc47..ff40b056 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -24,13 +24,22 @@ class ImageSaveParams:
         """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
 
 
-class CGFDenoiserParams:
-    def __init__(self, x_in, image_cond_in, sigma_in, sampling_step, total_sampling_steps):
-        self.x_in = x_in
-        self.image_cond_in = image_cond_in
-        self.sigma_in = sigma_in
+class CFGDenoiserParams:
+    def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
+        self.x = x
+        """Latent image representation in the process of being denoised"""
+        
+        self.image_cond = image_cond
+        """Conditioning image"""
+        
+        self.sigma = sigma
+        """Current sigma noise step value"""
+        
         self.sampling_step = sampling_step
+        """Current Sampling step number"""
+        
         self.total_sampling_steps = total_sampling_steps
+        """Total number of sampling steps planned"""
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
@@ -94,7 +103,7 @@ def image_saved_callback(params: ImageSaveParams):
             report_exception(c, 'image_saved_callback')
 
 
-def cfg_denoiser_callback(params: CGFDenoiserParams):
+def cfg_denoiser_callback(params: CFGDenoiserParams):
     for c in callbacks_cfg_denoiser:
         try:
             c.callback(params)
@@ -153,7 +162,7 @@ def on_image_saved(callback):
 def on_cfg_denoiser(callback):
     """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
     The callback is called with one argument:
-        - params: CGFDenoiserParams - parameters to be passed to the inner model and sampling state details.
+        - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
     """
     add_callback(callbacks_cfg_denoiser, callback)
 
-- 
cgit v1.2.3


From 5b6bedf6f2ebacb7f1f5809af8e26a6a1af16e2a Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 2 Nov 2022 00:38:17 +0000
Subject: Update class name and assign back to vars

---
 modules/sd_samplers.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 30cb5c4b..ebc0d896 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -11,7 +11,7 @@ from modules import prompt_parser, devices, processing, images
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
-from modules.script_callbacks import CGFDenoiserParams, cfg_denoiser_callback
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
@@ -279,7 +279,11 @@ class CFGDenoiser(torch.nn.Module):
         image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
-        cfg_denoiser_callback(CGFDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps))
+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
+        cfg_denoiser_callback(denoiser_params)
+        x_in = denoiser_params.x
+        image_cond_in = denoiser_params.image_cond
+        sigma_in = denoiser_params.sigma
 
         if tensor.shape[1] == uncond.shape[1]:
             cond_in = torch.cat([tensor, uncond])
-- 
cgit v1.2.3


From c9148b2312b36fee8727f5233da9dbe32aa1f58c Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Tue, 1 Nov 2022 21:56:47 -0300
Subject: Release processing resources after it finishes

---
 modules/img2img.py    | 2 ++
 modules/processing.py | 7 ++++---
 modules/txt2img.py    | 2 ++
 3 files changed, 8 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index 35c5df9b..fac010aa 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -137,6 +137,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         if processed is None:
             processed = process_images(p)
 
+    p.close()
+
     shared.total_tqdm.clear()
 
     generation_info_js = processed.js()
diff --git a/modules/processing.py b/modules/processing.py
index 57d3a523..b541ee2b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -202,6 +202,10 @@ class StableDiffusionProcessing():
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         raise NotImplementedError()
 
+    def close(self):
+        self.sd_model = None
+        self.sampler = None
+
 
 class Processed:
     def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
@@ -597,9 +601,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
 
-    p.sd_model = None
-    p.sampler = None
-
     return res
 
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index c9d5a090..8e4e8677 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -47,6 +47,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     if processed is None:
         processed = process_images(p)
 
+    p.close()
+
     shared.total_tqdm.clear()
 
     generation_info_js = processed.js()
-- 
cgit v1.2.3


From 5510c282b1f1974005790066b5e444f74a5178fb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 07:26:31 +0300
Subject: fix for extensions' javascript not loading

---
 modules/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 2c15abb7..a94f46ea 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -671,6 +671,8 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
+    reload_javascript()
+
     parameters_copypaste.reset()
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
@@ -1782,4 +1784,3 @@ def load_javascript(raw_response):
 
 
 reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)
-reload_javascript()
-- 
cgit v1.2.3


From 056f06d3738c267b1014e6e8e1ef5bd97af1fb45 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Wed, 2 Nov 2022 12:51:46 +0700
Subject: Reload VAE without reloading sd checkpoint

---
 modules/sd_models.py | 15 ++++----
 modules/sd_vae.py    | 97 ++++++++++++++++++++++++++++++++++++++++++++++++----
 2 files changed, 97 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6ab85b65..883639d1 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -159,15 +159,13 @@ def get_state_dict_from_checkpoint(pl_sd):
     return pl_sd
 
 
-vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
-
 def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
 
-    checkpoint_key = (checkpoint_info, vae_file)
+    checkpoint_key = checkpoint_info
 
     if checkpoint_key not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
@@ -190,13 +188,12 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
         devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
-        sd_vae.load_vae(model, vae_file)
-        model.first_stage_model.to(devices.dtype_vae)
-
         if shared.opts.sd_checkpoint_cache > 0:
+            # if PR #4035 were to get merged, restore base VAE first before caching
             checkpoints_loaded[checkpoint_key] = model.state_dict().copy()
             while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
                 checkpoints_loaded.popitem(last=False)  # LRU
+
     else:
         vae_name = sd_vae.get_filename(vae_file)
         print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")
@@ -207,6 +204,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    sd_vae.load_vae(model, vae_file)
+
 
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
@@ -254,14 +253,14 @@ def load_model(checkpoint_info=None):
     return sd_model
 
 
-def reload_model_weights(sd_model=None, info=None, force=False):
+def reload_model_weights(sd_model=None, info=None):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
  
     if not sd_model:
         sd_model = shared.sd_model
 
-    if sd_model.sd_model_checkpoint == checkpoint_info.filename and not force:
+    if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index e9239326..78e14e8a 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -1,26 +1,65 @@
 import torch
 import os
 from collections import namedtuple
-from modules import shared, devices
+from modules import shared, devices, script_callbacks
 from modules.paths import models_path
 import glob
 
+
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 vae_dir = "VAE"
 vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
 
+
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+
+
 default_vae_dict = {"auto": "auto", "None": "None"}
 default_vae_list = ["auto", "None"]
+
+
 default_vae_values = [default_vae_dict[x] for x in default_vae_list]
 vae_dict = dict(default_vae_dict)
 vae_list = list(default_vae_list)
 first_load = True
 
+
+base_vae = None
+loaded_vae_file = None
+checkpoint_info = None
+
+
+def get_base_vae(model):
+    if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
+        return base_vae
+    return None
+
+
+def store_base_vae(model):
+    global base_vae, checkpoint_info
+    if checkpoint_info != model.sd_checkpoint_info:
+        base_vae = model.first_stage_model.state_dict().copy()
+        checkpoint_info = model.sd_checkpoint_info
+
+
+def delete_base_vae():
+    global base_vae, checkpoint_info
+    base_vae = None
+    checkpoint_info = None
+
+
+def restore_base_vae(model):
+    global base_vae, checkpoint_info
+    if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
+        load_vae_dict(model, base_vae)
+    delete_base_vae()
+
+
 def get_filename(filepath):
     return os.path.splitext(os.path.basename(filepath))[0]
 
+
 def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     global vae_dict, vae_list
     res = {}
@@ -43,6 +82,7 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     vae_dict.update(res)
     return vae_list
 
+
 def resolve_vae(checkpoint_file, vae_file="auto"):
     global first_load, vae_dict, vae_list
     # save_settings = False
@@ -96,24 +136,26 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
 
     return vae_file
 
-def load_vae(model, vae_file):
-    global first_load, vae_dict, vae_list
+
+def load_vae(model, vae_file=None):
+    global first_load, vae_dict, vae_list, loaded_vae_file
     # save_settings = False
 
     if vae_file:
         print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-        model.first_stage_model.load_state_dict(vae_dict_1)
+        load_vae_dict(model, vae_dict_1)
 
-    # If vae used is not in dict, update it
-    # It will be removed on refresh though
-    if vae_file is not None:
+        # If vae used is not in dict, update it
+        # It will be removed on refresh though
         vae_opt = get_filename(vae_file)
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
             vae_list.append(vae_opt)
 
+    loaded_vae_file = vae_file
+
     """
     # Save current VAE to VAE settings, maybe? will it work?
     if save_settings:
@@ -124,4 +166,45 @@ def load_vae(model, vae_file):
     """
 
     first_load = False
+
+
+# don't call this from outside
+def load_vae_dict(model, vae_dict_1=None):
+    if vae_dict_1:
+        store_base_vae(model)
+        model.first_stage_model.load_state_dict(vae_dict_1)
+    else:
+        restore_base_vae()
     model.first_stage_model.to(devices.dtype_vae)
+
+
+def reload_vae_weights(sd_model=None, vae_file="auto"):
+    from modules import lowvram, devices, sd_hijack
+
+    if not sd_model:
+        sd_model = shared.sd_model
+
+    checkpoint_info = sd_model.sd_checkpoint_info
+    checkpoint_file = checkpoint_info.filename
+    vae_file = resolve_vae(checkpoint_file, vae_file=vae_file)
+
+    if loaded_vae_file == vae_file:
+        return
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.send_everything_to_cpu()
+    else:
+        sd_model.to(devices.cpu)
+
+    sd_hijack.model_hijack.undo_hijack(sd_model)
+
+    load_vae(sd_model, vae_file)
+
+    sd_hijack.model_hijack.hijack(sd_model)
+    script_callbacks.model_loaded_callback(sd_model)
+
+    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+        sd_model.to(devices.device)
+
+    print(f"VAE Weights loaded.")
+    return sd_model
-- 
cgit v1.2.3


From 95c6308ccd2e075d1fb804f5b98a4f0b07b87b7d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 09:47:53 +0300
Subject: switch to gradio 3.8

---
 modules/ui.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index a94f46ea..45cd8c3f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1572,8 +1572,7 @@ def create_ui(wrap_gradio_gpu_call):
         reload_script_bodies.click(
             fn=reload_scripts,
             inputs=[],
-            outputs=[],
-            _js='function(){}'
+            outputs=[]
         )
 
         def request_restart():
@@ -1585,7 +1584,7 @@ def create_ui(wrap_gradio_gpu_call):
             fn=request_restart,
             inputs=[],
             outputs=[],
-            _js='function(){restart_reload()}'
+            _js='restart_reload'
         )
 
         if column is not None:
-- 
cgit v1.2.3


From dd2108fdac2ebf943d4ac3563a49202222b88acf Mon Sep 17 00:00:00 2001
From: Maiko Tan <maiko.tan.coding@gmail.com>
Date: Wed, 2 Nov 2022 15:04:35 +0800
Subject: fix: should invoke callback as well in api only mode

---
 modules/script_callbacks.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index da88635b..c28e220e 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -2,6 +2,7 @@ import sys
 import traceback
 from collections import namedtuple
 import inspect
+from typing import Optional
 
 from fastapi import FastAPI
 from gradio import Blocks
@@ -62,7 +63,7 @@ def clear_callbacks():
     callbacks_image_saved.clear()
     callbacks_cfg_denoiser.clear()
 
-def app_started_callback(demo: Blocks, app: FastAPI):
+def app_started_callback(demo: Optional[Blocks], app: FastAPI):
     for c in callbacks_app_started:
         try:
             c.callback(demo, app)
-- 
cgit v1.2.3


From a5409a6e4bc3eaa9757a7505d4564ad8e0d899ea Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Wed, 2 Nov 2022 14:37:22 +0700
Subject: Save VAE provided by cmd_opts.vae_path

---
 modules/sd_vae.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 78e14e8a..71e7a6e6 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -78,27 +78,24 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     vae_list.extend(default_vae_list)
     vae_list.extend(list(res.keys()))
     vae_dict.clear()
-    vae_dict.update(default_vae_dict)
     vae_dict.update(res)
+    vae_dict.update(default_vae_dict)
     return vae_list
 
 
 def resolve_vae(checkpoint_file, vae_file="auto"):
     global first_load, vae_dict, vae_list
-    # save_settings = False
 
-    # if vae_file argument is provided, it takes priority
+    # if vae_file argument is provided, it takes priority, but not saved
     if vae_file and vae_file not in default_vae_list:
         if not os.path.isfile(vae_file):
             vae_file = "auto"
-            # save_settings = True
             print("VAE provided as function argument doesn't exist")
-    # for the first load, if vae-path is provided, it takes priority and failure is reported
+    # for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
     if first_load and shared.cmd_opts.vae_path is not None:
         if os.path.isfile(shared.cmd_opts.vae_path):
             vae_file = shared.cmd_opts.vae_path
-            # save_settings = True
-            # print("Using VAE provided as command line argument")
+            shared.opts.data['sd_vae'] = get_filename(vae_file)
         else:
             print("VAE provided as command line argument doesn't exist")
     # else, we load from settings
-- 
cgit v1.2.3


From 4a8cf01f6f7f072cc9c67d6b31662384b212dd9c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 12:12:32 +0300
Subject: remove duplicate code from #3970

---
 modules/api/api.py | 10 +---------
 modules/shared.py  | 14 ++++++++++++++
 modules/ui.py      | 10 +---------
 3 files changed, 16 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index b3d85e46..71c9c160 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -178,15 +178,7 @@ class Api:
 
         progress = min(progress, 1)
 
-        # copy from check_progress_call of ui.py
-
-        if shared.parallel_processing_allowed:
-            if shared.state.sampling_step - shared.state.current_image_sampling_step >= shared.opts.show_progress_every_n_steps and shared.state.current_latent is not None:
-                if shared.opts.show_progress_grid:
-                    shared.state.current_image = samples_to_image_grid(shared.state.current_latent)
-                else:
-                    shared.state.current_image = sample_to_image(shared.state.current_latent)
-                shared.state.current_image_sampling_step = shared.state.sampling_step
+        shared.state.set_current_image()
 
         current_image = None
         if shared.state.current_image and not req.skip_current_image:
diff --git a/modules/shared.py b/modules/shared.py
index 04aaa648..e65f6080 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -184,6 +184,20 @@ class State:
 
         devices.torch_gc()
 
+    """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
+    def set_current_image(self):
+        if not parallel_processing_allowed:
+            return
+
+        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and self.current_latent is not None:
+            if opts.show_progress_grid:
+                self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
+            else:
+                self.current_image = sd_samplers.sample_to_image(self.current_latent)
+
+            self.current_image_sampling_step = self.sampling_step
+
+
 state = State()
 
 artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
diff --git a/modules/ui.py b/modules/ui.py
index 45cd8c3f..784439ba 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -277,15 +277,7 @@ def check_progress_call(id_part):
     preview_visibility = gr_show(False)
 
     if opts.show_progress_every_n_steps > 0:
-        if shared.parallel_processing_allowed:
-
-            if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:
-                if opts.show_progress_grid:
-                    shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent)
-                else:
-                    shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
-                shared.state.current_image_sampling_step = shared.state.sampling_step
-
+        shared.state.set_current_image()
         image = shared.state.current_image
 
         if image is None:
-- 
cgit v1.2.3


From 9c67408004ed132637d10321bf44565f82055fd2 Mon Sep 17 00:00:00 2001
From: timntorres <116157310+timntorres@users.noreply.github.com>
Date: Wed, 2 Nov 2022 02:18:21 -0700
Subject: Allow saving "before-highres-fix. (#4150)

* Save image/s before doing highres fix.
---
 modules/processing.py  | 17 +++++++++++++++--
 modules/sd_samplers.py |  5 ++---
 modules/shared.py      |  1 +
 3 files changed, 18 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index b541ee2b..2dcf4879 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -521,7 +521,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             with devices.autocast():
-                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+                # Only Txt2Img needs an extra argument, n, when saving intermediate images pre highres fix. 
+                if isinstance(p, StableDiffusionProcessingTxt2Img):
+                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, n=n)
+                else:
+                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
 
             samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
@@ -649,7 +653,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, n=0):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
@@ -685,6 +689,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
+        # Save a copy of the image/s before doing highres fix, if applicable.
+        if opts.save and not self.do_not_save_samples and opts.save_images_before_highres_fix:
+            for i in range(self.batch_size):
+                # This batch's ith image.
+                img = sd_samplers.sample_to_image(samples, i)
+                # Index that accounts for both batch size and batch count.
+                ind = i + self.batch_size*n
+                images.save_image(img, self.outpath_samples, "", self.all_seeds[ind], self.all_prompts[ind], opts.samples_format, suffix=f"-before-highres-fix")
+
         shared.state.nextjob()
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 44d4c189..d7fa89a0 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -93,9 +93,8 @@ def single_sample_to_image(sample):
     return Image.fromarray(x_sample)
 
 
-def sample_to_image(samples):
-    return single_sample_to_image(samples[0])
-
+def sample_to_image(samples, index=0):
+    return single_sample_to_image(samples[index])
 
 def samples_to_image_grid(samples):
     return images.image_grid([single_sample_to_image(sample) for sample in samples])
diff --git a/modules/shared.py b/modules/shared.py
index e65f6080..ce991424 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -255,6 +255,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
     "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
     "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
+    "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
     "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
 
-- 
cgit v1.2.3


From eb5e82c7ddf5e72fa13b83bd1f12d3a07a4de1a4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 12:45:03 +0300
Subject: do not unnecessarily run VAE one more time when saving intermediate
 image with hires fix

---
 modules/processing.py  | 39 ++++++++++++++++++++-------------------
 modules/sd_samplers.py |  1 +
 modules/shared.py      |  2 +-
 3 files changed, 22 insertions(+), 20 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 2dcf4879..3a364b5f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -199,7 +199,7 @@ class StableDiffusionProcessing():
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         raise NotImplementedError()
 
     def close(self):
@@ -521,11 +521,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             with devices.autocast():
-                # Only Txt2Img needs an extra argument, n, when saving intermediate images pre highres fix. 
-                if isinstance(p, StableDiffusionProcessingTxt2Img):
-                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, n=n)
-                else:
-                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
 
             samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
@@ -653,7 +649,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, n=0):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
@@ -666,9 +662,21 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
+        """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images"""
+        def save_intermediate(image, index):
+            if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
+                return
+
+            if not isinstance(image, Image.Image):
+                image = sd_samplers.sample_to_image(image, index)
+
+            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
+
         if opts.use_scale_latent_for_hires_fix:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
 
+            for i in range(samples.shape[0]):
+                save_intermediate(samples, i)
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
@@ -678,6 +686,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
                 image = Image.fromarray(x_sample)
+
+                save_intermediate(image, i)
+
                 image = images.resize_image(0, image, self.width, self.height)
                 image = np.array(image).astype(np.float32) / 255.0
                 image = np.moveaxis(image, 2, 0)
@@ -689,15 +700,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
-        # Save a copy of the image/s before doing highres fix, if applicable.
-        if opts.save and not self.do_not_save_samples and opts.save_images_before_highres_fix:
-            for i in range(self.batch_size):
-                # This batch's ith image.
-                img = sd_samplers.sample_to_image(samples, i)
-                # Index that accounts for both batch size and batch count.
-                ind = i + self.batch_size*n
-                images.save_image(img, self.outpath_samples, "", self.all_seeds[ind], self.all_prompts[ind], opts.samples_format, suffix=f"-before-highres-fix")
-
         shared.state.nextjob()
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -844,8 +846,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
 
-
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
@@ -856,4 +857,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         del x
         devices.torch_gc()
 
-        return samples
\ No newline at end of file
+        return samples
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d7fa89a0..c7c414ef 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -96,6 +96,7 @@ def single_sample_to_image(sample):
 def sample_to_image(samples, index=0):
     return single_sample_to_image(samples[index])
 
+
 def samples_to_image_grid(samples):
     return images.image_grid([single_sample_to_image(sample) for sample in samples])
 
diff --git a/modules/shared.py b/modules/shared.py
index ce991424..01f47e38 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -256,6 +256,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
     "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
     "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
+    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
 
@@ -322,7 +323,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
-- 
cgit v1.2.3


From f2a5cbe6f55592c4c5527b8e0bf99ea8d658f057 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 14:41:29 +0300
Subject: fix #3986 breaking --no-half-vae

---
 modules/sd_models.py | 9 +++++++++
 1 file changed, 9 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 883639d1..5075fadb 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -183,11 +183,20 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
             model.to(memory_format=torch.channels_last)
 
         if not shared.cmd_opts.no_half:
+            vae = model.first_stage_model
+
+            # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
+            if shared.cmd_opts.no_half_vae:
+                model.first_stage_model = None
+
             model.half()
+            model.first_stage_model = vae
 
         devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
         devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
+        model.first_stage_model.to(devices.dtype_vae)
+
         if shared.opts.sd_checkpoint_cache > 0:
             # if PR #4035 were to get merged, restore base VAE first before caching
             checkpoints_loaded[checkpoint_key] = model.state_dict().copy()
-- 
cgit v1.2.3


From 3178c35224467893cf8dcedb1028c59c6c23db58 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Wed, 2 Nov 2022 22:16:32 +0900
Subject: resolve conflicts

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 065b893d..959937d7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -285,7 +285,7 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-- 
cgit v1.2.3


From 9b5f85ac83f864310fe19c9deab6670bad695b0d Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Wed, 2 Nov 2022 22:18:04 +0900
Subject: first revert

---
 modules/shared.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 959937d7..7e8c552b 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -286,7 +286,6 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-- 
cgit v1.2.3


From 7ea5956ad5fa925f92116e8a3bf78d7f6517b654 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Wed, 2 Nov 2022 22:18:55 +0900
Subject: now add

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index d8e99f85..7ecb40d8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -309,6 +309,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-- 
cgit v1.2.3


From e21fcd72fcf147904a1df060226c4df12acf251e Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Wed, 2 Nov 2022 22:37:45 +0800
Subject: add back png info in image api

---
 modules/api/api.py | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 71c9c160..ceaf08b0 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -7,8 +7,9 @@ from fastapi import APIRouter, Depends, HTTPException
 import modules.shared as shared
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid
+from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
+from PIL import PngImagePlugin
 
 
 def upscaler_to_index(name: str):
@@ -31,9 +32,21 @@ def setUpscalers(req: dict):
 
 
 def encode_pil_to_base64(image):
-    buffer = io.BytesIO()
-    image.save(buffer, format="png")
-    return base64.b64encode(buffer.getvalue())
+    with io.BytesIO() as output_bytes:
+
+        # Copy any text-only metadata
+        use_metadata = False
+        metadata = PngImagePlugin.PngInfo()
+        for key, value in image.info.items():
+            if isinstance(key, str) and isinstance(value, str):
+                metadata.add_text(key, value)
+                use_metadata = True
+
+        image.save(
+            output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
+        )
+        bytes_data = output_bytes.getvalue()
+    return base64.b64encode(bytes_data)
 
 
 class Api:
-- 
cgit v1.2.3


From a9e979977a8e3999b01b6a086bb1332ab7ab308b Mon Sep 17 00:00:00 2001
From: Artem Zagidulin <p0chta@yandex.ru>
Date: Wed, 2 Nov 2022 19:05:01 +0300
Subject: process_one

---
 modules/processing.py |  3 +++
 modules/scripts.py    | 16 ++++++++++++++++
 2 files changed, 19 insertions(+)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 3a364b5f..72a2ee4e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -509,6 +509,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if len(prompts) == 0:
                 break
 
+            if p.scripts is not None:
+                p.scripts.process_one(p)
+
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
diff --git a/modules/scripts.py b/modules/scripts.py
index 533db45c..9f82efea 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -70,6 +70,13 @@ class Script:
 
         pass
 
+    def process_one(self, p, *args):
+        """
+        Same as process(), but called for every iteration
+        """
+
+        pass
+
     def postprocess(self, p, processed, *args):
         """
         This function is called after processing ends for AlwaysVisible scripts.
@@ -294,6 +301,15 @@ class ScriptRunner:
                 print(f"Error running process: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
+    def process_one(self, p):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.process_one(p, *script_args)
+            except Exception:
+                print(f"Error running process_one: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
     def postprocess(self, p, processed):
         for script in self.alwayson_scripts:
             try:
-- 
cgit v1.2.3


From f1b6ac64e451036fb4dfabe66d79488c56c06776 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Kyu=E2=99=A5?= <3ad4gum@gmail.com>
Date: Wed, 2 Nov 2022 17:24:42 +0100
Subject: Added option to preview Created images on batch completion.

---
 modules/shared.py | 25 ++++++++++++++++---------
 modules/ui.py     |  2 +-
 2 files changed, 17 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index d8e99f85..d4cf32a4 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -146,6 +146,9 @@ class State:
         self.interrupted = True
 
     def nextjob(self):
+        if opts.show_progress_every_n_steps == -1: 
+            self.do_set_current_image()
+            
         self.job_no += 1
         self.sampling_step = 0
         self.current_image_sampling_step = 0
@@ -186,17 +189,21 @@ class State:
 
     """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
     def set_current_image(self):
+        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
+            self.do_set_current_image()
+
+    def do_set_current_image(self):
         if not parallel_processing_allowed:
             return
+        if self.current_latent is None:
+            return
+            
+        if opts.show_progress_grid:
+            self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
+        else:
+            self.current_image = sd_samplers.sample_to_image(self.current_latent)
 
-        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and self.current_latent is not None:
-            if opts.show_progress_grid:
-                self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
-            else:
-                self.current_image = sd_samplers.sample_to_image(self.current_latent)
-
-            self.current_image_sampling_step = self.sampling_step
-
+        self.current_image_sampling_step = self.sampling_step
 
 state = State()
 
@@ -351,7 +358,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
-    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
diff --git a/modules/ui.py b/modules/ui.py
index 2609857e..29de1e10 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -276,7 +276,7 @@ def check_progress_call(id_part):
     image = gr_show(False)
     preview_visibility = gr_show(False)
 
-    if opts.show_progress_every_n_steps > 0:
+    if opts.show_progress_every_n_steps != 0:
         shared.state.set_current_image()
         image = shared.state.current_image
 
-- 
cgit v1.2.3


From c07f1d0d7821f85b9ce1419992c118963d605bd7 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 2 Nov 2022 16:59:10 +0000
Subject: Convert callbacks into a private map, add utility functions for
 removing callbacks

---
 modules/script_callbacks.py | 68 +++++++++++++++++++++++++++------------------
 1 file changed, 41 insertions(+), 27 deletions(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index c28e220e..4a7fb944 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -46,25 +46,23 @@ class CFGDenoiserParams:
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-callbacks_app_started = []
-callbacks_model_loaded = []
-callbacks_ui_tabs = []
-callbacks_ui_settings = []
-callbacks_before_image_saved = []
-callbacks_image_saved = []
-callbacks_cfg_denoiser = []
+__callback_map = dict(
+    callbacks_app_started=[],
+    callbacks_model_loaded=[],
+    callbacks_ui_tabs=[],
+    callbacks_ui_settings=[],
+    callbacks_before_image_saved=[],
+    callbacks_image_saved=[],
+    callbacks_cfg_denoiser=[]
+)
 
 
 def clear_callbacks():
-    callbacks_model_loaded.clear()
-    callbacks_ui_tabs.clear()
-    callbacks_ui_settings.clear()
-    callbacks_before_image_saved.clear()
-    callbacks_image_saved.clear()
-    callbacks_cfg_denoiser.clear()
+    for callback_list in __callback_map.values():
+        callback_list.clear()
 
 def app_started_callback(demo: Optional[Blocks], app: FastAPI):
-    for c in callbacks_app_started:
+    for c in __callback_map['callbacks_app_started']:
         try:
             c.callback(demo, app)
         except Exception:
@@ -72,7 +70,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
 
 
 def model_loaded_callback(sd_model):
-    for c in callbacks_model_loaded:
+    for c in __callback_map['callbacks_model_loaded']:
         try:
             c.callback(sd_model)
         except Exception:
@@ -82,7 +80,7 @@ def model_loaded_callback(sd_model):
 def ui_tabs_callback():
     res = []
     
-    for c in callbacks_ui_tabs:
+    for c in __callback_map['callbacks_ui_tabs']:
         try:
             res += c.callback() or []
         except Exception:
@@ -92,7 +90,7 @@ def ui_tabs_callback():
 
 
 def ui_settings_callback():
-    for c in callbacks_ui_settings:
+    for c in __callback_map['callbacks_ui_settings']:
         try:
             c.callback()
         except Exception:
@@ -100,7 +98,7 @@ def ui_settings_callback():
 
 
 def before_image_saved_callback(params: ImageSaveParams):
-    for c in callbacks_before_image_saved:
+    for c in __callback_map['callbacks_before_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -108,7 +106,7 @@ def before_image_saved_callback(params: ImageSaveParams):
 
 
 def image_saved_callback(params: ImageSaveParams):
-    for c in callbacks_image_saved:
+    for c in __callback_map['callbacks_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -116,7 +114,7 @@ def image_saved_callback(params: ImageSaveParams):
 
 
 def cfg_denoiser_callback(params: CFGDenoiserParams):
-    for c in callbacks_cfg_denoiser:
+    for c in __callback_map['callbacks_cfg_denoiser']:
         try:
             c.callback(params)
         except Exception:
@@ -129,17 +127,33 @@ def add_callback(callbacks, fun):
 
     callbacks.append(ScriptCallback(filename, fun))
 
+    
+def remove_current_script_callbacks():
+    stack = [x for x in inspect.stack() if x.filename != __file__]
+    filename = stack[0].filename if len(stack) > 0 else 'unknown file'
+    if filename == 'unknown file':
+        return
+    for callback_list in __callback_map.values():
+        for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
+            callback_list.remove(callback_to_remove)
+
+
+def remove_callbacks_for_function(callback_func):
+    for callback_list in __callback_map.values():
+        for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
+            callback_list.remove(callback_to_remove)
+
 
 def on_app_started(callback):
     """register a function to be called when the webui started, the gradio `Block` component and
     fastapi `FastAPI` object are passed as the arguments"""
-    add_callback(callbacks_app_started, callback)
+    add_callback(__callback_map['callbacks_app_started'], callback)
 
 
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
-    add_callback(callbacks_model_loaded, callback)
+    add_callback(__callback_map['callbacks_model_loaded'], callback)
 
 
 def on_ui_tabs(callback):
@@ -152,13 +166,13 @@ def on_ui_tabs(callback):
     title is tab text displayed to user in the UI
     elem_id is HTML id for the tab
     """
-    add_callback(callbacks_ui_tabs, callback)
+    add_callback(__callback_map['callbacks_ui_tabs'], callback)
 
 
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
-    add_callback(callbacks_ui_settings, callback)
+    add_callback(__callback_map['callbacks_ui_settings'], callback)
 
 
 def on_before_image_saved(callback):
@@ -166,7 +180,7 @@ def on_before_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
     """
-    add_callback(callbacks_before_image_saved, callback)
+    add_callback(__callback_map['callbacks_before_image_saved'], callback)
 
 
 def on_image_saved(callback):
@@ -174,7 +188,7 @@ def on_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
     """
-    add_callback(callbacks_image_saved, callback)
+    add_callback(__callback_map['callbacks_image_saved'], callback)
 
 
 def on_cfg_denoiser(callback):
@@ -182,5 +196,5 @@ def on_cfg_denoiser(callback):
     The callback is called with one argument:
         - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
     """
-    add_callback(callbacks_cfg_denoiser, callback)
+    add_callback(__callback_map['callbacks_cfg_denoiser'], callback)
 
-- 
cgit v1.2.3


From de64146ad2fc2030a4cd3545676f9e18c93b8b18 Mon Sep 17 00:00:00 2001
From: Artem Zagidulin <p0chta@yandex.ru>
Date: Wed, 2 Nov 2022 21:30:50 +0300
Subject: add number of itter

---
 modules/processing.py | 2 +-
 modules/scripts.py    | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 72a2ee4e..17f4a5ec 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -510,7 +510,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 break
 
             if p.scripts is not None:
-                p.scripts.process_one(p)
+                p.scripts.process_one(p, n)
 
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
diff --git a/modules/scripts.py b/modules/scripts.py
index 9f82efea..7aa0d56a 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -70,7 +70,7 @@ class Script:
 
         pass
 
-    def process_one(self, p, *args):
+    def process_one(self, p, n, *args):
         """
         Same as process(), but called for every iteration
         """
@@ -301,11 +301,11 @@ class ScriptRunner:
                 print(f"Error running process: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
-    def process_one(self, p):
+    def process_one(self, p, n):
         for script in self.alwayson_scripts:
             try:
                 script_args = p.script_args[script.args_from:script.args_to]
-                script.process_one(p, *script_args)
+                script.process_one(p, n, *script_args)
             except Exception:
                 print(f"Error running process_one: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
-- 
cgit v1.2.3


From 2ac25ea64f31fd0e7dea35d27a52f3646618c3b6 Mon Sep 17 00:00:00 2001
From: digburn <digburned@gmail.com>
Date: Wed, 2 Nov 2022 21:52:23 +0000
Subject: fix: Add required parameter to API extras route

---
 modules/api/models.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index 9ee42a17..9069c0ac 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -131,6 +131,7 @@ class ExtrasBaseRequest(BaseModel):
     upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
+    upscale_first: bool = Field(default=True, title="Upscale first", description="Should the upscaler run before restoring faces?")
 
 class ExtraBaseResponse(BaseModel):
     html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
-- 
cgit v1.2.3


From 313e14de04d9955c6ad077341feceb0fc7f2f1d3 Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Wed, 2 Nov 2022 21:37:43 -0500
Subject: extras - skip unnecessary second hash of image

There is no need to re-hash the input image each iteration of the loop.
This also reverts PR #4026 as it was determined the cache hits it avoids
were actually valid.
---
 modules/extras.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 8e2ab35c..71b93a06 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -136,12 +136,13 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
         blended_result: Image.Image = None
+        image_hash: str = hash(np.array(image.getdata()).tobytes())
         for upscaler in params:
             upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
                             upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-            cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
+            cache_key = LruCache.Key(image_hash=image_hash,
                                      info_hash=hash(info),
-                                     args_hash=hash((upscale_args, upscale_first)))
+                                     args_hash=hash(upscale_args))
             cached_entry = cached_images.get(cache_key)
             if cached_entry is None:
                 res = upscale(image, *upscale_args)
-- 
cgit v1.2.3


From 7a2e36b583ef9eaefa44322e16faff6f9f1af169 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Thu, 3 Nov 2022 00:51:22 -0300
Subject: Add config and lists endpoints

---
 modules/api/api.py    | 97 ++++++++++++++++++++++++++++++++++++++++++++++++---
 modules/api/models.py | 70 +++++++++++++++++++++++++++++++++++--
 2 files changed, 159 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 71c9c160..ed2dce5d 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -2,14 +2,17 @@ import base64
 import io
 import time
 import uvicorn
-from gradio.processing_utils import decode_base64_to_file, decode_base64_to_image
-from fastapi import APIRouter, Depends, HTTPException
+from threading import Lock
+from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
+from fastapi import APIRouter, Depends, FastAPI, HTTPException
 import modules.shared as shared
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid
+from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
-
+from modules.sd_models import checkpoints_list
+from modules.realesrgan_model import get_realesrgan_models
+from typing import List
 
 def upscaler_to_index(name: str):
     try:
@@ -37,7 +40,7 @@ def encode_pil_to_base64(image):
 
 
 class Api:
-    def __init__(self, app, queue_lock):
+    def __init__(self, app: FastAPI, queue_lock: Lock):
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
@@ -48,6 +51,19 @@ class Api:
         self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
         self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
         self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
+        self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
+        self.app.add_api_route("/sdapi/v1/info", self.get_info, methods=["GET"])
+        self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
+        self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
+        self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
+        self.app.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
+        self.app.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
+        self.app.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
+        self.app.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
+        self.app.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
+        self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -190,6 +206,77 @@ class Api:
         shared.state.interrupt()
 
         return {}
+        
+    def get_config(self):
+        options = {}
+        for key in shared.opts.data.keys():
+            metadata = shared.opts.data_labels.get(key)
+            if(metadata is not None):
+                options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)})
+            else:
+                options.update({key: shared.opts.data.get(key, None)})
+        
+        return options
+        
+    def set_config(self, req: OptionsModel):
+        reqDict = vars(req)
+        for o in reqDict:
+            setattr(shared.opts, o, reqDict[o])
+
+        shared.opts.save(shared.config_filename)
+        return
+
+    def get_cmd_flags(self):
+        return vars(shared.cmd_opts)
+
+    def get_info(self):
+
+        return {
+            "hypernetworks": [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks],
+            "face_restorers": [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers],
+            "realesrgan_models":[{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)],
+            "promp_styles":[shared.prompt_styles.styles[k] for k in shared.prompt_styles.styles],
+            "artists_categories": shared.artist_db.cats,
+            # "artists": [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
+        }
+
+    def get_samplers(self):
+        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
+
+    def get_upscalers(self):
+        upscalers = []
+        
+        for upscaler in shared.sd_upscalers:
+            u = upscaler.scaler
+            upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url})
+        
+        return upscalers
+        
+    def get_sd_models(self):
+        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": x.config} for x in checkpoints_list.values()]
+
+    def get_hypernetworks(self):
+        return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
+
+    def get_face_restorers(self):
+        return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers]
+
+    def get_realesrgan_models(self):
+        return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
+    
+    def get_promp_styles(self):
+        styleList = []
+        for k in shared.prompt_styles.styles:
+            style = shared.prompt_styles.styles[k] 
+            styleList.append({"name":style[0], "prompt": style[1], "negative_prompr": style[2]})
+
+        return styleList
+
+    def get_artists_categories(self):
+        return shared.artist_db.cats
+
+    def get_artists(self):
+        return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)
diff --git a/modules/api/models.py b/modules/api/models.py
index 9ee42a17..b54b188a 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,11 +1,10 @@
 import inspect
-from click import prompt
 from pydantic import BaseModel, Field, create_model
-from typing import Any, Optional
+from typing import Any, Optional, Union
 from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
-from modules.shared import sd_upscalers
+from modules.shared import sd_upscalers, opts, parser
 
 API_NOT_ALLOWED = [
     "self",
@@ -165,3 +164,68 @@ class ProgressResponse(BaseModel):
     eta_relative: float = Field(title="ETA in secs")
     state: dict = Field(title="State", description="The current state snapshot")
     current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
+
+fields = {}
+for key, value in opts.data.items():
+    metadata = opts.data_labels.get(key)
+    optType = opts.typemap.get(type(value), type(value))
+
+    if (metadata is not None):
+        fields.update({key: (Optional[optType], Field(
+            default=metadata.default ,description=metadata.label))})
+    else:
+        fields.update({key: (Optional[optType], Field())})
+
+OptionsModel = create_model("Options", **fields)
+
+flags = {}
+_options = vars(parser)['_option_string_actions']
+for key in _options:
+    if(_options[key].dest != 'help'):
+        flag = _options[key]
+        _type = str 
+        if(_options[key].default != None): _type = type(_options[key].default) 
+        flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
+
+FlagsModel = create_model("Flags", **flags)
+
+class SamplerItem(BaseModel):
+    name: str = Field(title="Name")
+    aliases: list[str]  = Field(title="Aliases")
+    options: dict[str, str] = Field(title="Options")
+
+class UpscalerItem(BaseModel):
+    name: str = Field(title="Name")
+    model_name: str | None = Field(title="Model Name")
+    model_path: str | None = Field(title="Path")
+    model_url: str | None = Field(title="URL")
+
+class SDModelItem(BaseModel):
+    title: str = Field(title="Title")
+    model_name: str = Field(title="Model Name")
+    hash: str = Field(title="Hash")
+    filename: str = Field(title="Filename")
+    config: str = Field(title="Config file")
+
+class HypernetworkItem(BaseModel):
+    name: str = Field(title="Name")
+    path: str | None = Field(title="Path")
+
+class FaceRestorerItem(BaseModel):
+    name: str = Field(title="Name")
+    cmd_dir: str | None = Field(title="Path")
+
+class RealesrganItem(BaseModel):
+    name: str = Field(title="Name")
+    path: str | None = Field(title="Path")
+    scale: int | None = Field(title="Scale")
+
+class PromptStyleItem(BaseModel):
+    name: str = Field(title="Name")
+    prompt: str | None = Field(title="Prompt")
+    negative_prompt: str | None = Field(title="Negative Prompt")
+
+class ArtistItem(BaseModel):
+    name: str = Field(title="Name")
+    score: float = Field(title="Score")
+    category: str = Field(title="Category")
\ No newline at end of file
-- 
cgit v1.2.3


From 743fffa3d6c2e9e6bb5f48093a4c88f3b53e001d Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Thu, 3 Nov 2022 00:52:01 -0300
Subject: Remove unused endpoint

---
 modules/api/api.py | 12 ------------
 1 file changed, 12 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index ed2dce5d..a49f3755 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -54,7 +54,6 @@ class Api:
         self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
         self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
-        self.app.add_api_route("/sdapi/v1/info", self.get_info, methods=["GET"])
         self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
         self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
         self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
@@ -229,17 +228,6 @@ class Api:
     def get_cmd_flags(self):
         return vars(shared.cmd_opts)
 
-    def get_info(self):
-
-        return {
-            "hypernetworks": [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks],
-            "face_restorers": [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers],
-            "realesrgan_models":[{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)],
-            "promp_styles":[shared.prompt_styles.styles[k] for k in shared.prompt_styles.styles],
-            "artists_categories": shared.artist_db.cats,
-            # "artists": [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
-        }
-
     def get_samplers(self):
         return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
 
-- 
cgit v1.2.3


From e33d6cbddd08870e348d10a58af41fb677a39fd6 Mon Sep 17 00:00:00 2001
From: Ju1-js <40339350+Ju1-js@users.noreply.github.com>
Date: Wed, 2 Nov 2022 21:04:49 -0700
Subject: Make extension manager Remote links open a new tab

---
 modules/ui_extensions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index ab807722..a81de9a7 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -86,7 +86,7 @@ def extension_table():
         code += f"""
             <tr>
                 <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
-                <td><a href="{html.escape(ext.remote or '')}">{html.escape(ext.remote or '')}</a></td>
+                <td><a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape(ext.remote or '')}</a></td>
                 <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
             </tr>
     """
-- 
cgit v1.2.3


From 0b143c1163a96b193a4e8512be9c5831c661a50d Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Thu, 3 Nov 2022 14:30:53 +0900
Subject: Separate .optim file from model

---
 modules/hypernetworks/hypernetwork.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8f74cdea..63c25de8 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -161,6 +161,7 @@ class Hypernetwork:
 
     def save(self, filename):
         state_dict = {}
+        optimizer_saved_dict = {}
 
         for k, v in self.layers.items():
             state_dict[k] = (v[0].state_dict(), v[1].state_dict())
@@ -175,9 +176,10 @@ class Hypernetwork:
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
         if self.optimizer_name is not None:
-            state_dict['optimizer_name'] = self.optimizer_name
+            optimizer_saved_dict['optimizer_name'] = self.optimizer_name
         if self.optimizer_state_dict:
-            state_dict['optimizer_state_dict'] = self.optimizer_state_dict
+            optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
+            torch.save(optimizer_saved_dict, filename + '.optim')
 
         torch.save(state_dict, filename)
 
@@ -198,9 +200,11 @@ class Hypernetwork:
         print(f"Layer norm is set to {self.add_layer_norm}")
         self.use_dropout = state_dict.get('use_dropout', False)
         print(f"Dropout usage is set to {self.use_dropout}")
-        self.optimizer_name = state_dict.get('optimizer_name', 'AdamW')
+
+        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
+        self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
         print(f"Optimizer name is {self.optimizer_name}")
-        self.optimizer_state_dict = state_dict.get('optimizer_state_dict', None)
+        self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
         if self.optimizer_state_dict:
             print("Loaded existing optimizer from checkpoint")
         else:
-- 
cgit v1.2.3


From 1764ac3c8bc482bd575987850e96630d9115e51a Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Thu, 3 Nov 2022 14:49:26 +0900
Subject: use hash to check valid optim

---
 modules/hypernetworks/hypernetwork.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 63c25de8..4230b8cf 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -177,12 +177,13 @@ class Hypernetwork:
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
         if self.optimizer_name is not None:
             optimizer_saved_dict['optimizer_name'] = self.optimizer_name
+
+        torch.save(state_dict, filename)
         if self.optimizer_state_dict:
+            optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
             optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
             torch.save(optimizer_saved_dict, filename + '.optim')
 
-        torch.save(state_dict, filename)
-
     def load(self, filename):
         self.filename = filename
         if self.name is None:
@@ -204,7 +205,10 @@ class Hypernetwork:
         optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
         self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
         print(f"Optimizer name is {self.optimizer_name}")
-        self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+        if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
+            self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+        else:
+            self.optimizer_state_dict = None
         if self.optimizer_state_dict:
             print("Loaded existing optimizer from checkpoint")
         else:
@@ -229,7 +233,7 @@ def list_hypernetworks(path):
         name = os.path.splitext(os.path.basename(filename))[0]
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
-            res[name] = filename
+            res[name + f"({sd_models.model_hash(filename)})"] = filename
     return res
 
 
@@ -375,6 +379,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:
         hypernetwork_dir = None
 
+    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
     if create_image_every > 0:
         images_dir = os.path.join(log_directory, "images")
         os.makedirs(images_dir, exist_ok=True)
-- 
cgit v1.2.3


From 86b7fc6e5ed56327fa12b444ca2444b13eb98aa8 Mon Sep 17 00:00:00 2001
From: thesved <2893181+thesved@users.noreply.github.com>
Date: Thu, 3 Nov 2022 19:44:47 +0100
Subject: Make DDIM and PLMS work on Mac OS

Fix register_buffer error on Mac OS
---
 modules/sd_hijack_inpainting.py | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index fd92a335..202b42cf 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -1,4 +1,5 @@
 import torch
+import modules.devices as devices
 
 from einops import repeat
 from omegaconf import ListConfig
@@ -314,6 +315,20 @@ class LatentInpaintDiffusion(LatentDiffusion):
         self.masked_image_key = masked_image_key
         assert self.masked_image_key in concat_keys
         self.concat_keys = concat_keys
+            
+
+# =================================================================================================
+# Fix register buffer bug for Mac OS, Viktor Tabori, viktor.doklist.com/start-here
+# =================================================================================================
+def register_buffer(self, name, attr):
+    if type(attr) == torch.Tensor:
+        optimal_type = devices.get_optimal_device()
+        if attr.device != optimal_type:
+           if getattr(torch, 'has_mps', False):
+               attr = attr.to(device="mps", dtype=torch.float32)
+           else:
+               attr = attr.to(optimal_type)
+    setattr(self, name, attr)
 
 
 def should_hijack_inpainting(checkpoint_info):
@@ -326,6 +341,8 @@ def do_inpainting_hijack():
 
     ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
     ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
+    ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
 
     ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
-    ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
\ No newline at end of file
+    ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
+    ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer
-- 
cgit v1.2.3


From b2c48091db394c2b7d375a33f18d90c924cd4363 Mon Sep 17 00:00:00 2001
From: Gur <sevenflash@yandex.ru>
Date: Fri, 4 Nov 2022 06:55:03 +0800
Subject: fixed api compatibility with python 3.8

---
 modules/api/models.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index 9ee42a17..29a934ba 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -6,6 +6,7 @@ from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 from modules.shared import sd_upscalers
+from typing import List
 
 API_NOT_ALLOWED = [
     "self",
@@ -109,12 +110,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
 ).generate_model()
 
 class TextToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: dict
     info: str
 
 class ImageToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: dict
     info: str
 
@@ -146,10 +147,10 @@ class FileData(BaseModel):
     name: str = Field(title="File name")
 
 class ExtrasBatchImagesRequest(ExtrasBaseRequest):
-    imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+    imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
+    images: List[str] = Field(title="Images", description="The generated images in base64 format.")
 
 class PNGInfoRequest(BaseModel):
     image: str = Field(title="Image", description="The base64 encoded PNG image")
-- 
cgit v1.2.3


From 8eb64dab3e9e40531f6a3fa606a1c23a62987249 Mon Sep 17 00:00:00 2001
From: digburn <115176097+digburn@users.noreply.github.com>
Date: Fri, 4 Nov 2022 00:35:18 +0000
Subject: fix: correct default val of upscale_first to False

---
 modules/api/models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index 9069c0ac..68fb45c6 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -131,7 +131,7 @@ class ExtrasBaseRequest(BaseModel):
     upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
-    upscale_first: bool = Field(default=True, title="Upscale first", description="Should the upscaler run before restoring faces?")
+    upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?")
 
 class ExtraBaseResponse(BaseModel):
     html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
-- 
cgit v1.2.3


From 3780ad3ad837dd406da39eebd5d91009b5a58445 Mon Sep 17 00:00:00 2001
From: digburn <digburned@gmail.com>
Date: Fri, 4 Nov 2022 00:40:21 +0000
Subject: fix: loading models without vae from cache

---
 modules/sd_models.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5075fadb..ae427a5c 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -204,8 +204,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
                 checkpoints_loaded.popitem(last=False)  # LRU
 
     else:
-        vae_name = sd_vae.get_filename(vae_file)
-        print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")
+        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
+        vae_message = f" with {vae_name} VAE" if vae_name else ""
+        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
         checkpoints_loaded.move_to_end(checkpoint_key)
         model.load_state_dict(checkpoints_loaded[checkpoint_key])
 
-- 
cgit v1.2.3


From e533ff61c1baa4ad047f9c8dc05c17b64ee89ddf Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Thu, 3 Nov 2022 22:28:22 -0700
Subject: Lift extras generate button a la #4246.

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 2609857e..6461002a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1052,6 +1052,8 @@ def create_ui(wrap_gradio_gpu_call):
                         extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
                         show_extras_results = gr.Checkbox(label='Show result images', value=True)
 
+                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
                 with gr.Tabs(elem_id="extras_resize_mode"):
                     with gr.TabItem('Scale by'):
                         upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
@@ -1079,8 +1081,6 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
 
-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
             result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
-- 
cgit v1.2.3


From 4dd898b8c15e342f817d3fb1c8dc9f2d5d111022 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 08:38:11 +0300
Subject: do not mess with components' visibility for scripts; instead create
 group components and show/hide those; this will break scripts that create
 invisible components and rely on UI but the earlier i make this change the
 better

---
 modules/scripts.py | 34 ++++++++++++++++++----------------
 1 file changed, 18 insertions(+), 16 deletions(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 533db45c..28ce07f4 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -18,6 +18,9 @@ class Script:
     args_to = None
     alwayson = False
 
+    """A gr.Group component that has all script's UI inside it"""
+    group = None
+
     infotext_fields = None
     """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
     parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
@@ -218,8 +221,6 @@ class ScriptRunner:
 
             for control in controls:
                 control.custom_script_source = os.path.basename(script.filename)
-                if not script.alwayson:
-                    control.visible = False
 
             if script.infotext_fields is not None:
                 self.infotext_fields += script.infotext_fields
@@ -229,40 +230,41 @@ class ScriptRunner:
             script.args_to = len(inputs)
 
         for script in self.alwayson_scripts:
-            with gr.Group():
+            with gr.Group() as group:
                 create_script_ui(script, inputs, inputs_alwayson)
 
+            script.group = group
+
         dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
         dropdown.save_to_config = True
         inputs[0] = dropdown
 
         for script in self.selectable_scripts:
-            create_script_ui(script, inputs, inputs_alwayson)
+            with gr.Group(visible=False) as group:
+                create_script_ui(script, inputs, inputs_alwayson)
+
+            script.group = group
 
         def select_script(script_index):
-            if 0 < script_index <= len(self.selectable_scripts):
-                script = self.selectable_scripts[script_index-1]
-                args_from = script.args_from
-                args_to = script.args_to
-            else:
-                args_from = 0
-                args_to = 0
+            selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
 
-            return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]
+            return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
 
         def init_field(title):
+            """called when an initial value is set from ui-config.json to show script's UI components"""
+
             if title == 'None':
                 return
+
             script_index = self.titles.index(title)
-            script = self.selectable_scripts[script_index]
-            for i in range(script.args_from, script.args_to):
-                inputs[i].visible = True
+            self.selectable_scripts[script_index].group.visible = True
 
         dropdown.init_field = init_field
+
         dropdown.change(
             fn=select_script,
             inputs=[dropdown],
-            outputs=inputs
+            outputs=[script.group for script in self.selectable_scripts]
         )
 
         return inputs
-- 
cgit v1.2.3


From f2b69709eaff88fc3a2bd49585556ec0883bf5ea Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 09:42:25 +0300
Subject: move option access checking to options class out of various places
 scattered through code

---
 modules/processing.py |  4 ++--
 modules/shared.py     | 11 +++++++++++
 modules/ui.py         | 20 +++++---------------
 3 files changed, 18 insertions(+), 17 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 2168208c..a46e592d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -418,13 +418,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     try:
         for k, v in p.override_settings.items():
-            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model, hypernet impossible
+            setattr(opts, k, v) # we don't call onchange for simplicity which makes changing model, hypernet impossible
 
         res = process_images_inner(p)
 
     finally:
         for k, v in stored_opts.items():
-            opts.data[k] = v
+            setattr(opts, k, v)
 
     return res
 
diff --git a/modules/shared.py b/modules/shared.py
index d8e99f85..024c771a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -396,6 +396,15 @@ class Options:
     def __setattr__(self, key, value):
         if self.data is not None:
             if key in self.data or key in self.data_labels:
+                assert not cmd_opts.freeze_settings, "changing settings is disabled"
+
+                comp_args = opts.data_labels[key].component_args
+                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
+                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+
+                if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+
                 self.data[key] = value
                 return
 
@@ -412,6 +421,8 @@ class Options:
         return super(Options, self).__getattribute__(item)
 
     def save(self, filename):
+        assert not cmd_opts.freeze_settings, "saving settings is disabled"
+
         with open(filename, "w", encoding="utf8") as file:
             json.dump(self.data, file, indent=4)
 
diff --git a/modules/ui.py b/modules/ui.py
index b2b1c854..633b56ef 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1438,8 +1438,6 @@ def create_ui(wrap_gradio_gpu_call):
     def run_settings(*args):
         changed = 0
 
-        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
-
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
             if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
                 return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
@@ -1448,15 +1446,9 @@ def create_ui(wrap_gradio_gpu_call):
             if comp == dummy_component:
                 continue
 
-            comp_args = opts.data_labels[key].component_args
-            if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
-                continue
-
-            if cmd_opts.hide_ui_dir_config and key in restricted_opts:
-                continue
-
             oldval = opts.data.get(key, None)
-            opts.data[key] = value
+
+            setattr(opts, key, value)
 
             if oldval != value:
                 if opts.data_labels[key].onchange is not None:
@@ -1469,17 +1461,15 @@ def create_ui(wrap_gradio_gpu_call):
         return f'{changed} settings changed.', opts.dumpjson()
 
     def run_settings_single(value, key):
-        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
-
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
         oldval = opts.data.get(key, None)
-        if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+        try:
+            setattr(opts, key, value)
+        except Exception:
             return gr.update(value=oldval), opts.dumpjson()
 
-        opts.data[key] = value
-
         if oldval != value:
             if opts.data_labels[key].onchange is not None:
                 opts.data_labels[key].onchange()
-- 
cgit v1.2.3


From 0abb39f461baa343ae7c23abffb261e57c3168d4 Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 15:47:19 +0900
Subject: resolve conflict - first revert

---
 modules/hypernetworks/hypernetwork.py | 123 ++++++++++++++--------------------
 1 file changed, 52 insertions(+), 71 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4230b8cf..674fcedd 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -21,7 +21,6 @@ from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_norm
 from collections import defaultdict, deque
 from statistics import stdev, mean
 
-optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
@@ -34,9 +33,12 @@ class HypernetworkModule(torch.nn.Module):
         "tanh": torch.nn.Tanh,
         "sigmoid": torch.nn.Sigmoid,
     }
-    activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
+    activation_dict.update(
+        {cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if
+         inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
+                 add_layer_norm=False, use_dropout=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -47,7 +49,7 @@ class HypernetworkModule(torch.nn.Module):
         for i in range(len(layer_structure) - 1):
 
             # Add a fully-connected layer
-            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
+            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i + 1])))
 
             # Add an activation func
             if activation_func == "linear" or activation_func is None:
@@ -59,7 +61,7 @@ class HypernetworkModule(torch.nn.Module):
 
             # Add layer normalization
             if add_layer_norm:
-                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
+                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i + 1])))
 
             # Add dropout expect last layer
             if use_dropout and i < len(layer_structure) - 3:
@@ -128,7 +130,8 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None,
+                 add_layer_norm=False, use_dropout=False):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -140,13 +143,13 @@ class Hypernetwork:
         self.weight_init = weight_init
         self.add_layer_norm = add_layer_norm
         self.use_dropout = use_dropout
-        self.optimizer_name = None
-        self.optimizer_state_dict = None
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout),
             )
 
     def weights(self):
@@ -161,7 +164,6 @@ class Hypernetwork:
 
     def save(self, filename):
         state_dict = {}
-        optimizer_saved_dict = {}
 
         for k, v in self.layers.items():
             state_dict[k] = (v[0].state_dict(), v[1].state_dict())
@@ -175,14 +177,8 @@ class Hypernetwork:
         state_dict['use_dropout'] = self.use_dropout
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
-        if self.optimizer_name is not None:
-            optimizer_saved_dict['optimizer_name'] = self.optimizer_name
 
         torch.save(state_dict, filename)
-        if self.optimizer_state_dict:
-            optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
-            optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
-            torch.save(optimizer_saved_dict, filename + '.optim')
 
     def load(self, filename):
         self.filename = filename
@@ -202,23 +198,13 @@ class Hypernetwork:
         self.use_dropout = state_dict.get('use_dropout', False)
         print(f"Dropout usage is set to {self.use_dropout}")
 
-        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
-        self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
-        print(f"Optimizer name is {self.optimizer_name}")
-        if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
-            self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
-        else:
-            self.optimizer_state_dict = None
-        if self.optimizer_state_dict:
-            print("Loaded existing optimizer from checkpoint")
-        else:
-            print("No saved optimizer exists in checkpoint")
-
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)
@@ -233,7 +219,7 @@ def list_hypernetworks(path):
         name = os.path.splitext(os.path.basename(filename))[0]
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
-            res[name + f"({sd_models.model_hash(filename)})"] = filename
+            res[name] = filename
     return res
 
 
@@ -330,7 +316,7 @@ def statistics(data):
         std = 0
     else:
         std = stdev(data)
-    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
+    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std / (len(data) ** 0.5):.3f})"
     recent_data = data[-32:]
     if len(recent_data) < 2:
         std = 0
@@ -340,7 +326,7 @@ def statistics(data):
     return total_information, recent_information
 
 
-def report_statistics(loss_info:dict):
+def report_statistics(loss_info: dict):
     keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
     for key in keys:
         try:
@@ -352,14 +338,18 @@ def report_statistics(loss_info:dict):
             print(e)
 
 
-
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width,
+                       training_height, steps, create_image_every, save_hypernetwork_every, template_file,
+                       preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps,
+                       preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
     save_hypernetwork_every = save_hypernetwork_every or 0
     create_image_every = create_image_every or 0
-    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
+    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps,
+                                            save_hypernetwork_every, create_image_every, log_directory,
+                                            name="hypernetwork")
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
@@ -379,7 +369,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:
         hypernetwork_dir = None
 
-    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
     if create_image_every > 0:
         images_dir = os.path.join(log_directory, "images")
         os.makedirs(images_dir, exist_ok=True)
@@ -395,39 +384,34 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    
+
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width,
+                                                                height=training_height,
+                                                                repeats=shared.opts.training_image_repeats_per_epoch,
+                                                                placeholder_token=hypernetwork_name,
+                                                                model=shared.sd_model, device=devices.device,
+                                                                template_file=template_file, include_cond=True,
+                                                                batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     size = len(ds.indexes)
-    loss_dict = defaultdict(lambda : deque(maxlen = 1024))
+    loss_dict = defaultdict(lambda: deque(maxlen=1024))
     losses = torch.zeros((size,))
     previous_mean_losses = [0]
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
-    
+
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
-    # Here we use optimizer from saved HN, or we can specify as UI option.
-    if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict:
-        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
-    else:
-        print(f"Optimizer type {optimizer_name} is not defined!")
-        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
-        optimizer_name = 'AdamW'
-    if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
-        try:
-            optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
-        except RuntimeError as e:
-            print("Cannot resume from saved optimizer!")
-            print(e)
+    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
+    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     steps_without_grad = 0
 
@@ -441,7 +425,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         if len(loss_dict) > 0:
             previous_mean_losses = [i[-1] for i in loss_dict.values()]
             previous_mean_loss = mean(previous_mean_losses)
-            
+
         scheduler.apply(optimizer, hypernetwork.step)
         if scheduler.finished:
             break
@@ -460,7 +444,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
             for entry in entries:
                 loss_dict[entry.filename].append(loss.item())
-                
+
             optimizer.zero_grad()
             weights[0].grad = None
             loss.backward()
@@ -475,9 +459,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         steps_done = hypernetwork.step + 1
 
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
             raise RuntimeError("Loss diverged.")
-        
+
         if len(previous_mean_losses) > 1:
             std = stdev(previous_mean_losses)
         else:
@@ -489,11 +473,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             # Before saving, change name to match current checkpoint.
             hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
-            hypernetwork.optimizer_name = optimizer_name
-            if shared.opts.save_optimizer_state:
-                hypernetwork.optimizer_state_dict = optimizer.state_dict()
             save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
-            hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
+
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{previous_mean_loss:.7f}",
             "learn_rate": scheduler.learn_rate
@@ -529,7 +510,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             preview_text = p.prompt
 
             processed = processing.process_images(p)
-            image = processed.images[0] if len(processed.images)>0 else None
+            image = processed.images[0] if len(processed.images) > 0 else None
 
             if unload:
                 shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -537,7 +518,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             if image is not None:
                 shared.state.current_image = image
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt,
+                                                                     shared.opts.samples_format, processed.infotexts[0],
+                                                                     p=p, forced_filename=forced_filename,
+                                                                     save_to_dirs=False)
                 last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
@@ -551,15 +535,12 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
+
     report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
-    hypernetwork.optimizer_name = optimizer_name
-    if shared.opts.save_optimizer_state:
-        hypernetwork.optimizer_state_dict = optimizer.state_dict()
     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
-    del optimizer
-    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
+
     return hypernetwork, filename
 
 
@@ -576,4 +557,4 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
         hypernetwork.sd_checkpoint = old_sd_checkpoint
         hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
         hypernetwork.name = old_hypernetwork_name
-        raise
+        raise
\ No newline at end of file
-- 
cgit v1.2.3


From 0d07cbfa15d34294a4fa22d74359cdd6fe2f799c Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 15:50:54 +0900
Subject: I blame code autocomplete

---
 modules/hypernetworks/hypernetwork.py | 76 +++++++++++++----------------------
 1 file changed, 27 insertions(+), 49 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 674fcedd..a11e01d6 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -33,12 +33,9 @@ class HypernetworkModule(torch.nn.Module):
         "tanh": torch.nn.Tanh,
         "sigmoid": torch.nn.Sigmoid,
     }
-    activation_dict.update(
-        {cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if
-         inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
+    activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
-                 add_layer_norm=False, use_dropout=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -49,7 +46,7 @@ class HypernetworkModule(torch.nn.Module):
         for i in range(len(layer_structure) - 1):
 
             # Add a fully-connected layer
-            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i + 1])))
+            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
 
             # Add an activation func
             if activation_func == "linear" or activation_func is None:
@@ -61,7 +58,7 @@ class HypernetworkModule(torch.nn.Module):
 
             # Add layer normalization
             if add_layer_norm:
-                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i + 1])))
+                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
             # Add dropout expect last layer
             if use_dropout and i < len(layer_structure) - 3:
@@ -130,8 +127,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None,
-                 add_layer_norm=False, use_dropout=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -146,10 +142,8 @@ class Hypernetwork:
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
-                                   self.add_layer_norm, self.use_dropout),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
-                                   self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
             )
 
     def weights(self):
@@ -196,15 +190,13 @@ class Hypernetwork:
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
         print(f"Layer norm is set to {self.add_layer_norm}")
         self.use_dropout = state_dict.get('use_dropout', False)
-        print(f"Dropout usage is set to {self.use_dropout}")
+        print(f"Dropout usage is set to {self.use_dropout}" )
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
-                                       self.add_layer_norm, self.use_dropout),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
-                                       self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)
@@ -316,7 +308,7 @@ def statistics(data):
         std = 0
     else:
         std = stdev(data)
-    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std / (len(data) ** 0.5):.3f})"
+    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
     recent_data = data[-32:]
     if len(recent_data) < 2:
         std = 0
@@ -326,7 +318,7 @@ def statistics(data):
     return total_information, recent_information
 
 
-def report_statistics(loss_info: dict):
+def report_statistics(loss_info:dict):
     keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
     for key in keys:
         try:
@@ -338,18 +330,14 @@ def report_statistics(loss_info: dict):
             print(e)
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width,
-                       training_height, steps, create_image_every, save_hypernetwork_every, template_file,
-                       preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps,
-                       preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
     save_hypernetwork_every = save_hypernetwork_every or 0
     create_image_every = create_image_every or 0
-    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps,
-                                            save_hypernetwork_every, create_image_every, log_directory,
-                                            name="hypernetwork")
+    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
@@ -384,29 +372,23 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-
+    
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width,
-                                                                height=training_height,
-                                                                repeats=shared.opts.training_image_repeats_per_epoch,
-                                                                placeholder_token=hypernetwork_name,
-                                                                model=shared.sd_model, device=devices.device,
-                                                                template_file=template_file, include_cond=True,
-                                                                batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     size = len(ds.indexes)
-    loss_dict = defaultdict(lambda: deque(maxlen=1024))
+    loss_dict = defaultdict(lambda : deque(maxlen = 1024))
     losses = torch.zeros((size,))
     previous_mean_losses = [0]
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
-
+    
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
@@ -425,7 +407,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         if len(loss_dict) > 0:
             previous_mean_losses = [i[-1] for i in loss_dict.values()]
             previous_mean_loss = mean(previous_mean_losses)
-
+            
         scheduler.apply(optimizer, hypernetwork.step)
         if scheduler.finished:
             break
@@ -444,7 +426,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
             for entry in entries:
                 loss_dict[entry.filename].append(loss.item())
-
+                
             optimizer.zero_grad()
             weights[0].grad = None
             loss.backward()
@@ -459,9 +441,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         steps_done = hypernetwork.step + 1
 
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
             raise RuntimeError("Loss diverged.")
-
+        
         if len(previous_mean_losses) > 1:
             std = stdev(previous_mean_losses)
         else:
@@ -510,7 +492,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             preview_text = p.prompt
 
             processed = processing.process_images(p)
-            image = processed.images[0] if len(processed.images) > 0 else None
+            image = processed.images[0] if len(processed.images)>0 else None
 
             if unload:
                 shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -518,10 +500,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             if image is not None:
                 shared.state.current_image = image
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt,
-                                                                     shared.opts.samples_format, processed.infotexts[0],
-                                                                     p=p, forced_filename=forced_filename,
-                                                                     save_to_dirs=False)
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                 last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
@@ -535,7 +514,7 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-
+        
     report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
@@ -543,7 +522,6 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     return hypernetwork, filename
 
-
 def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
     old_hypernetwork_name = hypernetwork.name
     old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
@@ -557,4 +535,4 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
         hypernetwork.sd_checkpoint = old_sd_checkpoint
         hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
         hypernetwork.name = old_hypernetwork_name
-        raise
\ No newline at end of file
+        raise
-- 
cgit v1.2.3


From 283249d2390f0f3a1c8a55d5d9aa551e3e9b2f9c Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 15:57:17 +0900
Subject: apply

---
 modules/hypernetworks/hypernetwork.py | 54 +++++++++++++++++++++++++++++++----
 1 file changed, 49 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6e1a10cf..de8688a9 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -22,6 +22,8 @@ from collections import defaultdict, deque
 from statistics import stdev, mean
 
 
+optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
+
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
     activation_dict = {
@@ -142,6 +144,8 @@ class Hypernetwork:
         self.use_dropout = use_dropout
         self.activate_output = activate_output
         self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True
+        self.optimizer_name = None
+        self.optimizer_state_dict = None
 
         for size in enable_sizes or []:
             self.layers[size] = (
@@ -163,6 +167,7 @@ class Hypernetwork:
 
     def save(self, filename):
         state_dict = {}
+        optimizer_saved_dict = {}
 
         for k, v in self.layers.items():
             state_dict[k] = (v[0].state_dict(), v[1].state_dict())
@@ -178,8 +183,15 @@ class Hypernetwork:
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
         state_dict['activate_output'] = self.activate_output
         state_dict['last_layer_dropout'] = self.last_layer_dropout
-        
+
+        if self.optimizer_name is not None:
+            optimizer_saved_dict['optimizer_name'] = self.optimizer_name
+
         torch.save(state_dict, filename)
+        if self.optimizer_state_dict:
+            optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
+            optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
+            torch.save(optimizer_saved_dict, filename + '.optim')
 
     def load(self, filename):
         self.filename = filename
@@ -202,6 +214,18 @@ class Hypernetwork:
         print(f"Activate last layer is set to {self.activate_output}")
         self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
 
+        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
+        self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
+        print(f"Optimizer name is {self.optimizer_name}")
+        if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
+            self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+        else:
+            self.optimizer_state_dict = None
+        if self.optimizer_state_dict:
+            print("Loaded existing optimizer from checkpoint")
+        else:
+            print("No saved optimizer exists in checkpoint")
+
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
@@ -223,7 +247,7 @@ def list_hypernetworks(path):
         name = os.path.splitext(os.path.basename(filename))[0]
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
-            res[name] = filename
+            res[name + f"({sd_models.model_hash(filename)})"] = filename
     return res
 
 
@@ -369,6 +393,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:
         hypernetwork_dir = None
 
+    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
     if create_image_every > 0:
         images_dir = os.path.join(log_directory, "images")
         os.makedirs(images_dir, exist_ok=True)
@@ -404,8 +429,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
-    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
-    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
+    # Here we use optimizer from saved HN, or we can specify as UI option.
+    if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict:
+        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
+    else:
+        print(f"Optimizer type {optimizer_name} is not defined!")
+        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
+        optimizer_name = 'AdamW'
+    if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
+        try:
+            optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
+        except RuntimeError as e:
+            print("Cannot resume from saved optimizer!")
+            print(e)
 
     steps_without_grad = 0
 
@@ -467,7 +503,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             # Before saving, change name to match current checkpoint.
             hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+            hypernetwork.optimizer_name = optimizer_name
+            if shared.opts.save_optimizer_state:
+                hypernetwork.optimizer_state_dict = optimizer.state_dict()
             save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
+            hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{previous_mean_loss:.7f}",
@@ -530,8 +570,12 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+    hypernetwork.optimizer_name = optimizer_name
+    if shared.opts.save_optimizer_state:
+        hypernetwork.optimizer_state_dict = optimizer.state_dict()
     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
-
+    del optimizer
+    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
     return hypernetwork, filename
 
 def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
-- 
cgit v1.2.3


From f5d394214d6ee74a682d0a1016bcbebc4b43c13a Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 16:04:03 +0900
Subject: split before declaring file name

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index de8688a9..9b6a3e62 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -382,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     shared.state.textinfo = "Initializing hypernetwork training..."
     shared.state.job_count = steps
 
+    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
 
     log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
@@ -393,7 +394,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:
         hypernetwork_dir = None
 
-    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
     if create_image_every > 0:
         images_dir = os.path.join(log_directory, "images")
         os.makedirs(images_dir, exist_ok=True)
-- 
cgit v1.2.3


From 1ca0bcd3a7003dd2c1324de7d97fd2a6fc5ddc53 Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 16:09:19 +0900
Subject: only save if option is enabled

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 9b6a3e62..b1f308e2 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -188,7 +188,7 @@ class Hypernetwork:
             optimizer_saved_dict['optimizer_name'] = self.optimizer_name
 
         torch.save(state_dict, filename)
-        if self.optimizer_state_dict:
+        if shared.opts.save_optimizer_state and self.optimizer_state_dict:
             optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
             optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
             torch.save(optimizer_saved_dict, filename + '.optim')
-- 
cgit v1.2.3


From ccf1a15412ef6b518f9f54cc26a0ee5edf458108 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 10:16:19 +0300
Subject: add an option to enable installing extensions with --listen or
 --share

---
 modules/shared.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 024c771a..0a39cdf2 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -44,6 +44,7 @@ parser.add_argument("--precision", type=str, help="evaluate at this precision",
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
 parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
+parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
@@ -99,7 +100,7 @@ restricted_opts = {
     "outdir_save",
 }
 
-cmd_opts.disable_extension_access = cmd_opts.share or cmd_opts.listen
+cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen) and not cmd_opts.enable_insecure_extension_access
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
-- 
cgit v1.2.3


From 321e13ca176b256177c4a752d1f2bbee79b5532e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 10:35:30 +0300
Subject: produce a readable error message when setting an option fails on the
 settings screen

---
 modules/ui.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 633b56ef..3ac7540c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1439,8 +1439,7 @@ def create_ui(wrap_gradio_gpu_call):
         changed = 0
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
-                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
+            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
             if comp == dummy_component:
@@ -1458,7 +1457,7 @@ def create_ui(wrap_gradio_gpu_call):
 
         opts.save(shared.config_filename)
 
-        return f'{changed} settings changed.', opts.dumpjson()
+        return opts.dumpjson(), f'{changed} settings changed.'
 
     def run_settings_single(value, key):
         if not opts.same_type(value, opts.data_labels[key].default):
@@ -1622,9 +1621,9 @@ def create_ui(wrap_gradio_gpu_call):
 
         text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
         settings_submit.click(
-            fn=run_settings,
+            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
             inputs=components,
-            outputs=[result, text_settings],
+            outputs=[text_settings, result],
         )
 
         for i, k, item in quicksettings_list:
-- 
cgit v1.2.3


From f674c488d9701e577e2aaf25e331fb44ada4f1ef Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 10:45:34 +0300
Subject: bugfix: save image for hires fix BEFORE upscaling latent space

---
 modules/processing.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index a46e592d..7a2fc218 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -665,17 +665,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
 
         if opts.use_scale_latent_for_hires_fix:
+            for i in range(samples.shape[0]):
+                save_intermediate(samples, i)
+
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
-            
+
             # Avoid making the inpainting conditioning unless necessary as 
             # this does need some extra compute to decode / encode the image again.
             if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
                 image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
             else:
                 image_conditioning = self.txt2img_image_conditioning(samples)
-
-            for i in range(samples.shape[0]):
-                save_intermediate(samples, i)
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
-- 
cgit v1.2.3


From 7278897982bfb640ee95f144c97ed25fb3f77ea3 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 17:12:28 +0900
Subject: Update shared.py

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 4d6e1c8b..6e7a02e0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -309,7 +309,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-- 
cgit v1.2.3


From 99043f33606d3057f83ea52a403e10cd29d1f7e7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 11:20:42 +0300
Subject: fix one of previous merges breaking the program

---
 modules/sd_models.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 63e07a12..34c57bfa 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -167,6 +167,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         sd_vae.restore_base_vae(model)
         checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
 
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+
     if checkpoint_info not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-- 
cgit v1.2.3


From eeb07330131012c0294afb79165b90270679b9c7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 11:21:40 +0300
Subject: change process_one virtual function for script to process_batch, add
 extra args and docs

---
 modules/processing.py |  2 +-
 modules/scripts.py    | 16 +++++++++++-----
 2 files changed, 12 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index e20d8fc4..03c9143d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -502,7 +502,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 break
 
             if p.scripts is not None:
-                p.scripts.process_one(p, n)
+                p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
 
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
diff --git a/modules/scripts.py b/modules/scripts.py
index 75e47cd2..366c90d7 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -73,9 +73,15 @@ class Script:
 
         pass
 
-    def process_one(self, p, n, *args):
+    def process_batch(self, p, *args, **kwargs):
         """
-        Same as process(), but called for every iteration
+        Same as process(), but called for every batch.
+
+        **kwargs will have those items:
+          - batch_number - index of current batch, from 0 to number of batches-1
+          - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+          - seeds - list of seeds for current batch
+          - subseeds - list of subseeds for current batch
         """
 
         pass
@@ -303,13 +309,13 @@ class ScriptRunner:
                 print(f"Error running process: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
-    def process_one(self, p, n):
+    def process_batch(self, p, **kwargs):
         for script in self.alwayson_scripts:
             try:
                 script_args = p.script_args[script.args_from:script.args_to]
-                script.process_one(p, n, *script_args)
+                script.process_batch(p, *script_args, **kwargs)
             except Exception:
-                print(f"Error running process_one: {script.filename}", file=sys.stderr)
+                print(f"Error running process_batch: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
     def postprocess(self, p, processed):
-- 
cgit v1.2.3


From 39541d7725bc42f456a604b07c50aba503a5a09a Mon Sep 17 00:00:00 2001
From: Fampai <>
Date: Fri, 4 Nov 2022 04:50:22 -0400
Subject: Fixes race condition in training when VAE is unloaded

set_current_image can attempt to use the VAE when it is unloaded to
the CPU while training
---
 modules/hypernetworks/hypernetwork.py          | 4 ++++
 modules/textual_inversion/textual_inversion.py | 5 +++++
 2 files changed, 9 insertions(+)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6e1a10cf..fcb96059 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -390,7 +390,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
+    old_parallel_processing_allowed = shared.parallel_processing_allowed
+
     if unload:
+        shared.parallel_processing_allowed = False
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
 
@@ -531,6 +534,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
+    shared.parallel_processing_allowed = old_parallel_processing_allowed
 
     return hypernetwork, filename
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 0aeb0459..55892c57 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -273,7 +273,11 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+
+    old_parallel_processing_allowed = shared.parallel_processing_allowed
+
     if unload:
+        shared.parallel_processing_allowed = False
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     embedding.vec.requires_grad = True
@@ -410,6 +414,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
     save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
     shared.sd_model.first_stage_model.to(devices.device)
+    shared.parallel_processing_allowed = old_parallel_processing_allowed
 
     return embedding, filename
 
-- 
cgit v1.2.3


From 821e2b883dbb42a187bc37379175cd55b7cd7e81 Mon Sep 17 00:00:00 2001
From: TinkTheBoush <TinkTheBoush@github.com>
Date: Fri, 4 Nov 2022 19:39:03 +0900
Subject: change option position to Training setting

---
 modules/hypernetworks/hypernetwork.py          | 4 ++--
 modules/shared.py                              | 1 +
 modules/textual_inversion/dataset.py           | 5 ++---
 modules/textual_inversion/textual_inversion.py | 4 ++--
 4 files changed, 7 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7630fb81..a11e01d6 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -331,7 +331,7 @@ def report_statistics(loss_info:dict):
 
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, shuffle_tags, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
@@ -376,7 +376,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, shuffle_tags=shuffle_tags, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
diff --git a/modules/shared.py b/modules/shared.py
index 1ccb269a..e1d9bdf1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -290,6 +290,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
+    "shuffle_tags": OptionInfo(False, "Shuffleing tags by "," when create texts."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index e9d97cc1..df278dc2 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -24,7 +24,7 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", shuffle_tags=True, model=None, device=None, template_file=None, include_cond=False, batch_size=1):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
 
         self.placeholder_token = placeholder_token
@@ -33,7 +33,6 @@ class PersonalizedBase(Dataset):
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
-        self.shuffle_tags = shuffle_tags
 
         self.dataset = []
 
@@ -99,7 +98,7 @@ class PersonalizedBase(Dataset):
     def create_text(self, filename_text):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
-        if self.tag_shuffle:
+        if shared.opts.shuffle_tags:
             tags = filename_text.split(',')
             random.shuffle(tags)
             text = text.replace("[filewords]", ','.join(tags))
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 82dde931..0aeb0459 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -224,7 +224,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, data_root, templat
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 
-def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, shuffle_tags, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
     validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
@@ -272,7 +272,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, shuffle_tags=shuffle_tags, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
     if unload:
         shared.sd_model.first_stage_model.to(devices.cpu)
 
-- 
cgit v1.2.3


From 45b65e87e0ef64b3e457f7d20c62d591cdcd0e7b Mon Sep 17 00:00:00 2001
From: TinkTheBoush <TinkTheBoush@github.com>
Date: Fri, 4 Nov 2022 19:48:28 +0900
Subject: remove ui option

---
 modules/ui.py | 3 ---
 1 file changed, 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 6f3836c6..45cd8c3f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1269,7 +1269,6 @@ def create_ui(wrap_gradio_gpu_call):
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
-                    shuffle_tags = gr.Checkbox(label='Shuffleing tags by "," when create texts', value=True)
 
                     with gr.Row():
                         interrupt_training = gr.Button(value="Interrupt")
@@ -1364,7 +1363,6 @@ def create_ui(wrap_gradio_gpu_call):
                 template_file,
                 save_image_with_stored_embedding,
                 preview_from_txt2img,
-                shuffle_tags,
                 *txt2img_preview_params,
             ],
             outputs=[
@@ -1389,7 +1387,6 @@ def create_ui(wrap_gradio_gpu_call):
                 save_embedding_every,
                 template_file,
                 preview_from_txt2img,
-                shuffle_tags,
                 *txt2img_preview_params,
             ],
             outputs=[
-- 
cgit v1.2.3


From fd62727893f9face287b0a9620251afaa38a627d Mon Sep 17 00:00:00 2001
From: Isaac Poulton <flagredomega@gmail.com>
Date: Fri, 4 Nov 2022 18:34:35 +0700
Subject: Sort hypernetworks

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6e1a10cf..f1f04a70 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -224,7 +224,7 @@ def list_hypernetworks(path):
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
             res[name] = filename
-    return res
+    return dict(sorted(res.items()))
 
 
 def load_hypernetwork(filename):
-- 
cgit v1.2.3


From c3cd0d7a86f35a5bfc58fdc3ecfaf203c0aee06f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 4 Nov 2022 12:19:16 +0000
Subject: Should be one underscore for module privates not two

---
 modules/script_callbacks.py | 37 ++++++++++++++++++-------------------
 1 file changed, 18 insertions(+), 19 deletions(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 4a7fb944..83da7ca4 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -46,7 +46,7 @@ class CFGDenoiserParams:
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-__callback_map = dict(
+_callback_map = dict(
     callbacks_app_started=[],
     callbacks_model_loaded=[],
     callbacks_ui_tabs=[],
@@ -58,11 +58,11 @@ __callback_map = dict(
 
 
 def clear_callbacks():
-    for callback_list in __callback_map.values():
+    for callback_list in _callback_map.values():
         callback_list.clear()
 
 def app_started_callback(demo: Optional[Blocks], app: FastAPI):
-    for c in __callback_map['callbacks_app_started']:
+    for c in _callback_map['callbacks_app_started']:
         try:
             c.callback(demo, app)
         except Exception:
@@ -70,7 +70,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
 
 
 def model_loaded_callback(sd_model):
-    for c in __callback_map['callbacks_model_loaded']:
+    for c in _callback_map['callbacks_model_loaded']:
         try:
             c.callback(sd_model)
         except Exception:
@@ -80,7 +80,7 @@ def model_loaded_callback(sd_model):
 def ui_tabs_callback():
     res = []
     
-    for c in __callback_map['callbacks_ui_tabs']:
+    for c in _callback_map['callbacks_ui_tabs']:
         try:
             res += c.callback() or []
         except Exception:
@@ -90,7 +90,7 @@ def ui_tabs_callback():
 
 
 def ui_settings_callback():
-    for c in __callback_map['callbacks_ui_settings']:
+    for c in _callback_map['callbacks_ui_settings']:
         try:
             c.callback()
         except Exception:
@@ -98,7 +98,7 @@ def ui_settings_callback():
 
 
 def before_image_saved_callback(params: ImageSaveParams):
-    for c in __callback_map['callbacks_before_image_saved']:
+    for c in _callback_map['callbacks_before_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -106,7 +106,7 @@ def before_image_saved_callback(params: ImageSaveParams):
 
 
 def image_saved_callback(params: ImageSaveParams):
-    for c in __callback_map['callbacks_image_saved']:
+    for c in _callback_map['callbacks_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -114,7 +114,7 @@ def image_saved_callback(params: ImageSaveParams):
 
 
 def cfg_denoiser_callback(params: CFGDenoiserParams):
-    for c in __callback_map['callbacks_cfg_denoiser']:
+    for c in _callback_map['callbacks_cfg_denoiser']:
         try:
             c.callback(params)
         except Exception:
@@ -133,13 +133,13 @@ def remove_current_script_callbacks():
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
     if filename == 'unknown file':
         return
-    for callback_list in __callback_map.values():
+    for callback_list in _callback_map.values():
         for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
             callback_list.remove(callback_to_remove)
 
 
 def remove_callbacks_for_function(callback_func):
-    for callback_list in __callback_map.values():
+    for callback_list in _callback_map.values():
         for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
             callback_list.remove(callback_to_remove)
 
@@ -147,13 +147,13 @@ def remove_callbacks_for_function(callback_func):
 def on_app_started(callback):
     """register a function to be called when the webui started, the gradio `Block` component and
     fastapi `FastAPI` object are passed as the arguments"""
-    add_callback(__callback_map['callbacks_app_started'], callback)
+    add_callback(_callback_map['callbacks_app_started'], callback)
 
 
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
-    add_callback(__callback_map['callbacks_model_loaded'], callback)
+    add_callback(_callback_map['callbacks_model_loaded'], callback)
 
 
 def on_ui_tabs(callback):
@@ -166,13 +166,13 @@ def on_ui_tabs(callback):
     title is tab text displayed to user in the UI
     elem_id is HTML id for the tab
     """
-    add_callback(__callback_map['callbacks_ui_tabs'], callback)
+    add_callback(_callback_map['callbacks_ui_tabs'], callback)
 
 
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
-    add_callback(__callback_map['callbacks_ui_settings'], callback)
+    add_callback(_callback_map['callbacks_ui_settings'], callback)
 
 
 def on_before_image_saved(callback):
@@ -180,7 +180,7 @@ def on_before_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
     """
-    add_callback(__callback_map['callbacks_before_image_saved'], callback)
+    add_callback(_callback_map['callbacks_before_image_saved'], callback)
 
 
 def on_image_saved(callback):
@@ -188,7 +188,7 @@ def on_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
     """
-    add_callback(__callback_map['callbacks_image_saved'], callback)
+    add_callback(_callback_map['callbacks_image_saved'], callback)
 
 
 def on_cfg_denoiser(callback):
@@ -196,5 +196,4 @@ def on_cfg_denoiser(callback):
     The callback is called with one argument:
         - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
     """
-    add_callback(__callback_map['callbacks_cfg_denoiser'], callback)
-
+    add_callback(_callback_map['callbacks_cfg_denoiser'], callback)
-- 
cgit v1.2.3


From f316280ad3634a2343b086a6de0bfcd473e18599 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 16:48:40 +0300
Subject: fix the error that prevents from setting some options

---
 modules/shared.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index a9e28b9c..962115f6 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -406,7 +406,8 @@ class Options:
             if key in self.data or key in self.data_labels:
                 assert not cmd_opts.freeze_settings, "changing settings is disabled"
 
-                comp_args = opts.data_labels[key].component_args
+                info = opts.data_labels.get(key, None)
+                comp_args = info.component_args if info else None
                 if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
                     raise RuntimeError(f"not possible to set {key} because it is restricted")
 
-- 
cgit v1.2.3


From 116bcf730ade8d3ac5d76d04c5887b6bba000970 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 16:48:46 +0300
Subject: disable setting options via API until it is fixed by the author

---
 modules/api/api.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index a49f3755..8a7ab2f5 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -218,6 +218,10 @@ class Api:
         return options
         
     def set_config(self, req: OptionsModel):
+        # currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will
+        # overwrite all options with default values.
+        raise RuntimeError('Setting options via API is not supported')
+
         reqDict = vars(req)
         for o in reqDict:
             setattr(shared.opts, o, reqDict[o])
-- 
cgit v1.2.3


From 08feb4c364e8b2aed929fd7d22dfa21a93d78b2c Mon Sep 17 00:00:00 2001
From: Isaac Poulton <flagredomega@gmail.com>
Date: Fri, 4 Nov 2022 20:53:11 +0700
Subject: Sort straight out of the glob

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index f1f04a70..a441ab10 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -219,12 +219,12 @@ class Hypernetwork:
 
 def list_hypernetworks(path):
     res = {}
-    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
+    for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
         name = os.path.splitext(os.path.basename(filename))[0]
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
             res[name] = filename
-    return dict(sorted(res.items()))
+    return res
 
 
 def load_hypernetwork(filename):
-- 
cgit v1.2.3


From 5844ef8a9a165e0f456a4658bda830282cf5a55e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 4 Nov 2022 16:02:25 +0000
Subject: remove private underscore indicator

---
 modules/script_callbacks.py | 36 ++++++++++++++++++------------------
 1 file changed, 18 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 83da7ca4..74dfb880 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -46,7 +46,7 @@ class CFGDenoiserParams:
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-_callback_map = dict(
+callback_map = dict(
     callbacks_app_started=[],
     callbacks_model_loaded=[],
     callbacks_ui_tabs=[],
@@ -58,11 +58,11 @@ _callback_map = dict(
 
 
 def clear_callbacks():
-    for callback_list in _callback_map.values():
+    for callback_list in callback_map.values():
         callback_list.clear()
 
 def app_started_callback(demo: Optional[Blocks], app: FastAPI):
-    for c in _callback_map['callbacks_app_started']:
+    for c in callback_map['callbacks_app_started']:
         try:
             c.callback(demo, app)
         except Exception:
@@ -70,7 +70,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
 
 
 def model_loaded_callback(sd_model):
-    for c in _callback_map['callbacks_model_loaded']:
+    for c in callback_map['callbacks_model_loaded']:
         try:
             c.callback(sd_model)
         except Exception:
@@ -80,7 +80,7 @@ def model_loaded_callback(sd_model):
 def ui_tabs_callback():
     res = []
     
-    for c in _callback_map['callbacks_ui_tabs']:
+    for c in callback_map['callbacks_ui_tabs']:
         try:
             res += c.callback() or []
         except Exception:
@@ -90,7 +90,7 @@ def ui_tabs_callback():
 
 
 def ui_settings_callback():
-    for c in _callback_map['callbacks_ui_settings']:
+    for c in callback_map['callbacks_ui_settings']:
         try:
             c.callback()
         except Exception:
@@ -98,7 +98,7 @@ def ui_settings_callback():
 
 
 def before_image_saved_callback(params: ImageSaveParams):
-    for c in _callback_map['callbacks_before_image_saved']:
+    for c in callback_map['callbacks_before_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -106,7 +106,7 @@ def before_image_saved_callback(params: ImageSaveParams):
 
 
 def image_saved_callback(params: ImageSaveParams):
-    for c in _callback_map['callbacks_image_saved']:
+    for c in callback_map['callbacks_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -114,7 +114,7 @@ def image_saved_callback(params: ImageSaveParams):
 
 
 def cfg_denoiser_callback(params: CFGDenoiserParams):
-    for c in _callback_map['callbacks_cfg_denoiser']:
+    for c in callback_map['callbacks_cfg_denoiser']:
         try:
             c.callback(params)
         except Exception:
@@ -133,13 +133,13 @@ def remove_current_script_callbacks():
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
     if filename == 'unknown file':
         return
-    for callback_list in _callback_map.values():
+    for callback_list in callback_map.values():
         for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
             callback_list.remove(callback_to_remove)
 
 
 def remove_callbacks_for_function(callback_func):
-    for callback_list in _callback_map.values():
+    for callback_list in callback_map.values():
         for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
             callback_list.remove(callback_to_remove)
 
@@ -147,13 +147,13 @@ def remove_callbacks_for_function(callback_func):
 def on_app_started(callback):
     """register a function to be called when the webui started, the gradio `Block` component and
     fastapi `FastAPI` object are passed as the arguments"""
-    add_callback(_callback_map['callbacks_app_started'], callback)
+    add_callback(callback_map['callbacks_app_started'], callback)
 
 
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
-    add_callback(_callback_map['callbacks_model_loaded'], callback)
+    add_callback(callback_map['callbacks_model_loaded'], callback)
 
 
 def on_ui_tabs(callback):
@@ -166,13 +166,13 @@ def on_ui_tabs(callback):
     title is tab text displayed to user in the UI
     elem_id is HTML id for the tab
     """
-    add_callback(_callback_map['callbacks_ui_tabs'], callback)
+    add_callback(callback_map['callbacks_ui_tabs'], callback)
 
 
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
-    add_callback(_callback_map['callbacks_ui_settings'], callback)
+    add_callback(callback_map['callbacks_ui_settings'], callback)
 
 
 def on_before_image_saved(callback):
@@ -180,7 +180,7 @@ def on_before_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
     """
-    add_callback(_callback_map['callbacks_before_image_saved'], callback)
+    add_callback(callback_map['callbacks_before_image_saved'], callback)
 
 
 def on_image_saved(callback):
@@ -188,7 +188,7 @@ def on_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
     """
-    add_callback(_callback_map['callbacks_image_saved'], callback)
+    add_callback(callback_map['callbacks_image_saved'], callback)
 
 
 def on_cfg_denoiser(callback):
@@ -196,4 +196,4 @@ def on_cfg_denoiser(callback):
     The callback is called with one argument:
         - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
     """
-    add_callback(_callback_map['callbacks_cfg_denoiser'], callback)
+    add_callback(callback_map['callbacks_cfg_denoiser'], callback)
-- 
cgit v1.2.3


From 0d7e01d9950e013784c4b77c05aa7583ea69edc8 Mon Sep 17 00:00:00 2001
From: innovaciones <sonygarcia99@gmail.com>
Date: Fri, 4 Nov 2022 12:14:32 -0600
Subject: Open extensions links in new tab

Fixed for "Available" tab
---
 modules/ui_extensions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index a81de9a7..8e0d41d5 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -188,7 +188,7 @@ def refresh_available_extensions_from_data():
 
         code += f"""
             <tr>
-                <td><a href="{html.escape(url)}">{html.escape(name)}</a></td>
+                <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a></td>
                 <td>{html.escape(description)}</td>
                 <td>{install_code}</td>
             </tr>
-- 
cgit v1.2.3


From b8435e632f7ba0da12a2c8e9c788dda519279d24 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sat, 5 Nov 2022 02:36:47 +0800
Subject: add --cors-allow-origins cmd opt

---
 modules/shared.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index a9e28b9c..e83cbcdf 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -86,6 +86,7 @@ parser.add_argument("--nowebui", action='store_true', help="use api=True to laun
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
 parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origins", default=None)
 
 cmd_opts = parser.parse_args()
 restricted_opts = {
@@ -147,9 +148,9 @@ class State:
         self.interrupted = True
 
     def nextjob(self):
-        if opts.show_progress_every_n_steps == -1: 
+        if opts.show_progress_every_n_steps == -1:
             self.do_set_current_image()
-            
+
         self.job_no += 1
         self.sampling_step = 0
         self.current_image_sampling_step = 0
@@ -198,7 +199,7 @@ class State:
             return
         if self.current_latent is None:
             return
-            
+
         if opts.show_progress_grid:
             self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
         else:
-- 
cgit v1.2.3


From 467d8b967b5d1b1984ab113bec3fff217736e7ac Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sat, 5 Nov 2022 04:24:42 +0900
Subject: Fix errors from commit f2b697 with --hide-ui-dir-config

https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/f2b69709eaff88fc3a2bd49585556ec0883bf5ea
---
 modules/ui.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 4c2829af..76ca9b07 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1446,17 +1446,19 @@ def create_ui(wrap_gradio_gpu_call):
                 continue
 
             oldval = opts.data.get(key, None)
-
-            setattr(opts, key, value)
-
+            try:
+                setattr(opts, key, value)
+            except RuntimeError:
+                continue
             if oldval != value:
                 if opts.data_labels[key].onchange is not None:
                     opts.data_labels[key].onchange()
 
                 changed += 1
-
-        opts.save(shared.config_filename)
-
+        try:
+            opts.save(shared.config_filename)
+        except RuntimeError:
+            return opts.dumpjson(), f'{changed} settings changed without save.'
         return opts.dumpjson(), f'{changed} settings changed.'
 
     def run_settings_single(value, key):
-- 
cgit v1.2.3


From 30b1bcc64e67ad50c5d3af3a6fe1bd1e9553f34e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 22:56:18 +0300
Subject: fix upscale loop erroneously applied multiple times

---
 modules/upscaler.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/upscaler.py b/modules/upscaler.py
index 83fde7ca..c4e6e6bd 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -57,10 +57,18 @@ class Upscaler:
         self.scale = scale
         dest_w = img.width * scale
         dest_h = img.height * scale
+
         for i in range(3):
-            if img.width > dest_w and img.height > dest_h:
-                break
+            shape = (img.width, img.height)
+
             img = self.do_upscale(img, selected_model)
+
+            if shape == (img.width, img.height):
+                break
+
+            if img.width >= dest_w and img.height >= dest_h:
+                break
+
         if img.width != dest_w or img.height != dest_h:
             img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)
 
-- 
cgit v1.2.3


From 6008c0773ea575353f9b87da8a58454e20cc7857 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 4 Nov 2022 23:03:05 +0000
Subject: Add support for new DPM-Solver++ samplers

---
 modules/sd_samplers.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index c7c414ef..7ece6556 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -29,6 +29,10 @@ samplers_k_diffusion = [
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
     ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
+    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM-Solver++(2S) Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.3


From f92dc505a013af9e385c7edbdf97539be62503d6 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 4 Nov 2022 23:12:48 +0000
Subject: Fix name

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 7ece6556..b28a2e4c 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -31,7 +31,7 @@ samplers_k_diffusion = [
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
     ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
     ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
-    ('DPM-Solver++(2S) Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
 
-- 
cgit v1.2.3


From 1b6c2fc749e12f12bbee4705e65f217d23fa9072 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 4 Nov 2022 23:28:13 +0000
Subject: Reorder samplers

---
 modules/sd_samplers.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b28a2e4c..1e88f7ee 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -24,13 +24,13 @@ samplers_k_diffusion = [
     ('Heun', 'sample_heun', ['k_heun'], {}),
     ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
+    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
     ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
-    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
-    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
-- 
cgit v1.2.3


From ebce0c57c78a3f22178e3a38938d19ec0dfb703d Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Sat, 5 Nov 2022 11:38:24 +0800
Subject: Use typing.Optional instead of | to add support for Python 3.9 and
 below.

---
 modules/api/models.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index 2ae75f43..a44c5ddd 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,6 +1,6 @@
 import inspect
 from pydantic import BaseModel, Field, create_model
-from typing import Any, Optional, Union
+from typing import Any, Optional
 from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
@@ -185,22 +185,22 @@ _options = vars(parser)['_option_string_actions']
 for key in _options:
     if(_options[key].dest != 'help'):
         flag = _options[key]
-        _type = str 
-        if(_options[key].default != None): _type = type(_options[key].default) 
+        _type = str
+        if _options[key].default is not None: _type = type(_options[key].default)
         flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
 
 FlagsModel = create_model("Flags", **flags)
 
 class SamplerItem(BaseModel):
     name: str = Field(title="Name")
-    aliases: list[str]  = Field(title="Aliases")
+    aliases: list[str] = Field(title="Aliases")
     options: dict[str, str] = Field(title="Options")
 
 class UpscalerItem(BaseModel):
     name: str = Field(title="Name")
-    model_name: str | None = Field(title="Model Name")
-    model_path: str | None = Field(title="Path")
-    model_url: str | None = Field(title="URL")
+    model_name: Optional[str] = Field(title="Model Name")
+    model_path: Optional[str] = Field(title="Path")
+    model_url: Optional[str] = Field(title="URL")
 
 class SDModelItem(BaseModel):
     title: str = Field(title="Title")
@@ -211,21 +211,21 @@ class SDModelItem(BaseModel):
 
 class HypernetworkItem(BaseModel):
     name: str = Field(title="Name")
-    path: str | None = Field(title="Path")
+    path: Optional[str] = Field(title="Path")
 
 class FaceRestorerItem(BaseModel):
     name: str = Field(title="Name")
-    cmd_dir: str | None = Field(title="Path")
+    cmd_dir: Optional[str] = Field(title="Path")
 
 class RealesrganItem(BaseModel):
     name: str = Field(title="Name")
-    path: str | None = Field(title="Path")
-    scale: int | None = Field(title="Scale")
+    path: Optional[str] = Field(title="Path")
+    scale: Optional[int] = Field(title="Scale")
 
 class PromptStyleItem(BaseModel):
     name: str = Field(title="Name")
-    prompt: str | None = Field(title="Prompt")
-    negative_prompt: str | None = Field(title="Negative Prompt")
+    prompt: Optional[str] = Field(title="Prompt")
+    negative_prompt: Optional[str] = Field(title="Negative Prompt")
 
 class ArtistItem(BaseModel):
     name: str = Field(title="Name")
-- 
cgit v1.2.3


From e9a5562b9b27a1a4f9c282637b111cefd9727a41 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Sat, 5 Nov 2022 04:06:51 -0500
Subject: add support for tls (gradio tls options)

---
 modules/shared.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 962115f6..7a20c3af 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -86,6 +86,9 @@ parser.add_argument("--nowebui", action='store_true', help="use api=True to laun
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
 parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
+parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
+parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
+parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 
 cmd_opts = parser.parse_args()
 restricted_opts = {
-- 
cgit v1.2.3


From 03b08c4a6b0609f24ec789d40100529b92ef0612 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 5 Nov 2022 15:04:48 +0300
Subject: do not die when an extension's repo has no remote

---
 modules/extensions.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/extensions.py b/modules/extensions.py
index 897af96e..8e0977fd 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -34,8 +34,11 @@ class Extension:
         if repo is None or repo.bare:
             self.remote = None
         else:
-            self.remote = next(repo.remote().urls, None)
-            self.status = 'unknown'
+            try:
+                self.remote = next(repo.remote().urls, None)
+                self.status = 'unknown'
+            except Exception:
+                self.remote = None
 
     def list_files(self, subdir, extension):
         from modules import scripts
-- 
cgit v1.2.3


From a170e3d22231e145f42bb878a76ae5f76fdca230 Mon Sep 17 00:00:00 2001
From: Evgeniy <github@printerror.xyz>
Date: Sat, 5 Nov 2022 17:06:56 +0300
Subject: Python 3.8 typing compatibility

Solves problems with

```Traceback (most recent call last):
  File "webui.py", line 201, in <module>
    webui()
  File "webui.py", line 178, in webui
    create_api(app)
  File "webui.py", line 117, in create_api
    from modules.api.api import Api
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\api.py", line 9, in <module>
    from modules.api.models import *
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 194, in <module>
    class SamplerItem(BaseModel):
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 196, in SamplerItem
    aliases: list[str] = Field(title="Aliases")
TypeError: 'type' object is not subscriptable```

and

```Traceback (most recent call last):
  File "webui.py", line 201, in <module>
    webui()
  File "webui.py", line 178, in webui
    create_api(app)
  File "webui.py", line 117, in create_api
    from modules.api.api import Api
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\api.py", line 9, in <module>
    from modules.api.models import *
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 194, in <module>
    class SamplerItem(BaseModel):
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 197, in SamplerItem
    options: dict[str, str] = Field(title="Options")
TypeError: 'type' object is not subscriptable```
---
 modules/api/models.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index a44c5ddd..f89da1ff 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -5,7 +5,7 @@ from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 from modules.shared import sd_upscalers, opts, parser
-from typing import List
+from typing import Dict, List
 
 API_NOT_ALLOWED = [
     "self",
@@ -193,8 +193,8 @@ FlagsModel = create_model("Flags", **flags)
 
 class SamplerItem(BaseModel):
     name: str = Field(title="Name")
-    aliases: list[str] = Field(title="Aliases")
-    options: dict[str, str] = Field(title="Options")
+    aliases: List[str] = Field(title="Aliases")
+    options: Dict[str, str] = Field(title="Options")
 
 class UpscalerItem(BaseModel):
     name: str = Field(title="Name")
@@ -230,4 +230,4 @@ class PromptStyleItem(BaseModel):
 class ArtistItem(BaseModel):
     name: str = Field(title="Name")
     score: float = Field(title="Score")
-    category: str = Field(title="Category")
\ No newline at end of file
+    category: str = Field(title="Category")
-- 
cgit v1.2.3


From 62e3d71aa778928d63cab81d9d8cde33e55bebb3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 5 Nov 2022 17:09:42 +0300
Subject: rework the code to not use the walrus operator because colab's 3.7
 does not support it

---
 modules/hypernetworks/hypernetwork.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 5ceed6ee..7f182712 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -429,13 +429,16 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
+
     # Here we use optimizer from saved HN, or we can specify as UI option.
-    if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict:
+    if hypernetwork.optimizer_name in optimizer_dict:
         optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
+        optimizer_name = hypernetwork.optimizer_name
     else:
-        print(f"Optimizer type {optimizer_name} is not defined!")
+        print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
         optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
         optimizer_name = 'AdamW'
+
     if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
         try:
             optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
-- 
cgit v1.2.3


From 159475e072f2ed3db8235aab9c3fa18640b93b80 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 5 Nov 2022 18:32:22 +0300
Subject: tweak names a bit for new samplers

---
 modules/sd_samplers.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 1e88f7ee..783992d2 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -24,15 +24,15 @@ samplers_k_diffusion = [
     ('Heun', 'sample_heun', ['k_heun'], {}),
     ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
-    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
-    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
     ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
-    ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
-    ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.3


From 99b05addb1c98169d78957f13efef308aef0af94 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <breyxxi@gmail.com>
Date: Sat, 5 Nov 2022 18:46:47 -0300
Subject: Fix options endpoint not showing the full list of options

---
 modules/api/models.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/api/models.py b/modules/api/models.py
index f89da1ff..0ea62155 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -168,9 +168,9 @@ class ProgressResponse(BaseModel):
     current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
 
 fields = {}
-for key, value in opts.data.items():
-    metadata = opts.data_labels.get(key)
-    optType = opts.typemap.get(type(value), type(value))
+for key, metadata in opts.data_labels.items():
+    value = opts.data.get(key)
+    optType = opts.typemap.get(type(metadata.default), type(value))
 
     if (metadata is not None):
         fields.update({key: (Optional[optType], Field(
-- 
cgit v1.2.3


From 0ebf66b575f008a027097946eb2f6845feffd010 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <breyxxi@gmail.com>
Date: Sat, 5 Nov 2022 18:58:19 -0300
Subject: Fix set config endpoint

---
 modules/api/api.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 112000b8..a924c83a 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -230,14 +230,10 @@ class Api:
 
         return options
 
-    def set_config(self, req: OptionsModel):
-        # currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will
-        # overwrite all options with default values.
-        raise RuntimeError('Setting options via API is not supported')
-
-        reqDict = vars(req)
-        for o in reqDict:
-            setattr(shared.opts, o, reqDict[o])
+    def set_config(self, req: Dict[str, Any]):
+       
+        for o in req:
+            setattr(shared.opts, o, req[o])
 
         shared.opts.save(shared.config_filename)
         return
-- 
cgit v1.2.3


From 3c72055c22425dcde0739b5246e3501f4a3ec794 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <breyxxi@gmail.com>
Date: Sat, 5 Nov 2022 19:05:15 -0300
Subject: Add skip endpoint

---
 modules/api/api.py | 6 ++++++
 1 file changed, 6 insertions(+)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index a924c83a..c7ceb787 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -64,6 +64,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
         self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
         self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
         self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
@@ -219,6 +220,11 @@ class Api:
 
         return {}
 
+    def skip(self):
+        shared.state.skip()
+
+        return
+
     def get_config(self):
         options = {}
         for key in shared.opts.data.keys():
-- 
cgit v1.2.3


From 7f63980e479c7ffaec907fb659b5024e96eb72e7 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <breyxxi@gmail.com>
Date: Sat, 5 Nov 2022 19:09:13 -0300
Subject: Remove unnecesary return

---
 modules/api/api.py | 2 --
 1 file changed, 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index c7ceb787..33e6c6dc 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -223,8 +223,6 @@ class Api:
     def skip(self):
         shared.state.skip()
 
-        return
-
     def get_config(self):
         options = {}
         for key in shared.opts.data.keys():
-- 
cgit v1.2.3


From 6603f63b7b8af39ab815091460c5c2a12d3f253e Mon Sep 17 00:00:00 2001
From: Han Lin <hotdogee@gmail.com>
Date: Sun, 6 Nov 2022 11:08:20 +0800
Subject: Fixes LDSR upscaler producing black bars

---
 modules/ldsr_model_arch.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
index 14db5076..90e0a2f0 100644
--- a/modules/ldsr_model_arch.py
+++ b/modules/ldsr_model_arch.py
@@ -101,8 +101,8 @@ class LDSR:
         down_sample_rate = target_scale / 4
         wd = width_og * down_sample_rate
         hd = height_og * down_sample_rate
-        width_downsampled_pre = int(wd)
-        height_downsampled_pre = int(hd)
+        width_downsampled_pre = int(np.ceil(wd))
+        height_downsampled_pre = int(np.ceil(hd))
 
         if down_sample_rate != 1:
             print(
@@ -110,7 +110,12 @@ class LDSR:
             im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
         else:
             print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
-        logs = self.run(model["model"], im_og, diffusion_steps, eta)
+        
+        # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
+        pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
+        im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
+        
+        logs = self.run(model["model"], im_padded, diffusion_steps, eta)
 
         sample = logs["sample"]
         sample = sample.detach().cpu()
@@ -120,6 +125,9 @@ class LDSR:
         sample = np.transpose(sample, (0, 2, 3, 1))
         a = Image.fromarray(sample[0])
 
+        # remove padding
+        a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
+
         del model
         gc.collect()
         torch.cuda.empty_cache()
-- 
cgit v1.2.3


From a2a1a2f7270a865175f64475229838a8d64509ea Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 6 Nov 2022 09:02:25 +0300
Subject: add ability to create extensions that add localizations

---
 modules/localization.py | 6 ++++++
 modules/scripts.py      | 1 -
 modules/shared.py       | 2 --
 modules/ui.py           | 3 +--
 4 files changed, 7 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/localization.py b/modules/localization.py
index b1810cda..f6a6f2fb 100644
--- a/modules/localization.py
+++ b/modules/localization.py
@@ -3,6 +3,7 @@ import os
 import sys
 import traceback
 
+
 localizations = {}
 
 
@@ -16,6 +17,11 @@ def list_localizations(dirname):
 
         localizations[fn] = os.path.join(dirname, file)
 
+    from modules import scripts
+    for file in scripts.list_scripts("localizations", ".json"):
+        fn, ext = os.path.splitext(file.filename)
+        localizations[fn] = file.path
+
 
 def localization_js(current_localization_name):
     fn = localizations.get(current_localization_name, None)
diff --git a/modules/scripts.py b/modules/scripts.py
index 366c90d7..637b2329 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -3,7 +3,6 @@ import sys
 import traceback
 from collections import namedtuple
 
-import modules.ui as ui
 import gradio as gr
 
 from modules.processing import StableDiffusionProcessing
diff --git a/modules/shared.py b/modules/shared.py
index 70b998ff..e8bacd3c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -221,8 +221,6 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
 
-localization.list_localizations(cmd_opts.localizations_dir)
-
 
 def realesrgan_models_names():
     import modules.realesrgan_model
diff --git a/modules/ui.py b/modules/ui.py
index 76ca9b07..23643c22 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1563,11 +1563,10 @@ def create_ui(wrap_gradio_gpu_call):
             shared.state.need_restart = True
 
         restart_gradio.click(
-
             fn=request_restart,
+            _js='restart_reload',
             inputs=[],
             outputs=[],
-            _js='restart_reload'
         )
 
         if column is not None:
-- 
cgit v1.2.3


From e5b4e3f820cd09e751f1d168ab05d606d078a0d9 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 6 Nov 2022 10:12:53 +0300
Subject: add tags to extensions, and ability to filter out tags list changed
 Settings keys in UI do not print VRAM/etc stats everywhere but in calls that
 use GPU

---
 modules/ui.py            | 25 ++++++++++++----------
 modules/ui_extensions.py | 55 ++++++++++++++++++++++++++++++++++++++----------
 2 files changed, 58 insertions(+), 22 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 23643c22..c946ad59 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -174,9 +174,9 @@ def save_pil_to_file(pil_image, dir=None):
 gr.processing_utils.save_pil_to_file = save_pil_to_file
 
 
-def wrap_gradio_call(func, extra_outputs=None):
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
     def f(*args, extra_outputs_array=extra_outputs, **kwargs):
-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
+        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
         if run_memmon:
             shared.mem_mon.monitor()
         t = time.perf_counter()
@@ -203,11 +203,18 @@ def wrap_gradio_call(func, extra_outputs=None):
 
             res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
 
+        shared.state.skipped = False
+        shared.state.interrupted = False
+        shared.state.job_count = 0
+
+        if not add_stats:
+            return tuple(res)
+
         elapsed = time.perf_counter() - t
         elapsed_m = int(elapsed // 60)
         elapsed_s = elapsed % 60
         elapsed_text = f"{elapsed_s:.2f}s"
-        if (elapsed_m > 0):
+        if elapsed_m > 0:
             elapsed_text = f"{elapsed_m}m "+elapsed_text
 
         if run_memmon:
@@ -225,10 +232,6 @@ def wrap_gradio_call(func, extra_outputs=None):
         # last item is always HTML
         res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
 
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.job_count = 0
-
         return tuple(res)
 
     return f
@@ -1436,7 +1439,7 @@ def create_ui(wrap_gradio_gpu_call):
     opts.reorder()
 
     def run_settings(*args):
-        changed = 0
+        changed = []
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
             assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
@@ -1454,12 +1457,12 @@ def create_ui(wrap_gradio_gpu_call):
                 if opts.data_labels[key].onchange is not None:
                     opts.data_labels[key].onchange()
 
-                changed += 1
+                changed.append(key)
         try:
             opts.save(shared.config_filename)
         except RuntimeError:
-            return opts.dumpjson(), f'{changed} settings changed without save.'
-        return opts.dumpjson(), f'{changed} settings changed.'
+            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
 
     def run_settings_single(value, key):
         if not opts.same_type(value, opts.data_labels[key].default):
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 8e0d41d5..02ab9643 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -140,13 +140,15 @@ def install_extension_from_url(dirname, url):
         shutil.rmtree(tmpdir, True)
 
 
-def install_extension_from_index(url):
+def install_extension_from_index(url, hide_tags):
     ext_table, message = install_extension_from_url(None, url)
 
-    return refresh_available_extensions_from_data(), ext_table, message
+    code, _ = refresh_available_extensions_from_data(hide_tags)
 
+    return code, ext_table, message
 
-def refresh_available_extensions(url):
+
+def refresh_available_extensions(url, hide_tags):
     global available_extensions
 
     import urllib.request
@@ -155,13 +157,25 @@ def refresh_available_extensions(url):
 
     available_extensions = json.loads(text)
 
-    return url, refresh_available_extensions_from_data(), ''
+    code, tags = refresh_available_extensions_from_data(hide_tags)
+
+    return url, code, gr.CheckboxGroup.update(choices=tags), ''
+
+
+def refresh_available_extensions_for_tags(hide_tags):
+    code, _ = refresh_available_extensions_from_data(hide_tags)
 
+    return code, ''
 
-def refresh_available_extensions_from_data():
+
+def refresh_available_extensions_from_data(hide_tags):
     extlist = available_extensions["extensions"]
     installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
 
+    tags = available_extensions.get("tags", {})
+    tags_to_hide = set(hide_tags)
+    hidden = 0
+
     code = f"""<!-- {time.time()} -->
     <table id="available_extensions">
         <thead>
@@ -178,17 +192,24 @@ def refresh_available_extensions_from_data():
         name = ext.get("name", "noname")
         url = ext.get("url", None)
         description = ext.get("description", "")
+        extension_tags = ext.get("tags", [])
 
         if url is None:
             continue
 
+        if len([x for x in extension_tags if x in tags_to_hide]) > 0:
+            hidden += 1
+            continue
+
         existing = installed_extension_urls.get(normalize_git_url(url), None)
 
         install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
 
+        tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
+
         code += f"""
             <tr>
-                <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a></td>
+                <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a><br />{tags_text}</td>
                 <td>{html.escape(description)}</td>
                 <td>{install_code}</td>
             </tr>
@@ -199,7 +220,10 @@ def refresh_available_extensions_from_data():
     </table>
     """
 
-    return code
+    if hidden > 0:
+        code += f"<p>Extension hidden: {hidden}</p>"
+
+    return code, list(tags)
 
 
 def create_ui():
@@ -238,21 +262,30 @@ def create_ui():
                     extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
                     install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
 
+                with gr.Row():
+                    hide_tags = gr.CheckboxGroup(value=["ads", "localization"], label="Hide extensions with tags", choices=["script", "ads", "localization"])
+
                 install_result = gr.HTML()
                 available_extensions_table = gr.HTML()
 
                 refresh_available_extensions_button.click(
-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update()]),
-                    inputs=[available_extensions_index],
-                    outputs=[available_extensions_index, available_extensions_table, install_result],
+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
+                    inputs=[available_extensions_index, hide_tags],
+                    outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result],
                 )
 
                 install_extension_button.click(
                     fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
-                    inputs=[extension_to_install],
+                    inputs=[extension_to_install, hide_tags],
                     outputs=[available_extensions_table, extensions_table, install_result],
                 )
 
+                hide_tags.change(
+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
+                    inputs=[hide_tags],
+                    outputs=[available_extensions_table, install_result]
+                )
+
             with gr.TabItem("Install from URL"):
                 install_url = gr.Text(label="URL for extension's git repository")
                 install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
-- 
cgit v1.2.3


From 6e4de5b4422dfc0d45063b2c8c78b19f00321615 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 6 Nov 2022 11:20:23 +0300
Subject: add load_with_extra function for modules to load checkpoints with
 extended whitelist

---
 modules/safe.py | 40 +++++++++++++++++++++++++++++++++++++---
 1 file changed, 37 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/safe.py b/modules/safe.py
index 348a24fc..a9209e38 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -23,11 +23,18 @@ def encode(*args):
 
 
 class RestrictedUnpickler(pickle.Unpickler):
+    extra_handler = None
+
     def persistent_load(self, saved_id):
         assert saved_id[0] == 'storage'
         return TypedStorage()
 
     def find_class(self, module, name):
+        if self.extra_handler is not None:
+            res = self.extra_handler(module, name)
+            if res is not None:
+                return res
+
         if module == 'collections' and name == 'OrderedDict':
             return getattr(collections, name)
         if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
@@ -52,7 +59,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             return set
 
         # Forbid everything else.
-        raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
+        raise Exception(f"global '{module}/{name}' is forbidden")
 
 
 allowed_zip_names = ["archive/data.pkl", "archive/version"]
@@ -69,7 +76,7 @@ def check_zip_filenames(filename, names):
         raise Exception(f"bad file inside {filename}: {name}")
 
 
-def check_pt(filename):
+def check_pt(filename, extra_handler):
     try:
 
         # new pytorch format is a zip file
@@ -78,6 +85,7 @@ def check_pt(filename):
 
             with z.open('archive/data.pkl') as file:
                 unpickler = RestrictedUnpickler(file)
+                unpickler.extra_handler = extra_handler
                 unpickler.load()
 
     except zipfile.BadZipfile:
@@ -85,16 +93,42 @@ def check_pt(filename):
         # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
         with open(filename, "rb") as file:
             unpickler = RestrictedUnpickler(file)
+            unpickler.extra_handler = extra_handler
             for i in range(5):
                 unpickler.load()
 
 
 def load(filename, *args, **kwargs):
+    return load_with_extra(filename, *args, **kwargs)
+
+
+def load_with_extra(filename, extra_handler=None, *args, **kwargs):
+    """
+    this functon is intended to be used by extensions that want to load models with
+    some extra classes in them that the usual unpickler would find suspicious.
+
+    Use the extra_handler argument to specify a function that takes module and field name as text,
+    and returns that field's value:
+
+    ```python
+    def extra(module, name):
+        if module == 'collections' and name == 'OrderedDict':
+            return collections.OrderedDict
+
+        return None
+
+    safe.load_with_extra('model.pt', extra_handler=extra)
+    ```
+
+    The alternative to this is just to use safe.unsafe_torch_load('model.pt'), which as the name implies is
+    definitely unsafe.
+    """
+
     from modules import shared
 
     try:
         if not shared.cmd_opts.disable_safe_unpickle:
-            check_pt(filename)
+            check_pt(filename, extra_handler)
 
     except pickle.UnpicklingError:
         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
-- 
cgit v1.2.3


From 55ca04095845b41bf66333b3b7343e3ea0babed1 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Sun, 6 Nov 2022 16:31:44 +0800
Subject: Resolve conflict

---
 modules/processing.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 86d015af..db35983b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -422,14 +422,14 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     try:
         for k, v in p.override_settings.items():
-            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model impossible
+            setattr(opts, k, v)  # we don't call onchange for simplicity which makes changing model impossible
             if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet since it is relatively fast to load on-change, while SD models are not
 
         res = process_images_inner(p)
 
     finally:  # restore opts to original state
         for k, v in stored_opts.items():
-            opts.data[k] = v
+            setattr(opts, k, v)
             if k == 'sd_hypernetwork': shared.reload_hypernetworks()
 
     return res
-- 
cgit v1.2.3


From 32c0eab89538ba3900bf499291720f80ae4b43e5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 6 Nov 2022 14:39:41 +0300
Subject: load all settings in one call instead of one by one when the page
 loads

---
 modules/ui.py | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index c946ad59..34c31ef1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1141,7 +1141,7 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[html, generation_info, html2],
         )
 
-    with gr.Blocks() as modelmerger_interface:
+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
                 gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
@@ -1161,7 +1161,7 @@ def create_ui(wrap_gradio_gpu_call):
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
-    with gr.Blocks() as train_interface:
+    with gr.Blocks(analytics_enabled=False) as train_interface:
         with gr.Row().style(equal_height=False):
             gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
 
@@ -1420,15 +1420,14 @@ def create_ui(wrap_gradio_gpu_call):
 
         if info.refresh is not None:
             if is_quicksettings:
-                res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
+                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                 create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
             else:
                 with gr.Row(variant="compact"):
-                    res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
+                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                     create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
         else:
-            res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
-
+            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
 
         return res
 
@@ -1639,6 +1638,17 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[component, text_settings],
             )
 
+        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
+
+        def get_settings_values():
+            return [getattr(opts, key) for key in component_keys]
+
+        demo.load(
+            fn=get_settings_values,
+            inputs=[],
+            outputs=[component_dict[k] for k in component_keys],
+        )
+
         def modelmerger(*args):
             try:
                 results = modules.extras.run_modelmerger(*args)
-- 
cgit v1.2.3


From 67c8e11be74180be19341aebbd6a246c37a79fbb Mon Sep 17 00:00:00 2001
From: snowmeow2 <snowmeow2@example.com>
Date: Mon, 7 Nov 2022 02:32:06 +0800
Subject: Adding DeepDanbooru to the interrogation API

---
 modules/api/api.py    | 16 ++++++++++++++--
 modules/api/models.py |  1 +
 2 files changed, 15 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 688469ad..596a6616 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -15,6 +15,9 @@ from modules.sd_models import checkpoints_list
 from modules.realesrgan_model import get_realesrgan_models
 from typing import List
 
+if shared.cmd_opts.deepdanbooru:
+    from modules.deepbooru import get_deepbooru_tags
+
 def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
@@ -220,11 +223,20 @@ class Api:
         if image_b64 is None:
             raise HTTPException(status_code=404, detail="Image not found") 
 
-        img = self.__base64_to_image(image_b64)
+        img = decode_base64_to_image(image_b64)
+        img = img.convert('RGB')
 
         # Override object param
         with self.queue_lock:
-            processed = shared.interrogator.interrogate(img)
+            if interrogatereq.model == "clip":
+                processed = shared.interrogator.interrogate(img)
+            elif interrogatereq.model == "deepdanbooru":
+                if shared.cmd_opts.deepdanbooru:
+                    processed = get_deepbooru_tags(img)
+                else:
+                    raise HTTPException(status_code=404, detail="Model not found. Add --deepdanbooru when launching for using the model.")
+            else:
+                raise HTTPException(status_code=404, detail="Model not found")
         
         return InterrogateResponse(caption=processed)
 
diff --git a/modules/api/models.py b/modules/api/models.py
index 34dbfa16..f9cd929e 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -170,6 +170,7 @@ class ProgressResponse(BaseModel):
 
 class InterrogateRequest(BaseModel):
     image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+    model: str = Field(default="clip", title="Model", description="The interrogate model used.")
 
 class InterrogateResponse(BaseModel):
     caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
-- 
cgit v1.2.3


From cd6c55c1ab14fcab15329cde599cf79e8d555657 Mon Sep 17 00:00:00 2001
From: pepe10-gpu <pepe.dannyboy@gmail.com>
Date: Sun, 6 Nov 2022 17:05:51 -0800
Subject: 16xx card fix

cudnn
---
 modules/devices.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 7511e1dc..858bf399 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -39,10 +39,13 @@ def torch_gc():
 
 def enable_tf32():
     if torch.cuda.is_available():
+        torch.backends.cudnn.benchmark = True
+        torch.backends.cudnn.enabled = True
         torch.backends.cuda.matmul.allow_tf32 = True
         torch.backends.cudnn.allow_tf32 = True
 
 
+
 errors.run(enable_tf32, "Enabling TF32")
 
 device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
-- 
cgit v1.2.3


From a258fd60dbe2d68325339405a2aa72816d06d2fd Mon Sep 17 00:00:00 2001
From: Keavon Chambers <keavon@keavon.com>
Date: Mon, 7 Nov 2022 00:13:58 -0800
Subject: Add CORS-allow policy launch argument using regex

---
 modules/shared.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index e8bacd3c..55de286d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -81,12 +81,13 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
-parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
+parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
+parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
 parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
-parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origins", default=None)
+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
+parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
 parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
 parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
-- 
cgit v1.2.3


From 9ed4a126bd6421f91bf4a9bdd348b6aef0a378c6 Mon Sep 17 00:00:00 2001
From: kavorite <kavorite@gmail.com>
Date: Mon, 7 Nov 2022 19:58:49 -0500
Subject: add gradio-inpaint-tool; color-sketch

---
 modules/img2img.py | 19 +++++++++++++------
 modules/shared.py  |  1 +
 modules/ui.py      | 11 ++++++++++-
 3 files changed, 24 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index be9f3653..00c6f827 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -59,18 +59,25 @@ def process_batch(p, input_dir, output_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_with_mask_orig, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
     is_inpaint = mode == 1
     is_batch = mode == 2
 
     if is_inpaint:
         # Drawn mask
         if mask_mode == 0:
-            image = init_img_with_mask['image']
-            mask = init_img_with_mask['mask']
-            alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
-            mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
-            image = image.convert('RGB')
+            image = init_img_with_mask
+            is_mask_sketch = isinstance(image, dict)
+            if is_mask_sketch:  
+                # Sketch: mask iff. not transparent
+                image, mask = image["image"], image["mask"]
+                mask = np.array(mask)[..., -1] > 0
+            else:
+                # Color-sketch: mask iff. painted over
+                orig = init_img_with_mask_orig or image
+                mask = np.any(np.array(image) != np.array(orig), axis=-1)
+            mask = Image.fromarray(mask.astype(np.uint8) * 255, "L")
+            image = image.convert("RGB")
         # Uploaded mask
         else:
             image = init_img_inpaint
diff --git a/modules/shared.py b/modules/shared.py
index d8e99f85..325e37d9 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -71,6 +71,7 @@ parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
 parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
+parser.add_argument("--gradio-inpaint-tool", type=str, choices=["sketch", "color-sketch"], default="sketch", help="gradio inpainting editor: can be either sketch to only blur/noise the input, or color-sketch to paint over it")
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
diff --git a/modules/ui.py b/modules/ui.py
index 2609857e..db323e9c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -840,8 +840,17 @@ def create_ui(wrap_gradio_gpu_call):
                         init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
 
                     with gr.TabItem('Inpaint', id='inpaint'):
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
+                        init_img_with_mask_orig = gr.State(None)
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
 
+                        def update_orig(image, state):
+                            if image is not None:
+                                same_size = state is not None and state.size == image.size
+                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+                                edited = same_size and has_exact_match
+                                return image if not edited or state is None else state
+
+                        init_img_with_mask.change(update_orig, [init_img_with_mask, init_img_with_mask_orig], init_img_with_mask_orig)
                         init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
 
-- 
cgit v1.2.3


From 29eff4a194d22f0f0e7a7a976d746a71a4193cf5 Mon Sep 17 00:00:00 2001
From: pepe10-gpu <pepe.dannyboy@gmail.com>
Date: Mon, 7 Nov 2022 18:06:48 -0800
Subject: terrible hack

---
 modules/devices.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 858bf399..4c63f465 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -39,8 +39,15 @@ def torch_gc():
 
 def enable_tf32():
     if torch.cuda.is_available():
-        torch.backends.cudnn.benchmark = True
-        torch.backends.cudnn.enabled = True
+        #TODO: make this better; find a way to check if it is a turing card
+        turing = ["1630","1650","1660","Quadro RTX 3000","Quadro RTX 4000","Quadro RTX 4000","Quadro RTX 5000","Quadro RTX 5000","Quadro RTX 6000","Quadro RTX 6000","Quadro RTX 8000","Quadro RTX T400","Quadro RTX T400","Quadro RTX T600","Quadro RTX T1000","Quadro RTX T1000","2060","2070","2080","Titan RTX","Tesla T4","MX450","MX550"]
+        for devid in range(0,torch.cuda.device_count()):
+            for i in turing:
+                if i in torch.cuda.get_device_name(devid):
+                    shd = True
+        if shd:
+            torch.backends.cudnn.benchmark = True
+            torch.backends.cudnn.enabled = True
         torch.backends.cuda.matmul.allow_tf32 = True
         torch.backends.cudnn.allow_tf32 = True
 
-- 
cgit v1.2.3


From c5334fc56b3d44976425da2e6d0a303ae96836a1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 8 Nov 2022 08:35:01 +0300
Subject: fix javascript duplication bug after pressing the restart UI button

---
 modules/ui.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 34c31ef1..67cf1d6a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1752,7 +1752,7 @@ def create_ui(wrap_gradio_gpu_call):
     return demo
 
 
-def load_javascript(raw_response):
+def reload_javascript():
     with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
         javascript = f'<script>{jsfile.read()}</script>'
 
@@ -1768,7 +1768,7 @@ def load_javascript(raw_response):
     javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
 
     def template_response(*args, **kwargs):
-        res = raw_response(*args, **kwargs)
+        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
         res.body = res.body.replace(
             b'</head>', f'{javascript}</head>'.encode("utf8"))
         res.init_headers()
@@ -1777,4 +1777,5 @@ def load_javascript(raw_response):
     gradio.routes.templates.TemplateResponse = template_response
 
 
-reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)
+if not hasattr(shared, 'GradioTemplateResponseOriginal'):
+    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
-- 
cgit v1.2.3


From 8011be33c36eb7aa9e9498fc714614034e07f67a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 8 Nov 2022 08:37:05 +0300
Subject: move functions out of main body for image preprocessing for easier
 hijacking

---
 modules/textual_inversion/preprocess.py | 162 ++++++++++++++++++--------------
 1 file changed, 93 insertions(+), 69 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index e13b1894..488aa5b5 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -35,6 +35,84 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
             deepbooru.release_process()
 
 
+def listfiles(dirname):
+    return os.listdir(dirname)
+
+
+class PreprocessParams:
+    src = None
+    dstdir = None
+    subindex = 0
+    flip = False
+    process_caption = False
+    process_caption_deepbooru = False
+    preprocess_txt_action = None
+
+
+def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None):
+    caption = ""
+
+    if params.process_caption:
+        caption += shared.interrogator.generate_caption(image)
+
+    if params.process_caption_deepbooru:
+        if len(caption) > 0:
+            caption += ", "
+        caption += deepbooru.get_tags_from_process(image)
+
+    filename_part = params.src
+    filename_part = os.path.splitext(filename_part)[0]
+    filename_part = os.path.basename(filename_part)
+
+    basename = f"{index:05}-{params.subindex}-{filename_part}"
+    image.save(os.path.join(params.dstdir, f"{basename}.png"))
+
+    if params.preprocess_txt_action == 'prepend' and existing_caption:
+        caption = existing_caption + ' ' + caption
+    elif params.preprocess_txt_action == 'append' and existing_caption:
+        caption = caption + ' ' + existing_caption
+    elif params.preprocess_txt_action == 'copy' and existing_caption:
+        caption = existing_caption
+
+    caption = caption.strip()
+
+    if len(caption) > 0:
+        with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
+            file.write(caption)
+
+    params.subindex += 1
+
+
+def save_pic(image, index, params, existing_caption=None):
+    save_pic_with_caption(image, index, params, existing_caption=existing_caption)
+
+    if params.flip:
+        save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption)
+
+
+def split_pic(image, inverse_xy, width, height, overlap_ratio):
+    if inverse_xy:
+        from_w, from_h = image.height, image.width
+        to_w, to_h = height, width
+    else:
+        from_w, from_h = image.width, image.height
+        to_w, to_h = width, height
+    h = from_h * to_w // from_w
+    if inverse_xy:
+        image = image.resize((h, to_w))
+    else:
+        image = image.resize((to_w, h))
+
+    split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
+    y_step = (h - to_h) / (split_count - 1)
+    for i in range(split_count):
+        y = int(y_step * i)
+        if inverse_xy:
+            splitted = image.crop((y, 0, y + to_h, to_w))
+        else:
+            splitted = image.crop((0, y, to_w, y + to_h))
+        yield splitted
+
 
 def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):
     width = process_width
@@ -48,82 +126,28 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
 
     os.makedirs(dst, exist_ok=True)
 
-    files = os.listdir(src)
+    files = listfiles(src)
 
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
 
-    def save_pic_with_caption(image, index, existing_caption=None):
-        caption = ""
-
-        if process_caption:
-            caption += shared.interrogator.generate_caption(image)
-
-        if process_caption_deepbooru:
-            if len(caption) > 0:
-                caption += ", "
-            caption += deepbooru.get_tags_from_process(image)
-
-        filename_part = filename
-        filename_part = os.path.splitext(filename_part)[0]
-        filename_part = os.path.basename(filename_part)
-
-        basename = f"{index:05}-{subindex[0]}-{filename_part}"
-        image.save(os.path.join(dst, f"{basename}.png"))
-
-        if preprocess_txt_action == 'prepend' and existing_caption:
-            caption = existing_caption + ' ' + caption
-        elif preprocess_txt_action == 'append' and existing_caption:
-            caption = caption + ' ' + existing_caption
-        elif preprocess_txt_action == 'copy' and existing_caption:
-            caption = existing_caption
-
-        caption = caption.strip()
-        
-        if len(caption) > 0:
-            with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:
-                file.write(caption)
-
-        subindex[0] += 1
-
-    def save_pic(image, index, existing_caption=None):
-        save_pic_with_caption(image, index, existing_caption=existing_caption)
-
-        if process_flip:
-            save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)
-
-    def split_pic(image, inverse_xy):
-        if inverse_xy:
-            from_w, from_h = image.height, image.width
-            to_w, to_h = height, width
-        else:
-            from_w, from_h = image.width, image.height
-            to_w, to_h = width, height
-        h = from_h * to_w // from_w
-        if inverse_xy:
-            image = image.resize((h, to_w))
-        else:
-            image = image.resize((to_w, h))
-
-        split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
-        y_step = (h - to_h) / (split_count - 1)
-        for i in range(split_count):
-            y = int(y_step * i)
-            if inverse_xy:
-                splitted = image.crop((y, 0, y + to_h, to_w))
-            else:
-                splitted = image.crop((0, y, to_w, y + to_h))
-            yield splitted
-
+    params = PreprocessParams()
+    params.dstdir = dst
+    params.flip = process_flip
+    params.process_caption = process_caption
+    params.process_caption_deepbooru = process_caption_deepbooru
+    params.preprocess_txt_action = preprocess_txt_action
 
     for index, imagefile in enumerate(tqdm.tqdm(files)):
-        subindex = [0]
+        params.subindex = 0
         filename = os.path.join(src, imagefile)
         try:
             img = Image.open(filename).convert("RGB")
         except Exception:
             continue
 
+        params.src = filename
+
         existing_caption = None
         existing_caption_filename = os.path.splitext(filename)[0] + '.txt'
         if os.path.exists(existing_caption_filename):
@@ -143,8 +167,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
         process_default_resize = True
 
         if process_split and ratio < 1.0 and ratio <= split_threshold:
-            for splitted in split_pic(img, inverse_xy):
-                save_pic(splitted, index, existing_caption=existing_caption)
+            for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio):
+                save_pic(splitted, index, params, existing_caption=existing_caption)
             process_default_resize = False
 
         if process_focal_crop and img.height != img.width:
@@ -165,11 +189,11 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
                 dnn_model_path = dnn_model_path,
             )
             for focal in autocrop.crop_image(img, autocrop_settings):
-                save_pic(focal, index, existing_caption=existing_caption)
+                save_pic(focal, index, params, existing_caption=existing_caption)
             process_default_resize = False
 
         if process_default_resize:
             img = images.resize_image(1, img, width, height)
-            save_pic(img, index, existing_caption=existing_caption)
+            save_pic(img, index, params, existing_caption=existing_caption)
 
-        shared.state.nextjob()
\ No newline at end of file
+        shared.state.nextjob()
-- 
cgit v1.2.3


From 1610b3258458025025e9c4faae57d290e4519745 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 8 Nov 2022 08:38:10 +0300
Subject: add callback for creating a tab in train UI

---
 modules/script_callbacks.py | 27 +++++++++++++++++++++++++--
 modules/ui.py               |  4 ++++
 2 files changed, 29 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 74dfb880..f19e164c 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -7,6 +7,7 @@ from typing import Optional
 from fastapi import FastAPI
 from gradio import Blocks
 
+
 def report_exception(c, job):
     print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
     print(traceback.format_exc(), file=sys.stderr)
@@ -45,15 +46,21 @@ class CFGDenoiserParams:
         """Total number of sampling steps planned"""
 
 
+class UiTrainTabParams:
+    def __init__(self, txt2img_preview_params):
+        self.txt2img_preview_params = txt2img_preview_params
+
+
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
 callback_map = dict(
     callbacks_app_started=[],
     callbacks_model_loaded=[],
     callbacks_ui_tabs=[],
+    callbacks_ui_train_tabs=[],
     callbacks_ui_settings=[],
     callbacks_before_image_saved=[],
     callbacks_image_saved=[],
-    callbacks_cfg_denoiser=[]
+    callbacks_cfg_denoiser=[],
 )
 
 
@@ -61,6 +68,7 @@ def clear_callbacks():
     for callback_list in callback_map.values():
         callback_list.clear()
 
+
 def app_started_callback(demo: Optional[Blocks], app: FastAPI):
     for c in callback_map['callbacks_app_started']:
         try:
@@ -79,7 +87,7 @@ def model_loaded_callback(sd_model):
 
 def ui_tabs_callback():
     res = []
-    
+
     for c in callback_map['callbacks_ui_tabs']:
         try:
             res += c.callback() or []
@@ -89,6 +97,14 @@ def ui_tabs_callback():
     return res
 
 
+def ui_train_tabs_callback(params: UiTrainTabParams):
+    for c in callback_map['callbacks_ui_train_tabs']:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'callbacks_ui_train_tabs')
+
+
 def ui_settings_callback():
     for c in callback_map['callbacks_ui_settings']:
         try:
@@ -169,6 +185,13 @@ def on_ui_tabs(callback):
     add_callback(callback_map['callbacks_ui_tabs'], callback)
 
 
+def on_ui_train_tabs(callback):
+    """register a function to be called when the UI is creating new tabs for the train tab.
+    Create your new tabs with gr.Tab.
+    """
+    add_callback(callback_map['callbacks_ui_train_tabs'], callback)
+
+
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
diff --git a/modules/ui.py b/modules/ui.py
index 67cf1d6a..7ea1177f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1270,6 +1270,10 @@ def create_ui(wrap_gradio_gpu_call):
                         train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
                         train_embedding = gr.Button(value="Train Embedding", variant='primary')
 
+                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+                script_callbacks.ui_train_tabs_callback(params)
+
             with gr.Column():
                 progressbar = gr.HTML(elem_id="ti_progressbar")
                 ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-- 
cgit v1.2.3


From c34542a48376e4972de955aab00ffc8359f7d792 Mon Sep 17 00:00:00 2001
From: kavorite <kavorite@gmail.com>
Date: Tue, 8 Nov 2022 03:25:59 -0500
Subject: add new color-sketch state to img2img invocation

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index db323e9c..29954f2a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -941,6 +941,7 @@ def create_ui(wrap_gradio_gpu_call):
                     img2img_prompt_style2,
                     init_img,
                     init_img_with_mask,
+                    init_img_with_mask_orig,
                     init_img_inpaint,
                     init_mask_inpaint,
                     mask_mode,
-- 
cgit v1.2.3


From cfcadeae9a61e1aff32960864f90299412c86d5c Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Tue, 8 Nov 2022 10:03:56 -0600
Subject: Add option to preload extensions

By creating a file called "preload.py" in an extension folder and declaring a preload(parser) method, we can add extra command-line args for an extension.
---
 modules/extensions.py | 23 ++++++++++++++++++++++-
 modules/shared.py     |  5 ++++-
 2 files changed, 26 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/extensions.py b/modules/extensions.py
index 8e0977fd..544f3580 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -1,12 +1,12 @@
 import os
 import sys
 import traceback
+from importlib.machinery import SourceFileLoader
 
 import git
 
 from modules import paths, shared
 
-
 extensions = []
 extensions_dir = os.path.join(paths.script_path, "extensions")
 
@@ -84,3 +84,24 @@ def list_extensions():
 
         extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions)
         extensions.append(extension)
+
+
+def preload_extensions(parser):
+    if not os.path.isdir(extensions_dir):
+        return
+
+    for dirname in sorted(os.listdir(extensions_dir)):
+        path = os.path.join(extensions_dir, dirname)
+        if not os.path.isdir(path):
+            continue
+        for file in os.listdir(path):
+            if "preload.py" in file:
+                full_file = os.path.join(path, file)
+                print(f"Got preload file: {full_file}")
+
+                try:
+                    ext = SourceFileLoader("preload", full_file).load_module()
+                    parser = ext.preload(parser)
+                except Exception as e:
+                    print(f"Exception preloading script: {e}")
+    return parser
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index e8bacd3c..222ad4fb 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -15,7 +15,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models, localization, sd_vae
+from modules import sd_samplers, sd_models, localization, sd_vae, extensions
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -91,7 +91,10 @@ parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requ
 parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 
+extensions.preload_extensions(parser)
+
 cmd_opts = parser.parse_args()
+
 restricted_opts = {
     "samples_filename_pattern",
     "directories_filename_pattern",
-- 
cgit v1.2.3


From 62e9fec3df8518da3a2c35fa090bb54946c856b2 Mon Sep 17 00:00:00 2001
From: pepe10-gpu <pepe.dannyboy@gmail.com>
Date: Tue, 8 Nov 2022 15:19:09 -0800
Subject: actual better fix

thanks C43H66N12O12S2
---
 modules/devices.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 4c63f465..058a5e00 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -39,12 +39,9 @@ def torch_gc():
 
 def enable_tf32():
     if torch.cuda.is_available():
-        #TODO: make this better; find a way to check if it is a turing card
-        turing = ["1630","1650","1660","Quadro RTX 3000","Quadro RTX 4000","Quadro RTX 4000","Quadro RTX 5000","Quadro RTX 5000","Quadro RTX 6000","Quadro RTX 6000","Quadro RTX 8000","Quadro RTX T400","Quadro RTX T400","Quadro RTX T600","Quadro RTX T1000","Quadro RTX T1000","2060","2070","2080","Titan RTX","Tesla T4","MX450","MX550"]
         for devid in range(0,torch.cuda.device_count()):
-            for i in turing:
-                if i in torch.cuda.get_device_name(devid):
-                    shd = True
+            if torch.cuda.get_device_capability(devid) == (7, 5):
+                shd = True
         if shd:
             torch.backends.cudnn.benchmark = True
             torch.backends.cudnn.enabled = True
-- 
cgit v1.2.3


From 59bb1d36ea69db449cfe23be4988ab4f6711bf4b Mon Sep 17 00:00:00 2001
From: kavorite <kavorite@gmail.com>
Date: Tue, 8 Nov 2022 22:06:29 -0500
Subject: blur mask with color-sketch + add paint transparency slider

---
 modules/img2img.py | 21 +++++++++++++--------
 modules/ui.py      |  3 +++
 2 files changed, 16 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index 00c6f827..644297da 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -4,7 +4,7 @@ import sys
 import traceback
 
 import numpy as np
-from PIL import Image, ImageOps, ImageChops
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance
 
 from modules import devices
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
@@ -40,7 +40,7 @@ def process_batch(p, input_dir, output_dir, args):
 
         img = Image.open(image)
         # Use the EXIF orientation of photos taken by smartphones.
-        img = ImageOps.exif_transpose(img) 
+        img = ImageOps.exif_transpose(img)
         p.init_images = [img] * p.batch_size
 
         proc = modules.scripts.scripts_img2img.run(p, *args)
@@ -59,7 +59,7 @@ def process_batch(p, input_dir, output_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_with_mask_orig, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_with_mask_orig, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
     is_inpaint = mode == 1
     is_batch = mode == 2
 
@@ -68,15 +68,20 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         if mask_mode == 0:
             image = init_img_with_mask
             is_mask_sketch = isinstance(image, dict)
-            if is_mask_sketch:  
+            is_mask_paint = not is_mask_sketch
+            if is_mask_sketch:
                 # Sketch: mask iff. not transparent
                 image, mask = image["image"], image["mask"]
-                mask = np.array(mask)[..., -1] > 0
+                pred = np.array(mask)[..., -1] > 0
             else:
                 # Color-sketch: mask iff. painted over
                 orig = init_img_with_mask_orig or image
-                mask = np.any(np.array(image) != np.array(orig), axis=-1)
-            mask = Image.fromarray(mask.astype(np.uint8) * 255, "L")
+                pred = np.any(np.array(image) != np.array(orig), axis=-1)
+            mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
+            if is_mask_paint:
+                mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
+                blur = ImageFilter.GaussianBlur(mask_blur)
+                image = Image.composite(image.filter(blur), orig, mask.filter(blur))
             image = image.convert("RGB")
         # Uploaded mask
         else:
@@ -89,7 +94,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
 
     # Use the EXIF orientation of photos taken by smartphones.
     if image is not None:
-        image = ImageOps.exif_transpose(image) 
+        image = ImageOps.exif_transpose(image)
 
     assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
 
diff --git a/modules/ui.py b/modules/ui.py
index 29954f2a..16982abf 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -854,6 +854,8 @@ def create_ui(wrap_gradio_gpu_call):
                         init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
 
+                        show_mask_alpha = cmd_opts.gradio_inpaint_tool == "color-sketch"
+                        mask_alpha = gr.Slider(label="Mask transparency", interactive=show_mask_alpha, visible=show_mask_alpha)
                         mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
 
                         with gr.Row():
@@ -948,6 +950,7 @@ def create_ui(wrap_gradio_gpu_call):
                     steps,
                     sampler_index,
                     mask_blur,
+                    mask_alpha,
                     inpainting_fill,
                     restore_faces,
                     tiling,
-- 
cgit v1.2.3


From 3b51d239ac9201228c6032fc109111e347e8e6b0 Mon Sep 17 00:00:00 2001
From: cluder <1590330+cluder@users.noreply.github.com>
Date: Wed, 9 Nov 2022 04:54:21 +0100
Subject: - do not use ckpt cache, if disabled - cache model after is has been
 loaded from file

---
 modules/sd_models.py | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 34c57bfa..720c2a96 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -163,13 +163,21 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
+    cache_enabled = shared.opts.sd_checkpoint_cache > 0
+
+    if cache_enabled:
         sd_vae.restore_base_vae(model)
-        checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
 
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
 
-    if checkpoint_info not in checkpoints_loaded:
+    if cache_enabled and checkpoint_info in checkpoints_loaded:
+        # use checkpoint cache
+        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
+        vae_message = f" with {vae_name} VAE" if vae_name else ""
+        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
+        model.load_state_dict(checkpoints_loaded[checkpoint_info])
+    else:
+        # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
         pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
@@ -180,6 +188,10 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         del pl_sd
         model.load_state_dict(sd, strict=False)
         del sd
+        
+        if cache_enabled:
+            # cache newly loaded model
+            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)
@@ -199,13 +211,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 
         model.first_stage_model.to(devices.dtype_vae)
 
-    else:
-        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
-        vae_message = f" with {vae_name} VAE" if vae_name else ""
-        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
-        model.load_state_dict(checkpoints_loaded[checkpoint_info])
-
-    if shared.opts.sd_checkpoint_cache > 0:
+    # clean up cache if limit is reached
+    if cache_enabled:
         while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
             checkpoints_loaded.popitem(last=False)  # LRU
 
-- 
cgit v1.2.3


From eebf49592ad2c0933e58b06a098b92e48d47e4fe Mon Sep 17 00:00:00 2001
From: cluder <1590330+cluder@users.noreply.github.com>
Date: Wed, 9 Nov 2022 07:17:09 +0100
Subject: restore #4035 behavior

- if checkpoint cache is set to 1, keep 2 models in cache (current +1 more)
---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 720c2a96..80addf03 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -213,7 +213,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 
     # clean up cache if limit is reached
     if cache_enabled:
-        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1: # we need to count the current model
             checkpoints_loaded.popitem(last=False)  # LRU
 
     model.sd_model_hash = sd_model_hash
-- 
cgit v1.2.3


From 81f2575df91a50e4aa9ca816e02e3f77342eedc8 Mon Sep 17 00:00:00 2001
From: Liam <liamthekerr@gmail.com>
Date: Wed, 9 Nov 2022 15:24:31 -0500
Subject: updating the displayed generation info when user clicks images in the
 gallery. feature request 4415

---
 modules/ui.py | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 7ea1177f..756499d1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -566,6 +566,17 @@ def apply_setting(key, value):
     return value
 
 
+def update_generation_info(args):
+    generation_info, html_info, img_index = args
+    try:
+        generation_info = json.loads(generation_info)
+        return plaintext_to_html(generation_info["infotexts"][img_index])
+    except Exception:
+        pass
+    # if the json parse or anything else fails, just return the old html_info
+    return html_info
+
+
 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
     def refresh():
         refresh_method()
@@ -638,6 +649,15 @@ Requested path was: {f}
                     with gr.Group():
                         html_info = gr.HTML()
                         generation_info = gr.Textbox(visible=False)
+                        if tabname == 'txt2img' or tabname == 'img2img':
+                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+                            generation_info_button.click(
+                                fn=update_generation_info,
+                                _js="(x, y) => [x, y, selected_gallery_index()]",
+                                inputs=[generation_info, html_info],
+                                outputs=[html_info],
+                                preprocess=False
+                            )
 
                         save.click(
                             fn=wrap_gradio_call(save_files),
-- 
cgit v1.2.3


From 893191cab24cc3511135495d6d2c8d81f5ec63a3 Mon Sep 17 00:00:00 2001
From: Tong Zeng <tong.zeng@geely.com>
Date: Thu, 10 Nov 2022 10:34:03 +0800
Subject: fix a bug in list_files_with_name

---
 modules/scripts.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/scripts.py b/modules/scripts.py
index 637b2329..22d8908b 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -140,7 +140,7 @@ def list_files_with_name(filename):
             continue
 
         path = os.path.join(dirpath, filename)
-        if os.path.isfile(filename):
+        if os.path.isfile(path):
             res.append(path)
 
     return res
-- 
cgit v1.2.3


From 2505f39e28177452a92426f3b60d8edbe6ed1b14 Mon Sep 17 00:00:00 2001
From: JingShing <JingShingLai@gmail.com>
Date: Thu, 10 Nov 2022 20:39:20 +0800
Subject: Add username and password in ngrok.

---
 modules/ngrok.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ngrok.py b/modules/ngrok.py
index 5c5f349a..e506accb 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -1,14 +1,22 @@
 from pyngrok import ngrok, conf, exception
 
-
 def connect(token, port, region):
     if token == None:
         token = 'None'
+    else:
+        if ':' in token:
+            # token = authtoken:username:password
+            account = token.split(':')[1] + ':' + token.split(':')[-1]
+            token = token.split(':')[0]
+
     config = conf.PyngrokConfig(
         auth_token=token, region=region
     )
     try:
-        public_url = ngrok.connect(port, pyngrok_config=config).public_url
+        if account:
+            public_url = ngrok.connect(port, pyngrok_config=config, auth=account).public_url
+        else:
+            public_url = ngrok.connect(port, pyngrok_config=config).public_url
     except exception.PyngrokNgrokError:
         print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
-- 
cgit v1.2.3


From 1a01191e27545e9dae5255d59c920b6da5b236f4 Mon Sep 17 00:00:00 2001
From: JingShing <JingShingLai@gmail.com>
Date: Thu, 10 Nov 2022 20:42:41 +0800
Subject: Add username and password in ngrok.

---
 modules/ngrok.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/ngrok.py b/modules/ngrok.py
index e506accb..10d2179f 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -1,6 +1,7 @@
 from pyngrok import ngrok, conf, exception
 
 def connect(token, port, region):
+    account = None
     if token == None:
         token = 'None'
     else:
@@ -13,10 +14,10 @@ def connect(token, port, region):
         auth_token=token, region=region
     )
     try:
-        if account:
-            public_url = ngrok.connect(port, pyngrok_config=config, auth=account).public_url
-        else:
+        if account == None:
             public_url = ngrok.connect(port, pyngrok_config=config).public_url
+        else:
+            public_url = ngrok.connect(port, pyngrok_config=config, auth=account).public_url
     except exception.PyngrokNgrokError:
         print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
-- 
cgit v1.2.3


From b98740129c435f04a060369bd071fc4bafe021f5 Mon Sep 17 00:00:00 2001
From: Liam <liamthekerr@gmail.com>
Date: Thu, 10 Nov 2022 13:07:41 -0500
Subject: added event listener for the image gallery modal; moved js to
 separate file

---
 modules/ui.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 756499d1..5dce7f3b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -570,6 +570,8 @@ def update_generation_info(args):
     generation_info, html_info, img_index = args
     try:
         generation_info = json.loads(generation_info)
+        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
+            return html_info
         return plaintext_to_html(generation_info["infotexts"][img_index])
     except Exception:
         pass
-- 
cgit v1.2.3


From 6f8a807fe4eb41f6eb355c80fe96cd60b8e8a5a9 Mon Sep 17 00:00:00 2001
From: KyuSeok Jung <wjdrbtjr495@gmail.com>
Date: Fri, 11 Nov 2022 09:22:49 +0900
Subject: Update shared.py

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 89f4d5ee..82da5ce0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -321,7 +321,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
-    "shuffle_tags": OptionInfo(False, "Shuffleing tags by "," when create texts."),
+    "shuffle_tags": OptionInfo(False, "Shuffleing tags by ',' when create texts."),
     "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-- 
cgit v1.2.3


From 13a2f1dca32980339e1fb4d1995cde428db798c5 Mon Sep 17 00:00:00 2001
From: KyuSeok Jung <wjdrbtjr495@gmail.com>
Date: Fri, 11 Nov 2022 10:29:55 +0900
Subject: adding tag drop out option

---
 modules/textual_inversion/dataset.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index df278dc2..a95c7835 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -98,12 +98,12 @@ class PersonalizedBase(Dataset):
     def create_text(self, filename_text):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
+        tags = filename_text.split(',')
+        if shared.opt.tag_drop_out != 0:
+            tags = [t for t in tags if random.random() > shared.opt.tag_drop_out]
         if shared.opts.shuffle_tags:
-            tags = filename_text.split(',')
             random.shuffle(tags)
-            text = text.replace("[filewords]", ','.join(tags))
-        else:
-            text = text.replace("[filewords]", filename_text)
+        text = text.replace("[filewords]", ','.join(tags))
         return text
 
     def __len__(self):
-- 
cgit v1.2.3


From 0959907f87314cbee8a80036ec8ae24c65888f7f Mon Sep 17 00:00:00 2001
From: KyuSeok Jung <wjdrbtjr495@gmail.com>
Date: Fri, 11 Nov 2022 10:31:14 +0900
Subject: adding tag dropout option

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 82da5ce0..f2ea3baa 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -322,6 +322,7 @@ options_templates.update(options_section(('system', "System"), {
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "shuffle_tags": OptionInfo(False, "Shuffleing tags by ',' when create texts."),
+    "tag_drop_out": OptionInfo(0, "Dropout tags when create texts", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.1}),
     "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-- 
cgit v1.2.3


From b19af67d29356f97fea5cccfdfa12583f605243f Mon Sep 17 00:00:00 2001
From: KyuSeok Jung <wjdrbtjr495@gmail.com>
Date: Fri, 11 Nov 2022 10:54:19 +0900
Subject: Update dataset.py

---
 modules/textual_inversion/dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index a95c7835..e2cb8428 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -99,7 +99,7 @@ class PersonalizedBase(Dataset):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
         tags = filename_text.split(',')
-        if shared.opt.tag_drop_out != 0:
+        if shared.opts.tag_drop_out != 0:
             tags = [t for t in tags if random.random() > shared.opt.tag_drop_out]
         if shared.opts.shuffle_tags:
             random.shuffle(tags)
-- 
cgit v1.2.3


From c556d34523e8764bd66bf6a7bf97d06add420020 Mon Sep 17 00:00:00 2001
From: NoCrypt <57245077+NoCrypt@users.noreply.github.com>
Date: Fri, 11 Nov 2022 08:54:51 +0700
Subject: Forcing HTTPS instead of HTTP for ngrok

For security reason.
---
 modules/ngrok.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ngrok.py b/modules/ngrok.py
index 5c5f349a..25c53af8 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -8,7 +8,7 @@ def connect(token, port, region):
         auth_token=token, region=region
     )
     try:
-        public_url = ngrok.connect(port, pyngrok_config=config).public_url
+        public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
     except exception.PyngrokNgrokError:
         print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
-- 
cgit v1.2.3


From a1e271207dfc3e89b1286ba41d96b459f210c4b2 Mon Sep 17 00:00:00 2001
From: KyuSeok Jung <wjdrbtjr495@gmail.com>
Date: Fri, 11 Nov 2022 10:56:53 +0900
Subject: Update dataset.py

---
 modules/textual_inversion/dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index e2cb8428..eb75c376 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -100,7 +100,7 @@ class PersonalizedBase(Dataset):
         text = text.replace("[name]", self.placeholder_token)
         tags = filename_text.split(',')
         if shared.opts.tag_drop_out != 0:
-            tags = [t for t in tags if random.random() > shared.opt.tag_drop_out]
+            tags = [t for t in tags if random.random() > shared.opts.tag_drop_out]
         if shared.opts.shuffle_tags:
             random.shuffle(tags)
         text = text.replace("[filewords]", ','.join(tags))
-- 
cgit v1.2.3


From 7ba3923d5b494b7756d0b12f33acb3716d830b9a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 11 Nov 2022 18:20:18 +0300
Subject: move DDIM/PLMS fix for OSX out of the file with inpainting code.

---
 modules/sd_hijack.py            | 23 +++++++++++++++++++++++
 modules/sd_hijack_inpainting.py | 18 +-----------------
 2 files changed, 24 insertions(+), 17 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index bc49d235..75b2d22d 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -14,6 +14,8 @@ from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
 
 attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
@@ -406,3 +408,24 @@ def add_circular_option_to_conv_2d():
 
 
 model_hijack = StableDiffusionModelHijack()
+
+
+def register_buffer(self, name, attr):
+    """
+    Fix register buffer bug for Mac OS.
+    """
+
+    if type(attr) == torch.Tensor:
+        if attr.device != devices.device:
+
+            # would this not break cuda when torch adds has_mps() to main version?
+            if getattr(torch, 'has_mps', False):
+                attr = attr.to(device="mps", dtype=torch.float32)
+            else:
+                attr = attr.to(devices.device)
+
+    setattr(self, name, attr)
+
+
+ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
+ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 202b42cf..46714a4f 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -1,5 +1,4 @@
 import torch
-import modules.devices as devices
 
 from einops import repeat
 from omegaconf import ListConfig
@@ -315,20 +314,6 @@ class LatentInpaintDiffusion(LatentDiffusion):
         self.masked_image_key = masked_image_key
         assert self.masked_image_key in concat_keys
         self.concat_keys = concat_keys
-            
-
-# =================================================================================================
-# Fix register buffer bug for Mac OS, Viktor Tabori, viktor.doklist.com/start-here
-# =================================================================================================
-def register_buffer(self, name, attr):
-    if type(attr) == torch.Tensor:
-        optimal_type = devices.get_optimal_device()
-        if attr.device != optimal_type:
-           if getattr(torch, 'has_mps', False):
-               attr = attr.to(device="mps", dtype=torch.float32)
-           else:
-               attr = attr.to(optimal_type)
-    setattr(self, name, attr)
 
 
 def should_hijack_inpainting(checkpoint_info):
@@ -341,8 +326,7 @@ def do_inpainting_hijack():
 
     ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
     ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
-    ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
 
     ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
     ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
-    ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer
+
-- 
cgit v1.2.3


From 76ab31e18898d4c2aacb9725cfbe25b230bff974 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=BA=90=E6=96=87=E9=9B=A8?=
 <41315874+fumiama@users.noreply.github.com>
Date: Sat, 12 Nov 2022 11:02:40 +0800
Subject: Fix wrong mps selection below MasOS 12.3

---
 modules/devices.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 7511e1dc..9a3d29d7 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -3,8 +3,15 @@ import contextlib
 import torch
 from modules import errors
 
-# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
-has_mps = getattr(torch, 'has_mps', False)
+# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
+# check `getattr` and try it for compatibility
+def has_mps() -> bool:
+    if getattr(torch, 'has_mps', False): return False
+    try:
+        torch.zeros(1).to(torch.device("mps"))
+        return True
+    except Exception:
+        return False
 
 cpu = torch.device("cpu")
 
@@ -25,7 +32,7 @@ def get_optimal_device():
         else:
             return torch.device("cuda")
 
-    if has_mps:
+    if has_mps():
         return torch.device("mps")
 
     return cpu
-- 
cgit v1.2.3


From 1130d5df669911a5c67696be90bccca3ecf5f487 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=BA=90=E6=96=87=E9=9B=A8?=
 <41315874+fumiama@users.noreply.github.com>
Date: Sat, 12 Nov 2022 11:09:28 +0800
Subject: Update devices.py

---
 modules/devices.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 9a3d29d7..bd3e4ffb 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -6,7 +6,7 @@ from modules import errors
 # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
 # check `getattr` and try it for compatibility
 def has_mps() -> bool:
-    if getattr(torch, 'has_mps', False): return False
+    if not getattr(torch, 'has_mps', False): return False
     try:
         torch.zeros(1).to(torch.device("mps"))
         return True
-- 
cgit v1.2.3


From c62d17aee36b5f4ca24f9cfa7bf6d7aca0c923f8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 12 Nov 2022 10:00:22 +0300
Subject: use the new devices.has_mps() function in register_buffer for
 DDIM/PLMS fix for OSX

---
 modules/sd_hijack.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 75b2d22d..97979d05 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -418,8 +418,7 @@ def register_buffer(self, name, attr):
     if type(attr) == torch.Tensor:
         if attr.device != devices.device:
 
-            # would this not break cuda when torch adds has_mps() to main version?
-            if getattr(torch, 'has_mps', False):
+            if devices.has_mps():
                 attr = attr.to(device="mps", dtype=torch.float32)
             else:
                 attr = attr.to(devices.device)
-- 
cgit v1.2.3


From 0ab0a50f9ae14bd7ce7ec518323ebd31c7971155 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 12 Nov 2022 10:00:49 +0300
Subject: change formatting to match the main program in devices.py

---
 modules/devices.py | 21 ++++++++++++++++-----
 1 file changed, 16 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index bd3e4ffb..67165bf6 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -3,23 +3,27 @@ import contextlib
 import torch
 from modules import errors
 
+
 # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
 # check `getattr` and try it for compatibility
 def has_mps() -> bool:
-    if not getattr(torch, 'has_mps', False): return False
+    if not getattr(torch, 'has_mps', False):
+        return False
     try:
         torch.zeros(1).to(torch.device("mps"))
         return True
     except Exception:
         return False
 
-cpu = torch.device("cpu")
 
 def extract_device_id(args, name):
     for x in range(len(args)):
-        if name in args[x]: return args[x+1]
+        if name in args[x]:
+            return args[x + 1]
+
     return None
 
+
 def get_optimal_device():
     if torch.cuda.is_available():
         from modules import shared
@@ -52,10 +56,12 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
+cpu = torch.device("cpu")
 device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
 
+
 def randn(seed, shape):
     # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
     if device.type == 'mps':
@@ -89,6 +95,11 @@ def autocast(disable=False):
 
     return torch.autocast("cuda")
 
+
 # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
-def mps_contiguous(input_tensor, device): return input_tensor.contiguous() if device.type == 'mps' else input_tensor
-def mps_contiguous_to(input_tensor, device): return mps_contiguous(input_tensor, device).to(device)
+def mps_contiguous(input_tensor, device):
+    return input_tensor.contiguous() if device.type == 'mps' else input_tensor
+
+
+def mps_contiguous_to(input_tensor, device):
+    return mps_contiguous(input_tensor, device).to(device)
-- 
cgit v1.2.3


From a1a376331c9ecbbee77b86daeaba44587cc56557 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 12 Nov 2022 10:56:06 +0300
Subject: make existing script loading and new preload code use same code for
 loading modules limit extension preload scripts to just one file named
 preload.py

---
 modules/extensions.py     | 21 ---------------------
 modules/script_loading.py | 34 ++++++++++++++++++++++++++++++++++
 modules/scripts.py        | 46 +++++++++++++++++-----------------------------
 modules/shared.py         |  5 ++---
 4 files changed, 53 insertions(+), 53 deletions(-)
 create mode 100644 modules/script_loading.py

(limited to 'modules')

diff --git a/modules/extensions.py b/modules/extensions.py
index 544f3580..94ce479a 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -1,7 +1,6 @@
 import os
 import sys
 import traceback
-from importlib.machinery import SourceFileLoader
 
 import git
 
@@ -85,23 +84,3 @@ def list_extensions():
         extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions)
         extensions.append(extension)
 
-
-def preload_extensions(parser):
-    if not os.path.isdir(extensions_dir):
-        return
-
-    for dirname in sorted(os.listdir(extensions_dir)):
-        path = os.path.join(extensions_dir, dirname)
-        if not os.path.isdir(path):
-            continue
-        for file in os.listdir(path):
-            if "preload.py" in file:
-                full_file = os.path.join(path, file)
-                print(f"Got preload file: {full_file}")
-
-                try:
-                    ext = SourceFileLoader("preload", full_file).load_module()
-                    parser = ext.preload(parser)
-                except Exception as e:
-                    print(f"Exception preloading script: {e}")
-    return parser
\ No newline at end of file
diff --git a/modules/script_loading.py b/modules/script_loading.py
new file mode 100644
index 00000000..f93f0951
--- /dev/null
+++ b/modules/script_loading.py
@@ -0,0 +1,34 @@
+import os
+import sys
+import traceback
+from types import ModuleType
+
+
+def load_module(path):
+    with open(path, "r", encoding="utf8") as file:
+        text = file.read()
+
+    compiled = compile(text, path, 'exec')
+    module = ModuleType(os.path.basename(path))
+    exec(compiled, module.__dict__)
+
+    return module
+
+
+def preload_extensions(extensions_dir, parser):
+    if not os.path.isdir(extensions_dir):
+        return
+
+    for dirname in sorted(os.listdir(extensions_dir)):
+        preload_script = os.path.join(extensions_dir, dirname, "preload.py")
+        if not os.path.isfile(preload_script):
+            continue
+
+        try:
+            module = load_module(preload_script)
+            if hasattr(module, 'preload'):
+                module.preload(parser)
+
+        except Exception:
+            print(f"Error running preload() for {preload_script}", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
diff --git a/modules/scripts.py b/modules/scripts.py
index 22d8908b..986b1914 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -6,7 +6,7 @@ from collections import namedtuple
 import gradio as gr
 
 from modules.processing import StableDiffusionProcessing
-from modules import shared, paths, script_callbacks, extensions
+from modules import shared, paths, script_callbacks, extensions, script_loading
 
 AlwaysVisible = object()
 
@@ -161,13 +161,7 @@ def load_scripts():
                 sys.path = [scriptfile.basedir] + sys.path
             current_basedir = scriptfile.basedir
 
-            with open(scriptfile.path, "r", encoding="utf8") as file:
-                text = file.read()
-
-            from types import ModuleType
-            compiled = compile(text, scriptfile.path, 'exec')
-            module = ModuleType(scriptfile.filename)
-            exec(compiled, module.__dict__)
+            module = script_loading.load_module(scriptfile.path)
 
             for key, script_class in module.__dict__.items():
                 if type(script_class) == type and issubclass(script_class, Script):
@@ -328,27 +322,21 @@ class ScriptRunner:
 
     def reload_sources(self, cache):
         for si, script in list(enumerate(self.scripts)):
-            with open(script.filename, "r", encoding="utf8") as file:
-                args_from = script.args_from
-                args_to = script.args_to
-                filename = script.filename
-                text = file.read()
-
-                from types import ModuleType
-
-                module = cache.get(filename, None)
-                if module is None:
-                    compiled = compile(text, filename, 'exec')
-                    module = ModuleType(script.filename)
-                    exec(compiled, module.__dict__)
-                    cache[filename] = module
-
-                for key, script_class in module.__dict__.items():
-                    if type(script_class) == type and issubclass(script_class, Script):
-                        self.scripts[si] = script_class()
-                        self.scripts[si].filename = filename
-                        self.scripts[si].args_from = args_from
-                        self.scripts[si].args_to = args_to
+            args_from = script.args_from
+            args_to = script.args_to
+            filename = script.filename
+
+            module = cache.get(filename, None)
+            if module is None:
+                module = script_loading.load_module(script.filename)
+                cache[filename] = module
+
+            for key, script_class in module.__dict__.items():
+                if type(script_class) == type and issubclass(script_class, Script):
+                    self.scripts[si] = script_class()
+                    self.scripts[si].filename = filename
+                    self.scripts[si].args_from = args_from
+                    self.scripts[si].args_to = args_to
 
 
 scripts_txt2img = ScriptRunner()
diff --git a/modules/shared.py b/modules/shared.py
index 17132e42..6936cbe0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -3,7 +3,6 @@ import datetime
 import json
 import os
 import sys
-from collections import OrderedDict
 import time
 
 import gradio as gr
@@ -15,7 +14,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models, localization, sd_vae, extensions
+from modules import sd_samplers, sd_models, localization, sd_vae, extensions, script_loading
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -91,7 +90,7 @@ parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requ
 parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 
-extensions.preload_extensions(parser)
+script_loading.preload_extensions(extensions.extensions_dir, parser)
 
 cmd_opts = parser.parse_args()
 
-- 
cgit v1.2.3


From 98947d173e3f1667eba29c904f681047dea9de90 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 12 Nov 2022 11:11:47 +0300
Subject: run installers for newly installed extensions

---
 modules/ui_extensions.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 02ab9643..6671cb60 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -134,6 +134,9 @@ def install_extension_from_url(dirname, url):
 
         os.rename(tmpdir, target_dir)
 
+        import launch
+        launch.run_extension_installer(target_dir)
+
         extensions.list_extensions()
         return [extension_table(), html.escape(f"Installed into {target_dir}. Use Installed tab to restart.")]
     finally:
-- 
cgit v1.2.3


From f4a488f585c09b420dc05199240e68f8fb74337f Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 7 Nov 2022 20:12:31 -0500
Subject: Set device for facelib/facexlib and gfpgan

* FaceXLib/FaceLib doesn't pass the device argument to RetinaFace but instead chooses one itself and sets it to a global - in order to use a device other than its internally chosen default it is necessary to manually replace the default value
* The GFPGAN constructor needs the device argument to work with MPS or a CUDA device ID that differs from the default
---
 modules/codeformer_model.py | 3 +++
 modules/gfpgan_model.py     | 4 +++-
 2 files changed, 6 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index e6d9fa4f..ab40d842 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -36,6 +36,7 @@ def setup_model(dirname):
         from basicsr.utils.download_util import load_file_from_url
         from basicsr.utils import imwrite, img2tensor, tensor2img
         from facelib.utils.face_restoration_helper import FaceRestoreHelper
+        from facelib.detection.retinaface import retinaface
         from modules.shared import cmd_opts
 
         net_class = CodeFormer
@@ -65,6 +66,8 @@ def setup_model(dirname):
                 net.load_state_dict(checkpoint)
                 net.eval()
 
+                if hasattr(retinaface, 'device'):
+                    retinaface.device = devices.device_codeformer
                 face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
 
                 self.net = net
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index a9452dce..1e2dbc32 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -36,7 +36,9 @@ def gfpgann():
     else:
         print("Unable to load gfpgan model!")
         return None
-    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
+    if hasattr(facexlib.detection.retinaface, 'device'):
+        facexlib.detection.retinaface.device = devices.device_gfpgan
+    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
     loaded_gfpgan_model = model
 
     return model
-- 
cgit v1.2.3


From d671d1d45dfab61292ed788fd7778a33a82212ee Mon Sep 17 00:00:00 2001
From: Mrau Hu <mrauhu@yandex.ru>
Date: Sat, 12 Nov 2022 21:44:42 +0300
Subject: Fix: `error: Your local changes to the following files would be
 overwritten by merge` when run `pull()` method, because WSL2 Docker set 755
 file permissions instead of 644, this results to the error.

Updated `Extension` class: replaced `pull()` with `fetch_and_reset_hard()` method.

Updated `apply_and_restart()` function: replaced `ext.pull()` with `ext.fetch_and_reset_hard()` function.
---
 modules/extensions.py    | 7 +++++--
 modules/ui_extensions.py | 4 ++--
 2 files changed, 7 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/extensions.py b/modules/extensions.py
index 94ce479a..db9c4200 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -65,9 +65,12 @@ class Extension:
         self.can_update = False
         self.status = "latest"
 
-    def pull(self):
+    def fetch_and_reset_hard(self):
         repo = git.Repo(self.path)
-        repo.remotes.origin.pull()
+        # Fix: `error: Your local changes to the following files would be overwritten by merge`,
+        # because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
+        repo.git.fetch('--all')
+        repo.git.reset('--hard', 'origin')
 
 
 def list_extensions():
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 6671cb60..030f011e 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -36,9 +36,9 @@ def apply_and_restart(disable_list, update_list):
             continue
 
         try:
-            ext.pull()
+            ext.fetch_and_reset_hard()
         except Exception:
-            print(f"Error pulling updates for {ext.name}:", file=sys.stderr)
+            print(f"Error getting updates for {ext.name}:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
     shared.opts.disabled_extensions = disabled
-- 
cgit v1.2.3


From d20dbe47e06de7f6c0e65242a04c9bb1410ef7cb Mon Sep 17 00:00:00 2001
From: Xu Cuijie <975114697@qq.com>
Date: Sun, 13 Nov 2022 10:31:03 +0800
Subject: fix the model name error of Real-ESRGAN in the opts default value

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 6936cbe0..c46c29f7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -299,7 +299,7 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
 options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN x4+", "R-ESRGAN x4+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
+    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
     "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
-- 
cgit v1.2.3


From 6fa891b934ba854efa87315baffc4ff458ab2539 Mon Sep 17 00:00:00 2001
From: KEV <go2008to@gmail.com>
Date: Mon, 14 Nov 2022 00:25:38 +1000
Subject: Add 'Inpainting strength' to the 'generation_params' dictionary of
 'infotext' which is saved into the 'params.txt' or png chunks.

Value appears only if 'Denoising strength' appears too.
---
 modules/processing.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 03c9143d..01d7cbdc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -399,6 +399,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
+        "Inpainting strength": (None if getattr(p, 'denoising_strength', None) is None else shared.opts.inpainting_mask_weight),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
-- 
cgit v1.2.3


From 671c0e42b4167f4b7ff93e3b96922bf130c12718 Mon Sep 17 00:00:00 2001
From: Ryan Voots <simcop2387@simcop2387.info>
Date: Sun, 13 Nov 2022 13:39:41 -0500
Subject: Fix docker tmp/ and extensions/ handling for docker.  might also work
 for symlinks

---
 modules/ui_extensions.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 6671cb60..95b63f24 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -9,6 +9,8 @@ import git
 
 import gradio as gr
 import html
+import shutil
+import errno
 
 from modules import extensions, shared, paths
 
@@ -132,7 +134,18 @@ def install_extension_from_url(dirname, url):
         repo = git.Repo.clone_from(url, tmpdir)
         repo.remote().fetch()
 
-        os.rename(tmpdir, target_dir)
+        try:
+            os.rename(tmpdir, target_dir)
+        except OSError as err:
+            # TODO what does this do on windows? I think it'll be a different error code but I don't have a system to check it
+            # Shouldn't cause any new issues at least but we probably want to handle it there too.
+            if err.errno == errno.EXDEV:
+                # Cross device link, typical in docker or when tmp/ and extensions/ are on different file systems
+                # Since we can't use a rename, do the slower but more versitile shutil.move()
+                shutil.move(tmpdir, target_dir)
+            else:
+                # Something else, not enough free space, permissions, etc.  rethrow it so that it gets handled.
+                raise(err)
 
         import launch
         launch.run_extension_installer(target_dir)
-- 
cgit v1.2.3


From 9a1aff645a4bea745145c57c96950fbd3fcca27c Mon Sep 17 00:00:00 2001
From: parasi <kingofint22@gmail.com>
Date: Sun, 13 Nov 2022 13:44:27 -0600
Subject: resolve [name] after resolving [filewords] in training

---
 modules/textual_inversion/dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index eb75c376..06f271f9 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -97,13 +97,13 @@ class PersonalizedBase(Dataset):
 
     def create_text(self, filename_text):
         text = random.choice(self.lines)
-        text = text.replace("[name]", self.placeholder_token)
         tags = filename_text.split(',')
         if shared.opts.tag_drop_out != 0:
             tags = [t for t in tags if random.random() > shared.opts.tag_drop_out]
         if shared.opts.shuffle_tags:
             random.shuffle(tags)
         text = text.replace("[filewords]", ','.join(tags))
+        text = text.replace("[name]", self.placeholder_token)
         return text
 
     def __len__(self):
-- 
cgit v1.2.3


From 40ae95d53218b3b8f12fca50b5e4e98a1e50af4b Mon Sep 17 00:00:00 2001
From: KEV <go2008to@gmail.com>
Date: Mon, 14 Nov 2022 18:05:59 +1000
Subject: Fix retrieving value for 'x/y plot' script.

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 01d7cbdc..2fc9fe13 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -399,7 +399,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
-        "Inpainting strength": (None if getattr(p, 'denoising_strength', None) is None else shared.opts.inpainting_mask_weight),
+        "Inpainting strength": (None if getattr(p, 'denoising_strength', None) is None else getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
-- 
cgit v1.2.3


From 3405acc6a4dcef2b73782a04924a9a12422e54f0 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Mon, 14 Nov 2022 14:07:13 -0600
Subject: Give --server-name priority over --listen and add check for
 --server-name in addition to --share and --listen

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 6936cbe0..c628b580 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -106,7 +106,7 @@ restricted_opts = {
     "outdir_save",
 }
 
-cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen) and not cmd_opts.enable_insecure_extension_access
+cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
-- 
cgit v1.2.3


From 8f2ff861d31972d12de278075ea9c0c0deef99de Mon Sep 17 00:00:00 2001
From: Maiko Sinkyaet Tan <maiko.tan.coding@gmail.com>
Date: Tue, 15 Nov 2022 16:12:34 +0800
Subject: feat: add http basic authentication for api

---
 modules/api/api.py | 61 ++++++++++++++++++++++++++++++++++++------------------
 modules/shared.py  |  1 +
 2 files changed, 42 insertions(+), 20 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 596a6616..6bb01603 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -5,6 +5,9 @@ import uvicorn
 from threading import Lock
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, Depends, FastAPI, HTTPException
+from fastapi.security import HTTPBasic, HTTPBasicCredentials
+from secrets import compare_digest
+
 import modules.shared as shared
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -57,29 +60,47 @@ def encode_pil_to_base64(image):
 
 class Api:
     def __init__(self, app: FastAPI, queue_lock: Lock):
+        if shared.cmd_opts.api_auth:
+            self.credenticals = dict()
+            for auth in shared.cmd_opts.api_auth.split(","):
+                user, password = auth.split(":")
+                self.credenticals[user] = password
+
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
-        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
-        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
-        self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
-        self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
-        self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
-        self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
-        self.app.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
-        self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
-        self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
-        self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
-        self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
-        self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
-        self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
-        self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
-        self.app.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
-        self.app.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
-        self.app.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
-        self.app.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
-        self.app.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
-        self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
+        self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
+        self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
+        self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
+        self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
+        self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
+        self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
+        self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
+        self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
+        self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
+        self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
+        self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
+        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
+        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
+        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
+        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
+        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
+        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
+        self.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
+        self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
+        self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
+
+    def add_api_route(self, path: str, endpoint, **kwargs):
+        if shared.cmd_opts.api_auth:
+            return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
+        return self.app.add_api_route(path, endpoint, **kwargs)
+
+    def auth(self, credenticals: HTTPBasicCredentials = Depends(HTTPBasic())):
+        if credenticals.username in self.credenticals:
+            if compare_digest(credenticals.password, self.credenticals[credenticals.username]):
+                return True
+
+        raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
diff --git a/modules/shared.py b/modules/shared.py
index 6936cbe0..62d526fd 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -81,6 +81,7 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
+parser.add_argument("--api-auth", type=str, help='Set authentication for api like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-- 
cgit v1.2.3


From 72b52fbb77360f848cfa296b0c79d2bc0a1060f2 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Wed, 16 Nov 2022 13:08:03 +0900
Subject: add css override

---
 modules/ui.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 5dce7f3b..5e2a992f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -69,8 +69,11 @@ sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
 css_hide_progressbar = """
 .wrap .m-12 svg { display:none!important; }
 .wrap .m-12::before { content:"Loading..." }
+.wrap .z-20 svg { display:none!important; }
+.wrap .z-20::before { content:"Loading..." }
 .progress-bar { display:none!important; }
 .meta-text { display:none!important; }
+.meta-text-center { display:none!important; }
 """
 
 # Using constants for these since the variation selector isn't visible.
-- 
cgit v1.2.3


From 9bbe1e3c2e54f64283bb333ebb648d8f40f5d4ee Mon Sep 17 00:00:00 2001
From: Llewellyn Pritchard <xacc.ide@gmail.com>
Date: Wed, 16 Nov 2022 19:19:00 +0200
Subject: Fix unbounded prompt growth scripts that loop

---
 modules/processing.py | 5 +++++
 1 file changed, 5 insertions(+)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 03c9143d..2fd12288 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -450,6 +450,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     modules.sd_hijack.model_hijack.clear_comments()
 
     comments = {}
+    prompt_tmp = p.prompt
+    negative_prompt_tmp = p.negative_prompt
 
     shared.prompt_styles.apply_styles(p)
 
@@ -596,6 +598,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
 
+    p.prompt = prompt_tmp
+    p.negative_prompt = negative_prompt_tmp
+
     return res
 
 
-- 
cgit v1.2.3


From abfa22c16fb3d9b1ed8d049c7b68e94d1cca5b82 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 7 Nov 2022 19:25:43 -0500
Subject: Revert "MPS Upscalers Fix"

This reverts commit 768b95394a8500da639b947508f78296524f1836.
---
 modules/devices.py      | 9 ---------
 modules/esrgan_model.py | 2 +-
 modules/scunet_model.py | 3 ++-
 modules/swinir_model.py | 2 +-
 4 files changed, 4 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 67165bf6..a87d0d4c 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -94,12 +94,3 @@ def autocast(disable=False):
         return contextlib.nullcontext()
 
     return torch.autocast("cuda")
-
-
-# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
-def mps_contiguous(input_tensor, device):
-    return input_tensor.contiguous() if device.type == 'mps' else input_tensor
-
-
-def mps_contiguous_to(input_tensor, device):
-    return mps_contiguous(input_tensor, device).to(device)
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index c61669b4..9a9c38f1 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -199,7 +199,7 @@ def upscale_without_tiling(model, img):
     img = img[:, :, ::-1]
     img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
-    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan)
+    img = img.unsqueeze(0).to(devices.device_esrgan)
     with torch.no_grad():
         output = model(img)
     output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index 59532274..36a996bf 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -54,8 +54,9 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         img = img[:, :, ::-1]
         img = np.moveaxis(img, 2, 0) / 255
         img = torch.from_numpy(img).float()
-        img = devices.mps_contiguous_to(img.unsqueeze(0), device)
+        img = img.unsqueeze(0).to(device)
 
+        img = img.to(device)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index 4253b66d..facd262d 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -111,7 +111,7 @@ def upscale(
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
-    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_swinir)
+    img = img.unsqueeze(0).to(devices.device_swinir)
     with torch.no_grad(), precision_scope("cuda"):
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old
-- 
cgit v1.2.3


From a5106a7cdc24153332e4eb1d28e66ea1d7f1ef79 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 7 Nov 2022 19:44:27 -0500
Subject: Remove extra .to(device)

---
 modules/scunet_model.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index 36a996bf..52360241 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -56,7 +56,6 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         img = torch.from_numpy(img).float()
         img = img.unsqueeze(0).to(device)
 
-        img = img.to(device)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-- 
cgit v1.2.3


From c8c40c8a643f2d20e3475e4d9ae7aae6d36c7e85 Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Thu, 17 Nov 2022 18:03:57 -0800
Subject: Add interrupt button to preprocessing

---
 modules/textual_inversion/ui.py |  2 +-
 modules/ui.py                   | 10 +++++++++-
 2 files changed, 10 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index d679e6f4..35c4feef 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -18,7 +18,7 @@ def create_embedding(name, initialization_text, nvpt, overwrite_old):
 def preprocess(*args):
     modules.textual_inversion.preprocess.preprocess(*args)
 
-    return "Preprocessing finished.", ""
+    return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
 
 
 def train_embedding(*args):
diff --git a/modules/ui.py b/modules/ui.py
index 5dce7f3b..88e3c827 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1249,7 +1249,9 @@ def create_ui(wrap_gradio_gpu_call):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            run_preprocess = gr.Button(value="Preprocess", variant='primary')
+                            with gr.Row():
+                                interrupt_preprocessing = gr.Button("Interrupt")
+                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                     process_split.change(
                         fn=lambda show: gr_show(show),
@@ -1422,6 +1424,12 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[],
         )
 
+        interrupt_preprocessing.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
     def create_setting_component(key, is_quicksettings=False):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
-- 
cgit v1.2.3


From 17e44328204a09653bb89eea18b7b489cc118703 Mon Sep 17 00:00:00 2001
From: killfrenzy96 <evan298@gmail.com>
Date: Fri, 18 Nov 2022 21:22:55 +1100
Subject: cleanly undo circular hijack #4818

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 97979d05..eaedac13 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -96,8 +96,8 @@ class StableDiffusionModelHijack:
         if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
             model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
 
+        self.apply_circular(False)
         self.layers = None
-        self.circular_enabled = False
         self.clip = None
 
     def apply_circular(self, enable):
-- 
cgit v1.2.3


From 8ab4927452b04dcd30847eaf92ea7a9f3b9c74e1 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Wed, 2 Nov 2022 22:54:09 +0700
Subject: Fix model wasn't restored even when choosing "None"

---
 modules/sd_vae.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 71e7a6e6..7a79239f 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -50,8 +50,8 @@ def delete_base_vae():
 
 
 def restore_base_vae(model):
-    global base_vae, checkpoint_info
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
+        print("Restoring base VAE")
         load_vae_dict(model, base_vae)
     delete_base_vae()
 
@@ -143,6 +143,7 @@ def load_vae(model, vae_file=None):
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
         load_vae_dict(model, vae_dict_1)
+        store_base_vae(model)
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
@@ -150,6 +151,9 @@ def load_vae(model, vae_file=None):
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
             vae_list.append(vae_opt)
+            # shared.opts.data['sd_vae'] = vae_opt
+    else:
+        restore_base_vae(model)
 
     loaded_vae_file = vae_file
 
@@ -166,12 +170,8 @@ def load_vae(model, vae_file=None):
 
 
 # don't call this from outside
-def load_vae_dict(model, vae_dict_1=None):
-    if vae_dict_1:
-        store_base_vae(model)
-        model.first_stage_model.load_state_dict(vae_dict_1)
-    else:
-        restore_base_vae()
+def load_vae_dict(model, vae_dict_1):
+    model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
 
-- 
cgit v1.2.3


From abc1e79a5da24a1ea0f4bceedcdf225f32010aa8 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Thu, 3 Nov 2022 11:10:53 +0700
Subject: Fix base VAE caching was done after loading VAE, also add safeguard

---
 modules/sd_models.py |  1 +
 modules/sd_vae.py    | 19 ++++++++-----------
 2 files changed, 9 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 80addf03..e4dba62c 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -220,6 +220,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    sd_vae.clear_loaded_vae()
     sd_vae.load_vae(model, vae_file)
 
 
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 7a79239f..dd69a5e6 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -15,7 +15,7 @@ vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 
 
-default_vae_dict = {"auto": "auto", "None": "None"}
+default_vae_dict = {"auto": "auto", "None": None, None: None}
 default_vae_list = ["auto", "None"]
 
 
@@ -39,6 +39,7 @@ def get_base_vae(model):
 def store_base_vae(model):
     global base_vae, checkpoint_info
     if checkpoint_info != model.sd_checkpoint_info:
+        assert not loaded_vae_file, "Trying to store non-base VAE!"
         base_vae = model.first_stage_model.state_dict().copy()
         checkpoint_info = model.sd_checkpoint_info
 
@@ -50,9 +51,11 @@ def delete_base_vae():
 
 
 def restore_base_vae(model):
+    global loaded_vae_file
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
         print("Restoring base VAE")
         load_vae_dict(model, base_vae)
+        loaded_vae_file = None
     delete_base_vae()
 
 
@@ -140,10 +143,10 @@ def load_vae(model, vae_file=None):
 
     if vae_file:
         print(f"Loading VAE weights from: {vae_file}")
+        store_base_vae(model)
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
         load_vae_dict(model, vae_dict_1)
-        store_base_vae(model)
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
@@ -157,15 +160,6 @@ def load_vae(model, vae_file=None):
 
     loaded_vae_file = vae_file
 
-    """
-    # Save current VAE to VAE settings, maybe? will it work?
-    if save_settings:
-        if vae_file is None:
-            vae_opt = "None"
-
-        # shared.opts.sd_vae = vae_opt
-    """
-
     first_load = False
 
 
@@ -174,6 +168,9 @@ def load_vae_dict(model, vae_dict_1):
     model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
+def clear_loaded_vae():
+    global loaded_vae_file
+    loaded_vae_file = None
 
 def reload_vae_weights(sd_model=None, vae_file="auto"):
     from modules import lowvram, devices, sd_hijack
-- 
cgit v1.2.3


From c7be83bf0240498d9382e2afeaa3f0677d26c7f6 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 13 Nov 2022 11:11:14 +0700
Subject: Misc

Misc
---
 modules/sd_models.py | 1 +
 modules/sd_vae.py    | 3 +--
 modules/shared.py    | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index e4dba62c..cd7fe37a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -220,6 +220,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
     sd_vae.load_vae(model, vae_file)
 
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index dd69a5e6..13bf3d31 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -154,8 +154,7 @@ def load_vae(model, vae_file=None):
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
             vae_list.append(vae_opt)
-            # shared.opts.data['sd_vae'] = vae_opt
-    else:
+    elif loaded_vae_file:
         restore_base_vae(model)
 
     loaded_vae_file = vae_file
diff --git a/modules/shared.py b/modules/shared.py
index 17132e42..a9daf800 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -335,7 +335,7 @@ options_templates.update(options_section(('training', "Training"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
+    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-- 
cgit v1.2.3


From 9fdc343dcaee70f1a0ff15c0cc668dbd487abc61 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Thu, 17 Nov 2022 18:04:10 +0700
Subject: Fix model caching requiring deepcopy

---
 modules/sd_vae.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 13bf3d31..5b4709b5 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -4,6 +4,7 @@ from collections import namedtuple
 from modules import shared, devices, script_callbacks
 from modules.paths import models_path
 import glob
+from copy import deepcopy
 
 
 model_dir = "Stable-diffusion"
@@ -40,7 +41,7 @@ def store_base_vae(model):
     global base_vae, checkpoint_info
     if checkpoint_info != model.sd_checkpoint_info:
         assert not loaded_vae_file, "Trying to store non-base VAE!"
-        base_vae = model.first_stage_model.state_dict().copy()
+        base_vae = deepcopy(model.first_stage_model.state_dict())
         checkpoint_info = model.sd_checkpoint_info
 
 
-- 
cgit v1.2.3


From 028b67b6357b5a00ccbd6ea72d2f244a6664162b Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 19 Nov 2022 01:27:54 +0700
Subject: Use underscore naming for "private" functions in sd_vae

---
 modules/sd_vae.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 5b4709b5..d82a7bad 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -55,7 +55,7 @@ def restore_base_vae(model):
     global loaded_vae_file
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
         print("Restoring base VAE")
-        load_vae_dict(model, base_vae)
+        _load_vae_dict(model, base_vae)
         loaded_vae_file = None
     delete_base_vae()
 
@@ -147,7 +147,7 @@ def load_vae(model, vae_file=None):
         store_base_vae(model)
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-        load_vae_dict(model, vae_dict_1)
+        _load_vae_dict(model, vae_dict_1)
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
@@ -164,7 +164,7 @@ def load_vae(model, vae_file=None):
 
 
 # don't call this from outside
-def load_vae_dict(model, vae_dict_1):
+def _load_vae_dict(model, vae_dict_1):
     model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
-- 
cgit v1.2.3


From 0663706d4405b4f76ce653097f4f8989ee8b8684 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Thu, 3 Nov 2022 13:47:03 +0700
Subject: Option to use selected VAE as default fallback instead of primary
 option

---
 modules/sd_vae.py | 25 ++++++++++++++++---------
 modules/shared.py |  1 +
 2 files changed, 17 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 71e7a6e6..0b5f0213 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -83,7 +83,19 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     return vae_list
 
 
-def resolve_vae(checkpoint_file, vae_file="auto"):
+def get_vae_from_settings(vae_file="auto"):
+    # else, we load from settings, if not set to be default
+    if vae_file == "auto" and shared.opts.sd_vae is not None:
+        # if saved VAE settings isn't recognized, fallback to auto
+        vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
+        # if VAE selected but not found, fallback to auto
+        if vae_file not in default_vae_values and not os.path.isfile(vae_file):
+            vae_file = "auto"
+            print("Selected VAE doesn't exist")
+    return vae_file
+
+
+def resolve_vae(checkpoint_file=None, vae_file="auto"):
     global first_load, vae_dict, vae_list
 
     # if vae_file argument is provided, it takes priority, but not saved
@@ -98,14 +110,9 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
             shared.opts.data['sd_vae'] = get_filename(vae_file)
         else:
             print("VAE provided as command line argument doesn't exist")
-    # else, we load from settings
-    if vae_file == "auto" and shared.opts.sd_vae is not None:
-        # if saved VAE settings isn't recognized, fallback to auto
-        vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
-        # if VAE selected but not found, fallback to auto
-        if vae_file not in default_vae_values and not os.path.isfile(vae_file):
-            vae_file = "auto"
-            print("Selected VAE doesn't exist")
+    # fallback to selector in settings, if vae selector not set to act as default fallback
+    if not shared.opts.sd_vae_as_default:
+        vae_file = get_vae_from_settings(vae_file)
     # vae-path cmd arg takes priority for auto
     if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
         if os.path.isfile(shared.cmd_opts.vae_path):
diff --git a/modules/shared.py b/modules/shared.py
index 17132e42..b84767f0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -336,6 +336,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
+    "sd_vae_as_default": OptionInfo(False, "Use selected VAE as default fallback instead"),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-- 
cgit v1.2.3


From 2c5ca706a7e624d268545ba3318ba230b7b33477 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 13 Nov 2022 10:55:47 +0700
Subject: Remove no longer necessary parts and add vae_file safeguard

---
 modules/sd_models.py | 10 ++--------
 modules/sd_vae.py    |  1 +
 2 files changed, 3 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 80addf03..c59151e0 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -165,16 +165,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 
     cache_enabled = shared.opts.sd_checkpoint_cache > 0
 
-    if cache_enabled:
-        sd_vae.restore_base_vae(model)
-
-    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
-
     if cache_enabled and checkpoint_info in checkpoints_loaded:
         # use checkpoint cache
-        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
-        vae_message = f" with {vae_name} VAE" if vae_name else ""
-        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
+        print(f"Loading weights [{sd_model_hash}] from cache")
         model.load_state_dict(checkpoints_loaded[checkpoint_info])
     else:
         # load from file
@@ -220,6 +213,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
     sd_vae.load_vae(model, vae_file)
 
 
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 71e7a6e6..8bdb2c17 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -139,6 +139,7 @@ def load_vae(model, vae_file=None):
     # save_settings = False
 
     if vae_file:
+        assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
         print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-- 
cgit v1.2.3


From 271fd2d700a59e80d9dc9f23ad3ef08c988e8b24 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 13 Nov 2022 10:58:15 +0700
Subject: More verbose messages

---
 modules/sd_vae.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 8bdb2c17..fa8de905 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -89,15 +89,15 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
     # if vae_file argument is provided, it takes priority, but not saved
     if vae_file and vae_file not in default_vae_list:
         if not os.path.isfile(vae_file):
+            print(f"VAE provided as function argument doesn't exist: {vae_file}")
             vae_file = "auto"
-            print("VAE provided as function argument doesn't exist")
     # for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
     if first_load and shared.cmd_opts.vae_path is not None:
         if os.path.isfile(shared.cmd_opts.vae_path):
             vae_file = shared.cmd_opts.vae_path
             shared.opts.data['sd_vae'] = get_filename(vae_file)
         else:
-            print("VAE provided as command line argument doesn't exist")
+            print(f"VAE provided as command line argument doesn't exist: {vae_file}")
     # else, we load from settings
     if vae_file == "auto" and shared.opts.sd_vae is not None:
         # if saved VAE settings isn't recognized, fallback to auto
@@ -105,25 +105,25 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
         # if VAE selected but not found, fallback to auto
         if vae_file not in default_vae_values and not os.path.isfile(vae_file):
             vae_file = "auto"
-            print("Selected VAE doesn't exist")
+            print(f"Selected VAE doesn't exist: {vae_file}")
     # vae-path cmd arg takes priority for auto
     if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
         if os.path.isfile(shared.cmd_opts.vae_path):
             vae_file = shared.cmd_opts.vae_path
-            print("Using VAE provided as command line argument")
+            print(f"Using VAE provided as command line argument: {vae_file}")
     # if still not found, try look for ".vae.pt" beside model
     model_path = os.path.splitext(checkpoint_file)[0]
     if vae_file == "auto":
         vae_file_try = model_path + ".vae.pt"
         if os.path.isfile(vae_file_try):
             vae_file = vae_file_try
-            print("Using VAE found beside selected model")
+            print(f"Using VAE found similar to selected model: {vae_file}")
     # if still not found, try look for ".vae.ckpt" beside model
     if vae_file == "auto":
         vae_file_try = model_path + ".vae.ckpt"
         if os.path.isfile(vae_file_try):
             vae_file = vae_file_try
-            print("Using VAE found beside selected model")
+            print(f"Using VAE found similar to selected model: {vae_file}")
     # No more fallbacks for auto
     if vae_file == "auto":
         vae_file = None
-- 
cgit v1.2.3


From c8f7b5cdd73969d3d5027ceb71cbbd83d557702b Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 13 Nov 2022 11:11:14 +0700
Subject: Misc

Misc
---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 17132e42..a9daf800 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -335,7 +335,7 @@ options_templates.update(options_section(('training', "Training"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
+    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-- 
cgit v1.2.3


From d9fd4525a5d684100997130cc4132736bab1e4d9 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 11:09:44 +0300
Subject: change text for sd_vae_as_default that makes more sense to me

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 5528ab15..1c42641d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -335,7 +335,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
-    "sd_vae_as_default": OptionInfo(False, "Use selected VAE as default fallback instead"),
+    "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-- 
cgit v1.2.3


From cdc8020d13c5eef099c609b0a911ccf3568afc0d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 12:01:51 +0300
Subject: change StableDiffusionProcessing to internally use sampler name
 instead of sampler index

---
 modules/api/api.py                             | 26 ++++++++---------------
 modules/hypernetworks/hypernetwork.py          |  4 ++--
 modules/images.py                              |  2 +-
 modules/img2img.py                             |  4 ++--
 modules/processing.py                          | 29 +++++++++++---------------
 modules/sd_samplers.py                         | 13 +++++++++---
 modules/textual_inversion/textual_inversion.py |  4 ++--
 modules/txt2img.py                             |  3 ++-
 modules/ui.py                                  |  2 +-
 9 files changed, 41 insertions(+), 46 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 596a6616..0eccccbb 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -6,9 +6,9 @@ from threading import Lock
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, Depends, FastAPI, HTTPException
 import modules.shared as shared
+from modules import sd_samplers
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
 from PIL import PngImagePlugin
 from modules.sd_models import checkpoints_list
@@ -25,8 +25,12 @@ def upscaler_to_index(name: str):
         raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
 
 
-sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
+def validate_sampler_name(name):
+    config = sd_samplers.all_samplers_map.get(name, None)
+    if config is None:
+        raise HTTPException(status_code=404, detail="Sampler not found")
 
+    return name
 
 def setUpscalers(req: dict):
     reqDict = vars(req)
@@ -82,14 +86,9 @@ class Api:
         self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
-        sampler_index = sampler_to_index(txt2imgreq.sampler_index)
-
-        if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found")
-
         populate = txt2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
-            "sampler_index": sampler_index[0],
+            "sampler_name": validate_sampler_name(txt2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True
             }
@@ -109,12 +108,6 @@ class Api:
         return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
-        sampler_index = sampler_to_index(img2imgreq.sampler_index)
-
-        if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found")
-
-
         init_images = img2imgreq.init_images
         if init_images is None:
             raise HTTPException(status_code=404, detail="Init image not found")
@@ -123,10 +116,9 @@ class Api:
         if mask:
             mask = decode_base64_to_image(mask)
 
-
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
-            "sampler_index": sampler_index[0],
+            "sampler_name": validate_sampler_name(img2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True,
             "mask": mask
@@ -272,7 +264,7 @@ class Api:
         return vars(shared.cmd_opts)
 
     def get_samplers(self):
-        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
+        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
 
     def get_upscalers(self):
         upscalers = []
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7f182712..fbb87dd1 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -12,7 +12,7 @@ import torch
 import tqdm
 from einops import rearrange, repeat
 from ldm.util import default
-from modules import devices, processing, sd_models, shared
+from modules import devices, processing, sd_models, shared, sd_samplers
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
@@ -535,7 +535,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
                 p.prompt = preview_prompt
                 p.negative_prompt = preview_negative_prompt
                 p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
+                p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
                 p.cfg_scale = preview_cfg_scale
                 p.seed = preview_seed
                 p.width = preview_width
diff --git a/modules/images.py b/modules/images.py
index ae705cbd..26d5b7a9 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -303,7 +303,7 @@ class FilenameGenerator:
         'width': lambda self: self.image.width,
         'height': lambda self: self.image.height,
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
-        'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
+        'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
         'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
         'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
diff --git a/modules/img2img.py b/modules/img2img.py
index be9f3653..9fc5b693 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -6,7 +6,7 @@ import traceback
 import numpy as np
 from PIL import Image, ImageOps, ImageChops
 
-from modules import devices
+from modules import devices, sd_samplers
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
 import modules.shared as shared
@@ -99,7 +99,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
         seed_enable_extras=seed_enable_extras,
-        sampler_index=sampler_index,
+        sampler_index=sd_samplers.samplers_for_img2img[sampler_index].name,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,
diff --git a/modules/processing.py b/modules/processing.py
index 03c9143d..be2edf48 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -2,6 +2,7 @@ import json
 import math
 import os
 import sys
+import warnings
 
 import torch
 import numpy as np
@@ -66,19 +67,15 @@ def apply_overlay(image, paste_loc, index, overlays):
 
     return image
 
-def get_correct_sampler(p):
-    if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
-        return sd_samplers.samplers
-    elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
-        return sd_samplers.samplers_for_img2img
-    elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
-        return sd_samplers.samplers
 
 class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
     """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_index: int = 0, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, sampler_index: int = None):
+        if sampler_index is not None:
+            warnings.warn("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name")
+
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -91,7 +88,7 @@ class StableDiffusionProcessing():
         self.subseed_strength: float = subseed_strength
         self.seed_resize_from_h: int = seed_resize_from_h
         self.seed_resize_from_w: int = seed_resize_from_w
-        self.sampler_index: int = sampler_index
+        self.sampler_name: str = sampler_name
         self.batch_size: int = batch_size
         self.n_iter: int = n_iter
         self.steps: int = steps
@@ -210,8 +207,7 @@ class Processed:
         self.info = info
         self.width = p.width
         self.height = p.height
-        self.sampler_index = p.sampler_index
-        self.sampler = sd_samplers.samplers[p.sampler_index].name
+        self.sampler_name = p.sampler_name
         self.cfg_scale = p.cfg_scale
         self.steps = p.steps
         self.batch_size = p.batch_size
@@ -256,8 +252,7 @@ class Processed:
             "subseed_strength": self.subseed_strength,
             "width": self.width,
             "height": self.height,
-            "sampler_index": self.sampler_index,
-            "sampler": self.sampler,
+            "sampler_name": self.sampler_name,
             "cfg_scale": self.cfg_scale,
             "steps": self.steps,
             "batch_size": self.batch_size,
@@ -384,7 +379,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params = {
         "Steps": p.steps,
-        "Sampler": get_correct_sampler(p)[p.sampler_index].name,
+        "Sampler": p.sampler_name,
         "CFG scale": p.cfg_scale,
         "Seed": all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
@@ -645,7 +640,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
+        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@@ -706,7 +701,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         shared.state.nextjob()
 
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
+        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
@@ -743,7 +738,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.image_conditioning = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
+        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
         crop_region = None
 
         if self.image_mask is not None:
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 783992d2..4fe67854 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -46,16 +46,23 @@ all_samplers = [
     SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
     SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
 ]
+all_samplers_map = {x.name: x for x in all_samplers}
 
 samplers = []
 samplers_for_img2img = []
 
 
-def create_sampler_with_index(list_of_configs, index, model):
-    config = list_of_configs[index]
+def create_sampler(name, model):
+    if name is not None:
+        config = all_samplers_map.get(name, None)
+    else:
+        config = all_samplers[0]
+
+    assert config is not None, f'bad sampler name: {name}'
+
     sampler = config.constructor(model)
     sampler.config = config
-    
+
     return sampler
 
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 0aeb0459..5e4d8688 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -10,7 +10,7 @@ import csv
 
 from PIL import Image, PngImagePlugin
 
-from modules import shared, devices, sd_hijack, processing, sd_models, images
+from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -345,7 +345,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 p.prompt = preview_prompt
                 p.negative_prompt = preview_negative_prompt
                 p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
+                p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
                 p.cfg_scale = preview_cfg_scale
                 p.seed = preview_seed
                 p.width = preview_width
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 8e4e8677..c8f81176 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -1,4 +1,5 @@
 import modules.scripts
+from modules import sd_samplers
 from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
     StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, cmd_opts
@@ -21,7 +22,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
         seed_enable_extras=seed_enable_extras,
-        sampler_index=sampler_index,
+        sampler_name=sd_samplers.samplers[sampler_index].name,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,
diff --git a/modules/ui.py b/modules/ui.py
index 5dce7f3b..2d488741 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -142,7 +142,7 @@ def save_files(js_data, images, do_make_zip, index):
                 filenames.append(os.path.basename(txt_fullfn))
                 fullfns.append(txt_fullfn)
 
-        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
 
     # Make Zip
     if do_make_zip:
-- 
cgit v1.2.3


From 0d702930b068ca8da8eb0117613053a480d9439e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 12:47:52 +0300
Subject: renamed Inpainting strength infotext to Conditional mask weight, made
 it only appear if using inpainting model, made it possible to read the
 setting from it using the blue arrow button

---
 modules/generation_parameters_copypaste.py | 1 +
 modules/processing.py                      | 9 ++++++++-
 2 files changed, 9 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 985ec95e..1408ea05 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -73,6 +73,7 @@ def integrate_settings_paste_fields(component_dict):
         'sd_hypernetwork': 'Hypernet',
         'sd_hypernetwork_strength': 'Hypernet strength',
         'CLIP_stop_at_last_layers': 'Clip skip',
+        'inpainting_mask_weight': 'Conditional mask weight',
         'sd_model_checkpoint': 'Model hash',
     }
     settings_paste_fields = [
diff --git a/modules/processing.py b/modules/processing.py
index fb30aa81..def95846 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -113,6 +113,7 @@ class StableDiffusionProcessing():
         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
         self.s_noise = s_noise or opts.s_noise
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
+        self.is_using_inpainting_conditioning = False
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -133,6 +134,8 @@ class StableDiffusionProcessing():
             # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
             return x.new_zeros(x.shape[0], 5, 1, 1)
 
+        self.is_using_inpainting_conditioning = True
+
         height = height or self.height
         width = width or self.width
 
@@ -151,6 +154,8 @@ class StableDiffusionProcessing():
             # Dummy zero conditioning if we're not using inpainting model.
             return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
 
+        self.is_using_inpainting_conditioning = True
+
         # Handle the different mask inputs
         if image_mask is not None:
             if torch.is_tensor(image_mask):
@@ -234,6 +239,7 @@ class Processed:
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
         self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
         self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
+        self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
 
         self.all_prompts = all_prompts or [self.prompt]
         self.all_seeds = all_seeds or [self.seed]
@@ -268,6 +274,7 @@ class Processed:
             "styles": self.styles,
             "job_timestamp": self.job_timestamp,
             "clip_skip": self.clip_skip,
+            "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
         }
 
         return json.dumps(obj)
@@ -394,7 +401,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
-        "Inpainting strength": (None if getattr(p, 'denoising_strength', None) is None else getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)),
+        "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
-- 
cgit v1.2.3


From 617c5b486f42aa73062ee7699ee1147eb995c899 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 13:23:25 +0300
Subject: make it possible for StableDiffusionProcessing to accept multiple
 different negative prompts in a batch

---
 modules/processing.py | 46 ++++++++++++++++++++++++----------------------
 modules/styles.py     | 11 -----------
 2 files changed, 24 insertions(+), 33 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 604d822a..bc7e5311 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -124,6 +124,7 @@ class StableDiffusionProcessing():
         self.scripts = None
         self.script_args = None
         self.all_prompts = None
+        self.all_negative_prompts = None
         self.all_seeds = None
         self.all_subseeds = None
 
@@ -202,7 +203,7 @@ class StableDiffusionProcessing():
 
 
 class Processed:
-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
         self.images = images_list
         self.prompt = p.prompt
         self.negative_prompt = p.negative_prompt
@@ -241,16 +242,18 @@ class Processed:
         self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
         self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
 
-        self.all_prompts = all_prompts or [self.prompt]
-        self.all_seeds = all_seeds or [self.seed]
-        self.all_subseeds = all_subseeds or [self.subseed]
+        self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
+        self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
+        self.all_seeds = all_seeds or p.all_seeds or [self.seed]
+        self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
         self.infotexts = infotexts or [info]
 
     def js(self):
         obj = {
-            "prompt": self.prompt,
+            "prompt": self.all_prompts[0],
             "all_prompts": self.all_prompts,
-            "negative_prompt": self.negative_prompt,
+            "negative_prompt": self.all_negative_prompts[0],
+            "all_negative_prompts": self.all_negative_prompts,
             "seed": self.seed,
             "all_seeds": self.all_seeds,
             "subseed": self.subseed,
@@ -411,7 +414,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
+    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[0] if  p.all_negative_prompts[0] else ""
 
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
@@ -440,10 +443,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     else:
         assert p.prompt is not None
 
-    with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
-        processed = Processed(p, [], p.seed, "")
-        file.write(processed.infotext(p, 0))
-
     devices.torch_gc()
 
     seed = get_fixed_seed(p.seed)
@@ -453,15 +452,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     modules.sd_hijack.model_hijack.clear_comments()
 
     comments = {}
-    prompt_tmp = p.prompt
-    negative_prompt_tmp = p.negative_prompt
-
-    shared.prompt_styles.apply_styles(p)
 
     if type(p.prompt) == list:
-        p.all_prompts = p.prompt
+        p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
+    else:
+        p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
+
+    if type(p.negative_prompt) == list:
+        p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
     else:
-        p.all_prompts = p.batch_size * p.n_iter * [p.prompt]
+        p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
 
     if type(seed) == list:
         p.all_seeds = seed
@@ -476,6 +476,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     def infotext(iteration=0, position_in_batch=0):
         return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
 
+    with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
+        processed = Processed(p, [], p.seed, "")
+        file.write(processed.infotext(p, 0))
+
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
@@ -500,6 +504,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 break
 
             prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
             seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
             subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
@@ -510,7 +515,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
 
             with devices.autocast():
-                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
+                uc = prompt_parser.get_learned_conditioning(shared.sd_model, negative_prompts, p.steps)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
@@ -596,14 +601,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     devices.torch_gc()
 
-    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
 
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
 
-    p.prompt = prompt_tmp
-    p.negative_prompt = negative_prompt_tmp
-
     return res
 
 
diff --git a/modules/styles.py b/modules/styles.py
index 3bf5c5b6..ce6e71ca 100644
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -65,17 +65,6 @@ class StyleDatabase:
     def apply_negative_styles_to_prompt(self, prompt, styles):
         return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
 
-    def apply_styles(self, p: StableDiffusionProcessing) -> None:
-        if isinstance(p.prompt, list):
-            p.prompt = [self.apply_styles_to_prompt(prompt, p.styles) for prompt in p.prompt]
-        else:
-            p.prompt = self.apply_styles_to_prompt(p.prompt, p.styles)
-
-        if isinstance(p.negative_prompt, list):
-            p.negative_prompt = [self.apply_negative_styles_to_prompt(prompt, p.styles) for prompt in p.negative_prompt]
-        else:
-            p.negative_prompt = self.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)
-
     def save_styles(self, path: str) -> None:
         # Write to temporary file first, so we don't nuke the file if something goes wrong
         fd, temp_path = tempfile.mkstemp(".csv")
-- 
cgit v1.2.3


From 413c077969d35bc90a8b3218ab0db7e35c8c46f2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 13:47:37 +0300
Subject: prevent StableDiffusionProcessingImg2Img changing image_mask field as
 an alternative solution to #4765

---
 modules/processing.py | 33 ++++++++++++++++-----------------
 1 file changed, 16 insertions(+), 17 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index bc7e5311..accb31d1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -740,7 +740,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.denoising_strength: float = denoising_strength
         self.init_latent = None
         self.image_mask = mask
-        #self.image_unblurred_mask = None
         self.latent_mask = None
         self.mask_for_overlay = None
         self.mask_blur = mask_blur
@@ -756,36 +755,36 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
         crop_region = None
 
-        if self.image_mask is not None:
-            self.image_mask = self.image_mask.convert('L')
+        image_mask = self.image_mask
 
-            if self.inpainting_mask_invert:
-                self.image_mask = ImageOps.invert(self.image_mask)
+        if image_mask is not None:
+            image_mask = image_mask.convert('L')
 
-            #self.image_unblurred_mask = self.image_mask
+            if self.inpainting_mask_invert:
+                image_mask = ImageOps.invert(image_mask)
 
             if self.mask_blur > 0:
-                self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
+                image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
 
             if self.inpaint_full_res:
-                self.mask_for_overlay = self.image_mask
-                mask = self.image_mask.convert('L')
+                self.mask_for_overlay = image_mask
+                mask = image_mask.convert('L')
                 crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
                 crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
                 x1, y1, x2, y2 = crop_region
 
                 mask = mask.crop(crop_region)
-                self.image_mask = images.resize_image(2, mask, self.width, self.height)
+                image_mask = images.resize_image(2, mask, self.width, self.height)
                 self.paste_to = (x1, y1, x2-x1, y2-y1)
             else:
-                self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
-                np_mask = np.array(self.image_mask)
+                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
+                np_mask = np.array(image_mask)
                 np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
                 self.mask_for_overlay = Image.fromarray(np_mask)
 
             self.overlay_images = []
 
-        latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
+        latent_mask = self.latent_mask if self.latent_mask is not None else image_mask
 
         add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
         if add_color_corrections:
@@ -797,7 +796,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if crop_region is None:
                 image = images.resize_image(self.resize_mode, image, self.width, self.height)
 
-            if self.image_mask is not None:
+            if image_mask is not None:
                 image_masked = Image.new('RGBa', (image.width, image.height))
                 image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
 
@@ -807,7 +806,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                 image = image.crop(crop_region)
                 image = images.resize_image(2, image, self.width, self.height)
 
-            if self.image_mask is not None:
+            if image_mask is not None:
                 if self.inpainting_fill != 1:
                     image = masking.fill(image, latent_mask)
 
@@ -839,7 +838,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
 
-        if self.image_mask is not None:
+        if image_mask is not None:
             init_mask = latent_mask
             latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
             latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
@@ -856,7 +855,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
+        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-- 
cgit v1.2.3


From 5a6387e189dc365c47a7979b9040d5b6fdd7ba43 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 15:15:24 +0300
Subject: make it possible to change models etc by editing options using API

---
 modules/api/api.py |  7 +++----
 modules/shared.py  | 17 +++++++++++++++++
 modules/ui.py      | 22 ++++------------------
 3 files changed, 24 insertions(+), 22 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index bf700ed0..1e324d8d 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -253,9 +253,8 @@ class Api:
         return options
 
     def set_config(self, req: Dict[str, Any]):
-       
-        for o in req:
-            setattr(shared.opts, o, req[o])
+        for k, v in req.items():
+            shared.opts.set(k, v)
 
         shared.opts.save(shared.config_filename)
         return
@@ -264,7 +263,7 @@ class Api:
         return vars(shared.cmd_opts)
 
     def get_samplers(self):
-        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
+        return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
 
     def get_upscalers(self):
         upscalers = []
diff --git a/modules/shared.py b/modules/shared.py
index 84567c8e..58f53e54 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -437,6 +437,23 @@ class Options:
 
         return super(Options, self).__getattribute__(item)
 
+    def set(self, key, value):
+        """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
+
+        oldval = self.data.get(key, None)
+        if oldval == value:
+            return False
+
+        try:
+            setattr(self, key, value)
+        except RuntimeError:
+            return False
+
+        if self.data_labels[key].onchange is not None:
+            self.data_labels[key].onchange()
+
+        return True
+
     def save(self, filename):
         assert not cmd_opts.freeze_settings, "saving settings is disabled"
 
diff --git a/modules/ui.py b/modules/ui.py
index 5dd97754..bb090c62 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1484,16 +1484,9 @@ def create_ui(wrap_gradio_gpu_call):
             if comp == dummy_component:
                 continue
 
-            oldval = opts.data.get(key, None)
-            try:
-                setattr(opts, key, value)
-            except RuntimeError:
-                continue
-            if oldval != value:
-                if opts.data_labels[key].onchange is not None:
-                    opts.data_labels[key].onchange()
-
+            if opts.set(key, value):
                 changed.append(key)
+
         try:
             opts.save(shared.config_filename)
         except RuntimeError:
@@ -1504,15 +1497,8 @@ def create_ui(wrap_gradio_gpu_call):
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
-        oldval = opts.data.get(key, None)
-        try:
-            setattr(opts, key, value)
-        except Exception:
-            return gr.update(value=oldval), opts.dumpjson()
-
-        if oldval != value:
-            if opts.data_labels[key].onchange is not None:
-                opts.data_labels[key].onchange()
+        if not opts.set(key, value):
+            return gr.update(value=getattr(opts, key)), opts.dumpjson()
 
         opts.save(shared.config_filename)
 
-- 
cgit v1.2.3


From 3596af07493ab7981ef92074f979eeee8fa624c4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 19:10:17 +0300
Subject: Add API for scripts to add elements anywhere in UI.

---
 modules/script_callbacks.py | 35 +++++++++++++++++++++++
 modules/scripts.py          | 69 +++++++++++++++++++++++++++++++++++++++++++--
 modules/ui.py               | 12 ++++++--
 3 files changed, 111 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index f19e164c..8e22f875 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -61,6 +61,8 @@ callback_map = dict(
     callbacks_before_image_saved=[],
     callbacks_image_saved=[],
     callbacks_cfg_denoiser=[],
+    callbacks_before_component=[],
+    callbacks_after_component=[],
 )
 
 
@@ -137,6 +139,22 @@ def cfg_denoiser_callback(params: CFGDenoiserParams):
             report_exception(c, 'cfg_denoiser_callback')
 
 
+def before_component_callback(component, **kwargs):
+    for c in callback_map['callbacks_before_component']:
+        try:
+            c.callback(component, **kwargs)
+        except Exception:
+            report_exception(c, 'before_component_callback')
+
+
+def after_component_callback(component, **kwargs):
+    for c in callback_map['callbacks_after_component']:
+        try:
+            c.callback(component, **kwargs)
+        except Exception:
+            report_exception(c, 'after_component_callback')
+
+
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -220,3 +238,20 @@ def on_cfg_denoiser(callback):
         - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
     """
     add_callback(callback_map['callbacks_cfg_denoiser'], callback)
+
+
+def on_before_component(callback):
+    """register a function to be called before a component is created.
+    The callback is called with arguments:
+        - component - gradio component that is about to be created.
+        - **kwargs - args to gradio.components.IOComponent.__init__ function
+
+    Use elem_id/label fields of kwargs to figure out which component it is.
+    This can be useful to inject your own components somewhere in the middle of vanilla UI.
+    """
+    add_callback(callback_map['callbacks_before_component'], callback)
+
+
+def on_after_component(callback):
+    """register a function to be called after a component is created. See on_before_component for more."""
+    add_callback(callback_map['callbacks_after_component'], callback)
diff --git a/modules/scripts.py b/modules/scripts.py
index 986b1914..b934d881 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -17,6 +17,9 @@ class Script:
     args_to = None
     alwayson = False
 
+    is_txt2img = False
+    is_img2img = False
+
     """A gr.Group component that has all script's UI inside it"""
     group = None
 
@@ -93,6 +96,23 @@ class Script:
 
         pass
 
+    def before_component(self, component, **kwargs):
+        """
+        Called before a component is created.
+        Use elem_id/label fields of kwargs to figure out which component it is.
+        This can be useful to inject your own components somewhere in the middle of vanilla UI.
+        You can return created components in the ui() function to add them to the list of arguments for your processing functions
+        """
+
+        pass
+
+    def after_component(self, component, **kwargs):
+        """
+        Called after a component is created. Same as above.
+        """
+
+        pass
+
     def describe(self):
         """unused"""
         return ""
@@ -195,12 +215,18 @@ class ScriptRunner:
         self.titles = []
         self.infotext_fields = []
 
-    def setup_ui(self, is_img2img):
+    def initialize_scripts(self, is_img2img):
+        self.scripts.clear()
+        self.alwayson_scripts.clear()
+        self.selectable_scripts.clear()
+
         for script_class, path, basedir in scripts_data:
             script = script_class()
             script.filename = path
+            script.is_txt2img = not is_img2img
+            script.is_img2img = is_img2img
 
-            visibility = script.show(is_img2img)
+            visibility = script.show(script.is_img2img)
 
             if visibility == AlwaysVisible:
                 self.scripts.append(script)
@@ -211,6 +237,7 @@ class ScriptRunner:
                 self.scripts.append(script)
                 self.selectable_scripts.append(script)
 
+    def setup_ui(self):
         self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
 
         inputs = [None]
@@ -220,7 +247,7 @@ class ScriptRunner:
             script.args_from = len(inputs)
             script.args_to = len(inputs)
 
-            controls = wrap_call(script.ui, script.filename, "ui", is_img2img)
+            controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
 
             if controls is None:
                 return
@@ -320,6 +347,22 @@ class ScriptRunner:
                 print(f"Error running postprocess: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
+    def before_component(self, component, **kwargs):
+        for script in self.scripts:
+            try:
+                script.before_component(component, **kwargs)
+            except Exception:
+                print(f"Error running before_component: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
+    def after_component(self, component, **kwargs):
+        for script in self.scripts:
+            try:
+                script.after_component(component, **kwargs)
+            except Exception:
+                print(f"Error running after_component: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
     def reload_sources(self, cache):
         for si, script in list(enumerate(self.scripts)):
             args_from = script.args_from
@@ -341,6 +384,7 @@ class ScriptRunner:
 
 scripts_txt2img = ScriptRunner()
 scripts_img2img = ScriptRunner()
+scripts_current: ScriptRunner = None
 
 
 def reload_script_body_only():
@@ -357,3 +401,22 @@ def reload_scripts():
     scripts_txt2img = ScriptRunner()
     scripts_img2img = ScriptRunner()
 
+
+def IOComponent_init(self, *args, **kwargs):
+    if scripts_current is not None:
+        scripts_current.before_component(self, **kwargs)
+
+    script_callbacks.before_component_callback(self, **kwargs)
+
+    res = original_IOComponent_init(self, *args, **kwargs)
+
+    script_callbacks.after_component_callback(self, **kwargs)
+
+    if scripts_current is not None:
+        scripts_current.after_component(self, **kwargs)
+
+    return res
+
+
+original_IOComponent_init = gr.components.IOComponent.__init__
+gr.components.IOComponent.__init__ = IOComponent_init
diff --git a/modules/ui.py b/modules/ui.py
index bb090c62..a5953fce 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -695,6 +695,9 @@ def create_ui(wrap_gradio_gpu_call):
 
     parameters_copypaste.reset()
 
+    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
+    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
@@ -737,7 +740,7 @@ def create_ui(wrap_gradio_gpu_call):
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
 
                 with gr.Group():
-                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
+                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
             txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
             parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
@@ -846,6 +849,9 @@ def create_ui(wrap_gradio_gpu_call):
 
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
+    modules.scripts.scripts_current = modules.scripts.scripts_img2img
+    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
 
@@ -916,7 +922,7 @@ def create_ui(wrap_gradio_gpu_call):
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
 
                 with gr.Group():
-                    custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
+                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
 
             img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
             parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
@@ -1065,6 +1071,8 @@ def create_ui(wrap_gradio_gpu_call):
             parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
             parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
 
+    modules.scripts.scripts_current = None
+
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
-- 
cgit v1.2.3


From ac7ecd2d847bf4e3a9503db0f2a291e32b82302c Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Sat, 19 Nov 2022 14:49:22 -0500
Subject: Label and load SD .safetensors model files

---
 modules/modelloader.py |  1 +
 modules/sd_models.py   | 24 ++++++++++++++++--------
 2 files changed, 17 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/modelloader.py b/modules/modelloader.py
index e4a6f8ac..7d2f0ade 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -82,6 +82,7 @@ def cleanup_models():
     src_path = models_path
     dest_path = os.path.join(models_path, "Stable-diffusion")
     move_files(src_path, dest_path, ".ckpt")
+    move_files(src_path, dest_path, ".safetensors")
     src_path = os.path.join(root_path, "ESRGAN")
     dest_path = os.path.join(models_path, "ESRGAN")
     move_files(src_path, dest_path)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index c59151e0..4ccdf30b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,6 +4,7 @@ import sys
 import gc
 from collections import namedtuple
 import torch
+from safetensors.torch import load_file
 import re
 from omegaconf import OmegaConf
 
@@ -16,9 +17,10 @@ from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inp
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config', 'exttype'])
 checkpoints_list = {}
 checkpoints_loaded = collections.OrderedDict()
+checkpoint_types = {'.ckpt':'pickle','.safetensors':'safetensors'}
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -45,7 +47,7 @@ def checkpoint_tiles():
 
 def list_models():
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])
+    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt",".safetensors"])
 
     def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
@@ -60,15 +62,15 @@ def list_models():
         if name.startswith("\\") or name.startswith("/"):
             name = name[1:]
 
-        shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
+        shortname, ext = os.path.splitext(name.replace("/", "_").replace("\\", "_"))
 
-        return f'{name} [{shorthash}]', shortname
+        return f'{name} [{checkpoint_types[ext]}] [{shorthash}]', shortname
 
     cmd_ckpt = shared.cmd_opts.ckpt
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config, '')
         shared.opts.data['sd_model_checkpoint'] = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
@@ -76,12 +78,12 @@ def list_models():
         h = model_hash(filename)
         title, short_model_name = modeltitle(filename, h)
 
-        basename, _ = os.path.splitext(filename)
+        basename, ext = os.path.splitext(filename)
         config = basename + ".yaml"
         if not os.path.exists(config):
             config = shared.cmd_opts.config
 
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config, ext)
 
 
 def get_closet_checkpoint_match(searchString):
@@ -173,7 +175,13 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+        if(checkpoint_types[checkpoint_info.exttype] == 'safetensors'):
+            # safely load weights
+            # TODO: safetensors supports zero copy fast load to gpu, see issue #684
+            pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
+        else:
+            pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+
         if "global_step" in pl_sd:
             print(f"Global Step: {pl_sd['global_step']}")
 
-- 
cgit v1.2.3


From bd68e35de3b7cf7547ed97d8bdf60147402133cc Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Sun, 20 Nov 2022 12:35:26 +0900
Subject: Gradient accumulation, autocast fix, new latent sampling method, etc

---
 modules/hypernetworks/hypernetwork.py          | 269 +++++++++++----------
 modules/sd_hijack.py                           |   9 +-
 modules/sd_hijack_checkpoint.py                |  10 +
 modules/shared.py                              |   3 +-
 modules/textual_inversion/dataset.py           | 134 +++++++----
 modules/textual_inversion/textual_inversion.py | 320 ++++++++++++++-----------
 modules/ui.py                                  |  16 +-
 7 files changed, 448 insertions(+), 313 deletions(-)
 create mode 100644 modules/sd_hijack_checkpoint.py

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index fbb87dd1..3d3301b0 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -367,13 +367,13 @@ def report_statistics(loss_info:dict):
 
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
     save_hypernetwork_every = save_hypernetwork_every or 0
     create_image_every = create_image_every or 0
-    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
+    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
@@ -403,28 +403,24 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     hypernetwork = shared.loaded_hypernetwork
     checkpoint = sd_models.select_checkpoint()
 
-    ititial_step = hypernetwork.step or 0
-    if ititial_step >= steps:
+    initial_step = hypernetwork.step or 0
+    if initial_step >= steps:
         shared.state.textinfo = f"Model has already been trained beyond specified max steps"
         return hypernetwork, filename
 
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    
+    scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
+
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
-    with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+
+    pin_memory = shared.opts.pin_memory
+
+    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
+    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, batch_size=ds.batch_size, pin_memory=pin_memory)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
-
-    size = len(ds.indexes)
-    loss_dict = defaultdict(lambda : deque(maxlen = 1024))
-    losses = torch.zeros((size,))
-    previous_mean_losses = [0]
-    previous_mean_loss = 0
-    print("Mean loss of {} elements".format(size))
     
     weights = hypernetwork.weights()
     for weight in weights:
@@ -436,8 +432,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         optimizer_name = hypernetwork.optimizer_name
     else:
         print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
-        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
-        optimizer_name = 'AdamW'
+    optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
+    optimizer_name = 'AdamW'
 
     if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
         try:
@@ -446,131 +442,155 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             print("Cannot resume from saved optimizer!")
             print(e)
 
+    scaler = torch.cuda.amp.GradScaler()
+    
+    batch_size = ds.batch_size
+    gradient_step = ds.gradient_step
+    # n steps = batch_size * gradient_step * n image processed
+    steps_per_epoch = len(ds) // batch_size // gradient_step
+    max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step
+    loss_step = 0
+    _loss_step = 0 #internal
+    # size = len(ds.indexes)
+    # loss_dict = defaultdict(lambda : deque(maxlen = 1024))
+    # losses = torch.zeros((size,))
+    # previous_mean_losses = [0]
+    # previous_mean_loss = 0
+    # print("Mean loss of {} elements".format(size))
+
     steps_without_grad = 0
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
     forced_filename = "<none>"
 
-    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, entries in pbar:
-        hypernetwork.step = i + ititial_step
-        if len(loss_dict) > 0:
-            previous_mean_losses = [i[-1] for i in loss_dict.values()]
-            previous_mean_loss = mean(previous_mean_losses)
-            
-        scheduler.apply(optimizer, hypernetwork.step)
-        if scheduler.finished:
-            break
-
-        if shared.state.interrupted:
-            break
-
-        with torch.autocast("cuda"):
-            c = stack_conds([entry.cond for entry in entries]).to(devices.device)
-            # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
-            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-            loss = shared.sd_model(x, c)[0]
-            del x
-            del c
-
-            losses[hypernetwork.step % losses.shape[0]] = loss.item()
-            for entry in entries:
-                loss_dict[entry.filename].append(loss.item())
+    pbar = tqdm.tqdm(total=steps - initial_step)
+    try:
+        for i in range((steps-initial_step) * gradient_step):
+            if scheduler.finished:
+                break
+            if shared.state.interrupted:
+                break
+            for j, batch in enumerate(dl):
+                # works as a drop_last=True for gradient accumulation
+                if j == max_steps_per_epoch:
+                    break
+                scheduler.apply(optimizer, hypernetwork.step)
+                if scheduler.finished:
+                    break
+                if shared.state.interrupted:
+                    break
+
+                with torch.autocast("cuda"):
+                    x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+                    if tag_drop_out != 0 or shuffle_tags:
+                        shared.sd_model.cond_stage_model.to(devices.device)
+                        c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)
+                        shared.sd_model.cond_stage_model.to(devices.cpu)
+                    else:
+                        c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
+                    loss = shared.sd_model(x, c)[0] / gradient_step
+                    del x
+                    del c
+
+                    _loss_step += loss.item()
+                scaler.scale(loss).backward()
+                # go back until we reach gradient accumulation steps
+                if (j + 1) % gradient_step != 0:
+                    continue
+                # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.7f}")
+                # scaler.unscale_(optimizer)
+                # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}")
+                # torch.nn.utils.clip_grad_norm_(weights, max_norm=1.0)
+                # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}")
+                scaler.step(optimizer)
+                scaler.update()
+                hypernetwork.step += 1
+                pbar.update()
+                optimizer.zero_grad(set_to_none=True)
+                loss_step = _loss_step
+                _loss_step = 0
+
+                steps_done = hypernetwork.step + 1
                 
-            optimizer.zero_grad()
-            weights[0].grad = None
-            loss.backward()
-
-            if weights[0].grad is None:
-                steps_without_grad += 1
-            else:
-                steps_without_grad = 0
-            assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
-
-            optimizer.step()
-
-        steps_done = hypernetwork.step + 1
-
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
-            raise RuntimeError("Loss diverged.")
-        
-        if len(previous_mean_losses) > 1:
-            std = stdev(previous_mean_losses)
-        else:
-            std = 0
-        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
-        pbar.set_description(dataset_loss_info)
-
-        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
-            # Before saving, change name to match current checkpoint.
-            hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
-            hypernetwork.optimizer_name = optimizer_name
-            if shared.opts.save_optimizer_state:
-                hypernetwork.optimizer_state_dict = optimizer.state_dict()
-            save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
-            hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
-
-        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
-            "loss": f"{previous_mean_loss:.7f}",
-            "learn_rate": scheduler.learn_rate
-        })
-
-        if images_dir is not None and steps_done % create_image_every == 0:
-            forced_filename = f'{hypernetwork_name}-{steps_done}'
-            last_saved_image = os.path.join(images_dir, forced_filename)
-
-            optimizer.zero_grad()
-            shared.sd_model.cond_stage_model.to(devices.device)
-            shared.sd_model.first_stage_model.to(devices.device)
-
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-            )
-
-            if preview_from_txt2img:
-                p.prompt = preview_prompt
-                p.negative_prompt = preview_negative_prompt
-                p.steps = preview_steps
-                p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
-                p.cfg_scale = preview_cfg_scale
-                p.seed = preview_seed
-                p.width = preview_width
-                p.height = preview_height
-            else:
-                p.prompt = entries[0].cond_text
-                p.steps = 20
+                epoch_num = hypernetwork.step // steps_per_epoch
+                epoch_step = hypernetwork.step % steps_per_epoch
+
+                pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+                if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
+                    # Before saving, change name to match current checkpoint.
+                    hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
+                    last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+                    hypernetwork.optimizer_name = optimizer_name
+                    if shared.opts.save_optimizer_state:
+                        hypernetwork.optimizer_state_dict = optimizer.state_dict()
+                    save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
+                    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
+
+                textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, {
+                    "loss": f"{loss_step:.7f}",
+                    "learn_rate": scheduler.learn_rate
+                })
+
+                if images_dir is not None and steps_done % create_image_every == 0:
+                    forced_filename = f'{hypernetwork_name}-{steps_done}'
+                    last_saved_image = os.path.join(images_dir, forced_filename)
+
+                    shared.sd_model.cond_stage_model.to(devices.device)
+                    shared.sd_model.first_stage_model.to(devices.device)
+
+                    p = processing.StableDiffusionProcessingTxt2Img(
+                        sd_model=shared.sd_model,
+                        do_not_save_grid=True,
+                        do_not_save_samples=True,
+                    )
+
+                    if preview_from_txt2img:
+                        p.prompt = preview_prompt
+                        p.negative_prompt = preview_negative_prompt
+                        p.steps = preview_steps
+                        p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
+                        p.cfg_scale = preview_cfg_scale
+                        p.seed = preview_seed
+                        p.width = preview_width
+                        p.height = preview_height
+                    else:
+                        p.prompt = batch.cond_text[0]
+                        p.steps = 20
+                        p.width = training_width
+                        p.height = training_height
 
-            preview_text = p.prompt
+                    preview_text = p.prompt
 
-            processed = processing.process_images(p)
-            image = processed.images[0] if len(processed.images)>0 else None
+                    processed = processing.process_images(p)
+                    image = processed.images[0] if len(processed.images) > 0 else None
 
-            if unload:
-                shared.sd_model.cond_stage_model.to(devices.cpu)
-                shared.sd_model.first_stage_model.to(devices.cpu)
+                    if unload:
+                        shared.sd_model.cond_stage_model.to(devices.cpu)
+                        shared.sd_model.first_stage_model.to(devices.cpu)
 
-            if image is not None:
-                shared.state.current_image = image
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-                last_saved_image += f", prompt: {preview_text}"
+                    if image is not None:
+                        shared.state.current_image = image
+                        last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                        last_saved_image += f", prompt: {preview_text}"
 
-        shared.state.job_no = hypernetwork.step
+                shared.state.job_no = hypernetwork.step
 
-        shared.state.textinfo = f"""
+                shared.state.textinfo = f"""
 <p>
-Loss: {previous_mean_loss:.7f}<br/>
+Loss: {loss_step:.7f}<br/>
 Step: {hypernetwork.step}<br/>
-Last prompt: {html.escape(entries[0].cond_text)}<br/>
+Last prompt: {html.escape(batch.cond_text[0])}<br/>
 Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-        
-    report_statistics(loss_dict)
+    except Exception:
+        print(traceback.format_exc(), file=sys.stderr)
+    finally:
+        pbar.leave = False
+        pbar.close()
+        #report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
     hypernetwork.optimizer_name = optimizer_name
@@ -579,6 +599,9 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
     del optimizer
     hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
+    shared.sd_model.cond_stage_model.to(devices.device)
+    shared.sd_model.first_stage_model.to(devices.device)
+
     return hypernetwork, filename
 
 def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index eaedac13..29c8b561 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -8,7 +8,7 @@ from torch import einsum
 from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.shared import opts, device, cmd_opts
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
@@ -59,6 +59,10 @@ def undo_optimizations():
 def get_target_prompt_token_count(token_count):
     return math.ceil(max(token_count, 1) / 75) * 75
 
+def fix_checkpoint():
+    ldm.modules.attention.BasicTransformerBlock.forward = sd_hijack_checkpoint.BasicTransformerBlock_forward
+    ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = sd_hijack_checkpoint.ResBlock_forward
+    ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = sd_hijack_checkpoint.AttentionBlock_forward
 
 class StableDiffusionModelHijack:
     fixes = None
@@ -78,6 +82,7 @@ class StableDiffusionModelHijack:
         self.clip = m.cond_stage_model
 
         apply_optimizations()
+        fix_checkpoint()
 
         def flatten(el):
             flattened = [flatten(children) for children in el.children()]
@@ -303,7 +308,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
         else:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
-
+        
         self.hijack.comments += hijack_comments
 
         if len(used_custom_terms) > 0:
diff --git a/modules/sd_hijack_checkpoint.py b/modules/sd_hijack_checkpoint.py
new file mode 100644
index 00000000..5712972f
--- /dev/null
+++ b/modules/sd_hijack_checkpoint.py
@@ -0,0 +1,10 @@
+from torch.utils.checkpoint import checkpoint
+
+def BasicTransformerBlock_forward(self, x, context=None):
+    return checkpoint(self._forward, x, context)
+
+def AttentionBlock_forward(self, x):
+    return checkpoint(self._forward, x)
+
+def ResBlock_forward(self, x, emb):
+    return checkpoint(self._forward, x, emb)
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index a4457305..3704ce23 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -322,8 +322,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
-    "shuffle_tags": OptionInfo(False, "Shuffleing tags by ',' when create texts."),
-    "tag_drop_out": OptionInfo(0, "Dropout tags when create texts", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.1}),
+    "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
     "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index eb75c376..d594b49d 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -3,7 +3,7 @@ import numpy as np
 import PIL
 import torch
 from PIL import Image
-from torch.utils.data import Dataset
+from torch.utils.data import Dataset, DataLoader
 from torchvision import transforms
 
 import random
@@ -11,25 +11,28 @@ import tqdm
 from modules import devices, shared
 import re
 
+from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+
 re_numbers_at_start = re.compile(r"^[-\d]+\s*")
 
 
 class DatasetEntry:
-    def __init__(self, filename=None, latent=None, filename_text=None):
+    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None):
         self.filename = filename
-        self.latent = latent
         self.filename_text = filename_text
-        self.cond = None
-        self.cond_text = None
+        self.latent_dist = latent_dist
+        self.latent_sample = latent_sample
+        self.cond = cond
+        self.cond_text = cond_text
+        self.pixel_values = pixel_values
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):        
         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
-
+        
         self.placeholder_token = placeholder_token
 
-        self.batch_size = batch_size
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
@@ -45,11 +48,16 @@ class PersonalizedBase(Dataset):
         assert os.path.isdir(data_root), "Dataset directory doesn't exist"
         assert os.listdir(data_root), "Dataset directory is empty"
 
-        cond_model = shared.sd_model.cond_stage_model
-
         self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
+
+        
+        self.shuffle_tags = shuffle_tags
+        self.tag_drop_out = tag_drop_out
+
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
+            if shared.state.interrupted:
+                raise Exception("inturrupted")
             try:
                 image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
             except Exception:
@@ -71,37 +79,58 @@ class PersonalizedBase(Dataset):
             npimage = np.array(image).astype(np.uint8)
             npimage = (npimage / 127.5 - 1.0).astype(np.float32)
 
-            torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
-            torchdata = torch.moveaxis(torchdata, 2, 0)
-
-            init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
-            init_latent = init_latent.to(devices.cpu)
-
-            entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent)
-
-            if include_cond:
+            torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
+            latent_sample = None
+
+            with torch.autocast("cuda"):
+                latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
+
+            if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
+                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
+                latent_sampling_method = "once"
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
+            elif latent_sampling_method == "deterministic":
+                # Works only for DiagonalGaussianDistribution
+                latent_dist.std = 0
+                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
+            elif latent_sampling_method == "random":
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
+
+            if not (self.tag_drop_out != 0 or self.shuffle_tags):
                 entry.cond_text = self.create_text(filename_text)
-                entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
 
-            self.dataset.append(entry)
-
-        assert len(self.dataset) > 0, "No images have been found in the dataset."
-        self.length = len(self.dataset) * repeats // batch_size
+            if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
+                with torch.autocast("cuda"):
+                    entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
+            # elif not include_cond:
+            #     _, _, _, _, hijack_fixes, token_count = cond_model.process_text([entry.cond_text])
+            #     max_n = token_count // 75
+            #     index_list = [ [] for _ in range(max_n + 1) ]
+            #     for n, (z, _) in hijack_fixes[0]:
+            #         index_list[n].append(z)
+            #     with torch.autocast("cuda"):
+            #         entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
+            #     entry.emb_index = index_list
 
-        self.dataset_length = len(self.dataset)
-        self.indexes = None
-        self.shuffle()
+            self.dataset.append(entry)
+            del torchdata
+            del latent_dist
+            del latent_sample
 
-    def shuffle(self):
-        self.indexes = np.random.permutation(self.dataset_length)
+        self.length = len(self.dataset)
+        assert self.length > 0, "No images have been found in the dataset."
+        self.batch_size = min(batch_size, self.length)
+        self.gradient_step = min(gradient_step, self.length // self.batch_size)
+        self.latent_sampling_method = latent_sampling_method
 
     def create_text(self, filename_text):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
         tags = filename_text.split(',')
-        if shared.opts.tag_drop_out != 0:
-            tags = [t for t in tags if random.random() > shared.opts.tag_drop_out]
-        if shared.opts.shuffle_tags:
+        if self.tag_drop_out != 0:
+            tags = [t for t in tags if random.random() > self.tag_drop_out]
+        if self.shuffle_tags:
             random.shuffle(tags)
         text = text.replace("[filewords]", ','.join(tags))
         return text
@@ -110,19 +139,28 @@ class PersonalizedBase(Dataset):
         return self.length
 
     def __getitem__(self, i):
-        res = []
-
-        for j in range(self.batch_size):
-            position = i * self.batch_size + j
-            if position % len(self.indexes) == 0:
-                self.shuffle()
-
-            index = self.indexes[position % len(self.indexes)]
-            entry = self.dataset[index]
-
-            if entry.cond is None:
-                entry.cond_text = self.create_text(entry.filename_text)
-
-            res.append(entry)
-
-        return res
+        entry = self.dataset[i]
+        if self.tag_drop_out != 0 or self.shuffle_tags:
+            entry.cond_text = self.create_text(entry.filename_text)
+        if self.latent_sampling_method == "random":
+            entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist)
+        return entry
+
+class PersonalizedDataLoader(DataLoader):
+    def __init__(self, *args, **kwargs):
+        super(PersonalizedDataLoader, self).__init__(shuffle=True, drop_last=True, *args, **kwargs)
+        self.collate_fn = collate_wrapper
+        
+
+class BatchLoader:
+    def __init__(self, data):
+        self.cond_text = [entry.cond_text for entry in data]
+        self.cond = [entry.cond for entry in data]
+        self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
+
+    def pin_memory(self):
+        self.latent_sample = self.latent_sample.pin_memory()
+        return self
+
+def collate_wrapper(batch):
+    return BatchLoader(batch)
\ No newline at end of file
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5e4d8688..1d5e3a32 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -184,7 +184,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
     if shared.opts.training_write_csv_every == 0:
         return
 
-    if (step + 1) % shared.opts.training_write_csv_every != 0:
+    if step % shared.opts.training_write_csv_every != 0:
         return
     write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
 
@@ -194,21 +194,23 @@ def write_loss(log_directory, filename, step, epoch_len, values):
         if write_csv_header:
             csv_writer.writeheader()
 
-        epoch = step // epoch_len
-        epoch_step = step % epoch_len 
+        epoch = (step - 1) // epoch_len
+        epoch_step = (step - 1) % epoch_len 
 
         csv_writer.writerow({
-            "step": step + 1,
+            "step": step,
             "epoch": epoch,
-            "epoch_step": epoch_step + 1,
+            "epoch_step": epoch_step,
             **values,
         })
 
-def validate_train_inputs(model_name, learn_rate, batch_size, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
+def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
     assert model_name, f"{name} not selected"
     assert learn_rate, "Learning rate is empty or 0"
     assert isinstance(batch_size, int), "Batch size must be integer"
     assert batch_size > 0, "Batch size must be positive"
+    assert isinstance(gradient_step, int), "Gradient accumulation step must be integer"
+    assert gradient_step > 0, "Gradient accumulation step must be positive"
     assert data_root, "Dataset directory is empty"
     assert os.path.isdir(data_root), "Dataset directory doesn't exist"
     assert os.listdir(data_root), "Dataset directory is empty"
@@ -224,10 +226,10 @@ def validate_train_inputs(model_name, learn_rate, batch_size, data_root, templat
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 
-def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
-    validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
+    validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
 
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
@@ -255,161 +257,205 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     else:
         images_embeds_dir = None
 
-    cond_model = shared.sd_model.cond_stage_model
-
     hijack = sd_hijack.model_hijack
 
     embedding = hijack.embedding_db.word_embeddings[embedding_name]
     checkpoint = sd_models.select_checkpoint()
 
-    ititial_step = embedding.step or 0
-    if ititial_step >= steps:
+    initial_step = embedding.step or 0
+    if initial_step >= steps:
         shared.state.textinfo = f"Model has already been trained beyond specified max steps"
         return embedding, filename
+    scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
 
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-
-    # dataset loading may take a while, so input validations and early returns should be done before this
+   # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
-    with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+    
+    pin_memory = shared.opts.pin_memory
+    
+    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
+
+    latent_sampling_method = ds.latent_sampling_method
+
+    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, batch_size=ds.batch_size, pin_memory=False)
+
     if unload:
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     embedding.vec.requires_grad = True
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
+    scaler = torch.cuda.amp.GradScaler()
 
-    losses = torch.zeros((32,))
+    batch_size = ds.batch_size
+    gradient_step = ds.gradient_step
+    # n steps = batch_size * gradient_step * n image processed
+    steps_per_epoch = len(ds) // batch_size // gradient_step
+    max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step
+    loss_step = 0
+    _loss_step = 0 #internal
 
+    
     last_saved_file = "<none>"
     last_saved_image = "<none>"
     forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
-
-    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, entries in pbar:
-        embedding.step = i + ititial_step
-
-        scheduler.apply(optimizer, embedding.step)
-        if scheduler.finished:
-            break
-
-        if shared.state.interrupted:
-            break
-
-        with torch.autocast("cuda"):
-            c = cond_model([entry.cond_text for entry in entries])
-            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-            loss = shared.sd_model(x, c)[0]
-            del x
-
-            losses[embedding.step % losses.shape[0]] = loss.item()
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-        steps_done = embedding.step + 1
-
-        epoch_num = embedding.step // len(ds)
-        epoch_step = embedding.step % len(ds)
-
-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
-
-        if embedding_dir is not None and steps_done % save_embedding_every == 0:
-            # Before saving, change name to match current checkpoint.
-            embedding_name_every = f'{embedding_name}-{steps_done}'
-            last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
-            save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
-            embedding_yet_to_be_embedded = True
-
-        write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
-            "loss": f"{losses.mean():.7f}",
-            "learn_rate": scheduler.learn_rate
-        })
-
-        if images_dir is not None and steps_done % create_image_every == 0:
-            forced_filename = f'{embedding_name}-{steps_done}'
-            last_saved_image = os.path.join(images_dir, forced_filename)
-
-            shared.sd_model.first_stage_model.to(devices.device)
-
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-                do_not_reload_embeddings=True,
-            )
-
-            if preview_from_txt2img:
-                p.prompt = preview_prompt
-                p.negative_prompt = preview_negative_prompt
-                p.steps = preview_steps
-                p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
-                p.cfg_scale = preview_cfg_scale
-                p.seed = preview_seed
-                p.width = preview_width
-                p.height = preview_height
-            else:
-                p.prompt = entries[0].cond_text
-                p.steps = 20
-                p.width = training_width
-                p.height = training_height
-
-            preview_text = p.prompt
-
-            processed = processing.process_images(p)
-            image = processed.images[0]
-
-            if unload:
-                shared.sd_model.first_stage_model.to(devices.cpu)
-
-            shared.state.current_image = image
-
-            if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
-
-                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
-
-                info = PngImagePlugin.PngInfo()
-                data = torch.load(last_saved_file)
-                info.add_text("sd-ti-embedding", embedding_to_b64(data))
-
-                title = "<{}>".format(data.get('name', '???'))
-
-                try:
-                    vectorSize = list(data['string_to_param'].values())[0].shape[0]
-                except Exception as e:
-                    vectorSize = '?'
-
-                checkpoint = sd_models.select_checkpoint()
-                footer_left = checkpoint.model_name
-                footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}v {}s'.format(vectorSize, steps_done)
-
-                captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
-                captioned_image = insert_image_data_embed(captioned_image, data)
-
-                captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
-                embedding_yet_to_be_embedded = False
-
-            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-            last_saved_image += f", prompt: {preview_text}"
-
-        shared.state.job_no = embedding.step
-
-        shared.state.textinfo = f"""
+    
+    pbar = tqdm.tqdm(total=steps - initial_step)
+    try:
+        for i in range((steps-initial_step) * gradient_step):
+            if scheduler.finished:
+                break
+            if shared.state.interrupted:
+                break
+            for j, batch in enumerate(dl):
+                # works as a drop_last=True for gradient accumulation
+                if j == max_steps_per_epoch:
+                    break
+                scheduler.apply(optimizer, embedding.step)
+                if scheduler.finished:
+                    break
+                if shared.state.interrupted:
+                    break
+
+                with torch.autocast("cuda"):
+                    # c = stack_conds(batch.cond).to(devices.device)
+                    # mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
+                    # print(mask)
+                    # c[:, 1:1+embedding.vec.shape[0]] = embedding.vec.to(devices.device, non_blocking=pin_memory)
+                    x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+                    c = shared.sd_model.cond_stage_model(batch.cond_text)
+                    loss = shared.sd_model(x, c)[0] / gradient_step
+                    del x
+                    
+                    _loss_step += loss.item()
+                scaler.scale(loss).backward()
+                
+                # go back until we reach gradient accumulation steps
+                if (j + 1) % gradient_step != 0:
+                    continue
+                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
+                #scaler.unscale_(optimizer)
+                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
+                #torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=1.0)
+                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
+                scaler.step(optimizer)
+                scaler.update()
+                embedding.step += 1
+                pbar.update()
+                optimizer.zero_grad(set_to_none=True)
+                loss_step = _loss_step
+                _loss_step = 0
+
+                steps_done = embedding.step + 1
+
+                epoch_num = embedding.step // steps_per_epoch
+                epoch_step = embedding.step % steps_per_epoch
+
+                pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+                if embedding_dir is not None and steps_done % save_embedding_every == 0:
+                    # Before saving, change name to match current checkpoint.
+                    embedding_name_every = f'{embedding_name}-{steps_done}'
+                    last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
+                    #if shared.opts.save_optimizer_state:
+                        #embedding.optimizer_state_dict = optimizer.state_dict()
+                    save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
+                    embedding_yet_to_be_embedded = True
+
+                write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, {
+                    "loss": f"{loss_step:.7f}",
+                    "learn_rate": scheduler.learn_rate
+                })
+
+                if images_dir is not None and steps_done % create_image_every == 0:
+                    forced_filename = f'{embedding_name}-{steps_done}'
+                    last_saved_image = os.path.join(images_dir, forced_filename)
+
+                    shared.sd_model.first_stage_model.to(devices.device)
+
+                    p = processing.StableDiffusionProcessingTxt2Img(
+                        sd_model=shared.sd_model,
+                        do_not_save_grid=True,
+                        do_not_save_samples=True,
+                        do_not_reload_embeddings=True,
+                    )
+
+                    if preview_from_txt2img:
+                        p.prompt = preview_prompt
+                        p.negative_prompt = preview_negative_prompt
+                        p.steps = preview_steps
+                        p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
+                        p.cfg_scale = preview_cfg_scale
+                        p.seed = preview_seed
+                        p.width = preview_width
+                        p.height = preview_height
+                    else:
+                        p.prompt = batch.cond_text[0]
+                        p.steps = 20
+                        p.width = training_width
+                        p.height = training_height
+
+                    preview_text = p.prompt
+
+                    processed = processing.process_images(p)
+                    image = processed.images[0] if len(processed.images) > 0 else None
+
+                    if unload:
+                        shared.sd_model.first_stage_model.to(devices.cpu)
+
+                    if image is not None:
+                        shared.state.current_image = image
+                        last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                        last_saved_image += f", prompt: {preview_text}"
+
+                    if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
+
+                        last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
+
+                        info = PngImagePlugin.PngInfo()
+                        data = torch.load(last_saved_file)
+                        info.add_text("sd-ti-embedding", embedding_to_b64(data))
+
+                        title = "<{}>".format(data.get('name', '???'))
+
+                        try:
+                            vectorSize = list(data['string_to_param'].values())[0].shape[0]
+                        except Exception as e:
+                            vectorSize = '?'
+
+                        checkpoint = sd_models.select_checkpoint()
+                        footer_left = checkpoint.model_name
+                        footer_mid = '[{}]'.format(checkpoint.hash)
+                        footer_right = '{}v {}s'.format(vectorSize, steps_done)
+
+                        captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
+                        captioned_image = insert_image_data_embed(captioned_image, data)
+
+                        captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+                        embedding_yet_to_be_embedded = False
+
+                    last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                    last_saved_image += f", prompt: {preview_text}"
+
+                shared.state.job_no = embedding.step
+
+                shared.state.textinfo = f"""
 <p>
-Loss: {losses.mean():.7f}<br/>
+Loss: {loss_step:.7f}<br/>
 Step: {embedding.step}<br/>
-Last prompt: {html.escape(entries[0].cond_text)}<br/>
+Last prompt: {html.escape(batch.cond_text[0])}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-
-    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
-    save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
-    shared.sd_model.first_stage_model.to(devices.device)
+        filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+        save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+    except Exception:
+        print(traceback.format_exc(), file=sys.stderr)
+        pass
+    finally:
+        pbar.leave = False
+        pbar.close()
+        shared.sd_model.first_stage_model.to(devices.device)
 
     return embedding, filename
 
diff --git a/modules/ui.py b/modules/ui.py
index a5953fce..9d2a1cbf 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1262,7 +1262,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             with gr.Row():
                                 interrupt_preprocessing = gr.Button("Interrupt")
-                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
+                            run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                     process_split.change(
                         fn=lambda show: gr_show(show),
@@ -1289,6 +1289,7 @@ def create_ui(wrap_gradio_gpu_call):
                         hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
 
                     batch_size = gr.Number(label='Batch size', value=1, precision=0)
+                    gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
@@ -1299,6 +1300,11 @@ def create_ui(wrap_gradio_gpu_call):
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+                    with gr.Row():
+                        shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False)
+                        tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0)
+                    with gr.Row():
+                        latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
 
                     with gr.Row():
                         interrupt_training = gr.Button(value="Interrupt")
@@ -1387,11 +1393,15 @@ def create_ui(wrap_gradio_gpu_call):
                 train_embedding_name,
                 embedding_learn_rate,
                 batch_size,
+                gradient_step,
                 dataset_directory,
                 log_directory,
                 training_width,
                 training_height,
                 steps,
+                shuffle_tags,
+                tag_drop_out,
+                latent_sampling_method,
                 create_image_every,
                 save_embedding_every,
                 template_file,
@@ -1412,11 +1422,15 @@ def create_ui(wrap_gradio_gpu_call):
                 train_hypernetwork_name,
                 hypernetwork_learn_rate,
                 batch_size,
+                gradient_step,
                 dataset_directory,
                 log_directory,
                 training_width,
                 training_height,
                 steps,
+                shuffle_tags,
+                tag_drop_out,
+                latent_sampling_method,
                 create_image_every,
                 save_embedding_every,
                 template_file,
-- 
cgit v1.2.3


From a4a5735d0a80218e59f8a6e8401726f7209a6a8d Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Sun, 20 Nov 2022 12:38:18 +0900
Subject: remove unnecessary comment

---
 modules/textual_inversion/dataset.py | 9 ---------
 1 file changed, 9 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index d594b49d..1dd53b85 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -103,15 +103,6 @@ class PersonalizedBase(Dataset):
             if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
                 with torch.autocast("cuda"):
                     entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
-            # elif not include_cond:
-            #     _, _, _, _, hijack_fixes, token_count = cond_model.process_text([entry.cond_text])
-            #     max_n = token_count // 75
-            #     index_list = [ [] for _ in range(max_n + 1) ]
-            #     for n, (z, _) in hijack_fixes[0]:
-            #         index_list[n].append(z)
-            #     with torch.autocast("cuda"):
-            #         entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
-            #     entry.emb_index = index_list
 
             self.dataset.append(entry)
             del torchdata
-- 
cgit v1.2.3


From 2d22d72cdaaf2b78b2986b841d478c11ac855dd2 Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Sun, 20 Nov 2022 16:14:27 +0900
Subject: fix random sampling with pin_memory

---
 modules/textual_inversion/dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 1dd53b85..110c0e09 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -134,7 +134,7 @@ class PersonalizedBase(Dataset):
         if self.tag_drop_out != 0 or self.shuffle_tags:
             entry.cond_text = self.create_text(entry.filename_text)
         if self.latent_sampling_method == "random":
-            entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist)
+            entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
         return entry
 
 class PersonalizedDataLoader(DataLoader):
-- 
cgit v1.2.3


From c81d440d876dfd2ab3560410f37442ef56fc6632 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 20 Nov 2022 16:39:20 +0300
Subject: moved deepdanbooru to pure pytorch implementation

---
 modules/api/api.py                      |  10 +-
 modules/deepbooru.py                    | 258 +++++-------
 modules/deepbooru_model.py              | 676 ++++++++++++++++++++++++++++++++
 modules/shared.py                       |   2 +-
 modules/textual_inversion/preprocess.py |  12 +-
 modules/ui.py                           |   7 +-
 6 files changed, 776 insertions(+), 189 deletions(-)
 create mode 100644 modules/deepbooru_model.py

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 79b2c818..7a567be3 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -9,7 +9,7 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers
+from modules import sd_samplers, deepbooru
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.extras import run_extras, run_pnginfo
@@ -18,9 +18,6 @@ from modules.sd_models import checkpoints_list
 from modules.realesrgan_model import get_realesrgan_models
 from typing import List
 
-if shared.cmd_opts.deepdanbooru:
-    from modules.deepbooru import get_deepbooru_tags
-
 def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
@@ -245,10 +242,7 @@ class Api:
             if interrogatereq.model == "clip":
                 processed = shared.interrogator.interrogate(img)
             elif interrogatereq.model == "deepdanbooru":
-                if shared.cmd_opts.deepdanbooru:
-                    processed = get_deepbooru_tags(img)
-                else:
-                    raise HTTPException(status_code=404, detail="Model not found. Add --deepdanbooru when launching for using the model.")
+                processed = deepbooru.model.tag(img)
             else:
                 raise HTTPException(status_code=404, detail="Model not found")
         
diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 8bbc90a4..b9066d81 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,173 +1,97 @@
-import os.path
-from concurrent.futures import ProcessPoolExecutor
-import multiprocessing
-import time
+import os
 import re
 
+import torch
+from PIL import Image
+import numpy as np
+
+from modules import modelloader, paths, deepbooru_model, devices, images, shared
+
 re_special = re.compile(r'([\\()])')
 
-def get_deepbooru_tags(pil_image):
-    """
-    This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
-    """
-    from modules import shared  # prevents circular reference
-
-    try:
-        create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts())
-        return get_tags_from_process(pil_image)
-    finally:
-        release_process()
-
-
-OPT_INCLUDE_RANKS = "include_ranks"
-def create_deepbooru_opts():
-    from modules import shared
-
-    return {
-        "use_spaces": shared.opts.deepbooru_use_spaces,
-        "use_escape": shared.opts.deepbooru_escape,
-        "alpha_sort": shared.opts.deepbooru_sort_alpha,
-        OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks,
-    }
-
-
-def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts):
-    model, tags = get_deepbooru_tags_model()
-    while True: # while process is running, keep monitoring queue for new image
-        pil_image = queue.get()
-        if pil_image == "QUIT":
-            break
-        else:
-            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts)
-
-
-def create_deepbooru_process(threshold, deepbooru_opts):
-    """
-    Creates deepbooru process.  A queue is created to send images into the process.  This enables multiple images
-    to be processed in a row without reloading the model or creating a new process.  To return the data, a shared
-    dictionary is created to hold the tags created.  To wait for tags to be returned, a value of -1 is assigned
-    to the dictionary and the method adding the image to the queue should wait for this value to be updated with
-    the tags.
-    """
-    from modules import shared  # prevents circular reference
-    context = multiprocessing.get_context("spawn")
-    shared.deepbooru_process_manager = context.Manager()
-    shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
-    shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
-    shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process = context.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
-    shared.deepbooru_process.start()
-
-
-def get_tags_from_process(image):
-    from modules import shared
-
-    shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process_queue.put(image)
-    while shared.deepbooru_process_return["value"] == -1:
-        time.sleep(0.2)
-    caption = shared.deepbooru_process_return["value"]
-    shared.deepbooru_process_return["value"] = -1
-
-    return caption
-
-
-def release_process():
-    """
-    Stops the deepbooru process to return used memory
-    """
-    from modules import shared  # prevents circular reference
-    shared.deepbooru_process_queue.put("QUIT")
-    shared.deepbooru_process.join()
-    shared.deepbooru_process_queue = None
-    shared.deepbooru_process = None
-    shared.deepbooru_process_return = None
-    shared.deepbooru_process_manager = None
-
-def get_deepbooru_tags_model():
-    import deepdanbooru as dd
-    import tensorflow as tf
-    import numpy as np
-    this_folder = os.path.dirname(__file__)
-    model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
-    if not os.path.exists(os.path.join(model_path, 'project.json')):
-        # there is no point importing these every time
-        import zipfile
-        from basicsr.utils.download_util import load_file_from_url
-        load_file_from_url(
-            r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
-            model_path)
-        with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
-            zip_ref.extractall(model_path)
-        os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
-
-    tags = dd.project.load_tags_from_project(model_path)
-    model = dd.project.load_model_from_project(
-        model_path, compile_model=False
-    )
-    return model, tags
-
-
-def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts):
-    import deepdanbooru as dd
-    import tensorflow as tf
-    import numpy as np
-
-    alpha_sort = deepbooru_opts['alpha_sort']
-    use_spaces = deepbooru_opts['use_spaces']
-    use_escape = deepbooru_opts['use_escape']
-    include_ranks = deepbooru_opts['include_ranks']
-
-    width = model.input_shape[2]
-    height = model.input_shape[1]
-    image = np.array(pil_image)
-    image = tf.image.resize(
-        image,
-        size=(height, width),
-        method=tf.image.ResizeMethod.AREA,
-        preserve_aspect_ratio=True,
-    )
-    image = image.numpy()  # EagerTensor to np.array
-    image = dd.image.transform_and_pad_image(image, width, height)
-    image = image / 255.0
-    image_shape = image.shape
-    image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
-
-    y = model.predict(image)[0]
-
-    result_dict = {}
-
-    for i, tag in enumerate(tags):
-        result_dict[tag] = y[i]
-
-    unsorted_tags_in_theshold = []
-    result_tags_print = []
-    for tag in tags:
-        if result_dict[tag] >= threshold:
+
+class DeepDanbooru:
+    def __init__(self):
+        self.model = None
+
+    def load(self):
+        if self.model is not None:
+            return
+
+        files = modelloader.load_models(
+            model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
+            model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
+            ext_filter=".pt",
+            download_name='model-resnet_custom_v3.pt',
+        )
+
+        self.model = deepbooru_model.DeepDanbooruModel()
+        self.model.load_state_dict(torch.load(files[0], map_location="cpu"))
+
+        self.model.eval()
+        self.model.to(devices.cpu, devices.dtype)
+
+    def start(self):
+        self.load()
+        self.model.to(devices.device)
+
+    def stop(self):
+        if not shared.opts.interrogate_keep_models_in_memory:
+            self.model.to(devices.cpu)
+            devices.torch_gc()
+
+    def tag(self, pil_image):
+        self.start()
+        res = self.tag_multi(pil_image)
+        self.stop()
+
+        return res
+
+    def tag_multi(self, pil_image, force_disable_ranks=False):
+        threshold = shared.opts.interrogate_deepbooru_score_threshold
+        use_spaces = shared.opts.deepbooru_use_spaces
+        use_escape = shared.opts.deepbooru_escape
+        alpha_sort = shared.opts.deepbooru_sort_alpha
+        include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks
+
+        pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512)
+        a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
+
+        with torch.no_grad(), devices.autocast():
+            x = torch.from_numpy(a).cuda()
+            y = self.model(x)[0].detach().cpu().numpy()
+
+        probability_dict = {}
+
+        for tag, probability in zip(self.model.tags, y):
+            if probability < threshold:
+                continue
+
             if tag.startswith("rating:"):
                 continue
-            unsorted_tags_in_theshold.append((result_dict[tag], tag))
-            result_tags_print.append(f'{result_dict[tag]} {tag}')
-
-    # sort tags
-    result_tags_out = []
-    sort_ndx = 0
-    if alpha_sort:
-        sort_ndx = 1
-
-    # sort by reverse by likelihood and normal for alpha, and format tag text as requested
-    unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
-    for weight, tag in unsorted_tags_in_theshold:
-        tag_outformat = tag
-        if use_spaces:
-            tag_outformat = tag_outformat.replace('_', ' ')
-        if use_escape:
-            tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
-        if include_ranks:
-            tag_outformat = f"({tag_outformat}:{weight:.3f})"
-
-        result_tags_out.append(tag_outformat)
-
-    print('\n'.join(sorted(result_tags_print, reverse=True)))
-
-    return ', '.join(result_tags_out)
+
+            probability_dict[tag] = probability
+
+        if alpha_sort:
+            tags = sorted(probability_dict)
+        else:
+            tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])]
+
+        res = []
+
+        for tag in tags:
+            probability = probability_dict[tag]
+            tag_outformat = tag
+            if use_spaces:
+                tag_outformat = tag_outformat.replace('_', ' ')
+            if use_escape:
+                tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
+            if include_ranks:
+                tag_outformat = f"({tag_outformat}:{probability:.3f})"
+
+            res.append(tag_outformat)
+
+        return ", ".join(res)
+
+
+model = DeepDanbooru()
diff --git a/modules/deepbooru_model.py b/modules/deepbooru_model.py
new file mode 100644
index 00000000..edd40c81
--- /dev/null
+++ b/modules/deepbooru_model.py
@@ -0,0 +1,676 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
+
+
+class DeepDanbooruModel(nn.Module):
+    def __init__(self):
+        super(DeepDanbooruModel, self).__init__()
+
+        self.tags = []
+
+        self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2))
+        self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))
+        self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+        self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64)
+        self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+        self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+        self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
+        self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+        self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+        self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
+        self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+        self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+        self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2))
+        self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128)
+        self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2))
+        self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2))
+        self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256)
+        self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
+        self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
+        self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2))
+        self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2))
+        self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512)
+        self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2))
+        self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+        self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
+        self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
+        self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+        self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
+        self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
+        self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+        self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2))
+        self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024)
+        self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2))
+        self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+        self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
+        self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
+        self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+        self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
+        self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
+        self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+        self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False)
+
+    def forward(self, *inputs):
+        t_358, = inputs
+        t_359 = t_358.permute(*[0, 3, 1, 2])
+        t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
+        t_360 = self.n_Conv_0(t_359_padded)
+        t_361 = F.relu(t_360)
+        t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
+        t_362 = self.n_MaxPool_0(t_361)
+        t_363 = self.n_Conv_1(t_362)
+        t_364 = self.n_Conv_2(t_362)
+        t_365 = F.relu(t_364)
+        t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0)
+        t_366 = self.n_Conv_3(t_365_padded)
+        t_367 = F.relu(t_366)
+        t_368 = self.n_Conv_4(t_367)
+        t_369 = torch.add(t_368, t_363)
+        t_370 = F.relu(t_369)
+        t_371 = self.n_Conv_5(t_370)
+        t_372 = F.relu(t_371)
+        t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0)
+        t_373 = self.n_Conv_6(t_372_padded)
+        t_374 = F.relu(t_373)
+        t_375 = self.n_Conv_7(t_374)
+        t_376 = torch.add(t_375, t_370)
+        t_377 = F.relu(t_376)
+        t_378 = self.n_Conv_8(t_377)
+        t_379 = F.relu(t_378)
+        t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0)
+        t_380 = self.n_Conv_9(t_379_padded)
+        t_381 = F.relu(t_380)
+        t_382 = self.n_Conv_10(t_381)
+        t_383 = torch.add(t_382, t_377)
+        t_384 = F.relu(t_383)
+        t_385 = self.n_Conv_11(t_384)
+        t_386 = self.n_Conv_12(t_384)
+        t_387 = F.relu(t_386)
+        t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0)
+        t_388 = self.n_Conv_13(t_387_padded)
+        t_389 = F.relu(t_388)
+        t_390 = self.n_Conv_14(t_389)
+        t_391 = torch.add(t_390, t_385)
+        t_392 = F.relu(t_391)
+        t_393 = self.n_Conv_15(t_392)
+        t_394 = F.relu(t_393)
+        t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0)
+        t_395 = self.n_Conv_16(t_394_padded)
+        t_396 = F.relu(t_395)
+        t_397 = self.n_Conv_17(t_396)
+        t_398 = torch.add(t_397, t_392)
+        t_399 = F.relu(t_398)
+        t_400 = self.n_Conv_18(t_399)
+        t_401 = F.relu(t_400)
+        t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0)
+        t_402 = self.n_Conv_19(t_401_padded)
+        t_403 = F.relu(t_402)
+        t_404 = self.n_Conv_20(t_403)
+        t_405 = torch.add(t_404, t_399)
+        t_406 = F.relu(t_405)
+        t_407 = self.n_Conv_21(t_406)
+        t_408 = F.relu(t_407)
+        t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0)
+        t_409 = self.n_Conv_22(t_408_padded)
+        t_410 = F.relu(t_409)
+        t_411 = self.n_Conv_23(t_410)
+        t_412 = torch.add(t_411, t_406)
+        t_413 = F.relu(t_412)
+        t_414 = self.n_Conv_24(t_413)
+        t_415 = F.relu(t_414)
+        t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0)
+        t_416 = self.n_Conv_25(t_415_padded)
+        t_417 = F.relu(t_416)
+        t_418 = self.n_Conv_26(t_417)
+        t_419 = torch.add(t_418, t_413)
+        t_420 = F.relu(t_419)
+        t_421 = self.n_Conv_27(t_420)
+        t_422 = F.relu(t_421)
+        t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0)
+        t_423 = self.n_Conv_28(t_422_padded)
+        t_424 = F.relu(t_423)
+        t_425 = self.n_Conv_29(t_424)
+        t_426 = torch.add(t_425, t_420)
+        t_427 = F.relu(t_426)
+        t_428 = self.n_Conv_30(t_427)
+        t_429 = F.relu(t_428)
+        t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0)
+        t_430 = self.n_Conv_31(t_429_padded)
+        t_431 = F.relu(t_430)
+        t_432 = self.n_Conv_32(t_431)
+        t_433 = torch.add(t_432, t_427)
+        t_434 = F.relu(t_433)
+        t_435 = self.n_Conv_33(t_434)
+        t_436 = F.relu(t_435)
+        t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0)
+        t_437 = self.n_Conv_34(t_436_padded)
+        t_438 = F.relu(t_437)
+        t_439 = self.n_Conv_35(t_438)
+        t_440 = torch.add(t_439, t_434)
+        t_441 = F.relu(t_440)
+        t_442 = self.n_Conv_36(t_441)
+        t_443 = self.n_Conv_37(t_441)
+        t_444 = F.relu(t_443)
+        t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0)
+        t_445 = self.n_Conv_38(t_444_padded)
+        t_446 = F.relu(t_445)
+        t_447 = self.n_Conv_39(t_446)
+        t_448 = torch.add(t_447, t_442)
+        t_449 = F.relu(t_448)
+        t_450 = self.n_Conv_40(t_449)
+        t_451 = F.relu(t_450)
+        t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0)
+        t_452 = self.n_Conv_41(t_451_padded)
+        t_453 = F.relu(t_452)
+        t_454 = self.n_Conv_42(t_453)
+        t_455 = torch.add(t_454, t_449)
+        t_456 = F.relu(t_455)
+        t_457 = self.n_Conv_43(t_456)
+        t_458 = F.relu(t_457)
+        t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0)
+        t_459 = self.n_Conv_44(t_458_padded)
+        t_460 = F.relu(t_459)
+        t_461 = self.n_Conv_45(t_460)
+        t_462 = torch.add(t_461, t_456)
+        t_463 = F.relu(t_462)
+        t_464 = self.n_Conv_46(t_463)
+        t_465 = F.relu(t_464)
+        t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0)
+        t_466 = self.n_Conv_47(t_465_padded)
+        t_467 = F.relu(t_466)
+        t_468 = self.n_Conv_48(t_467)
+        t_469 = torch.add(t_468, t_463)
+        t_470 = F.relu(t_469)
+        t_471 = self.n_Conv_49(t_470)
+        t_472 = F.relu(t_471)
+        t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0)
+        t_473 = self.n_Conv_50(t_472_padded)
+        t_474 = F.relu(t_473)
+        t_475 = self.n_Conv_51(t_474)
+        t_476 = torch.add(t_475, t_470)
+        t_477 = F.relu(t_476)
+        t_478 = self.n_Conv_52(t_477)
+        t_479 = F.relu(t_478)
+        t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0)
+        t_480 = self.n_Conv_53(t_479_padded)
+        t_481 = F.relu(t_480)
+        t_482 = self.n_Conv_54(t_481)
+        t_483 = torch.add(t_482, t_477)
+        t_484 = F.relu(t_483)
+        t_485 = self.n_Conv_55(t_484)
+        t_486 = F.relu(t_485)
+        t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0)
+        t_487 = self.n_Conv_56(t_486_padded)
+        t_488 = F.relu(t_487)
+        t_489 = self.n_Conv_57(t_488)
+        t_490 = torch.add(t_489, t_484)
+        t_491 = F.relu(t_490)
+        t_492 = self.n_Conv_58(t_491)
+        t_493 = F.relu(t_492)
+        t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0)
+        t_494 = self.n_Conv_59(t_493_padded)
+        t_495 = F.relu(t_494)
+        t_496 = self.n_Conv_60(t_495)
+        t_497 = torch.add(t_496, t_491)
+        t_498 = F.relu(t_497)
+        t_499 = self.n_Conv_61(t_498)
+        t_500 = F.relu(t_499)
+        t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0)
+        t_501 = self.n_Conv_62(t_500_padded)
+        t_502 = F.relu(t_501)
+        t_503 = self.n_Conv_63(t_502)
+        t_504 = torch.add(t_503, t_498)
+        t_505 = F.relu(t_504)
+        t_506 = self.n_Conv_64(t_505)
+        t_507 = F.relu(t_506)
+        t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0)
+        t_508 = self.n_Conv_65(t_507_padded)
+        t_509 = F.relu(t_508)
+        t_510 = self.n_Conv_66(t_509)
+        t_511 = torch.add(t_510, t_505)
+        t_512 = F.relu(t_511)
+        t_513 = self.n_Conv_67(t_512)
+        t_514 = F.relu(t_513)
+        t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0)
+        t_515 = self.n_Conv_68(t_514_padded)
+        t_516 = F.relu(t_515)
+        t_517 = self.n_Conv_69(t_516)
+        t_518 = torch.add(t_517, t_512)
+        t_519 = F.relu(t_518)
+        t_520 = self.n_Conv_70(t_519)
+        t_521 = F.relu(t_520)
+        t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0)
+        t_522 = self.n_Conv_71(t_521_padded)
+        t_523 = F.relu(t_522)
+        t_524 = self.n_Conv_72(t_523)
+        t_525 = torch.add(t_524, t_519)
+        t_526 = F.relu(t_525)
+        t_527 = self.n_Conv_73(t_526)
+        t_528 = F.relu(t_527)
+        t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0)
+        t_529 = self.n_Conv_74(t_528_padded)
+        t_530 = F.relu(t_529)
+        t_531 = self.n_Conv_75(t_530)
+        t_532 = torch.add(t_531, t_526)
+        t_533 = F.relu(t_532)
+        t_534 = self.n_Conv_76(t_533)
+        t_535 = F.relu(t_534)
+        t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0)
+        t_536 = self.n_Conv_77(t_535_padded)
+        t_537 = F.relu(t_536)
+        t_538 = self.n_Conv_78(t_537)
+        t_539 = torch.add(t_538, t_533)
+        t_540 = F.relu(t_539)
+        t_541 = self.n_Conv_79(t_540)
+        t_542 = F.relu(t_541)
+        t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0)
+        t_543 = self.n_Conv_80(t_542_padded)
+        t_544 = F.relu(t_543)
+        t_545 = self.n_Conv_81(t_544)
+        t_546 = torch.add(t_545, t_540)
+        t_547 = F.relu(t_546)
+        t_548 = self.n_Conv_82(t_547)
+        t_549 = F.relu(t_548)
+        t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0)
+        t_550 = self.n_Conv_83(t_549_padded)
+        t_551 = F.relu(t_550)
+        t_552 = self.n_Conv_84(t_551)
+        t_553 = torch.add(t_552, t_547)
+        t_554 = F.relu(t_553)
+        t_555 = self.n_Conv_85(t_554)
+        t_556 = F.relu(t_555)
+        t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0)
+        t_557 = self.n_Conv_86(t_556_padded)
+        t_558 = F.relu(t_557)
+        t_559 = self.n_Conv_87(t_558)
+        t_560 = torch.add(t_559, t_554)
+        t_561 = F.relu(t_560)
+        t_562 = self.n_Conv_88(t_561)
+        t_563 = F.relu(t_562)
+        t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0)
+        t_564 = self.n_Conv_89(t_563_padded)
+        t_565 = F.relu(t_564)
+        t_566 = self.n_Conv_90(t_565)
+        t_567 = torch.add(t_566, t_561)
+        t_568 = F.relu(t_567)
+        t_569 = self.n_Conv_91(t_568)
+        t_570 = F.relu(t_569)
+        t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0)
+        t_571 = self.n_Conv_92(t_570_padded)
+        t_572 = F.relu(t_571)
+        t_573 = self.n_Conv_93(t_572)
+        t_574 = torch.add(t_573, t_568)
+        t_575 = F.relu(t_574)
+        t_576 = self.n_Conv_94(t_575)
+        t_577 = F.relu(t_576)
+        t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0)
+        t_578 = self.n_Conv_95(t_577_padded)
+        t_579 = F.relu(t_578)
+        t_580 = self.n_Conv_96(t_579)
+        t_581 = torch.add(t_580, t_575)
+        t_582 = F.relu(t_581)
+        t_583 = self.n_Conv_97(t_582)
+        t_584 = F.relu(t_583)
+        t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0)
+        t_585 = self.n_Conv_98(t_584_padded)
+        t_586 = F.relu(t_585)
+        t_587 = self.n_Conv_99(t_586)
+        t_588 = self.n_Conv_100(t_582)
+        t_589 = torch.add(t_587, t_588)
+        t_590 = F.relu(t_589)
+        t_591 = self.n_Conv_101(t_590)
+        t_592 = F.relu(t_591)
+        t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0)
+        t_593 = self.n_Conv_102(t_592_padded)
+        t_594 = F.relu(t_593)
+        t_595 = self.n_Conv_103(t_594)
+        t_596 = torch.add(t_595, t_590)
+        t_597 = F.relu(t_596)
+        t_598 = self.n_Conv_104(t_597)
+        t_599 = F.relu(t_598)
+        t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0)
+        t_600 = self.n_Conv_105(t_599_padded)
+        t_601 = F.relu(t_600)
+        t_602 = self.n_Conv_106(t_601)
+        t_603 = torch.add(t_602, t_597)
+        t_604 = F.relu(t_603)
+        t_605 = self.n_Conv_107(t_604)
+        t_606 = F.relu(t_605)
+        t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0)
+        t_607 = self.n_Conv_108(t_606_padded)
+        t_608 = F.relu(t_607)
+        t_609 = self.n_Conv_109(t_608)
+        t_610 = torch.add(t_609, t_604)
+        t_611 = F.relu(t_610)
+        t_612 = self.n_Conv_110(t_611)
+        t_613 = F.relu(t_612)
+        t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0)
+        t_614 = self.n_Conv_111(t_613_padded)
+        t_615 = F.relu(t_614)
+        t_616 = self.n_Conv_112(t_615)
+        t_617 = torch.add(t_616, t_611)
+        t_618 = F.relu(t_617)
+        t_619 = self.n_Conv_113(t_618)
+        t_620 = F.relu(t_619)
+        t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0)
+        t_621 = self.n_Conv_114(t_620_padded)
+        t_622 = F.relu(t_621)
+        t_623 = self.n_Conv_115(t_622)
+        t_624 = torch.add(t_623, t_618)
+        t_625 = F.relu(t_624)
+        t_626 = self.n_Conv_116(t_625)
+        t_627 = F.relu(t_626)
+        t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0)
+        t_628 = self.n_Conv_117(t_627_padded)
+        t_629 = F.relu(t_628)
+        t_630 = self.n_Conv_118(t_629)
+        t_631 = torch.add(t_630, t_625)
+        t_632 = F.relu(t_631)
+        t_633 = self.n_Conv_119(t_632)
+        t_634 = F.relu(t_633)
+        t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0)
+        t_635 = self.n_Conv_120(t_634_padded)
+        t_636 = F.relu(t_635)
+        t_637 = self.n_Conv_121(t_636)
+        t_638 = torch.add(t_637, t_632)
+        t_639 = F.relu(t_638)
+        t_640 = self.n_Conv_122(t_639)
+        t_641 = F.relu(t_640)
+        t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0)
+        t_642 = self.n_Conv_123(t_641_padded)
+        t_643 = F.relu(t_642)
+        t_644 = self.n_Conv_124(t_643)
+        t_645 = torch.add(t_644, t_639)
+        t_646 = F.relu(t_645)
+        t_647 = self.n_Conv_125(t_646)
+        t_648 = F.relu(t_647)
+        t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0)
+        t_649 = self.n_Conv_126(t_648_padded)
+        t_650 = F.relu(t_649)
+        t_651 = self.n_Conv_127(t_650)
+        t_652 = torch.add(t_651, t_646)
+        t_653 = F.relu(t_652)
+        t_654 = self.n_Conv_128(t_653)
+        t_655 = F.relu(t_654)
+        t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0)
+        t_656 = self.n_Conv_129(t_655_padded)
+        t_657 = F.relu(t_656)
+        t_658 = self.n_Conv_130(t_657)
+        t_659 = torch.add(t_658, t_653)
+        t_660 = F.relu(t_659)
+        t_661 = self.n_Conv_131(t_660)
+        t_662 = F.relu(t_661)
+        t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0)
+        t_663 = self.n_Conv_132(t_662_padded)
+        t_664 = F.relu(t_663)
+        t_665 = self.n_Conv_133(t_664)
+        t_666 = torch.add(t_665, t_660)
+        t_667 = F.relu(t_666)
+        t_668 = self.n_Conv_134(t_667)
+        t_669 = F.relu(t_668)
+        t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0)
+        t_670 = self.n_Conv_135(t_669_padded)
+        t_671 = F.relu(t_670)
+        t_672 = self.n_Conv_136(t_671)
+        t_673 = torch.add(t_672, t_667)
+        t_674 = F.relu(t_673)
+        t_675 = self.n_Conv_137(t_674)
+        t_676 = F.relu(t_675)
+        t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0)
+        t_677 = self.n_Conv_138(t_676_padded)
+        t_678 = F.relu(t_677)
+        t_679 = self.n_Conv_139(t_678)
+        t_680 = torch.add(t_679, t_674)
+        t_681 = F.relu(t_680)
+        t_682 = self.n_Conv_140(t_681)
+        t_683 = F.relu(t_682)
+        t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0)
+        t_684 = self.n_Conv_141(t_683_padded)
+        t_685 = F.relu(t_684)
+        t_686 = self.n_Conv_142(t_685)
+        t_687 = torch.add(t_686, t_681)
+        t_688 = F.relu(t_687)
+        t_689 = self.n_Conv_143(t_688)
+        t_690 = F.relu(t_689)
+        t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0)
+        t_691 = self.n_Conv_144(t_690_padded)
+        t_692 = F.relu(t_691)
+        t_693 = self.n_Conv_145(t_692)
+        t_694 = torch.add(t_693, t_688)
+        t_695 = F.relu(t_694)
+        t_696 = self.n_Conv_146(t_695)
+        t_697 = F.relu(t_696)
+        t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0)
+        t_698 = self.n_Conv_147(t_697_padded)
+        t_699 = F.relu(t_698)
+        t_700 = self.n_Conv_148(t_699)
+        t_701 = torch.add(t_700, t_695)
+        t_702 = F.relu(t_701)
+        t_703 = self.n_Conv_149(t_702)
+        t_704 = F.relu(t_703)
+        t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0)
+        t_705 = self.n_Conv_150(t_704_padded)
+        t_706 = F.relu(t_705)
+        t_707 = self.n_Conv_151(t_706)
+        t_708 = torch.add(t_707, t_702)
+        t_709 = F.relu(t_708)
+        t_710 = self.n_Conv_152(t_709)
+        t_711 = F.relu(t_710)
+        t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0)
+        t_712 = self.n_Conv_153(t_711_padded)
+        t_713 = F.relu(t_712)
+        t_714 = self.n_Conv_154(t_713)
+        t_715 = torch.add(t_714, t_709)
+        t_716 = F.relu(t_715)
+        t_717 = self.n_Conv_155(t_716)
+        t_718 = F.relu(t_717)
+        t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0)
+        t_719 = self.n_Conv_156(t_718_padded)
+        t_720 = F.relu(t_719)
+        t_721 = self.n_Conv_157(t_720)
+        t_722 = torch.add(t_721, t_716)
+        t_723 = F.relu(t_722)
+        t_724 = self.n_Conv_158(t_723)
+        t_725 = self.n_Conv_159(t_723)
+        t_726 = F.relu(t_725)
+        t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0)
+        t_727 = self.n_Conv_160(t_726_padded)
+        t_728 = F.relu(t_727)
+        t_729 = self.n_Conv_161(t_728)
+        t_730 = torch.add(t_729, t_724)
+        t_731 = F.relu(t_730)
+        t_732 = self.n_Conv_162(t_731)
+        t_733 = F.relu(t_732)
+        t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0)
+        t_734 = self.n_Conv_163(t_733_padded)
+        t_735 = F.relu(t_734)
+        t_736 = self.n_Conv_164(t_735)
+        t_737 = torch.add(t_736, t_731)
+        t_738 = F.relu(t_737)
+        t_739 = self.n_Conv_165(t_738)
+        t_740 = F.relu(t_739)
+        t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0)
+        t_741 = self.n_Conv_166(t_740_padded)
+        t_742 = F.relu(t_741)
+        t_743 = self.n_Conv_167(t_742)
+        t_744 = torch.add(t_743, t_738)
+        t_745 = F.relu(t_744)
+        t_746 = self.n_Conv_168(t_745)
+        t_747 = self.n_Conv_169(t_745)
+        t_748 = F.relu(t_747)
+        t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0)
+        t_749 = self.n_Conv_170(t_748_padded)
+        t_750 = F.relu(t_749)
+        t_751 = self.n_Conv_171(t_750)
+        t_752 = torch.add(t_751, t_746)
+        t_753 = F.relu(t_752)
+        t_754 = self.n_Conv_172(t_753)
+        t_755 = F.relu(t_754)
+        t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0)
+        t_756 = self.n_Conv_173(t_755_padded)
+        t_757 = F.relu(t_756)
+        t_758 = self.n_Conv_174(t_757)
+        t_759 = torch.add(t_758, t_753)
+        t_760 = F.relu(t_759)
+        t_761 = self.n_Conv_175(t_760)
+        t_762 = F.relu(t_761)
+        t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0)
+        t_763 = self.n_Conv_176(t_762_padded)
+        t_764 = F.relu(t_763)
+        t_765 = self.n_Conv_177(t_764)
+        t_766 = torch.add(t_765, t_760)
+        t_767 = F.relu(t_766)
+        t_768 = self.n_Conv_178(t_767)
+        t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:])
+        t_770 = torch.squeeze(t_769, 3)
+        t_770 = torch.squeeze(t_770, 2)
+        t_771 = torch.sigmoid(t_770)
+        return t_771
+
+    def load_state_dict(self, state_dict, **kwargs):
+        self.tags = state_dict.get('tags', [])
+
+        super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'})
+
diff --git a/modules/shared.py b/modules/shared.py
index a4457305..c93ae2a3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -55,7 +55,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
-parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
+parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 488aa5b5..56b9b2eb 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -6,12 +6,10 @@ import sys
 import tqdm
 import time
 
-from modules import shared, images
+from modules import shared, images, deepbooru
 from modules.paths import models_path
 from modules.shared import opts, cmd_opts
 from modules.textual_inversion import autocrop
-if cmd_opts.deepdanbooru:
-    import modules.deepbooru as deepbooru
 
 
 def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):
@@ -20,9 +18,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
             shared.interrogator.load()
 
         if process_caption_deepbooru:
-            db_opts = deepbooru.create_deepbooru_opts()
-            db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
-            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
+            deepbooru.model.start()
 
         preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug)
 
@@ -32,7 +28,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
             shared.interrogator.send_blip_to_ram()
 
         if process_caption_deepbooru:
-            deepbooru.release_process()
+            deepbooru.model.stop()
 
 
 def listfiles(dirname):
@@ -58,7 +54,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
     if params.process_caption_deepbooru:
         if len(caption) > 0:
             caption += ", "
-        caption += deepbooru.get_tags_from_process(image)
+        caption += deepbooru.model.tag_multi(image)
 
     filename_part = params.src
     filename_part = os.path.splitext(filename_part)[0]
diff --git a/modules/ui.py b/modules/ui.py
index a5953fce..e6da1b2a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -19,14 +19,11 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 
 
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru
 from modules.paths import script_path
 
 from modules.shared import opts, cmd_opts, restricted_opts
 
-if cmd_opts.deepdanbooru:
-    from modules.deepbooru import get_deepbooru_tags
-
 import modules.codeformer_model
 import modules.generation_parameters_copypaste as parameters_copypaste
 import modules.gfpgan_model
@@ -352,7 +349,7 @@ def interrogate(image):
 
 
 def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image)
+    prompt = deepbooru.model.tag(image)
     return gr_show(True) if prompt is None else prompt
 
 
-- 
cgit v1.2.3


From 637815632f9f362c9959e53139d37e88ea9ace6f Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Sun, 20 Nov 2022 13:36:05 -0500
Subject: Generalize SD torch load/save to implement safetensor merging compat

---
 modules/extras.py    |   15 +-
 modules/sd_models.py |   25 +-
 modules/ui.py        | 3626 +++++++++++++++++++++++++-------------------------
 3 files changed, 1840 insertions(+), 1826 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 71b93a06..820427de 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -249,7 +249,7 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, save_as_safetensors, custom_name):
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
@@ -264,16 +264,16 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
     teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
 
     print(f"Loading {primary_model_info.filename}...")
-    primary_model = torch.load(primary_model_info.filename, map_location='cpu')
+    primary_model = sd_models.torch_load(primary_model_info.filename, primary_model_info, map_override='cpu')
     theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
 
     print(f"Loading {secondary_model_info.filename}...")
-    secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
+    secondary_model = sd_models.torch_load(secondary_model_info.filename, primary_model_info, map_override='cpu')
     theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
 
     if teritary_model_info is not None:
         print(f"Loading {teritary_model_info.filename}...")
-        teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
+        teritary_model = sd_models.torch_load(teritary_model_info.filename, teritary_model_info, map_override='cpu')
         theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
     else:
         teritary_model = None
@@ -314,12 +314,13 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
-    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
-    filename = filename if custom_name == '' else (custom_name + '.ckpt')
+    output_exttype = '.safetensors' if save_as_safetensors else '.ckpt'
+    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged' + output_exttype
+    filename = filename if custom_name == '' else (custom_name + output_exttype)
     output_modelname = os.path.join(ckpt_dir, filename)
 
     print(f"Saving to {output_modelname}...")
-    torch.save(primary_model, output_modelname)
+    sd_models.torch_save(primary_model, output_modelname)
 
     sd_models.list_models()
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4ccdf30b..2f8c2c48 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,7 +4,7 @@ import sys
 import gc
 from collections import namedtuple
 import torch
-from safetensors.torch import load_file
+from safetensors.torch import load_file, save_file
 import re
 from omegaconf import OmegaConf
 
@@ -143,6 +143,22 @@ def transform_checkpoint_dict_key(k):
 
     return k
 
+def torch_load(model_filename, model_info, map_override=None):
+    map_override=shared.weight_load_location if not map_override else map_override
+    if(checkpoint_types[model_info.exttype] == 'safetensors'):
+        # safely load weights
+        # TODO: safetensors supports zero copy fast load to gpu, see issue #684
+        return load_file(model_filename, device=map_override)
+    else:
+        return torch.load(model_filename, map_location=map_override)
+
+def torch_save(model, output_filename):
+    basename, exttype = os.path.splitext(output_filename)
+    if(checkpoint_types[exttype] == 'safetensors'):
+        # [=====  >] Reticulating brines...
+        save_file(model, output_filename, metadata={"format": "pt"})
+    else:
+        torch.save(model, output_filename)
 
 def get_state_dict_from_checkpoint(pl_sd):
     if "state_dict" in pl_sd:
@@ -175,12 +191,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        if(checkpoint_types[checkpoint_info.exttype] == 'safetensors'):
-            # safely load weights
-            # TODO: safetensors supports zero copy fast load to gpu, see issue #684
-            pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
-        else:
-            pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+        pl_sd = torch_load(checkpoint_file, checkpoint_info)
 
         if "global_step" in pl_sd:
             print(f"Global Step: {pl_sd['global_step']}")
diff --git a/modules/ui.py b/modules/ui.py
index a5953fce..a2b06aae 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1,1812 +1,1814 @@
-import html
-import json
-import math
-import mimetypes
-import os
-import platform
-import random
-import subprocess as sp
-import sys
-import tempfile
-import time
-import traceback
-from functools import partial, reduce
-
-import gradio as gr
-import gradio.routes
-import gradio.utils
-import numpy as np
-from PIL import Image, PngImagePlugin
-
-
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
-from modules.paths import script_path
-
-from modules.shared import opts, cmd_opts, restricted_opts
-
-if cmd_opts.deepdanbooru:
-    from modules.deepbooru import get_deepbooru_tags
-
-import modules.codeformer_model
-import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.ldsr_model
-import modules.scripts
-import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
-from modules import prompt_parser
-from modules.images import save_image
-from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-from modules.generation_parameters_copypaste import image_from_url_text
-
-# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
-mimetypes.init()
-mimetypes.add_type('application/javascript', '.js')
-
-if not cmd_opts.share and not cmd_opts.listen:
-    # fix gradio phoning home
-    gradio.utils.version_check = lambda: None
-    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
-
-if cmd_opts.ngrok != None:
-    import modules.ngrok as ngrok
-    print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
-
-
-def gr_show(visible=True):
-    return {"visible": visible, "__type__": "update"}
-
-
-sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
-sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-
-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
-# Using constants for these since the variation selector isn't visible.
-# Important that they exactly match script.js for tooltip to work.
-random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
-reuse_symbol = '\u267b\ufe0f'  # ♻️
-art_symbol = '\U0001f3a8'  # 🎨
-paste_symbol = '\u2199\ufe0f'  # ↙
-folder_symbol = '\U0001f4c2'  # 📂
-refresh_symbol = '\U0001f504'  # 🔄
-save_style_symbol = '\U0001f4be'  # 💾
-apply_style_symbol = '\U0001f4cb'  # 📋
-
-
-def plaintext_to_html(text):
-    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-    return text
-
-def send_gradio_gallery_to_image(x):
-    if len(x) == 0:
-        return None
-    return image_from_url_text(x[0])
-
-def save_files(js_data, images, do_make_zip, index):
-    import csv
-    filenames = []
-    fullfns = []
-
-    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
-    class MyObject:
-        def __init__(self, d=None):
-            if d is not None:
-                for key, value in d.items():
-                    setattr(self, key, value)
-
-    data = json.loads(js_data)
-
-    p = MyObject(data)
-    path = opts.outdir_save
-    save_to_dirs = opts.use_save_to_dirs_for_ui
-    extension: str = opts.samples_format
-    start_index = 0
-
-    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-
-        images = [images[index]]
-        start_index = index
-
-    os.makedirs(opts.outdir_save, exist_ok=True)
-
-    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
-        at_start = file.tell() == 0
-        writer = csv.writer(file)
-        if at_start:
-            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
-
-        for image_index, filedata in enumerate(images, start_index):
-            image = image_from_url_text(filedata)
-
-            is_grid = image_index < p.index_of_first_image
-            i = 0 if is_grid else (image_index - p.index_of_first_image)
-
-            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
-
-            filename = os.path.relpath(fullfn, path)
-            filenames.append(filename)
-            fullfns.append(fullfn)
-            if txt_fullfn:
-                filenames.append(os.path.basename(txt_fullfn))
-                fullfns.append(txt_fullfn)
-
-        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
-
-    # Make Zip
-    if do_make_zip:
-        zip_filepath = os.path.join(path, "images.zip")
-
-        from zipfile import ZipFile
-        with ZipFile(zip_filepath, "w") as zip_file:
-            for i in range(len(fullfns)):
-                with open(fullfns[i], mode="rb") as f:
-                    zip_file.writestr(filenames[i], f.read())
-        fullfns.insert(0, zip_filepath)
-
-    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
-
-def save_pil_to_file(pil_image, dir=None):
-    use_metadata = False
-    metadata = PngImagePlugin.PngInfo()
-    for key, value in pil_image.info.items():
-        if isinstance(key, str) and isinstance(value, str):
-            metadata.add_text(key, value)
-            use_metadata = True
-
-    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
-    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
-    return file_obj
-
-
-# override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
-
-
-def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
-    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
-        if run_memmon:
-            shared.mem_mon.monitor()
-        t = time.perf_counter()
-
-        try:
-            res = list(func(*args, **kwargs))
-        except Exception as e:
-            # When printing out our debug argument list, do not print out more than a MB of text
-            max_debug_str_len = 131072 # (1024*1024)/8
-
-            print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
-            print(argStr[:max_debug_str_len], file=sys.stderr)
-            if len(argStr) > max_debug_str_len:
-                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
-            print(traceback.format_exc(), file=sys.stderr)
-
-            shared.state.job = ""
-            shared.state.job_count = 0
-
-            if extra_outputs_array is None:
-                extra_outputs_array = [None, '']
-
-            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
-
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.job_count = 0
-
-        if not add_stats:
-            return tuple(res)
-
-        elapsed = time.perf_counter() - t
-        elapsed_m = int(elapsed // 60)
-        elapsed_s = elapsed % 60
-        elapsed_text = f"{elapsed_s:.2f}s"
-        if elapsed_m > 0:
-            elapsed_text = f"{elapsed_m}m "+elapsed_text
-
-        if run_memmon:
-            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
-            active_peak = mem_stats['active_peak']
-            reserved_peak = mem_stats['reserved_peak']
-            sys_peak = mem_stats['system_peak']
-            sys_total = mem_stats['total']
-            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
-
-            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
-        else:
-            vram_html = ''
-
-        # last item is always HTML
-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
-
-        return tuple(res)
-
-    return f
-
-
-def calc_time_left(progress, threshold, label, force_display):
-    if progress == 0:
-        return ""
-    else:
-        time_since_start = time.time() - shared.state.time_start
-        eta = (time_since_start/progress)
-        eta_relative = eta-time_since_start
-        if (eta_relative > threshold and progress > 0.02) or force_display:
-            if eta_relative > 3600:
-                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
-            elif eta_relative > 60:
-                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))
-            else:
-                return label + time.strftime('%Ss',  time.gmtime(eta_relative))
-        else:
-            return ""
-
-
-def check_progress_call(id_part):
-    if shared.state.job_count == 0:
-        return "", gr_show(False), gr_show(False), gr_show(False)
-
-    progress = 0
-
-    if shared.state.job_count > 0:
-        progress += shared.state.job_no / shared.state.job_count
-    if shared.state.sampling_steps > 0:
-        progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
-
-    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
-    if time_left != "":
-        shared.state.time_left_force_display = True
-
-    progress = min(progress, 1)
-
-    progressbar = ""
-    if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
-
-    image = gr_show(False)
-    preview_visibility = gr_show(False)
-
-    if opts.show_progress_every_n_steps != 0:
-        shared.state.set_current_image()
-        image = shared.state.current_image
-
-        if image is None:
-            image = gr.update(value=None)
-        else:
-            preview_visibility = gr_show(True)
-
-    if shared.state.textinfo is not None:
-        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
-    else:
-        textinfo_result = gr_show(False)
-
-    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
-
-
-def check_progress_call_initial(id_part):
-    shared.state.job_count = -1
-    shared.state.current_latent = None
-    shared.state.current_image = None
-    shared.state.textinfo = None
-    shared.state.time_start = time.time()
-    shared.state.time_left_force_display = False
-
-    return check_progress_call(id_part)
-
-
-def roll_artist(prompt):
-    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
-    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
-
-    return prompt + ", " + artist.name if prompt != '' else artist.name
-
-
-def visit(x, func, path=""):
-    if hasattr(x, 'children'):
-        for c in x.children:
-            visit(c, func, path)
-    elif x.label is not None:
-        func(path + "/" + str(x.label), x)
-
-
-def add_style(name: str, prompt: str, negative_prompt: str):
-    if name is None:
-        return [gr_show() for x in range(4)]
-
-    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
-    shared.prompt_styles.styles[style.name] = style
-    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
-    # reserialize all styles every time we save them
-    shared.prompt_styles.save_styles(shared.styles_filename)
-
-    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)]
-
-
-def apply_styles(prompt, prompt_neg, style1_name, style2_name):
-    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
-    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
-
-    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
-
-
-def interrogate(image):
-    prompt = shared.interrogator.interrogate(image)
-
-    return gr_show(True) if prompt is None else prompt
-
-
-def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image)
-    return gr_show(True) if prompt is None else prompt
-
-
-def create_seed_inputs():
-    with gr.Row():
-        with gr.Box():
-            with gr.Row(elem_id='seed_row'):
-                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
-                seed.style(container=False)
-                random_seed = gr.Button(random_symbol, elem_id='random_seed')
-                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
-
-        with gr.Box(elem_id='subseed_show_box'):
-            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
-
-    # Components to show/hide based on the 'Extra' checkbox
-    seed_extras = []
-
-    with gr.Row(visible=False) as seed_extra_row_1:
-        seed_extras.append(seed_extra_row_1)
-        with gr.Box():
-            with gr.Row(elem_id='subseed_row'):
-                subseed = gr.Number(label='Variation seed', value=-1)
-                subseed.style(container=False)
-                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
-                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
-        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
-
-    with gr.Row(visible=False) as seed_extra_row_2:
-        seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
-
-    random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
-    random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
-
-    def change_visibility(show):
-        return {comp: gr_show(show) for comp in seed_extras}
-
-    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
-
-    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
-
-
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
-    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
-        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
-        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
-    def copy_seed(gen_info_string: str, index):
-        res = -1
-
-        try:
-            gen_info = json.loads(gen_info_string)
-            index -= gen_info.get('index_of_first_image', 0)
-
-            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
-                all_subseeds = gen_info.get('all_subseeds', [-1])
-                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
-            else:
-                all_seeds = gen_info.get('all_seeds', [-1])
-                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
-        except json.decoder.JSONDecodeError as e:
-            if gen_info_string != '':
-                print("Error parsing JSON generation info:", file=sys.stderr)
-                print(gen_info_string, file=sys.stderr)
-
-        return [res, gr_show(False)]
-
-    reuse_seed.click(
-        fn=copy_seed,
-        _js="(x, y) => [x, selected_gallery_index()]",
-        show_progress=False,
-        inputs=[generation_info, dummy_component],
-        outputs=[seed, dummy_component]
-    )
-
-
-def update_token_counter(text, steps):
-    try:
-        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
-        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
-
-    except Exception:
-        # a parsing error can happen here during typing, and we don't want to bother the user with
-        # messages related to it in console
-        prompt_schedules = [[[steps, text]]]
-
-    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
-    prompts = [prompt_text for step, prompt_text in flat_prompts]
-    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
-    style_class = ' class="red"' if (token_count > max_length) else ""
-    return f"<span {style_class}>{token_count}/{max_length}</span>"
-
-
-def create_toprow(is_img2img):
-    id_part = "img2img" if is_img2img else "txt2img"
-
-    with gr.Row(elem_id="toprow"):
-        with gr.Column(scale=6):
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
-                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                        )
-
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
-                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                        )
-
-        with gr.Column(scale=1, elem_id="roll_col"):
-            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
-            paste = gr.Button(value=paste_symbol, elem_id="paste")
-            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
-            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-
-            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
-            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-
-        button_interrogate = None
-        button_deepbooru = None
-        if is_img2img:
-            with gr.Column(scale=1, elem_id="interrogate_col"):
-                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-
-                if cmd_opts.deepdanbooru:
-                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
-        with gr.Column(scale=1):
-            with gr.Row():
-                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
-                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
-                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
-                skip.click(
-                    fn=lambda: shared.state.skip(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                interrupt.click(
-                    fn=lambda: shared.state.interrupt(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-            with gr.Row():
-                with gr.Column(scale=1, elem_id="style_pos_col"):
-                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
-                    prompt_style.save_to_config = True
-
-                with gr.Column(scale=1, elem_id="style_neg_col"):
-                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
-                    prompt_style2.save_to_config = True
-
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
-
-
-def setup_progressbar(progressbar, preview, id_part, textinfo=None):
-    if textinfo is None:
-        textinfo = gr.HTML(visible=False)
-
-    check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
-    check_progress.click(
-        fn=lambda: check_progress_call(id_part),
-        show_progress=False,
-        inputs=[],
-        outputs=[progressbar, preview, preview, textinfo],
-    )
-
-    check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
-    check_progress_initial.click(
-        fn=lambda: check_progress_call_initial(id_part),
-        show_progress=False,
-        inputs=[],
-        outputs=[progressbar, preview, preview, textinfo],
-    )
-
-
-def apply_setting(key, value):
-    if value is None:
-        return gr.update()
-
-    if shared.cmd_opts.freeze_settings:
-        return gr.update()
-
-    # dont allow model to be swapped when model hash exists in prompt
-    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
-        return gr.update()
-
-    if key == "sd_model_checkpoint":
-        ckpt_info = sd_models.get_closet_checkpoint_match(value)
-
-        if ckpt_info is not None:
-            value = ckpt_info.title
-        else:
-            return gr.update()
-
-    comp_args = opts.data_labels[key].component_args
-    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
-        return
-
-    valtype = type(opts.data_labels[key].default)
-    oldval = opts.data[key]
-    opts.data[key] = valtype(value) if valtype != type(None) else value
-    if oldval != value and opts.data_labels[key].onchange is not None:
-        opts.data_labels[key].onchange()
-
-    opts.save(shared.config_filename)
-    return value
-
-
-def update_generation_info(args):
-    generation_info, html_info, img_index = args
-    try:
-        generation_info = json.loads(generation_info)
-        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
-            return html_info
-        return plaintext_to_html(generation_info["infotexts"][img_index])
-    except Exception:
-        pass
-    # if the json parse or anything else fails, just return the old html_info
-    return html_info
-
-
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
-    def refresh():
-        refresh_method()
-        args = refreshed_args() if callable(refreshed_args) else refreshed_args
-
-        for k, v in args.items():
-            setattr(refresh_component, k, v)
-
-        return gr.update(**(args or {}))
-
-    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
-    refresh_button.click(
-        fn=refresh,
-        inputs=[],
-        outputs=[refresh_component]
-    )
-    return refresh_button
-
-
-def create_output_panel(tabname, outdir):
-    def open_folder(f):
-        if not os.path.exists(f):
-            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
-            return
-        elif not os.path.isdir(f):
-            print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
-            return
-
-        if not shared.cmd_opts.hide_ui_dir_config:
-            path = os.path.normpath(f)
-            if platform.system() == "Windows":
-                os.startfile(path)
-            elif platform.system() == "Darwin":
-                sp.Popen(["open", path])
-            else:
-                sp.Popen(["xdg-open", path])
-
-    with gr.Column(variant='panel'):
-            with gr.Group():
-                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
-
-            generation_info = None
-            with gr.Column():
-                with gr.Row():
-                    if tabname != "extras":
-                        save = gr.Button('Save', elem_id=f'save_{tabname}')
-
-                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
-                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
-
-                open_folder_button.click(
-                    fn=lambda: open_folder(opts.outdir_samples or outdir),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                if tabname != "extras":
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
-                        if tabname == 'txt2img' or tabname == 'img2img':
-                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
-                            generation_info_button.click(
-                                fn=update_generation_info,
-                                _js="(x, y) => [x, y, selected_gallery_index()]",
-                                inputs=[generation_info, html_info],
-                                outputs=[html_info],
-                                preprocess=False
-                            )
-
-                        save.click(
-                            fn=wrap_gradio_call(save_files),
-                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                            inputs=[
-                                generation_info,
-                                result_gallery,
-                                do_make_zip,
-                                html_info,
-                            ],
-                            outputs=[
-                                download_files,
-                                html_info,
-                                html_info,
-                                html_info,
-                            ]
-                        )
-                else:
-                    html_info_x = gr.HTML()
-                    html_info = gr.HTML()
-                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
-                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
-
-
-def create_ui(wrap_gradio_gpu_call):
-    import modules.img2img
-    import modules.txt2img
-
-    reload_javascript()
-
-    parameters_copypaste.reset()
-
-    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
-    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
-
-    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
-        dummy_component = gr.Label(visible=False)
-        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
-
-        with gr.Row(elem_id='txt2img_progress_row'):
-            with gr.Column(scale=1):
-                pass
-
-            with gr.Column(scale=1):
-                progressbar = gr.HTML(elem_id="txt2img_progressbar")
-                txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
-                setup_progressbar(progressbar, txt2img_preview, 'txt2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
-
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)
-
-                with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
-
-                with gr.Row(equal_height=True):
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
-                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
-                with gr.Group():
-                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
-
-            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
-            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            txt2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
-                _js="submit",
-                inputs=[
-                    txt2img_prompt,
-                    txt2img_negative_prompt,
-                    txt2img_prompt_style,
-                    txt2img_prompt_style2,
-                    steps,
-                    sampler_index,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    enable_hr,
-                    denoising_strength,
-                    firstphase_width,
-                    firstphase_height,
-                ] + custom_inputs,
-
-                outputs=[
-                    txt2img_gallery,
-                    generation_info,
-                    html_info
-                ],
-                show_progress=False,
-            )
-
-            txt2img_prompt.submit(**txt2img_args)
-            submit.click(**txt2img_args)
-
-            txt_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    txt_prompt_img
-                ],
-                outputs=[
-                    txt2img_prompt,
-                    txt_prompt_img
-                ]
-            )
-
-            enable_hr.change(
-                fn=lambda x: gr_show(x),
-                inputs=[enable_hr],
-                outputs=[hr_options],
-            )
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_txt2img_tokens",
-                inputs=[
-                    txt2img_prompt,
-                ],
-                outputs=[
-                    txt2img_prompt,
-                ]
-            )
-
-            txt2img_paste_fields = [
-                (txt2img_prompt, "Prompt"),
-                (txt2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (denoising_strength, "Denoising strength"),
-                (enable_hr, lambda d: "Denoising strength" in d),
-                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (firstphase_width, "First pass size-1"),
-                (firstphase_height, "First pass size-2"),
-                *modules.scripts.scripts_txt2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
-
-            txt2img_preview_params = [
-                txt2img_prompt,
-                txt2img_negative_prompt,
-                steps,
-                sampler_index,
-                cfg_scale,
-                seed,
-                width,
-                height,
-            ]
-
-            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-
-    modules.scripts.scripts_current = modules.scripts.scripts_img2img
-    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
-
-    with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
-
-        with gr.Row(elem_id='img2img_progress_row'):
-            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-
-            with gr.Column(scale=1):
-                pass
-
-            with gr.Column(scale=1):
-                progressbar = gr.HTML(elem_id="img2img_progressbar")
-                img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
-                setup_progressbar(progressbar, img2img_preview, 'img2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-
-                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
-                    with gr.TabItem('img2img', id='img2img'):
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
-
-                    with gr.TabItem('Inpaint', id='inpaint'):
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
-
-                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
-                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
-
-                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
-
-                        with gr.Row():
-                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
-
-                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
-
-                        with gr.Row():
-                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
-
-                    with gr.TabItem('Batch img2img', id='batch'):
-                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
-                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
-                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
-
-                with gr.Row():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
-
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-
-                with gr.Row():
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
-                with gr.Group():
-                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
-                with gr.Group():
-                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
-
-            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
-            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            img2img_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    img2img_prompt_img
-                ],
-                outputs=[
-                    img2img_prompt,
-                    img2img_prompt_img
-                ]
-            )
-
-            mask_mode.change(
-                lambda mode, img: {
-                    init_img_with_mask: gr_show(mode == 0),
-                    init_img_inpaint: gr_show(mode == 1),
-                    init_mask_inpaint: gr_show(mode == 1),
-                },
-                inputs=[mask_mode, init_img_with_mask],
-                outputs=[
-                    init_img_with_mask,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                ],
-            )
-
-            img2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
-                _js="submit_img2img",
-                inputs=[
-                    dummy_component,
-                    img2img_prompt,
-                    img2img_negative_prompt,
-                    img2img_prompt_style,
-                    img2img_prompt_style2,
-                    init_img,
-                    init_img_with_mask,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                    mask_mode,
-                    steps,
-                    sampler_index,
-                    mask_blur,
-                    inpainting_fill,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    denoising_strength,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    resize_mode,
-                    inpaint_full_res,
-                    inpaint_full_res_padding,
-                    inpainting_mask_invert,
-                    img2img_batch_input_dir,
-                    img2img_batch_output_dir,
-                ] + custom_inputs,
-                outputs=[
-                    img2img_gallery,
-                    generation_info,
-                    html_info
-                ],
-                show_progress=False,
-            )
-
-            img2img_prompt.submit(**img2img_args)
-            submit.click(**img2img_args)
-
-            img2img_interrogate.click(
-                fn=interrogate,
-                inputs=[init_img],
-                outputs=[img2img_prompt],
-            )
-
-            if cmd_opts.deepdanbooru:
-                img2img_deepbooru.click(
-                    fn=interrogate_deepbooru,
-                    inputs=[init_img],
-                    outputs=[img2img_prompt],
-            )
-
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_img2img_tokens",
-                inputs=[
-                    img2img_prompt,
-                ],
-                outputs=[
-                    img2img_prompt,
-                ]
-            )
-
-            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
-            style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
-            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-
-            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
-                button.click(
-                    fn=add_style,
-                    _js="ask_for_style_name",
-                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
-                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
-                    inputs=[dummy_component, prompt, negative_prompt],
-                    outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
-                )
-
-            for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
-                button.click(
-                    fn=apply_styles,
-                    _js=js_func,
-                    inputs=[prompt, negative_prompt, style1, style2],
-                    outputs=[prompt, negative_prompt, style1, style2],
-                )
-
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-
-            img2img_paste_fields = [
-                (img2img_prompt, "Prompt"),
-                (img2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (denoising_strength, "Denoising strength"),
-                *modules.scripts.scripts_img2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
-            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
-
-    modules.scripts.scripts_current = None
-
-    with gr.Blocks(analytics_enabled=False) as extras_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                with gr.Tabs(elem_id="mode_extras"):
-                    with gr.TabItem('Single Image'):
-                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
-
-                    with gr.TabItem('Batch Process'):
-                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
-
-                    with gr.TabItem('Batch from Directory'):
-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
-                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
-
-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
-                with gr.Tabs(elem_id="extras_resize_mode"):
-                    with gr.TabItem('Scale by'):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
-                    with gr.TabItem('Scale to'):
-                        with gr.Group():
-                            with gr.Row():
-                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
-                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-
-                with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-
-                with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
-
-                with gr.Group():
-                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
-
-                with gr.Group():
-                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
-                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
-
-                with gr.Group():
-                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
-
-            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
-
-        submit.click(
-            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
-            _js="get_extras_tab_index",
-            inputs=[
-                dummy_component,
-                dummy_component,
-                extras_image,
-                image_batch,
-                extras_batch_input_dir,
-                extras_batch_output_dir,
-                show_extras_results,
-                gfpgan_visibility,
-                codeformer_visibility,
-                codeformer_weight,
-                upscaling_resize,
-                upscaling_resize_w,
-                upscaling_resize_h,
-                upscaling_crop,
-                extras_upscaler_1,
-                extras_upscaler_2,
-                extras_upscaler_2_visibility,
-                upscale_before_face_fix,
-            ],
-            outputs=[
-                result_images,
-                html_info_x,
-                html_info,
-            ]
-        )
-        parameters_copypaste.add_paste_fields("extras", extras_image, None)
-
-        extras_image.change(
-            fn=modules.extras.clear_cache,
-            inputs=[], outputs=[]
-        )
-
-    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
-
-            with gr.Column(variant='panel'):
-                html = gr.HTML()
-                generation_info = gr.Textbox(visible=False)
-                html2 = gr.HTML()
-                with gr.Row():
-                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-                parameters_copypaste.bind_buttons(buttons, image, generation_info)
-
-        image.change(
-            fn=wrap_gradio_call(modules.extras.run_pnginfo),
-            inputs=[image],
-            outputs=[html, generation_info, html2],
-        )
-
-    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
-
-                with gr.Row():
-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
-                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
-                custom_name = gr.Textbox(label="Custom Name (Optional)")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
-                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
-                save_as_half = gr.Checkbox(value=False, label="Save as float16")
-                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
-
-            with gr.Column(variant='panel'):
-                submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
-
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
-
-    with gr.Blocks(analytics_enabled=False) as train_interface:
-        with gr.Row().style(equal_height=False):
-            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
-
-        with gr.Row().style(equal_height=False):
-            with gr.Tabs(elem_id="train_tabs"):
-
-                with gr.Tab(label="Create embedding"):
-                    new_embedding_name = gr.Textbox(label="Name")
-                    initialization_text = gr.Textbox(label="Initialization text", value="*")
-                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
-                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_embedding = gr.Button(value="Create embedding", variant='primary')
-
-                with gr.Tab(label="Create hypernetwork"):
-                    new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
-                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
-                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
-                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
-                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
-
-                with gr.Tab(label="Preprocess images"):
-                    process_src = gr.Textbox(label='Source directory')
-                    process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
-
-                    with gr.Row():
-                        process_flip = gr.Checkbox(label='Create flipped copies')
-                        process_split = gr.Checkbox(label='Split oversized images')
-                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
-                        process_caption = gr.Checkbox(label='Use BLIP for caption')
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
-
-                    with gr.Row(visible=False) as process_split_extra_row:
-                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
-
-                    with gr.Row(visible=False) as process_focal_crop_row:
-                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            with gr.Row():
-                                interrupt_preprocessing = gr.Button("Interrupt")
-                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
-
-                    process_split.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_split],
-                        outputs=[process_split_extra_row],
-                    )
-
-                    process_focal_crop.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_focal_crop],
-                        outputs=[process_focal_crop_row],
-                    )
-
-                with gr.Tab(label="Train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
-                    with gr.Row():
-                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-                    with gr.Row():
-                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
-                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
-                    with gr.Row():
-                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
-                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
-
-                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
-                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
-                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
-                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    steps = gr.Number(label='Max steps', value=100000, precision=0)
-                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
-                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
-
-                    with gr.Row():
-                        interrupt_training = gr.Button(value="Interrupt")
-                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
-                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
-
-                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
-
-                script_callbacks.ui_train_tabs_callback(params)
-
-            with gr.Column():
-                progressbar = gr.HTML(elem_id="ti_progressbar")
-                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-
-                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
-                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
-                ti_progress = gr.HTML(elem_id="ti_progress", value="")
-                ti_outcome = gr.HTML(elem_id="ti_error", value="")
-                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
-
-        create_embedding.click(
-            fn=modules.textual_inversion.ui.create_embedding,
-            inputs=[
-                new_embedding_name,
-                initialization_text,
-                nvpt,
-                overwrite_old_embedding,
-            ],
-            outputs=[
-                train_embedding_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        create_hypernetwork.click(
-            fn=modules.hypernetworks.ui.create_hypernetwork,
-            inputs=[
-                new_hypernetwork_name,
-                new_hypernetwork_sizes,
-                overwrite_old_hypernetwork,
-                new_hypernetwork_layer_structure,
-                new_hypernetwork_activation_func,
-                new_hypernetwork_initialization_option,
-                new_hypernetwork_add_layer_norm,
-                new_hypernetwork_use_dropout
-            ],
-            outputs=[
-                train_hypernetwork_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        run_preprocess.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                process_src,
-                process_dst,
-                process_width,
-                process_height,
-                preprocess_txt_action,
-                process_flip,
-                process_split,
-                process_caption,
-                process_caption_deepbooru,
-                process_split_threshold,
-                process_overlap_ratio,
-                process_focal_crop,
-                process_focal_crop_face_weight,
-                process_focal_crop_entropy_weight,
-                process_focal_crop_edges_weight,
-                process_focal_crop_debug,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ],
-        )
-
-        train_embedding.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                train_embedding_name,
-                embedding_learn_rate,
-                batch_size,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                steps,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                save_image_with_stored_embedding,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        train_hypernetwork.click(
-            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                train_hypernetwork_name,
-                hypernetwork_learn_rate,
-                batch_size,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                steps,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        interrupt_training.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-        interrupt_preprocessing.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-    def create_setting_component(key, is_quicksettings=False):
-        def fun():
-            return opts.data[key] if key in opts.data else opts.data_labels[key].default
-
-        info = opts.data_labels[key]
-        t = type(info.default)
-
-        args = info.component_args() if callable(info.component_args) else info.component_args
-
-        if info.component is not None:
-            comp = info.component
-        elif t == str:
-            comp = gr.Textbox
-        elif t == int:
-            comp = gr.Number
-        elif t == bool:
-            comp = gr.Checkbox
-        else:
-            raise Exception(f'bad options item type: {str(t)} for key {key}')
-
-        elem_id = "setting_"+key
-
-        if info.refresh is not None:
-            if is_quicksettings:
-                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
-            else:
-                with gr.Row(variant="compact"):
-                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
-        else:
-            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-
-        return res
-
-    components = []
-    component_dict = {}
-
-    script_callbacks.ui_settings_callback()
-    opts.reorder()
-
-    def run_settings(*args):
-        changed = []
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if comp == dummy_component:
-                continue
-
-            if opts.set(key, value):
-                changed.append(key)
-
-        try:
-            opts.save(shared.config_filename)
-        except RuntimeError:
-            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
-        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
-
-    def run_settings_single(value, key):
-        if not opts.same_type(value, opts.data_labels[key].default):
-            return gr.update(visible=True), opts.dumpjson()
-
-        if not opts.set(key, value):
-            return gr.update(value=getattr(opts, key)), opts.dumpjson()
-
-        opts.save(shared.config_filename)
-
-        return gr.update(value=value), opts.dumpjson()
-
-    with gr.Blocks(analytics_enabled=False) as settings_interface:
-        settings_submit = gr.Button(value="Apply settings", variant='primary')
-        result = gr.HTML()
-
-        settings_cols = 3
-        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
-
-        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
-        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
-
-        quicksettings_list = []
-
-        cols_displayed = 0
-        items_displayed = 0
-        previous_section = None
-        column = None
-        with gr.Row(elem_id="settings").style(equal_height=False):
-            for i, (k, item) in enumerate(opts.data_labels.items()):
-                section_must_be_skipped = item.section[0] is None
-
-                if previous_section != item.section and not section_must_be_skipped:
-                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
-                        if column is not None:
-                            column.__exit__()
-
-                        column = gr.Column(variant='panel')
-                        column.__enter__()
-
-                        items_displayed = 0
-                        cols_displayed += 1
-
-                    previous_section = item.section
-
-                    elem_id, text = item.section
-                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
-
-                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
-                    quicksettings_list.append((i, k, item))
-                    components.append(dummy_component)
-                elif section_must_be_skipped:
-                    components.append(dummy_component)
-                else:
-                    component = create_setting_component(k)
-                    component_dict[k] = component
-                    components.append(component)
-                    items_displayed += 1
-
-        with gr.Row():
-            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
-            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
-
-        with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-
-        request_notifications.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='function(){}'
-        )
-
-        download_localization.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='download_localization'
-        )
-
-        def reload_scripts():
-            modules.scripts.reload_script_body_only()
-            reload_javascript()  # need to refresh the html page
-
-        reload_script_bodies.click(
-            fn=reload_scripts,
-            inputs=[],
-            outputs=[]
-        )
-
-        def request_restart():
-            shared.state.interrupt()
-            shared.state.need_restart = True
-
-        restart_gradio.click(
-            fn=request_restart,
-            _js='restart_reload',
-            inputs=[],
-            outputs=[],
-        )
-
-        if column is not None:
-            column.__exit__()
-
-    interfaces = [
-        (txt2img_interface, "txt2img", "txt2img"),
-        (img2img_interface, "img2img", "img2img"),
-        (extras_interface, "Extras", "extras"),
-        (pnginfo_interface, "PNG Info", "pnginfo"),
-        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
-        (train_interface, "Train", "ti"),
-    ]
-
-    css = ""
-
-    for cssfile in modules.scripts.list_files_with_name("style.css"):
-        if not os.path.isfile(cssfile):
-            continue
-
-        with open(cssfile, "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if os.path.exists(os.path.join(script_path, "user.css")):
-        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if not cmd_opts.no_progressbar_hiding:
-        css += css_hide_progressbar
-
-    interfaces += script_callbacks.ui_tabs_callback()
-    interfaces += [(settings_interface, "Settings", "settings")]
-
-    extensions_interface = ui_extensions.create_ui()
-    interfaces += [(extensions_interface, "Extensions", "extensions")]
-
-    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
-        with gr.Row(elem_id="quicksettings"):
-            for i, k, item in quicksettings_list:
-                component = create_setting_component(k, is_quicksettings=True)
-                component_dict[k] = component
-
-        parameters_copypaste.integrate_settings_paste_fields(component_dict)
-        parameters_copypaste.run_bind()
-
-        with gr.Tabs(elem_id="tabs") as tabs:
-            for interface, label, ifid in interfaces:
-                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
-                    interface.render()
-
-        if os.path.exists(os.path.join(script_path, "notification.mp3")):
-            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
-
-        text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
-        settings_submit.click(
-            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
-            inputs=components,
-            outputs=[text_settings, result],
-        )
-
-        for i, k, item in quicksettings_list:
-            component = component_dict[k]
-
-            component.change(
-                fn=lambda value, k=k: run_settings_single(value, key=k),
-                inputs=[component],
-                outputs=[component, text_settings],
-            )
-
-        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
-
-        def get_settings_values():
-            return [getattr(opts, key) for key in component_keys]
-
-        demo.load(
-            fn=get_settings_values,
-            inputs=[],
-            outputs=[component_dict[k] for k in component_keys],
-        )
-
-        def modelmerger(*args):
-            try:
-                results = modules.extras.run_modelmerger(*args)
-            except Exception as e:
-                print("Error loading/saving model file:", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-                modules.sd_models.list_models()  # to remove the potentially missing models from the list
-                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
-            return results
-
-        modelmerger_merge.click(
-            fn=modelmerger,
-            inputs=[
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                interp_method,
-                interp_amount,
-                save_as_half,
-                custom_name,
-            ],
-            outputs=[
-                submit_result,
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                component_dict['sd_model_checkpoint'],
-            ]
-        )
-
-    ui_config_file = cmd_opts.ui_config_file
-    ui_settings = {}
-    settings_count = len(ui_settings)
-    error_loading = False
-
-    try:
-        if os.path.exists(ui_config_file):
-            with open(ui_config_file, "r", encoding="utf8") as file:
-                ui_settings = json.load(file)
-    except Exception:
-        error_loading = True
-        print("Error loading settings:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-
-    def loadsave(path, x):
-        def apply_field(obj, field, condition=None, init_field=None):
-            key = path + "/" + field
-
-            if getattr(obj, 'custom_script_source', None) is not None:
-              key = 'customscript/' + obj.custom_script_source + '/' + key
-
-            if getattr(obj, 'do_not_save_to_config', False):
-                return
-
-            saved_value = ui_settings.get(key, None)
-            if saved_value is None:
-                ui_settings[key] = getattr(obj, field)
-            elif condition and not condition(saved_value):
-                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
-            else:
-                setattr(obj, field, saved_value)
-                if init_field is not None:
-                    init_field(saved_value)
-
-        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
-            apply_field(x, 'visible')
-
-        if type(x) == gr.Slider:
-            apply_field(x, 'value')
-            apply_field(x, 'minimum')
-            apply_field(x, 'maximum')
-            apply_field(x, 'step')
-
-        if type(x) == gr.Radio:
-            apply_field(x, 'value', lambda val: val in x.choices)
-
-        if type(x) == gr.Checkbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Textbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Number:
-            apply_field(x, 'value')
-
-        # Since there are many dropdowns that shouldn't be saved,
-        # we only mark dropdowns that should be saved.
-        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
-            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
-            apply_field(x, 'visible')
-
-    visit(txt2img_interface, loadsave, "txt2img")
-    visit(img2img_interface, loadsave, "img2img")
-    visit(extras_interface, loadsave, "extras")
-    visit(modelmerger_interface, loadsave, "modelmerger")
-
-    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
-        with open(ui_config_file, "w", encoding="utf8") as file:
-            json.dump(ui_settings, file, indent=4)
-
-    return demo
-
-
-def reload_javascript():
-    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
-        javascript = f'<script>{jsfile.read()}</script>'
-
-    scripts_list = modules.scripts.list_scripts("javascript", ".js")
-
-    for basedir, filename, path in scripts_list:
-        with open(path, "r", encoding="utf8") as jsfile:
-            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-
-    if cmd_opts.theme is not None:
-        javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
-
-    javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
-
-    def template_response(*args, **kwargs):
-        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(
-            b'</head>', f'{javascript}</head>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gradio.routes.templates.TemplateResponse = template_response
-
-
-if not hasattr(shared, 'GradioTemplateResponseOriginal'):
-    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
+import html
+import json
+import math
+import mimetypes
+import os
+import platform
+import random
+import subprocess as sp
+import sys
+import tempfile
+import time
+import traceback
+from functools import partial, reduce
+
+import gradio as gr
+import gradio.routes
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin
+
+
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
+from modules.paths import script_path
+
+from modules.shared import opts, cmd_opts, restricted_opts
+
+if cmd_opts.deepdanbooru:
+    from modules.deepbooru import get_deepbooru_tags
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.ldsr_model
+import modules.scripts
+import modules.shared as shared
+import modules.styles
+import modules.textual_inversion.ui
+from modules import prompt_parser
+from modules.images import save_image
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
+import modules.textual_inversion.ui
+import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
+
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
+mimetypes.init()
+mimetypes.add_type('application/javascript', '.js')
+
+if not cmd_opts.share and not cmd_opts.listen:
+    # fix gradio phoning home
+    gradio.utils.version_check = lambda: None
+    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
+
+if cmd_opts.ngrok != None:
+    import modules.ngrok as ngrok
+    print('ngrok authtoken detected, trying to connect...')
+    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
+
+
+def gr_show(visible=True):
+    return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+css_hide_progressbar = """
+.wrap .m-12 svg { display:none!important; }
+.wrap .m-12::before { content:"Loading..." }
+.wrap .z-20 svg { display:none!important; }
+.wrap .z-20::before { content:"Loading..." }
+.progress-bar { display:none!important; }
+.meta-text { display:none!important; }
+.meta-text-center { display:none!important; }
+"""
+
+# Using constants for these since the variation selector isn't visible.
+# Important that they exactly match script.js for tooltip to work.
+random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
+reuse_symbol = '\u267b\ufe0f'  # ♻️
+art_symbol = '\U0001f3a8'  # 🎨
+paste_symbol = '\u2199\ufe0f'  # ↙
+folder_symbol = '\U0001f4c2'  # 📂
+refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+apply_style_symbol = '\U0001f4cb'  # 📋
+
+
+def plaintext_to_html(text):
+    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
+    return text
+
+def send_gradio_gallery_to_image(x):
+    if len(x) == 0:
+        return None
+    return image_from_url_text(x[0])
+
+def save_files(js_data, images, do_make_zip, index):
+    import csv
+    filenames = []
+    fullfns = []
+
+    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
+    class MyObject:
+        def __init__(self, d=None):
+            if d is not None:
+                for key, value in d.items():
+                    setattr(self, key, value)
+
+    data = json.loads(js_data)
+
+    p = MyObject(data)
+    path = opts.outdir_save
+    save_to_dirs = opts.use_save_to_dirs_for_ui
+    extension: str = opts.samples_format
+    start_index = 0
+
+    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
+
+        images = [images[index]]
+        start_index = index
+
+    os.makedirs(opts.outdir_save, exist_ok=True)
+
+    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
+        at_start = file.tell() == 0
+        writer = csv.writer(file)
+        if at_start:
+            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
+
+        for image_index, filedata in enumerate(images, start_index):
+            image = image_from_url_text(filedata)
+
+            is_grid = image_index < p.index_of_first_image
+            i = 0 if is_grid else (image_index - p.index_of_first_image)
+
+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+
+            filename = os.path.relpath(fullfn, path)
+            filenames.append(filename)
+            fullfns.append(fullfn)
+            if txt_fullfn:
+                filenames.append(os.path.basename(txt_fullfn))
+                fullfns.append(txt_fullfn)
+
+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+
+    # Make Zip
+    if do_make_zip:
+        zip_filepath = os.path.join(path, "images.zip")
+
+        from zipfile import ZipFile
+        with ZipFile(zip_filepath, "w") as zip_file:
+            for i in range(len(fullfns)):
+                with open(fullfns[i], mode="rb") as f:
+                    zip_file.writestr(filenames[i], f.read())
+        fullfns.insert(0, zip_filepath)
+
+    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+
+def save_pil_to_file(pil_image, dir=None):
+    use_metadata = False
+    metadata = PngImagePlugin.PngInfo()
+    for key, value in pil_image.info.items():
+        if isinstance(key, str) and isinstance(value, str):
+            metadata.add_text(key, value)
+            use_metadata = True
+
+    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+    return file_obj
+
+
+# override save to file function so that it also writes PNG info
+gr.processing_utils.save_pil_to_file = save_pil_to_file
+
+
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
+        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
+        if run_memmon:
+            shared.mem_mon.monitor()
+        t = time.perf_counter()
+
+        try:
+            res = list(func(*args, **kwargs))
+        except Exception as e:
+            # When printing out our debug argument list, do not print out more than a MB of text
+            max_debug_str_len = 131072 # (1024*1024)/8
+
+            print("Error completing request", file=sys.stderr)
+            argStr = f"Arguments: {str(args)} {str(kwargs)}"
+            print(argStr[:max_debug_str_len], file=sys.stderr)
+            if len(argStr) > max_debug_str_len:
+                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
+
+            print(traceback.format_exc(), file=sys.stderr)
+
+            shared.state.job = ""
+            shared.state.job_count = 0
+
+            if extra_outputs_array is None:
+                extra_outputs_array = [None, '']
+
+            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+
+        shared.state.skipped = False
+        shared.state.interrupted = False
+        shared.state.job_count = 0
+
+        if not add_stats:
+            return tuple(res)
+
+        elapsed = time.perf_counter() - t
+        elapsed_m = int(elapsed // 60)
+        elapsed_s = elapsed % 60
+        elapsed_text = f"{elapsed_s:.2f}s"
+        if elapsed_m > 0:
+            elapsed_text = f"{elapsed_m}m "+elapsed_text
+
+        if run_memmon:
+            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
+            active_peak = mem_stats['active_peak']
+            reserved_peak = mem_stats['reserved_peak']
+            sys_peak = mem_stats['system_peak']
+            sys_total = mem_stats['total']
+            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
+
+            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
+        else:
+            vram_html = ''
+
+        # last item is always HTML
+        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
+
+        return tuple(res)
+
+    return f
+
+
+def calc_time_left(progress, threshold, label, force_display):
+    if progress == 0:
+        return ""
+    else:
+        time_since_start = time.time() - shared.state.time_start
+        eta = (time_since_start/progress)
+        eta_relative = eta-time_since_start
+        if (eta_relative > threshold and progress > 0.02) or force_display:
+            if eta_relative > 3600:
+                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
+            elif eta_relative > 60:
+                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))
+            else:
+                return label + time.strftime('%Ss',  time.gmtime(eta_relative))
+        else:
+            return ""
+
+
+def check_progress_call(id_part):
+    if shared.state.job_count == 0:
+        return "", gr_show(False), gr_show(False), gr_show(False)
+
+    progress = 0
+
+    if shared.state.job_count > 0:
+        progress += shared.state.job_no / shared.state.job_count
+    if shared.state.sampling_steps > 0:
+        progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
+
+    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
+    if time_left != "":
+        shared.state.time_left_force_display = True
+
+    progress = min(progress, 1)
+
+    progressbar = ""
+    if opts.show_progressbar:
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
+
+    image = gr_show(False)
+    preview_visibility = gr_show(False)
+
+    if opts.show_progress_every_n_steps != 0:
+        shared.state.set_current_image()
+        image = shared.state.current_image
+
+        if image is None:
+            image = gr.update(value=None)
+        else:
+            preview_visibility = gr_show(True)
+
+    if shared.state.textinfo is not None:
+        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
+    else:
+        textinfo_result = gr_show(False)
+
+    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
+
+
+def check_progress_call_initial(id_part):
+    shared.state.job_count = -1
+    shared.state.current_latent = None
+    shared.state.current_image = None
+    shared.state.textinfo = None
+    shared.state.time_start = time.time()
+    shared.state.time_left_force_display = False
+
+    return check_progress_call(id_part)
+
+
+def roll_artist(prompt):
+    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
+    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
+
+    return prompt + ", " + artist.name if prompt != '' else artist.name
+
+
+def visit(x, func, path=""):
+    if hasattr(x, 'children'):
+        for c in x.children:
+            visit(c, func, path)
+    elif x.label is not None:
+        func(path + "/" + str(x.label), x)
+
+
+def add_style(name: str, prompt: str, negative_prompt: str):
+    if name is None:
+        return [gr_show() for x in range(4)]
+
+    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
+    shared.prompt_styles.styles[style.name] = style
+    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
+    # reserialize all styles every time we save them
+    shared.prompt_styles.save_styles(shared.styles_filename)
+
+    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)]
+
+
+def apply_styles(prompt, prompt_neg, style1_name, style2_name):
+    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
+    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
+
+    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
+
+
+def interrogate(image):
+    prompt = shared.interrogator.interrogate(image)
+
+    return gr_show(True) if prompt is None else prompt
+
+
+def interrogate_deepbooru(image):
+    prompt = get_deepbooru_tags(image)
+    return gr_show(True) if prompt is None else prompt
+
+
+def create_seed_inputs():
+    with gr.Row():
+        with gr.Box():
+            with gr.Row(elem_id='seed_row'):
+                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
+                seed.style(container=False)
+                random_seed = gr.Button(random_symbol, elem_id='random_seed')
+                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
+
+        with gr.Box(elem_id='subseed_show_box'):
+            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
+
+    # Components to show/hide based on the 'Extra' checkbox
+    seed_extras = []
+
+    with gr.Row(visible=False) as seed_extra_row_1:
+        seed_extras.append(seed_extra_row_1)
+        with gr.Box():
+            with gr.Row(elem_id='subseed_row'):
+                subseed = gr.Number(label='Variation seed', value=-1)
+                subseed.style(container=False)
+                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
+                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
+
+    with gr.Row(visible=False) as seed_extra_row_2:
+        seed_extras.append(seed_extra_row_2)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
+
+    random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
+    random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
+
+    def change_visibility(show):
+        return {comp: gr_show(show) for comp in seed_extras}
+
+    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
+
+    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
+    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+    def copy_seed(gen_info_string: str, index):
+        res = -1
+
+        try:
+            gen_info = json.loads(gen_info_string)
+            index -= gen_info.get('index_of_first_image', 0)
+
+            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+                all_subseeds = gen_info.get('all_subseeds', [-1])
+                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+            else:
+                all_seeds = gen_info.get('all_seeds', [-1])
+                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+        except json.decoder.JSONDecodeError as e:
+            if gen_info_string != '':
+                print("Error parsing JSON generation info:", file=sys.stderr)
+                print(gen_info_string, file=sys.stderr)
+
+        return [res, gr_show(False)]
+
+    reuse_seed.click(
+        fn=copy_seed,
+        _js="(x, y) => [x, selected_gallery_index()]",
+        show_progress=False,
+        inputs=[generation_info, dummy_component],
+        outputs=[seed, dummy_component]
+    )
+
+
+def update_token_counter(text, steps):
+    try:
+        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+
+    except Exception:
+        # a parsing error can happen here during typing, and we don't want to bother the user with
+        # messages related to it in console
+        prompt_schedules = [[[steps, text]]]
+
+    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+    prompts = [prompt_text for step, prompt_text in flat_prompts]
+    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
+    style_class = ' class="red"' if (token_count > max_length) else ""
+    return f"<span {style_class}>{token_count}/{max_length}</span>"
+
+
+def create_toprow(is_img2img):
+    id_part = "img2img" if is_img2img else "txt2img"
+
+    with gr.Row(elem_id="toprow"):
+        with gr.Column(scale=6):
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
+                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
+                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
+        with gr.Column(scale=1, elem_id="roll_col"):
+            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+            paste = gr.Button(value=paste_symbol, elem_id="paste")
+            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
+            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
+
+            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+
+        button_interrogate = None
+        button_deepbooru = None
+        if is_img2img:
+            with gr.Column(scale=1, elem_id="interrogate_col"):
+                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+
+                if cmd_opts.deepdanbooru:
+                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+        with gr.Column(scale=1):
+            with gr.Row():
+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
+                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
+                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+
+                skip.click(
+                    fn=lambda: shared.state.skip(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                interrupt.click(
+                    fn=lambda: shared.state.interrupt(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+            with gr.Row():
+                with gr.Column(scale=1, elem_id="style_pos_col"):
+                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style.save_to_config = True
+
+                with gr.Column(scale=1, elem_id="style_neg_col"):
+                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style2.save_to_config = True
+
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+
+
+def setup_progressbar(progressbar, preview, id_part, textinfo=None):
+    if textinfo is None:
+        textinfo = gr.HTML(visible=False)
+
+    check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
+    check_progress.click(
+        fn=lambda: check_progress_call(id_part),
+        show_progress=False,
+        inputs=[],
+        outputs=[progressbar, preview, preview, textinfo],
+    )
+
+    check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
+    check_progress_initial.click(
+        fn=lambda: check_progress_call_initial(id_part),
+        show_progress=False,
+        inputs=[],
+        outputs=[progressbar, preview, preview, textinfo],
+    )
+
+
+def apply_setting(key, value):
+    if value is None:
+        return gr.update()
+
+    if shared.cmd_opts.freeze_settings:
+        return gr.update()
+
+    # dont allow model to be swapped when model hash exists in prompt
+    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+        return gr.update()
+
+    if key == "sd_model_checkpoint":
+        ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+        if ckpt_info is not None:
+            value = ckpt_info.title
+        else:
+            return gr.update()
+
+    comp_args = opts.data_labels[key].component_args
+    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+        return
+
+    valtype = type(opts.data_labels[key].default)
+    oldval = opts.data[key]
+    opts.data[key] = valtype(value) if valtype != type(None) else value
+    if oldval != value and opts.data_labels[key].onchange is not None:
+        opts.data_labels[key].onchange()
+
+    opts.save(shared.config_filename)
+    return value
+
+
+def update_generation_info(args):
+    generation_info, html_info, img_index = args
+    try:
+        generation_info = json.loads(generation_info)
+        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
+            return html_info
+        return plaintext_to_html(generation_info["infotexts"][img_index])
+    except Exception:
+        pass
+    # if the json parse or anything else fails, just return the old html_info
+    return html_info
+
+
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+    def refresh():
+        refresh_method()
+        args = refreshed_args() if callable(refreshed_args) else refreshed_args
+
+        for k, v in args.items():
+            setattr(refresh_component, k, v)
+
+        return gr.update(**(args or {}))
+
+    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
+    refresh_button.click(
+        fn=refresh,
+        inputs=[],
+        outputs=[refresh_component]
+    )
+    return refresh_button
+
+
+def create_output_panel(tabname, outdir):
+    def open_folder(f):
+        if not os.path.exists(f):
+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+            return
+        elif not os.path.isdir(f):
+            print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+            return
+
+        if not shared.cmd_opts.hide_ui_dir_config:
+            path = os.path.normpath(f)
+            if platform.system() == "Windows":
+                os.startfile(path)
+            elif platform.system() == "Darwin":
+                sp.Popen(["open", path])
+            else:
+                sp.Popen(["xdg-open", path])
+
+    with gr.Column(variant='panel'):
+            with gr.Group():
+                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+
+            generation_info = None
+            with gr.Column():
+                with gr.Row():
+                    if tabname != "extras":
+                        save = gr.Button('Save', elem_id=f'save_{tabname}')
+
+                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
+
+                open_folder_button.click(
+                    fn=lambda: open_folder(opts.outdir_samples or outdir),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                if tabname != "extras":
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
+                        if tabname == 'txt2img' or tabname == 'img2img':
+                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+                            generation_info_button.click(
+                                fn=update_generation_info,
+                                _js="(x, y) => [x, y, selected_gallery_index()]",
+                                inputs=[generation_info, html_info],
+                                outputs=[html_info],
+                                preprocess=False
+                            )
+
+                        save.click(
+                            fn=wrap_gradio_call(save_files),
+                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+                            inputs=[
+                                generation_info,
+                                result_gallery,
+                                do_make_zip,
+                                html_info,
+                            ],
+                            outputs=[
+                                download_files,
+                                html_info,
+                                html_info,
+                                html_info,
+                            ]
+                        )
+                else:
+                    html_info_x = gr.HTML()
+                    html_info = gr.HTML()
+                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+
+
+def create_ui(wrap_gradio_gpu_call):
+    import modules.img2img
+    import modules.txt2img
+
+    reload_javascript()
+
+    parameters_copypaste.reset()
+
+    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
+    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
+    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
+        dummy_component = gr.Label(visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+        with gr.Row(elem_id='txt2img_progress_row'):
+            with gr.Column(scale=1):
+                pass
+
+            with gr.Column(scale=1):
+                progressbar = gr.HTML(elem_id="txt2img_progressbar")
+                txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
+                setup_progressbar(progressbar, txt2img_preview, 'txt2img')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
+
+                with gr.Group():
+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+
+                with gr.Row():
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+                    tiling = gr.Checkbox(label='Tiling', value=False)
+                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)
+
+                with gr.Row(visible=False) as hr_options:
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+
+                with gr.Row(equal_height=True):
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+                with gr.Group():
+                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+
+            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            txt2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
+                _js="submit",
+                inputs=[
+                    txt2img_prompt,
+                    txt2img_negative_prompt,
+                    txt2img_prompt_style,
+                    txt2img_prompt_style2,
+                    steps,
+                    sampler_index,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    height,
+                    width,
+                    enable_hr,
+                    denoising_strength,
+                    firstphase_width,
+                    firstphase_height,
+                ] + custom_inputs,
+
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info
+                ],
+                show_progress=False,
+            )
+
+            txt2img_prompt.submit(**txt2img_args)
+            submit.click(**txt2img_args)
+
+            txt_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    txt_prompt_img
+                ],
+                outputs=[
+                    txt2img_prompt,
+                    txt_prompt_img
+                ]
+            )
+
+            enable_hr.change(
+                fn=lambda x: gr_show(x),
+                inputs=[enable_hr],
+                outputs=[hr_options],
+            )
+
+            roll.click(
+                fn=roll_artist,
+                _js="update_txt2img_tokens",
+                inputs=[
+                    txt2img_prompt,
+                ],
+                outputs=[
+                    txt2img_prompt,
+                ]
+            )
+
+            txt2img_paste_fields = [
+                (txt2img_prompt, "Prompt"),
+                (txt2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (denoising_strength, "Denoising strength"),
+                (enable_hr, lambda d: "Denoising strength" in d),
+                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+                (firstphase_width, "First pass size-1"),
+                (firstphase_height, "First pass size-2"),
+                *modules.scripts.scripts_txt2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
+
+            txt2img_preview_params = [
+                txt2img_prompt,
+                txt2img_negative_prompt,
+                steps,
+                sampler_index,
+                cfg_scale,
+                seed,
+                width,
+                height,
+            ]
+
+            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
+
+    modules.scripts.scripts_current = modules.scripts.scripts_img2img
+    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
+    with gr.Blocks(analytics_enabled=False) as img2img_interface:
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
+
+        with gr.Row(elem_id='img2img_progress_row'):
+            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+            with gr.Column(scale=1):
+                pass
+
+            with gr.Column(scale=1):
+                progressbar = gr.HTML(elem_id="img2img_progressbar")
+                img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
+                setup_progressbar(progressbar, img2img_preview, 'img2img')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+
+                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
+                    with gr.TabItem('img2img', id='img2img'):
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
+
+                    with gr.TabItem('Inpaint', id='inpaint'):
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
+
+                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
+                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
+
+                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+
+                        with gr.Row():
+                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
+                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
+
+                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
+
+                        with gr.Row():
+                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
+                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+
+                    with gr.TabItem('Batch img2img', id='batch'):
+                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
+                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
+                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
+
+                with gr.Row():
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
+
+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
+
+                with gr.Group():
+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
+
+                with gr.Row():
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+                    tiling = gr.Checkbox(label='Tiling', value=False)
+
+                with gr.Row():
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+                with gr.Group():
+                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
+
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+                with gr.Group():
+                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+
+            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
+            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            img2img_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    img2img_prompt_img
+                ],
+                outputs=[
+                    img2img_prompt,
+                    img2img_prompt_img
+                ]
+            )
+
+            mask_mode.change(
+                lambda mode, img: {
+                    init_img_with_mask: gr_show(mode == 0),
+                    init_img_inpaint: gr_show(mode == 1),
+                    init_mask_inpaint: gr_show(mode == 1),
+                },
+                inputs=[mask_mode, init_img_with_mask],
+                outputs=[
+                    init_img_with_mask,
+                    init_img_inpaint,
+                    init_mask_inpaint,
+                ],
+            )
+
+            img2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
+                _js="submit_img2img",
+                inputs=[
+                    dummy_component,
+                    img2img_prompt,
+                    img2img_negative_prompt,
+                    img2img_prompt_style,
+                    img2img_prompt_style2,
+                    init_img,
+                    init_img_with_mask,
+                    init_img_inpaint,
+                    init_mask_inpaint,
+                    mask_mode,
+                    steps,
+                    sampler_index,
+                    mask_blur,
+                    inpainting_fill,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    denoising_strength,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    height,
+                    width,
+                    resize_mode,
+                    inpaint_full_res,
+                    inpaint_full_res_padding,
+                    inpainting_mask_invert,
+                    img2img_batch_input_dir,
+                    img2img_batch_output_dir,
+                ] + custom_inputs,
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info
+                ],
+                show_progress=False,
+            )
+
+            img2img_prompt.submit(**img2img_args)
+            submit.click(**img2img_args)
+
+            img2img_interrogate.click(
+                fn=interrogate,
+                inputs=[init_img],
+                outputs=[img2img_prompt],
+            )
+
+            if cmd_opts.deepdanbooru:
+                img2img_deepbooru.click(
+                    fn=interrogate_deepbooru,
+                    inputs=[init_img],
+                    outputs=[img2img_prompt],
+            )
+
+
+            roll.click(
+                fn=roll_artist,
+                _js="update_img2img_tokens",
+                inputs=[
+                    img2img_prompt,
+                ],
+                outputs=[
+                    img2img_prompt,
+                ]
+            )
+
+            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
+            style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
+            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
+
+            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
+                button.click(
+                    fn=add_style,
+                    _js="ask_for_style_name",
+                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
+                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
+                    inputs=[dummy_component, prompt, negative_prompt],
+                    outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
+                )
+
+            for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
+                button.click(
+                    fn=apply_styles,
+                    _js=js_func,
+                    inputs=[prompt, negative_prompt, style1, style2],
+                    outputs=[prompt, negative_prompt, style1, style2],
+                )
+
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+
+            img2img_paste_fields = [
+                (img2img_prompt, "Prompt"),
+                (img2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (denoising_strength, "Denoising strength"),
+                *modules.scripts.scripts_img2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+
+    modules.scripts.scripts_current = None
+
+    with gr.Blocks(analytics_enabled=False) as extras_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                with gr.Tabs(elem_id="mode_extras"):
+                    with gr.TabItem('Single Image'):
+                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
+
+                    with gr.TabItem('Batch Process'):
+                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
+
+                    with gr.TabItem('Batch from Directory'):
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
+                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
+
+                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
+                with gr.Tabs(elem_id="extras_resize_mode"):
+                    with gr.TabItem('Scale by'):
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
+                    with gr.TabItem('Scale to'):
+                        with gr.Group():
+                            with gr.Row():
+                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
+                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
+
+                with gr.Group():
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+
+                with gr.Group():
+                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
+
+                with gr.Group():
+                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
+
+                with gr.Group():
+                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
+                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
+
+                with gr.Group():
+                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
+
+            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
+
+        submit.click(
+            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
+            _js="get_extras_tab_index",
+            inputs=[
+                dummy_component,
+                dummy_component,
+                extras_image,
+                image_batch,
+                extras_batch_input_dir,
+                extras_batch_output_dir,
+                show_extras_results,
+                gfpgan_visibility,
+                codeformer_visibility,
+                codeformer_weight,
+                upscaling_resize,
+                upscaling_resize_w,
+                upscaling_resize_h,
+                upscaling_crop,
+                extras_upscaler_1,
+                extras_upscaler_2,
+                extras_upscaler_2_visibility,
+                upscale_before_face_fix,
+            ],
+            outputs=[
+                result_images,
+                html_info_x,
+                html_info,
+            ]
+        )
+        parameters_copypaste.add_paste_fields("extras", extras_image, None)
+
+        extras_image.change(
+            fn=modules.extras.clear_cache,
+            inputs=[], outputs=[]
+        )
+
+    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
+
+            with gr.Column(variant='panel'):
+                html = gr.HTML()
+                generation_info = gr.Textbox(visible=False)
+                html2 = gr.HTML()
+                with gr.Row():
+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+                parameters_copypaste.bind_buttons(buttons, image, generation_info)
+
+        image.change(
+            fn=wrap_gradio_call(modules.extras.run_pnginfo),
+            inputs=[image],
+            outputs=[html, generation_info, html2],
+        )
+
+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
+
+                with gr.Row():
+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+                custom_name = gr.Textbox(label="Custom Name (Optional)")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
+                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
+                save_as_half = gr.Checkbox(value=False, label="Save as float16")
+                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
+                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
+
+            with gr.Column(variant='panel'):
+                submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
+
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+    with gr.Blocks(analytics_enabled=False) as train_interface:
+        with gr.Row().style(equal_height=False):
+            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
+        with gr.Row().style(equal_height=False):
+            with gr.Tabs(elem_id="train_tabs"):
+
+                with gr.Tab(label="Create embedding"):
+                    new_embedding_name = gr.Textbox(label="Name")
+                    initialization_text = gr.Textbox(label="Initialization text", value="*")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding = gr.Button(value="Create embedding", variant='primary')
+
+                with gr.Tab(label="Create hypernetwork"):
+                    new_hypernetwork_name = gr.Textbox(label="Name")
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
+
+                with gr.Tab(label="Preprocess images"):
+                    process_src = gr.Textbox(label='Source directory')
+                    process_dst = gr.Textbox(label='Destination directory')
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
+
+                    with gr.Row():
+                        process_flip = gr.Checkbox(label='Create flipped copies')
+                        process_split = gr.Checkbox(label='Split oversized images')
+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
+                        process_caption = gr.Checkbox(label='Use BLIP for caption')
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
+
+                    with gr.Row(visible=False) as process_split_extra_row:
+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
+
+                    with gr.Row(visible=False) as process_focal_crop_row:
+                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            with gr.Row():
+                                interrupt_preprocessing = gr.Button("Interrupt")
+                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
+
+                    process_split.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_split],
+                        outputs=[process_split_extra_row],
+                    )
+
+                    process_focal_crop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_focal_crop],
+                        outputs=[process_focal_crop_row],
+                    )
+
+                with gr.Tab(label="Train"):
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+                    with gr.Row():
+                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+                    with gr.Row():
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
+                    with gr.Row():
+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
+
+                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
+                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+
+                    with gr.Row():
+                        interrupt_training = gr.Button(value="Interrupt")
+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
+                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
+
+                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+                script_callbacks.ui_train_tabs_callback(params)
+
+            with gr.Column():
+                progressbar = gr.HTML(elem_id="ti_progressbar")
+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+
+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
+                ti_progress = gr.HTML(elem_id="ti_progress", value="")
+                ti_outcome = gr.HTML(elem_id="ti_error", value="")
+                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
+
+        create_embedding.click(
+            fn=modules.textual_inversion.ui.create_embedding,
+            inputs=[
+                new_embedding_name,
+                initialization_text,
+                nvpt,
+                overwrite_old_embedding,
+            ],
+            outputs=[
+                train_embedding_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        create_hypernetwork.click(
+            fn=modules.hypernetworks.ui.create_hypernetwork,
+            inputs=[
+                new_hypernetwork_name,
+                new_hypernetwork_sizes,
+                overwrite_old_hypernetwork,
+                new_hypernetwork_layer_structure,
+                new_hypernetwork_activation_func,
+                new_hypernetwork_initialization_option,
+                new_hypernetwork_add_layer_norm,
+                new_hypernetwork_use_dropout
+            ],
+            outputs=[
+                train_hypernetwork_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        run_preprocess.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                process_src,
+                process_dst,
+                process_width,
+                process_height,
+                preprocess_txt_action,
+                process_flip,
+                process_split,
+                process_caption,
+                process_caption_deepbooru,
+                process_split_threshold,
+                process_overlap_ratio,
+                process_focal_crop,
+                process_focal_crop_face_weight,
+                process_focal_crop_entropy_weight,
+                process_focal_crop_edges_weight,
+                process_focal_crop_debug,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ],
+        )
+
+        train_embedding.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_embedding_name,
+                embedding_learn_rate,
+                batch_size,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                save_image_with_stored_embedding,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        train_hypernetwork.click(
+            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_hypernetwork_name,
+                hypernetwork_learn_rate,
+                batch_size,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        interrupt_training.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+        interrupt_preprocessing.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+    def create_setting_component(key, is_quicksettings=False):
+        def fun():
+            return opts.data[key] if key in opts.data else opts.data_labels[key].default
+
+        info = opts.data_labels[key]
+        t = type(info.default)
+
+        args = info.component_args() if callable(info.component_args) else info.component_args
+
+        if info.component is not None:
+            comp = info.component
+        elif t == str:
+            comp = gr.Textbox
+        elif t == int:
+            comp = gr.Number
+        elif t == bool:
+            comp = gr.Checkbox
+        else:
+            raise Exception(f'bad options item type: {str(t)} for key {key}')
+
+        elem_id = "setting_"+key
+
+        if info.refresh is not None:
+            if is_quicksettings:
+                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+            else:
+                with gr.Row(variant="compact"):
+                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+        else:
+            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+
+        return res
+
+    components = []
+    component_dict = {}
+
+    script_callbacks.ui_settings_callback()
+    opts.reorder()
+
+    def run_settings(*args):
+        changed = []
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, components):
+            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, components):
+            if comp == dummy_component:
+                continue
+
+            if opts.set(key, value):
+                changed.append(key)
+
+        try:
+            opts.save(shared.config_filename)
+        except RuntimeError:
+            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
+
+    def run_settings_single(value, key):
+        if not opts.same_type(value, opts.data_labels[key].default):
+            return gr.update(visible=True), opts.dumpjson()
+
+        if not opts.set(key, value):
+            return gr.update(value=getattr(opts, key)), opts.dumpjson()
+
+        opts.save(shared.config_filename)
+
+        return gr.update(value=value), opts.dumpjson()
+
+    with gr.Blocks(analytics_enabled=False) as settings_interface:
+        settings_submit = gr.Button(value="Apply settings", variant='primary')
+        result = gr.HTML()
+
+        settings_cols = 3
+        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
+
+        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
+        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
+
+        quicksettings_list = []
+
+        cols_displayed = 0
+        items_displayed = 0
+        previous_section = None
+        column = None
+        with gr.Row(elem_id="settings").style(equal_height=False):
+            for i, (k, item) in enumerate(opts.data_labels.items()):
+                section_must_be_skipped = item.section[0] is None
+
+                if previous_section != item.section and not section_must_be_skipped:
+                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
+                        if column is not None:
+                            column.__exit__()
+
+                        column = gr.Column(variant='panel')
+                        column.__enter__()
+
+                        items_displayed = 0
+                        cols_displayed += 1
+
+                    previous_section = item.section
+
+                    elem_id, text = item.section
+                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
+
+                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
+                    quicksettings_list.append((i, k, item))
+                    components.append(dummy_component)
+                elif section_must_be_skipped:
+                    components.append(dummy_component)
+                else:
+                    component = create_setting_component(k)
+                    component_dict[k] = component
+                    components.append(component)
+                    items_displayed += 1
+
+        with gr.Row():
+            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+
+        with gr.Row():
+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+
+        request_notifications.click(
+            fn=lambda: None,
+            inputs=[],
+            outputs=[],
+            _js='function(){}'
+        )
+
+        download_localization.click(
+            fn=lambda: None,
+            inputs=[],
+            outputs=[],
+            _js='download_localization'
+        )
+
+        def reload_scripts():
+            modules.scripts.reload_script_body_only()
+            reload_javascript()  # need to refresh the html page
+
+        reload_script_bodies.click(
+            fn=reload_scripts,
+            inputs=[],
+            outputs=[]
+        )
+
+        def request_restart():
+            shared.state.interrupt()
+            shared.state.need_restart = True
+
+        restart_gradio.click(
+            fn=request_restart,
+            _js='restart_reload',
+            inputs=[],
+            outputs=[],
+        )
+
+        if column is not None:
+            column.__exit__()
+
+    interfaces = [
+        (txt2img_interface, "txt2img", "txt2img"),
+        (img2img_interface, "img2img", "img2img"),
+        (extras_interface, "Extras", "extras"),
+        (pnginfo_interface, "PNG Info", "pnginfo"),
+        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (train_interface, "Train", "ti"),
+    ]
+
+    css = ""
+
+    for cssfile in modules.scripts.list_files_with_name("style.css"):
+        if not os.path.isfile(cssfile):
+            continue
+
+        with open(cssfile, "r", encoding="utf8") as file:
+            css += file.read() + "\n"
+
+    if os.path.exists(os.path.join(script_path, "user.css")):
+        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
+            css += file.read() + "\n"
+
+    if not cmd_opts.no_progressbar_hiding:
+        css += css_hide_progressbar
+
+    interfaces += script_callbacks.ui_tabs_callback()
+    interfaces += [(settings_interface, "Settings", "settings")]
+
+    extensions_interface = ui_extensions.create_ui()
+    interfaces += [(extensions_interface, "Extensions", "extensions")]
+
+    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
+        with gr.Row(elem_id="quicksettings"):
+            for i, k, item in quicksettings_list:
+                component = create_setting_component(k, is_quicksettings=True)
+                component_dict[k] = component
+
+        parameters_copypaste.integrate_settings_paste_fields(component_dict)
+        parameters_copypaste.run_bind()
+
+        with gr.Tabs(elem_id="tabs") as tabs:
+            for interface, label, ifid in interfaces:
+                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
+                    interface.render()
+
+        if os.path.exists(os.path.join(script_path, "notification.mp3")):
+            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+        text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
+        settings_submit.click(
+            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
+            inputs=components,
+            outputs=[text_settings, result],
+        )
+
+        for i, k, item in quicksettings_list:
+            component = component_dict[k]
+
+            component.change(
+                fn=lambda value, k=k: run_settings_single(value, key=k),
+                inputs=[component],
+                outputs=[component, text_settings],
+            )
+
+        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
+
+        def get_settings_values():
+            return [getattr(opts, key) for key in component_keys]
+
+        demo.load(
+            fn=get_settings_values,
+            inputs=[],
+            outputs=[component_dict[k] for k in component_keys],
+        )
+
+        def modelmerger(*args):
+            try:
+                results = modules.extras.run_modelmerger(*args)
+            except Exception as e:
+                print("Error loading/saving model file:", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+                modules.sd_models.list_models()  # to remove the potentially missing models from the list
+                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
+            return results
+
+        modelmerger_merge.click(
+            fn=modelmerger,
+            inputs=[
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                interp_method,
+                interp_amount,
+                save_as_half,
+                save_as_safetensors,
+                custom_name,
+            ],
+            outputs=[
+                submit_result,
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                component_dict['sd_model_checkpoint'],
+            ]
+        )
+
+    ui_config_file = cmd_opts.ui_config_file
+    ui_settings = {}
+    settings_count = len(ui_settings)
+    error_loading = False
+
+    try:
+        if os.path.exists(ui_config_file):
+            with open(ui_config_file, "r", encoding="utf8") as file:
+                ui_settings = json.load(file)
+    except Exception:
+        error_loading = True
+        print("Error loading settings:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+    def loadsave(path, x):
+        def apply_field(obj, field, condition=None, init_field=None):
+            key = path + "/" + field
+
+            if getattr(obj, 'custom_script_source', None) is not None:
+              key = 'customscript/' + obj.custom_script_source + '/' + key
+
+            if getattr(obj, 'do_not_save_to_config', False):
+                return
+
+            saved_value = ui_settings.get(key, None)
+            if saved_value is None:
+                ui_settings[key] = getattr(obj, field)
+            elif condition and not condition(saved_value):
+                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
+            else:
+                setattr(obj, field, saved_value)
+                if init_field is not None:
+                    init_field(saved_value)
+
+        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
+            apply_field(x, 'visible')
+
+        if type(x) == gr.Slider:
+            apply_field(x, 'value')
+            apply_field(x, 'minimum')
+            apply_field(x, 'maximum')
+            apply_field(x, 'step')
+
+        if type(x) == gr.Radio:
+            apply_field(x, 'value', lambda val: val in x.choices)
+
+        if type(x) == gr.Checkbox:
+            apply_field(x, 'value')
+
+        if type(x) == gr.Textbox:
+            apply_field(x, 'value')
+
+        if type(x) == gr.Number:
+            apply_field(x, 'value')
+
+        # Since there are many dropdowns that shouldn't be saved,
+        # we only mark dropdowns that should be saved.
+        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
+            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
+            apply_field(x, 'visible')
+
+    visit(txt2img_interface, loadsave, "txt2img")
+    visit(img2img_interface, loadsave, "img2img")
+    visit(extras_interface, loadsave, "extras")
+    visit(modelmerger_interface, loadsave, "modelmerger")
+
+    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
+        with open(ui_config_file, "w", encoding="utf8") as file:
+            json.dump(ui_settings, file, indent=4)
+
+    return demo
+
+
+def reload_javascript():
+    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
+        javascript = f'<script>{jsfile.read()}</script>'
+
+    scripts_list = modules.scripts.list_scripts("javascript", ".js")
+
+    for basedir, filename, path in scripts_list:
+        with open(path, "r", encoding="utf8") as jsfile:
+            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
+
+    if cmd_opts.theme is not None:
+        javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
+
+    javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
+
+    def template_response(*args, **kwargs):
+        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
+        res.body = res.body.replace(
+            b'</head>', f'{javascript}</head>'.encode("utf8"))
+        res.init_headers()
+        return res
+
+    gradio.routes.templates.TemplateResponse = template_response
+
+
+if not hasattr(shared, 'GradioTemplateResponseOriginal'):
+    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
-- 
cgit v1.2.3


From 5b57f61ba47f8b11d19a5b46e7fb5a52458abae5 Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Mon, 21 Nov 2022 10:15:46 +0900
Subject: fix pin_memory with different latent sampling method

---
 modules/hypernetworks/hypernetwork.py          |  5 ++++-
 modules/textual_inversion/dataset.py           | 23 +++++++++++++++++++----
 modules/textual_inversion/textual_inversion.py |  7 +------
 3 files changed, 24 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3d3301b0..0128419b 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -416,7 +416,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
     pin_memory = shared.opts.pin_memory
 
     ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
-    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, batch_size=ds.batch_size, pin_memory=pin_memory)
+    
+    latent_sampling_method = ds.latent_sampling_method
+
+    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 110c0e09..f470324a 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -138,9 +138,12 @@ class PersonalizedBase(Dataset):
         return entry
 
 class PersonalizedDataLoader(DataLoader):
-    def __init__(self, *args, **kwargs):
-        super(PersonalizedDataLoader, self).__init__(shuffle=True, drop_last=True, *args, **kwargs)
-        self.collate_fn = collate_wrapper
+    def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
+        super(PersonalizedDataLoader, self).__init__(dataset, shuffle=True, drop_last=True, batch_size=batch_size, pin_memory=pin_memory)
+        if latent_sampling_method == "random":
+            self.collate_fn = collate_wrapper_random
+        else:
+            self.collate_fn = collate_wrapper
         
 
 class BatchLoader:
@@ -148,10 +151,22 @@ class BatchLoader:
         self.cond_text = [entry.cond_text for entry in data]
         self.cond = [entry.cond for entry in data]
         self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
+        #self.emb_index = [entry.emb_index for entry in data]
+        #print(self.latent_sample.device)
 
     def pin_memory(self):
         self.latent_sample = self.latent_sample.pin_memory()
         return self
 
 def collate_wrapper(batch):
-    return BatchLoader(batch)
\ No newline at end of file
+    return BatchLoader(batch)
+
+class BatchLoaderRandom(BatchLoader):
+    def __init__(self, data):
+        super().__init__(data)
+
+    def pin_memory(self):
+        return self
+
+def collate_wrapper_random(batch):
+    return BatchLoaderRandom(batch)
\ No newline at end of file
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1d5e3a32..3036e48a 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -277,7 +277,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 
     latent_sampling_method = ds.latent_sampling_method
 
-    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, batch_size=ds.batch_size, pin_memory=False)
+    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
 
     if unload:
         shared.sd_model.first_stage_model.to(devices.cpu)
@@ -333,11 +333,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                 # go back until we reach gradient accumulation steps
                 if (j + 1) % gradient_step != 0:
                     continue
-                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
-                #scaler.unscale_(optimizer)
-                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
-                #torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=1.0)
-                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
                 scaler.step(optimizer)
                 scaler.update()
                 embedding.step += 1
-- 
cgit v1.2.3


From 9ae30b34508770c941f4d09187c0d8e82bb009c8 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 21 Nov 2022 12:53:55 +0900
Subject: remove cmd args requirement for deepdanbooru

---
 modules/ui.py | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index e6da1b2a..aba13926 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -478,9 +478,7 @@ def create_toprow(is_img2img):
         if is_img2img:
             with gr.Column(scale=1, elem_id="interrogate_col"):
                 button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-
-                if cmd_opts.deepdanbooru:
-                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
 
         with gr.Column(scale=1):
             with gr.Row():
@@ -1004,11 +1002,10 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[img2img_prompt],
             )
 
-            if cmd_opts.deepdanbooru:
-                img2img_deepbooru.click(
-                    fn=interrogate_deepbooru,
-                    inputs=[init_img],
-                    outputs=[img2img_prompt],
+            img2img_deepbooru.click(
+                fn=interrogate_deepbooru,
+                inputs=[init_img],
+                outputs=[img2img_prompt],
             )
 
 
@@ -1240,7 +1237,7 @@ def create_ui(wrap_gradio_gpu_call):
                         process_split = gr.Checkbox(label='Split oversized images')
                         process_focal_crop = gr.Checkbox(label='Auto focal point crop')
                         process_caption = gr.Checkbox(label='Use BLIP for caption')
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True)
 
                     with gr.Row(visible=False) as process_split_extra_row:
                         process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-- 
cgit v1.2.3


From e247b7400a592c0a19c197cd080aeec38ee02b68 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Thu, 17 Nov 2022 03:52:17 -0500
Subject: Add fixes for PyTorch 1.12.1

Fix typo "MasOS" -> "macOS"

If MPS is available and PyTorch is an earlier version than 1.13:
* Monkey patch torch.Tensor.to to ensure all tensors sent to MPS are contiguous
* Monkey patch torch.nn.functional.layer_norm to ensure input tensor is contiguous (required for this program to work with MPS on unmodified PyTorch 1.12.1)
---
 modules/devices.py | 28 +++++++++++++++++++++++++++-
 1 file changed, 27 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index a87d0d4c..6e8277e5 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -2,9 +2,10 @@ import sys, os, shlex
 import contextlib
 import torch
 from modules import errors
+from packaging import version
 
 
-# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
+# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
 # check `getattr` and try it for compatibility
 def has_mps() -> bool:
     if not getattr(torch, 'has_mps', False):
@@ -94,3 +95,28 @@ def autocast(disable=False):
         return contextlib.nullcontext()
 
     return torch.autocast("cuda")
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+orig_tensor_to = torch.Tensor.to
+def tensor_to_fix(self, *args, **kwargs):
+    if self.device.type != 'mps' and \
+       ((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \
+       (isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):
+        self = self.contiguous()
+    return orig_tensor_to(self, *args, **kwargs)
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
+orig_layer_norm = torch.nn.functional.layer_norm
+def layer_norm_fix(*args, **kwargs):
+    if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':
+        args = list(args)
+        args[0] = args[0].contiguous()
+    return orig_layer_norm(*args, **kwargs)
+
+
+# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
+if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):
+    torch.Tensor.to = tensor_to_fix
+    torch.nn.functional.layer_norm = layer_norm_fix
-- 
cgit v1.2.3


From 563ea3f6ff66e0eba44033163d24e42297465a47 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 21 Nov 2022 02:56:00 -0500
Subject: Change .cuda() to .to(devices.device)

---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index b9066d81..31ec7e17 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -58,7 +58,7 @@ class DeepDanbooru:
         a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
 
         with torch.no_grad(), devices.autocast():
-            x = torch.from_numpy(a).cuda()
+            x = torch.from_numpy(a).to(devices.device)
             y = self.model(x)[0].detach().cpu().numpy()
 
         probability_dict = {}
-- 
cgit v1.2.3


From 0efffbb407a9d07eae6850374099775385ce176c Mon Sep 17 00:00:00 2001
From: Nicolas Patry <patry.nicolas@protonmail.com>
Date: Mon, 21 Nov 2022 14:04:25 +0100
Subject: Supporting `*.safetensors` format.

If a model file exists with extension `.safetensors` then we can load it
more safely than with PyTorch weights.
---
 modules/sd_models.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 80addf03..0164cc1b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -45,7 +45,7 @@ def checkpoint_tiles():
 
 def list_models():
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])
+    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"])
 
     def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
@@ -180,7 +180,14 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+        if checkpoint_file.endswith(".safetensors"):
+            try:
+                from safetensors.torch import load_file
+            except ImportError as e:
+                raise ImportError(f"The model is in safetensors format and it is not installed, use `pip install safetensors`: {e}")
+            pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
+        else:
+            pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
         if "global_step" in pl_sd:
             print(f"Global Step: {pl_sd['global_step']}")
 
-- 
cgit v1.2.3


From 162fef394f8d80b54df7ede9e3b7ba65da23d3c5 Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Mon, 21 Nov 2022 10:50:57 -0500
Subject: Patch line ui endings

---
 modules/ui.py | 3628 ++++++++++++++++++++++++++++-----------------------------
 1 file changed, 1814 insertions(+), 1814 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index a2b06aae..54d3293a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1,1814 +1,1814 @@
-import html
-import json
-import math
-import mimetypes
-import os
-import platform
-import random
-import subprocess as sp
-import sys
-import tempfile
-import time
-import traceback
-from functools import partial, reduce
-
-import gradio as gr
-import gradio.routes
-import gradio.utils
-import numpy as np
-from PIL import Image, PngImagePlugin
-
-
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
-from modules.paths import script_path
-
-from modules.shared import opts, cmd_opts, restricted_opts
-
-if cmd_opts.deepdanbooru:
-    from modules.deepbooru import get_deepbooru_tags
-
-import modules.codeformer_model
-import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.ldsr_model
-import modules.scripts
-import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
-from modules import prompt_parser
-from modules.images import save_image
-from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-from modules.generation_parameters_copypaste import image_from_url_text
-
-# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
-mimetypes.init()
-mimetypes.add_type('application/javascript', '.js')
-
-if not cmd_opts.share and not cmd_opts.listen:
-    # fix gradio phoning home
-    gradio.utils.version_check = lambda: None
-    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
-
-if cmd_opts.ngrok != None:
-    import modules.ngrok as ngrok
-    print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
-
-
-def gr_show(visible=True):
-    return {"visible": visible, "__type__": "update"}
-
-
-sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
-sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-
-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
-# Using constants for these since the variation selector isn't visible.
-# Important that they exactly match script.js for tooltip to work.
-random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
-reuse_symbol = '\u267b\ufe0f'  # ♻️
-art_symbol = '\U0001f3a8'  # 🎨
-paste_symbol = '\u2199\ufe0f'  # ↙
-folder_symbol = '\U0001f4c2'  # 📂
-refresh_symbol = '\U0001f504'  # 🔄
-save_style_symbol = '\U0001f4be'  # 💾
-apply_style_symbol = '\U0001f4cb'  # 📋
-
-
-def plaintext_to_html(text):
-    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-    return text
-
-def send_gradio_gallery_to_image(x):
-    if len(x) == 0:
-        return None
-    return image_from_url_text(x[0])
-
-def save_files(js_data, images, do_make_zip, index):
-    import csv
-    filenames = []
-    fullfns = []
-
-    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
-    class MyObject:
-        def __init__(self, d=None):
-            if d is not None:
-                for key, value in d.items():
-                    setattr(self, key, value)
-
-    data = json.loads(js_data)
-
-    p = MyObject(data)
-    path = opts.outdir_save
-    save_to_dirs = opts.use_save_to_dirs_for_ui
-    extension: str = opts.samples_format
-    start_index = 0
-
-    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-
-        images = [images[index]]
-        start_index = index
-
-    os.makedirs(opts.outdir_save, exist_ok=True)
-
-    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
-        at_start = file.tell() == 0
-        writer = csv.writer(file)
-        if at_start:
-            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
-
-        for image_index, filedata in enumerate(images, start_index):
-            image = image_from_url_text(filedata)
-
-            is_grid = image_index < p.index_of_first_image
-            i = 0 if is_grid else (image_index - p.index_of_first_image)
-
-            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
-
-            filename = os.path.relpath(fullfn, path)
-            filenames.append(filename)
-            fullfns.append(fullfn)
-            if txt_fullfn:
-                filenames.append(os.path.basename(txt_fullfn))
-                fullfns.append(txt_fullfn)
-
-        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
-
-    # Make Zip
-    if do_make_zip:
-        zip_filepath = os.path.join(path, "images.zip")
-
-        from zipfile import ZipFile
-        with ZipFile(zip_filepath, "w") as zip_file:
-            for i in range(len(fullfns)):
-                with open(fullfns[i], mode="rb") as f:
-                    zip_file.writestr(filenames[i], f.read())
-        fullfns.insert(0, zip_filepath)
-
-    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
-
-def save_pil_to_file(pil_image, dir=None):
-    use_metadata = False
-    metadata = PngImagePlugin.PngInfo()
-    for key, value in pil_image.info.items():
-        if isinstance(key, str) and isinstance(value, str):
-            metadata.add_text(key, value)
-            use_metadata = True
-
-    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
-    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
-    return file_obj
-
-
-# override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
-
-
-def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
-    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
-        if run_memmon:
-            shared.mem_mon.monitor()
-        t = time.perf_counter()
-
-        try:
-            res = list(func(*args, **kwargs))
-        except Exception as e:
-            # When printing out our debug argument list, do not print out more than a MB of text
-            max_debug_str_len = 131072 # (1024*1024)/8
-
-            print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
-            print(argStr[:max_debug_str_len], file=sys.stderr)
-            if len(argStr) > max_debug_str_len:
-                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
-            print(traceback.format_exc(), file=sys.stderr)
-
-            shared.state.job = ""
-            shared.state.job_count = 0
-
-            if extra_outputs_array is None:
-                extra_outputs_array = [None, '']
-
-            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
-
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.job_count = 0
-
-        if not add_stats:
-            return tuple(res)
-
-        elapsed = time.perf_counter() - t
-        elapsed_m = int(elapsed // 60)
-        elapsed_s = elapsed % 60
-        elapsed_text = f"{elapsed_s:.2f}s"
-        if elapsed_m > 0:
-            elapsed_text = f"{elapsed_m}m "+elapsed_text
-
-        if run_memmon:
-            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
-            active_peak = mem_stats['active_peak']
-            reserved_peak = mem_stats['reserved_peak']
-            sys_peak = mem_stats['system_peak']
-            sys_total = mem_stats['total']
-            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
-
-            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
-        else:
-            vram_html = ''
-
-        # last item is always HTML
-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
-
-        return tuple(res)
-
-    return f
-
-
-def calc_time_left(progress, threshold, label, force_display):
-    if progress == 0:
-        return ""
-    else:
-        time_since_start = time.time() - shared.state.time_start
-        eta = (time_since_start/progress)
-        eta_relative = eta-time_since_start
-        if (eta_relative > threshold and progress > 0.02) or force_display:
-            if eta_relative > 3600:
-                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
-            elif eta_relative > 60:
-                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))
-            else:
-                return label + time.strftime('%Ss',  time.gmtime(eta_relative))
-        else:
-            return ""
-
-
-def check_progress_call(id_part):
-    if shared.state.job_count == 0:
-        return "", gr_show(False), gr_show(False), gr_show(False)
-
-    progress = 0
-
-    if shared.state.job_count > 0:
-        progress += shared.state.job_no / shared.state.job_count
-    if shared.state.sampling_steps > 0:
-        progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
-
-    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
-    if time_left != "":
-        shared.state.time_left_force_display = True
-
-    progress = min(progress, 1)
-
-    progressbar = ""
-    if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
-
-    image = gr_show(False)
-    preview_visibility = gr_show(False)
-
-    if opts.show_progress_every_n_steps != 0:
-        shared.state.set_current_image()
-        image = shared.state.current_image
-
-        if image is None:
-            image = gr.update(value=None)
-        else:
-            preview_visibility = gr_show(True)
-
-    if shared.state.textinfo is not None:
-        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
-    else:
-        textinfo_result = gr_show(False)
-
-    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
-
-
-def check_progress_call_initial(id_part):
-    shared.state.job_count = -1
-    shared.state.current_latent = None
-    shared.state.current_image = None
-    shared.state.textinfo = None
-    shared.state.time_start = time.time()
-    shared.state.time_left_force_display = False
-
-    return check_progress_call(id_part)
-
-
-def roll_artist(prompt):
-    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
-    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
-
-    return prompt + ", " + artist.name if prompt != '' else artist.name
-
-
-def visit(x, func, path=""):
-    if hasattr(x, 'children'):
-        for c in x.children:
-            visit(c, func, path)
-    elif x.label is not None:
-        func(path + "/" + str(x.label), x)
-
-
-def add_style(name: str, prompt: str, negative_prompt: str):
-    if name is None:
-        return [gr_show() for x in range(4)]
-
-    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
-    shared.prompt_styles.styles[style.name] = style
-    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
-    # reserialize all styles every time we save them
-    shared.prompt_styles.save_styles(shared.styles_filename)
-
-    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)]
-
-
-def apply_styles(prompt, prompt_neg, style1_name, style2_name):
-    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
-    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
-
-    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
-
-
-def interrogate(image):
-    prompt = shared.interrogator.interrogate(image)
-
-    return gr_show(True) if prompt is None else prompt
-
-
-def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image)
-    return gr_show(True) if prompt is None else prompt
-
-
-def create_seed_inputs():
-    with gr.Row():
-        with gr.Box():
-            with gr.Row(elem_id='seed_row'):
-                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
-                seed.style(container=False)
-                random_seed = gr.Button(random_symbol, elem_id='random_seed')
-                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
-
-        with gr.Box(elem_id='subseed_show_box'):
-            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
-
-    # Components to show/hide based on the 'Extra' checkbox
-    seed_extras = []
-
-    with gr.Row(visible=False) as seed_extra_row_1:
-        seed_extras.append(seed_extra_row_1)
-        with gr.Box():
-            with gr.Row(elem_id='subseed_row'):
-                subseed = gr.Number(label='Variation seed', value=-1)
-                subseed.style(container=False)
-                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
-                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
-        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
-
-    with gr.Row(visible=False) as seed_extra_row_2:
-        seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
-
-    random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
-    random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
-
-    def change_visibility(show):
-        return {comp: gr_show(show) for comp in seed_extras}
-
-    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
-
-    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
-
-
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
-    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
-        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
-        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
-    def copy_seed(gen_info_string: str, index):
-        res = -1
-
-        try:
-            gen_info = json.loads(gen_info_string)
-            index -= gen_info.get('index_of_first_image', 0)
-
-            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
-                all_subseeds = gen_info.get('all_subseeds', [-1])
-                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
-            else:
-                all_seeds = gen_info.get('all_seeds', [-1])
-                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
-        except json.decoder.JSONDecodeError as e:
-            if gen_info_string != '':
-                print("Error parsing JSON generation info:", file=sys.stderr)
-                print(gen_info_string, file=sys.stderr)
-
-        return [res, gr_show(False)]
-
-    reuse_seed.click(
-        fn=copy_seed,
-        _js="(x, y) => [x, selected_gallery_index()]",
-        show_progress=False,
-        inputs=[generation_info, dummy_component],
-        outputs=[seed, dummy_component]
-    )
-
-
-def update_token_counter(text, steps):
-    try:
-        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
-        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
-
-    except Exception:
-        # a parsing error can happen here during typing, and we don't want to bother the user with
-        # messages related to it in console
-        prompt_schedules = [[[steps, text]]]
-
-    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
-    prompts = [prompt_text for step, prompt_text in flat_prompts]
-    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
-    style_class = ' class="red"' if (token_count > max_length) else ""
-    return f"<span {style_class}>{token_count}/{max_length}</span>"
-
-
-def create_toprow(is_img2img):
-    id_part = "img2img" if is_img2img else "txt2img"
-
-    with gr.Row(elem_id="toprow"):
-        with gr.Column(scale=6):
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
-                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                        )
-
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
-                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                        )
-
-        with gr.Column(scale=1, elem_id="roll_col"):
-            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
-            paste = gr.Button(value=paste_symbol, elem_id="paste")
-            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
-            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-
-            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
-            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-
-        button_interrogate = None
-        button_deepbooru = None
-        if is_img2img:
-            with gr.Column(scale=1, elem_id="interrogate_col"):
-                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-
-                if cmd_opts.deepdanbooru:
-                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
-        with gr.Column(scale=1):
-            with gr.Row():
-                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
-                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
-                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
-                skip.click(
-                    fn=lambda: shared.state.skip(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                interrupt.click(
-                    fn=lambda: shared.state.interrupt(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-            with gr.Row():
-                with gr.Column(scale=1, elem_id="style_pos_col"):
-                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
-                    prompt_style.save_to_config = True
-
-                with gr.Column(scale=1, elem_id="style_neg_col"):
-                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
-                    prompt_style2.save_to_config = True
-
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
-
-
-def setup_progressbar(progressbar, preview, id_part, textinfo=None):
-    if textinfo is None:
-        textinfo = gr.HTML(visible=False)
-
-    check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
-    check_progress.click(
-        fn=lambda: check_progress_call(id_part),
-        show_progress=False,
-        inputs=[],
-        outputs=[progressbar, preview, preview, textinfo],
-    )
-
-    check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
-    check_progress_initial.click(
-        fn=lambda: check_progress_call_initial(id_part),
-        show_progress=False,
-        inputs=[],
-        outputs=[progressbar, preview, preview, textinfo],
-    )
-
-
-def apply_setting(key, value):
-    if value is None:
-        return gr.update()
-
-    if shared.cmd_opts.freeze_settings:
-        return gr.update()
-
-    # dont allow model to be swapped when model hash exists in prompt
-    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
-        return gr.update()
-
-    if key == "sd_model_checkpoint":
-        ckpt_info = sd_models.get_closet_checkpoint_match(value)
-
-        if ckpt_info is not None:
-            value = ckpt_info.title
-        else:
-            return gr.update()
-
-    comp_args = opts.data_labels[key].component_args
-    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
-        return
-
-    valtype = type(opts.data_labels[key].default)
-    oldval = opts.data[key]
-    opts.data[key] = valtype(value) if valtype != type(None) else value
-    if oldval != value and opts.data_labels[key].onchange is not None:
-        opts.data_labels[key].onchange()
-
-    opts.save(shared.config_filename)
-    return value
-
-
-def update_generation_info(args):
-    generation_info, html_info, img_index = args
-    try:
-        generation_info = json.loads(generation_info)
-        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
-            return html_info
-        return plaintext_to_html(generation_info["infotexts"][img_index])
-    except Exception:
-        pass
-    # if the json parse or anything else fails, just return the old html_info
-    return html_info
-
-
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
-    def refresh():
-        refresh_method()
-        args = refreshed_args() if callable(refreshed_args) else refreshed_args
-
-        for k, v in args.items():
-            setattr(refresh_component, k, v)
-
-        return gr.update(**(args or {}))
-
-    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
-    refresh_button.click(
-        fn=refresh,
-        inputs=[],
-        outputs=[refresh_component]
-    )
-    return refresh_button
-
-
-def create_output_panel(tabname, outdir):
-    def open_folder(f):
-        if not os.path.exists(f):
-            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
-            return
-        elif not os.path.isdir(f):
-            print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
-            return
-
-        if not shared.cmd_opts.hide_ui_dir_config:
-            path = os.path.normpath(f)
-            if platform.system() == "Windows":
-                os.startfile(path)
-            elif platform.system() == "Darwin":
-                sp.Popen(["open", path])
-            else:
-                sp.Popen(["xdg-open", path])
-
-    with gr.Column(variant='panel'):
-            with gr.Group():
-                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
-
-            generation_info = None
-            with gr.Column():
-                with gr.Row():
-                    if tabname != "extras":
-                        save = gr.Button('Save', elem_id=f'save_{tabname}')
-
-                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
-                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
-
-                open_folder_button.click(
-                    fn=lambda: open_folder(opts.outdir_samples or outdir),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                if tabname != "extras":
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
-                        if tabname == 'txt2img' or tabname == 'img2img':
-                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
-                            generation_info_button.click(
-                                fn=update_generation_info,
-                                _js="(x, y) => [x, y, selected_gallery_index()]",
-                                inputs=[generation_info, html_info],
-                                outputs=[html_info],
-                                preprocess=False
-                            )
-
-                        save.click(
-                            fn=wrap_gradio_call(save_files),
-                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                            inputs=[
-                                generation_info,
-                                result_gallery,
-                                do_make_zip,
-                                html_info,
-                            ],
-                            outputs=[
-                                download_files,
-                                html_info,
-                                html_info,
-                                html_info,
-                            ]
-                        )
-                else:
-                    html_info_x = gr.HTML()
-                    html_info = gr.HTML()
-                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
-                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
-
-
-def create_ui(wrap_gradio_gpu_call):
-    import modules.img2img
-    import modules.txt2img
-
-    reload_javascript()
-
-    parameters_copypaste.reset()
-
-    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
-    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
-
-    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
-        dummy_component = gr.Label(visible=False)
-        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
-
-        with gr.Row(elem_id='txt2img_progress_row'):
-            with gr.Column(scale=1):
-                pass
-
-            with gr.Column(scale=1):
-                progressbar = gr.HTML(elem_id="txt2img_progressbar")
-                txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
-                setup_progressbar(progressbar, txt2img_preview, 'txt2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
-
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)
-
-                with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
-
-                with gr.Row(equal_height=True):
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
-                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
-                with gr.Group():
-                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
-
-            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
-            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            txt2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
-                _js="submit",
-                inputs=[
-                    txt2img_prompt,
-                    txt2img_negative_prompt,
-                    txt2img_prompt_style,
-                    txt2img_prompt_style2,
-                    steps,
-                    sampler_index,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    enable_hr,
-                    denoising_strength,
-                    firstphase_width,
-                    firstphase_height,
-                ] + custom_inputs,
-
-                outputs=[
-                    txt2img_gallery,
-                    generation_info,
-                    html_info
-                ],
-                show_progress=False,
-            )
-
-            txt2img_prompt.submit(**txt2img_args)
-            submit.click(**txt2img_args)
-
-            txt_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    txt_prompt_img
-                ],
-                outputs=[
-                    txt2img_prompt,
-                    txt_prompt_img
-                ]
-            )
-
-            enable_hr.change(
-                fn=lambda x: gr_show(x),
-                inputs=[enable_hr],
-                outputs=[hr_options],
-            )
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_txt2img_tokens",
-                inputs=[
-                    txt2img_prompt,
-                ],
-                outputs=[
-                    txt2img_prompt,
-                ]
-            )
-
-            txt2img_paste_fields = [
-                (txt2img_prompt, "Prompt"),
-                (txt2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (denoising_strength, "Denoising strength"),
-                (enable_hr, lambda d: "Denoising strength" in d),
-                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (firstphase_width, "First pass size-1"),
-                (firstphase_height, "First pass size-2"),
-                *modules.scripts.scripts_txt2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
-
-            txt2img_preview_params = [
-                txt2img_prompt,
-                txt2img_negative_prompt,
-                steps,
-                sampler_index,
-                cfg_scale,
-                seed,
-                width,
-                height,
-            ]
-
-            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-
-    modules.scripts.scripts_current = modules.scripts.scripts_img2img
-    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
-
-    with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
-
-        with gr.Row(elem_id='img2img_progress_row'):
-            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-
-            with gr.Column(scale=1):
-                pass
-
-            with gr.Column(scale=1):
-                progressbar = gr.HTML(elem_id="img2img_progressbar")
-                img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
-                setup_progressbar(progressbar, img2img_preview, 'img2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-
-                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
-                    with gr.TabItem('img2img', id='img2img'):
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
-
-                    with gr.TabItem('Inpaint', id='inpaint'):
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
-
-                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
-                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
-
-                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
-
-                        with gr.Row():
-                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
-
-                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
-
-                        with gr.Row():
-                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
-
-                    with gr.TabItem('Batch img2img', id='batch'):
-                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
-                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
-                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
-
-                with gr.Row():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
-
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-
-                with gr.Row():
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
-                with gr.Group():
-                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
-                with gr.Group():
-                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
-
-            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
-            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            img2img_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    img2img_prompt_img
-                ],
-                outputs=[
-                    img2img_prompt,
-                    img2img_prompt_img
-                ]
-            )
-
-            mask_mode.change(
-                lambda mode, img: {
-                    init_img_with_mask: gr_show(mode == 0),
-                    init_img_inpaint: gr_show(mode == 1),
-                    init_mask_inpaint: gr_show(mode == 1),
-                },
-                inputs=[mask_mode, init_img_with_mask],
-                outputs=[
-                    init_img_with_mask,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                ],
-            )
-
-            img2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
-                _js="submit_img2img",
-                inputs=[
-                    dummy_component,
-                    img2img_prompt,
-                    img2img_negative_prompt,
-                    img2img_prompt_style,
-                    img2img_prompt_style2,
-                    init_img,
-                    init_img_with_mask,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                    mask_mode,
-                    steps,
-                    sampler_index,
-                    mask_blur,
-                    inpainting_fill,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    denoising_strength,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    resize_mode,
-                    inpaint_full_res,
-                    inpaint_full_res_padding,
-                    inpainting_mask_invert,
-                    img2img_batch_input_dir,
-                    img2img_batch_output_dir,
-                ] + custom_inputs,
-                outputs=[
-                    img2img_gallery,
-                    generation_info,
-                    html_info
-                ],
-                show_progress=False,
-            )
-
-            img2img_prompt.submit(**img2img_args)
-            submit.click(**img2img_args)
-
-            img2img_interrogate.click(
-                fn=interrogate,
-                inputs=[init_img],
-                outputs=[img2img_prompt],
-            )
-
-            if cmd_opts.deepdanbooru:
-                img2img_deepbooru.click(
-                    fn=interrogate_deepbooru,
-                    inputs=[init_img],
-                    outputs=[img2img_prompt],
-            )
-
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_img2img_tokens",
-                inputs=[
-                    img2img_prompt,
-                ],
-                outputs=[
-                    img2img_prompt,
-                ]
-            )
-
-            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
-            style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
-            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-
-            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
-                button.click(
-                    fn=add_style,
-                    _js="ask_for_style_name",
-                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
-                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
-                    inputs=[dummy_component, prompt, negative_prompt],
-                    outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
-                )
-
-            for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
-                button.click(
-                    fn=apply_styles,
-                    _js=js_func,
-                    inputs=[prompt, negative_prompt, style1, style2],
-                    outputs=[prompt, negative_prompt, style1, style2],
-                )
-
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-
-            img2img_paste_fields = [
-                (img2img_prompt, "Prompt"),
-                (img2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (denoising_strength, "Denoising strength"),
-                *modules.scripts.scripts_img2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
-            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
-
-    modules.scripts.scripts_current = None
-
-    with gr.Blocks(analytics_enabled=False) as extras_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                with gr.Tabs(elem_id="mode_extras"):
-                    with gr.TabItem('Single Image'):
-                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
-
-                    with gr.TabItem('Batch Process'):
-                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
-
-                    with gr.TabItem('Batch from Directory'):
-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
-                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
-
-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
-                with gr.Tabs(elem_id="extras_resize_mode"):
-                    with gr.TabItem('Scale by'):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
-                    with gr.TabItem('Scale to'):
-                        with gr.Group():
-                            with gr.Row():
-                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
-                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-
-                with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-
-                with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
-
-                with gr.Group():
-                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
-
-                with gr.Group():
-                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
-                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
-
-                with gr.Group():
-                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
-
-            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
-
-        submit.click(
-            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
-            _js="get_extras_tab_index",
-            inputs=[
-                dummy_component,
-                dummy_component,
-                extras_image,
-                image_batch,
-                extras_batch_input_dir,
-                extras_batch_output_dir,
-                show_extras_results,
-                gfpgan_visibility,
-                codeformer_visibility,
-                codeformer_weight,
-                upscaling_resize,
-                upscaling_resize_w,
-                upscaling_resize_h,
-                upscaling_crop,
-                extras_upscaler_1,
-                extras_upscaler_2,
-                extras_upscaler_2_visibility,
-                upscale_before_face_fix,
-            ],
-            outputs=[
-                result_images,
-                html_info_x,
-                html_info,
-            ]
-        )
-        parameters_copypaste.add_paste_fields("extras", extras_image, None)
-
-        extras_image.change(
-            fn=modules.extras.clear_cache,
-            inputs=[], outputs=[]
-        )
-
-    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
-
-            with gr.Column(variant='panel'):
-                html = gr.HTML()
-                generation_info = gr.Textbox(visible=False)
-                html2 = gr.HTML()
-                with gr.Row():
-                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-                parameters_copypaste.bind_buttons(buttons, image, generation_info)
-
-        image.change(
-            fn=wrap_gradio_call(modules.extras.run_pnginfo),
-            inputs=[image],
-            outputs=[html, generation_info, html2],
-        )
-
-    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
-
-                with gr.Row():
-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
-                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
-                custom_name = gr.Textbox(label="Custom Name (Optional)")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
-                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
-                save_as_half = gr.Checkbox(value=False, label="Save as float16")
-                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
-                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
-
-            with gr.Column(variant='panel'):
-                submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
-
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
-
-    with gr.Blocks(analytics_enabled=False) as train_interface:
-        with gr.Row().style(equal_height=False):
-            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
-
-        with gr.Row().style(equal_height=False):
-            with gr.Tabs(elem_id="train_tabs"):
-
-                with gr.Tab(label="Create embedding"):
-                    new_embedding_name = gr.Textbox(label="Name")
-                    initialization_text = gr.Textbox(label="Initialization text", value="*")
-                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
-                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_embedding = gr.Button(value="Create embedding", variant='primary')
-
-                with gr.Tab(label="Create hypernetwork"):
-                    new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
-                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
-                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
-                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
-                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
-
-                with gr.Tab(label="Preprocess images"):
-                    process_src = gr.Textbox(label='Source directory')
-                    process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
-
-                    with gr.Row():
-                        process_flip = gr.Checkbox(label='Create flipped copies')
-                        process_split = gr.Checkbox(label='Split oversized images')
-                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
-                        process_caption = gr.Checkbox(label='Use BLIP for caption')
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
-
-                    with gr.Row(visible=False) as process_split_extra_row:
-                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
-
-                    with gr.Row(visible=False) as process_focal_crop_row:
-                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            with gr.Row():
-                                interrupt_preprocessing = gr.Button("Interrupt")
-                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
-
-                    process_split.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_split],
-                        outputs=[process_split_extra_row],
-                    )
-
-                    process_focal_crop.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_focal_crop],
-                        outputs=[process_focal_crop_row],
-                    )
-
-                with gr.Tab(label="Train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
-                    with gr.Row():
-                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-                    with gr.Row():
-                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
-                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
-                    with gr.Row():
-                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
-                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
-
-                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
-                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
-                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
-                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    steps = gr.Number(label='Max steps', value=100000, precision=0)
-                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
-                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
-
-                    with gr.Row():
-                        interrupt_training = gr.Button(value="Interrupt")
-                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
-                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
-
-                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
-
-                script_callbacks.ui_train_tabs_callback(params)
-
-            with gr.Column():
-                progressbar = gr.HTML(elem_id="ti_progressbar")
-                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-
-                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
-                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
-                ti_progress = gr.HTML(elem_id="ti_progress", value="")
-                ti_outcome = gr.HTML(elem_id="ti_error", value="")
-                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
-
-        create_embedding.click(
-            fn=modules.textual_inversion.ui.create_embedding,
-            inputs=[
-                new_embedding_name,
-                initialization_text,
-                nvpt,
-                overwrite_old_embedding,
-            ],
-            outputs=[
-                train_embedding_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        create_hypernetwork.click(
-            fn=modules.hypernetworks.ui.create_hypernetwork,
-            inputs=[
-                new_hypernetwork_name,
-                new_hypernetwork_sizes,
-                overwrite_old_hypernetwork,
-                new_hypernetwork_layer_structure,
-                new_hypernetwork_activation_func,
-                new_hypernetwork_initialization_option,
-                new_hypernetwork_add_layer_norm,
-                new_hypernetwork_use_dropout
-            ],
-            outputs=[
-                train_hypernetwork_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        run_preprocess.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                process_src,
-                process_dst,
-                process_width,
-                process_height,
-                preprocess_txt_action,
-                process_flip,
-                process_split,
-                process_caption,
-                process_caption_deepbooru,
-                process_split_threshold,
-                process_overlap_ratio,
-                process_focal_crop,
-                process_focal_crop_face_weight,
-                process_focal_crop_entropy_weight,
-                process_focal_crop_edges_weight,
-                process_focal_crop_debug,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ],
-        )
-
-        train_embedding.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                train_embedding_name,
-                embedding_learn_rate,
-                batch_size,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                steps,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                save_image_with_stored_embedding,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        train_hypernetwork.click(
-            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                train_hypernetwork_name,
-                hypernetwork_learn_rate,
-                batch_size,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                steps,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        interrupt_training.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-        interrupt_preprocessing.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-    def create_setting_component(key, is_quicksettings=False):
-        def fun():
-            return opts.data[key] if key in opts.data else opts.data_labels[key].default
-
-        info = opts.data_labels[key]
-        t = type(info.default)
-
-        args = info.component_args() if callable(info.component_args) else info.component_args
-
-        if info.component is not None:
-            comp = info.component
-        elif t == str:
-            comp = gr.Textbox
-        elif t == int:
-            comp = gr.Number
-        elif t == bool:
-            comp = gr.Checkbox
-        else:
-            raise Exception(f'bad options item type: {str(t)} for key {key}')
-
-        elem_id = "setting_"+key
-
-        if info.refresh is not None:
-            if is_quicksettings:
-                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
-            else:
-                with gr.Row(variant="compact"):
-                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
-        else:
-            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-
-        return res
-
-    components = []
-    component_dict = {}
-
-    script_callbacks.ui_settings_callback()
-    opts.reorder()
-
-    def run_settings(*args):
-        changed = []
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if comp == dummy_component:
-                continue
-
-            if opts.set(key, value):
-                changed.append(key)
-
-        try:
-            opts.save(shared.config_filename)
-        except RuntimeError:
-            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
-        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
-
-    def run_settings_single(value, key):
-        if not opts.same_type(value, opts.data_labels[key].default):
-            return gr.update(visible=True), opts.dumpjson()
-
-        if not opts.set(key, value):
-            return gr.update(value=getattr(opts, key)), opts.dumpjson()
-
-        opts.save(shared.config_filename)
-
-        return gr.update(value=value), opts.dumpjson()
-
-    with gr.Blocks(analytics_enabled=False) as settings_interface:
-        settings_submit = gr.Button(value="Apply settings", variant='primary')
-        result = gr.HTML()
-
-        settings_cols = 3
-        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
-
-        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
-        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
-
-        quicksettings_list = []
-
-        cols_displayed = 0
-        items_displayed = 0
-        previous_section = None
-        column = None
-        with gr.Row(elem_id="settings").style(equal_height=False):
-            for i, (k, item) in enumerate(opts.data_labels.items()):
-                section_must_be_skipped = item.section[0] is None
-
-                if previous_section != item.section and not section_must_be_skipped:
-                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
-                        if column is not None:
-                            column.__exit__()
-
-                        column = gr.Column(variant='panel')
-                        column.__enter__()
-
-                        items_displayed = 0
-                        cols_displayed += 1
-
-                    previous_section = item.section
-
-                    elem_id, text = item.section
-                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
-
-                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
-                    quicksettings_list.append((i, k, item))
-                    components.append(dummy_component)
-                elif section_must_be_skipped:
-                    components.append(dummy_component)
-                else:
-                    component = create_setting_component(k)
-                    component_dict[k] = component
-                    components.append(component)
-                    items_displayed += 1
-
-        with gr.Row():
-            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
-            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
-
-        with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-
-        request_notifications.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='function(){}'
-        )
-
-        download_localization.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='download_localization'
-        )
-
-        def reload_scripts():
-            modules.scripts.reload_script_body_only()
-            reload_javascript()  # need to refresh the html page
-
-        reload_script_bodies.click(
-            fn=reload_scripts,
-            inputs=[],
-            outputs=[]
-        )
-
-        def request_restart():
-            shared.state.interrupt()
-            shared.state.need_restart = True
-
-        restart_gradio.click(
-            fn=request_restart,
-            _js='restart_reload',
-            inputs=[],
-            outputs=[],
-        )
-
-        if column is not None:
-            column.__exit__()
-
-    interfaces = [
-        (txt2img_interface, "txt2img", "txt2img"),
-        (img2img_interface, "img2img", "img2img"),
-        (extras_interface, "Extras", "extras"),
-        (pnginfo_interface, "PNG Info", "pnginfo"),
-        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
-        (train_interface, "Train", "ti"),
-    ]
-
-    css = ""
-
-    for cssfile in modules.scripts.list_files_with_name("style.css"):
-        if not os.path.isfile(cssfile):
-            continue
-
-        with open(cssfile, "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if os.path.exists(os.path.join(script_path, "user.css")):
-        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if not cmd_opts.no_progressbar_hiding:
-        css += css_hide_progressbar
-
-    interfaces += script_callbacks.ui_tabs_callback()
-    interfaces += [(settings_interface, "Settings", "settings")]
-
-    extensions_interface = ui_extensions.create_ui()
-    interfaces += [(extensions_interface, "Extensions", "extensions")]
-
-    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
-        with gr.Row(elem_id="quicksettings"):
-            for i, k, item in quicksettings_list:
-                component = create_setting_component(k, is_quicksettings=True)
-                component_dict[k] = component
-
-        parameters_copypaste.integrate_settings_paste_fields(component_dict)
-        parameters_copypaste.run_bind()
-
-        with gr.Tabs(elem_id="tabs") as tabs:
-            for interface, label, ifid in interfaces:
-                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
-                    interface.render()
-
-        if os.path.exists(os.path.join(script_path, "notification.mp3")):
-            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
-
-        text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
-        settings_submit.click(
-            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
-            inputs=components,
-            outputs=[text_settings, result],
-        )
-
-        for i, k, item in quicksettings_list:
-            component = component_dict[k]
-
-            component.change(
-                fn=lambda value, k=k: run_settings_single(value, key=k),
-                inputs=[component],
-                outputs=[component, text_settings],
-            )
-
-        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
-
-        def get_settings_values():
-            return [getattr(opts, key) for key in component_keys]
-
-        demo.load(
-            fn=get_settings_values,
-            inputs=[],
-            outputs=[component_dict[k] for k in component_keys],
-        )
-
-        def modelmerger(*args):
-            try:
-                results = modules.extras.run_modelmerger(*args)
-            except Exception as e:
-                print("Error loading/saving model file:", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-                modules.sd_models.list_models()  # to remove the potentially missing models from the list
-                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
-            return results
-
-        modelmerger_merge.click(
-            fn=modelmerger,
-            inputs=[
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                interp_method,
-                interp_amount,
-                save_as_half,
-                save_as_safetensors,
-                custom_name,
-            ],
-            outputs=[
-                submit_result,
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                component_dict['sd_model_checkpoint'],
-            ]
-        )
-
-    ui_config_file = cmd_opts.ui_config_file
-    ui_settings = {}
-    settings_count = len(ui_settings)
-    error_loading = False
-
-    try:
-        if os.path.exists(ui_config_file):
-            with open(ui_config_file, "r", encoding="utf8") as file:
-                ui_settings = json.load(file)
-    except Exception:
-        error_loading = True
-        print("Error loading settings:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-
-    def loadsave(path, x):
-        def apply_field(obj, field, condition=None, init_field=None):
-            key = path + "/" + field
-
-            if getattr(obj, 'custom_script_source', None) is not None:
-              key = 'customscript/' + obj.custom_script_source + '/' + key
-
-            if getattr(obj, 'do_not_save_to_config', False):
-                return
-
-            saved_value = ui_settings.get(key, None)
-            if saved_value is None:
-                ui_settings[key] = getattr(obj, field)
-            elif condition and not condition(saved_value):
-                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
-            else:
-                setattr(obj, field, saved_value)
-                if init_field is not None:
-                    init_field(saved_value)
-
-        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
-            apply_field(x, 'visible')
-
-        if type(x) == gr.Slider:
-            apply_field(x, 'value')
-            apply_field(x, 'minimum')
-            apply_field(x, 'maximum')
-            apply_field(x, 'step')
-
-        if type(x) == gr.Radio:
-            apply_field(x, 'value', lambda val: val in x.choices)
-
-        if type(x) == gr.Checkbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Textbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Number:
-            apply_field(x, 'value')
-
-        # Since there are many dropdowns that shouldn't be saved,
-        # we only mark dropdowns that should be saved.
-        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
-            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
-            apply_field(x, 'visible')
-
-    visit(txt2img_interface, loadsave, "txt2img")
-    visit(img2img_interface, loadsave, "img2img")
-    visit(extras_interface, loadsave, "extras")
-    visit(modelmerger_interface, loadsave, "modelmerger")
-
-    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
-        with open(ui_config_file, "w", encoding="utf8") as file:
-            json.dump(ui_settings, file, indent=4)
-
-    return demo
-
-
-def reload_javascript():
-    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
-        javascript = f'<script>{jsfile.read()}</script>'
-
-    scripts_list = modules.scripts.list_scripts("javascript", ".js")
-
-    for basedir, filename, path in scripts_list:
-        with open(path, "r", encoding="utf8") as jsfile:
-            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-
-    if cmd_opts.theme is not None:
-        javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
-
-    javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
-
-    def template_response(*args, **kwargs):
-        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(
-            b'</head>', f'{javascript}</head>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gradio.routes.templates.TemplateResponse = template_response
-
-
-if not hasattr(shared, 'GradioTemplateResponseOriginal'):
-    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
+import html
+import json
+import math
+import mimetypes
+import os
+import platform
+import random
+import subprocess as sp
+import sys
+import tempfile
+import time
+import traceback
+from functools import partial, reduce
+
+import gradio as gr
+import gradio.routes
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin
+
+
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
+from modules.paths import script_path
+
+from modules.shared import opts, cmd_opts, restricted_opts
+
+if cmd_opts.deepdanbooru:
+    from modules.deepbooru import get_deepbooru_tags
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.ldsr_model
+import modules.scripts
+import modules.shared as shared
+import modules.styles
+import modules.textual_inversion.ui
+from modules import prompt_parser
+from modules.images import save_image
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
+import modules.textual_inversion.ui
+import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
+
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
+mimetypes.init()
+mimetypes.add_type('application/javascript', '.js')
+
+if not cmd_opts.share and not cmd_opts.listen:
+    # fix gradio phoning home
+    gradio.utils.version_check = lambda: None
+    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
+
+if cmd_opts.ngrok != None:
+    import modules.ngrok as ngrok
+    print('ngrok authtoken detected, trying to connect...')
+    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
+
+
+def gr_show(visible=True):
+    return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+css_hide_progressbar = """
+.wrap .m-12 svg { display:none!important; }
+.wrap .m-12::before { content:"Loading..." }
+.wrap .z-20 svg { display:none!important; }
+.wrap .z-20::before { content:"Loading..." }
+.progress-bar { display:none!important; }
+.meta-text { display:none!important; }
+.meta-text-center { display:none!important; }
+"""
+
+# Using constants for these since the variation selector isn't visible.
+# Important that they exactly match script.js for tooltip to work.
+random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
+reuse_symbol = '\u267b\ufe0f'  # ♻️
+art_symbol = '\U0001f3a8'  # 🎨
+paste_symbol = '\u2199\ufe0f'  # ↙
+folder_symbol = '\U0001f4c2'  # 📂
+refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+apply_style_symbol = '\U0001f4cb'  # 📋
+
+
+def plaintext_to_html(text):
+    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
+    return text
+
+def send_gradio_gallery_to_image(x):
+    if len(x) == 0:
+        return None
+    return image_from_url_text(x[0])
+
+def save_files(js_data, images, do_make_zip, index):
+    import csv
+    filenames = []
+    fullfns = []
+
+    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
+    class MyObject:
+        def __init__(self, d=None):
+            if d is not None:
+                for key, value in d.items():
+                    setattr(self, key, value)
+
+    data = json.loads(js_data)
+
+    p = MyObject(data)
+    path = opts.outdir_save
+    save_to_dirs = opts.use_save_to_dirs_for_ui
+    extension: str = opts.samples_format
+    start_index = 0
+
+    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
+
+        images = [images[index]]
+        start_index = index
+
+    os.makedirs(opts.outdir_save, exist_ok=True)
+
+    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
+        at_start = file.tell() == 0
+        writer = csv.writer(file)
+        if at_start:
+            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
+
+        for image_index, filedata in enumerate(images, start_index):
+            image = image_from_url_text(filedata)
+
+            is_grid = image_index < p.index_of_first_image
+            i = 0 if is_grid else (image_index - p.index_of_first_image)
+
+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+
+            filename = os.path.relpath(fullfn, path)
+            filenames.append(filename)
+            fullfns.append(fullfn)
+            if txt_fullfn:
+                filenames.append(os.path.basename(txt_fullfn))
+                fullfns.append(txt_fullfn)
+
+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+
+    # Make Zip
+    if do_make_zip:
+        zip_filepath = os.path.join(path, "images.zip")
+
+        from zipfile import ZipFile
+        with ZipFile(zip_filepath, "w") as zip_file:
+            for i in range(len(fullfns)):
+                with open(fullfns[i], mode="rb") as f:
+                    zip_file.writestr(filenames[i], f.read())
+        fullfns.insert(0, zip_filepath)
+
+    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+
+def save_pil_to_file(pil_image, dir=None):
+    use_metadata = False
+    metadata = PngImagePlugin.PngInfo()
+    for key, value in pil_image.info.items():
+        if isinstance(key, str) and isinstance(value, str):
+            metadata.add_text(key, value)
+            use_metadata = True
+
+    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+    return file_obj
+
+
+# override save to file function so that it also writes PNG info
+gr.processing_utils.save_pil_to_file = save_pil_to_file
+
+
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
+        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
+        if run_memmon:
+            shared.mem_mon.monitor()
+        t = time.perf_counter()
+
+        try:
+            res = list(func(*args, **kwargs))
+        except Exception as e:
+            # When printing out our debug argument list, do not print out more than a MB of text
+            max_debug_str_len = 131072 # (1024*1024)/8
+
+            print("Error completing request", file=sys.stderr)
+            argStr = f"Arguments: {str(args)} {str(kwargs)}"
+            print(argStr[:max_debug_str_len], file=sys.stderr)
+            if len(argStr) > max_debug_str_len:
+                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
+
+            print(traceback.format_exc(), file=sys.stderr)
+
+            shared.state.job = ""
+            shared.state.job_count = 0
+
+            if extra_outputs_array is None:
+                extra_outputs_array = [None, '']
+
+            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+
+        shared.state.skipped = False
+        shared.state.interrupted = False
+        shared.state.job_count = 0
+
+        if not add_stats:
+            return tuple(res)
+
+        elapsed = time.perf_counter() - t
+        elapsed_m = int(elapsed // 60)
+        elapsed_s = elapsed % 60
+        elapsed_text = f"{elapsed_s:.2f}s"
+        if elapsed_m > 0:
+            elapsed_text = f"{elapsed_m}m "+elapsed_text
+
+        if run_memmon:
+            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
+            active_peak = mem_stats['active_peak']
+            reserved_peak = mem_stats['reserved_peak']
+            sys_peak = mem_stats['system_peak']
+            sys_total = mem_stats['total']
+            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
+
+            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
+        else:
+            vram_html = ''
+
+        # last item is always HTML
+        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
+
+        return tuple(res)
+
+    return f
+
+
+def calc_time_left(progress, threshold, label, force_display):
+    if progress == 0:
+        return ""
+    else:
+        time_since_start = time.time() - shared.state.time_start
+        eta = (time_since_start/progress)
+        eta_relative = eta-time_since_start
+        if (eta_relative > threshold and progress > 0.02) or force_display:
+            if eta_relative > 3600:
+                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
+            elif eta_relative > 60:
+                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))
+            else:
+                return label + time.strftime('%Ss',  time.gmtime(eta_relative))
+        else:
+            return ""
+
+
+def check_progress_call(id_part):
+    if shared.state.job_count == 0:
+        return "", gr_show(False), gr_show(False), gr_show(False)
+
+    progress = 0
+
+    if shared.state.job_count > 0:
+        progress += shared.state.job_no / shared.state.job_count
+    if shared.state.sampling_steps > 0:
+        progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
+
+    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
+    if time_left != "":
+        shared.state.time_left_force_display = True
+
+    progress = min(progress, 1)
+
+    progressbar = ""
+    if opts.show_progressbar:
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
+
+    image = gr_show(False)
+    preview_visibility = gr_show(False)
+
+    if opts.show_progress_every_n_steps != 0:
+        shared.state.set_current_image()
+        image = shared.state.current_image
+
+        if image is None:
+            image = gr.update(value=None)
+        else:
+            preview_visibility = gr_show(True)
+
+    if shared.state.textinfo is not None:
+        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
+    else:
+        textinfo_result = gr_show(False)
+
+    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
+
+
+def check_progress_call_initial(id_part):
+    shared.state.job_count = -1
+    shared.state.current_latent = None
+    shared.state.current_image = None
+    shared.state.textinfo = None
+    shared.state.time_start = time.time()
+    shared.state.time_left_force_display = False
+
+    return check_progress_call(id_part)
+
+
+def roll_artist(prompt):
+    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
+    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
+
+    return prompt + ", " + artist.name if prompt != '' else artist.name
+
+
+def visit(x, func, path=""):
+    if hasattr(x, 'children'):
+        for c in x.children:
+            visit(c, func, path)
+    elif x.label is not None:
+        func(path + "/" + str(x.label), x)
+
+
+def add_style(name: str, prompt: str, negative_prompt: str):
+    if name is None:
+        return [gr_show() for x in range(4)]
+
+    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
+    shared.prompt_styles.styles[style.name] = style
+    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
+    # reserialize all styles every time we save them
+    shared.prompt_styles.save_styles(shared.styles_filename)
+
+    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)]
+
+
+def apply_styles(prompt, prompt_neg, style1_name, style2_name):
+    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
+    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
+
+    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
+
+
+def interrogate(image):
+    prompt = shared.interrogator.interrogate(image)
+
+    return gr_show(True) if prompt is None else prompt
+
+
+def interrogate_deepbooru(image):
+    prompt = get_deepbooru_tags(image)
+    return gr_show(True) if prompt is None else prompt
+
+
+def create_seed_inputs():
+    with gr.Row():
+        with gr.Box():
+            with gr.Row(elem_id='seed_row'):
+                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
+                seed.style(container=False)
+                random_seed = gr.Button(random_symbol, elem_id='random_seed')
+                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
+
+        with gr.Box(elem_id='subseed_show_box'):
+            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
+
+    # Components to show/hide based on the 'Extra' checkbox
+    seed_extras = []
+
+    with gr.Row(visible=False) as seed_extra_row_1:
+        seed_extras.append(seed_extra_row_1)
+        with gr.Box():
+            with gr.Row(elem_id='subseed_row'):
+                subseed = gr.Number(label='Variation seed', value=-1)
+                subseed.style(container=False)
+                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
+                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
+
+    with gr.Row(visible=False) as seed_extra_row_2:
+        seed_extras.append(seed_extra_row_2)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
+
+    random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
+    random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
+
+    def change_visibility(show):
+        return {comp: gr_show(show) for comp in seed_extras}
+
+    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
+
+    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
+    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+    def copy_seed(gen_info_string: str, index):
+        res = -1
+
+        try:
+            gen_info = json.loads(gen_info_string)
+            index -= gen_info.get('index_of_first_image', 0)
+
+            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+                all_subseeds = gen_info.get('all_subseeds', [-1])
+                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+            else:
+                all_seeds = gen_info.get('all_seeds', [-1])
+                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+        except json.decoder.JSONDecodeError as e:
+            if gen_info_string != '':
+                print("Error parsing JSON generation info:", file=sys.stderr)
+                print(gen_info_string, file=sys.stderr)
+
+        return [res, gr_show(False)]
+
+    reuse_seed.click(
+        fn=copy_seed,
+        _js="(x, y) => [x, selected_gallery_index()]",
+        show_progress=False,
+        inputs=[generation_info, dummy_component],
+        outputs=[seed, dummy_component]
+    )
+
+
+def update_token_counter(text, steps):
+    try:
+        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+
+    except Exception:
+        # a parsing error can happen here during typing, and we don't want to bother the user with
+        # messages related to it in console
+        prompt_schedules = [[[steps, text]]]
+
+    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+    prompts = [prompt_text for step, prompt_text in flat_prompts]
+    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
+    style_class = ' class="red"' if (token_count > max_length) else ""
+    return f"<span {style_class}>{token_count}/{max_length}</span>"
+
+
+def create_toprow(is_img2img):
+    id_part = "img2img" if is_img2img else "txt2img"
+
+    with gr.Row(elem_id="toprow"):
+        with gr.Column(scale=6):
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
+                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
+                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
+        with gr.Column(scale=1, elem_id="roll_col"):
+            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+            paste = gr.Button(value=paste_symbol, elem_id="paste")
+            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
+            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
+
+            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+
+        button_interrogate = None
+        button_deepbooru = None
+        if is_img2img:
+            with gr.Column(scale=1, elem_id="interrogate_col"):
+                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+
+                if cmd_opts.deepdanbooru:
+                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+        with gr.Column(scale=1):
+            with gr.Row():
+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
+                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
+                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+
+                skip.click(
+                    fn=lambda: shared.state.skip(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                interrupt.click(
+                    fn=lambda: shared.state.interrupt(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+            with gr.Row():
+                with gr.Column(scale=1, elem_id="style_pos_col"):
+                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style.save_to_config = True
+
+                with gr.Column(scale=1, elem_id="style_neg_col"):
+                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style2.save_to_config = True
+
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+
+
+def setup_progressbar(progressbar, preview, id_part, textinfo=None):
+    if textinfo is None:
+        textinfo = gr.HTML(visible=False)
+
+    check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
+    check_progress.click(
+        fn=lambda: check_progress_call(id_part),
+        show_progress=False,
+        inputs=[],
+        outputs=[progressbar, preview, preview, textinfo],
+    )
+
+    check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
+    check_progress_initial.click(
+        fn=lambda: check_progress_call_initial(id_part),
+        show_progress=False,
+        inputs=[],
+        outputs=[progressbar, preview, preview, textinfo],
+    )
+
+
+def apply_setting(key, value):
+    if value is None:
+        return gr.update()
+
+    if shared.cmd_opts.freeze_settings:
+        return gr.update()
+
+    # dont allow model to be swapped when model hash exists in prompt
+    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+        return gr.update()
+
+    if key == "sd_model_checkpoint":
+        ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+        if ckpt_info is not None:
+            value = ckpt_info.title
+        else:
+            return gr.update()
+
+    comp_args = opts.data_labels[key].component_args
+    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+        return
+
+    valtype = type(opts.data_labels[key].default)
+    oldval = opts.data[key]
+    opts.data[key] = valtype(value) if valtype != type(None) else value
+    if oldval != value and opts.data_labels[key].onchange is not None:
+        opts.data_labels[key].onchange()
+
+    opts.save(shared.config_filename)
+    return value
+
+
+def update_generation_info(args):
+    generation_info, html_info, img_index = args
+    try:
+        generation_info = json.loads(generation_info)
+        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
+            return html_info
+        return plaintext_to_html(generation_info["infotexts"][img_index])
+    except Exception:
+        pass
+    # if the json parse or anything else fails, just return the old html_info
+    return html_info
+
+
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+    def refresh():
+        refresh_method()
+        args = refreshed_args() if callable(refreshed_args) else refreshed_args
+
+        for k, v in args.items():
+            setattr(refresh_component, k, v)
+
+        return gr.update(**(args or {}))
+
+    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
+    refresh_button.click(
+        fn=refresh,
+        inputs=[],
+        outputs=[refresh_component]
+    )
+    return refresh_button
+
+
+def create_output_panel(tabname, outdir):
+    def open_folder(f):
+        if not os.path.exists(f):
+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+            return
+        elif not os.path.isdir(f):
+            print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+            return
+
+        if not shared.cmd_opts.hide_ui_dir_config:
+            path = os.path.normpath(f)
+            if platform.system() == "Windows":
+                os.startfile(path)
+            elif platform.system() == "Darwin":
+                sp.Popen(["open", path])
+            else:
+                sp.Popen(["xdg-open", path])
+
+    with gr.Column(variant='panel'):
+            with gr.Group():
+                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+
+            generation_info = None
+            with gr.Column():
+                with gr.Row():
+                    if tabname != "extras":
+                        save = gr.Button('Save', elem_id=f'save_{tabname}')
+
+                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
+
+                open_folder_button.click(
+                    fn=lambda: open_folder(opts.outdir_samples or outdir),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                if tabname != "extras":
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
+                        if tabname == 'txt2img' or tabname == 'img2img':
+                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+                            generation_info_button.click(
+                                fn=update_generation_info,
+                                _js="(x, y) => [x, y, selected_gallery_index()]",
+                                inputs=[generation_info, html_info],
+                                outputs=[html_info],
+                                preprocess=False
+                            )
+
+                        save.click(
+                            fn=wrap_gradio_call(save_files),
+                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+                            inputs=[
+                                generation_info,
+                                result_gallery,
+                                do_make_zip,
+                                html_info,
+                            ],
+                            outputs=[
+                                download_files,
+                                html_info,
+                                html_info,
+                                html_info,
+                            ]
+                        )
+                else:
+                    html_info_x = gr.HTML()
+                    html_info = gr.HTML()
+                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+
+
+def create_ui(wrap_gradio_gpu_call):
+    import modules.img2img
+    import modules.txt2img
+
+    reload_javascript()
+
+    parameters_copypaste.reset()
+
+    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
+    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
+    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
+        dummy_component = gr.Label(visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+        with gr.Row(elem_id='txt2img_progress_row'):
+            with gr.Column(scale=1):
+                pass
+
+            with gr.Column(scale=1):
+                progressbar = gr.HTML(elem_id="txt2img_progressbar")
+                txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
+                setup_progressbar(progressbar, txt2img_preview, 'txt2img')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
+
+                with gr.Group():
+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+
+                with gr.Row():
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+                    tiling = gr.Checkbox(label='Tiling', value=False)
+                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)
+
+                with gr.Row(visible=False) as hr_options:
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+
+                with gr.Row(equal_height=True):
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+                with gr.Group():
+                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+
+            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            txt2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
+                _js="submit",
+                inputs=[
+                    txt2img_prompt,
+                    txt2img_negative_prompt,
+                    txt2img_prompt_style,
+                    txt2img_prompt_style2,
+                    steps,
+                    sampler_index,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    height,
+                    width,
+                    enable_hr,
+                    denoising_strength,
+                    firstphase_width,
+                    firstphase_height,
+                ] + custom_inputs,
+
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info
+                ],
+                show_progress=False,
+            )
+
+            txt2img_prompt.submit(**txt2img_args)
+            submit.click(**txt2img_args)
+
+            txt_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    txt_prompt_img
+                ],
+                outputs=[
+                    txt2img_prompt,
+                    txt_prompt_img
+                ]
+            )
+
+            enable_hr.change(
+                fn=lambda x: gr_show(x),
+                inputs=[enable_hr],
+                outputs=[hr_options],
+            )
+
+            roll.click(
+                fn=roll_artist,
+                _js="update_txt2img_tokens",
+                inputs=[
+                    txt2img_prompt,
+                ],
+                outputs=[
+                    txt2img_prompt,
+                ]
+            )
+
+            txt2img_paste_fields = [
+                (txt2img_prompt, "Prompt"),
+                (txt2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (denoising_strength, "Denoising strength"),
+                (enable_hr, lambda d: "Denoising strength" in d),
+                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+                (firstphase_width, "First pass size-1"),
+                (firstphase_height, "First pass size-2"),
+                *modules.scripts.scripts_txt2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
+
+            txt2img_preview_params = [
+                txt2img_prompt,
+                txt2img_negative_prompt,
+                steps,
+                sampler_index,
+                cfg_scale,
+                seed,
+                width,
+                height,
+            ]
+
+            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
+
+    modules.scripts.scripts_current = modules.scripts.scripts_img2img
+    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
+    with gr.Blocks(analytics_enabled=False) as img2img_interface:
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
+
+        with gr.Row(elem_id='img2img_progress_row'):
+            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+            with gr.Column(scale=1):
+                pass
+
+            with gr.Column(scale=1):
+                progressbar = gr.HTML(elem_id="img2img_progressbar")
+                img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
+                setup_progressbar(progressbar, img2img_preview, 'img2img')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+
+                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
+                    with gr.TabItem('img2img', id='img2img'):
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
+
+                    with gr.TabItem('Inpaint', id='inpaint'):
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
+
+                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
+                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
+
+                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+
+                        with gr.Row():
+                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
+                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
+
+                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
+
+                        with gr.Row():
+                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
+                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+
+                    with gr.TabItem('Batch img2img', id='batch'):
+                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
+                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
+                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
+
+                with gr.Row():
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
+
+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
+
+                with gr.Group():
+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
+
+                with gr.Row():
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+                    tiling = gr.Checkbox(label='Tiling', value=False)
+
+                with gr.Row():
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+                with gr.Group():
+                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
+
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+                with gr.Group():
+                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+
+            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
+            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            img2img_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    img2img_prompt_img
+                ],
+                outputs=[
+                    img2img_prompt,
+                    img2img_prompt_img
+                ]
+            )
+
+            mask_mode.change(
+                lambda mode, img: {
+                    init_img_with_mask: gr_show(mode == 0),
+                    init_img_inpaint: gr_show(mode == 1),
+                    init_mask_inpaint: gr_show(mode == 1),
+                },
+                inputs=[mask_mode, init_img_with_mask],
+                outputs=[
+                    init_img_with_mask,
+                    init_img_inpaint,
+                    init_mask_inpaint,
+                ],
+            )
+
+            img2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
+                _js="submit_img2img",
+                inputs=[
+                    dummy_component,
+                    img2img_prompt,
+                    img2img_negative_prompt,
+                    img2img_prompt_style,
+                    img2img_prompt_style2,
+                    init_img,
+                    init_img_with_mask,
+                    init_img_inpaint,
+                    init_mask_inpaint,
+                    mask_mode,
+                    steps,
+                    sampler_index,
+                    mask_blur,
+                    inpainting_fill,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    denoising_strength,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    height,
+                    width,
+                    resize_mode,
+                    inpaint_full_res,
+                    inpaint_full_res_padding,
+                    inpainting_mask_invert,
+                    img2img_batch_input_dir,
+                    img2img_batch_output_dir,
+                ] + custom_inputs,
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info
+                ],
+                show_progress=False,
+            )
+
+            img2img_prompt.submit(**img2img_args)
+            submit.click(**img2img_args)
+
+            img2img_interrogate.click(
+                fn=interrogate,
+                inputs=[init_img],
+                outputs=[img2img_prompt],
+            )
+
+            if cmd_opts.deepdanbooru:
+                img2img_deepbooru.click(
+                    fn=interrogate_deepbooru,
+                    inputs=[init_img],
+                    outputs=[img2img_prompt],
+            )
+
+
+            roll.click(
+                fn=roll_artist,
+                _js="update_img2img_tokens",
+                inputs=[
+                    img2img_prompt,
+                ],
+                outputs=[
+                    img2img_prompt,
+                ]
+            )
+
+            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
+            style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
+            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
+
+            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
+                button.click(
+                    fn=add_style,
+                    _js="ask_for_style_name",
+                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
+                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
+                    inputs=[dummy_component, prompt, negative_prompt],
+                    outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
+                )
+
+            for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
+                button.click(
+                    fn=apply_styles,
+                    _js=js_func,
+                    inputs=[prompt, negative_prompt, style1, style2],
+                    outputs=[prompt, negative_prompt, style1, style2],
+                )
+
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+
+            img2img_paste_fields = [
+                (img2img_prompt, "Prompt"),
+                (img2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (denoising_strength, "Denoising strength"),
+                *modules.scripts.scripts_img2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+
+    modules.scripts.scripts_current = None
+
+    with gr.Blocks(analytics_enabled=False) as extras_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                with gr.Tabs(elem_id="mode_extras"):
+                    with gr.TabItem('Single Image'):
+                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
+
+                    with gr.TabItem('Batch Process'):
+                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
+
+                    with gr.TabItem('Batch from Directory'):
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
+                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
+
+                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
+                with gr.Tabs(elem_id="extras_resize_mode"):
+                    with gr.TabItem('Scale by'):
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
+                    with gr.TabItem('Scale to'):
+                        with gr.Group():
+                            with gr.Row():
+                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
+                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
+
+                with gr.Group():
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+
+                with gr.Group():
+                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
+
+                with gr.Group():
+                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
+
+                with gr.Group():
+                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
+                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
+
+                with gr.Group():
+                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
+
+            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
+
+        submit.click(
+            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
+            _js="get_extras_tab_index",
+            inputs=[
+                dummy_component,
+                dummy_component,
+                extras_image,
+                image_batch,
+                extras_batch_input_dir,
+                extras_batch_output_dir,
+                show_extras_results,
+                gfpgan_visibility,
+                codeformer_visibility,
+                codeformer_weight,
+                upscaling_resize,
+                upscaling_resize_w,
+                upscaling_resize_h,
+                upscaling_crop,
+                extras_upscaler_1,
+                extras_upscaler_2,
+                extras_upscaler_2_visibility,
+                upscale_before_face_fix,
+            ],
+            outputs=[
+                result_images,
+                html_info_x,
+                html_info,
+            ]
+        )
+        parameters_copypaste.add_paste_fields("extras", extras_image, None)
+
+        extras_image.change(
+            fn=modules.extras.clear_cache,
+            inputs=[], outputs=[]
+        )
+
+    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
+
+            with gr.Column(variant='panel'):
+                html = gr.HTML()
+                generation_info = gr.Textbox(visible=False)
+                html2 = gr.HTML()
+                with gr.Row():
+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+                parameters_copypaste.bind_buttons(buttons, image, generation_info)
+
+        image.change(
+            fn=wrap_gradio_call(modules.extras.run_pnginfo),
+            inputs=[image],
+            outputs=[html, generation_info, html2],
+        )
+
+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
+
+                with gr.Row():
+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+                custom_name = gr.Textbox(label="Custom Name (Optional)")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
+                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
+                save_as_half = gr.Checkbox(value=False, label="Save as float16")
+                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
+                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
+
+            with gr.Column(variant='panel'):
+                submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
+
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+    with gr.Blocks(analytics_enabled=False) as train_interface:
+        with gr.Row().style(equal_height=False):
+            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
+        with gr.Row().style(equal_height=False):
+            with gr.Tabs(elem_id="train_tabs"):
+
+                with gr.Tab(label="Create embedding"):
+                    new_embedding_name = gr.Textbox(label="Name")
+                    initialization_text = gr.Textbox(label="Initialization text", value="*")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding = gr.Button(value="Create embedding", variant='primary')
+
+                with gr.Tab(label="Create hypernetwork"):
+                    new_hypernetwork_name = gr.Textbox(label="Name")
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
+
+                with gr.Tab(label="Preprocess images"):
+                    process_src = gr.Textbox(label='Source directory')
+                    process_dst = gr.Textbox(label='Destination directory')
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
+
+                    with gr.Row():
+                        process_flip = gr.Checkbox(label='Create flipped copies')
+                        process_split = gr.Checkbox(label='Split oversized images')
+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
+                        process_caption = gr.Checkbox(label='Use BLIP for caption')
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
+
+                    with gr.Row(visible=False) as process_split_extra_row:
+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
+
+                    with gr.Row(visible=False) as process_focal_crop_row:
+                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            with gr.Row():
+                                interrupt_preprocessing = gr.Button("Interrupt")
+                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
+
+                    process_split.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_split],
+                        outputs=[process_split_extra_row],
+                    )
+
+                    process_focal_crop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_focal_crop],
+                        outputs=[process_focal_crop_row],
+                    )
+
+                with gr.Tab(label="Train"):
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+                    with gr.Row():
+                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+                    with gr.Row():
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
+                    with gr.Row():
+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
+
+                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
+                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+
+                    with gr.Row():
+                        interrupt_training = gr.Button(value="Interrupt")
+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
+                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
+
+                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+                script_callbacks.ui_train_tabs_callback(params)
+
+            with gr.Column():
+                progressbar = gr.HTML(elem_id="ti_progressbar")
+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+
+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
+                ti_progress = gr.HTML(elem_id="ti_progress", value="")
+                ti_outcome = gr.HTML(elem_id="ti_error", value="")
+                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
+
+        create_embedding.click(
+            fn=modules.textual_inversion.ui.create_embedding,
+            inputs=[
+                new_embedding_name,
+                initialization_text,
+                nvpt,
+                overwrite_old_embedding,
+            ],
+            outputs=[
+                train_embedding_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        create_hypernetwork.click(
+            fn=modules.hypernetworks.ui.create_hypernetwork,
+            inputs=[
+                new_hypernetwork_name,
+                new_hypernetwork_sizes,
+                overwrite_old_hypernetwork,
+                new_hypernetwork_layer_structure,
+                new_hypernetwork_activation_func,
+                new_hypernetwork_initialization_option,
+                new_hypernetwork_add_layer_norm,
+                new_hypernetwork_use_dropout
+            ],
+            outputs=[
+                train_hypernetwork_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        run_preprocess.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                process_src,
+                process_dst,
+                process_width,
+                process_height,
+                preprocess_txt_action,
+                process_flip,
+                process_split,
+                process_caption,
+                process_caption_deepbooru,
+                process_split_threshold,
+                process_overlap_ratio,
+                process_focal_crop,
+                process_focal_crop_face_weight,
+                process_focal_crop_entropy_weight,
+                process_focal_crop_edges_weight,
+                process_focal_crop_debug,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ],
+        )
+
+        train_embedding.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_embedding_name,
+                embedding_learn_rate,
+                batch_size,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                save_image_with_stored_embedding,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        train_hypernetwork.click(
+            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_hypernetwork_name,
+                hypernetwork_learn_rate,
+                batch_size,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        interrupt_training.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+        interrupt_preprocessing.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+    def create_setting_component(key, is_quicksettings=False):
+        def fun():
+            return opts.data[key] if key in opts.data else opts.data_labels[key].default
+
+        info = opts.data_labels[key]
+        t = type(info.default)
+
+        args = info.component_args() if callable(info.component_args) else info.component_args
+
+        if info.component is not None:
+            comp = info.component
+        elif t == str:
+            comp = gr.Textbox
+        elif t == int:
+            comp = gr.Number
+        elif t == bool:
+            comp = gr.Checkbox
+        else:
+            raise Exception(f'bad options item type: {str(t)} for key {key}')
+
+        elem_id = "setting_"+key
+
+        if info.refresh is not None:
+            if is_quicksettings:
+                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+            else:
+                with gr.Row(variant="compact"):
+                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+        else:
+            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+
+        return res
+
+    components = []
+    component_dict = {}
+
+    script_callbacks.ui_settings_callback()
+    opts.reorder()
+
+    def run_settings(*args):
+        changed = []
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, components):
+            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, components):
+            if comp == dummy_component:
+                continue
+
+            if opts.set(key, value):
+                changed.append(key)
+
+        try:
+            opts.save(shared.config_filename)
+        except RuntimeError:
+            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
+
+    def run_settings_single(value, key):
+        if not opts.same_type(value, opts.data_labels[key].default):
+            return gr.update(visible=True), opts.dumpjson()
+
+        if not opts.set(key, value):
+            return gr.update(value=getattr(opts, key)), opts.dumpjson()
+
+        opts.save(shared.config_filename)
+
+        return gr.update(value=value), opts.dumpjson()
+
+    with gr.Blocks(analytics_enabled=False) as settings_interface:
+        settings_submit = gr.Button(value="Apply settings", variant='primary')
+        result = gr.HTML()
+
+        settings_cols = 3
+        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
+
+        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
+        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
+
+        quicksettings_list = []
+
+        cols_displayed = 0
+        items_displayed = 0
+        previous_section = None
+        column = None
+        with gr.Row(elem_id="settings").style(equal_height=False):
+            for i, (k, item) in enumerate(opts.data_labels.items()):
+                section_must_be_skipped = item.section[0] is None
+
+                if previous_section != item.section and not section_must_be_skipped:
+                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
+                        if column is not None:
+                            column.__exit__()
+
+                        column = gr.Column(variant='panel')
+                        column.__enter__()
+
+                        items_displayed = 0
+                        cols_displayed += 1
+
+                    previous_section = item.section
+
+                    elem_id, text = item.section
+                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
+
+                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
+                    quicksettings_list.append((i, k, item))
+                    components.append(dummy_component)
+                elif section_must_be_skipped:
+                    components.append(dummy_component)
+                else:
+                    component = create_setting_component(k)
+                    component_dict[k] = component
+                    components.append(component)
+                    items_displayed += 1
+
+        with gr.Row():
+            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+
+        with gr.Row():
+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+
+        request_notifications.click(
+            fn=lambda: None,
+            inputs=[],
+            outputs=[],
+            _js='function(){}'
+        )
+
+        download_localization.click(
+            fn=lambda: None,
+            inputs=[],
+            outputs=[],
+            _js='download_localization'
+        )
+
+        def reload_scripts():
+            modules.scripts.reload_script_body_only()
+            reload_javascript()  # need to refresh the html page
+
+        reload_script_bodies.click(
+            fn=reload_scripts,
+            inputs=[],
+            outputs=[]
+        )
+
+        def request_restart():
+            shared.state.interrupt()
+            shared.state.need_restart = True
+
+        restart_gradio.click(
+            fn=request_restart,
+            _js='restart_reload',
+            inputs=[],
+            outputs=[],
+        )
+
+        if column is not None:
+            column.__exit__()
+
+    interfaces = [
+        (txt2img_interface, "txt2img", "txt2img"),
+        (img2img_interface, "img2img", "img2img"),
+        (extras_interface, "Extras", "extras"),
+        (pnginfo_interface, "PNG Info", "pnginfo"),
+        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (train_interface, "Train", "ti"),
+    ]
+
+    css = ""
+
+    for cssfile in modules.scripts.list_files_with_name("style.css"):
+        if not os.path.isfile(cssfile):
+            continue
+
+        with open(cssfile, "r", encoding="utf8") as file:
+            css += file.read() + "\n"
+
+    if os.path.exists(os.path.join(script_path, "user.css")):
+        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
+            css += file.read() + "\n"
+
+    if not cmd_opts.no_progressbar_hiding:
+        css += css_hide_progressbar
+
+    interfaces += script_callbacks.ui_tabs_callback()
+    interfaces += [(settings_interface, "Settings", "settings")]
+
+    extensions_interface = ui_extensions.create_ui()
+    interfaces += [(extensions_interface, "Extensions", "extensions")]
+
+    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
+        with gr.Row(elem_id="quicksettings"):
+            for i, k, item in quicksettings_list:
+                component = create_setting_component(k, is_quicksettings=True)
+                component_dict[k] = component
+
+        parameters_copypaste.integrate_settings_paste_fields(component_dict)
+        parameters_copypaste.run_bind()
+
+        with gr.Tabs(elem_id="tabs") as tabs:
+            for interface, label, ifid in interfaces:
+                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
+                    interface.render()
+
+        if os.path.exists(os.path.join(script_path, "notification.mp3")):
+            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+        text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
+        settings_submit.click(
+            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
+            inputs=components,
+            outputs=[text_settings, result],
+        )
+
+        for i, k, item in quicksettings_list:
+            component = component_dict[k]
+
+            component.change(
+                fn=lambda value, k=k: run_settings_single(value, key=k),
+                inputs=[component],
+                outputs=[component, text_settings],
+            )
+
+        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
+
+        def get_settings_values():
+            return [getattr(opts, key) for key in component_keys]
+
+        demo.load(
+            fn=get_settings_values,
+            inputs=[],
+            outputs=[component_dict[k] for k in component_keys],
+        )
+
+        def modelmerger(*args):
+            try:
+                results = modules.extras.run_modelmerger(*args)
+            except Exception as e:
+                print("Error loading/saving model file:", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+                modules.sd_models.list_models()  # to remove the potentially missing models from the list
+                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
+            return results
+
+        modelmerger_merge.click(
+            fn=modelmerger,
+            inputs=[
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                interp_method,
+                interp_amount,
+                save_as_half,
+                save_as_safetensors,
+                custom_name,
+            ],
+            outputs=[
+                submit_result,
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                component_dict['sd_model_checkpoint'],
+            ]
+        )
+
+    ui_config_file = cmd_opts.ui_config_file
+    ui_settings = {}
+    settings_count = len(ui_settings)
+    error_loading = False
+
+    try:
+        if os.path.exists(ui_config_file):
+            with open(ui_config_file, "r", encoding="utf8") as file:
+                ui_settings = json.load(file)
+    except Exception:
+        error_loading = True
+        print("Error loading settings:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+    def loadsave(path, x):
+        def apply_field(obj, field, condition=None, init_field=None):
+            key = path + "/" + field
+
+            if getattr(obj, 'custom_script_source', None) is not None:
+              key = 'customscript/' + obj.custom_script_source + '/' + key
+
+            if getattr(obj, 'do_not_save_to_config', False):
+                return
+
+            saved_value = ui_settings.get(key, None)
+            if saved_value is None:
+                ui_settings[key] = getattr(obj, field)
+            elif condition and not condition(saved_value):
+                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
+            else:
+                setattr(obj, field, saved_value)
+                if init_field is not None:
+                    init_field(saved_value)
+
+        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
+            apply_field(x, 'visible')
+
+        if type(x) == gr.Slider:
+            apply_field(x, 'value')
+            apply_field(x, 'minimum')
+            apply_field(x, 'maximum')
+            apply_field(x, 'step')
+
+        if type(x) == gr.Radio:
+            apply_field(x, 'value', lambda val: val in x.choices)
+
+        if type(x) == gr.Checkbox:
+            apply_field(x, 'value')
+
+        if type(x) == gr.Textbox:
+            apply_field(x, 'value')
+
+        if type(x) == gr.Number:
+            apply_field(x, 'value')
+
+        # Since there are many dropdowns that shouldn't be saved,
+        # we only mark dropdowns that should be saved.
+        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
+            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
+            apply_field(x, 'visible')
+
+    visit(txt2img_interface, loadsave, "txt2img")
+    visit(img2img_interface, loadsave, "img2img")
+    visit(extras_interface, loadsave, "extras")
+    visit(modelmerger_interface, loadsave, "modelmerger")
+
+    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
+        with open(ui_config_file, "w", encoding="utf8") as file:
+            json.dump(ui_settings, file, indent=4)
+
+    return demo
+
+
+def reload_javascript():
+    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
+        javascript = f'<script>{jsfile.read()}</script>'
+
+    scripts_list = modules.scripts.list_scripts("javascript", ".js")
+
+    for basedir, filename, path in scripts_list:
+        with open(path, "r", encoding="utf8") as jsfile:
+            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
+
+    if cmd_opts.theme is not None:
+        javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
+
+    javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
+
+    def template_response(*args, **kwargs):
+        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
+        res.body = res.body.replace(
+            b'</head>', f'{javascript}</head>'.encode("utf8"))
+        res.init_headers()
+        return res
+
+    gradio.routes.templates.TemplateResponse = template_response
+
+
+if not hasattr(shared, 'GradioTemplateResponseOriginal'):
+    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
-- 
cgit v1.2.3


From 210cb4c128afdd65fa998229a97d0694154983ea Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Mon, 21 Nov 2022 16:40:18 -0500
Subject: Use GPU for loading safetensors, disable export

---
 modules/sd_models.py | 5 +++--
 modules/ui.py        | 3 ++-
 2 files changed, 5 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2f8c2c48..2bbb3bf5 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -147,8 +147,9 @@ def torch_load(model_filename, model_info, map_override=None):
     map_override=shared.weight_load_location if not map_override else map_override
     if(checkpoint_types[model_info.exttype] == 'safetensors'):
         # safely load weights
-        # TODO: safetensors supports zero copy fast load to gpu, see issue #684
-        return load_file(model_filename, device=map_override)
+        # TODO: safetensors supports zero copy fast load to gpu, see issue #684.  
+        # GPU only for now, see https://github.com/huggingface/safetensors/issues/95
+        return load_file(model_filename, device='cuda')
     else:
         return torch.load(model_filename, map_location=map_override)
 
diff --git a/modules/ui.py b/modules/ui.py
index 54d3293a..c376a59d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1187,7 +1187,8 @@ def create_ui(wrap_gradio_gpu_call):
                 interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
                 interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
                 save_as_half = gr.Checkbox(value=False, label="Save as float16")
-                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
+                # invisible until feature can be verified
+                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format", visible=False)
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
             with gr.Column(variant='panel'):
-- 
cgit v1.2.3


From 745f1e8f8008ea4906b0f5eb8b8f71d205fedf9e Mon Sep 17 00:00:00 2001
From: "Tiago F. Santos" <tiagofsantos81@sapo.pt>
Date: Tue, 22 Nov 2022 12:48:25 +0000
Subject: [CLIP interrogator] use local file, if available

---
 modules/interrogate.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 9769aa34..1a9c758e 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -14,6 +14,7 @@ import modules.shared as shared
 from modules import devices, paths, lowvram
 
 blip_image_eval_size = 384
+blip_model_local = os.path.join('models', 'Interrogator', 'BLIP_model.pth')
 blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
 clip_model_name = 'ViT-L/14'
 
@@ -47,7 +48,13 @@ class InterrogateModels:
     def load_blip_model(self):
         import models.blip
 
-        blip_model = models.blip.blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
+        if not os.path.isfile(blip_model_local):
+            print("Downloading BLIP...")
+            import requests as req
+            open(blip_model_local, 'wb').write(req.get(blip_model_url, allow_redirects=True).content)
+            print("BLIP downloaded to", blip_model_local + '.')
+
+        blip_model = models.blip.blip_decoder(pretrained=blip_model_local, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
         blip_model.eval()
 
         return blip_model
-- 
cgit v1.2.3


From c27a973c82c374f47fb279c1b9b8de7288fd729d Mon Sep 17 00:00:00 2001
From: Rogerooo <hugo.lopesmatias@gmail.com>
Date: Tue, 22 Nov 2022 14:02:59 +0000
Subject: fix null negative_prompt on get requests

Small typo that causes a bug when returning negative prompts from the get request.
---
 modules/api/api.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 7a567be3..08e03c13 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -305,7 +305,7 @@ class Api:
         styleList = []
         for k in shared.prompt_styles.styles:
             style = shared.prompt_styles.styles[k]
-            styleList.append({"name":style[0], "prompt": style[1], "negative_prompr": style[2]})
+            styleList.append({"name":style[0], "prompt": style[1], "negative_prompt": style[2]})
 
         return styleList
 
-- 
cgit v1.2.3


From 0a01f5089127f1ab86625036526082f544344a10 Mon Sep 17 00:00:00 2001
From: uservar <63248296+uservar@users.noreply.github.com>
Date: Tue, 22 Nov 2022 14:24:50 +0000
Subject: Add DPM++ SDE sampler

---
 modules/sd_samplers.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4fe67854..80e91d62 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -26,6 +26,7 @@ samplers_k_diffusion = [
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
     ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
     ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
@@ -33,6 +34,7 @@ samplers_k_diffusion = [
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
     ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.3


From ac90cf38c6b55d57d37923aa1fe86c7374e32d0b Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Tue, 22 Nov 2022 10:13:07 -0500
Subject: safetensors optional for now

---
 modules/sd_models.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2bbb3bf5..75f7ab09 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,7 +4,6 @@ import sys
 import gc
 from collections import namedtuple
 import torch
-from safetensors.torch import load_file, save_file
 import re
 from omegaconf import OmegaConf
 
@@ -149,6 +148,10 @@ def torch_load(model_filename, model_info, map_override=None):
         # safely load weights
         # TODO: safetensors supports zero copy fast load to gpu, see issue #684.  
         # GPU only for now, see https://github.com/huggingface/safetensors/issues/95
+        try:
+            from safetensors.torch import load_file
+        except ImportError as e:
+            raise ImportError(f"The model is in safetensors format and it is not installed, use `pip install safetensors`: {e}")
         return load_file(model_filename, device='cuda')
     else:
         return torch.load(model_filename, map_location=map_override)
@@ -157,6 +160,10 @@ def torch_save(model, output_filename):
     basename, exttype = os.path.splitext(output_filename)
     if(checkpoint_types[exttype] == 'safetensors'):
         # [=====  >] Reticulating brines...
+        try:
+            from safetensors.torch import save_file
+        except ImportError as e:
+            raise ImportError(f"Export as safetensors selected, yet it is not installed, use `pip install safetensors`: {e}")
         save_file(model, output_filename, metadata={"format": "pt"})
     else:
         torch.save(model, output_filename)
-- 
cgit v1.2.3


From 89d8ecff09b426ddc89eb5b432825f8f4c218051 Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Wed, 23 Nov 2022 02:49:01 +0900
Subject: small fixes

---
 modules/hypernetworks/hypernetwork.py          | 6 +++---
 modules/textual_inversion/textual_inversion.py | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 0128419b..4541af18 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -435,8 +435,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
         optimizer_name = hypernetwork.optimizer_name
     else:
         print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
-    optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
-    optimizer_name = 'AdamW'
+        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
+        optimizer_name = 'AdamW'
 
     if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
         try:
@@ -582,7 +582,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                 shared.state.textinfo = f"""
 <p>
 Loss: {loss_step:.7f}<br/>
-Step: {hypernetwork.step}<br/>
+Step: {steps_done}<br/>
 Last prompt: {html.escape(batch.cond_text[0])}<br/>
 Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 3036e48a..fee08e33 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -436,7 +436,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                 shared.state.textinfo = f"""
 <p>
 Loss: {loss_step:.7f}<br/>
-Step: {embedding.step}<br/>
+Step: {steps_done}<br/>
 Last prompt: {html.escape(batch.cond_text[0])}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
-- 
cgit v1.2.3


From 75b67eebf21f72f5b693926476d9c3b12471f0d6 Mon Sep 17 00:00:00 2001
From: Sena <34237511+sena-nana@users.noreply.github.com>
Date: Wed, 23 Nov 2022 17:43:58 +0800
Subject: Fix bare base64 not accept

---
 modules/api/api.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 7a567be3..648bd6a8 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -3,6 +3,7 @@ import io
 import time
 import uvicorn
 from threading import Lock
+from io import BytesIO
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, Depends, FastAPI, HTTPException
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
@@ -13,7 +14,7 @@ from modules import sd_samplers, deepbooru
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.extras import run_extras, run_pnginfo
-from PIL import PngImagePlugin
+from PIL import PngImagePlugin,Image
 from modules.sd_models import checkpoints_list
 from modules.realesrgan_model import get_realesrgan_models
 from typing import List
@@ -133,7 +134,10 @@ class Api:
 
         mask = img2imgreq.mask
         if mask:
-            mask = decode_base64_to_image(mask)
+            if mask.startswith("data:image/"):
+                mask = decode_base64_to_image(mask)
+            else:
+                mask = Image.open(BytesIO(base64.b64decode(mask)))
 
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
@@ -147,7 +151,10 @@ class Api:
 
         imgs = []
         for img in init_images:
-            img = decode_base64_to_image(img)
+            if img.startswith("data:image/"):
+                img = decode_base64_to_image(img)
+            else:
+                img = Image.open(BytesIO(base64.b64decode(img)))
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
-- 
cgit v1.2.3


From adb6cb7619989cbc7a271cc6c2ae27bb936c43d9 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Wed, 23 Nov 2022 18:11:24 +0800
Subject: Patch UNet Forward to support resolutions that are not multiples of
 64 Also modifed the UI to no longer step in 64

---
 modules/sd_hijack.py               |  2 ++
 modules/sd_hijack_optimizations.py | 31 +++++++++++++++++++++++++++++++
 modules/ui.py                      | 24 ++++++++++++------------
 3 files changed, 45 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index eaedac13..6141f705 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -16,6 +16,7 @@ import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
+import ldm.modules.diffusionmodules.openaimodel
 
 attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
@@ -26,6 +27,7 @@ def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
+    ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_hijack_optimizations.patched_unet_forward
 
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 98123fbf..8cd4c954 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -5,6 +5,7 @@ import importlib
 
 import torch
 from torch import einsum
+import torch.nn.functional as F
 
 from ldm.util import default
 from einops import rearrange
@@ -12,6 +13,8 @@ from einops import rearrange
 from modules import shared
 from modules.hypernetworks import hypernetwork
 
+from ldm.modules.diffusionmodules.util import timestep_embedding
+
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
@@ -310,3 +313,31 @@ def xformers_attnblock_forward(self, x):
         return x + out
     except NotImplementedError:
         return cross_attention_attnblock_forward(self, x)
+
+def patched_unet_forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+    assert (y is not None) == (
+        self.num_classes is not None
+    ), "must specify y if and only if the model is class-conditional"
+    hs = []
+    t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+    emb = self.time_embed(t_emb)
+
+    if self.num_classes is not None:
+        assert y.shape == (x.shape[0],)
+        emb = emb + self.label_emb(y)
+
+    h = x.type(self.dtype)
+    for module in self.input_blocks:
+        h = module(h, emb, context)
+        hs.append(h)
+    h = self.middle_block(h, emb, context)
+    for module in self.output_blocks:
+        if h.shape[-2:] != hs[-1].shape[-2:]:
+            h = F.interpolate(h, hs[-1].shape[-2:], mode="nearest")
+        h = torch.cat([h, hs.pop()], dim=1)
+        h = module(h, emb, context)
+    h = h.type(x.dtype)
+    if self.predict_codebook_ids:
+        return self.id_predictor(h)
+    else:
+        return self.out(h)
diff --git a/modules/ui.py b/modules/ui.py
index e6da1b2a..85e531af 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -380,8 +380,8 @@ def create_seed_inputs():
 
     with gr.Row(visible=False) as seed_extra_row_2:
         seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=1, label="Resize seed from width", value=0)
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=1, label="Resize seed from height", value=0)
 
     random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
     random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
@@ -715,8 +715,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
+                    height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
@@ -724,8 +724,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=1, label="Firstpass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=1, label="Firstpass height", value=0)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row(equal_height=True):
@@ -901,8 +901,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
+                    width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512, elem_id="img2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512, elem_id="img2img_height")
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
@@ -1231,8 +1231,8 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Tab(label="Preprocess images"):
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
                     preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
 
                     with gr.Row():
@@ -1289,8 +1289,8 @@ def create_ui(wrap_gradio_gpu_call):
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-- 
cgit v1.2.3


From d2c97fc3fe5857d6fba9ad1695ed3ac6ec455ca9 Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Wed, 23 Nov 2022 20:00:00 +0900
Subject: fix dropout, implement train/eval mode

---
 modules/hypernetworks/hypernetwork.py | 24 ++++++++++++++++++------
 1 file changed, 18 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4541af18..9388959f 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -154,16 +154,28 @@ class Hypernetwork:
                 HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
                                    self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
             )
+        self.eval_mode()
 
     def weights(self):
         res = []
+        for k, layers in self.layers.items():
+            for layer in layers:
+                res += layer.parameters()
+        return res
 
+    def train_mode(self):
         for k, layers in self.layers.items():
             for layer in layers:
                 layer.train()
-                res += layer.trainables()
+                for param in layer.parameters():
+                    param.requires_grad = True
 
-        return res
+    def eval_mode(self):
+        for k, layers in self.layers.items():
+            for layer in layers:
+                layer.eval()
+                for param in layer.parameters():
+                    param.requires_grad = False
 
     def save(self, filename):
         state_dict = {}
@@ -426,8 +438,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
         shared.sd_model.first_stage_model.to(devices.cpu)
     
     weights = hypernetwork.weights()
-    for weight in weights:
-        weight.requires_grad = True
+    hypernetwork.train_mode()
 
     # Here we use optimizer from saved HN, or we can specify as UI option.
     if hypernetwork.optimizer_name in optimizer_dict:
@@ -538,7 +549,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                 if images_dir is not None and steps_done % create_image_every == 0:
                     forced_filename = f'{hypernetwork_name}-{steps_done}'
                     last_saved_image = os.path.join(images_dir, forced_filename)
-
+                    hypernetwork.eval_mode()
                     shared.sd_model.cond_stage_model.to(devices.device)
                     shared.sd_model.first_stage_model.to(devices.device)
 
@@ -571,7 +582,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                     if unload:
                         shared.sd_model.cond_stage_model.to(devices.cpu)
                         shared.sd_model.first_stage_model.to(devices.cpu)
-
+                    hypernetwork.train_mode()
                     if image is not None:
                         shared.state.current_image = image
                         last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
@@ -593,6 +604,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     finally:
         pbar.leave = False
         pbar.close()
+        hypernetwork.eval_mode()
         #report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
-- 
cgit v1.2.3


From 1bd57cc9791e2e742f72a3d74d589f2c289e8e92 Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Wed, 23 Nov 2022 20:21:52 +0900
Subject: last_layer_dropout default to False

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 9388959f..8466887f 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -38,7 +38,7 @@ class HypernetworkModule(torch.nn.Module):
     activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
     def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
-                 add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=True):
+                 add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
-- 
cgit v1.2.3


From 6001684be3e7b023346326b9dfc771219b8fe47e Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Wed, 23 Nov 2022 06:35:44 -0800
Subject: add model_name pattern for saving

---
 modules/images.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 26d5b7a9..420828b0 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -305,6 +305,7 @@ class FilenameGenerator:
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
+        'model_name': lambda self: shared.sd_model.sd_checkpoint_info.model_name,
         'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
         'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
         'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
-- 
cgit v1.2.3


From ffcbbcf385eb847ced957510ab726291a8b20207 Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Wed, 23 Nov 2022 06:44:20 -0800
Subject: add filename santization

Probably redundant, considering the model name *is* a filename, but I suppose better safe than sorry.
---
 modules/images.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 420828b0..8fa96b16 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -305,7 +305,7 @@ class FilenameGenerator:
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
-        'model_name': lambda self: shared.sd_model.sd_checkpoint_info.model_name,
+        'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.model_name, replace_spaces=False),
         'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
         'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
         'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
-- 
cgit v1.2.3


From 904121fecc0a1f11db76a73ca8649fb21e05ac5b Mon Sep 17 00:00:00 2001
From: Nandaka <nchek2000@gmail.com>
Date: Thu, 24 Nov 2022 02:39:09 +0000
Subject: Support NAI exif for PNG Info

---
 modules/extras.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 71b93a06..af4cd97d 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -233,6 +233,20 @@ def run_pnginfo(image):
 
     geninfo = items.get('parameters', geninfo)
 
+    # nai prompt
+    if "Software" in items.keys() and items["Software"] == "NovelAI":
+        import json
+        json_info = json.loads(items["Comment"])
+        geninfo = f'{items["Description"]}\r\nNegative prompt: {json_info["uc"]}\r\n'
+        sampler = "Euler a"
+        if json_info["sampler"] == "k_euler_ancestral":
+            sampler = "Euler a"
+        elif json_info["sampler"] == "k_euler":
+            sampler = "Euler"
+        model_hash = '925997e9'  # assuming this is the correct model hash
+        # not sure with noise and strength parameter
+        geninfo += f'Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Model hash: {model_hash}'  # , Denoising strength: {json_info["noise"]}'
+
     info = ''
     for key, text in items.items():
         info += f"""
-- 
cgit v1.2.3


From fcd75bd8740855e0c7bc80c0e8a4e1033b76d007 Mon Sep 17 00:00:00 2001
From: Sena <34237511+sena-nana@users.noreply.github.com>
Date: Thu, 24 Nov 2022 13:10:40 +0800
Subject: Fix other apis

---
 modules/api/api.py | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 648bd6a8..efcedbba 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -4,7 +4,7 @@ import time
 import uvicorn
 from threading import Lock
 from io import BytesIO
-from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
+from gradio.processing_utils import decode_base64_to_file
 from fastapi import APIRouter, Depends, FastAPI, HTTPException
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
@@ -41,6 +41,10 @@ def setUpscalers(req: dict):
     reqDict.pop('upscaler_2')
     return reqDict
 
+def decode_base64_to_image(encoding):
+    if encoding.startswith("data:image/"):
+        encoding = encoding.split(";")[1].split(",")[1]
+    return Image.open(BytesIO(base64.b64decode(encoding)))
 
 def encode_pil_to_base64(image):
     with io.BytesIO() as output_bytes:
@@ -134,10 +138,7 @@ class Api:
 
         mask = img2imgreq.mask
         if mask:
-            if mask.startswith("data:image/"):
-                mask = decode_base64_to_image(mask)
-            else:
-                mask = Image.open(BytesIO(base64.b64decode(mask)))
+            mask = decode_base64_to_image(mask)
 
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
@@ -151,10 +152,7 @@ class Api:
 
         imgs = []
         for img in init_images:
-            if img.startswith("data:image/"):
-                img = decode_base64_to_image(img)
-            else:
-                img = Image.open(BytesIO(base64.b64decode(img)))
+            img = decode_base64_to_image(img)
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
-- 
cgit v1.2.3


From a2ae5a655518b150a34b95d7afecc87a43280406 Mon Sep 17 00:00:00 2001
From: "Tiago F. Santos" <tiagofsantos81@sapo.pt>
Date: Thu, 24 Nov 2022 13:04:45 +0000
Subject: [interrogator] mkdir check

---
 modules/interrogate.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 1a9c758e..f177a5a8 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -14,7 +14,8 @@ import modules.shared as shared
 from modules import devices, paths, lowvram
 
 blip_image_eval_size = 384
-blip_model_local = os.path.join('models', 'Interrogator', 'BLIP_model.pth')
+blip_local_dir = os.path.join('models', 'Interrogator')
+blip_local_file = os.path.join(blip_local_dir, 'model_base_caption_capfilt_large.pth')
 blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
 clip_model_name = 'ViT-L/14'
 
@@ -48,13 +49,16 @@ class InterrogateModels:
     def load_blip_model(self):
         import models.blip
 
-        if not os.path.isfile(blip_model_local):
+        if not os.path.isfile(blip_local_file):
+            if not os.path.isdir(blip_local_dir):
+                os.mkdir(blip_local_dir)
+
             print("Downloading BLIP...")
-            import requests as req
-            open(blip_model_local, 'wb').write(req.get(blip_model_url, allow_redirects=True).content)
-            print("BLIP downloaded to", blip_model_local + '.')
+            from requests import get as reqget
+            open(blip_local_file, 'wb').write(reqget(blip_model_url, allow_redirects=True).content)
+            print("BLIP downloaded to", blip_local_file + '.')
 
-        blip_model = models.blip.blip_decoder(pretrained=blip_model_local, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
+        blip_model = models.blip.blip_decoder(pretrained=blip_local_file, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
         blip_model.eval()
 
         return blip_model
-- 
cgit v1.2.3


From c833d5bfaae05de41d8e795aba5b15822673ef04 Mon Sep 17 00:00:00 2001
From: Jay Smith <jayvsmith@gmail.com>
Date: Fri, 25 Nov 2022 20:12:23 -0600
Subject: fixes #3449 - VRAM leak when switching to/from inpainting model

---
 modules/sd_samplers.py | 33 +++++++++++++++------------------
 1 file changed, 15 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4fe67854..44112f99 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -1,4 +1,4 @@
-from collections import namedtuple
+from collections import namedtuple, deque
 import numpy as np
 from math import floor
 import torch
@@ -335,18 +335,28 @@ class CFGDenoiser(torch.nn.Module):
 
 
 class TorchHijack:
-    def __init__(self, kdiff_sampler):
-        self.kdiff_sampler = kdiff_sampler
+    def __init__(self, sampler_noises):
+        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
+        # implementation.
+        self.sampler_noises = deque(sampler_noises)
 
     def __getattr__(self, item):
         if item == 'randn_like':
-            return self.kdiff_sampler.randn_like
+            return self.randn_like
 
         if hasattr(torch, item):
             return getattr(torch, item)
 
         raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
 
+    def randn_like(self, x):
+        if self.sampler_noises:
+            noise = self.sampler_noises.popleft()
+            if noise.shape == x.shape:
+                return noise
+
+        return torch.randn_like(x)
+
 
 class KDiffusionSampler:
     def __init__(self, funcname, sd_model):
@@ -356,7 +366,6 @@ class KDiffusionSampler:
         self.extra_params = sampler_extra_params.get(funcname, [])
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
         self.sampler_noises = None
-        self.sampler_noise_index = 0
         self.stop_at = None
         self.eta = None
         self.default_eta = 1.0
@@ -389,26 +398,14 @@ class KDiffusionSampler:
     def number_of_needed_noises(self, p):
         return p.steps
 
-    def randn_like(self, x):
-        noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None
-
-        if noise is not None and x.shape == noise.shape:
-            res = noise
-        else:
-            res = torch.randn_like(x)
-
-        self.sampler_noise_index += 1
-        return res
-
     def initialize(self, p):
         self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
         self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
         self.model_wrap.step = 0
-        self.sampler_noise_index = 0
         self.eta = p.eta or opts.eta_ancestral
 
         if self.sampler_noises is not None:
-            k_diffusion.sampling.torch = TorchHijack(self)
+            k_diffusion.sampling.torch = TorchHijack(self.sampler_noises)
 
         extra_params_kwargs = {}
         for param_name in self.extra_params:
-- 
cgit v1.2.3


From ce6911158b5b2f9cf79b405a1f368f875492044d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 26 Nov 2022 16:10:46 +0300
Subject: Add support Stable Diffusion 2.0

---
 modules/paths.py                               |   2 +-
 modules/sd_hijack.py                           | 297 +++---------------------
 modules/sd_hijack_clip.py                      | 301 +++++++++++++++++++++++++
 modules/sd_hijack_inpainting.py                |  20 +-
 modules/sd_hijack_open_clip.py                 |  37 +++
 modules/sd_samplers.py                         |  14 +-
 modules/shared.py                              |  34 ++-
 modules/textual_inversion/textual_inversion.py |   7 +-
 modules/ui.py                                  |  13 +-
 9 files changed, 421 insertions(+), 304 deletions(-)
 create mode 100644 modules/sd_hijack_clip.py
 create mode 100644 modules/sd_hijack_open_clip.py

(limited to 'modules')

diff --git a/modules/paths.py b/modules/paths.py
index 1e7a2fbc..4dd03a35 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -9,7 +9,7 @@ sys.path.insert(0, script_path)
 
 # search for directory of stable diffusion in following places
 sd_path = None
-possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'), '.', os.path.dirname(script_path)]
+possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion-stability-ai'), '.', os.path.dirname(script_path)]
 for possible_sd_path in possible_sd_paths:
     if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')):
         sd_path = os.path.abspath(possible_sd_path)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index eaedac13..d5243fd3 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -9,18 +9,29 @@ from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared
-from modules.shared import opts, device, cmd_opts
+from modules.shared import cmd_opts
+from modules import sd_hijack_clip, sd_hijack_open_clip
+
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
+import ldm.modules.encoders.modules
 
 attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
 
+# new memory efficient cross attention blocks do not support hypernets and we already
+# have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
+ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
+ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
+
+# silence new console spam from SD2
+ldm.modules.attention.print = lambda *args: None
+ldm.modules.diffusionmodules.model.print = lambda *args: None
 
 def apply_optimizations():
     undo_optimizations()
@@ -49,16 +60,11 @@ def apply_optimizations():
 
 
 def undo_optimizations():
-    from modules.hypernetworks import hypernetwork
-
-    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+    ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward  # this stops hypernets from working
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
 
-def get_target_prompt_token_count(token_count):
-    return math.ceil(max(token_count, 1) / 75) * 75
-
 
 class StableDiffusionModelHijack:
     fixes = None
@@ -70,10 +76,13 @@ class StableDiffusionModelHijack:
     embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
 
     def hijack(self, m):
-        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
-
-        model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
-        m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+        if type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
+            model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
+            m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+        elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder:
+            m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
+            m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
 
         self.clip = m.cond_stage_model
 
@@ -89,12 +98,15 @@ class StableDiffusionModelHijack:
         self.layers = flatten(m)
 
     def undo_hijack(self, m):
-        if type(m.cond_stage_model) == FrozenCLIPEmbedderWithCustomWords:
+        if type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped
 
-        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
-        if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
-            model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
+            model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+            if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
+                model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
+        elif type(m.cond_stage_model) == sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords:
+            m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped
+            m.cond_stage_model = m.cond_stage_model.wrapped
 
         self.apply_circular(False)
         self.layers = None
@@ -114,261 +126,8 @@ class StableDiffusionModelHijack:
 
     def tokenize(self, text):
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
-        return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
-
-
-class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
-    def __init__(self, wrapped, hijack):
-        super().__init__()
-        self.wrapped = wrapped
-        self.hijack: StableDiffusionModelHijack = hijack
-        self.tokenizer = wrapped.tokenizer
-        self.token_mults = {}
-
-        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
-
-        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
-        for text, ident in tokens_with_parens:
-            mult = 1.0
-            for c in text:
-                if c == '[':
-                    mult /= 1.1
-                if c == ']':
-                    mult *= 1.1
-                if c == '(':
-                    mult *= 1.1
-                if c == ')':
-                    mult /= 1.1
-
-            if mult != 1.0:
-                self.token_mults[ident] = mult
-
-    def tokenize_line(self, line, used_custom_terms, hijack_comments):
-        id_end = self.wrapped.tokenizer.eos_token_id
-
-        if opts.enable_emphasis:
-            parsed = prompt_parser.parse_prompt_attention(line)
-        else:
-            parsed = [[line, 1.0]]
-
-        tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]
-
-        fixes = []
-        remade_tokens = []
-        multipliers = []
-        last_comma = -1
-
-        for tokens, (text, weight) in zip(tokenized, parsed):
-            i = 0
-            while i < len(tokens):
-                token = tokens[i]
-
-                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
-
-                if token == self.comma_token:
-                    last_comma = len(remade_tokens)
-                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
-                    last_comma += 1
-                    reloc_tokens = remade_tokens[last_comma:]
-                    reloc_mults = multipliers[last_comma:]
-
-                    remade_tokens = remade_tokens[:last_comma]
-                    length = len(remade_tokens)
-
-                    rem = int(math.ceil(length / 75)) * 75 - length
-                    remade_tokens += [id_end] * rem + reloc_tokens
-                    multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
-
-                if embedding is None:
-                    remade_tokens.append(token)
-                    multipliers.append(weight)
-                    i += 1
-                else:
-                    emb_len = int(embedding.vec.shape[0])
-                    iteration = len(remade_tokens) // 75
-                    if (len(remade_tokens) + emb_len) // 75 != iteration:
-                        rem = (75 * (iteration + 1) - len(remade_tokens))
-                        remade_tokens += [id_end] * rem
-                        multipliers += [1.0] * rem
-                        iteration += 1
-                    fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
-                    remade_tokens += [0] * emb_len
-                    multipliers += [weight] * emb_len
-                    used_custom_terms.append((embedding.name, embedding.checksum()))
-                    i += embedding_length_in_tokens
-
-        token_count = len(remade_tokens)
-        prompt_target_length = get_target_prompt_token_count(token_count)
-        tokens_to_add = prompt_target_length - len(remade_tokens)
-
-        remade_tokens = remade_tokens + [id_end] * tokens_to_add
-        multipliers = multipliers + [1.0] * tokens_to_add
-
-        return remade_tokens, fixes, multipliers, token_count
-
-    def process_text(self, texts):
-        used_custom_terms = []
-        remade_batch_tokens = []
-        hijack_comments = []
-        hijack_fixes = []
-        token_count = 0
-
-        cache = {}
-        batch_multipliers = []
-        for line in texts:
-            if line in cache:
-                remade_tokens, fixes, multipliers = cache[line]
-            else:
-                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
-                token_count = max(current_token_count, token_count)
-
-                cache[line] = (remade_tokens, fixes, multipliers)
-
-            remade_batch_tokens.append(remade_tokens)
-            hijack_fixes.append(fixes)
-            batch_multipliers.append(multipliers)
-
-        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-
-    def process_text_old(self, text):
-        id_start = self.wrapped.tokenizer.bos_token_id
-        id_end = self.wrapped.tokenizer.eos_token_id
-        maxlen = self.wrapped.max_length  # you get to stay at 77
-        used_custom_terms = []
-        remade_batch_tokens = []
-        overflowing_words = []
-        hijack_comments = []
-        hijack_fixes = []
-        token_count = 0
-
-        cache = {}
-        batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
-        batch_multipliers = []
-        for tokens in batch_tokens:
-            tuple_tokens = tuple(tokens)
-
-            if tuple_tokens in cache:
-                remade_tokens, fixes, multipliers = cache[tuple_tokens]
-            else:
-                fixes = []
-                remade_tokens = []
-                multipliers = []
-                mult = 1.0
-
-                i = 0
-                while i < len(tokens):
-                    token = tokens[i]
-
-                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
-
-                    mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
-                    if mult_change is not None:
-                        mult *= mult_change
-                        i += 1
-                    elif embedding is None:
-                        remade_tokens.append(token)
-                        multipliers.append(mult)
-                        i += 1
-                    else:
-                        emb_len = int(embedding.vec.shape[0])
-                        fixes.append((len(remade_tokens), embedding))
-                        remade_tokens += [0] * emb_len
-                        multipliers += [mult] * emb_len
-                        used_custom_terms.append((embedding.name, embedding.checksum()))
-                        i += embedding_length_in_tokens
-
-                if len(remade_tokens) > maxlen - 2:
-                    vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
-                    ovf = remade_tokens[maxlen - 2:]
-                    overflowing_words = [vocab.get(int(x), "") for x in ovf]
-                    overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
-                    hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-
-                token_count = len(remade_tokens)
-                remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
-                remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
-                cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
-
-            multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
-            multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
-
-            remade_batch_tokens.append(remade_tokens)
-            hijack_fixes.append(fixes)
-            batch_multipliers.append(multipliers)
-        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-
-    def forward(self, text):
-        use_old = opts.use_old_emphasis_implementation
-        if use_old:
-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
-        else:
-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
-
-        self.hijack.comments += hijack_comments
-
-        if len(used_custom_terms) > 0:
-            self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
-
-        if use_old:
-            self.hijack.fixes = hijack_fixes
-            return self.process_tokens(remade_batch_tokens, batch_multipliers)
-
-        z = None
-        i = 0
-        while max(map(len, remade_batch_tokens)) != 0:
-            rem_tokens = [x[75:] for x in remade_batch_tokens]
-            rem_multipliers = [x[75:] for x in batch_multipliers]
-
-            self.hijack.fixes = []
-            for unfiltered in hijack_fixes:
-                fixes = []
-                for fix in unfiltered:
-                    if fix[0] == i:
-                        fixes.append(fix[1])
-                self.hijack.fixes.append(fixes)
-
-            tokens = []
-            multipliers = []
-            for j in range(len(remade_batch_tokens)):
-                if len(remade_batch_tokens[j]) > 0:
-                    tokens.append(remade_batch_tokens[j][:75])
-                    multipliers.append(batch_multipliers[j][:75])
-                else:
-                    tokens.append([self.wrapped.tokenizer.eos_token_id] * 75)
-                    multipliers.append([1.0] * 75)
-
-            z1 = self.process_tokens(tokens, multipliers)
-            z = z1 if z is None else torch.cat((z, z1), axis=-2)
-
-            remade_batch_tokens = rem_tokens
-            batch_multipliers = rem_multipliers
-            i += 1
-
-        return z
-
-    def process_tokens(self, remade_batch_tokens, batch_multipliers):
-        if not opts.use_old_emphasis_implementation:
-            remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
-            batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
-
-        tokens = torch.asarray(remade_batch_tokens).to(device)
-        outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
-
-        if opts.CLIP_stop_at_last_layers > 1:
-            z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
-            z = self.wrapped.transformer.text_model.final_layer_norm(z)
-        else:
-            z = outputs.last_hidden_state
-
-        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-        batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
-        batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
-        original_mean = z.mean()
-        z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
-        new_mean = z.mean()
-        z *= original_mean / new_mean
+        return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count)
 
-        return z
 
 
 class EmbeddingsWithFixes(torch.nn.Module):
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
new file mode 100644
index 00000000..b451d1cf
--- /dev/null
+++ b/modules/sd_hijack_clip.py
@@ -0,0 +1,301 @@
+import math
+
+import torch
+
+from modules import prompt_parser, devices
+from modules.shared import opts
+
+
+def get_target_prompt_token_count(token_count):
+    return math.ceil(max(token_count, 1) / 75) * 75
+
+
+class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
+    def __init__(self, wrapped, hijack):
+        super().__init__()
+        self.wrapped = wrapped
+        self.hijack = hijack
+
+    def tokenize(self, texts):
+        raise NotImplementedError
+
+    def encode_with_transformers(self, tokens):
+        raise NotImplementedError
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        raise NotImplementedError
+
+    def tokenize_line(self, line, used_custom_terms, hijack_comments):
+        if opts.enable_emphasis:
+            parsed = prompt_parser.parse_prompt_attention(line)
+        else:
+            parsed = [[line, 1.0]]
+
+        tokenized = self.tokenize([text for text, _ in parsed])
+
+        fixes = []
+        remade_tokens = []
+        multipliers = []
+        last_comma = -1
+
+        for tokens, (text, weight) in zip(tokenized, parsed):
+            i = 0
+            while i < len(tokens):
+                token = tokens[i]
+
+                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+
+                if token == self.comma_token:
+                    last_comma = len(remade_tokens)
+                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
+                    last_comma += 1
+                    reloc_tokens = remade_tokens[last_comma:]
+                    reloc_mults = multipliers[last_comma:]
+
+                    remade_tokens = remade_tokens[:last_comma]
+                    length = len(remade_tokens)
+
+                    rem = int(math.ceil(length / 75)) * 75 - length
+                    remade_tokens += [self.id_end] * rem + reloc_tokens
+                    multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
+
+                if embedding is None:
+                    remade_tokens.append(token)
+                    multipliers.append(weight)
+                    i += 1
+                else:
+                    emb_len = int(embedding.vec.shape[0])
+                    iteration = len(remade_tokens) // 75
+                    if (len(remade_tokens) + emb_len) // 75 != iteration:
+                        rem = (75 * (iteration + 1) - len(remade_tokens))
+                        remade_tokens += [self.id_end] * rem
+                        multipliers += [1.0] * rem
+                        iteration += 1
+                    fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
+                    remade_tokens += [0] * emb_len
+                    multipliers += [weight] * emb_len
+                    used_custom_terms.append((embedding.name, embedding.checksum()))
+                    i += embedding_length_in_tokens
+
+        token_count = len(remade_tokens)
+        prompt_target_length = get_target_prompt_token_count(token_count)
+        tokens_to_add = prompt_target_length - len(remade_tokens)
+
+        remade_tokens = remade_tokens + [self.id_end] * tokens_to_add
+        multipliers = multipliers + [1.0] * tokens_to_add
+
+        return remade_tokens, fixes, multipliers, token_count
+
+    def process_text(self, texts):
+        used_custom_terms = []
+        remade_batch_tokens = []
+        hijack_comments = []
+        hijack_fixes = []
+        token_count = 0
+
+        cache = {}
+        batch_multipliers = []
+        for line in texts:
+            if line in cache:
+                remade_tokens, fixes, multipliers = cache[line]
+            else:
+                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+                token_count = max(current_token_count, token_count)
+
+                cache[line] = (remade_tokens, fixes, multipliers)
+
+            remade_batch_tokens.append(remade_tokens)
+            hijack_fixes.append(fixes)
+            batch_multipliers.append(multipliers)
+
+        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
+
+    def process_text_old(self, texts):
+        id_start = self.id_start
+        id_end = self.id_end
+        maxlen = self.wrapped.max_length  # you get to stay at 77
+        used_custom_terms = []
+        remade_batch_tokens = []
+        hijack_comments = []
+        hijack_fixes = []
+        token_count = 0
+
+        cache = {}
+        batch_tokens = self.tokenize(texts)
+        batch_multipliers = []
+        for tokens in batch_tokens:
+            tuple_tokens = tuple(tokens)
+
+            if tuple_tokens in cache:
+                remade_tokens, fixes, multipliers = cache[tuple_tokens]
+            else:
+                fixes = []
+                remade_tokens = []
+                multipliers = []
+                mult = 1.0
+
+                i = 0
+                while i < len(tokens):
+                    token = tokens[i]
+
+                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+
+                    mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
+                    if mult_change is not None:
+                        mult *= mult_change
+                        i += 1
+                    elif embedding is None:
+                        remade_tokens.append(token)
+                        multipliers.append(mult)
+                        i += 1
+                    else:
+                        emb_len = int(embedding.vec.shape[0])
+                        fixes.append((len(remade_tokens), embedding))
+                        remade_tokens += [0] * emb_len
+                        multipliers += [mult] * emb_len
+                        used_custom_terms.append((embedding.name, embedding.checksum()))
+                        i += embedding_length_in_tokens
+
+                if len(remade_tokens) > maxlen - 2:
+                    vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
+                    ovf = remade_tokens[maxlen - 2:]
+                    overflowing_words = [vocab.get(int(x), "") for x in ovf]
+                    overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
+                    hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+
+                token_count = len(remade_tokens)
+                remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
+                remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
+                cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
+
+            multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
+            multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
+
+            remade_batch_tokens.append(remade_tokens)
+            hijack_fixes.append(fixes)
+            batch_multipliers.append(multipliers)
+        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
+
+    def forward(self, text):
+        use_old = opts.use_old_emphasis_implementation
+        if use_old:
+            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
+        else:
+            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
+
+        self.hijack.comments += hijack_comments
+
+        if len(used_custom_terms) > 0:
+            self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
+
+        if use_old:
+            self.hijack.fixes = hijack_fixes
+            return self.process_tokens(remade_batch_tokens, batch_multipliers)
+
+        z = None
+        i = 0
+        while max(map(len, remade_batch_tokens)) != 0:
+            rem_tokens = [x[75:] for x in remade_batch_tokens]
+            rem_multipliers = [x[75:] for x in batch_multipliers]
+
+            self.hijack.fixes = []
+            for unfiltered in hijack_fixes:
+                fixes = []
+                for fix in unfiltered:
+                    if fix[0] == i:
+                        fixes.append(fix[1])
+                self.hijack.fixes.append(fixes)
+
+            tokens = []
+            multipliers = []
+            for j in range(len(remade_batch_tokens)):
+                if len(remade_batch_tokens[j]) > 0:
+                    tokens.append(remade_batch_tokens[j][:75])
+                    multipliers.append(batch_multipliers[j][:75])
+                else:
+                    tokens.append([self.id_end] * 75)
+                    multipliers.append([1.0] * 75)
+
+            z1 = self.process_tokens(tokens, multipliers)
+            z = z1 if z is None else torch.cat((z, z1), axis=-2)
+
+            remade_batch_tokens = rem_tokens
+            batch_multipliers = rem_multipliers
+            i += 1
+
+        return z
+
+    def process_tokens(self, remade_batch_tokens, batch_multipliers):
+        if not opts.use_old_emphasis_implementation:
+            remade_batch_tokens = [[self.id_start] + x[:75] + [self.id_end] for x in remade_batch_tokens]
+            batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
+
+        tokens = torch.asarray(remade_batch_tokens).to(devices.device)
+
+        if self.id_end != self.id_pad:
+            for batch_pos in range(len(remade_batch_tokens)):
+                index = remade_batch_tokens[batch_pos].index(self.id_end)
+                tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad
+
+        z = self.encode_with_transformers(tokens)
+
+        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
+        batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
+        batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(devices.device)
+        original_mean = z.mean()
+        z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
+        new_mean = z.mean()
+        z *= original_mean / new_mean
+
+        return z
+
+
+class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
+    def __init__(self, wrapped, hijack):
+        super().__init__(wrapped, hijack)
+        self.tokenizer = wrapped.tokenizer
+        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+
+        self.token_mults = {}
+        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
+        for text, ident in tokens_with_parens:
+            mult = 1.0
+            for c in text:
+                if c == '[':
+                    mult /= 1.1
+                if c == ']':
+                    mult *= 1.1
+                if c == '(':
+                    mult *= 1.1
+                if c == ')':
+                    mult /= 1.1
+
+            if mult != 1.0:
+                self.token_mults[ident] = mult
+
+        self.id_start = self.wrapped.tokenizer.bos_token_id
+        self.id_end = self.wrapped.tokenizer.eos_token_id
+        self.id_pad = self.id_end
+
+    def tokenize(self, texts):
+        tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
+
+        return tokenized
+
+    def encode_with_transformers(self, tokens):
+        outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
+
+        if opts.CLIP_stop_at_last_layers > 1:
+            z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
+            z = self.wrapped.transformer.text_model.final_layer_norm(z)
+        else:
+            z = outputs.last_hidden_state
+
+        return z
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        embedding_layer = self.wrapped.transformer.text_model.embeddings
+        ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
+        embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+
+        return embedded
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 46714a4f..938f9a58 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -199,8 +199,8 @@ def sample_plms(self,
 
 @torch.no_grad()
 def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                    unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
+                  temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                  unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None):
     b, *_, device = *x.shape, x.device
 
     def get_model_output(x, t):
@@ -249,6 +249,8 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
         pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
         if quantize_denoised:
             pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        if dynamic_threshold is not None:
+            pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
         # direction pointing to x_t
         dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
         noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
@@ -321,12 +323,16 @@ def should_hijack_inpainting(checkpoint_info):
 
 
 def do_inpainting_hijack():
-    ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
+    # most of this stuff seems to no longer be needed because it is already included into SD2.0
+    # LatentInpaintDiffusion remains because SD2.0's LatentInpaintDiffusion can't be loaded without specifying a checkpoint
+    # p_sample_plms is needed because PLMS can't work with dicts as conditionings
+    # this file should be cleaned up later if weverything tuens out to work fine
+
+    # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
     ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
 
-    ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
-    ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
+    # ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
+    # ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
 
     ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
-    ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
-
+    # ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
diff --git a/modules/sd_hijack_open_clip.py b/modules/sd_hijack_open_clip.py
new file mode 100644
index 00000000..f733e852
--- /dev/null
+++ b/modules/sd_hijack_open_clip.py
@@ -0,0 +1,37 @@
+import open_clip.tokenizer
+import torch
+
+from modules import sd_hijack_clip, devices
+from modules.shared import opts
+
+tokenizer = open_clip.tokenizer._tokenizer
+
+
+class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
+    def __init__(self, wrapped, hijack):
+        super().__init__(wrapped, hijack)
+
+        self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
+        self.id_start = tokenizer.encoder["<start_of_text>"]
+        self.id_end = tokenizer.encoder["<end_of_text>"]
+        self.id_pad = 0
+
+    def tokenize(self, texts):
+        assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
+
+        tokenized = [tokenizer.encode(text) for text in texts]
+
+        return tokenized
+
+    def encode_with_transformers(self, tokens):
+        # set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
+        z = self.wrapped.encode_with_transformer(tokens)
+
+        return z
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        ids = tokenizer.encode(init_text)
+        ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
+        embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
+
+        return embedded
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4fe67854..4edd8c60 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -127,7 +127,8 @@ class InterruptedException(BaseException):
 class VanillaStableDiffusionSampler:
     def __init__(self, constructor, sd_model):
         self.sampler = constructor(sd_model)
-        self.orig_p_sample_ddim = self.sampler.p_sample_ddim if hasattr(self.sampler, 'p_sample_ddim') else self.sampler.p_sample_plms
+        self.is_plms = hasattr(self.sampler, 'p_sample_plms')
+        self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim
         self.mask = None
         self.nmask = None
         self.init_latent = None
@@ -218,7 +219,6 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None
         self.nmask = p.nmask if hasattr(p, 'nmask') else None
 
-
     def adjust_steps_if_invalid(self, p, num_steps):
         if  (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
             valid_step = 999 / (1000 // num_steps)
@@ -227,7 +227,6 @@ class VanillaStableDiffusionSampler:
         
         return num_steps
 
-
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
         steps = self.adjust_steps_if_invalid(p, steps)
@@ -260,9 +259,10 @@ class VanillaStableDiffusionSampler:
         steps = self.adjust_steps_if_invalid(p, steps or p.steps)
 
         # Wrap the conditioning models with additional image conditioning for inpainting model
+        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
         if image_conditioning is not None:
-            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
-            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+            conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
+            unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
 
         samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
 
@@ -350,7 +350,9 @@ class TorchHijack:
 
 class KDiffusionSampler:
     def __init__(self, funcname, sd_model):
-        self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
+        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+
+        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
         self.funcname = funcname
         self.func = getattr(k_diffusion.sampling, self.funcname)
         self.extra_params = sampler_extra_params.get(funcname, [])
diff --git a/modules/shared.py b/modules/shared.py
index c93ae2a3..8fb1387a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -11,17 +11,15 @@ import tqdm
 import modules.artists
 import modules.interrogate
 import modules.memmon
-import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models, localization, sd_vae, extensions, script_loading
-from modules.hypernetworks import hypernetwork
+from modules import localization, sd_vae, extensions, script_loading
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
+parser.add_argument("--config", type=str, default=os.path.join(script_path, "v1-inference.yaml"), help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
 parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
@@ -121,10 +119,12 @@ xformers_available = False
 config_filename = cmd_opts.ui_settings_file
 
 os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
-hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
+hypernetworks = {}
 loaded_hypernetwork = None
 
+
 def reload_hypernetworks():
+    from modules.hypernetworks import hypernetwork
     global hypernetworks
 
     hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
@@ -206,10 +206,11 @@ class State:
         if self.current_latent is None:
             return
 
+        import modules.sd_samplers
         if opts.show_progress_grid:
-            self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
+            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent)
         else:
-            self.current_image = sd_samplers.sample_to_image(self.current_latent)
+            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent)
 
         self.current_image_sampling_step = self.sampling_step
 
@@ -248,6 +249,21 @@ def options_section(section_identifier, options_dict):
     return options_dict
 
 
+def list_checkpoint_tiles():
+    import modules.sd_models
+    return modules.sd_models.checkpoint_tiles()
+
+
+def refresh_checkpoints():
+    import modules.sd_models
+    return modules.sd_models.list_models()
+
+
+def list_samplers():
+    import modules.sd_samplers
+    return modules.sd_samplers.all_samplers
+
+
 hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
 
 options_templates = {}
@@ -333,7 +349,7 @@ options_templates.update(options_section(('training', "Training"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
     "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
@@ -385,7 +401,7 @@ options_templates.update(options_section(('ui', "User interface"), {
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-    "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}),
+    "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
     "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5e4d8688..a273e663 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -64,7 +64,8 @@ class EmbeddingDatabase:
 
         self.word_embeddings[embedding.name] = embedding
 
-        ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0]
+        # TODO changing between clip and open clip changes tokenization, which will cause embeddings to stop working
+        ids = model.cond_stage_model.tokenize([embedding.name])[0]
 
         first_id = ids[0]
         if first_id not in self.ids_lookup:
@@ -155,13 +156,11 @@ class EmbeddingDatabase:
 
 def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     cond_model = shared.sd_model.cond_stage_model
-    embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
 
     with devices.autocast():
         cond_model([""])  # will send cond model to GPU if lowvram/medvram is active
 
-    ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
-    embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+    embedded = cond_model.encode_embedding_init_text(init_text, num_vectors_per_token)
     vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
 
     for i in range(num_vectors_per_token):
diff --git a/modules/ui.py b/modules/ui.py
index e6da1b2a..e5cb69d0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -478,9 +478,7 @@ def create_toprow(is_img2img):
         if is_img2img:
             with gr.Column(scale=1, elem_id="interrogate_col"):
                 button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-
-                if cmd_opts.deepdanbooru:
-                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
 
         with gr.Column(scale=1):
             with gr.Row():
@@ -1004,11 +1002,10 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[img2img_prompt],
             )
 
-            if cmd_opts.deepdanbooru:
-                img2img_deepbooru.click(
-                    fn=interrogate_deepbooru,
-                    inputs=[init_img],
-                    outputs=[img2img_prompt],
+            img2img_deepbooru.click(
+                fn=interrogate_deepbooru,
+                inputs=[init_img],
+                outputs=[img2img_prompt],
             )
 
 
-- 
cgit v1.2.3


From 1123f52cadf8d86c006177791b3191e5b8388b5a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 26 Nov 2022 16:37:37 +0300
Subject: add 1024 module for hypernets for the new open clip

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index e5cb69d0..16f262c4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1210,7 +1210,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "1024", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
                     new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
-- 
cgit v1.2.3


From 64c7b7975cedeb2aaa1a9c8eb4a479fc575843f8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 26 Nov 2022 16:45:57 +0300
Subject: restore hypernetworks to seemingly working state

---
 modules/sd_hijack.py | 3 ++-
 modules/ui.py        | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index d5243fd3..64655eb1 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -9,6 +9,7 @@ from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared
+from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
 from modules import sd_hijack_clip, sd_hijack_open_clip
 
@@ -60,7 +61,7 @@ def apply_optimizations():
 
 
 def undo_optimizations():
-    ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward  # this stops hypernets from working
+    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
diff --git a/modules/ui.py b/modules/ui.py
index 16f262c4..c8b8fecd 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1210,7 +1210,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "1024", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
                     new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
-- 
cgit v1.2.3


From 755df94b2aa62eabd96f900e0dd7ddc83c2f692c Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Sun, 27 Nov 2022 00:35:44 +0900
Subject: set TI AdamW default weight decay to 0

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fee08e33..b9b1394f 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -283,7 +283,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     embedding.vec.requires_grad = True
-    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
+    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
     scaler = torch.cuda.amp.GradScaler()
 
     batch_size = ds.batch_size
-- 
cgit v1.2.3


From b5050ad2071644f7b4c99660dc66a8a95136102f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 26 Nov 2022 20:52:16 +0300
Subject: make SD2 compatible with --medvram setting

---
 modules/lowvram.py | 8 ++++++++
 1 file changed, 8 insertions(+)

(limited to 'modules')

diff --git a/modules/lowvram.py b/modules/lowvram.py
index a4652cb1..aa464a95 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -51,6 +51,10 @@ def setup_for_low_vram(sd_model, use_medvram):
         send_me_to_gpu(first_stage_model, None)
         return first_stage_model_decode(z)
 
+    # for SD1, cond_stage_model is CLIP and its NN is in the tranformer frield, but for SD2, it's open clip, and it's in model field
+    if hasattr(sd_model.cond_stage_model, 'model'):
+        sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
+
     # remove three big modules, cond, first_stage, and unet from the model and then
     # send the model to GPU. Then put modules back. the modules will be in CPU.
     stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
@@ -65,6 +69,10 @@ def setup_for_low_vram(sd_model, use_medvram):
     sd_model.first_stage_model.decode = first_stage_model_decode_wrap
     parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
+    if hasattr(sd_model.cond_stage_model, 'model'):
+        sd_model.cond_stage_model.model = sd_model.cond_stage_model.transformer
+        del sd_model.cond_stage_model.transformer
+
     if use_medvram:
         sd_model.model.register_forward_pre_hook(send_me_to_gpu)
     else:
-- 
cgit v1.2.3


From 1e506657e1cb732a5f0e567ba2585fba2bbb1327 Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sat, 26 Nov 2022 13:28:44 -0500
Subject: no-half support for SD 2.0

---
 modules/sd_models.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index c59151e0..0e0bd79e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -244,6 +244,9 @@ def load_model(checkpoint_info=None):
 
     do_inpainting_hijack()
 
+    if shared.cmd_opts.no_half:
+        sd_config.model.params.unet_config.params.use_fp16 = False
+
     sd_model = instantiate_from_config(sd_config.model)
     load_model_weights(sd_model, checkpoint_info)
 
-- 
cgit v1.2.3


From c67c40f983997594f76b2312f92c3761e8d83715 Mon Sep 17 00:00:00 2001
From: Matthew McGoogan <mlmcgoogan@gmail.com>
Date: Sat, 26 Nov 2022 23:25:16 +0000
Subject: torch.cuda.empty_cache() defaults to cuda:0 device unless explicitly
 set otherwise first. Updating torch_gc() to use the device set by --device-id
 if specified to avoid OOM edge cases on multi-GPU systems.

---
 modules/devices.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 67165bf6..93d82bbc 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -44,8 +44,18 @@ def get_optimal_device():
 
 def torch_gc():
     if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        torch.cuda.ipc_collect()
+        from modules import shared
+
+        device_id = shared.cmd_opts.device_id
+        
+        if device_id is not None:
+            cuda_device = f"cuda:{device_id}"
+        else:
+            cuda_device = "cuda"
+        
+        with torch.cuda.device(cuda_device):
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
 
 
 def enable_tf32():
-- 
cgit v1.2.3


From b006382784a2f0887317bb60ea49d19b50a5dc7e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 11:52:53 +0300
Subject: serve images from where they are saved instead of a temporary
 directory add an option to choose a different temporary directory in the UI
 add an option to cleanup the selected temporary directory at startup

---
 modules/images.py     |  2 ++
 modules/shared.py     |  7 ++++++
 modules/ui.py         | 16 -------------
 modules/ui_tempdir.py | 62 +++++++++++++++++++++++++++++++++++++++++++++++++++
 4 files changed, 71 insertions(+), 16 deletions(-)
 create mode 100644 modules/ui_tempdir.py

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 26d5b7a9..8737ccff 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -524,6 +524,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     else:
         image.save(fullfn, quality=opts.jpeg_quality)
 
+    image.already_saved_as = fullfn
+
     target_side_length = 4000
     oversize = image.width > target_side_length or image.height > target_side_length
     if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
diff --git a/modules/shared.py b/modules/shared.py
index 8fb1387a..af975f54 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -16,6 +16,9 @@ import modules.devices as devices
 from modules import localization, sd_vae, extensions, script_loading
 from modules.paths import models_path, script_path, sd_path
 
+
+demo = None
+
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 parser = argparse.ArgumentParser()
@@ -292,6 +295,10 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
     "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
+
+    "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
+    "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
+
 }))
 
 options_templates.update(options_section(('saving-paths', "Paths for saving"), {
diff --git a/modules/ui.py b/modules/ui.py
index c8b8fecd..ea925c40 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -157,22 +157,6 @@ def save_files(js_data, images, do_make_zip, index):
 
     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
-def save_pil_to_file(pil_image, dir=None):
-    use_metadata = False
-    metadata = PngImagePlugin.PngInfo()
-    for key, value in pil_image.info.items():
-        if isinstance(key, str) and isinstance(value, str):
-            metadata.add_text(key, value)
-            use_metadata = True
-
-    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
-    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
-    return file_obj
-
-
-# override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
-
 
 def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
     def f(*args, extra_outputs_array=extra_outputs, **kwargs):
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
new file mode 100644
index 00000000..9c6d3a9d
--- /dev/null
+++ b/modules/ui_tempdir.py
@@ -0,0 +1,62 @@
+import os
+import tempfile
+from collections import namedtuple
+
+import gradio as gr
+
+from PIL import PngImagePlugin
+
+from modules import shared
+
+
+Savedfile = namedtuple("Savedfile", ["name"])
+
+
+def save_pil_to_file(pil_image, dir=None):
+    already_saved_as = getattr(pil_image, 'already_saved_as', None)
+    if already_saved_as:
+        shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(os.path.dirname(already_saved_as))}
+        file_obj = Savedfile(already_saved_as)
+        return file_obj
+
+    if shared.opts.temp_dir != "":
+        dir = shared.opts.temp_dir
+
+    use_metadata = False
+    metadata = PngImagePlugin.PngInfo()
+    for key, value in pil_image.info.items():
+        if isinstance(key, str) and isinstance(value, str):
+            metadata.add_text(key, value)
+            use_metadata = True
+
+    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+    return file_obj
+
+
+# override save to file function so that it also writes PNG info
+gr.processing_utils.save_pil_to_file = save_pil_to_file
+
+
+def on_tmpdir_changed():
+    if shared.opts.temp_dir == "" or shared.demo is None:
+        return
+
+    os.makedirs(shared.opts.temp_dir, exist_ok=True)
+
+    shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(shared.opts.temp_dir)}
+
+
+def cleanup_tmpdr():
+    temp_dir = shared.opts.temp_dir
+    if temp_dir == "" or not os.path.isdir(temp_dir):
+        return
+
+    for root, dirs, files in os.walk(temp_dir, topdown=False):
+        for name in files:
+            _, extension = os.path.splitext(name)
+            if extension != ".png":
+                continue
+
+            filename = os.path.join(root, name)
+            os.remove(filename)
-- 
cgit v1.2.3


From 5b2c316890b7b8af95f0d0334d1fd34b9a687b99 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 13:08:54 +0300
Subject: eliminate duplicated code from #5095

---
 modules/devices.py | 30 +++++++++++-------------------
 1 file changed, 11 insertions(+), 19 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 93d82bbc..dd50fe24 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -24,17 +24,18 @@ def extract_device_id(args, name):
     return None
 
 
-def get_optimal_device():
-    if torch.cuda.is_available():
-        from modules import shared
+def get_cuda_device_string():
+    from modules import shared
+
+    if shared.cmd_opts.device_id is not None:
+        return f"cuda:{shared.cmd_opts.device_id}"
 
-        device_id = shared.cmd_opts.device_id
+    return "cuda"
 
-        if device_id is not None:
-            cuda_device = f"cuda:{device_id}"
-            return torch.device(cuda_device)
-        else:
-            return torch.device("cuda")
+
+def get_optimal_device():
+    if torch.cuda.is_available():
+        return torch.device(get_cuda_device_string())
 
     if has_mps():
         return torch.device("mps")
@@ -44,16 +45,7 @@ def get_optimal_device():
 
 def torch_gc():
     if torch.cuda.is_available():
-        from modules import shared
-
-        device_id = shared.cmd_opts.device_id
-        
-        if device_id is not None:
-            cuda_device = f"cuda:{device_id}"
-        else:
-            cuda_device = "cuda"
-        
-        with torch.cuda.device(cuda_device):
+        with torch.cuda.device(get_cuda_device_string()):
             torch.cuda.empty_cache()
             torch.cuda.ipc_collect()
 
-- 
cgit v1.2.3


From 40ca34b837b5068ec35b8d5681bae32cf28f5816 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 13:17:39 +0300
Subject: fix for broken sampler selection in img2img and xy plot #4860 #4909

---
 modules/img2img.py    | 2 +-
 modules/processing.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index 9fc5b693..7e58994a 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -99,7 +99,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
         seed_enable_extras=seed_enable_extras,
-        sampler_index=sd_samplers.samplers_for_img2img[sampler_index].name,
+        sampler_name=sd_samplers.samplers_for_img2img[sampler_index].name,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,
diff --git a/modules/processing.py b/modules/processing.py
index c310df6a..edceb532 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -74,7 +74,7 @@ class StableDiffusionProcessing():
     """
     def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, sampler_index: int = None):
         if sampler_index is not None:
-            warnings.warn("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name")
+            print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
-- 
cgit v1.2.3


From 10923f9b3a10a9af20429e51242614e259fbd434 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 13:43:10 +0300
Subject: calculate dictionary for sampler names only once

---
 modules/sd_samplers.py | 7 +++++++
 1 file changed, 7 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 43ce34eb..6f8ccf1d 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -52,6 +52,7 @@ all_samplers_map = {x.name: x for x in all_samplers}
 
 samplers = []
 samplers_for_img2img = []
+samplers_map = {}
 
 
 def create_sampler(name, model):
@@ -77,6 +78,12 @@ def set_samplers():
     samplers = [x for x in all_samplers if x.name not in hidden]
     samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
 
+    samplers_map.clear()
+    for sampler in all_samplers:
+        samplers_map[sampler.name.lower()] = sampler.name
+        for alias in sampler.aliases:
+            samplers_map[alias.lower()] = sampler.name
+
 
 set_samplers()
 
-- 
cgit v1.2.3


From 6074175faa751dde933aa8e15cd687ca4e4b4a23 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 14:46:40 +0300
Subject: add safetensors to requirements

---
 modules/sd_models.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ae36841a..77236480 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -5,6 +5,7 @@ import gc
 from collections import namedtuple
 import torch
 import re
+import safetensors.torch
 from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
@@ -173,14 +174,12 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        if checkpoint_file.endswith(".safetensors"):
-            try:
-                from safetensors.torch import load_file
-            except ImportError as e:
-                raise ImportError(f"The model is in safetensors format and it is not installed, use `pip install safetensors`: {e}")
-            pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
+        _, extension = os.path.splitext(checkpoint_file)
+        if extension.lower() == ".safetensors":
+            pl_sd = safetensors.torch.load_file(checkpoint_file, device=shared.weight_load_location)
         else:
             pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+
         if "global_step" in pl_sd:
             print(f"Global Step: {pl_sd['global_step']}")
 
-- 
cgit v1.2.3


From dac9b6f15de5e675053d9490a20e0457dcd1a23e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 15:51:29 +0300
Subject: add safetensors support for model merging #4869

---
 modules/extras.py    | 26 ++++++++++++++------------
 modules/sd_models.py | 26 +++++++++++++++-----------
 modules/ui.py        |  7 ++++++-
 3 files changed, 35 insertions(+), 24 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 71b93a06..3d65d90a 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -20,6 +20,7 @@ import modules.codeformer_model
 import piexif
 import piexif.helper
 import gradio as gr
+import safetensors.torch
 
 
 class LruCache(OrderedDict):
@@ -249,7 +250,7 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
@@ -264,19 +265,15 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
     teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
 
     print(f"Loading {primary_model_info.filename}...")
-    primary_model = torch.load(primary_model_info.filename, map_location='cpu')
-    theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
+    theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
 
     print(f"Loading {secondary_model_info.filename}...")
-    secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
-    theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
+    theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
 
     if teritary_model_info is not None:
         print(f"Loading {teritary_model_info.filename}...")
-        teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
-        theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
+        theta_2 = sd_models.read_state_dict(teritary_model_info.filename, map_location='cpu')
     else:
-        teritary_model = None
         theta_2 = None
 
     theta_funcs = {
@@ -295,7 +292,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
                     theta_1[key] = theta_func1(theta_1[key], t2)
                 else:
                     theta_1[key] = torch.zeros_like(theta_1[key])
-    del theta_2, teritary_model
+    del theta_2
 
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
@@ -314,12 +311,17 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
-    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
-    filename = filename if custom_name == '' else (custom_name + '.ckpt')
+    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.' + checkpoint_format
+    filename = filename if custom_name == '' else (custom_name + '.' + checkpoint_format)
     output_modelname = os.path.join(ckpt_dir, filename)
 
     print(f"Saving to {output_modelname}...")
-    torch.save(primary_model, output_modelname)
+
+    _, extension = os.path.splitext(output_modelname)
+    if extension.lower() == ".safetensors":
+        safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
+    else:
+        torch.save(theta_0, output_modelname)
 
     sd_models.list_models()
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 77236480..a1ea5611 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -160,6 +160,20 @@ def get_state_dict_from_checkpoint(pl_sd):
     return pl_sd
 
 
+def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
+    _, extension = os.path.splitext(checkpoint_file)
+    if extension.lower() == ".safetensors":
+        pl_sd = safetensors.torch.load_file(checkpoint_file, device=map_location or shared.weight_load_location)
+    else:
+        pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
+
+    if print_global_state and "global_step" in pl_sd:
+        print(f"Global Step: {pl_sd['global_step']}")
+
+    sd = get_state_dict_from_checkpoint(pl_sd)
+    return sd
+
+
 def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
@@ -174,17 +188,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        _, extension = os.path.splitext(checkpoint_file)
-        if extension.lower() == ".safetensors":
-            pl_sd = safetensors.torch.load_file(checkpoint_file, device=shared.weight_load_location)
-        else:
-            pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
-
-        if "global_step" in pl_sd:
-            print(f"Global Step: {pl_sd['global_step']}")
-
-        sd = get_state_dict_from_checkpoint(pl_sd)
-        del pl_sd
+        sd = read_state_dict(checkpoint_file)
         model.load_state_dict(sd, strict=False)
         del sd
         
diff --git a/modules/ui.py b/modules/ui.py
index de2b5544..aa13978d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1164,7 +1164,11 @@ def create_ui(wrap_gradio_gpu_call):
                 custom_name = gr.Textbox(label="Custom Name (Optional)")
                 interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
                 interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
-                save_as_half = gr.Checkbox(value=False, label="Save as float16")
+
+                with gr.Row():
+                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format")
+                    save_as_half = gr.Checkbox(value=False, label="Save as float16")
+
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
             with gr.Column(variant='panel'):
@@ -1692,6 +1696,7 @@ def create_ui(wrap_gradio_gpu_call):
                 interp_amount,
                 save_as_half,
                 custom_name,
+                checkpoint_format,
             ],
             outputs=[
                 submit_result,
-- 
cgit v1.2.3


From 3cf93de24f90247af33ab9cf743a6eb45308d668 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Sun, 27 Nov 2022 21:12:37 +0800
Subject: Fix sampler_name for API requests are being ignored

---
 modules/api/api.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index efcedbba..53980551 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -112,7 +112,7 @@ class Api:
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         populate = txt2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
-            "sampler_name": validate_sampler_name(txt2imgreq.sampler_index),
+            "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True
             }
@@ -142,7 +142,7 @@ class Api:
 
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
-            "sampler_name": validate_sampler_name(img2imgreq.sampler_index),
+            "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True,
             "mask": mask
-- 
cgit v1.2.3


From 06ada734c7f85e5e6e2e6ae78fb873be0222bfd5 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Sun, 27 Nov 2022 21:19:47 +0800
Subject: Prevent warning on sampler_index if sampler_name is being used

---
 modules/api/api.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 53980551..2f450fc4 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -117,6 +117,8 @@ class Api:
             "do_not_save_grid": True
             }
         )
+        if populate.sampler_name:
+            populate.sampler_index = None  # prevent a warning later on
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
         # Override object param
 
@@ -148,6 +150,8 @@ class Api:
             "mask": mask
             }
         )
+        if populate.sampler_name:
+            populate.sampler_index = None  # prevent a warning later on
         p = StableDiffusionProcessingImg2Img(**vars(populate))
 
         imgs = []
-- 
cgit v1.2.3


From 185ab3cbd1330f6dbc87e5dd72cf115b3edfd153 Mon Sep 17 00:00:00 2001
From: cat <cat@wolfgirl.org>
Date: Sat, 26 Nov 2022 20:07:51 +0500
Subject: Atomically rename saved image to avoid race condition with other
 processes.

---
 modules/images.py | 51 +++++++++++++++++++++++++++++----------------------
 1 file changed, 29 insertions(+), 22 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index b968d6a6..51b1ff9c 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -500,30 +500,39 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     image = params.image
     fullfn = params.filename
     info = params.pnginfo.get(pnginfo_section_name, None)
-    fullfn_without_extension, extension = os.path.splitext(params.filename)
 
-    def exif_bytes():
-        return piexif.dump({
-            "Exif": {
-                piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode")
-            },
-        })
+    def _atomically_save_image(image_to_save, filename_without_extension, extension):
+        # save image with .tmp extension to avoid race condition when another process detects new image in the directory
+        temp_file_path = filename_without_extension + ".tmp"
+        image_format = Image.registered_extensions()[extension]
 
-    if extension.lower() == '.png':
-        pnginfo_data = PngImagePlugin.PngInfo()
-        if opts.enable_pnginfo:
-            for k, v in params.pnginfo.items():
-                pnginfo_data.add_text(k, str(v))
+        if extension.lower() == '.png':
+            pnginfo_data = PngImagePlugin.PngInfo()
+            if opts.enable_pnginfo:
+                for k, v in params.pnginfo.items():
+                    pnginfo_data.add_text(k, str(v))
 
-        image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
+            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
 
-    elif extension.lower() in (".jpg", ".jpeg", ".webp"):
-        image.save(fullfn, quality=opts.jpeg_quality)
+        elif extension.lower() in (".jpg", ".jpeg", ".webp"):
+            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
 
-        if opts.enable_pnginfo and info is not None:
-            piexif.insert(exif_bytes(), fullfn)
-    else:
-        image.save(fullfn, quality=opts.jpeg_quality)
+            if opts.enable_pnginfo and info is not None:
+                exif_bytes = piexif.dump({
+                    "Exif": {
+                        piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode")
+                    },
+                })
+
+                piexif.insert(exif_bytes, temp_file_path)
+        else:
+            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
+
+        # atomically rename the file with correct extension
+        os.replace(temp_file_path, filename_without_extension + extension)
+
+    fullfn_without_extension, extension = os.path.splitext(params.filename)
+    _atomically_save_image(image, fullfn_without_extension, extension)
 
     image.already_saved_as = fullfn
 
@@ -537,9 +546,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         elif oversize:
             image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
 
-        image.save(fullfn_without_extension + ".jpg", quality=opts.jpeg_quality)
-        if opts.enable_pnginfo and info is not None:
-            piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg")
+        _atomically_save_image(image, fullfn_without_extension, ".jpg")
 
     if opts.save_txt and info is not None:
         txt_fullfn = f"{fullfn_without_extension}.txt"
-- 
cgit v1.2.3


From 506d529d19f135f57e142371271f84d4971b456f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 16:28:32 +0300
Subject: rework #5012 to also work for pictures dragged into the prompt and
 also add Clip skip + ENSD to parameters

---
 modules/extras.py                          | 40 ++++--------------------------
 modules/generation_parameters_copypaste.py |  1 +
 modules/images.py                          | 38 +++++++++++++++++++++++++++-
 modules/sd_samplers.py                     |  2 +-
 4 files changed, 44 insertions(+), 37 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 0057bf9c..6021a024 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -1,6 +1,8 @@
 from __future__ import annotations
 import math
 import os
+import sys
+import traceback
 
 import numpy as np
 from PIL import Image
@@ -12,7 +14,7 @@ from typing import Callable, List, OrderedDict, Tuple
 from functools import partial
 from dataclasses import dataclass
 
-from modules import processing, shared, images, devices, sd_models
+from modules import processing, shared, images, devices, sd_models, sd_samplers
 from modules.shared import opts
 import modules.gfpgan_model
 from modules.ui import plaintext_to_html
@@ -22,7 +24,6 @@ import piexif.helper
 import gradio as gr
 import safetensors.torch
 
-
 class LruCache(OrderedDict):
     @dataclass(frozen=True)
     class Key:
@@ -214,39 +215,8 @@ def run_pnginfo(image):
     if image is None:
         return '', '', ''
 
-    items = image.info
-    geninfo = ''
-
-    if "exif" in image.info:
-        exif = piexif.load(image.info["exif"])
-        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
-        try:
-            exif_comment = piexif.helper.UserComment.load(exif_comment)
-        except ValueError:
-            exif_comment = exif_comment.decode('utf8', errors="ignore")
-
-        items['exif comment'] = exif_comment
-        geninfo = exif_comment
-
-        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
-                      'loop', 'background', 'timestamp', 'duration']:
-            items.pop(field, None)
-
-    geninfo = items.get('parameters', geninfo)
-
-    # nai prompt
-    if "Software" in items.keys() and items["Software"] == "NovelAI":
-        import json
-        json_info = json.loads(items["Comment"])
-        geninfo = f'{items["Description"]}\r\nNegative prompt: {json_info["uc"]}\r\n'
-        sampler = "Euler a"
-        if json_info["sampler"] == "k_euler_ancestral":
-            sampler = "Euler a"
-        elif json_info["sampler"] == "k_euler":
-            sampler = "Euler"
-        model_hash = '925997e9'  # assuming this is the correct model hash
-        # not sure with noise and strength parameter
-        geninfo += f'Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Model hash: {model_hash}'  # , Denoising strength: {json_info["noise"]}'
+    geninfo, items = images.read_info_from_image(image)
+    items = {**{'parameters': geninfo}, **items}
 
     info = ''
     for key, text in items.items():
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 1408ea05..0973c695 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -75,6 +75,7 @@ def integrate_settings_paste_fields(component_dict):
         'CLIP_stop_at_last_layers': 'Clip skip',
         'inpainting_mask_weight': 'Conditional mask weight',
         'sd_model_checkpoint': 'Model hash',
+        'eta_noise_seed_delta': 'ENSD',
     }
     settings_paste_fields = [
         (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))
diff --git a/modules/images.py b/modules/images.py
index b968d6a6..08a72e67 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -15,6 +15,7 @@ import piexif.helper
 from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
 from fonts.ttf import Roboto
 import string
+import json
 
 from modules import sd_samplers, shared, script_callbacks
 from modules.shared import opts, cmd_opts
@@ -553,10 +554,45 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     return fullfn, txt_fullfn
 
 
+def read_info_from_image(image):
+    items = image.info or {}
+
+    geninfo = items.pop('parameters', None)
+
+    if "exif" in items:
+        exif = piexif.load(items["exif"])
+        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
+        try:
+            exif_comment = piexif.helper.UserComment.load(exif_comment)
+        except ValueError:
+            exif_comment = exif_comment.decode('utf8', errors="ignore")
+
+        items['exif comment'] = exif_comment
+        geninfo = exif_comment
+
+        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
+                      'loop', 'background', 'timestamp', 'duration']:
+            items.pop(field, None)
+
+    if items.get("Software", None) == "NovelAI":
+        try:
+            json_info = json.loads(items["Comment"])
+            sampler = sd_samplers.samplers_map.get(json_info["sampler"], "Euler a")
+
+            geninfo = f"""{items["Description"]}
+Negative prompt: {json_info["uc"]}
+Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
+        except Exception:
+            print(f"Error parsing NovelAI iamge generation parameters:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    return geninfo, items
+
+
 def image_data(data):
     try:
         image = Image.open(io.BytesIO(data))
-        textinfo = image.text["parameters"]
+        textinfo, _ = read_info_from_image(image)
         return textinfo, None
     except Exception:
         pass
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 2ca17d8b..5fefb227 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -18,7 +18,7 @@ from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
 
 samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
     ('Euler', 'sample_euler', ['k_euler'], {}),
     ('LMS', 'sample_lms', ['k_lms'], {}),
     ('Heun', 'sample_heun', ['k_heun'], {}),
-- 
cgit v1.2.3


From 8c13f3a2a56b2ab8d6ff0bc5b2ffe0313e74f323 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 16:35:35 +0300
Subject: cherrypick from #4971

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index aa13978d..446bee40 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1044,6 +1044,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
                 (denoising_strength, "Denoising strength"),
+                (mask_blur, "Mask blur"),
                 *modules.scripts.scripts_img2img.infotext_fields
             ]
             parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
-- 
cgit v1.2.3


From 9146a5884cbdf67c019685950f7ad0b3f7bd9230 Mon Sep 17 00:00:00 2001
From: uservar <63248296+uservar@users.noreply.github.com>
Date: Sun, 27 Nov 2022 19:11:50 +0000
Subject: Better should hijack inpainting detection

---
 modules/sd_hijack_inpainting.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 938f9a58..5dcbbed9 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -1,3 +1,4 @@
+import os
 import torch
 
 from einops import repeat
@@ -319,7 +320,9 @@ class LatentInpaintDiffusion(LatentDiffusion):
 
 
 def should_hijack_inpainting(checkpoint_info):
-    return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
+    ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
+    cfg_basename = os.path.basename(checkpoint_info.config).lower()
+    return "inpainting" in ckpt_basename and not "inpainting" in cfg_basename
 
 
 def do_inpainting_hijack():
-- 
cgit v1.2.3


From aa12dfada05a1f5bef558f24f3a318a1c293a01f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 23:04:42 +0300
Subject: fix the bug that makes it impossible to send images to other tabs

---
 modules/generation_parameters_copypaste.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 0973c695..01980dca 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -2,6 +2,8 @@ import base64
 import io
 import os
 import re
+from pathlib import Path
+
 import gradio as gr
 from modules.shared import script_path
 from modules import shared
@@ -35,9 +37,8 @@ def quote(text):
 def image_from_url_text(filedata):
     if type(filedata) == dict and filedata["is_file"]:
         filename = filedata["name"]
-        tempdir = os.path.normpath(tempfile.gettempdir())
-        normfn = os.path.normpath(filename)
-        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
+        is_in_right_dir = any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in shared.demo.temp_dirs)
+        assert is_in_right_dir, 'trying to open image file outside of allowed directories'
 
         return Image.open(filename)
 
-- 
cgit v1.2.3


From bb11bee22ab02aa2fb5b96baa9be8103fff19e6a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 23:14:13 +0300
Subject: if image on disk was deleted between being generated and request
 being completed, do use temporary dir to store it for the browser

---
 modules/ui_tempdir.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
index 9c6d3a9d..07210d14 100644
--- a/modules/ui_tempdir.py
+++ b/modules/ui_tempdir.py
@@ -14,7 +14,7 @@ Savedfile = namedtuple("Savedfile", ["name"])
 
 def save_pil_to_file(pil_image, dir=None):
     already_saved_as = getattr(pil_image, 'already_saved_as', None)
-    if already_saved_as:
+    if already_saved_as and os.path.isfile(already_saved_as):
         shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(os.path.dirname(already_saved_as))}
         file_obj = Savedfile(already_saved_as)
         return file_obj
-- 
cgit v1.2.3


From 0376da180c81a11880a2587903d69d85541051e7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 28 Nov 2022 08:39:59 +0300
Subject: make it possible to save nai model using safetensors

---
 modules/sd_models.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index a1ea5611..283cf1cd 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -144,8 +144,8 @@ def transform_checkpoint_dict_key(k):
 
 
 def get_state_dict_from_checkpoint(pl_sd):
-    if "state_dict" in pl_sd:
-        pl_sd = pl_sd["state_dict"]
+    pl_sd = pl_sd.pop("state_dict", pl_sd)
+    pl_sd.pop("state_dict", None)
 
     sd = {}
     for k, v in pl_sd.items():
-- 
cgit v1.2.3


From 0b5dcb3d7ce397ad38312dbfc70febe7bb42dcc3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 28 Nov 2022 09:00:10 +0300
Subject: fix an error that happens when you type into prompt while switching
 model, put queue stuff into separate file

---
 modules/call_queue.py | 98 +++++++++++++++++++++++++++++++++++++++++++++++++++
 modules/ui.py         | 67 ++---------------------------------
 2 files changed, 101 insertions(+), 64 deletions(-)
 create mode 100644 modules/call_queue.py

(limited to 'modules')

diff --git a/modules/call_queue.py b/modules/call_queue.py
new file mode 100644
index 00000000..4cd49533
--- /dev/null
+++ b/modules/call_queue.py
@@ -0,0 +1,98 @@
+import html
+import sys
+import threading
+import traceback
+import time
+
+from modules import shared
+
+queue_lock = threading.Lock()
+
+
+def wrap_queued_call(func):
+    def f(*args, **kwargs):
+        with queue_lock:
+            res = func(*args, **kwargs)
+
+        return res
+
+    return f
+
+
+def wrap_gradio_gpu_call(func, extra_outputs=None):
+    def f(*args, **kwargs):
+
+        shared.state.begin()
+
+        with queue_lock:
+            res = func(*args, **kwargs)
+
+        shared.state.end()
+
+        return res
+
+    return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True)
+
+
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
+        run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
+        if run_memmon:
+            shared.mem_mon.monitor()
+        t = time.perf_counter()
+
+        try:
+            res = list(func(*args, **kwargs))
+        except Exception as e:
+            # When printing out our debug argument list, do not print out more than a MB of text
+            max_debug_str_len = 131072 # (1024*1024)/8
+
+            print("Error completing request", file=sys.stderr)
+            argStr = f"Arguments: {str(args)} {str(kwargs)}"
+            print(argStr[:max_debug_str_len], file=sys.stderr)
+            if len(argStr) > max_debug_str_len:
+                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
+
+            print(traceback.format_exc(), file=sys.stderr)
+
+            shared.state.job = ""
+            shared.state.job_count = 0
+
+            if extra_outputs_array is None:
+                extra_outputs_array = [None, '']
+
+            res = extra_outputs_array + [f"<div class='error'>{html.escape(type(e).__name__+': '+str(e))}</div>"]
+
+        shared.state.skipped = False
+        shared.state.interrupted = False
+        shared.state.job_count = 0
+
+        if not add_stats:
+            return tuple(res)
+
+        elapsed = time.perf_counter() - t
+        elapsed_m = int(elapsed // 60)
+        elapsed_s = elapsed % 60
+        elapsed_text = f"{elapsed_s:.2f}s"
+        if elapsed_m > 0:
+            elapsed_text = f"{elapsed_m}m "+elapsed_text
+
+        if run_memmon:
+            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
+            active_peak = mem_stats['active_peak']
+            reserved_peak = mem_stats['reserved_peak']
+            sys_peak = mem_stats['system_peak']
+            sys_total = mem_stats['total']
+            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
+
+            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
+        else:
+            vram_html = ''
+
+        # last item is always HTML
+        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
+
+        return tuple(res)
+
+    return f
+
diff --git a/modules/ui.py b/modules/ui.py
index 446bee40..00809361 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -17,7 +17,7 @@ import gradio.routes
 import gradio.utils
 import numpy as np
 from PIL import Image, PngImagePlugin
-
+from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
 from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru
 from modules.paths import script_path
@@ -158,67 +158,6 @@ def save_files(js_data, images, do_make_zip, index):
     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
-def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
-    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
-        if run_memmon:
-            shared.mem_mon.monitor()
-        t = time.perf_counter()
-
-        try:
-            res = list(func(*args, **kwargs))
-        except Exception as e:
-            # When printing out our debug argument list, do not print out more than a MB of text
-            max_debug_str_len = 131072 # (1024*1024)/8
-
-            print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
-            print(argStr[:max_debug_str_len], file=sys.stderr)
-            if len(argStr) > max_debug_str_len:
-                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
-            print(traceback.format_exc(), file=sys.stderr)
-
-            shared.state.job = ""
-            shared.state.job_count = 0
-
-            if extra_outputs_array is None:
-                extra_outputs_array = [None, '']
-
-            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
-
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.job_count = 0
-
-        if not add_stats:
-            return tuple(res)
-
-        elapsed = time.perf_counter() - t
-        elapsed_m = int(elapsed // 60)
-        elapsed_s = elapsed % 60
-        elapsed_text = f"{elapsed_s:.2f}s"
-        if elapsed_m > 0:
-            elapsed_text = f"{elapsed_m}m "+elapsed_text
-
-        if run_memmon:
-            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
-            active_peak = mem_stats['active_peak']
-            reserved_peak = mem_stats['reserved_peak']
-            sys_peak = mem_stats['system_peak']
-            sys_total = mem_stats['total']
-            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
-
-            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
-        else:
-            vram_html = ''
-
-        # last item is always HTML
-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
-
-        return tuple(res)
-
-    return f
 
 
 def calc_time_left(progress, threshold, label, force_display):
@@ -666,7 +605,7 @@ Requested path was: {f}
                 return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
 
 
-def create_ui(wrap_gradio_gpu_call):
+def create_ui():
     import modules.img2img
     import modules.txt2img
 
@@ -826,7 +765,7 @@ def create_ui(wrap_gradio_gpu_call):
                 height,
             ]
 
-            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
+            token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     modules.scripts.scripts_current = modules.scripts.scripts_img2img
     modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
-- 
cgit v1.2.3


From 67efee33a6c65e58b3f6c788993d0e68a33e4fd0 Mon Sep 17 00:00:00 2001
From: klimaleksus <klimaleksus@gmail.com>
Date: Mon, 28 Nov 2022 16:29:43 +0500
Subject: Make VAE step sequential to prevent VRAM spikes

---
 modules/processing.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index edceb532..fd995b8a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -530,8 +530,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             with devices.autocast():
                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
 
-            samples_ddim = samples_ddim.to(devices.dtype_vae)
-            x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
+            x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
+            x_samples_ddim = torch.stack(x_samples_ddim).float()
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
             del samples_ddim
-- 
cgit v1.2.3


From 98ca437edfbf71dd956d67d37f2136b12d13be0d Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sat, 12 Nov 2022 02:17:55 -0500
Subject: Refactor and instead check if mps is being used, not availability

---
 modules/sd_hijack.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index b824b5bf..ce583950 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -182,11 +182,7 @@ def register_buffer(self, name, attr):
 
     if type(attr) == torch.Tensor:
         if attr.device != devices.device:
-
-            if devices.has_mps():
-                attr = attr.to(device="mps", dtype=torch.float32)
-            else:
-                attr = attr.to(devices.device)
+            attr = attr.to(device=devices.device, dtype=(torch.float32 if devices.device.type == 'mps' else None))
 
     setattr(self, name, attr)
 
-- 
cgit v1.2.3


From 75c4511e6b81ae8fb0dbd932043e8eb35cd09f72 Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Tue, 29 Nov 2022 10:28:41 +0800
Subject: add AltDiffusion to webui

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 modules/devices.py   |  4 ++--
 modules/sd_hijack.py | 23 +++++++++++++++++------
 modules/shared.py    |  6 +++++-
 3 files changed, 24 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 67165bf6..f30b6ebc 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -36,8 +36,8 @@ def get_optimal_device():
         else:
             return torch.device("cuda")
 
-    if has_mps():
-        return torch.device("mps")
+    # if has_mps():
+    #     return torch.device("mps")
 
     return cpu
 
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index eaedac13..26280fe4 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -70,14 +70,19 @@ class StableDiffusionModelHijack:
     embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
 
     def hijack(self, m):
-        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+       
+        if shared.text_model_name == "XLMR-Large":
+            model_embeddings = m.cond_stage_model.roberta.embeddings
+            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
+        else :
+            model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embeddings, self)
 
-        model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
         m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
 
         self.clip = m.cond_stage_model
 
-        apply_optimizations()
+        # apply_optimizations()
 
         def flatten(el):
             flattened = [flatten(children) for children in el.children()]
@@ -125,8 +130,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         self.tokenizer = wrapped.tokenizer
         self.token_mults = {}
 
-        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
-
+        try:
+            self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+        except:
+            self.comma_token = None
+           
         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
         for text, ident in tokens_with_parens:
             mult = 1.0
@@ -298,6 +306,9 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
 
     def forward(self, text):
+        if shared.text_model_name == "XLMR-Large":
+            return self.wrapped.encode(text)
+        
         use_old = opts.use_old_emphasis_implementation
         if use_old:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
@@ -359,7 +370,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             z = self.wrapped.transformer.text_model.final_layer_norm(z)
         else:
             z = outputs.last_hidden_state
-
+        
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
         batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
diff --git a/modules/shared.py b/modules/shared.py
index c93ae2a3..9941d2f4 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -21,7 +21,7 @@ from modules.paths import models_path, script_path, sd_path
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
+parser.add_argument("--config", type=str, default="configs/altdiffusion/ad-inference.yaml", help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
 parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
@@ -106,6 +106,10 @@ restricted_opts = {
     "outdir_txt2img_grids",
     "outdir_save",
 }
+from omegaconf import OmegaConf
+config = OmegaConf.load(f"{cmd_opts.config}")
+# XLMR-Large
+text_model_name = config.model.params.cond_stage_config.params.name
 
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
-- 
cgit v1.2.3


From 9a8678f61eff172811498a682c171399b7216e12 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Tue, 29 Nov 2022 11:11:29 +0800
Subject: Support changing checkpoint and vae through override_settings

---
 modules/processing.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index edceb532..a5c72e3d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -20,6 +20,8 @@ import modules.shared as shared
 import modules.face_restoration
 import modules.images as images
 import modules.styles
+import modules.sd_models as sd_models
+import modules.sd_vae as sd_vae
 import logging
 
 
@@ -424,8 +426,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     try:
         for k, v in p.override_settings.items():
-            setattr(opts, k, v)  # we don't call onchange for simplicity which makes changing model impossible
-            if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet since it is relatively fast to load on-change, while SD models are not
+            setattr(opts, k, v)
+            if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet
+            if k == 'sd_model_checkpoint': sd_models.reload_model_weights()  # make onchange call for changing SD model
+            if k == 'sd_vae': sd_vae.reload_vae_weights()  # make onchange call for changing VAE
 
         res = process_images_inner(p)
 
@@ -433,6 +437,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         for k, v in stored_opts.items():
             setattr(opts, k, v)
             if k == 'sd_hypernetwork': shared.reload_hypernetworks()
+            if k == 'sd_model_checkpoint': sd_models.reload_model_weights()
+            if k == 'sd_vae': sd_vae.reload_vae_weights()
 
     return res
 
-- 
cgit v1.2.3


From 241cbc4d2fed71d33e5dc62c3d61b63a54e9d790 Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Tue, 29 Nov 2022 17:38:16 +0000
Subject: Hijack VQModelInterface back to AutoEncoder

---
 modules/sd_hijack_autoencoder.py | 282 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 282 insertions(+)
 create mode 100644 modules/sd_hijack_autoencoder.py

(limited to 'modules')

diff --git a/modules/sd_hijack_autoencoder.py b/modules/sd_hijack_autoencoder.py
new file mode 100644
index 00000000..ffa72f90
--- /dev/null
+++ b/modules/sd_hijack_autoencoder.py
@@ -0,0 +1,282 @@
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.util import instantiate_from_config
+
+import ldm.models.autoencoder
+
+class VQModel(pl.LightningModule):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 n_embed,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 image_key="image",
+                 colorize_nlabels=None,
+                 monitor=None,
+                 batch_resize_range=None,
+                 scheduler_config=None,
+                 lr_g_factor=1.0,
+                 remap=None,
+                 sane_index_shape=False, # tell vector quantizer to return indices as bhw
+                 use_ema=False
+                 ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.n_embed = n_embed
+        self.image_key = image_key
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = Decoder(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
+                                        remap=remap,
+                                        sane_index_shape=sane_index_shape)
+        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+        self.batch_resize_range = batch_resize_range
+        if self.batch_resize_range is not None:
+            print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
+
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+        self.scheduler_config = scheduler_config
+        self.lr_g_factor = lr_g_factor
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.parameters())
+            self.model_ema.copy_to(self)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        sd = torch.load(path, map_location="cpu")["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+            print(f"Unexpected Keys: {unexpected}")
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self)
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        quant, emb_loss, info = self.quantize(h)
+        return quant, emb_loss, info
+
+    def encode_to_prequant(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, quant):
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+    def decode_code(self, code_b):
+        quant_b = self.quantize.embed_code(code_b)
+        dec = self.decode(quant_b)
+        return dec
+
+    def forward(self, input, return_pred_indices=False):
+        quant, diff, (_,_,ind) = self.encode(input)
+        dec = self.decode(quant)
+        if return_pred_indices:
+            return dec, diff, ind
+        return dec, diff
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+        if self.batch_resize_range is not None:
+            lower_size = self.batch_resize_range[0]
+            upper_size = self.batch_resize_range[1]
+            if self.global_step <= 4:
+                # do the first few batches with max size to avoid later oom
+                new_resize = upper_size
+            else:
+                new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
+            if new_resize != x.shape[2]:
+                x = F.interpolate(x, size=new_resize, mode="bicubic")
+            x = x.detach()
+        return x
+
+    def training_step(self, batch, batch_idx, optimizer_idx):
+        # https://github.com/pytorch/pytorch/issues/37142
+        # try not to fool the heuristics
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+
+        if optimizer_idx == 0:
+            # autoencode
+            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train",
+                                            predicted_indices=ind)
+
+            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return aeloss
+
+        if optimizer_idx == 1:
+            # discriminator
+            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return discloss
+
+    def validation_step(self, batch, batch_idx):
+        log_dict = self._validation_step(batch, batch_idx)
+        with self.ema_scope():
+            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
+        return log_dict
+
+    def _validation_step(self, batch, batch_idx, suffix=""):
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
+                                        self.global_step,
+                                        last_layer=self.get_last_layer(),
+                                        split="val"+suffix,
+                                        predicted_indices=ind
+                                        )
+
+        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
+                                            self.global_step,
+                                            last_layer=self.get_last_layer(),
+                                            split="val"+suffix,
+                                            predicted_indices=ind
+                                            )
+        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log(f"val{suffix}/rec_loss", rec_loss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        self.log(f"val{suffix}/aeloss", aeloss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        if version.parse(pl.__version__) >= version.parse('1.4.0'):
+            del log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log_dict(log_dict_ae)
+        self.log_dict(log_dict_disc)
+        return self.log_dict
+
+    def configure_optimizers(self):
+        lr_d = self.learning_rate
+        lr_g = self.lr_g_factor*self.learning_rate
+        print("lr_d", lr_d)
+        print("lr_g", lr_g)
+        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+                                  list(self.decoder.parameters())+
+                                  list(self.quantize.parameters())+
+                                  list(self.quant_conv.parameters())+
+                                  list(self.post_quant_conv.parameters()),
+                                  lr=lr_g, betas=(0.5, 0.9))
+        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+                                    lr=lr_d, betas=(0.5, 0.9))
+
+        if self.scheduler_config is not None:
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+                {
+                    'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+            ]
+            return [opt_ae, opt_disc], scheduler
+        return [opt_ae, opt_disc], []
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        if only_inputs:
+            log["inputs"] = x
+            return log
+        xrec, _ = self(x)
+        if x.shape[1] > 3:
+            # colorize with random projection
+            assert xrec.shape[1] > 3
+            x = self.to_rgb(x)
+            xrec = self.to_rgb(xrec)
+        log["inputs"] = x
+        log["reconstructions"] = xrec
+        if plot_ema:
+            with self.ema_scope():
+                xrec_ema, _ = self(x)
+                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
+                log["reconstructions_ema"] = xrec_ema
+        return log
+
+    def to_rgb(self, x):
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        x = F.conv2d(x, weight=self.colorize)
+        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        return x
+
+
+class VQModelInterface(VQModel):
+    def __init__(self, embed_dim, *args, **kwargs):
+        super().__init__(embed_dim=embed_dim, *args, **kwargs)
+        self.embed_dim = embed_dim
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, h, force_not_quantize=False):
+        # also go through quantization layer
+        if not force_not_quantize:
+            quant, emb_loss, info = self.quantize(h)
+        else:
+            quant = h
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+setattr(ldm.models.autoencoder, "VQModel", VQModel)
+setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
-- 
cgit v1.2.3


From 36c3613d16c523e43ec4dedbcbe9a3b93ad7d139 Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Tue, 29 Nov 2022 17:40:02 +0000
Subject: Add autoencoder to sd_hijack

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index b824b5bf..26f9b951 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -11,7 +11,7 @@ import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
 from modules.shared import opts, device, cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_autoencoder
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
-- 
cgit v1.2.3


From 7193814cf7b052fe52596cab89c5f0fd95823950 Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Tue, 29 Nov 2022 19:22:53 +0000
Subject: Added purpose of this hijack to comments

---
 modules/sd_hijack_autoencoder.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_hijack_autoencoder.py b/modules/sd_hijack_autoencoder.py
index ffa72f90..8e03c7f8 100644
--- a/modules/sd_hijack_autoencoder.py
+++ b/modules/sd_hijack_autoencoder.py
@@ -1,3 +1,7 @@
+# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
+# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
+# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
+
 import torch
 import pytorch_lightning as pl
 import torch.nn.functional as F
-- 
cgit v1.2.3


From 52cc83d36b7663a77b79fd2258d2ca871af73e55 Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Wed, 30 Nov 2022 14:56:12 +0800
Subject: fix bugs

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 modules/sd_hijack.py      |  15 ++---
 modules/sd_hijack_clip.py |  10 +++-
 modules/xlmr.py           | 137 ++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 153 insertions(+), 9 deletions(-)
 create mode 100644 modules/xlmr.py

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 3ec3f98a..edb8b420 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -28,7 +28,7 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 # new memory efficient cross attention blocks do not support hypernets and we already
 # have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
 ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
-ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
+# ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
 
 # silence new console spam from SD2
 ldm.modules.attention.print = lambda *args: None
@@ -82,7 +82,12 @@ class StableDiffusionModelHijack:
 
     def hijack(self, m):
 
-        if type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
+        if shared.text_model_name == "XLMR-Large":
+            model_embeddings = m.cond_stage_model.roberta.embeddings
+            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
+            m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+            
+        elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
             model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
             m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
@@ -91,11 +96,7 @@ class StableDiffusionModelHijack:
             m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
             m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
             apply_optimizations()
-        elif shared.text_model_name == "XLMR-Large":
-            model_embeddings = m.cond_stage_model.roberta.embeddings
-            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
-            m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-            
+        
         self.clip = m.cond_stage_model
         
         fix_checkpoint()
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index b451d1cf..9ea6e1ce 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -4,7 +4,7 @@ import torch
 
 from modules import prompt_parser, devices
 from modules.shared import opts
-
+import modules.shared as shared
 
 def get_target_prompt_token_count(token_count):
     return math.ceil(max(token_count, 1) / 75) * 75
@@ -177,6 +177,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
 
     def forward(self, text):
+        if shared.text_model_name == "XLMR-Large":
+            return self.wrapped.encode(text)
+
         use_old = opts.use_old_emphasis_implementation
         if use_old:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
@@ -254,7 +257,10 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
     def __init__(self, wrapped, hijack):
         super().__init__(wrapped, hijack)
         self.tokenizer = wrapped.tokenizer
-        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+        if shared.text_model_name == "XLMR-Large":
+            self.comma_token = None 
+        else :
+            self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
 
         self.token_mults = {}
         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
diff --git a/modules/xlmr.py b/modules/xlmr.py
new file mode 100644
index 00000000..beab3fdf
--- /dev/null
+++ b/modules/xlmr.py
@@ -0,0 +1,137 @@
+from transformers import BertPreTrainedModel,BertModel,BertConfig
+import torch.nn as nn
+import torch
+from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
+from transformers import XLMRobertaModel,XLMRobertaTokenizer
+from typing import Optional
+
+class BertSeriesConfig(BertConfig):
+    def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
+
+        super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
+        self.project_dim = project_dim
+        self.pooler_fn = pooler_fn
+        self.learn_encoder = learn_encoder
+
+class RobertaSeriesConfig(XLMRobertaConfig):
+    def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
+        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+        self.project_dim = project_dim
+        self.pooler_fn = pooler_fn
+        self.learn_encoder = learn_encoder
+
+
+class BertSeriesModelWithTransformation(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+    config_class = BertSeriesConfig
+
+    def __init__(self, config=None, **kargs):
+        # modify initialization for autoloading 
+        if config is None:
+            config = XLMRobertaConfig()
+            config.attention_probs_dropout_prob= 0.1
+            config.bos_token_id=0
+            config.eos_token_id=2
+            config.hidden_act='gelu'
+            config.hidden_dropout_prob=0.1
+            config.hidden_size=1024
+            config.initializer_range=0.02
+            config.intermediate_size=4096
+            config.layer_norm_eps=1e-05
+            config.max_position_embeddings=514
+
+            config.num_attention_heads=16
+            config.num_hidden_layers=24
+            config.output_past=True
+            config.pad_token_id=1
+            config.position_embedding_type= "absolute"
+
+            config.type_vocab_size= 1
+            config.use_cache=True
+            config.vocab_size= 250002
+            config.project_dim = 768
+            config.learn_encoder = False
+        super().__init__(config)
+        self.roberta = XLMRobertaModel(config)
+        self.transformation = nn.Linear(config.hidden_size,config.project_dim)
+        self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
+        self.pooler = lambda x: x[:,0]
+        self.post_init()
+
+    def encode(self,c):
+        device = next(self.parameters()).device
+        text = self.tokenizer(c,
+                        truncation=True,
+                        max_length=77,
+                        return_length=False,
+                        return_overflowing_tokens=False,
+                        padding="max_length",
+                        return_tensors="pt")
+        text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
+        text["attention_mask"] = torch.tensor(
+            text['attention_mask']).to(device)
+        features = self(**text)
+        return features['projection_state'] 
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) :
+        r"""
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        outputs = self.roberta(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            return_dict=return_dict,
+        )
+
+        # last module outputs
+        sequence_output = outputs[0]
+
+
+        # project every module
+        sequence_output_ln = self.pre_LN(sequence_output)
+
+        # pooler
+        pooler_output = self.pooler(sequence_output_ln)
+        pooler_output = self.transformation(pooler_output)
+        projection_state = self.transformation(outputs.last_hidden_state)
+
+        return {
+            'pooler_output':pooler_output,
+            'last_hidden_state':outputs.last_hidden_state,
+            'hidden_states':outputs.hidden_states,
+            'attentions':outputs.attentions,
+            'projection_state':projection_state,
+            'sequence_out': sequence_output
+        }
+
+
+class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
+    base_model_prefix = 'roberta'
+    config_class= RobertaSeriesConfig
\ No newline at end of file
-- 
cgit v1.2.3


From 3a724e91a23a715987e3034f31d8c8f5d416bd3a Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Wed, 30 Nov 2022 20:52:32 +0800
Subject: Change to steps of 8

---
 modules/ui.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 36dd754e..acf99bda 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -380,8 +380,8 @@ def create_seed_inputs():
 
     with gr.Row(visible=False) as seed_extra_row_2:
         seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=1, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=1, label="Resize seed from height", value=0)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0)
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0)
 
     random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
     random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
@@ -713,8 +713,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
+                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
+                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
@@ -722,8 +722,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=1, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=1, label="Firstpass height", value=0)
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row(equal_height=True):
@@ -899,8 +899,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512, elem_id="img2img_height")
+                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
@@ -1228,8 +1228,8 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Tab(label="Preprocess images"):
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
                     preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
 
                     with gr.Row():
@@ -1286,8 +1286,8 @@ def create_ui(wrap_gradio_gpu_call):
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-- 
cgit v1.2.3


From a44994e2c926fc1f8479281e5b1e08d7fe9db2bb Mon Sep 17 00:00:00 2001
From: Adi Eyal <adi@clearforest.io>
Date: Wed, 30 Nov 2022 15:23:53 +0200
Subject: Fixed incorrect negative prompt text in infotext

Previously only the first negative prompt in all_negative_prompts was
being used for infotext. This fixes that by selecting the index-th
negative prompt
---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index edceb532..0a73ccbb 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -414,7 +414,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[0] if  p.all_negative_prompts[0] else ""
+    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if  p.all_negative_prompts[index] else ""
 
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
-- 
cgit v1.2.3


From 21effd629d0fdfdbbff2b20a9f4a3767e7e8bd33 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 28 Nov 2022 21:24:06 -0500
Subject: Add workaround for using MPS with torchsde

---
 modules/sd_samplers.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 5fefb227..8b11f569 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -6,6 +6,7 @@ import tqdm
 from PIL import Image
 import inspect
 import k_diffusion.sampling
+import torchsde._brownian.brownian_interval
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 from modules import prompt_parser, devices, processing, images
@@ -367,6 +368,19 @@ class TorchHijack:
         return torch.randn_like(x)
 
 
+# MPS fix for randn in torchsde
+def torchsde_randn(size, dtype, device, seed):
+    if device.type == 'mps':
+        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+    else:
+        generator = torch.Generator(device).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=device, generator=generator)
+
+
+torchsde._brownian.brownian_interval._randn = torchsde_randn
+
+
 class KDiffusionSampler:
     def __init__(self, funcname, sd_model):
         denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-- 
cgit v1.2.3


From 4d5f1691dda971ec7b461dd880426300fd54ccee Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 28 Nov 2022 21:36:35 -0500
Subject: Use devices.autocast instead of torch.autocast

---
 modules/hypernetworks/hypernetwork.py          | 2 +-
 modules/interrogate.py                         | 3 +--
 modules/swinir_model.py                        | 6 +-----
 modules/textual_inversion/dataset.py           | 4 ++--
 modules/textual_inversion/textual_inversion.py | 2 +-
 5 files changed, 6 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8466887f..eb5ae372 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -495,7 +495,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                 if shared.state.interrupted:
                     break
 
-                with torch.autocast("cuda"):
+                with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                     if tag_drop_out != 0 or shuffle_tags:
                         shared.sd_model.cond_stage_model.to(devices.device)
diff --git a/modules/interrogate.py b/modules/interrogate.py
index 9769aa34..40c6b082 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -148,8 +148,7 @@ class InterrogateModels:
 
             clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
 
-            precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
-            with torch.no_grad(), precision_scope("cuda"):
+            with torch.no_grad(), devices.autocast():
                 image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
 
                 image_features /= image_features.norm(dim=-1, keepdim=True)
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index facd262d..483eabd4 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -13,10 +13,6 @@ from modules.swinir_model_arch import SwinIR as net
 from modules.swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
 
-precision_scope = (
-    torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
-)
-
 
 class UpscalerSwinIR(Upscaler):
     def __init__(self, dirname):
@@ -112,7 +108,7 @@ def upscale(
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
     img = img.unsqueeze(0).to(devices.device_swinir)
-    with torch.no_grad(), precision_scope("cuda"):
+    with torch.no_grad(), devices.autocast():
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old
         w_pad = (w_old // window_size + 1) * window_size - w_old
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index e5725f33..2dc64c3c 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -82,7 +82,7 @@ class PersonalizedBase(Dataset):
             torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
             latent_sample = None
 
-            with torch.autocast("cuda"):
+            with devices.autocast():
                 latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
 
             if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
@@ -101,7 +101,7 @@ class PersonalizedBase(Dataset):
                 entry.cond_text = self.create_text(filename_text)
 
             if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
-                with torch.autocast("cuda"):
+                with devices.autocast():
                     entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
 
             self.dataset.append(entry)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 4eb75cb5..daf8d1b8 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -316,7 +316,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                 if shared.state.interrupted:
                     break
 
-                with torch.autocast("cuda"):
+                with devices.autocast():
                     # c = stack_conds(batch.cond).to(devices.device)
                     # mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
                     # print(mask)
-- 
cgit v1.2.3


From 0fddb4a1c06a6e2122add7eee3b001a6d473baee Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Wed, 30 Nov 2022 08:02:39 -0500
Subject: Rework MPS randn fix, add randn_like fix

torch.manual_seed() already sets a CPU generator, so there is no reason to create a CPU generator manually. torch.randn_like also needs a MPS fix for k-diffusion, but a torch hijack with randn_like already exists so it can also be used for that.
---
 modules/devices.py     | 15 +++------------
 modules/sd_samplers.py |  8 +++++---
 2 files changed, 8 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index f00079c6..046460fa 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -66,24 +66,15 @@ dtype_vae = torch.float16
 
 
 def randn(seed, shape):
-    # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
-    if device.type == 'mps':
-        generator = torch.Generator(device=cpu)
-        generator.manual_seed(seed)
-        noise = torch.randn(shape, generator=generator, device=cpu).to(device)
-        return noise
-
     torch.manual_seed(seed)
+    if device.type == 'mps':
+        return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 
 
 def randn_without_seed(shape):
-    # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
     if device.type == 'mps':
-        generator = torch.Generator(device=cpu)
-        noise = torch.randn(shape, generator=generator, device=cpu).to(device)
-        return noise
-
+        return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 
 
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 8b11f569..4c123d3b 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -365,7 +365,10 @@ class TorchHijack:
             if noise.shape == x.shape:
                 return noise
 
-        return torch.randn_like(x)
+        if x.device.type == 'mps':
+            return torch.randn_like(x, device=devices.cpu).to(x.device)
+        else:
+            return torch.randn_like(x)
 
 
 # MPS fix for randn in torchsde
@@ -429,8 +432,7 @@ class KDiffusionSampler:
         self.model_wrap.step = 0
         self.eta = p.eta or opts.eta_ancestral
 
-        if self.sampler_noises is not None:
-            k_diffusion.sampling.torch = TorchHijack(self.sampler_noises)
+        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
 
         extra_params_kwargs = {}
         for param_name in self.extra_params:
-- 
cgit v1.2.3


From be2e6de94a5d40bff6d65497fd5ebc275b389f3f Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Thu, 1 Dec 2022 11:34:16 -0800
Subject: Fix clip skip of 1 not being restored from prompts

---
 modules/generation_parameters_copypaste.py | 4 ++++
 modules/shared.py                          | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 01980dca..44fe1a6c 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -184,6 +184,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         else:
             res[k] = v
 
+    # Missing CLIP skip means it was set to 1 (the default)
+    if "Clip skip" not in res:
+        res["Clip skip"] = "1"
+
     return res
 
 
diff --git a/modules/shared.py b/modules/shared.py
index c36ee211..b4ecc7ca 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -371,7 +371,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-    'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
+    'CLIP_stop_at_last_layers': OptionInfo(1, "Stop at last layers of CLIP model (CLIP skip)", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.3


From 9c86fb8cace6d8ac0843e0ddad0ba5ae7f3148c9 Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Fri, 2 Dec 2022 16:08:46 +0800
Subject: fix bug

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 modules/shared.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 1408dee3..ac7678c3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -111,7 +111,11 @@ restricted_opts = {
 from omegaconf import OmegaConf
 config = OmegaConf.load(f"{cmd_opts.config}")
 # XLMR-Large
-text_model_name = config.model.params.cond_stage_config.params.name
+try:
+    text_model_name = config.model.params.cond_stage_config.params.name
+
+except :
+    text_model_name = "stable_diffusion"
 
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
-- 
cgit v1.2.3


From e46147786914484b422899ee7154ae1685d96ae5 Mon Sep 17 00:00:00 2001
From: SmirkingFace <116507648+smirkingface@users.noreply.github.com>
Date: Fri, 2 Dec 2022 11:12:13 +0100
Subject: Fixed safe.py for pytorch 1.13 ckpt files

---
 modules/safe.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/safe.py b/modules/safe.py
index a9209e38..10460ad0 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -62,14 +62,12 @@ class RestrictedUnpickler(pickle.Unpickler):
         raise Exception(f"global '{module}/{name}' is forbidden")
 
 
-allowed_zip_names = ["archive/data.pkl", "archive/version"]
-allowed_zip_names_re = re.compile(r"^archive/data/\d+$")
-
+# Regular expression that accepts 'dirname/version', 'dirname/data.pkl', and 'dirname/data/<number>'
+allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|(data\.pkl))$")
+data_pkl_re = re.compile(r"^([^/]+)/data\.pkl$")
 
 def check_zip_filenames(filename, names):
     for name in names:
-        if name in allowed_zip_names:
-            continue
         if allowed_zip_names_re.match(name):
             continue
 
@@ -82,8 +80,14 @@ def check_pt(filename, extra_handler):
         # new pytorch format is a zip file
         with zipfile.ZipFile(filename) as z:
             check_zip_filenames(filename, z.namelist())
-
-            with z.open('archive/data.pkl') as file:
+            
+            # find filename of data.pkl in zip file: '<directory name>/data.pkl'
+            data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
+            if len(data_pkl_filenames) == 0:
+                raise Exception(f"data.pkl not found in {filename}")
+            if len(data_pkl_filenames) > 1:
+                raise Exception(f"Multiple data.pkl found in {filename}")
+            with z.open(data_pkl_filenames[0]) as file:
                 unpickler = RestrictedUnpickler(file)
                 unpickler.extra_handler = extra_handler
                 unpickler.load()
-- 
cgit v1.2.3


From 99b19b1a8f5d25ac43e6a031d7423e541ed31b0e Mon Sep 17 00:00:00 2001
From: jcowens <jeremy7235@gmail.com>
Date: Fri, 2 Dec 2022 02:53:26 -0800
Subject: fix typo

---
 modules/ui_extensions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 030f011e..42667941 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -17,7 +17,7 @@ available_extensions = {"extensions": []}
 
 
 def check_access():
-    assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
+    assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"
 
 
 def apply_and_restart(disable_list, update_list):
-- 
cgit v1.2.3


From da698ca92ed79b9104a62f34291d9b842c433a1b Mon Sep 17 00:00:00 2001
From: SmirkingFace <116507648+smirkingface@users.noreply.github.com>
Date: Fri, 2 Dec 2022 13:47:02 +0100
Subject: Fixed AttributeError where openaimodel is not found

---
 modules/sd_hijack.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index b824b5bf..eef6efd2 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -17,6 +17,7 @@ from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
+import ldm.modules.diffusionmodules.openaimodel
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 import ldm.modules.encoders.modules
-- 
cgit v1.2.3


From 119a945ef7569128eb7d6772468ffc5567c2e161 Mon Sep 17 00:00:00 2001
From: PhytoEpidemic <64293310+PhytoEpidemic@users.noreply.github.com>
Date: Fri, 2 Dec 2022 12:16:29 -0600
Subject: Fix divide by 0 error

Fix of the edge case 0 weight that occasionally will pop up in some specific situations. This was crashing the script.
---
 modules/textual_inversion/autocrop.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 9859974a..68e1103c 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -276,8 +276,8 @@ def poi_average(pois, settings):
         weight += poi.weight
         x += poi.x * poi.weight
         y += poi.y * poi.weight
-    avg_x = round(x / weight)
-    avg_y = round(y / weight)
+    avg_x = round(weight and x / weight)
+    avg_y = round(weight and y / weight)
 
     return PointOfInterest(avg_x, avg_y)
 
@@ -338,4 +338,4 @@ class Settings:
     self.face_points_weight = face_points_weight
     self.annotate_image = annotate_image
     self.destop_view_image = False
-    self.dnn_model_path = dnn_model_path
\ No newline at end of file
+    self.dnn_model_path = dnn_model_path
-- 
cgit v1.2.3


From b2f17dd367c5758e406dd22b78ad7456dac1957a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 09:15:24 +0300
Subject: prevent include_init_images from being passed to
 StableDiffusionProcessingImg2Img in API #4989

---
 modules/api/api.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 1de3f98f..54ee7cb0 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -152,7 +152,10 @@ class Api:
         )
         if populate.sampler_name:
             populate.sampler_index = None  # prevent a warning later on
-        p = StableDiffusionProcessingImg2Img(**vars(populate))
+
+        args = vars(populate)
+        args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
+        p = StableDiffusionProcessingImg2Img(**args)
 
         imgs = []
         for img in init_images:
@@ -170,7 +173,7 @@ class Api:
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
-        if (not img2imgreq.include_init_images):
+        if not img2imgreq.include_init_images:
             img2imgreq.init_images = None
             img2imgreq.mask = None
 
-- 
cgit v1.2.3


From c7af672186ec09a514f0e78aa21155264e56c130 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 09:41:39 +0300
Subject: more simple config option name plus mouseover hint for clip skip

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index b4ecc7ca..42ec4120 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -371,7 +371,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-    'CLIP_stop_at_last_layers': OptionInfo(1, "Stop at last layers of CLIP model (CLIP skip)", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
+    'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.3


From 2651267e3af5886b8b6b1dc3023f2507f7079118 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 15:57:52 +0300
Subject: fix #4407 breaking UI entirely for card other than ones related to
 the PR

---
 modules/devices.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 1325569c..547ea46c 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -53,12 +53,10 @@ def torch_gc():
 
 def enable_tf32():
     if torch.cuda.is_available():
-        for devid in range(0,torch.cuda.device_count()):
-            if torch.cuda.get_device_capability(devid) == (7, 5):
-                shd = True
-        if shd:
+        if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]):
             torch.backends.cudnn.benchmark = True
             torch.backends.cudnn.enabled = True
+
         torch.backends.cuda.matmul.allow_tf32 = True
         torch.backends.cudnn.allow_tf32 = True
 
-- 
cgit v1.2.3


From 46b0d230e7c13e247eabb22e1103ce512e7ed6b1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 16:01:23 +0300
Subject: add comment for #4407 and remove seemingly unnecessary cudnn.enabled

---
 modules/devices.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index 547ea46c..d6a76844 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -53,9 +53,11 @@ def torch_gc():
 
 def enable_tf32():
     if torch.cuda.is_available():
+
+        # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
+        # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
         if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]):
             torch.backends.cudnn.benchmark = True
-            torch.backends.cudnn.enabled = True
 
         torch.backends.cuda.matmul.allow_tf32 = True
         torch.backends.cudnn.allow_tf32 = True
-- 
cgit v1.2.3


From cf3e844d1d31d64f3234a0fbdfcac91cc5834657 Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sat, 3 Dec 2022 18:05:47 +0300
Subject: add noise strength parameter similar to NAI

---
 modules/processing.py | 1 +
 modules/shared.py     | 1 +
 2 files changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 3d2c4dc9..b9cb6d32 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -861,6 +861,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        x = x*shared.opts.initial_noise_multiplier
 
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 
diff --git a/modules/shared.py b/modules/shared.py
index 8202d8e5..4182e2ac 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -364,6 +364,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "initial_noise_multiplier": OptionInfo(1.0, "Multiply initial noise by this factor, may result in less or more detailed img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-- 
cgit v1.2.3


From b6e5edd74657e3fd1fbd04f341b7a84625d4aa7a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 18:06:33 +0300
Subject: add built-in extension system add support for adding upscalers in
 extensions move LDSR, ScuNET and SwinIR to built-in extensions

---
 modules/devices.py              |   11 +-
 modules/extensions.py           |   22 +-
 modules/ldsr_model.py           |   54 ---
 modules/ldsr_model_arch.py      |  230 ---------
 modules/modelloader.py          |   20 +-
 modules/scunet_model.py         |   87 ----
 modules/scunet_model_arch.py    |  265 ----------
 modules/shared.py               |   13 +-
 modules/swinir_model.py         |  157 ------
 modules/swinir_model_arch.py    |  867 ---------------------------------
 modules/swinir_model_arch_v2.py | 1017 ---------------------------------------
 modules/ui.py                   |    1 -
 modules/ui_extensions.py        |    8 +-
 13 files changed, 42 insertions(+), 2710 deletions(-)
 delete mode 100644 modules/ldsr_model.py
 delete mode 100644 modules/ldsr_model_arch.py
 delete mode 100644 modules/scunet_model.py
 delete mode 100644 modules/scunet_model_arch.py
 delete mode 100644 modules/swinir_model.py
 delete mode 100644 modules/swinir_model_arch.py
 delete mode 100644 modules/swinir_model_arch_v2.py

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index d6a76844..f8cffae1 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -44,6 +44,15 @@ def get_optimal_device():
     return cpu
 
 
+def get_device_for(task):
+    from modules import shared
+
+    if task in shared.cmd_opts.use_cpu:
+        return cpu
+
+    return get_optimal_device()
+
+
 def torch_gc():
     if torch.cuda.is_available():
         with torch.cuda.device(get_cuda_device_string()):
@@ -67,7 +76,7 @@ def enable_tf32():
 errors.run(enable_tf32, "Enabling TF32")
 
 cpu = torch.device("cpu")
-device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
+device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
 
diff --git a/modules/extensions.py b/modules/extensions.py
index db9c4200..b522125c 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -8,6 +8,7 @@ from modules import paths, shared
 
 extensions = []
 extensions_dir = os.path.join(paths.script_path, "extensions")
+extensions_builtin_dir = os.path.join(paths.script_path, "extensions-builtin")
 
 
 def active():
@@ -15,12 +16,13 @@ def active():
 
 
 class Extension:
-    def __init__(self, name, path, enabled=True):
+    def __init__(self, name, path, enabled=True, is_builtin=False):
         self.name = name
         self.path = path
         self.enabled = enabled
         self.status = ''
         self.can_update = False
+        self.is_builtin = is_builtin
 
         repo = None
         try:
@@ -79,11 +81,19 @@ def list_extensions():
     if not os.path.isdir(extensions_dir):
         return
 
-    for dirname in sorted(os.listdir(extensions_dir)):
-        path = os.path.join(extensions_dir, dirname)
-        if not os.path.isdir(path):
-            continue
+    paths = []
+    for dirname in [extensions_dir, extensions_builtin_dir]:
+        if not os.path.isdir(dirname):
+            return
 
-        extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions)
+        for extension_dirname in sorted(os.listdir(dirname)):
+            path = os.path.join(dirname, extension_dirname)
+            if not os.path.isdir(path):
+                continue
+
+            paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
+
+    for dirname, path, is_builtin in paths:
+        extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
         extensions.append(extension)
 
diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
deleted file mode 100644
index 8c4db44a..00000000
--- a/modules/ldsr_model.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import os
-import sys
-import traceback
-
-from basicsr.utils.download_util import load_file_from_url
-
-from modules.upscaler import Upscaler, UpscalerData
-from modules.ldsr_model_arch import LDSR
-from modules import shared
-
-
-class UpscalerLDSR(Upscaler):
-    def __init__(self, user_path):
-        self.name = "LDSR"
-        self.user_path = user_path
-        self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
-        self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
-        super().__init__()
-        scaler_data = UpscalerData("LDSR", None, self)
-        self.scalers = [scaler_data]
-
-    def load_model(self, path: str):
-        # Remove incorrect project.yaml file if too big
-        yaml_path = os.path.join(self.model_path, "project.yaml")
-        old_model_path = os.path.join(self.model_path, "model.pth")
-        new_model_path = os.path.join(self.model_path, "model.ckpt")
-        if os.path.exists(yaml_path):
-            statinfo = os.stat(yaml_path)
-            if statinfo.st_size >= 10485760:
-                print("Removing invalid LDSR YAML file.")
-                os.remove(yaml_path)
-        if os.path.exists(old_model_path):
-            print("Renaming model from model.pth to model.ckpt")
-            os.rename(old_model_path, new_model_path)
-        model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
-                                   file_name="model.ckpt", progress=True)
-        yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
-                                  file_name="project.yaml", progress=True)
-
-        try:
-            return LDSR(model, yaml)
-
-        except Exception:
-            print("Error importing LDSR:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-        return None
-
-    def do_upscale(self, img, path):
-        ldsr = self.load_model(path)
-        if ldsr is None:
-            print("NO LDSR!")
-            return img
-        ddim_steps = shared.opts.ldsr_steps
-        return ldsr.super_resolution(img, ddim_steps, self.scale)
diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
deleted file mode 100644
index 90e0a2f0..00000000
--- a/modules/ldsr_model_arch.py
+++ /dev/null
@@ -1,230 +0,0 @@
-import gc
-import time
-import warnings
-
-import numpy as np
-import torch
-import torchvision
-from PIL import Image
-from einops import rearrange, repeat
-from omegaconf import OmegaConf
-
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.util import instantiate_from_config, ismap
-
-warnings.filterwarnings("ignore", category=UserWarning)
-
-
-# Create LDSR Class
-class LDSR:
-    def load_model_from_config(self, half_attention):
-        print(f"Loading model from {self.modelPath}")
-        pl_sd = torch.load(self.modelPath, map_location="cpu")
-        sd = pl_sd["state_dict"]
-        config = OmegaConf.load(self.yamlPath)
-        model = instantiate_from_config(config.model)
-        model.load_state_dict(sd, strict=False)
-        model.cuda()
-        if half_attention:
-            model = model.half()
-
-        model.eval()
-        return {"model": model}
-
-    def __init__(self, model_path, yaml_path):
-        self.modelPath = model_path
-        self.yamlPath = yaml_path
-
-    @staticmethod
-    def run(model, selected_path, custom_steps, eta):
-        example = get_cond(selected_path)
-
-        n_runs = 1
-        guider = None
-        ckwargs = None
-        ddim_use_x0_pred = False
-        temperature = 1.
-        eta = eta
-        custom_shape = None
-
-        height, width = example["image"].shape[1:3]
-        split_input = height >= 128 and width >= 128
-
-        if split_input:
-            ks = 128
-            stride = 64
-            vqf = 4  #
-            model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
-                                        "vqf": vqf,
-                                        "patch_distributed_vq": True,
-                                        "tie_braker": False,
-                                        "clip_max_weight": 0.5,
-                                        "clip_min_weight": 0.01,
-                                        "clip_max_tie_weight": 0.5,
-                                        "clip_min_tie_weight": 0.01}
-        else:
-            if hasattr(model, "split_input_params"):
-                delattr(model, "split_input_params")
-
-        x_t = None
-        logs = None
-        for n in range(n_runs):
-            if custom_shape is not None:
-                x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
-                x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
-
-            logs = make_convolutional_sample(example, model,
-                                             custom_steps=custom_steps,
-                                             eta=eta, quantize_x0=False,
-                                             custom_shape=custom_shape,
-                                             temperature=temperature, noise_dropout=0.,
-                                             corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
-                                             ddim_use_x0_pred=ddim_use_x0_pred
-                                             )
-        return logs
-
-    def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
-        model = self.load_model_from_config(half_attention)
-
-        # Run settings
-        diffusion_steps = int(steps)
-        eta = 1.0
-
-        down_sample_method = 'Lanczos'
-
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        im_og = image
-        width_og, height_og = im_og.size
-        # If we can adjust the max upscale size, then the 4 below should be our variable
-        down_sample_rate = target_scale / 4
-        wd = width_og * down_sample_rate
-        hd = height_og * down_sample_rate
-        width_downsampled_pre = int(np.ceil(wd))
-        height_downsampled_pre = int(np.ceil(hd))
-
-        if down_sample_rate != 1:
-            print(
-                f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
-            im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
-        else:
-            print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
-        
-        # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
-        pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
-        im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
-        
-        logs = self.run(model["model"], im_padded, diffusion_steps, eta)
-
-        sample = logs["sample"]
-        sample = sample.detach().cpu()
-        sample = torch.clamp(sample, -1., 1.)
-        sample = (sample + 1.) / 2. * 255
-        sample = sample.numpy().astype(np.uint8)
-        sample = np.transpose(sample, (0, 2, 3, 1))
-        a = Image.fromarray(sample[0])
-
-        # remove padding
-        a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
-
-        del model
-        gc.collect()
-        torch.cuda.empty_cache()
-        return a
-
-
-def get_cond(selected_path):
-    example = dict()
-    up_f = 4
-    c = selected_path.convert('RGB')
-    c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
-    c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
-                                                    antialias=True)
-    c_up = rearrange(c_up, '1 c h w -> 1 h w c')
-    c = rearrange(c, '1 c h w -> 1 h w c')
-    c = 2. * c - 1.
-
-    c = c.to(torch.device("cuda"))
-    example["LR_image"] = c
-    example["image"] = c_up
-
-    return example
-
-
-@torch.no_grad()
-def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
-                    mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
-                    corrector_kwargs=None, x_t=None
-                    ):
-    ddim = DDIMSampler(model)
-    bs = shape[0]
-    shape = shape[1:]
-    print(f"Sampling with eta = {eta}; steps: {steps}")
-    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
-                                         normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
-                                         mask=mask, x0=x0, temperature=temperature, verbose=False,
-                                         score_corrector=score_corrector,
-                                         corrector_kwargs=corrector_kwargs, x_t=x_t)
-
-    return samples, intermediates
-
-
-@torch.no_grad()
-def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
-                              corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
-    log = dict()
-
-    z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
-                                        return_first_stage_outputs=True,
-                                        force_c_encode=not (hasattr(model, 'split_input_params')
-                                                            and model.cond_stage_key == 'coordinates_bbox'),
-                                        return_original_cond=True)
-
-    if custom_shape is not None:
-        z = torch.randn(custom_shape)
-        print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
-
-    z0 = None
-
-    log["input"] = x
-    log["reconstruction"] = xrec
-
-    if ismap(xc):
-        log["original_conditioning"] = model.to_rgb(xc)
-        if hasattr(model, 'cond_stage_key'):
-            log[model.cond_stage_key] = model.to_rgb(xc)
-
-    else:
-        log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
-        if model.cond_stage_model:
-            log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
-            if model.cond_stage_key == 'class_label':
-                log[model.cond_stage_key] = xc[model.cond_stage_key]
-
-    with model.ema_scope("Plotting"):
-        t0 = time.time()
-
-        sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
-                                                eta=eta,
-                                                quantize_x0=quantize_x0, mask=None, x0=z0,
-                                                temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
-                                                x_t=x_T)
-        t1 = time.time()
-
-        if ddim_use_x0_pred:
-            sample = intermediates['pred_x0'][-1]
-
-    x_sample = model.decode_first_stage(sample)
-
-    try:
-        x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
-        log["sample_noquant"] = x_sample_noquant
-        log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
-    except:
-        pass
-
-    log["sample"] = x_sample
-    log["time"] = t1 - t0
-
-    return log
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 7d2f0ade..e647f6fa 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -124,10 +124,9 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
 
 
 def load_upscalers():
-    sd = shared.script_path
     # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
     # so we'll try to import any _model.py files before looking in __subclasses__
-    modules_dir = os.path.join(sd, "modules")
+    modules_dir = os.path.join(shared.script_path, "modules")
     for file in os.listdir(modules_dir):
         if "_model.py" in file:
             model_name = file.replace("_model.py", "")
@@ -136,22 +135,13 @@ def load_upscalers():
                 importlib.import_module(full_model)
             except:
                 pass
+
     datas = []
-    c_o = vars(shared.cmd_opts)
+    commandline_options = vars(shared.cmd_opts)
     for cls in Upscaler.__subclasses__():
         name = cls.__name__
-        module_name = cls.__module__
-        module = importlib.import_module(module_name)
-        class_ = getattr(module, name)
         cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
-        opt_string = None
-        try:
-            if cmd_name in c_o:
-                opt_string = c_o[cmd_name]
-        except:
-            pass
-        scaler = class_(opt_string)
-        for child in scaler.scalers:
-            datas.append(child)
+        scaler = cls(commandline_options.get(cmd_name, None))
+        datas += scaler.scalers
 
     shared.sd_upscalers = datas
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
deleted file mode 100644
index 52360241..00000000
--- a/modules/scunet_model.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import os.path
-import sys
-import traceback
-
-import PIL.Image
-import numpy as np
-import torch
-from basicsr.utils.download_util import load_file_from_url
-
-import modules.upscaler
-from modules import devices, modelloader
-from modules.scunet_model_arch import SCUNet as net
-
-
-class UpscalerScuNET(modules.upscaler.Upscaler):
-    def __init__(self, dirname):
-        self.name = "ScuNET"
-        self.model_name = "ScuNET GAN"
-        self.model_name2 = "ScuNET PSNR"
-        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
-        self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
-        self.user_path = dirname
-        super().__init__()
-        model_paths = self.find_models(ext_filter=[".pth"])
-        scalers = []
-        add_model2 = True
-        for file in model_paths:
-            if "http" in file:
-                name = self.model_name
-            else:
-                name = modelloader.friendly_name(file)
-            if name == self.model_name2 or file == self.model_url2:
-                add_model2 = False
-            try:
-                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
-                scalers.append(scaler_data)
-            except Exception:
-                print(f"Error loading ScuNET model: {file}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-        if add_model2:
-            scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
-            scalers.append(scaler_data2)
-        self.scalers = scalers
-
-    def do_upscale(self, img: PIL.Image, selected_file):
-        torch.cuda.empty_cache()
-
-        model = self.load_model(selected_file)
-        if model is None:
-            return img
-
-        device = devices.device_scunet
-        img = np.array(img)
-        img = img[:, :, ::-1]
-        img = np.moveaxis(img, 2, 0) / 255
-        img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(device)
-
-        with torch.no_grad():
-            output = model(img)
-        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-        output = 255. * np.moveaxis(output, 0, 2)
-        output = output.astype(np.uint8)
-        output = output[:, :, ::-1]
-        torch.cuda.empty_cache()
-        return PIL.Image.fromarray(output, 'RGB')
-
-    def load_model(self, path: str):
-        device = devices.device_scunet
-        if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
-                                          progress=True)
-        else:
-            filename = path
-        if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
-            print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
-            return None
-
-        model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
-        model.load_state_dict(torch.load(filename), strict=True)
-        model.eval()
-        for k, v in model.named_parameters():
-            v.requires_grad = False
-        model = model.to(device)
-
-        return model
-
diff --git a/modules/scunet_model_arch.py b/modules/scunet_model_arch.py
deleted file mode 100644
index 43ca8d36..00000000
--- a/modules/scunet_model_arch.py
+++ /dev/null
@@ -1,265 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import torch
-import torch.nn as nn
-from einops import rearrange
-from einops.layers.torch import Rearrange
-from timm.models.layers import trunc_normal_, DropPath
-
-
-class WMSA(nn.Module):
-    """ Self-attention module in Swin Transformer
-    """
-
-    def __init__(self, input_dim, output_dim, head_dim, window_size, type):
-        super(WMSA, self).__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        self.head_dim = head_dim
-        self.scale = self.head_dim ** -0.5
-        self.n_heads = input_dim // head_dim
-        self.window_size = window_size
-        self.type = type
-        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
-
-        self.relative_position_params = nn.Parameter(
-            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
-
-        self.linear = nn.Linear(self.input_dim, self.output_dim)
-
-        trunc_normal_(self.relative_position_params, std=.02)
-        self.relative_position_params = torch.nn.Parameter(
-            self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
-                                                                                                                 2).transpose(
-                0, 1))
-
-    def generate_mask(self, h, w, p, shift):
-        """ generating the mask of SW-MSA
-        Args:
-            shift: shift parameters in CyclicShift.
-        Returns:
-            attn_mask: should be (1 1 w p p),
-        """
-        # supporting square.
-        attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
-        if self.type == 'W':
-            return attn_mask
-
-        s = p - shift
-        attn_mask[-1, :, :s, :, s:, :] = True
-        attn_mask[-1, :, s:, :, :s, :] = True
-        attn_mask[:, -1, :, :s, :, s:] = True
-        attn_mask[:, -1, :, s:, :, :s] = True
-        attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
-        return attn_mask
-
-    def forward(self, x):
-        """ Forward pass of Window Multi-head Self-attention module.
-        Args:
-            x: input tensor with shape of [b h w c];
-            attn_mask: attention mask, fill -inf where the value is True;
-        Returns:
-            output: tensor shape [b h w c]
-        """
-        if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
-        x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
-        h_windows = x.size(1)
-        w_windows = x.size(2)
-        # square validation
-        # assert h_windows == w_windows
-
-        x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
-        qkv = self.embedding_layer(x)
-        q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
-        sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
-        # Adding learnable relative embedding
-        sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
-        # Using Attn Mask to distinguish different subwindows.
-        if self.type != 'W':
-            attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
-            sim = sim.masked_fill_(attn_mask, float("-inf"))
-
-        probs = nn.functional.softmax(sim, dim=-1)
-        output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
-        output = rearrange(output, 'h b w p c -> b w p (h c)')
-        output = self.linear(output)
-        output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
-
-        if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
-                                                 dims=(1, 2))
-        return output
-
-    def relative_embedding(self):
-        cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
-        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
-        # negative is allowed
-        return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
-
-
-class Block(nn.Module):
-    def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
-        """ SwinTransformer Block
-        """
-        super(Block, self).__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        assert type in ['W', 'SW']
-        self.type = type
-        if input_resolution <= window_size:
-            self.type = 'W'
-
-        self.ln1 = nn.LayerNorm(input_dim)
-        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.ln2 = nn.LayerNorm(input_dim)
-        self.mlp = nn.Sequential(
-            nn.Linear(input_dim, 4 * input_dim),
-            nn.GELU(),
-            nn.Linear(4 * input_dim, output_dim),
-        )
-
-    def forward(self, x):
-        x = x + self.drop_path(self.msa(self.ln1(x)))
-        x = x + self.drop_path(self.mlp(self.ln2(x)))
-        return x
-
-
-class ConvTransBlock(nn.Module):
-    def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
-        """ SwinTransformer and Conv Block
-        """
-        super(ConvTransBlock, self).__init__()
-        self.conv_dim = conv_dim
-        self.trans_dim = trans_dim
-        self.head_dim = head_dim
-        self.window_size = window_size
-        self.drop_path = drop_path
-        self.type = type
-        self.input_resolution = input_resolution
-
-        assert self.type in ['W', 'SW']
-        if self.input_resolution <= self.window_size:
-            self.type = 'W'
-
-        self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
-                                 self.type, self.input_resolution)
-        self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
-        self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
-
-        self.conv_block = nn.Sequential(
-            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
-            nn.ReLU(True),
-            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
-        )
-
-    def forward(self, x):
-        conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
-        conv_x = self.conv_block(conv_x) + conv_x
-        trans_x = Rearrange('b c h w -> b h w c')(trans_x)
-        trans_x = self.trans_block(trans_x)
-        trans_x = Rearrange('b h w c -> b c h w')(trans_x)
-        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
-        x = x + res
-
-        return x
-
-
-class SCUNet(nn.Module):
-    # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
-    def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
-        super(SCUNet, self).__init__()
-        if config is None:
-            config = [2, 2, 2, 2, 2, 2, 2]
-        self.config = config
-        self.dim = dim
-        self.head_dim = 32
-        self.window_size = 8
-
-        # drop path rate for each layer
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
-
-        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
-
-        begin = 0
-        self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
-                                       'W' if not i % 2 else 'SW', input_resolution)
-                        for i in range(config[0])] + \
-                       [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[0]
-        self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
-                                       'W' if not i % 2 else 'SW', input_resolution // 2)
-                        for i in range(config[1])] + \
-                       [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[1]
-        self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
-                                       'W' if not i % 2 else 'SW', input_resolution // 4)
-                        for i in range(config[2])] + \
-                       [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[2]
-        self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
-                                      'W' if not i % 2 else 'SW', input_resolution // 8)
-                       for i in range(config[3])]
-
-        begin += config[3]
-        self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
-                     [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
-                                     'W' if not i % 2 else 'SW', input_resolution // 4)
-                      for i in range(config[4])]
-
-        begin += config[4]
-        self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
-                     [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
-                                     'W' if not i % 2 else 'SW', input_resolution // 2)
-                      for i in range(config[5])]
-
-        begin += config[5]
-        self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
-                     [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
-                                     'W' if not i % 2 else 'SW', input_resolution)
-                      for i in range(config[6])]
-
-        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
-
-        self.m_head = nn.Sequential(*self.m_head)
-        self.m_down1 = nn.Sequential(*self.m_down1)
-        self.m_down2 = nn.Sequential(*self.m_down2)
-        self.m_down3 = nn.Sequential(*self.m_down3)
-        self.m_body = nn.Sequential(*self.m_body)
-        self.m_up3 = nn.Sequential(*self.m_up3)
-        self.m_up2 = nn.Sequential(*self.m_up2)
-        self.m_up1 = nn.Sequential(*self.m_up1)
-        self.m_tail = nn.Sequential(*self.m_tail)
-        # self.apply(self._init_weights)
-
-    def forward(self, x0):
-
-        h, w = x0.size()[-2:]
-        paddingBottom = int(np.ceil(h / 64) * 64 - h)
-        paddingRight = int(np.ceil(w / 64) * 64 - w)
-        x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
-
-        x1 = self.m_head(x0)
-        x2 = self.m_down1(x1)
-        x3 = self.m_down2(x2)
-        x4 = self.m_down3(x3)
-        x = self.m_body(x4)
-        x = self.m_up3(x + x4)
-        x = self.m_up2(x + x3)
-        x = self.m_up1(x + x2)
-        x = self.m_tail(x + x1)
-
-        x = x[..., :h, :w]
-
-        return x
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index 8202d8e5..dc45fcaa 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -50,9 +50,6 @@ parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory wi
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
 parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
 parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
-parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
-parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
-parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
 parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
@@ -61,7 +58,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en
 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -95,6 +92,7 @@ parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, req
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 
 script_loading.preload_extensions(extensions.extensions_dir, parser)
+script_loading.preload_extensions(extensions.extensions_builtin_dir, parser)
 
 cmd_opts = parser.parse_args()
 
@@ -112,8 +110,8 @@ restricted_opts = {
 
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
-devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
-(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
+    (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
 
 device = devices.device
 weight_load_location = None if cmd_opts.lowram else "cpu"
@@ -326,9 +324,6 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
-    "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
-    "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-    "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
     "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
 }))
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
deleted file mode 100644
index 483eabd4..00000000
--- a/modules/swinir_model.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import contextlib
-import os
-
-import numpy as np
-import torch
-from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
-from tqdm import tqdm
-
-from modules import modelloader, devices
-from modules.shared import cmd_opts, opts
-from modules.swinir_model_arch import SwinIR as net
-from modules.swinir_model_arch_v2 import Swin2SR as net2
-from modules.upscaler import Upscaler, UpscalerData
-
-
-class UpscalerSwinIR(Upscaler):
-    def __init__(self, dirname):
-        self.name = "SwinIR"
-        self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
-                         "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
-                         "-L_x4_GAN.pth "
-        self.model_name = "SwinIR 4x"
-        self.user_path = dirname
-        super().__init__()
-        scalers = []
-        model_files = self.find_models(ext_filter=[".pt", ".pth"])
-        for model in model_files:
-            if "http" in model:
-                name = self.model_name
-            else:
-                name = modelloader.friendly_name(model)
-            model_data = UpscalerData(name, model, self)
-            scalers.append(model_data)
-        self.scalers = scalers
-
-    def do_upscale(self, img, model_file):
-        model = self.load_model(model_file)
-        if model is None:
-            return img
-        model = model.to(devices.device_swinir)
-        img = upscale(img, model)
-        try:
-            torch.cuda.empty_cache()
-        except:
-            pass
-        return img
-
-    def load_model(self, path, scale=4):
-        if "http" in path:
-            dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
-            filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
-        else:
-            filename = path
-        if filename is None or not os.path.exists(filename):
-            return None
-        if filename.endswith(".v2.pth"):
-            model = net2(
-            upscale=scale,
-            in_chans=3,
-            img_size=64,
-            window_size=8,
-            img_range=1.0,
-            depths=[6, 6, 6, 6, 6, 6],
-            embed_dim=180,
-            num_heads=[6, 6, 6, 6, 6, 6],
-            mlp_ratio=2,
-            upsampler="nearest+conv",
-            resi_connection="1conv",
-            )
-            params = None
-        else:
-            model = net(
-                upscale=scale,
-                in_chans=3,
-                img_size=64,
-                window_size=8,
-                img_range=1.0,
-                depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
-                embed_dim=240,
-                num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
-                mlp_ratio=2,
-                upsampler="nearest+conv",
-                resi_connection="3conv",
-            )
-            params = "params_ema"
-
-        pretrained_model = torch.load(filename)
-        if params is not None:
-            model.load_state_dict(pretrained_model[params], strict=True)
-        else:
-            model.load_state_dict(pretrained_model, strict=True)
-        if not cmd_opts.no_half:
-            model = model.half()
-        return model
-
-
-def upscale(
-        img,
-        model,
-        tile=opts.SWIN_tile,
-        tile_overlap=opts.SWIN_tile_overlap,
-        window_size=8,
-        scale=4,
-):
-    img = np.array(img)
-    img = img[:, :, ::-1]
-    img = np.moveaxis(img, 2, 0) / 255
-    img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(devices.device_swinir)
-    with torch.no_grad(), devices.autocast():
-        _, _, h_old, w_old = img.size()
-        h_pad = (h_old // window_size + 1) * window_size - h_old
-        w_pad = (w_old // window_size + 1) * window_size - w_old
-        img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
-        img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
-        output = inference(img, model, tile, tile_overlap, window_size, scale)
-        output = output[..., : h_old * scale, : w_old * scale]
-        output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-        if output.ndim == 3:
-            output = np.transpose(
-                output[[2, 1, 0], :, :], (1, 2, 0)
-            )  # CHW-RGB to HCW-BGR
-        output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
-        return Image.fromarray(output, "RGB")
-
-
-def inference(img, model, tile, tile_overlap, window_size, scale):
-    # test the image tile by tile
-    b, c, h, w = img.size()
-    tile = min(tile, h, w)
-    assert tile % window_size == 0, "tile size should be a multiple of window_size"
-    sf = scale
-
-    stride = tile - tile_overlap
-    h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
-    w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
-    E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=devices.device_swinir).type_as(img)
-    W = torch.zeros_like(E, dtype=torch.half, device=devices.device_swinir)
-
-    with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
-        for h_idx in h_idx_list:
-            for w_idx in w_idx_list:
-                in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
-                out_patch = model(in_patch)
-                out_patch_mask = torch.ones_like(out_patch)
-
-                E[
-                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-                ].add_(out_patch)
-                W[
-                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-                ].add_(out_patch_mask)
-                pbar.update(1)
-    output = E.div_(W)
-
-    return output
diff --git a/modules/swinir_model_arch.py b/modules/swinir_model_arch.py
deleted file mode 100644
index 863f42db..00000000
--- a/modules/swinir_model_arch.py
+++ /dev/null
@@ -1,867 +0,0 @@
-# -----------------------------------------------------------------------------------
-# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
-# Originally Written by Ze Liu, Modified by Jingyun Liang.
-# -----------------------------------------------------------------------------------
-
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.dim
-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 img_size=224, patch_size=4, resi_connection='1conv'):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(dim=dim,
-                                         input_resolution=input_resolution,
-                                         depth=depth,
-                                         num_heads=num_heads,
-                                         window_size=window_size,
-                                         mlp_ratio=mlp_ratio,
-                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                         drop=drop, attn_drop=attn_drop,
-                                         drop_path=drop_path,
-                                         norm_layer=norm_layer,
-                                         downsample=downsample,
-                                         use_checkpoint=use_checkpoint)
-
-        if resi_connection == '1conv':
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-    def forward(self, x, x_size):
-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        flops = 0
-        H, W = self.img_size
-        if self.norm is not None:
-            flops += H * W * self.embed_dim
-        return flops
-
-
-class PatchUnEmbed(nn.Module):
-    r""" Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-
-
-class SwinIR(nn.Module):
-    r""" SwinIR
-        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
-                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
-                 **kwargs):
-        super(SwinIR, self).__init__()
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(dim=embed_dim,
-                         input_resolution=(patches_resolution[0],
-                                           patches_resolution[1]),
-                         depth=depths[i_layer],
-                         num_heads=num_heads[i_layer],
-                         window_size=window_size,
-                         mlp_ratio=self.mlp_ratio,
-                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                         drop=drop_rate, attn_drop=attn_drop_rate,
-                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                         norm_layer=norm_layer,
-                         downsample=None,
-                         use_checkpoint=use_checkpoint,
-                         img_size=img_size,
-                         patch_size=patch_size,
-                         resi_connection=resi_connection
-
-                         )
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == '1conv':
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
-                                            (patches_resolution[0], patches_resolution[1]))
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR (less artifacts)
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            if self.upscale == 4:
-                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-        
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            if self.upscale == 4:
-                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-
-        return x[:, :, :H*self.upscale, :W*self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()
-        return flops
-
-
-if __name__ == '__main__':
-    upscale = 4
-    window_size = 8
-    height = (1024 // upscale // window_size + 1) * window_size
-    width = (720 // upscale // window_size + 1) * window_size
-    model = SwinIR(upscale=2, img_size=(height, width),
-                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
-                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
-    print(model)
-    print(height, width, model.flops() / 1e9)
-
-    x = torch.randn((1, 3, height, width))
-    x = model(x)
-    print(x.shape)
diff --git a/modules/swinir_model_arch_v2.py b/modules/swinir_model_arch_v2.py
deleted file mode 100644
index 0e28ae6e..00000000
--- a/modules/swinir_model_arch_v2.py
+++ /dev/null
@@ -1,1017 +0,0 @@
-# -----------------------------------------------------------------------------------
-# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
-# Written by Conde and Choi et al.
-# -----------------------------------------------------------------------------------
-
-import math
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
-                 pretrained_window_size=[0, 0]):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.pretrained_window_size = pretrained_window_size
-        self.num_heads = num_heads
-
-        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
-
-        # mlp to generate continuous relative position bias
-        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
-                                     nn.ReLU(inplace=True),
-                                     nn.Linear(512, num_heads, bias=False))
-
-        # get relative_coords_table
-        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
-        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
-        relative_coords_table = torch.stack(
-            torch.meshgrid([relative_coords_h,
-                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
-        if pretrained_window_size[0] > 0:
-            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
-            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
-        else:
-            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
-            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
-        relative_coords_table *= 8  # normalize to -8, 8
-        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
-            torch.abs(relative_coords_table) + 1.0) / np.log2(8)
-
-        self.register_buffer("relative_coords_table", relative_coords_table)
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=False)
-        if qkv_bias:
-            self.q_bias = nn.Parameter(torch.zeros(dim))
-            self.v_bias = nn.Parameter(torch.zeros(dim))
-        else:
-            self.q_bias = None
-            self.v_bias = None
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv_bias = None
-        if self.q_bias is not None:
-            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
-        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
-        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        # cosine attention
-        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
-        logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
-        attn = attn * logit_scale
-
-        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
-        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, ' \
-               f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-        pretrained_window_size (int): Window size in pre-training.
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
-            pretrained_window_size=to_2tuple(pretrained_window_size))
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-        
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask        
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        #assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-            
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-        x = shortcut + self.drop_path(self.norm1(x))
-
-        # FFN
-        x = x + self.drop_path(self.norm2(self.mlp(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(2 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.reduction(x)
-        x = self.norm(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        flops += H * W * self.dim // 2
-        return flops    
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        pretrained_window_size (int): Local window size in pre-training.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 pretrained_window_size=0):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer,
-                                 pretrained_window_size=pretrained_window_size)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-    def _init_respostnorm(self):
-        for blk in self.blocks:
-            nn.init.constant_(blk.norm1.bias, 0)
-            nn.init.constant_(blk.norm1.weight, 0)
-            nn.init.constant_(blk.norm2.bias, 0)
-            nn.init.constant_(blk.norm2.weight, 0)
-            
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        # assert H == self.img_size[0] and W == self.img_size[1],
-        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        Ho, Wo = self.patches_resolution
-        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
-        if self.norm is not None:
-            flops += Ho * Wo * self.embed_dim
-        return flops           
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 img_size=224, patch_size=4, resi_connection='1conv'):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(dim=dim,
-                                         input_resolution=input_resolution,
-                                         depth=depth,
-                                         num_heads=num_heads,
-                                         window_size=window_size,
-                                         mlp_ratio=mlp_ratio,
-                                         qkv_bias=qkv_bias, 
-                                         drop=drop, attn_drop=attn_drop,
-                                         drop_path=drop_path,
-                                         norm_layer=norm_layer,
-                                         downsample=downsample,
-                                         use_checkpoint=use_checkpoint)
-
-        if resi_connection == '1conv':
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
-            norm_layer=None)
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
-            norm_layer=None)
-
-    def forward(self, x, x_size):
-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-class PatchUnEmbed(nn.Module):
-    r""" Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-        
-class Upsample_hf(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample_hf, self).__init__(*m)        
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-    
-    
-
-class Swin2SR(nn.Module):
-    r""" Swin2SR
-        A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
-                 window_size=7, mlp_ratio=4., qkv_bias=True, 
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
-                 **kwargs):
-        super(Swin2SR, self).__init__()
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(dim=embed_dim,
-                         input_resolution=(patches_resolution[0],
-                                           patches_resolution[1]),
-                         depth=depths[i_layer],
-                         num_heads=num_heads[i_layer],
-                         window_size=window_size,
-                         mlp_ratio=self.mlp_ratio,
-                         qkv_bias=qkv_bias, 
-                         drop=drop_rate, attn_drop=attn_drop_rate,
-                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                         norm_layer=norm_layer,
-                         downsample=None,
-                         use_checkpoint=use_checkpoint,
-                         img_size=img_size,
-                         patch_size=patch_size,
-                         resi_connection=resi_connection
-
-                         )
-            self.layers.append(layer)
-            
-        if self.upsampler == 'pixelshuffle_hf':
-            self.layers_hf = nn.ModuleList()
-            for i_layer in range(self.num_layers):
-                layer = RSTB(dim=embed_dim,
-                             input_resolution=(patches_resolution[0],
-                                               patches_resolution[1]),
-                             depth=depths[i_layer],
-                             num_heads=num_heads[i_layer],
-                             window_size=window_size,
-                             mlp_ratio=self.mlp_ratio,
-                             qkv_bias=qkv_bias, 
-                             drop=drop_rate, attn_drop=attn_drop_rate,
-                             drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                             norm_layer=norm_layer,
-                             downsample=None,
-                             use_checkpoint=use_checkpoint,
-                             img_size=img_size,
-                             patch_size=patch_size,
-                             resi_connection=resi_connection
-
-                             )
-                self.layers_hf.append(layer)
-                        
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == '1conv':
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == 'pixelshuffle_aux':
-            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                nn.LeakyReLU(inplace=True))
-            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.conv_after_aux = nn.Sequential(
-                nn.Conv2d(3, num_feat, 3, 1, 1),
-                nn.LeakyReLU(inplace=True))            
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            
-        elif self.upsampler == 'pixelshuffle_hf':
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.upsample_hf = Upsample_hf(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-            self.conv_before_upsample_hf = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                nn.LeakyReLU(inplace=True))
-            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
-                                            (patches_resolution[0], patches_resolution[1]))
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR (less artifacts)
-            assert self.upscale == 4, 'only support x4 now.'
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-    
-    def forward_features_hf(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers_hf:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x    
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == 'pixelshuffle_aux':
-            bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
-            bicubic = self.conv_bicubic(bicubic)
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            aux = self.conv_aux(x) # b, 3, LR_H, LR_W
-            x = self.conv_after_aux(aux)
-            x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
-            x = self.conv_last(x)
-            aux = aux / self.img_range + self.mean
-        elif self.upsampler == 'pixelshuffle_hf':
-            # for classical SR with HF
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x_before = self.conv_before_upsample(x)
-            x_out = self.conv_last(self.upsample(x_before))
-            
-            x_hf = self.conv_first_hf(x_before)
-            x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
-            x_hf = self.conv_before_upsample_hf(x_hf)
-            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
-            x = x_out + x_hf
-            x_hf = x_hf / self.img_range + self.mean
-
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-        
-        x = x / self.img_range + self.mean
-        if self.upsampler == "pixelshuffle_aux":
-            return x[:, :, :H*self.upscale, :W*self.upscale], aux
-        
-        elif self.upsampler == "pixelshuffle_hf":
-            x_out = x_out / self.img_range + self.mean
-            return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
-        
-        else:
-            return x[:, :, :H*self.upscale, :W*self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()
-        return flops
-
-
-if __name__ == '__main__':
-    upscale = 4
-    window_size = 8
-    height = (1024 // upscale // window_size + 1) * window_size
-    width = (720 // upscale // window_size + 1) * window_size
-    model = Swin2SR(upscale=2, img_size=(height, width),
-                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
-                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
-    print(model)
-    print(height, width, model.flops() / 1e9)
-
-    x = torch.randn((1, 3, height, width))
-    x = model(x)
-    print(x.shape)
\ No newline at end of file
diff --git a/modules/ui.py b/modules/ui.py
index 2eb0b684..3acb9b48 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -28,7 +28,6 @@ import modules.codeformer_model
 import modules.generation_parameters_copypaste as parameters_copypaste
 import modules.gfpgan_model
 import modules.hypernetworks.ui
-import modules.ldsr_model
 import modules.scripts
 import modules.shared as shared
 import modules.styles
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 42667941..b487ac25 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -78,6 +78,12 @@ def extension_table():
     """
 
     for ext in extensions.extensions:
+        remote = ""
+        if ext.is_builtin:
+            remote = "built-in"
+        elif ext.remote:
+            remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
+
         if ext.can_update:
             ext_status = f"""<label><input class="gr-check-radio gr-checkbox" name="update_{html.escape(ext.name)}" checked="checked" type="checkbox">{html.escape(ext.status)}</label>"""
         else:
@@ -86,7 +92,7 @@ def extension_table():
         code += f"""
             <tr>
                 <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
-                <td><a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape(ext.remote or '')}</a></td>
+                <td>{remote}</td>
                 <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
             </tr>
     """
-- 
cgit v1.2.3


From 0d21624ceef52b843c731ddc7fdcd7b8d108a42e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 18:16:19 +0300
Subject: move #5216 to the extension

---
 modules/sd_hijack.py             |   2 +-
 modules/sd_hijack_autoencoder.py | 286 ---------------------------------------
 2 files changed, 1 insertion(+), 287 deletions(-)
 delete mode 100644 modules/sd_hijack_autoencoder.py

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 303b1397..95a17093 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -11,7 +11,7 @@ import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
 from modules.shared import opts, device, cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_autoencoder
+from modules import sd_hijack_clip, sd_hijack_open_clip
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
diff --git a/modules/sd_hijack_autoencoder.py b/modules/sd_hijack_autoencoder.py
deleted file mode 100644
index 8e03c7f8..00000000
--- a/modules/sd_hijack_autoencoder.py
+++ /dev/null
@@ -1,286 +0,0 @@
-# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
-# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
-# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
-
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.util import instantiate_from_config
-
-import ldm.models.autoencoder
-
-class VQModel(pl.LightningModule):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 n_embed,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 batch_resize_range=None,
-                 scheduler_config=None,
-                 lr_g_factor=1.0,
-                 remap=None,
-                 sane_index_shape=False, # tell vector quantizer to return indices as bhw
-                 use_ema=False
-                 ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.n_embed = n_embed
-        self.image_key = image_key
-        self.encoder = Encoder(**ddconfig)
-        self.decoder = Decoder(**ddconfig)
-        self.loss = instantiate_from_config(lossconfig)
-        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
-                                        remap=remap,
-                                        sane_index_shape=sane_index_shape)
-        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
-        if colorize_nlabels is not None:
-            assert type(colorize_nlabels)==int
-            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
-        if monitor is not None:
-            self.monitor = monitor
-        self.batch_resize_range = batch_resize_range
-        if self.batch_resize_range is not None:
-            print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
-
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.model_ema = LitEma(self)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-        self.scheduler_config = scheduler_config
-        self.lr_g_factor = lr_g_factor
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.parameters())
-            self.model_ema.copy_to(self)
-            if context is not None:
-                print(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.parameters())
-                if context is not None:
-                    print(f"{context}: Restored training weights")
-
-    def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-            print(f"Unexpected Keys: {unexpected}")
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self)
-
-    def encode(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        quant, emb_loss, info = self.quantize(h)
-        return quant, emb_loss, info
-
-    def encode_to_prequant(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        return h
-
-    def decode(self, quant):
-        quant = self.post_quant_conv(quant)
-        dec = self.decoder(quant)
-        return dec
-
-    def decode_code(self, code_b):
-        quant_b = self.quantize.embed_code(code_b)
-        dec = self.decode(quant_b)
-        return dec
-
-    def forward(self, input, return_pred_indices=False):
-        quant, diff, (_,_,ind) = self.encode(input)
-        dec = self.decode(quant)
-        if return_pred_indices:
-            return dec, diff, ind
-        return dec, diff
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
-        if self.batch_resize_range is not None:
-            lower_size = self.batch_resize_range[0]
-            upper_size = self.batch_resize_range[1]
-            if self.global_step <= 4:
-                # do the first few batches with max size to avoid later oom
-                new_resize = upper_size
-            else:
-                new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
-            if new_resize != x.shape[2]:
-                x = F.interpolate(x, size=new_resize, mode="bicubic")
-            x = x.detach()
-        return x
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        # https://github.com/pytorch/pytorch/issues/37142
-        # try not to fool the heuristics
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss, ind = self(x, return_pred_indices=True)
-
-        if optimizer_idx == 0:
-            # autoencode
-            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train",
-                                            predicted_indices=ind)
-
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return aeloss
-
-        if optimizer_idx == 1:
-            # discriminator
-            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return discloss
-
-    def validation_step(self, batch, batch_idx):
-        log_dict = self._validation_step(batch, batch_idx)
-        with self.ema_scope():
-            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
-        return log_dict
-
-    def _validation_step(self, batch, batch_idx, suffix=""):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss, ind = self(x, return_pred_indices=True)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
-                                        self.global_step,
-                                        last_layer=self.get_last_layer(),
-                                        split="val"+suffix,
-                                        predicted_indices=ind
-                                        )
-
-        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
-                                            self.global_step,
-                                            last_layer=self.get_last_layer(),
-                                            split="val"+suffix,
-                                            predicted_indices=ind
-                                            )
-        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
-        self.log(f"val{suffix}/rec_loss", rec_loss,
-                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
-        self.log(f"val{suffix}/aeloss", aeloss,
-                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
-        if version.parse(pl.__version__) >= version.parse('1.4.0'):
-            del log_dict_ae[f"val{suffix}/rec_loss"]
-        self.log_dict(log_dict_ae)
-        self.log_dict(log_dict_disc)
-        return self.log_dict
-
-    def configure_optimizers(self):
-        lr_d = self.learning_rate
-        lr_g = self.lr_g_factor*self.learning_rate
-        print("lr_d", lr_d)
-        print("lr_g", lr_g)
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quantize.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr_g, betas=(0.5, 0.9))
-        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
-                                    lr=lr_d, betas=(0.5, 0.9))
-
-        if self.scheduler_config is not None:
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                },
-                {
-                    'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                },
-            ]
-            return [opt_ae, opt_disc], scheduler
-        return [opt_ae, opt_disc], []
-
-    def get_last_layer(self):
-        return self.decoder.conv_out.weight
-
-    def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        if only_inputs:
-            log["inputs"] = x
-            return log
-        xrec, _ = self(x)
-        if x.shape[1] > 3:
-            # colorize with random projection
-            assert xrec.shape[1] > 3
-            x = self.to_rgb(x)
-            xrec = self.to_rgb(xrec)
-        log["inputs"] = x
-        log["reconstructions"] = xrec
-        if plot_ema:
-            with self.ema_scope():
-                xrec_ema, _ = self(x)
-                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
-                log["reconstructions_ema"] = xrec_ema
-        return log
-
-    def to_rgb(self, x):
-        assert self.image_key == "segmentation"
-        if not hasattr(self, "colorize"):
-            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
-        x = F.conv2d(x, weight=self.colorize)
-        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
-        return x
-
-
-class VQModelInterface(VQModel):
-    def __init__(self, embed_dim, *args, **kwargs):
-        super().__init__(embed_dim=embed_dim, *args, **kwargs)
-        self.embed_dim = embed_dim
-
-    def encode(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        return h
-
-    def decode(self, h, force_not_quantize=False):
-        # also go through quantization layer
-        if not force_not_quantize:
-            quant, emb_loss, info = self.quantize(h)
-        else:
-            quant = h
-        quant = self.post_quant_conv(quant)
-        dec = self.decoder(quant)
-        return dec
-
-setattr(ldm.models.autoencoder, "VQModel", VQModel)
-setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
-- 
cgit v1.2.3


From 4b0dc206edbad90affe609ac0bf2e9be7e197674 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 18:45:51 +0300
Subject: use modelloader for #4956

---
 modules/interrogate.py | 22 ++++++++--------------
 1 file changed, 8 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 3a09b366..0068b81c 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -1,4 +1,3 @@
-import contextlib
 import os
 import sys
 import traceback
@@ -11,12 +10,9 @@ from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
 import modules.shared as shared
-from modules import devices, paths, lowvram
+from modules import devices, paths, lowvram, modelloader
 
 blip_image_eval_size = 384
-blip_local_dir = os.path.join('models', 'Interrogator')
-blip_local_file = os.path.join(blip_local_dir, 'model_base_caption_capfilt_large.pth')
-blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
 clip_model_name = 'ViT-L/14'
 
 Category = namedtuple("Category", ["name", "topn", "items"])
@@ -49,16 +45,14 @@ class InterrogateModels:
     def load_blip_model(self):
         import models.blip
 
-        if not os.path.isfile(blip_local_file):
-            if not os.path.isdir(blip_local_dir):
-                os.mkdir(blip_local_dir)
+        files = modelloader.load_models(
+            model_path=os.path.join(paths.models_path, "BLIP"),
+            model_url='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth',
+            ext_filter=[".pth"],
+            download_name='model_base_caption_capfilt_large.pth',
+        )
 
-            print("Downloading BLIP...")
-            from requests import get as reqget
-            open(blip_local_file, 'wb').write(reqget(blip_model_url, allow_redirects=True).content)
-            print("BLIP downloaded to", blip_local_file + '.')
-
-        blip_model = models.blip.blip_decoder(pretrained=blip_local_file, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
+        blip_model = models.blip.blip_decoder(pretrained=files[0], image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
         blip_model.eval()
 
         return blip_model
-- 
cgit v1.2.3


From 60bd4d52a658838c5ee3f6ddfe8d4db55cf1d764 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 18:46:09 +0300
Subject: fix incorrect file extension filter for deepdanbooru models

---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 31ec7e17..dfc83357 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -21,7 +21,7 @@ class DeepDanbooru:
         files = modelloader.load_models(
             model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
             model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
-            ext_filter=".pt",
+            ext_filter=[".pt"],
             download_name='model-resnet_custom_v3.pt',
         )
 
-- 
cgit v1.2.3


From 8504db51704d238cc7616f6bf59eb049d3eb101d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 4 Dec 2022 01:04:24 +0300
Subject: fix #4459 breaking inpainting when the option is not specified.

---
 modules/img2img.py | 17 +++++++++--------
 modules/ui.py      | 25 ++++++++++++++-----------
 2 files changed, 23 insertions(+), 19 deletions(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index 830cfa15..81da4b13 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -4,7 +4,7 @@ import sys
 import traceback
 
 import numpy as np
-from PIL import Image, ImageOps, ImageFilter, ImageEnhance
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
 
 from modules import devices, sd_samplers
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
@@ -66,22 +66,23 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     if is_inpaint:
         # Drawn mask
         if mask_mode == 0:
-            image = init_img_with_mask
-            is_mask_sketch = isinstance(image, dict)
+            is_mask_sketch = isinstance(init_img_with_mask, dict)
             is_mask_paint = not is_mask_sketch
             if is_mask_sketch:
                 # Sketch: mask iff. not transparent
-                image, mask = image["image"], image["mask"]
-                pred = np.array(mask)[..., -1] > 0
+                image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
+                alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
+                mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
             else:
                 # Color-sketch: mask iff. painted over
-                orig = init_img_with_mask_orig or image
+                image = init_img_with_mask
+                orig = init_img_with_mask_orig or init_img_with_mask
                 pred = np.any(np.array(image) != np.array(orig), axis=-1)
-            mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
-            if is_mask_paint:
+                mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
                 mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
                 blur = ImageFilter.GaussianBlur(mask_blur)
                 image = Image.composite(image.filter(blur), orig, mask.filter(blur))
+
             image = image.convert("RGB")
         # Uploaded mask
         else:
diff --git a/modules/ui.py b/modules/ui.py
index 3acb9b48..b2b8de90 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -791,23 +791,26 @@ def create_ui():
                         init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
 
                     with gr.TabItem('Inpaint', id='inpaint'):
-                        init_img_with_mask_orig = gr.State(None)
                         init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
+                        init_img_with_mask_orig = gr.State(None)
 
-                        def update_orig(image, state):
-                            if image is not None:
-                                same_size = state is not None and state.size == image.size
-                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
-                                edited = same_size and has_exact_match
-                                return image if not edited or state is None else state
+                        use_color_sketch = cmd_opts.gradio_inpaint_tool == "color-sketch"
+                        if use_color_sketch:
+                            def update_orig(image, state):
+                                if image is not None:
+                                    same_size = state is not None and state.size == image.size
+                                    has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+                                    edited = same_size and has_exact_match
+                                    return image if not edited or state is None else state
+
+                            init_img_with_mask.change(update_orig, [init_img_with_mask, init_img_with_mask_orig], init_img_with_mask_orig)
 
-                        init_img_with_mask.change(update_orig, [init_img_with_mask, init_img_with_mask_orig], init_img_with_mask_orig)
                         init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
 
-                        show_mask_alpha = cmd_opts.gradio_inpaint_tool == "color-sketch"
-                        mask_alpha = gr.Slider(label="Mask transparency", interactive=show_mask_alpha, visible=show_mask_alpha)
-                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+                        with gr.Row():
+                            mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+                            mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch)
 
                         with gr.Row():
                             mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-- 
cgit v1.2.3


From 44c46f0ed395967cd3830dd481a2db759fda5b3b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 4 Dec 2022 12:30:44 +0300
Subject: make it possible to merge inpainting model with non-inpainting one

---
 modules/extras.py | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 6021a024..bc349d5e 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -247,6 +247,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
     teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
+    result_is_inpainting_model = False
 
     print(f"Loading {primary_model_info.filename}...")
     theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
@@ -280,8 +281,22 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
+            a = theta_0[key]
+            b = theta_1[key]
 
-            theta_0[key] = theta_func2(theta_0[key], theta_1[key], multiplier)
+            # this enables merging an inpainting model (A) with another one (B);
+            # where normal model would have 4 channels, for latenst space, inpainting model would
+            # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
+            if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]:
+                if a.shape[1] == 4 and b.shape[1] == 9:
+                    raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.")
+
+                assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
+
+                theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
+                result_is_inpainting_model = True
+            else:
+                theta_0[key] = theta_func2(a, b, multiplier)
 
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
@@ -295,8 +310,16 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
-    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.' + checkpoint_format
+    filename = \
+        primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + \
+        secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + \
+        interp_method.replace(" ", "_") + \
+        '-merged.' +  \
+        ("inpainting." if result_is_inpainting_model else "") + \
+        checkpoint_format
+
     filename = filename if custom_name == '' else (custom_name + '.' + checkpoint_format)
+
     output_modelname = os.path.join(ckpt_dir, filename)
 
     print(f"Saving to {output_modelname}...")
-- 
cgit v1.2.3


From 681c450ecd8f0999cbaf562c5e734c7105320ad9 Mon Sep 17 00:00:00 2001
From: Mackerel <mackerel@chickatrice.com>
Date: Sun, 4 Dec 2022 01:13:36 -0500
Subject: extras.py: use as little RAM as possible, misc fixes

maximum of 2 models loaded at once. delete unneeded model before next
step. fix 'teritary' -> 'tertiary'. gracefully fail when "add
difference" is selected without a tertiary model
---
 modules/extras.py | 37 +++++++++++++++++++------------------
 1 file changed, 19 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index bc349d5e..0ad8deec 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -62,7 +62,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     # Also keep track of original file names
     imageNameArr = []
     outputs = []
-    
+
     if extras_mode == 1:
         #convert file to pillow image
         for img in image_folder:
@@ -234,7 +234,7 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
+def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
@@ -246,30 +246,25 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
-    teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
+    tertiary_model_info = sd_models.checkpoints_list.get(tertiary_model_name, None)
     result_is_inpainting_model = False
 
-    print(f"Loading {primary_model_info.filename}...")
-    theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
-
-    print(f"Loading {secondary_model_info.filename}...")
-    theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
-
-    if teritary_model_info is not None:
-        print(f"Loading {teritary_model_info.filename}...")
-        theta_2 = sd_models.read_state_dict(teritary_model_info.filename, map_location='cpu')
-    else:
-        theta_2 = None
-
     theta_funcs = {
         "Weighted sum": (None, weighted_sum),
         "Add difference": (get_difference, add_difference),
     }
     theta_func1, theta_func2 = theta_funcs[interp_method]
 
-    print(f"Merging...")
+    if theta_func1 and not tertiary_model_info:
+        return ["Failed: Interpolation method requires a tertiary model."] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
+
+    print(f"Loading {secondary_model_info.filename}...")
+    theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
 
     if theta_func1:
+        print(f"Loading {tertiary_model_info.filename}...")
+        theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
+
         for key in tqdm.tqdm(theta_1.keys()):
             if 'model' in key:
                 if key in theta_2:
@@ -277,7 +272,12 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
                     theta_1[key] = theta_func1(theta_1[key], t2)
                 else:
                     theta_1[key] = torch.zeros_like(theta_1[key])
-    del theta_2
+        del theta_2
+
+    print(f"Loading {primary_model_info.filename}...")
+    theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
+
+    print("Merging...")
 
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
@@ -307,6 +307,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
             theta_0[key] = theta_1[key]
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
+    del theta_1
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
@@ -332,5 +333,5 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     sd_models.list_models()
 
-    print(f"Checkpoint saved.")
+    print("Checkpoint saved.")
     return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
-- 
cgit v1.2.3


From 7057c72ae3f697381a6ccdd1527b954a1280cb40 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Mon, 5 Dec 2022 03:41:36 -0800
Subject: Add opt. to avoid sending size between interfaces.

---
 modules/generation_parameters_copypaste.py | 3 +--
 modules/shared.py                          | 1 +
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 44fe1a6c..e8d5250a 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -121,8 +121,7 @@ def run_bind():
 
             if send_generate_info and paste_fields[tab]["fields"] is not None:
                 if send_generate_info in paste_fields:
-                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2'] + (["Seed"] if shared.opts.send_seed else [])
-
+                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] +  (['Size-1', 'Size-2'] if shared.opts.send_size else []) + (["Seed"] if shared.opts.send_seed else [])
                     button.click(
                         fn=lambda *x: x,
                         inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
diff --git a/modules/shared.py b/modules/shared.py
index dc45fcaa..ab9012af 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -395,6 +395,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
     "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
     "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
+    "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-- 
cgit v1.2.3


From 4929503258d80abbc4b5f40da034298fe3803906 Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Tue, 6 Dec 2022 09:03:55 +0800
Subject: fix bugs

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 modules/devices.py   | 4 ++--
 modules/sd_hijack.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index e69c1fe3..f00079c6 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -38,8 +38,8 @@ def get_optimal_device():
     if torch.cuda.is_available():
         return torch.device(get_cuda_device_string())
 
-    # if has_mps():
-    #     return torch.device("mps")
+    if has_mps():
+        return torch.device("mps")
 
     return cpu
 
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index edb8b420..cd65d356 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -28,7 +28,7 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 # new memory efficient cross attention blocks do not support hypernets and we already
 # have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
 ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
-# ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
+ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
 
 # silence new console spam from SD2
 ldm.modules.attention.print = lambda *args: None
-- 
cgit v1.2.3


From 5dcc22606d05ebe5ae89c990bd83a3eb068fcb78 Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Tue, 6 Dec 2022 16:04:50 +0800
Subject: add hash and fix undo hijack bug

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 modules/sd_hijack.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 9b5890e7..9fed1b6f 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -112,7 +112,11 @@ class StableDiffusionModelHijack:
         self.layers = flatten(m)
 
     def undo_hijack(self, m):
-        if type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
+
+        if shared.text_model_name == "XLMR-Large":
+            m.cond_stage_model = m.cond_stage_model.wrapped 
+
+        elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped
 
             model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
-- 
cgit v1.2.3


From 965fc5ac5a6ccdf38342e21c97183011a04e799e Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Tue, 6 Dec 2022 16:15:15 +0800
Subject: delete a file

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 522c56c1..8419b531 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -22,7 +22,7 @@ demo = None
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default="configs/altdiffusion/ad-inference.yaml", help="path to config which constructs model",)
+parser.add_argument("--config", type=str, default=os.path.join(script_path, "v1-inference.yaml"), help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
 parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
-- 
cgit v1.2.3


From 358a8628f6abb4ca1e1bfddf122687c6fb13be0c Mon Sep 17 00:00:00 2001
From: Andrew Ryan <andrewryanchama@gmail.com>
Date: Thu, 8 Dec 2022 07:09:09 +0000
Subject: Add latent upscale option to img2img

Recently, the option to do latent upscale was added to txt2img highres
fix. This feature runs by scaling the latent sample of the image, and
then running a second pass of img2img.

But, in this edition of highres fix, the image and parameters cannot be
changed between the first pass and second pass. We might want to do a
fixup in img2img before doing the second pass, or might want to run the
second pass at a different resolution.

This change adds the option for img2img to perform its upscale in latent
space, rather than image space, giving very similar results to highres
fix with latent upscale.  The result is not exactly the same because
there is an additional latent -> decoder -> image -> encoder -> latent
that won't happen in highres fix, but this conversion has relatively
small losses
---
 modules/processing.py | 6 +++++-
 modules/ui.py         | 2 +-
 2 files changed, 6 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 3d2c4dc9..ab5a34d0 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -795,7 +795,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         for img in self.init_images:
             image = img.convert("RGB")
 
-            if crop_region is None:
+            if crop_region is None and self.resize_mode != 3:
                 image = images.resize_image(self.resize_mode, image, self.width, self.height)
 
             if image_mask is not None:
@@ -804,6 +804,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
                 self.overlay_images.append(image_masked.convert('RGBA'))
 
+            # crop_region is not none iif we are doing inpaint full res
             if crop_region is not None:
                 image = image.crop(crop_region)
                 image = images.resize_image(2, image, self.width, self.height)
@@ -840,6 +841,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
 
+        if self.resize_mode == 3:
+            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+
         if image_mask is not None:
             init_mask = latent_mask
             latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
diff --git a/modules/ui.py b/modules/ui.py
index b2b8de90..fe4abe05 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -829,7 +829,7 @@ def create_ui():
                         img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
 
                 with gr.Row():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Upscale Latent Space"], type="index", value="Just resize")
 
                 steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-- 
cgit v1.2.3


From 1ed4f0e22807f3afef925210182cbbee51f0cb2c Mon Sep 17 00:00:00 2001
From: Jay Smith <jayvsmith@gmail.com>
Date: Thu, 8 Dec 2022 18:14:35 -0600
Subject: Depth2img model support

---
 modules/processing.py | 38 ++++++++++++++++++++++++++++++++++----
 modules/sd_models.py  | 46 ++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 80 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 3d2c4dc9..0417ffc5 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -21,7 +21,10 @@ import modules.face_restoration
 import modules.images as images
 import modules.styles
 import logging
+from ldm.data.util import AddMiDaS
+from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 
+from einops import repeat, rearrange
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -150,11 +153,26 @@ class StableDiffusionProcessing():
 
         return image_conditioning
 
-    def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
-        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
-            # Dummy zero conditioning if we're not using inpainting model.
-            return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+    def depth2img_image_conditioning(self, source_image):
+        # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
+        transformer = AddMiDaS(model_type="dpt_hybrid")
+        transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")})
+        midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
+        midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
+
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
+        conditioning = torch.nn.functional.interpolate(
+            self.sd_model.depth_model(midas_in),
+            size=conditioning_image.shape[2:],
+            mode="bicubic",
+            align_corners=False,
+        )
+
+        (depth_min, depth_max) = torch.aminmax(conditioning)
+        conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
+        return conditioning
 
+    def inpainting_image_conditioning(self, source_image, latent_image, image_mask = None):
         self.is_using_inpainting_conditioning = True
 
         # Handle the different mask inputs
@@ -191,6 +209,18 @@ class StableDiffusionProcessing():
 
         return image_conditioning
 
+    def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+        # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
+        # identify itself with a field common to all models. The conditioning_key is also hybrid.
+        if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
+            return self.depth2img_image_conditioning(source_image)
+
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+
+        # Dummy zero conditioning if we're not using inpainting or depth model.
+        return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 283cf1cd..139952ba 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -7,6 +7,9 @@ import torch
 import re
 import safetensors.torch
 from omegaconf import OmegaConf
+from os import mkdir
+from urllib import request
+import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
@@ -36,6 +39,7 @@ def setup_model():
         os.makedirs(model_path)
 
     list_models()
+    enable_midas_autodownload()
 
 
 def checkpoint_tiles(): 
@@ -227,6 +231,48 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     sd_vae.load_vae(model, vae_file)
 
 
+def enable_midas_autodownload():
+    """
+    Gives the ldm.modules.midas.api.load_model function automatic downloading.
+
+    When the 512-depth-ema model, and other future models like it, is loaded,
+    it calls midas.api.load_model to load the associated midas depth model.
+    This function applies a wrapper to download the model to the correct
+    location automatically.
+    """
+
+    midas_path = os.path.join(models_path, 'midas')
+
+    # stable-diffusion-stability-ai hard-codes the midas model path to
+    # a location that differs from where other scripts using this model look.
+    # HACK: Overriding the path here.
+    for k, v in midas.api.ISL_PATHS.items():
+        file_name = os.path.basename(v)
+        midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
+
+    midas_urls = {
+        "dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
+        "dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
+        "midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
+        "midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
+    }
+
+    midas.api.load_model_inner = midas.api.load_model
+
+    def load_model_wrapper(model_type):
+        path = midas.api.ISL_PATHS[model_type]
+        if not os.path.exists(path):
+            if not os.path.exists(midas_path):
+                mkdir(midas_path)
+    
+            print(f"Downloading midas model weights for {model_type} to {path}")
+            request.urlretrieve(midas_urls[model_type], path)
+            print(f"{model_type} downloaded")
+
+        return midas.api.load_model_inner(model_type)
+
+    midas.api.load_model = load_model_wrapper
+
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
-- 
cgit v1.2.3


From ce04ba71b880aeb97b7d47d404ba4ea430891618 Mon Sep 17 00:00:00 2001
From: Ju1-js <40339350+Ju1-js@users.noreply.github.com>
Date: Thu, 8 Dec 2022 22:47:45 -0800
Subject: Make # settings changed message grammatically correct

Make the ": " in the settings changed message not show if 0 settings were changed.
"0 settings changed: ." -> "0 settings changed."
---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index b2b8de90..79bb3d1f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1450,7 +1450,7 @@ def create_ui():
             opts.save(shared.config_filename)
         except RuntimeError:
             return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
-        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
+        return opts.dumpjson(), f'{len(changed)} settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}.'
 
     def run_settings_single(value, key):
         if not opts.same_type(value, opts.data_labels[key].default):
-- 
cgit v1.2.3


From 9539c2045a2de9e900b96acbf67e41fafe93c6f6 Mon Sep 17 00:00:00 2001
From: ywx9 <yw.ninefold@gmail.com>
Date: Fri, 9 Dec 2022 23:03:06 +0900
Subject: Bug fix

---
 modules/api/api.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 54ee7cb0..89935a70 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -157,12 +157,7 @@ class Api:
         args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
         p = StableDiffusionProcessingImg2Img(**args)
 
-        imgs = []
-        for img in init_images:
-            img = decode_base64_to_image(img)
-            imgs = [img] * p.batch_size
-
-        p.init_images = imgs
+        p.init_images = [decode_base64_to_image(x) for x in init_images]
 
         shared.state.begin()
 
-- 
cgit v1.2.3


From 7dbfd8a7d8aefec7283b456c6f5b000ae4d3496d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 09:14:30 +0300
Subject: do not replace entire unet for the resolution hack

---
 modules/sd_hijack.py               |  5 +++--
 modules/sd_hijack_optimizations.py | 28 ----------------------------
 modules/sd_hijack_unet.py          | 30 ++++++++++++++++++++++++++++++
 3 files changed, 33 insertions(+), 30 deletions(-)
 create mode 100644 modules/sd_hijack_unet.py

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 92874a79..47dbc1b7 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -11,7 +11,7 @@ import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
 from modules.shared import opts, device, cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
@@ -35,11 +35,12 @@ ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"]
 ldm.modules.attention.print = lambda *args: None
 ldm.modules.diffusionmodules.model.print = lambda *args: None
 
+
 def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_hijack_optimizations.patched_unet_forward
+    ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
 
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 8cd4c954..85909eb9 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -313,31 +313,3 @@ def xformers_attnblock_forward(self, x):
         return x + out
     except NotImplementedError:
         return cross_attention_attnblock_forward(self, x)
-
-def patched_unet_forward(self, x, timesteps=None, context=None, y=None,**kwargs):
-    assert (y is not None) == (
-        self.num_classes is not None
-    ), "must specify y if and only if the model is class-conditional"
-    hs = []
-    t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-    emb = self.time_embed(t_emb)
-
-    if self.num_classes is not None:
-        assert y.shape == (x.shape[0],)
-        emb = emb + self.label_emb(y)
-
-    h = x.type(self.dtype)
-    for module in self.input_blocks:
-        h = module(h, emb, context)
-        hs.append(h)
-    h = self.middle_block(h, emb, context)
-    for module in self.output_blocks:
-        if h.shape[-2:] != hs[-1].shape[-2:]:
-            h = F.interpolate(h, hs[-1].shape[-2:], mode="nearest")
-        h = torch.cat([h, hs.pop()], dim=1)
-        h = module(h, emb, context)
-    h = h.type(x.dtype)
-    if self.predict_codebook_ids:
-        return self.id_predictor(h)
-    else:
-        return self.out(h)
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
new file mode 100644
index 00000000..1b9d7757
--- /dev/null
+++ b/modules/sd_hijack_unet.py
@@ -0,0 +1,30 @@
+import torch
+
+
+class TorchHijackForUnet:
+    """
+    This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
+    this makes it possible to create pictures with dimensions that are muliples of 8 rather than 64
+    """
+
+    def __getattr__(self, item):
+        if item == 'cat':
+            return self.cat
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+
+    def cat(self, tensors, *args, **kwargs):
+        if len(tensors) == 2:
+            a, b = tensors
+            if a.shape[-2:] != b.shape[-2:]:
+                a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
+
+            tensors = (a, b)
+
+        return torch.cat(tensors, *args, **kwargs)
+
+
+th = TorchHijackForUnet()
-- 
cgit v1.2.3


From 505ec7e4d960e7bea579182509050fafb10bd00c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 09:17:39 +0300
Subject: cleanup some unneeded imports for hijack files

---
 modules/sd_hijack.py               | 10 ++--------
 modules/sd_hijack_optimizations.py |  3 ---
 2 files changed, 2 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 47dbc1b7..690a9ec2 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -1,16 +1,10 @@
-import math
-import os
-import sys
-import traceback
 import torch
-import numpy as np
-from torch import einsum
 from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
+from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
-from modules.shared import opts, device, cmd_opts
+from modules.shared import cmd_opts
 from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 85909eb9..98123fbf 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -5,7 +5,6 @@ import importlib
 
 import torch
 from torch import einsum
-import torch.nn.functional as F
 
 from ldm.util import default
 from einops import rearrange
@@ -13,8 +12,6 @@ from einops import rearrange
 from modules import shared
 from modules.hypernetworks import hypernetwork
 
-from ldm.modules.diffusionmodules.util import timestep_embedding
-
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
-- 
cgit v1.2.3


From bab91b12798f67c19a2b14dab13a08d5d3e3de26 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 09:51:26 +0300
Subject: add Noise multiplier option to infotext

---
 modules/generation_parameters_copypaste.py | 1 +
 modules/processing.py                      | 8 ++++++--
 modules/shared.py                          | 2 +-
 3 files changed, 8 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 44fe1a6c..53f34b0a 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -77,6 +77,7 @@ def integrate_settings_paste_fields(component_dict):
         'inpainting_mask_weight': 'Conditional mask weight',
         'sd_model_checkpoint': 'Model hash',
         'eta_noise_seed_delta': 'ENSD',
+        'initial_noise_multiplier': 'Noise multiplier',
     }
     settings_paste_fields = [
         (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))
diff --git a/modules/processing.py b/modules/processing.py
index dd22a2fa..81400d14 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -764,7 +764,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: Any=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
+    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
@@ -779,6 +779,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.inpaint_full_res = inpaint_full_res
         self.inpaint_full_res_padding = inpaint_full_res_padding
         self.inpainting_mask_invert = inpainting_mask_invert
+        self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
         self.mask = None
         self.nmask = None
         self.image_conditioning = None
@@ -891,7 +892,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        x = x*shared.opts.initial_noise_multiplier
+
+        if self.initial_noise_multiplier != 1.0:
+            self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
+            x *= self.initial_noise_multiplier
 
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 
diff --git a/modules/shared.py b/modules/shared.py
index 67f8f77b..200693fe 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -359,7 +359,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "initial_noise_multiplier": OptionInfo(1.0, "Multiply initial noise by this factor, may result in less or more detailed img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }),
+    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-- 
cgit v1.2.3


From d06592267c745b4732026c4e0c499c9a4b3900a1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 13:46:18 +0300
Subject: use less javascript for this non-js-only implementation of the clear
 prompt button.

---
 modules/shared.py |  1 -
 modules/ui.py     | 23 +++++++++++------------
 2 files changed, 11 insertions(+), 13 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 4223c017..44922c91 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -401,7 +401,6 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
-    "clear_prompt_visible": OptionInfo(True, "Show clear prompt button"),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
diff --git a/modules/ui.py b/modules/ui.py
index c0a7ca8b..28481e33 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -403,16 +403,17 @@ def create_toprow(is_img2img):
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-
-            clear_prompt_button = gr.Button(
-                value=clear_prompt_symbol,
-                elem_id="clear_prompt",
-                visible=opts.clear_prompt_visible
-            )
-
+            clear_prompt_button = gr.Button(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
             token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
             token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
 
+            clear_prompt_button.click(
+                fn=lambda *x: x,
+                _js="confirm_clear_prompt",
+                inputs=[prompt, negative_prompt],
+                outputs=[prompt, negative_prompt],
+            )
+
         button_interrogate = None
         button_deepbooru = None
         if is_img2img:
@@ -447,7 +448,7 @@ def create_toprow(is_img2img):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
                     prompt_style2.save_to_config = True
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button, clear_prompt_button
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -634,7 +635,7 @@ def create_ui():
     modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -686,7 +687,6 @@ def create_ui():
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button)
 
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
@@ -793,7 +793,7 @@ def create_ui():
     modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True)
 
 
         with gr.Row(elem_id='img2img_progress_row'):
@@ -884,7 +884,6 @@ def create_ui():
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button)
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-- 
cgit v1.2.3


From 991e2dcee9d6baa66b5c0b1969c4c07407be933a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 14:54:02 +0300
Subject: remove NSFW filter and its dependency; if you still want it, find it
 in the extensions section

---
 modules/processing.py |  7 +++----
 modules/safety.py     | 42 ------------------------------------------
 modules/scripts.py    | 20 ++++++++++++++++++++
 modules/shared.py     |  1 -
 4 files changed, 23 insertions(+), 47 deletions(-)
 delete mode 100644 modules/safety.py

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 81400d14..056c9322 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -13,7 +13,7 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -571,9 +571,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
             devices.torch_gc()
 
-            if opts.filter_nsfw:
-                import modules.safety as safety
-                x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
+            if p.scripts is not None:
+                p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
             for i, x_sample in enumerate(x_samples_ddim):
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
diff --git a/modules/safety.py b/modules/safety.py
deleted file mode 100644
index cff4b278..00000000
--- a/modules/safety.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import torch
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from transformers import AutoFeatureExtractor
-from PIL import Image
-
-import modules.shared as shared
-
-safety_model_id = "CompVis/stable-diffusion-safety-checker"
-safety_feature_extractor = None
-safety_checker = None
-
-def numpy_to_pil(images):
-    """
-    Convert a numpy image or a batch of images to a PIL image.
-    """
-    if images.ndim == 3:
-        images = images[None, ...]
-    images = (images * 255).round().astype("uint8")
-    pil_images = [Image.fromarray(image) for image in images]
-
-    return pil_images
-
-# check and replace nsfw content
-def check_safety(x_image):
-    global safety_feature_extractor, safety_checker
-
-    if safety_feature_extractor is None:
-        safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
-        safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
-
-    safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
-    x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
-
-    return x_checked_image, has_nsfw_concept
-
-
-def censor_batch(x):
-    x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy()
-    x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim_numpy)
-    x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
-
-    return x
diff --git a/modules/scripts.py b/modules/scripts.py
index b934d881..23ca195d 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -88,6 +88,17 @@ class Script:
 
         pass
 
+    def postprocess_batch(self, p, *args, **kwargs):
+        """
+        Same as process_batch(), but called for every batch after it has been generated.
+
+        **kwargs will have same items as process_batch, and also:
+          - batch_number - index of current batch, from 0 to number of batches-1
+          - images - torch tensor with all generated images, with values ranging from 0 to 1;
+        """
+
+        pass
+
     def postprocess(self, p, processed, *args):
         """
         This function is called after processing ends for AlwaysVisible scripts.
@@ -347,6 +358,15 @@ class ScriptRunner:
                 print(f"Error running postprocess: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
+    def postprocess_batch(self, p, images, **kwargs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess_batch(p, *script_args, images=images, **kwargs)
+            except Exception:
+                print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
     def before_component(self, component, **kwargs):
         for script in self.scripts:
             try:
diff --git a/modules/shared.py b/modules/shared.py
index 44922c91..272267c1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -367,7 +367,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
-    "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
-- 
cgit v1.2.3


From 713c48ddd7f296fe064cf58af7baa31aa5fcffb3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 15:05:22 +0300
Subject: add an 'installed' tag to extensions

---
 modules/ui_extensions.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index b487ac25..1434f25f 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -206,12 +206,13 @@ def refresh_available_extensions_from_data(hide_tags):
         if url is None:
             continue
 
+        existing = installed_extension_urls.get(normalize_git_url(url), None)
+        extension_tags = extension_tags + ["installed"] if existing else extension_tags
+
         if len([x for x in extension_tags if x in tags_to_hide]) > 0:
             hidden += 1
             continue
 
-        existing = installed_extension_urls.get(normalize_git_url(url), None)
-
         install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
 
         tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
@@ -222,7 +223,11 @@ def refresh_available_extensions_from_data(hide_tags):
                 <td>{html.escape(description)}</td>
                 <td>{install_code}</td>
             </tr>
-    """
+        
+        """
+
+        for tag in [x for x in extension_tags if x not in tags]:
+            tags[tag] = tag
 
     code += """
         </tbody>
@@ -272,7 +277,7 @@ def create_ui():
                     install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
 
                 with gr.Row():
-                    hide_tags = gr.CheckboxGroup(value=["ads", "localization"], label="Hide extensions with tags", choices=["script", "ads", "localization"])
+                    hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
 
                 install_result = gr.HTML()
                 available_extensions_table = gr.HTML()
-- 
cgit v1.2.3


From a1c8ad88283f7b3e861e4722c71e39bf71eec744 Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sat, 10 Dec 2022 11:02:47 -0500
Subject: unload depth model if medvram/lowvram enabled

---
 modules/lowvram.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/lowvram.py b/modules/lowvram.py
index aa464a95..042a0254 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -55,18 +55,20 @@ def setup_for_low_vram(sd_model, use_medvram):
     if hasattr(sd_model.cond_stage_model, 'model'):
         sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
 
-    # remove three big modules, cond, first_stage, and unet from the model and then
+    # remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
     # send the model to GPU. Then put modules back. the modules will be in CPU.
-    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None
+    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
+    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None
     sd_model.to(devices.device)
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored
+    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
 
-    # register hooks for those the first two models
+    # register hooks for those the first three models
     sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
     sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
     sd_model.first_stage_model.encode = first_stage_model_encode_wrap
     sd_model.first_stage_model.decode = first_stage_model_decode_wrap
+    if sd_model.depth_model:
+        sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
     parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
     if hasattr(sd_model.cond_stage_model, 'model'):
-- 
cgit v1.2.3


From bd81a09eacf02dad095b98094ab936f276d0343f Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sat, 10 Dec 2022 11:29:26 -0500
Subject: fix support for 2.0 inpainting model while maintaining support for
 1.5 inpainting model

---
 modules/sd_hijack_inpainting.py | 3 +--
 modules/sd_models.py            | 1 +
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 938f9a58..5018b047 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -324,12 +324,11 @@ def should_hijack_inpainting(checkpoint_info):
 
 def do_inpainting_hijack():
     # most of this stuff seems to no longer be needed because it is already included into SD2.0
-    # LatentInpaintDiffusion remains because SD2.0's LatentInpaintDiffusion can't be loaded without specifying a checkpoint
     # p_sample_plms is needed because PLMS can't work with dicts as conditionings
     # this file should be cleaned up later if weverything tuens out to work fine
 
     # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
-    ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
+    # ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
 
     # ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
     # ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5b37f3fe..b64f573f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -296,6 +296,7 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.use_ema = False
         sd_config.model.params.conditioning_key = "hybrid"
         sd_config.model.params.unet_config.params.in_channels = 9
+        sd_config.model.params.finetune_keys = None
 
         # Create a "fake" config with a different name so that we know to unload it when switching models.
         checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
-- 
cgit v1.2.3


From 59c6511494c55a578eecdf71fb4590b6bd5d04a7 Mon Sep 17 00:00:00 2001
From: Dean van Dugteren <31391056+deanpress@users.noreply.github.com>
Date: Sun, 11 Dec 2022 17:08:51 +0100
Subject: fix: fallback model_checkpoint if it's empty

This fixes the following error when SD attempts to start with a deleted checkpoint:

```
Traceback (most recent call last):
  File "D:\Web\stable-diffusion-webui\launch.py", line 295, in <module>
    start()
  File "D:\Web\stable-diffusion-webui\launch.py", line 290, in start
    webui.webui()
  File "D:\Web\stable-diffusion-webui\webui.py", line 132, in webui
    initialize()
  File "D:\Web\stable-diffusion-webui\webui.py", line 62, in initialize
    modules.sd_models.load_model()
  File "D:\Web\stable-diffusion-webui\modules\sd_models.py", line 283, in load_model
    checkpoint_info = checkpoint_info or select_checkpoint()
  File "D:\Web\stable-diffusion-webui\modules\sd_models.py", line 117, in select_checkpoint
    checkpoint_info = checkpoints_list.get(model_checkpoint, None)
TypeError: unhashable type: 'list'
```
---
 modules/sd_models.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5b37f3fe..b6d75db7 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -111,6 +111,10 @@ def model_hash(filename):
 
 def select_checkpoint():
     model_checkpoint = shared.opts.sd_model_checkpoint
+    
+    if len(model_checkpoint) == 0:
+        model_checkpoint = shared.default_sd_model_file
+        
     checkpoint_info = checkpoints_list.get(model_checkpoint, None)
     if checkpoint_info is not None:
         return checkpoint_info
-- 
cgit v1.2.3


From ec0a48826fb41c1b1baab45a9030f7eb55568fd0 Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sun, 11 Dec 2022 10:19:46 -0500
Subject: unconditionally set use_ema=False if value not specified (True never
 worked, and all configs except v1-inpainting-inference.yaml already correctly
 set it to False)

---
 modules/sd_models.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index b64f573f..f36b299f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -293,7 +293,6 @@ def load_model(checkpoint_info=None):
     if should_hijack_inpainting(checkpoint_info):
         # Hardcoded config for now...
         sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
-        sd_config.model.params.use_ema = False
         sd_config.model.params.conditioning_key = "hybrid"
         sd_config.model.params.unet_config.params.in_channels = 9
         sd_config.model.params.finetune_keys = None
@@ -301,6 +300,9 @@ def load_model(checkpoint_info=None):
         # Create a "fake" config with a different name so that we know to unload it when switching models.
         checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
 
+    if not hasattr(sd_config.model.params, "use_ema"):
+        sd_config.model.params.use_ema = False
+
     do_inpainting_hijack()
 
     if shared.cmd_opts.no_half:
-- 
cgit v1.2.3


From 960293d6b24f380f5744c94c9a46acaae6cc8c04 Mon Sep 17 00:00:00 2001
From: Dean Hopkins <deanhopkins@gmail.com>
Date: Sun, 11 Dec 2022 19:16:44 +0000
Subject: API endpoint to refresh checkpoints

API endpoint to refresh checkpoints
---
 modules/api/api.py | 4 ++++
 1 file changed, 4 insertions(+)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 89935a70..14d0baaa 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -96,6 +96,7 @@ class Api:
         self.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
         self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
         self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
+        self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
 
     def add_api_route(self, path: str, endpoint, **kwargs):
         if shared.cmd_opts.api_auth:
@@ -321,6 +322,9 @@ class Api:
 
     def get_artists(self):
         return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
+    
+    def refresh_checkpoints(self):
+        shared.refresh_checkpoints()
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)
-- 
cgit v1.2.3


From 2e8b5418e3cd4e9212f2fcdb36305d7a40f97916 Mon Sep 17 00:00:00 2001
From: ThereforeGames <95403634+ThereforeGames@users.noreply.github.com>
Date: Sun, 11 Dec 2022 18:03:36 -0500
Subject: Improve color correction with luminosity blend

---
 modules/processing.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 24c537d1..bc841837 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -27,6 +27,7 @@ from ldm.data.util import AddMiDaS
 from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 
 from einops import repeat, rearrange
+from blendmodes.blend import blendLayers, BlendType
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -39,17 +40,19 @@ def setup_color_correction(image):
     return correction_target
 
 
-def apply_color_correction(correction, image):
+def apply_color_correction(correction, original_image):
     logging.info("Applying color correction.")
     image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
         cv2.cvtColor(
-            np.asarray(image),
+            np.asarray(original_image),
             cv2.COLOR_RGB2LAB
         ),
         correction,
         channel_axis=2
     ), cv2.COLOR_LAB2RGB).astype("uint8"))
-
+    
+    image = blendLayers(image, original_image, BlendType.LUMINOSITY)
+    
     return image
 
 
-- 
cgit v1.2.3


From 7077428209cd02f7da23ef843e5027e960f6aa39 Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Tue, 13 Dec 2022 13:05:40 -0800
Subject: Save hypernetwork hash in infotext

---
 modules/processing.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 24c537d1..6dd7491b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -314,7 +314,7 @@ class Processed:
 
         return json.dumps(obj)
 
-    def infotext(self,  p: StableDiffusionProcessing, index):
+    def infotext(self, p: StableDiffusionProcessing, index):
         return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
 
 
@@ -429,6 +429,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
         "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
+        "Hypernet hash": (None if shared.loaded_hypernetwork is None else sd_models.model_hash(shared.loaded_hypernetwork.filename)),
         "Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
@@ -446,7 +447,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if  p.all_negative_prompts[index] else ""
+    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
 
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
-- 
cgit v1.2.3


From 9d5948e5f7324b98fa7445accb2fe14487ff809d Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Tue, 13 Dec 2022 14:25:16 -0800
Subject: Correctly restore hypernetwork from hash

---
 modules/generation_parameters_copypaste.py | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 565e342d..e4e1d41c 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -14,6 +14,7 @@ re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
+re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
 type_of_gr_update = type(gr.update())
 paste_fields = {}
 bind_list = []
@@ -139,6 +140,30 @@ def run_bind():
             )
 
 
+def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
+    """Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
+
+    Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config
+    parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to.
+
+    If the infotext has no hash, then a hypernet with the same name and the most steps will be selected instead.
+    """
+    hypernet_name = hypernet_name.lower()
+    if hypernet_hash is not None:
+        # Try to match the hash in the name
+        for hypernet_key in shared.hypernetworks.keys():
+            result = re_hypernet_hash.search(hypernet_key)
+            if result is not None and result[1] == hypernet_hash:
+                return hypernet_key
+    else:
+        # Fall back to a hypernet with the same name
+        for hypernet_key in shared.hypernetworks.keys():
+            if hypernet_key.lower().startswith(hypernet_name):
+                return hypernet_key
+
+    return None
+
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
@@ -188,6 +213,11 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "Clip skip" not in res:
         res["Clip skip"] = "1"
 
+    if "Hypernet" in res:
+        hypernet_name = res["Hypernet"]
+        hypernet_hash = res.get("Hypernet hash", None)
+        res["Hypernet"] = find_hypernetwork_key(hypernet_name, hypernet_hash)
+
     return res
 
 
-- 
cgit v1.2.3


From 1fcb9595143fc352240635959ea5b9929c02dca6 Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Tue, 13 Dec 2022 14:30:54 -0800
Subject: Correctly restore default hypernetwork strength

---
 modules/generation_parameters_copypaste.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index e4e1d41c..a33f8d5c 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -213,6 +213,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "Clip skip" not in res:
         res["Clip skip"] = "1"
 
+    if "Hypernet strength" not in res:
+        res["Hypernet strength"] = "1"
+
     if "Hypernet" in res:
         hypernet_name = res["Hypernet"]
         hypernet_hash = res.get("Hypernet hash", None)
-- 
cgit v1.2.3


From 5f407ebd61bb5c1ca025c5d7fa642e32ac0526ce Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Tue, 13 Dec 2022 14:32:26 -0800
Subject: Fix comment

---
 modules/generation_parameters_copypaste.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index a33f8d5c..fbd91300 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -146,7 +146,7 @@ def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
     Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config
     parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to.
 
-    If the infotext has no hash, then a hypernet with the same name and the most steps will be selected instead.
+    If the infotext has no hash, then a hypernet with the same name will be selected instead.
     """
     hypernet_name = hypernet_name.lower()
     if hypernet_hash is not None:
-- 
cgit v1.2.3


From 957e15c4642199e0792eba817a15e244246fb012 Mon Sep 17 00:00:00 2001
From: Yuval Aboulafia <yuval.abou@gmail.com>
Date: Wed, 14 Dec 2022 20:59:33 +0200
Subject: Correct singleton comparisons

---
 modules/extras.py | 2 +-
 modules/ngrok.py  | 4 ++--
 modules/ui.py     | 8 ++++++--
 3 files changed, 9 insertions(+), 5 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 0ad8deec..69b85465 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -188,7 +188,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         for op in extras_ops:
             image, info = op(image, info)
 
-        if opts.use_original_name_batch and image_name != None:
+        if opts.use_original_name_batch and image_name is not None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
             basename = ''
diff --git a/modules/ngrok.py b/modules/ngrok.py
index 64c9a3c2..3df2c06b 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -2,7 +2,7 @@ from pyngrok import ngrok, conf, exception
 
 def connect(token, port, region):
     account = None
-    if token == None:
+    if token is None:
         token = 'None'
     else:
         if ':' in token:
@@ -14,7 +14,7 @@ def connect(token, port, region):
         auth_token=token, region=region
     )
     try:
-        if account == None:
+        if account is None:
             public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
         else:
             public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True, auth=account).public_url
diff --git a/modules/ui.py b/modules/ui.py
index 28481e33..c4bb186d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -49,10 +49,14 @@ if not cmd_opts.share and not cmd_opts.listen:
     gradio.utils.version_check = lambda: None
     gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
 
-if cmd_opts.ngrok != None:
+if cmd_opts.ngrok is not None:
     import modules.ngrok as ngrok
     print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
+    ngrok.connect(
+        cmd_opts.ngrok,
+        cmd_opts.port if cmd_opts.port is not None else 7860,
+        cmd_opts.ngrok_region
+        )
 
 
 def gr_show(visible=True):
-- 
cgit v1.2.3


From c0355caefe3d82e304e6d832699d581fc8f9fbf9 Mon Sep 17 00:00:00 2001
From: Jim Hays <jim@emporatitle.com>
Date: Wed, 14 Dec 2022 21:01:32 -0500
Subject: Fix various typos

---
 modules/api/api.py                             | 18 +++++++++---------
 modules/api/models.py                          |  2 +-
 modules/images.py                              |  4 ++--
 modules/processing.py                          | 14 +++++++-------
 modules/safe.py                                |  4 ++--
 modules/scripts.py                             |  4 ++--
 modules/sd_hijack_inpainting.py                |  6 +++---
 modules/sd_hijack_unet.py                      |  2 +-
 modules/textual_inversion/dataset.py           | 10 +++++-----
 modules/textual_inversion/textual_inversion.py | 16 ++++++++--------
 10 files changed, 40 insertions(+), 40 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 89935a70..33845045 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -67,10 +67,10 @@ def encode_pil_to_base64(image):
 class Api:
     def __init__(self, app: FastAPI, queue_lock: Lock):
         if shared.cmd_opts.api_auth:
-            self.credenticals = dict()
+            self.credentials = dict()
             for auth in shared.cmd_opts.api_auth.split(","):
                 user, password = auth.split(":")
-                self.credenticals[user] = password
+                self.credentials[user] = password
 
         self.router = APIRouter()
         self.app = app
@@ -93,7 +93,7 @@ class Api:
         self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
         self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
         self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
-        self.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
+        self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
         self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
         self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
 
@@ -102,9 +102,9 @@ class Api:
             return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
         return self.app.add_api_route(path, endpoint, **kwargs)
 
-    def auth(self, credenticals: HTTPBasicCredentials = Depends(HTTPBasic())):
-        if credenticals.username in self.credenticals:
-            if compare_digest(credenticals.password, self.credenticals[credenticals.username]):
+    def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())):
+        if credentials.username in self.credentials:
+            if compare_digest(credentials.password, self.credentials[credentials.username]):
                 return True
 
         raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
@@ -239,7 +239,7 @@ class Api:
     def interrogateapi(self, interrogatereq: InterrogateRequest):
         image_b64 = interrogatereq.image
         if image_b64 is None:
-            raise HTTPException(status_code=404, detail="Image not found") 
+            raise HTTPException(status_code=404, detail="Image not found")
 
         img = decode_base64_to_image(image_b64)
         img = img.convert('RGB')
@@ -252,7 +252,7 @@ class Api:
                 processed = deepbooru.model.tag(img)
             else:
                 raise HTTPException(status_code=404, detail="Model not found")
-        
+
         return InterrogateResponse(caption=processed)
 
     def interruptapi(self):
@@ -308,7 +308,7 @@ class Api:
     def get_realesrgan_models(self):
         return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
 
-    def get_promp_styles(self):
+    def get_prompt_styles(self):
         styleList = []
         for k in shared.prompt_styles.styles:
             style = shared.prompt_styles.styles[k]
diff --git a/modules/api/models.py b/modules/api/models.py
index f77951fc..a22bc6b3 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -128,7 +128,7 @@ class ExtrasBaseRequest(BaseModel):
     upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
     upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
     upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
-    upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?")
+    upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
     upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
diff --git a/modules/images.py b/modules/images.py
index 8146f580..93a14289 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -429,7 +429,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
         basename (`str`):
             The base filename which will be applied to `filename pattern`.
-        seed, prompt, short_filename, 
+        seed, prompt, short_filename,
         extension (`str`):
             Image file extension, default is `png`.
         pngsectionname (`str`):
@@ -590,7 +590,7 @@ def read_info_from_image(image):
 Negative prompt: {json_info["uc"]}
 Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
         except Exception:
-            print(f"Error parsing NovelAI iamge generation parameters:", file=sys.stderr)
+            print(f"Error parsing NovelAI image generation parameters:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
     return geninfo, items
diff --git a/modules/processing.py b/modules/processing.py
index 24c537d1..fe7f4faf 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -147,11 +147,11 @@ class StableDiffusionProcessing():
 
         # The "masked-image" in this case will just be all zeros since the entire image is masked.
         image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
 
         # Add the fake full 1s mask to the first dimension.
         image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-        image_conditioning = image_conditioning.to(x.dtype)            
+        image_conditioning = image_conditioning.to(x.dtype)
 
         return image_conditioning
 
@@ -199,7 +199,7 @@ class StableDiffusionProcessing():
             source_image * (1.0 - conditioning_mask),
             getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
         )
-        
+
         # Encode the new masked image using first stage of network.
         conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
 
@@ -537,7 +537,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         for n in range(p.n_iter):
             if state.skipped:
                 state.skipped = False
-            
+
             if state.interrupted:
                 break
 
@@ -612,7 +612,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image.info["parameters"] = text
                 output_images.append(image)
 
-            del x_samples_ddim 
+            del x_samples_ddim
 
             devices.torch_gc()
 
@@ -704,7 +704,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
-        """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images"""
+        """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
         def save_intermediate(image, index):
             if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
                 return
@@ -720,7 +720,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
 
-            # Avoid making the inpainting conditioning unless necessary as 
+            # Avoid making the inpainting conditioning unless necessary as
             # this does need some extra compute to decode / encode the image again.
             if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
                 image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
diff --git a/modules/safe.py b/modules/safe.py
index 10460ad0..20e9d2fa 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -80,7 +80,7 @@ def check_pt(filename, extra_handler):
         # new pytorch format is a zip file
         with zipfile.ZipFile(filename) as z:
             check_zip_filenames(filename, z.namelist())
-            
+
             # find filename of data.pkl in zip file: '<directory name>/data.pkl'
             data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
             if len(data_pkl_filenames) == 0:
@@ -108,7 +108,7 @@ def load(filename, *args, **kwargs):
 
 def load_with_extra(filename, extra_handler=None, *args, **kwargs):
     """
-    this functon is intended to be used by extensions that want to load models with
+    this function is intended to be used by extensions that want to load models with
     some extra classes in them that the usual unpickler would find suspicious.
 
     Use the extra_handler argument to specify a function that takes module and field name as text,
diff --git a/modules/scripts.py b/modules/scripts.py
index 23ca195d..722f8685 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -36,7 +36,7 @@ class Script:
     def ui(self, is_img2img):
         """this function should create gradio UI elements. See https://gradio.app/docs/#components
         The return value should be an array of all components that are used in processing.
-        Values of those returned componenbts will be passed to run() and process() functions.
+        Values of those returned components will be passed to run() and process() functions.
         """
 
         pass
@@ -47,7 +47,7 @@ class Script:
 
         This function should return:
          - False if the script should not be shown in UI at all
-         - True if the script should be shown in UI if it's scelected in the scripts drowpdown
+         - True if the script should be shown in UI if it's selected in the scripts dropdown
          - script.AlwaysVisible if the script should be shown in UI at all times
          """
 
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 938f9a58..d72f83fd 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -209,7 +209,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
         else:
             x_in = torch.cat([x] * 2)
             t_in = torch.cat([t] * 2)
-            
+
             if isinstance(c, dict):
                 assert isinstance(unconditional_conditioning, dict)
                 c_in = dict()
@@ -278,7 +278,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
     x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
 
     return x_prev, pred_x0, e_t
-    
+
 # =================================================================================================
 # Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
 # Adapted from:
@@ -326,7 +326,7 @@ def do_inpainting_hijack():
     # most of this stuff seems to no longer be needed because it is already included into SD2.0
     # LatentInpaintDiffusion remains because SD2.0's LatentInpaintDiffusion can't be loaded without specifying a checkpoint
     # p_sample_plms is needed because PLMS can't work with dicts as conditionings
-    # this file should be cleaned up later if weverything tuens out to work fine
+    # this file should be cleaned up later if everything turns out to work fine
 
     # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
     ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
index 1b9d7757..18daf8c1 100644
--- a/modules/sd_hijack_unet.py
+++ b/modules/sd_hijack_unet.py
@@ -4,7 +4,7 @@ import torch
 class TorchHijackForUnet:
     """
     This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
-    this makes it possible to create pictures with dimensions that are muliples of 8 rather than 64
+    this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
     """
 
     def __getattr__(self, item):
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 2dc64c3c..88d68c76 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -28,9 +28,9 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):        
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):
         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
-        
+
         self.placeholder_token = placeholder_token
 
         self.width = width
@@ -50,14 +50,14 @@ class PersonalizedBase(Dataset):
 
         self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
 
-        
+
         self.shuffle_tags = shuffle_tags
         self.tag_drop_out = tag_drop_out
 
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
             if shared.state.interrupted:
-                raise Exception("inturrupted")
+                raise Exception("interrupted")
             try:
                 image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
             except Exception:
@@ -144,7 +144,7 @@ class PersonalizedDataLoader(DataLoader):
             self.collate_fn = collate_wrapper_random
         else:
             self.collate_fn = collate_wrapper
-        
+
 
 class BatchLoader:
     def __init__(self, data):
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e28c357a..daf3997b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -133,7 +133,7 @@ class EmbeddingDatabase:
 
                 process_file(fullfn, fn)
             except Exception:
-                print(f"Error loading emedding {fn}:", file=sys.stderr)
+                print(f"Error loading embedding {fn}:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
                 continue
 
@@ -194,7 +194,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             csv_writer.writeheader()
 
         epoch = (step - 1) // epoch_len
-        epoch_step = (step - 1) % epoch_len 
+        epoch_step = (step - 1) % epoch_len
 
         csv_writer.writerow({
             "step": step,
@@ -270,9 +270,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
    # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     old_parallel_processing_allowed = shared.parallel_processing_allowed
-    
+
     pin_memory = shared.opts.pin_memory
-    
+
     ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
 
     latent_sampling_method = ds.latent_sampling_method
@@ -295,12 +295,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     loss_step = 0
     _loss_step = 0 #internal
 
-    
+
     last_saved_file = "<none>"
     last_saved_image = "<none>"
     forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
-    
+
     pbar = tqdm.tqdm(total=steps - initial_step)
     try:
         for i in range((steps-initial_step) * gradient_step):
@@ -327,10 +327,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     c = shared.sd_model.cond_stage_model(batch.cond_text)
                     loss = shared.sd_model(x, c)[0] / gradient_step
                     del x
-                    
+
                     _loss_step += loss.item()
                 scaler.scale(loss).backward()
-                
+
                 # go back until we reach gradient accumulation steps
                 if (j + 1) % gradient_step != 0:
                     continue
-- 
cgit v1.2.3


From 35e1017e3ea0a3ad9ec28c9b447200a70a65c0ae Mon Sep 17 00:00:00 2001
From: Akiba <akiba@anzu.link>
Date: Fri, 16 Dec 2022 20:43:09 +0800
Subject: fix: xformers

---
 modules/import_hook.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 modules/import_hook.py

(limited to 'modules')

diff --git a/modules/import_hook.py b/modules/import_hook.py
new file mode 100644
index 00000000..eb10e4fd
--- /dev/null
+++ b/modules/import_hook.py
@@ -0,0 +1,18 @@
+import builtins
+import sys
+
+old_import = builtins.__import__
+IMPORT_BLACKLIST = []
+
+
+if "xformers" not in "".join(sys.argv):
+    IMPORT_BLACKLIST.append("xformers")
+
+
+def import_hook(*args, **kwargs):
+    if args[0] in IMPORT_BLACKLIST:
+        raise ImportError("Import of %s is blacklisted" % args[0])
+    return old_import(*args, **kwargs)
+
+
+builtins.__import__ = import_hook
-- 
cgit v1.2.3


From 8b0703b8fcdab153958b11f0dd5e5b6b58565fed Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Fri, 16 Dec 2022 08:18:29 -0800
Subject: Add a workaround patch for DPM2 a issue

DPM2 a and DPM2 a Karras samplers are both affected by an issue described by https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/3483 and can be resolved by a workaround suggested by the k-diffusion author at https://github.com/crowsonkb/k-diffusion/issues/43#issuecomment-1305195666
---
 modules/sd_samplers.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4c123d3b..b8e0ce53 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -494,6 +494,9 @@ class KDiffusionSampler:
 
         x = x * sigmas[0]
 
+        if self.funcname == "sample_dpm_2_ancestral": # workaround dpm2 a issue
+            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
         extra_params_kwargs = self.initialize(p)
         if 'sigma_min' in inspect.signature(self.func).parameters:
             extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-- 
cgit v1.2.3


From 180fdf7809ea18de2d3b04618846d5a4e33c002e Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Fri, 16 Dec 2022 08:42:00 -0800
Subject: apply to DPM2 (non-ancestral) as well

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b8e0ce53..ae3d8bfa 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -494,7 +494,7 @@ class KDiffusionSampler:
 
         x = x * sigmas[0]
 
-        if self.funcname == "sample_dpm_2_ancestral": # workaround dpm2 a issue
+        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
             sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
 
         extra_params_kwargs = self.initialize(p)
-- 
cgit v1.2.3


From b7c478c3ebb2b1844efd5d6bddb69095dd10808f Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Sat, 17 Dec 2022 00:45:43 -0500
Subject: fix: Modify font size when unable to fit in plot

This prevents scenarios where text without line breaks will start overlapping with each other when generating X/Y plots. This is most evident when generating X/Y plots with checkpoints, as most don't contain spaces and sometimes include extra information such as the epoch, making it extra long.
---
 modules/images.py | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 8146f580..ad97980c 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -136,8 +136,19 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
                 lines.append(word)
         return lines
 
-    def draw_texts(drawing, draw_x, draw_y, lines):
+    def get_font(fontsize):
+        try:
+            return ImageFont.truetype(opts.font or Roboto, fontsize)
+        except Exception:
+            return ImageFont.truetype(Roboto, fontsize)
+
+    def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
         for i, line in enumerate(lines):
+            fnt = initial_fnt
+            fontsize = initial_fontsize
+            while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
+                fontsize -= 1
+                fnt = get_font(fontsize)
             drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
 
             if not line.is_active:
@@ -148,10 +159,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
     fontsize = (width + height) // 25
     line_spacing = fontsize // 2
 
-    try:
-        fnt = ImageFont.truetype(opts.font or Roboto, fontsize)
-    except Exception:
-        fnt = ImageFont.truetype(Roboto, fontsize)
+    fnt = get_font(fontsize)
 
     color_active = (0, 0, 0)
     color_inactive = (153, 153, 153)
@@ -178,6 +186,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
         for line in texts:
             bbox = calc_d.multiline_textbbox((0, 0), line.text, font=fnt)
             line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1])
+            line.allowed_width = allowed_width
 
     hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
     ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in
@@ -194,13 +203,13 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
         x = pad_left + width * col + width / 2
         y = pad_top / 2 - hor_text_heights[col] / 2
 
-        draw_texts(d, x, y, hor_texts[col])
+        draw_texts(d, x, y, hor_texts[col], fnt, fontsize)
 
     for row in range(rows):
         x = pad_left / 2
         y = pad_top + height * row + height / 2 - ver_text_heights[row] / 2
 
-        draw_texts(d, x, y, ver_texts[row])
+        draw_texts(d, x, y, ver_texts[row], fnt, fontsize)
 
     return result
 
-- 
cgit v1.2.3


From 16b4509fa60ec03102b2452b41799dafccd35970 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sat, 17 Dec 2022 03:21:19 -0500
Subject: Add numpy fix for MPS on PyTorch 1.12.1

When saving training results with torch.save(), an exception is thrown:
"RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead."

So for MPS, check if Tensor.requires_grad and detach() if necessary.
---
 modules/devices.py | 9 +++++++++
 1 file changed, 9 insertions(+)

(limited to 'modules')

diff --git a/modules/devices.py b/modules/devices.py
index f8cffae1..800510b7 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -125,7 +125,16 @@ def layer_norm_fix(*args, **kwargs):
     return orig_layer_norm(*args, **kwargs)
 
 
+# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
+orig_tensor_numpy = torch.Tensor.numpy
+def numpy_fix(self, *args, **kwargs):
+    if self.requires_grad:
+        self = self.detach()
+    return orig_tensor_numpy(self, *args, **kwargs)
+
+
 # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
 if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):
     torch.Tensor.to = tensor_to_fix
     torch.nn.functional.layer_norm = layer_norm_fix
+    torch.Tensor.numpy = numpy_fix
-- 
cgit v1.2.3


From cca16373def60bfc6d159a3c2dca91d0ba48112a Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sat, 17 Dec 2022 03:24:54 -0500
Subject: Add attributes used by MPS

---
 modules/safe.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/safe.py b/modules/safe.py
index 10460ad0..7c89c4c2 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -37,16 +37,16 @@ class RestrictedUnpickler(pickle.Unpickler):
 
         if module == 'collections' and name == 'OrderedDict':
             return getattr(collections, name)
-        if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
+        if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']:
             return getattr(torch._utils, name)
-        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage']:
+        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32']:
             return getattr(torch, name)
         if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
             return getattr(torch.nn.modules.container, name)
-        if module == 'numpy.core.multiarray' and name == 'scalar':
-            return numpy.core.multiarray.scalar
-        if module == 'numpy' and name == 'dtype':
-            return numpy.dtype
+        if module == 'numpy.core.multiarray' and name in ['scalar', '_reconstruct']:
+            return getattr(numpy.core.multiarray, name)
+        if module == 'numpy' and name in ['dtype', 'ndarray']:
+            return getattr(numpy, name)
         if module == '_codecs' and name == 'encode':
             return encode
         if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
-- 
cgit v1.2.3


From a26fe85056cf0dacef2d78cccf6ab100fd16da1c Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Sat, 17 Dec 2022 04:31:03 -0800
Subject: Add upscaler name as a suffix.

---
 modules/extras.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index bc349d5e..9b60e360 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -193,8 +193,13 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         else:
             basename = ''
 
+        # Add upscaler name as a suffix.
+        suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}"
+        if extras_upscaler_2 and extras_upscaler_2_visibility:
+            suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
+
         images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.3


From a7a039d53a69f8c32cb889fe322e769b238fec27 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Sat, 17 Dec 2022 06:28:51 -0800
Subject: Add option to include upscaler name in filename.

---
 modules/extras.py | 5 +++--
 modules/shared.py | 1 +
 2 files changed, 4 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 9b60e360..074a7c22 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -194,8 +194,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             basename = ''
 
         # Add upscaler name as a suffix.
-        suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}"
-        if extras_upscaler_2 and extras_upscaler_2_visibility:
+        suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
+        # Add second upscaler if applicable.
+        if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:
             suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
 
         images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
diff --git a/modules/shared.py b/modules/shared.py
index dc45fcaa..218894e8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -293,6 +293,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
 
     "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
+    "use_upscaler_name_as_suffix": OptionInfo(False, "Add upscaler name to the end of filename in the extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
     "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
 
-- 
cgit v1.2.3


From 6fd91c9179f51dd2f73f03eeabd12bfd081941c5 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Sat, 17 Dec 2022 08:59:02 -0800
Subject: Update OptionInfo to match preexisting option.

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 218894e8..230c377e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -293,7 +293,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
 
     "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
-    "use_upscaler_name_as_suffix": OptionInfo(False, "Add upscaler name to the end of filename in the extras tab"),
+    "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
     "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
 
-- 
cgit v1.2.3


From c02ef0f4286c618d30ee028778f58ca7809c7d93 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Sun, 18 Dec 2022 20:51:59 +0800
Subject: Fix PIL being imported before its installed (for new users only)

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index c36ee211..734ea2fe 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -5,6 +5,7 @@ import os
 import sys
 import time
 
+from PIL import Image
 import gradio as gr
 import tqdm
 
-- 
cgit v1.2.3


From 7ba9bc2fdbfae8115294962510492faafeb48573 Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Sun, 18 Dec 2022 19:16:42 -0800
Subject: fix dpm2 in img2img as well

---
 modules/sd_samplers.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index ae3d8bfa..1a1b8919 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -454,6 +454,9 @@ class KDiffusionSampler:
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 
+        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
+            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
         sigma_sched = sigmas[steps - t_enc - 1:]
         xi = x + noise * sigma_sched[0]
         
-- 
cgit v1.2.3


From 22f1527fa79a03dbc8b1a4eec3b22369a877f4bd Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Tue, 20 Dec 2022 20:36:49 +1100
Subject: feat(api): add override_settings_restore_afterwards

---
 modules/processing.py | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 24c537d1..f7335da2 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -77,7 +77,7 @@ class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
     """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, sampler_index: int = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None):
         if sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
@@ -118,6 +118,7 @@ class StableDiffusionProcessing():
         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
         self.s_noise = s_noise or opts.s_noise
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
+        self.override_settings_restore_afterwards = override_settings_restore_afterwards
         self.is_using_inpainting_conditioning = False
 
         if not seed_enable_extras:
@@ -147,11 +148,11 @@ class StableDiffusionProcessing():
 
         # The "masked-image" in this case will just be all zeros since the entire image is masked.
         image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
 
         # Add the fake full 1s mask to the first dimension.
         image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-        image_conditioning = image_conditioning.to(x.dtype)            
+        image_conditioning = image_conditioning.to(x.dtype)
 
         return image_conditioning
 
@@ -199,7 +200,7 @@ class StableDiffusionProcessing():
             source_image * (1.0 - conditioning_mask),
             getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
         )
-        
+
         # Encode the new masked image using first stage of network.
         conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
 
@@ -463,12 +464,14 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
         res = process_images_inner(p)
 
-    finally:  # restore opts to original state
-        for k, v in stored_opts.items():
-            setattr(opts, k, v)
-            if k == 'sd_hypernetwork': shared.reload_hypernetworks()
-            if k == 'sd_model_checkpoint': sd_models.reload_model_weights()
-            if k == 'sd_vae': sd_vae.reload_vae_weights()
+    finally:
+        # restore opts to original state
+        if p.override_settings_restore_afterwards:
+            for k, v in stored_opts.items():
+                setattr(opts, k, v)
+                if k == 'sd_hypernetwork': shared.reload_hypernetworks()
+                if k == 'sd_model_checkpoint': sd_models.reload_model_weights()
+                if k == 'sd_vae': sd_vae.reload_vae_weights()
 
     return res
 
@@ -537,7 +540,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         for n in range(p.n_iter):
             if state.skipped:
                 state.skipped = False
-            
+
             if state.interrupted:
                 break
 
@@ -612,7 +615,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image.info["parameters"] = text
                 output_images.append(image)
 
-            del x_samples_ddim 
+            del x_samples_ddim
 
             devices.torch_gc()
 
@@ -720,7 +723,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
 
-            # Avoid making the inpainting conditioning unless necessary as 
+            # Avoid making the inpainting conditioning unless necessary as
             # this does need some extra compute to decode / encode the image again.
             if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
                 image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
-- 
cgit v1.2.3


From 35b1775b32a07f1b7c9dccad61f7aa77027a00fa Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 19 Dec 2022 17:25:14 -0500
Subject: Use other MPS optimization for large q.shape[0] * q.shape[1]

Check if q.shape[0] * q.shape[1] is 2**18 or larger and use the lower memory usage MPS optimization if it is. This should prevent most crashes that were occurring at certain resolutions (e.g. 1024x1024, 2048x512, 512x2048).

Also included is a change to check slice_size and prevent it from being divisible by 4096 which also results in a crash. Otherwise a crash can occur at 1024x512 or 512x1024 resolution.
---
 modules/sd_hijack_optimizations.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 98123fbf..02c87f40 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -127,7 +127,7 @@ def check_for_psutil():
 
 invokeAI_mps_available = check_for_psutil()
 
-# -- Taken from https://github.com/invoke-ai/InvokeAI --
+# -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
 if invokeAI_mps_available:
     import psutil
     mem_total_gb = psutil.virtual_memory().total // (1 << 30)
@@ -152,14 +152,16 @@ def einsum_op_slice_1(q, k, v, slice_size):
     return r
 
 def einsum_op_mps_v1(q, k, v):
-    if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
+    if q.shape[0] * q.shape[1] <= 2**16: # (512x512) max q.shape[1]: 4096
         return einsum_op_compvis(q, k, v)
     else:
         slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
+        if slice_size % 4096 == 0:
+            slice_size -= 1
         return einsum_op_slice_1(q, k, v, slice_size)
 
 def einsum_op_mps_v2(q, k, v):
-    if mem_total_gb > 8 and q.shape[1] <= 4096:
+    if mem_total_gb > 8 and q.shape[0] * q.shape[1] <= 2**16:
         return einsum_op_compvis(q, k, v)
     else:
         return einsum_op_slice_0(q, k, v, 1)
@@ -188,7 +190,7 @@ def einsum_op(q, k, v):
         return einsum_op_cuda(q, k, v)
 
     if q.device.type == 'mps':
-        if mem_total_gb >= 32:
+        if mem_total_gb >= 32 and q.shape[0] % 32 != 0 and q.shape[0] * q.shape[1] < 2**18:
             return einsum_op_mps_v1(q, k, v)
         return einsum_op_mps_v2(q, k, v)
 
-- 
cgit v1.2.3


From 13e0295ab682299e3280eb6ff28be0870f2bc57c Mon Sep 17 00:00:00 2001
From: Akiba <akiba@anzu.link>
Date: Sat, 24 Dec 2022 11:17:21 +0800
Subject: fix: xformers use importlib

---
 modules/import_hook.py | 15 +--------------
 1 file changed, 1 insertion(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/import_hook.py b/modules/import_hook.py
index eb10e4fd..7403135d 100644
--- a/modules/import_hook.py
+++ b/modules/import_hook.py
@@ -1,18 +1,5 @@
-import builtins
 import sys
 
-old_import = builtins.__import__
-IMPORT_BLACKLIST = []
-
 
 if "xformers" not in "".join(sys.argv):
-    IMPORT_BLACKLIST.append("xformers")
-
-
-def import_hook(*args, **kwargs):
-    if args[0] in IMPORT_BLACKLIST:
-        raise ImportError("Import of %s is blacklisted" % args[0])
-    return old_import(*args, **kwargs)
-
-
-builtins.__import__ = import_hook
+    sys.modules["xformers"] = None
-- 
cgit v1.2.3


From 0c747d4013f41f6c887a63d256af884aa8872f91 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 07:57:56 +0300
Subject: add a comment for disable xformers hack

---
 modules/import_hook.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/import_hook.py b/modules/import_hook.py
index 7403135d..28c67dfa 100644
--- a/modules/import_hook.py
+++ b/modules/import_hook.py
@@ -1,5 +1,5 @@
 import sys
 
-
-if "xformers" not in "".join(sys.argv):
+# this will break any attempt to import xformers which will prevent stability diffusion repo from trying to use it
+if "--xformers" not in "".join(sys.argv):
     sys.modules["xformers"] = None
-- 
cgit v1.2.3


From 399b229783a7b5fddab0a258740b4d59d668ee12 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 09:03:45 +0300
Subject: eliminate duplicated code add an option to samplers for skipping next
 to last sigma

---
 modules/sd_samplers.py | 31 ++++++++++++++-----------------
 1 file changed, 14 insertions(+), 17 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 1a1b8919..d26e48dc 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -23,16 +23,16 @@ samplers_k_diffusion = [
     ('Euler', 'sample_euler', ['k_euler'], {}),
     ('LMS', 'sample_lms', ['k_lms'], {}),
     ('Heun', 'sample_heun', ['k_heun'], {}),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
     ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
     ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
-    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
-    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
     ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
     ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
@@ -444,9 +444,7 @@ class KDiffusionSampler:
 
         return extra_params_kwargs
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = setup_img2img_steps(p, steps)
-
+    def get_sigmas(self, p, steps):
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
@@ -454,9 +452,16 @@ class KDiffusionSampler:
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 
-        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
+        if self.config is not None and self.config.options.get('discard_next_to_last_sigma', False):
             sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
 
+        return sigmas
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps, t_enc = setup_img2img_steps(p, steps)
+
+        sigmas = self.get_sigmas(p, steps)
+
         sigma_sched = sigmas[steps - t_enc - 1:]
         xi = x + noise * sigma_sched[0]
         
@@ -488,18 +493,10 @@ class KDiffusionSampler:
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
         steps = steps or p.steps
 
-        if p.sampler_noise_scheduler_override:
-            sigmas = p.sampler_noise_scheduler_override(steps)
-        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
-        else:
-            sigmas = self.model_wrap.get_sigmas(steps)
+        sigmas = self.get_sigmas(p, steps)
 
         x = x * sigmas[0]
 
-        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
-            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
         extra_params_kwargs = self.initialize(p)
         if 'sigma_min' in inspect.signature(self.func).parameters:
             extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-- 
cgit v1.2.3


From 9441c28c947588d756e279a8cd5db6c0b4a8d2e4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 09:46:35 +0300
Subject: add an option for img2img background color

---
 modules/images.py     | 11 +++++++++++
 modules/processing.py |  2 +-
 modules/shared.py     |  1 +
 modules/ui.py         |  2 +-
 4 files changed, 14 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index ad97980c..8bcbc8d9 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -622,3 +622,14 @@ def image_data(data):
         pass
 
     return '', None
+
+
+def flatten(img, bgcolor):
+    """replaces transparency with bgcolor (example: "#ffffff"), returning an RGB mode image with no transparency"""
+
+    if img.mode == "RGBA":
+        background = Image.new('RGBA', img.size, bgcolor)
+        background.paste(img, mask=img)
+        img = background
+
+    return img.convert('RGB')
diff --git a/modules/processing.py b/modules/processing.py
index bc841837..7c4bcd74 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -832,7 +832,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.color_corrections = []
         imgs = []
         for img in self.init_images:
-            image = img.convert("RGB")
+            image = images.flatten(img, opts.img2img_background_color)
 
             if crop_region is None:
                 image = images.resize_image(self.resize_mode, image, self.width, self.height)
diff --git a/modules/shared.py b/modules/shared.py
index 215c1358..dcce9299 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -363,6 +363,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
+    "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", gr.ColorPicker, {}),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
diff --git a/modules/ui.py b/modules/ui.py
index 28481e33..76919b0f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -812,7 +812,7 @@ def create_ui():
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
                     with gr.TabItem('img2img', id='img2img'):
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool, image_mode="RGBA").style(height=480)
 
                     with gr.TabItem('Inpaint', id='inpaint'):
                         init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
-- 
cgit v1.2.3


From c0a8401b5a8368d03bb14fc63abbdedb1e802d8d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 11:12:17 +0300
Subject: rename the option for img2img latent upscale

---
 modules/processing.py | 2 +-
 modules/ui.py         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 75b0067c..d2288f26 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -846,7 +846,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
                 self.overlay_images.append(image_masked.convert('RGBA'))
 
-            # crop_region is not none iif we are doing inpaint full res
+            # crop_region is not None if we are doing inpaint full res
             if crop_region is not None:
                 image = image.crop(crop_region)
                 image = images.resize_image(2, image, self.width, self.height)
diff --git a/modules/ui.py b/modules/ui.py
index faba69a4..9dec61d5 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -857,7 +857,7 @@ def create_ui():
                         img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
 
                 with gr.Row():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Upscale Latent Space"], type="index", value="Just resize")
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
                 steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-- 
cgit v1.2.3


From f23a822f1c9cb3bd2e8772c75af429e06515eaef Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Sat, 24 Dec 2022 20:45:16 +1100
Subject: feat(api): include job_timestamp in progress

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 8ea3b441..f356dbf7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -171,6 +171,7 @@ class State:
             "interrupted": self.skipped,
             "job": self.job,
             "job_count": self.job_count,
+            "job_timestamp": self.job_timestamp,
             "job_no": self.job_no,
             "sampling_step": self.sampling_step,
             "sampling_steps": self.sampling_steps,
-- 
cgit v1.2.3


From 11dd79e346bd780bc5c3119df962e7a9c20f2493 Mon Sep 17 00:00:00 2001
From: AbstractQbit <38468635+AbstractQbit@users.noreply.github.com>
Date: Sat, 24 Dec 2022 14:00:17 +0300
Subject: Add an option for faster low quality previews

---
 modules/sd_samplers.py | 23 ++++++++++++++++-------
 modules/shared.py      |  5 +++--
 2 files changed, 19 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d26e48dc..fbb56af4 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -106,20 +106,29 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc
 
 
-def single_sample_to_image(sample):
-    x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
+def single_sample_to_image(sample, approximation=False):
+    if approximation:
+        # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
+        coefs = torch.tensor(
+            [[ 0.298,  0.207,  0.208],
+             [ 0.187,  0.286,  0.173],
+             [-0.158,  0.189,  0.264],
+             [-0.184, -0.271, -0.473]]).to(sample.device)
+        x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
+    else:
+        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)
 
 
-def sample_to_image(samples, index=0):
-    return single_sample_to_image(samples[index])
+def sample_to_image(samples, index=0, approximation=False):
+    return single_sample_to_image(samples[index], approximation)
 
 
-def samples_to_image_grid(samples):
-    return images.image_grid([single_sample_to_image(sample) for sample in samples])
+def samples_to_image_grid(samples, approximation=False):
+    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
 
 
 def store_latent(decoded):
@@ -127,7 +136,7 @@ def store_latent(decoded):
 
     if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
         if not shared.parallel_processing_allowed:
-            shared.state.current_image = sample_to_image(decoded)
+            shared.state.current_image = sample_to_image(decoded, approximation=opts.show_progress_approximate)
 
 
 class InterruptedException(BaseException):
diff --git a/modules/shared.py b/modules/shared.py
index 8ea3b441..1067b1d3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -212,9 +212,9 @@ class State:
 
         import modules.sd_samplers
         if opts.show_progress_grid:
-            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent)
+            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent, approximation=opts.show_progress_approximate)
         else:
-            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent)
+            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent, approximation=opts.show_progress_approximate)
 
         self.current_image_sampling_step = self.sampling_step
 
@@ -391,6 +391,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
+    "show_progress_approximate": OptionInfo(False, "Calculate small previews using fast linear approximation instead of VAE"),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-- 
cgit v1.2.3


From 6247f21a637399900643a4915e8a223688e0ed22 Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Sat, 24 Dec 2022 22:04:53 +1100
Subject: fix(api): don't save extras output to disk

---
 modules/api/api.py |  6 +++---
 modules/extras.py  | 17 +++++++++--------
 2 files changed, 12 insertions(+), 11 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 3257445d..b43dd16b 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -181,7 +181,7 @@ class Api:
         reqDict['image'] = decode_base64_to_image(reqDict['image'])
 
         with self.queue_lock:
-            result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", **reqDict)
+            result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
 
         return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
 
@@ -197,7 +197,7 @@ class Api:
         reqDict.pop('imageList')
 
         with self.queue_lock:
-            result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict)
+            result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
 
         return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
 
@@ -322,7 +322,7 @@ class Api:
 
     def get_artists(self):
         return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
-    
+
     def refresh_checkpoints(self):
         shared.refresh_checkpoints()
 
diff --git a/modules/extras.py b/modules/extras.py
index 6fa7d856..68939dea 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -55,7 +55,7 @@ class LruCache(OrderedDict):
 cached_images: LruCache = LruCache(max_size=5)
 
 
-def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool):
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
     devices.torch_gc()
 
     imageArr = []
@@ -193,14 +193,15 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         else:
             basename = ''
 
-        # Add upscaler name as a suffix.
-        suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
-        # Add second upscaler if applicable.
-        if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:
-            suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
+        if save_output:
+            # Add upscaler name as a suffix.
+            suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
+            # Add second upscaler if applicable.
+            if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:
+                suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
 
-        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
+            images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                            no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.3


From 5a650055de3792223a91925aba8130ebdee29e35 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?linuxmobile=20=28=20=E3=83=AA=E3=83=8A=E3=83=83=E3=82=AF?=
 =?UTF-8?q?=E3=82=B9=20=29?= <bdiez19@gmail.com>
Date: Sat, 24 Dec 2022 09:25:35 -0300
Subject: Removed lenght in sd_model at line 115

Commit eba60a4 is what is causing this error, delete the length check in sd_model starting at line 115 and it's fine.

https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/5971#issuecomment-1364507379
---
 modules/sd_models.py | 3 ---
 1 file changed, 3 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 1254e5ae..6ca06211 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -111,9 +111,6 @@ def model_hash(filename):
 
 def select_checkpoint():
     model_checkpoint = shared.opts.sd_model_checkpoint
-    
-    if len(model_checkpoint) == 0:
-        model_checkpoint = shared.default_sd_model_file
         
     checkpoint_info = checkpoints_list.get(model_checkpoint, None)
     if checkpoint_info is not None:
-- 
cgit v1.2.3


From 03d7b394539558f6f560155d87a4fc66eb675e30 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 12:40:32 +0300
Subject: added an option to filter out deepbooru tags

---
 modules/deepbooru.py | 4 +++-
 modules/shared.py    | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index dfc83357..122fce7f 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -79,7 +79,9 @@ class DeepDanbooru:
 
         res = []
 
-        for tag in tags:
+        filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")])
+
+        for tag in [x for x in tags if x not in filtertags]:
             probability = probability_dict[tag]
             tag_outformat = tag
             if use_spaces:
diff --git a/modules/shared.py b/modules/shared.py
index 8ea3b441..a75de535 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -386,6 +386,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
     "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
     "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
     "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
+    "deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
 }))
 
 options_templates.update(options_section(('ui', "User interface"), {
-- 
cgit v1.2.3


From 0b8acce6a9a1418fa88a506450cd1b92e2d48986 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 18:38:16 +0300
Subject: separate part of denoiser code into a function to make it easier for
 extensions to override it

---
 modules/sd_samplers.py | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d26e48dc..8efe74df 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -288,6 +288,16 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None
         self.step = 0
 
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+        return denoised
+
     def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
         if state.interrupted or state.skipped:
             raise InterruptedException
@@ -329,12 +339,7 @@ class CFGDenoiser(torch.nn.Module):
 
             x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
 
-        denoised_uncond = x_out[-uncond.shape[0]:]
-        denoised = torch.clone(denoised_uncond)
-
-        for i, conds in enumerate(conds_list):
-            for cond_index, weight in conds:
-                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+        denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
 
         if self.mask is not None:
             denoised = self.init_latent * self.mask + self.nmask * denoised
-- 
cgit v1.2.3


From 3bf5591efe9a9f219c6088be322a87adc4f48f95 Mon Sep 17 00:00:00 2001
From: Yuval Aboulafia <yuval.abou@gmail.com>
Date: Sat, 24 Dec 2022 21:35:29 +0200
Subject: fix F541 f-string without any placeholders

---
 modules/codeformer/vqgan_arch.py               | 4 ++--
 modules/hypernetworks/hypernetwork.py          | 4 ++--
 modules/images.py                              | 2 +-
 modules/interrogate.py                         | 2 +-
 modules/safe.py                                | 8 ++++----
 modules/sd_models.py                           | 8 ++++----
 modules/sd_vae.py                              | 2 +-
 modules/textual_inversion/textual_inversion.py | 2 +-
 8 files changed, 16 insertions(+), 16 deletions(-)

(limited to 'modules')

diff --git a/modules/codeformer/vqgan_arch.py b/modules/codeformer/vqgan_arch.py
index c06c590c..e7293683 100644
--- a/modules/codeformer/vqgan_arch.py
+++ b/modules/codeformer/vqgan_arch.py
@@ -382,7 +382,7 @@ class VQAutoEncoder(nn.Module):
                 self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
                 logger.info(f'vqgan is loaded from: {model_path} [params]')
             else:
-                raise ValueError(f'Wrong params!')
+                raise ValueError('Wrong params!')
 
 
     def forward(self, x):
@@ -431,7 +431,7 @@ class VQGANDiscriminator(nn.Module):
             elif 'params' in chkpt:
                 self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
             else:
-                raise ValueError(f'Wrong params!')
+                raise ValueError('Wrong params!')
 
     def forward(self, x):
         return self.main(x)
\ No newline at end of file
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index c406ffb3..9d3034ae 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -277,7 +277,7 @@ def load_hypernetwork(filename):
             print(traceback.format_exc(), file=sys.stderr)
     else:
         if shared.loaded_hypernetwork is not None:
-            print(f"Unloading hypernetwork")
+            print("Unloading hypernetwork")
 
         shared.loaded_hypernetwork = None
 
@@ -417,7 +417,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
 
     initial_step = hypernetwork.step or 0
     if initial_step >= steps:
-        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        shared.state.textinfo = "Model has already been trained beyond specified max steps"
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
diff --git a/modules/images.py b/modules/images.py
index 809ad9f7..31d4528d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -599,7 +599,7 @@ def read_info_from_image(image):
 Negative prompt: {json_info["uc"]}
 Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
         except Exception:
-            print(f"Error parsing NovelAI image generation parameters:", file=sys.stderr)
+            print("Error parsing NovelAI image generation parameters:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
     return geninfo, items
diff --git a/modules/interrogate.py b/modules/interrogate.py
index 0068b81c..46935210 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -172,7 +172,7 @@ class InterrogateModels:
                             res += ", " + match
 
         except Exception:
-            print(f"Error interrogating", file=sys.stderr)
+            print("Error interrogating", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
             res += "<error>"
 
diff --git a/modules/safe.py b/modules/safe.py
index 479c8b86..1d4c20b9 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -137,15 +137,15 @@ def load_with_extra(filename, extra_handler=None, *args, **kwargs):
     except pickle.UnpicklingError:
         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
-        print(f"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
-        print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
+        print("-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
+        print("You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
         return None
 
     except Exception:
         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
-        print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
-        print(f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
+        print("\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
+        print("You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
         return None
 
     return unsafe_torch_load(filename, *args, **kwargs)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6ca06211..ecdd91c5 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -117,13 +117,13 @@ def select_checkpoint():
         return checkpoint_info
 
     if len(checkpoints_list) == 0:
-        print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
+        print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
         if shared.cmd_opts.ckpt is not None:
             print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
         print(f" - directory {model_path}", file=sys.stderr)
         if shared.cmd_opts.ckpt_dir is not None:
             print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
-        print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
+        print("Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
         exit(1)
 
     checkpoint_info = next(iter(checkpoints_list.values()))
@@ -324,7 +324,7 @@ def load_model(checkpoint_info=None):
 
     script_callbacks.model_loaded_callback(sd_model)
 
-    print(f"Model loaded.")
+    print("Model loaded.")
     return sd_model
 
 
@@ -359,5 +359,5 @@ def reload_model_weights(sd_model=None, info=None):
     if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
         sd_model.to(devices.device)
 
-    print(f"Weights loaded.")
+    print("Weights loaded.")
     return sd_model
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 25638a83..3856418e 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -208,5 +208,5 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
     if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
         sd_model.to(devices.device)
 
-    print(f"VAE Weights loaded.")
+    print("VAE Weights loaded.")
     return sd_model
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index daf3997b..f6112578 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -263,7 +263,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 
     initial_step = embedding.step or 0
     if initial_step >= steps:
-        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        shared.state.textinfo = "Model has already been trained beyond specified max steps"
         return embedding, filename
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
 
-- 
cgit v1.2.3


From 56e557c6ff8a6480887c9c585bf908045ee8e791 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 22:39:00 +0300
Subject: added cheap NN approximation for VAE

---
 modules/sd_samplers.py   | 29 +++++++++++++-----------
 modules/sd_vae_approx.py | 58 ++++++++++++++++++++++++++++++++++++++++++++++++
 modules/shared.py        |  6 ++---
 3 files changed, 77 insertions(+), 16 deletions(-)
 create mode 100644 modules/sd_vae_approx.py

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 27ef4ff8..177b5338 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -9,7 +9,7 @@ import k_diffusion.sampling
 import torchsde._brownian.brownian_interval
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
-from modules import prompt_parser, devices, processing, images
+from modules import prompt_parser, devices, processing, images, sd_vae_approx
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -106,28 +106,31 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc
 
 
-def single_sample_to_image(sample, approximation=False):
-    if approximation:
-        # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
-        coefs = torch.tensor(
-            [[ 0.298,  0.207,  0.208],
-             [ 0.187,  0.286,  0.173],
-             [-0.158,  0.189,  0.264],
-             [-0.184, -0.271, -0.473]]).to(sample.device)
-        x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
+approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
+
+
+def single_sample_to_image(sample, approximation=None):
+    if approximation is None:
+        approximation = approximation_indexes.get(opts.show_progress_type, 0)
+
+    if approximation == 2:
+        x_sample = sd_vae_approx.cheap_approximation(sample)
+    elif approximation == 1:
+        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
     else:
         x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
+
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)
 
 
-def sample_to_image(samples, index=0, approximation=False):
+def sample_to_image(samples, index=0, approximation=None):
     return single_sample_to_image(samples[index], approximation)
 
 
-def samples_to_image_grid(samples, approximation=False):
+def samples_to_image_grid(samples, approximation=None):
     return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
 
 
@@ -136,7 +139,7 @@ def store_latent(decoded):
 
     if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
         if not shared.parallel_processing_allowed:
-            shared.state.current_image = sample_to_image(decoded, approximation=opts.show_progress_approximate)
+            shared.state.current_image = sample_to_image(decoded)
 
 
 class InterruptedException(BaseException):
diff --git a/modules/sd_vae_approx.py b/modules/sd_vae_approx.py
new file mode 100644
index 00000000..0a58542d
--- /dev/null
+++ b/modules/sd_vae_approx.py
@@ -0,0 +1,58 @@
+import os
+
+import torch
+from torch import nn
+from modules import devices, paths
+
+sd_vae_approx_model = None
+
+
+class VAEApprox(nn.Module):
+    def __init__(self):
+        super(VAEApprox, self).__init__()
+        self.conv1 = nn.Conv2d(4, 8, (7, 7))
+        self.conv2 = nn.Conv2d(8, 16, (5, 5))
+        self.conv3 = nn.Conv2d(16, 32, (3, 3))
+        self.conv4 = nn.Conv2d(32, 64, (3, 3))
+        self.conv5 = nn.Conv2d(64, 32, (3, 3))
+        self.conv6 = nn.Conv2d(32, 16, (3, 3))
+        self.conv7 = nn.Conv2d(16, 8, (3, 3))
+        self.conv8 = nn.Conv2d(8, 3, (3, 3))
+
+    def forward(self, x):
+        extra = 11
+        x = nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2))
+        x = nn.functional.pad(x, (extra, extra, extra, extra))
+
+        for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8, ]:
+            x = layer(x)
+            x = nn.functional.leaky_relu(x, 0.1)
+
+        return x
+
+
+def model():
+    global sd_vae_approx_model
+
+    if sd_vae_approx_model is None:
+        sd_vae_approx_model = VAEApprox()
+        sd_vae_approx_model.load_state_dict(torch.load(os.path.join(paths.models_path, "VAE-approx", "model.pt")))
+        sd_vae_approx_model.eval()
+        sd_vae_approx_model.to(devices.device, devices.dtype)
+
+    return sd_vae_approx_model
+
+
+def cheap_approximation(sample):
+    # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
+
+    coefs = torch.tensor([
+        [0.298, 0.207, 0.208],
+        [0.187, 0.286, 0.173],
+        [-0.158, 0.189, 0.264],
+        [-0.184, -0.271, -0.473],
+    ]).to(sample.device)
+
+    x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
+
+    return x_sample
diff --git a/modules/shared.py b/modules/shared.py
index eb3e5aec..3cc3c724 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -212,9 +212,9 @@ class State:
 
         import modules.sd_samplers
         if opts.show_progress_grid:
-            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent, approximation=opts.show_progress_approximate)
+            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent)
         else:
-            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent, approximation=opts.show_progress_approximate)
+            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent)
 
         self.current_image_sampling_step = self.sampling_step
 
@@ -392,7 +392,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
-    "show_progress_approximate": OptionInfo(False, "Calculate small previews using fast linear approximation instead of VAE"),
+    "show_progress_type": OptionInfo("Full", "Image creation progress mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-- 
cgit v1.2.3


From c5bdba2089dc7060be2631bcbc83313b6358cbf2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 22:41:35 +0300
Subject: change wording a bit

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 3cc3c724..d4ddeea0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -392,7 +392,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
-    "show_progress_type": OptionInfo("Full", "Image creation progress mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
+    "show_progress_type": OptionInfo("Full", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-- 
cgit v1.2.3


From 5f1dfbbc959855fd90ba80c0c76301d2063772fa Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 24 Dec 2022 18:02:22 -0500
Subject: implement train api

---
 modules/api/api.py                    | 94 ++++++++++++++++++++++++++++++++++-
 modules/api/models.py                 |  9 ++++
 modules/hypernetworks/hypernetwork.py | 26 ++++++++++
 modules/hypernetworks/ui.py           | 31 ++----------
 4 files changed, 132 insertions(+), 28 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index b43dd16b..1ceba75d 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -10,13 +10,17 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru
+from modules import sd_samplers, deepbooru, sd_hijack
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.extras import run_extras, run_pnginfo
+from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
+from modules.textual_inversion.preprocess import preprocess
+from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
 from modules.sd_models import checkpoints_list
 from modules.realesrgan_model import get_realesrgan_models
+from modules import devices
 from typing import List
 
 def upscaler_to_index(name: str):
@@ -97,6 +101,11 @@ class Api:
         self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
         self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
         self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
+        self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
+        self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
+        self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse)
+        self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
+        self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
 
     def add_api_route(self, path: str, endpoint, **kwargs):
         if shared.cmd_opts.api_auth:
@@ -326,6 +335,89 @@ class Api:
     def refresh_checkpoints(self):
         shared.refresh_checkpoints()
 
+    def create_embedding(self, args: dict):
+        try:
+            shared.state.begin()
+            filename = create_embedding(**args) # create empty embedding
+            sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
+            shared.state.end()
+            return CreateResponse(info = "create embedding filename: {filename}".format(filename = filename))
+        except AssertionError as e:
+            shared.state.end()
+            return TrainResponse(info = "create embedding error: {error}".format(error = e))
+
+    def create_hypernetwork(self, args: dict):
+        try:
+            shared.state.begin()
+            filename = create_hypernetwork(**args) # create empty embedding
+            shared.state.end()
+            return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename))
+        except AssertionError as e:
+            shared.state.end()
+            return TrainResponse(info = "create hypernetwork error: {error}".format(error = e))
+
+    def preprocess(self, args: dict):
+        try:
+            shared.state.begin()
+            preprocess(**args) # quick operation unless blip/booru interrogation is enabled
+            shared.state.end()
+            return PreprocessResponse(info = 'preprocess complete')
+        except KeyError as e:
+            shared.state.end()
+            return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e))
+        except AssertionError as e:
+            shared.state.end()
+            return PreprocessResponse(info = "preprocess error: {error}".format(error = e))
+        except FileNotFoundError as e:
+            shared.state.end()
+            return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e))
+
+    def train_embedding(self, args: dict):
+        try:
+            shared.state.begin()
+            apply_optimizations = shared.opts.training_xattention_optimizations
+            error = None
+            filename = ''
+            if not apply_optimizations:
+                sd_hijack.undo_optimizations()
+            try:
+                embedding, filename = train_embedding(**args) # can take a long time to complete
+            except Exception as e:
+                error = e
+            finally:
+                if not apply_optimizations:
+                    sd_hijack.apply_optimizations()
+                shared.state.end()
+            return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
+        except AssertionError as msg:
+            shared.state.end()
+            return TrainResponse(info = "train embedding error: {msg}".format(msg = msg))
+
+    def train_hypernetwork(self, args: dict):
+        try:
+            shared.state.begin()
+            initial_hypernetwork = shared.loaded_hypernetwork
+            apply_optimizations = shared.opts.training_xattention_optimizations
+            error = None
+            filename = ''
+            if not apply_optimizations:
+                sd_hijack.undo_optimizations()
+            try:
+                hypernetwork, filename = train_hypernetwork(*args)
+            except Exception as e:
+                error = e
+            finally:
+                shared.loaded_hypernetwork = initial_hypernetwork
+                shared.sd_model.cond_stage_model.to(devices.device)
+                shared.sd_model.first_stage_model.to(devices.device)
+                if not apply_optimizations:
+                    sd_hijack.apply_optimizations()
+                shared.state.end()
+            return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
+        except AssertionError as msg:
+            shared.state.end()
+            return TrainResponse(info = "train embedding error: {error}".format(error = error))
+
     def launch(self, server_name, port):
         self.app.include_router(self.router)
         uvicorn.run(self.app, host=server_name, port=port)
diff --git a/modules/api/models.py b/modules/api/models.py
index a22bc6b3..c446ce7a 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -175,6 +175,15 @@ class InterrogateRequest(BaseModel):
 class InterrogateResponse(BaseModel):
     caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
 
+class TrainResponse(BaseModel):
+    info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.")
+
+class CreateResponse(BaseModel):
+    info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
+
+class PreprocessResponse(BaseModel):
+    info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
+
 fields = {}
 for key, metadata in opts.data_labels.items():
     value = opts.data.get(key)
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index c406ffb3..3182ff03 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -378,6 +378,32 @@ def report_statistics(loss_info:dict):
             print(e)
 
 
+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
+    # Remove illegal characters from name.
+    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
+
+    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
+    if not overwrite_old:
+        assert not os.path.exists(fn), f"file {fn} already exists"
+
+    if type(layer_structure) == str:
+        layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
+
+    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
+        name=name,
+        enable_sizes=[int(x) for x in enable_sizes],
+        layer_structure=layer_structure,
+        activation_func=activation_func,
+        weight_init=weight_init,
+        add_layer_norm=add_layer_norm,
+        use_dropout=use_dropout,
+    )
+    hypernet.save(fn)
+
+    shared.reload_hypernetworks()
+
+    return fn
+
 
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index c2d4b51c..e7f9e593 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -3,39 +3,16 @@ import os
 import re
 
 import gradio as gr
-import modules.textual_inversion.preprocess
-import modules.textual_inversion.textual_inversion
+import modules.hypernetworks.hypernetwork
 from modules import devices, sd_hijack, shared
-from modules.hypernetworks import hypernetwork
 
 not_available = ["hardswish", "multiheadattention"]
-keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
-    # Remove illegal characters from name.
-    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
+    filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout)
 
-    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
-    if not overwrite_old:
-        assert not os.path.exists(fn), f"file {fn} already exists"
-
-    if type(layer_structure) == str:
-        layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
-
-    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
-        name=name,
-        enable_sizes=[int(x) for x in enable_sizes],
-        layer_structure=layer_structure,
-        activation_func=activation_func,
-        weight_init=weight_init,
-        add_layer_norm=add_layer_norm,
-        use_dropout=use_dropout,
-    )
-    hypernet.save(fn)
-
-    shared.reload_hypernetworks()
-
-    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
+    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {filename}", ""
 
 
 def train_hypernetwork(*args):
-- 
cgit v1.2.3


From f60c24f8121186f8d85f1096a96ddf685f625d04 Mon Sep 17 00:00:00 2001
From: eaglgenes101 <eaglgenes101@gmail.com>
Date: Sat, 24 Dec 2022 22:16:01 -0500
Subject: Add CSS classes for the settings panels

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 9dec61d5..65af8966 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -657,7 +657,7 @@ def create_ui():
                 setup_progressbar(progressbar, txt2img_preview, 'txt2img')
 
         with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
+            with gr.Column(variant='panel', elem_id="txt2img_settings"):
                 steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
                 sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
 
@@ -812,7 +812,7 @@ def create_ui():
                 setup_progressbar(progressbar, img2img_preview, 'img2img')
 
         with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
+            with gr.Column(variant='panel', elem_id="img2img_settings"):
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
                     with gr.TabItem('img2img', id='img2img'):
-- 
cgit v1.2.3


From 61a273236ffd1366456cac7040e30972ca65dc2c Mon Sep 17 00:00:00 2001
From: Allen Benz <allenbenz@users.noreply.github.com>
Date: Sat, 24 Dec 2022 20:23:12 -0800
Subject: Fix clip interrogate from the webui

A recent change made the image RGBA, which makes the clip interrogator unhappy.
deepbooru and calling the interrogator from the api already do the conversion so this is the only place that needed it.
---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 9dec61d5..7bf5abd9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -270,7 +270,7 @@ def apply_styles(prompt, prompt_neg, style1_name, style2_name):
 
 
 def interrogate(image):
-    prompt = shared.interrogator.interrogate(image)
+    prompt = shared.interrogator.interrogate(image.convert("RGB"))
 
     return gr_show(True) if prompt is None else prompt
 
-- 
cgit v1.2.3


From 8eef9d8e782aa0655241e43f67059aa7bef3bdca Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 25 Dec 2022 09:03:56 +0300
Subject: a way to add an exception to unpickler without explicitly calling
 load_with_extra

---
 modules/safe.py | 39 ++++++++++++++++++++++++++++++++++++++-
 1 file changed, 38 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/safe.py b/modules/safe.py
index 479c8b86..ec23a53c 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -103,7 +103,7 @@ def check_pt(filename, extra_handler):
 
 
 def load(filename, *args, **kwargs):
-    return load_with_extra(filename, *args, **kwargs)
+    return load_with_extra(filename, extra_handler=global_extra_handler, *args, **kwargs)
 
 
 def load_with_extra(filename, extra_handler=None, *args, **kwargs):
@@ -151,5 +151,42 @@ def load_with_extra(filename, extra_handler=None, *args, **kwargs):
     return unsafe_torch_load(filename, *args, **kwargs)
 
 
+class Extra:
+    """
+    A class for temporarily setting the global handler for when you can't explicitly call load_with_extra
+    (because it's not your code making the torch.load call). The intended use is like this:
+
+```
+import torch
+from modules import safe
+
+def handler(module, name):
+    if module == 'torch' and name in ['float64', 'float16']:
+        return getattr(torch, name)
+
+    return None
+
+with safe.Extra(handler):
+    x = torch.load('model.pt')
+```
+    """
+
+    def __init__(self, handler):
+        self.handler = handler
+
+    def __enter__(self):
+        global global_extra_handler
+
+        assert global_extra_handler is None, 'already inside an Extra() block'
+        global_extra_handler = self.handler
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        global global_extra_handler
+
+        global_extra_handler = None
+
+
 unsafe_torch_load = torch.load
 torch.load = load
+global_extra_handler = None
+
-- 
cgit v1.2.3


From 5be9387b230794a8c771120577cb213490c940c0 Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Sun, 25 Dec 2022 21:45:44 +1100
Subject: fix(api): only begin/end state in lock

---
 modules/api/api.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 1ceba75d..59b81c93 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -130,14 +130,12 @@ class Api:
         if populate.sampler_name:
             populate.sampler_index = None  # prevent a warning later on
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
-        # Override object param
-
-        shared.state.begin()
 
         with self.queue_lock:
+            shared.state.begin()
             processed = process_images(p)
+            shared.state.end()
 
-        shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
@@ -169,12 +167,10 @@ class Api:
 
         p.init_images = [decode_base64_to_image(x) for x in init_images]
 
-        shared.state.begin()
-
         with self.queue_lock:
+            shared.state.begin()
             processed = process_images(p)
-
-        shared.state.end()
+            shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
-- 
cgit v1.2.3


From 893933e05ad267778111b4fad6d1ecb80937afdf Mon Sep 17 00:00:00 2001
From: hitomi <huahuohe@foxmail.com>
Date: Sun, 25 Dec 2022 20:49:25 +0800
Subject: Add memory cache for VAE weights

---
 modules/sd_vae.py | 31 +++++++++++++++++++++++++------
 modules/shared.py |  1 +
 2 files changed, 26 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 3856418e..ac71d62d 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -1,5 +1,6 @@
 import torch
 import os
+import collections
 from collections import namedtuple
 from modules import shared, devices, script_callbacks
 from modules.paths import models_path
@@ -30,6 +31,7 @@ base_vae = None
 loaded_vae_file = None
 checkpoint_info = None
 
+checkpoints_loaded = collections.OrderedDict()
 
 def get_base_vae(model):
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
@@ -149,13 +151,30 @@ def load_vae(model, vae_file=None):
     global first_load, vae_dict, vae_list, loaded_vae_file
     # save_settings = False
 
+    cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
+
     if vae_file:
-        assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
-        print(f"Loading VAE weights from: {vae_file}")
-        store_base_vae(model)
-        vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-        vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-        _load_vae_dict(model, vae_dict_1)
+        if cache_enabled and vae_file in checkpoints_loaded:
+            # use vae checkpoint cache
+            print(f"Loading VAE weights [{get_filename(vae_file)}] from cache")
+            store_base_vae(model)
+            _load_vae_dict(model, checkpoints_loaded[vae_file])
+        else:
+            assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
+            print(f"Loading VAE weights from: {vae_file}")
+            store_base_vae(model)
+            vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
+            vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
+            _load_vae_dict(model, vae_dict_1)
+
+            if cache_enabled:
+                # cache newly loaded vae
+                checkpoints_loaded[vae_file] = vae_dict_1.copy()
+
+        # clean up cache if limit is reached
+        if cache_enabled:
+            while len(checkpoints_loaded) > shared.opts.sd_vae_checkpoint_cache + 1: # we need to count the current model
+                checkpoints_loaded.popitem(last=False)  # LRU
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
diff --git a/modules/shared.py b/modules/shared.py
index d4ddeea0..671d30e1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -356,6 +356,7 @@ options_templates.update(options_section(('training', "Training"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+    "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
     "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
-- 
cgit v1.2.3


From 4af3ca5393151d61363c30eef4965e694eeac15e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 26 Dec 2022 10:11:28 +0300
Subject: make it so that blank ENSD does not break image generation

---
 modules/processing.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 4a406084..0a9a8f95 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -338,13 +338,14 @@ def slerp(val, low, high):
 
 
 def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
+    eta_noise_seed_delta = opts.eta_noise_seed_delta or 0
     xs = []
 
     # if we have multiple seeds, this means we are working with batch size>1; this then
     # enables the generation of additional tensors with noise that the sampler will use during its processing.
     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
     # produce the same images as with two batches [100], [101].
-    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
+    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or eta_noise_seed_delta > 0):
         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
     else:
         sampler_noises = None
@@ -384,8 +385,8 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
         if sampler_noises is not None:
             cnt = p.sampler.number_of_needed_noises(p)
 
-            if opts.eta_noise_seed_delta > 0:
-                torch.manual_seed(seed + opts.eta_noise_seed_delta)
+            if eta_noise_seed_delta > 0:
+                torch.manual_seed(seed + eta_noise_seed_delta)
 
             for j in range(cnt):
                 sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
-- 
cgit v1.2.3


From ae955b0146a52ea2474c79655ede0d361829ef63 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 26 Dec 2022 09:53:26 -0500
Subject: fix rgba to rgb when using jpeg output

---
 modules/images.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 31d4528d..962a955d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -525,6 +525,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
 
         elif extension.lower() in (".jpg", ".jpeg", ".webp"):
+            if image_to_save.mode == 'RGBA':
+                image_to_save = image_to_save.convert("RGB")
+
             image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
 
             if opts.enable_pnginfo and info is not None:
-- 
cgit v1.2.3


From 5ba04f9ec050a66e918571f07e8863f157f05b44 Mon Sep 17 00:00:00 2001
From: Nicolas Patry <patry.nicolas@protonmail.com>
Date: Wed, 21 Dec 2022 13:45:58 +0100
Subject: Attempting to solve slow loads for `safetensors`.

Fixes #5893
---
 modules/sd_models.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ecdd91c5..cd938656 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -168,7 +168,10 @@ def get_state_dict_from_checkpoint(pl_sd):
 def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
     _, extension = os.path.splitext(checkpoint_file)
     if extension.lower() == ".safetensors":
-        pl_sd = safetensors.torch.load_file(checkpoint_file, device=map_location or shared.weight_load_location)
+        device = map_location or shared.weight_load_location
+        if device is None:
+            device = "cuda:0" if torch.cuda.is_available() else "cpu"
+        pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
     else:
         pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
 
-- 
cgit v1.2.3


From 5958bbd244703f7c248a91e86dea5d52acc85505 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 30 Dec 2022 19:36:36 -0500
Subject: add additional memory states

---
 modules/memmon.py | 3 +++
 1 file changed, 3 insertions(+)

(limited to 'modules')

diff --git a/modules/memmon.py b/modules/memmon.py
index 9fb9b687..a7060f58 100644
--- a/modules/memmon.py
+++ b/modules/memmon.py
@@ -71,10 +71,13 @@ class MemUsageMonitor(threading.Thread):
     def read(self):
         if not self.disabled:
             free, total = torch.cuda.mem_get_info()
+            self.data["free"] = free
             self.data["total"] = total
 
             torch_stats = torch.cuda.memory_stats(self.device)
+            self.data["active"] = torch_stats["active.all.current"]
             self.data["active_peak"] = torch_stats["active_bytes.all.peak"]
+            self.data["reserved"] = torch_stats["reserved_bytes.all.current"]
             self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"]
             self.data["system_peak"] = total - self.data["min_free"]
 
-- 
cgit v1.2.3


From d3aa2a48e1e896b6ffafda5367200a4bbd46b0d7 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 30 Dec 2022 19:38:53 -0500
Subject: remove unnecessary console message

---
 modules/sd_hijack_inpainting.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index bb5499b3..06b75772 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -178,7 +178,7 @@ def sample_plms(self,
     # sampling
     C, H, W = shape
     size = (batch_size, C, H, W)
-    print(f'Data shape for PLMS sampling is {size}')
+    # print(f'Data shape for PLMS sampling is {size}') # remove unnecessary message
 
     samples, intermediates = self.plms_sampling(conditioning, size,
                                                 callback=callback,
-- 
cgit v1.2.3


From 463048344fc036b262aa132584b65ee6e9fec6cf Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 30 Dec 2022 19:41:47 -0500
Subject: fix shared state dictionary

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index d4ddeea0..9a13fb60 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -168,7 +168,7 @@ class State:
     def dict(self):
         obj = {
             "skipped": self.skipped,
-            "interrupted": self.skipped,
+            "interrupted": self.interrupted,
             "job": self.job,
             "job_count": self.job_count,
             "job_no": self.job_no,
-- 
cgit v1.2.3


From fef98723b2b1c7a9893ead41bbefcb36192babd6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 31 Dec 2022 12:44:26 +0300
Subject: set sd_model for API later, inside the lock, to prevent multiple
 requests with different models ending up with incorrect results #5877 #6012

---
 modules/api/api.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 59b81c93..11daff0d 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -121,7 +121,6 @@ class Api:
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         populate = txt2imgreq.copy(update={ # Override __init__ params
-            "sd_model": shared.sd_model,
             "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True
@@ -129,9 +128,10 @@ class Api:
         )
         if populate.sampler_name:
             populate.sampler_index = None  # prevent a warning later on
-        p = StableDiffusionProcessingTxt2Img(**vars(populate))
 
         with self.queue_lock:
+            p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **vars(populate))
+
             shared.state.begin()
             processed = process_images(p)
             shared.state.end()
@@ -151,7 +151,6 @@ class Api:
             mask = decode_base64_to_image(mask)
 
         populate = img2imgreq.copy(update={ # Override __init__ params
-            "sd_model": shared.sd_model,
             "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True,
@@ -163,11 +162,11 @@ class Api:
 
         args = vars(populate)
         args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
-        p = StableDiffusionProcessingImg2Img(**args)
-
-        p.init_images = [decode_base64_to_image(x) for x in init_images]
 
         with self.queue_lock:
+            p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
+            p.init_images = [decode_base64_to_image(x) for x in init_images]
+
             shared.state.begin()
             processed = process_images(p)
             shared.state.end()
-- 
cgit v1.2.3


From 65be1df7bb55b21a3d76630a397c820218cbd12a Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 31 Dec 2022 07:46:04 -0500
Subject: initialize result so not to cause exception on empty results

---
 modules/interrogate.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 46935210..6f761c5a 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -135,7 +135,7 @@ class InterrogateModels:
         return caption[0]
 
     def interrogate(self, pil_image):
-        res = None
+        res = ""
 
         try:
 
-- 
cgit v1.2.3


From f34c7341720fb2059992926c9f9ae6ff25f7385b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 31 Dec 2022 18:06:35 +0300
Subject: alt-diffusion integration

---
 modules/sd_hijack.py      | 18 ++++++++++--------
 modules/sd_hijack_clip.py | 14 +++++---------
 modules/sd_hijack_xlmr.py | 34 ++++++++++++++++++++++++++++++++++
 modules/shared.py         | 10 +---------
 4 files changed, 50 insertions(+), 26 deletions(-)
 create mode 100644 modules/sd_hijack_xlmr.py

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index bce23b03..edcbaf52 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -5,7 +5,7 @@ import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
@@ -68,6 +68,7 @@ def fix_checkpoint():
     ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = sd_hijack_checkpoint.ResBlock_forward
     ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = sd_hijack_checkpoint.AttentionBlock_forward
 
+
 class StableDiffusionModelHijack:
     fixes = None
     comments = []
@@ -79,21 +80,22 @@ class StableDiffusionModelHijack:
 
     def hijack(self, m):
 
-        if shared.text_model_name == "XLMR-Large":
+        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             model_embeddings = m.cond_stage_model.roberta.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
-            m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-            
+            m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
+
         elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
             model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
             m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-            apply_optimizations()
+
         elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder:
             m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
             m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-            apply_optimizations()
-        
+
+        apply_optimizations()
+
         self.clip = m.cond_stage_model
         
         fix_checkpoint()
@@ -109,7 +111,7 @@ class StableDiffusionModelHijack:
 
     def undo_hijack(self, m):
 
-        if shared.text_model_name == "XLMR-Large":
+        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             m.cond_stage_model = m.cond_stage_model.wrapped 
 
         elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 9ea6e1ce..6ec50cca 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -4,7 +4,6 @@ import torch
 
 from modules import prompt_parser, devices
 from modules.shared import opts
-import modules.shared as shared
 
 def get_target_prompt_token_count(token_count):
     return math.ceil(max(token_count, 1) / 75) * 75
@@ -177,9 +176,6 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
 
     def forward(self, text):
-        if shared.text_model_name == "XLMR-Large":
-            return self.wrapped.encode(text)
-
         use_old = opts.use_old_emphasis_implementation
         if use_old:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
@@ -257,13 +253,13 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
     def __init__(self, wrapped, hijack):
         super().__init__(wrapped, hijack)
         self.tokenizer = wrapped.tokenizer
-        if shared.text_model_name == "XLMR-Large":
-            self.comma_token = None 
-        else :
-            self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+
+        vocab = self.tokenizer.get_vocab()
+
+        self.comma_token = vocab.get(',</w>', None)
 
         self.token_mults = {}
-        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
+        tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k]
         for text, ident in tokens_with_parens:
             mult = 1.0
             for c in text:
diff --git a/modules/sd_hijack_xlmr.py b/modules/sd_hijack_xlmr.py
new file mode 100644
index 00000000..4ac51c38
--- /dev/null
+++ b/modules/sd_hijack_xlmr.py
@@ -0,0 +1,34 @@
+import open_clip.tokenizer
+import torch
+
+from modules import sd_hijack_clip, devices
+from modules.shared import opts
+
+
+class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):
+    def __init__(self, wrapped, hijack):
+        super().__init__(wrapped, hijack)
+
+        self.id_start = wrapped.config.bos_token_id
+        self.id_end = wrapped.config.eos_token_id
+        self.id_pad = wrapped.config.pad_token_id
+
+        self.comma_token = self.tokenizer.get_vocab().get(',', None)  # alt diffusion doesn't have </w> bits for comma
+
+    def encode_with_transformers(self, tokens):
+        # there's no CLIP Skip here because all hidden layers have size of 1024 and the last one uses a
+        # trained layer to transform those 1024 into 768 for unet; so you can't choose which transformer
+        # layer to work with - you have to use the last
+
+        attention_mask = (tokens != self.id_pad).to(device=tokens.device, dtype=torch.int64)
+        features = self.wrapped(input_ids=tokens, attention_mask=attention_mask)
+        z = features['projection_state']
+
+        return z
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        embedding_layer = self.wrapped.roberta.embeddings
+        ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
+        embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+
+        return embedded
diff --git a/modules/shared.py b/modules/shared.py
index 2b31e717..715b9169 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -23,7 +23,7 @@ demo = None
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default=os.path.join(script_path, "v1-inference.yaml"), help="path to config which constructs model",)
+parser.add_argument("--config", type=str, default=os.path.join(script_path, "configs/v1-inference.yaml"), help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
 parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
@@ -108,14 +108,6 @@ restricted_opts = {
     "outdir_txt2img_grids",
     "outdir_save",
 }
-from omegaconf import OmegaConf
-config = OmegaConf.load(f"{cmd_opts.config}")
-# XLMR-Large
-try:
-    text_model_name = config.model.params.cond_stage_config.params.name
-
-except :
-    text_model_name = "stable_diffusion"
 
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
-- 
cgit v1.2.3


From f55ac33d446185680604e872ceda2ae858821d5c Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 31 Dec 2022 11:27:02 -0500
Subject: validate textual inversion embeddings

---
 modules/sd_models.py                           |  3 ++
 modules/textual_inversion/textual_inversion.py | 43 +++++++++++++++++++++++---
 modules/ui.py                                  |  2 --
 3 files changed, 41 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ecdd91c5..ebd4dff7 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -325,6 +325,9 @@ def load_model(checkpoint_info=None):
     script_callbacks.model_loaded_callback(sd_model)
 
     print("Model loaded.")
+
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload = True) # Reload embeddings after model load as they may or may not fit the model
+
     return sd_model
 
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index f6112578..103ace60 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -23,6 +23,8 @@ class Embedding:
         self.vec = vec
         self.name = name
         self.step = step
+        self.shape = None
+        self.vectors = 0
         self.cached_checksum = None
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
@@ -57,8 +59,10 @@ class EmbeddingDatabase:
     def __init__(self, embeddings_dir):
         self.ids_lookup = {}
         self.word_embeddings = {}
+        self.skipped_embeddings = []
         self.dir_mtime = None
         self.embeddings_dir = embeddings_dir
+        self.expected_shape = -1
 
     def register_embedding(self, embedding, model):
 
@@ -75,14 +79,35 @@ class EmbeddingDatabase:
 
         return embedding
 
-    def load_textual_inversion_embeddings(self):
+    def get_expected_shape(self):
+        expected_shape = -1 # initialize with unknown
+        idx = torch.tensor(0).to(shared.device)
+        if expected_shape == -1:
+            try: # matches sd15 signature
+                first_embedding = shared.sd_model.cond_stage_model.wrapped.transformer.text_model.embeddings.token_embedding.wrapped(idx)
+                expected_shape = first_embedding.shape[0]
+            except:
+                pass
+        if expected_shape == -1:
+            try: # matches sd20 signature
+                first_embedding = shared.sd_model.cond_stage_model.wrapped.model.token_embedding.wrapped(idx)
+                expected_shape = first_embedding.shape[0]
+            except:
+                pass
+        if expected_shape == -1:
+            print('Could not determine expected embeddings shape from model')
+        return expected_shape
+
+    def load_textual_inversion_embeddings(self, force_reload = False):
         mt = os.path.getmtime(self.embeddings_dir)
-        if self.dir_mtime is not None and mt <= self.dir_mtime:
+        if not force_reload and self.dir_mtime is not None and mt <= self.dir_mtime:
             return
 
         self.dir_mtime = mt
         self.ids_lookup.clear()
         self.word_embeddings.clear()
+        self.skipped_embeddings = []
+        self.expected_shape = self.get_expected_shape()
 
         def process_file(path, filename):
             name = os.path.splitext(filename)[0]
@@ -122,7 +147,14 @@ class EmbeddingDatabase:
             embedding.step = data.get('step', None)
             embedding.sd_checkpoint = data.get('sd_checkpoint', None)
             embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
-            self.register_embedding(embedding, shared.sd_model)
+            embedding.vectors = vec.shape[0]
+            embedding.shape = vec.shape[-1]
+
+            if (self.expected_shape == -1) or (self.expected_shape == embedding.shape):
+                self.register_embedding(embedding, shared.sd_model)
+            else:
+                self.skipped_embeddings.append(name)
+                # print('Skipping embedding {name}: shape was {shape} expected {expected}'.format(name = name, shape = embedding.shape, expected = self.expected_shape))
 
         for fn in os.listdir(self.embeddings_dir):
             try:
@@ -137,8 +169,9 @@ class EmbeddingDatabase:
                 print(traceback.format_exc(), file=sys.stderr)
                 continue
 
-        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
-        print("Embeddings:", ', '.join(self.word_embeddings.keys()))
+        print("Textual inversion embeddings {num} loaded: {val}".format(num = len(self.word_embeddings), val = ', '.join(self.word_embeddings.keys())))
+        if (len(self.skipped_embeddings) > 0):
+            print("Textual inversion embeddings {num} skipped: {val}".format(num = len(self.skipped_embeddings), val = ', '.join(self.skipped_embeddings)))
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
diff --git a/modules/ui.py b/modules/ui.py
index 57ee0465..397dd804 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1157,8 +1157,6 @@ def create_ui():
             with gr.Column(variant='panel'):
                 submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
 
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
-
     with gr.Blocks(analytics_enabled=False) as train_interface:
         with gr.Row().style(equal_height=False):
             gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
-- 
cgit v1.2.3


From bdbe09827b39be63c9c0b3636132ca58da38ebf6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 31 Dec 2022 22:49:09 +0300
Subject: changed embedding accepted shape detection to use existing code and
 support the new alt-diffusion model, and reformatted messages a bit #6149

---
 modules/textual_inversion/textual_inversion.py | 30 ++++++--------------------
 1 file changed, 6 insertions(+), 24 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 103ace60..66f40367 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -80,23 +80,8 @@ class EmbeddingDatabase:
         return embedding
 
     def get_expected_shape(self):
-        expected_shape = -1 # initialize with unknown
-        idx = torch.tensor(0).to(shared.device)
-        if expected_shape == -1:
-            try: # matches sd15 signature
-                first_embedding = shared.sd_model.cond_stage_model.wrapped.transformer.text_model.embeddings.token_embedding.wrapped(idx)
-                expected_shape = first_embedding.shape[0]
-            except:
-                pass
-        if expected_shape == -1:
-            try: # matches sd20 signature
-                first_embedding = shared.sd_model.cond_stage_model.wrapped.model.token_embedding.wrapped(idx)
-                expected_shape = first_embedding.shape[0]
-            except:
-                pass
-        if expected_shape == -1:
-            print('Could not determine expected embeddings shape from model')
-        return expected_shape
+        vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
+        return vec.shape[1]
 
     def load_textual_inversion_embeddings(self, force_reload = False):
         mt = os.path.getmtime(self.embeddings_dir)
@@ -112,8 +97,6 @@ class EmbeddingDatabase:
         def process_file(path, filename):
             name = os.path.splitext(filename)[0]
 
-            data = []
-
             if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
                 embed_image = Image.open(path)
                 if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
@@ -150,11 +133,10 @@ class EmbeddingDatabase:
             embedding.vectors = vec.shape[0]
             embedding.shape = vec.shape[-1]
 
-            if (self.expected_shape == -1) or (self.expected_shape == embedding.shape):
+            if self.expected_shape == -1 or self.expected_shape == embedding.shape:
                 self.register_embedding(embedding, shared.sd_model)
             else:
                 self.skipped_embeddings.append(name)
-                # print('Skipping embedding {name}: shape was {shape} expected {expected}'.format(name = name, shape = embedding.shape, expected = self.expected_shape))
 
         for fn in os.listdir(self.embeddings_dir):
             try:
@@ -169,9 +151,9 @@ class EmbeddingDatabase:
                 print(traceback.format_exc(), file=sys.stderr)
                 continue
 
-        print("Textual inversion embeddings {num} loaded: {val}".format(num = len(self.word_embeddings), val = ', '.join(self.word_embeddings.keys())))
-        if (len(self.skipped_embeddings) > 0):
-            print("Textual inversion embeddings {num} skipped: {val}".format(num = len(self.skipped_embeddings), val = ', '.join(self.skipped_embeddings)))
+        print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
+        if len(self.skipped_embeddings) > 0:
+            print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings)}")
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
-- 
cgit v1.2.3


From f4535f6e4f001314bd155bc6e1b6908e02792b9a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 31 Dec 2022 23:40:55 +0300
Subject: make it so that memory/embeddings info is displayed in a separate UI
 element from generation parameters, and is preserved when you change the
 displayed infotext by clicking on gallery images

---
 modules/img2img.py    |  2 +-
 modules/processing.py |  5 +++--
 modules/txt2img.py    |  2 +-
 modules/ui.py         | 31 +++++++++++++++++--------------
 4 files changed, 22 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/img2img.py b/modules/img2img.py
index 81da4b13..ca58b5d8 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -162,4 +162,4 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     if opts.do_not_show_images:
         processed.images = []
 
-    return processed.images, generation_info_js, plaintext_to_html(processed.info)
+    return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)
diff --git a/modules/processing.py b/modules/processing.py
index 0a9a8f95..42dc19ea 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -239,7 +239,7 @@ class StableDiffusionProcessing():
 
 
 class Processed:
-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
         self.images = images_list
         self.prompt = p.prompt
         self.negative_prompt = p.negative_prompt
@@ -247,6 +247,7 @@ class Processed:
         self.subseed = subseed
         self.subseed_strength = p.subseed_strength
         self.info = info
+        self.comments = comments
         self.width = p.width
         self.height = p.height
         self.sampler_name = p.sampler_name
@@ -646,7 +647,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     devices.torch_gc()
 
-    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
+    res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
 
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
diff --git a/modules/txt2img.py b/modules/txt2img.py
index c8f81176..7f61e19a 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -59,4 +59,4 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     if opts.do_not_show_images:
         processed.images = []
 
-    return processed.images, generation_info_js, plaintext_to_html(processed.info)
+    return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)
diff --git a/modules/ui.py b/modules/ui.py
index 397dd804..f550ad00 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -159,7 +159,7 @@ def save_files(js_data, images, do_make_zip, index):
                     zip_file.writestr(filenames[i], f.read())
         fullfns.insert(0, zip_filepath)
 
-    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+    return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
 
 
 
@@ -593,6 +593,8 @@ Requested path was: {f}
 
                     with gr.Group():
                         html_info = gr.HTML()
+                        html_log = gr.HTML()
+
                         generation_info = gr.Textbox(visible=False)
                         if tabname == 'txt2img' or tabname == 'img2img':
                             generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
@@ -615,16 +617,16 @@ Requested path was: {f}
                             ],
                             outputs=[
                                 download_files,
-                                html_info,
-                                html_info,
-                                html_info,
+                                html_log,
                             ]
                         )
                 else:
                     html_info_x = gr.HTML()
                     html_info = gr.HTML()
+                    html_log = gr.HTML()
+
                 parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
-                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
 
 
 def create_ui():
@@ -686,14 +688,14 @@ def create_ui():
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
-            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+            txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
             parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
             txt2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
                 _js="submit",
                 inputs=[
                     txt2img_prompt,
@@ -720,7 +722,8 @@ def create_ui():
                 outputs=[
                     txt2img_gallery,
                     generation_info,
-                    html_info
+                    html_info,
+                    html_log,
                 ],
                 show_progress=False,
             )
@@ -799,7 +802,6 @@ def create_ui():
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True)
 
-
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
 
@@ -883,7 +885,7 @@ def create_ui():
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui()
 
-            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
+            img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
             parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
@@ -915,7 +917,7 @@ def create_ui():
             )
 
             img2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
                 _js="submit_img2img",
                 inputs=[
                     dummy_component,
@@ -954,7 +956,8 @@ def create_ui():
                 outputs=[
                     img2img_gallery,
                     generation_info,
-                    html_info
+                    html_info,
+                    html_log,
                 ],
                 show_progress=False,
             )
@@ -1078,10 +1081,10 @@ def create_ui():
                 with gr.Group():
                     upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
 
-            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
+            result_images, html_info_x, html_info, html_log = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
-            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
+            fn=wrap_gradio_gpu_call(modules.extras.run_extras, extra_outputs=[None, '']),
             _js="get_extras_tab_index",
             inputs=[
                 dummy_component,
-- 
cgit v1.2.3


From 360feed9b55fb03060c236773867b08b4265645d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 00:38:58 +0300
Subject: HAPPY NEW YEAR

make save to zip into its own button instead of a checkbox
---
 modules/ui.py | 30 ++++++++++++++++++++++--------
 1 file changed, 22 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index f550ad00..279b5110 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -570,13 +570,14 @@ Requested path was: {f}
 
             generation_info = None
             with gr.Column():
-                with gr.Row():
+                with gr.Row(elem_id=f"image_buttons_{tabname}"):
+                    open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder')
+
                     if tabname != "extras":
                         save = gr.Button('Save', elem_id=f'save_{tabname}')
+                        save_zip = gr.Button('Zip', elem_id=f'save_zip_{tabname}')
 
                     buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
-                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
 
                 open_folder_button.click(
                     fn=lambda: open_folder(opts.outdir_samples or outdir),
@@ -585,9 +586,6 @@ Requested path was: {f}
                 )
 
                 if tabname != "extras":
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
@@ -608,11 +606,11 @@ Requested path was: {f}
 
                         save.click(
                             fn=wrap_gradio_call(save_files),
-                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+                            _js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
                             inputs=[
                                 generation_info,
                                 result_gallery,
-                                do_make_zip,
+                                html_info,
                                 html_info,
                             ],
                             outputs=[
@@ -620,6 +618,22 @@ Requested path was: {f}
                                 html_log,
                             ]
                         )
+
+                        save_zip.click(
+                            fn=wrap_gradio_call(save_files),
+                            _js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
+                            inputs=[
+                                generation_info,
+                                result_gallery,
+                                html_info,
+                                html_info,
+                            ],
+                            outputs=[
+                                download_files,
+                                html_log,
+                            ]
+                        )
+
                 else:
                     html_info_x = gr.HTML()
                     html_info = gr.HTML()
-- 
cgit v1.2.3


From 29a3a7eb13478297bc7093971b48827ab8246f45 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 01:19:10 +0300
Subject: show sampler selection in dropdown, add option selection to revert to
 old radio group

---
 modules/shared.py |  1 +
 modules/ui.py     | 22 +++++++++++++++-------
 2 files changed, 16 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 715b9169..948b9542 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -406,6 +406,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
diff --git a/modules/ui.py b/modules/ui.py
index 279b5110..c7b8ea5d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -643,6 +643,19 @@ Requested path was: {f}
                 return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
 
 
+def create_sampler_and_steps_selection(choices, tabname):
+    if opts.samplers_in_dropdown:
+        with gr.Row(elem_id=f"sampler_selection_{tabname}"):
+            sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)
+    else:
+        with gr.Group(elem_id=f"sampler_selection_{tabname}"):
+            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)
+            sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+
+    return steps, sampler_index
+
+
 def create_ui():
     import modules.img2img
     import modules.txt2img
@@ -660,9 +673,6 @@ def create_ui():
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
-
-
-
         with gr.Row(elem_id='txt2img_progress_row'):
             with gr.Column(scale=1):
                 pass
@@ -674,8 +684,7 @@ def create_ui():
 
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel', elem_id="txt2img_settings"):
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
+                steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
 
                 with gr.Group():
                     width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
@@ -875,8 +884,7 @@ def create_ui():
                 with gr.Row():
                     resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
+                steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
 
                 with gr.Group():
                     width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
-- 
cgit v1.2.3


From 210449b374d522c94a67fe54289a9eb515933a9f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 02:41:15 +0300
Subject: fix 'RuntimeError: Expected all tensors to be on the same device'
 error preventing models from loading on lowvram/medvram.

---
 modules/sd_hijack_clip.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 6ec50cca..ca92b142 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -298,6 +298,6 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
     def encode_embedding_init_text(self, init_text, nvpt):
         embedding_layer = self.wrapped.transformer.text_model.embeddings
         ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
-        embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+        embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
 
         return embedded
-- 
cgit v1.2.3


From 16b9661d2741b241c3964fcbd56559c078b84822 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 09:51:37 +0300
Subject: change karras scheduler sigmas to values recommended by SD from old
 0.1 to 10 with an option to revert to old

---
 modules/sd_samplers.py | 4 +++-
 modules/shared.py      | 6 +++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 177b5338..e904d860 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -465,7 +465,9 @@ class KDiffusionSampler:
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 
diff --git a/modules/shared.py b/modules/shared.py
index 948b9542..7f430b93 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -368,13 +368,17 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", gr.ColorPicker, {}),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
-    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
+options_templates.update(options_section(('compatibility', "Compatibility"), {
+    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
+    "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
+}))
+
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
-- 
cgit v1.2.3


From 11d432d92d63660c516540dcb48faac87669b4f0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 10:35:38 +0300
Subject: add refresh buttons to checkpoint merger

---
 modules/ui.py | 6 ++++++
 1 file changed, 6 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index c7b8ea5d..4cc2ce4f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1167,8 +1167,14 @@ def create_ui():
 
                 with gr.Row():
                     primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                    create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
+
                     secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                    create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
+
                     tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+                    create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
+
                 custom_name = gr.Textbox(label="Custom Name (Optional)")
                 interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
                 interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
-- 
cgit v1.2.3


From 76f256fe8f844641f4e9b41f35c7dd2cba5090d6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 11:08:39 +0300
Subject: Bump gradio version #YOLO

---
 modules/ui_tempdir.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
index 07210d14..8d519310 100644
--- a/modules/ui_tempdir.py
+++ b/modules/ui_tempdir.py
@@ -15,7 +15,8 @@ Savedfile = namedtuple("Savedfile", ["name"])
 def save_pil_to_file(pil_image, dir=None):
     already_saved_as = getattr(pil_image, 'already_saved_as', None)
     if already_saved_as and os.path.isfile(already_saved_as):
-        shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(os.path.dirname(already_saved_as))}
+        shared.demo.temp_file_sets[0] = shared.demo.temp_file_sets[0] | {os.path.abspath(already_saved_as)}
+
         file_obj = Savedfile(already_saved_as)
         return file_obj
 
-- 
cgit v1.2.3


From b46b97fa297b3a4a654da77cf98a775a2bcab4c7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 11:38:17 +0300
Subject: more fixes for gradio update

---
 modules/generation_parameters_copypaste.py | 2 +-
 modules/ui_tempdir.py                      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index fbd91300..54b3372d 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -38,7 +38,7 @@ def quote(text):
 def image_from_url_text(filedata):
     if type(filedata) == dict and filedata["is_file"]:
         filename = filedata["name"]
-        is_in_right_dir = any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in shared.demo.temp_dirs)
+        is_in_right_dir = any([filename in fileset for fileset in shared.demo.temp_file_sets])
         assert is_in_right_dir, 'trying to open image file outside of allowed directories'
 
         return Image.open(filename)
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
index 8d519310..363d449d 100644
--- a/modules/ui_tempdir.py
+++ b/modules/ui_tempdir.py
@@ -45,7 +45,7 @@ def on_tmpdir_changed():
 
     os.makedirs(shared.opts.temp_dir, exist_ok=True)
 
-    shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(shared.opts.temp_dir)}
+    shared.demo.temp_file_sets[0] = shared.demo.temp_file_sets[0] | {os.path.abspath(shared.opts.temp_dir)}
 
 
 def cleanup_tmpdr():
-- 
cgit v1.2.3


From e5f1a37cb9b537d95b2df47c96b4a4f7242fd294 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 13:08:40 +0300
Subject: make refresh buttons look more nice

---
 modules/ui.py            |  6 +++---
 modules/ui_components.py | 18 ++++++++++++++++++
 2 files changed, 21 insertions(+), 3 deletions(-)
 create mode 100644 modules/ui_components.py

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 4cc2ce4f..32fa80d1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -19,7 +19,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, ui_components
 from modules.paths import script_path
 
 from modules.shared import opts, cmd_opts, restricted_opts
@@ -532,7 +532,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
 
         return gr.update(**(args or {}))
 
-    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
+    refresh_button = ui_components.ToolButton(value=refresh_symbol, elem_id=elem_id)
     refresh_button.click(
         fn=refresh,
         inputs=[],
@@ -1476,7 +1476,7 @@ def create_ui():
                 res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                 create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
             else:
-                with gr.Row(variant="compact"):
+                with ui_components.FormRow():
                     res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                     create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
         else:
diff --git a/modules/ui_components.py b/modules/ui_components.py
new file mode 100644
index 00000000..d0519d2d
--- /dev/null
+++ b/modules/ui_components.py
@@ -0,0 +1,18 @@
+import gradio as gr
+
+
+class ToolButton(gr.Button, gr.components.FormComponent):
+    """Small button with single emoji as text, fits inside gradio forms"""
+
+    def __init__(self, **kwargs):
+        super().__init__(variant="tool", **kwargs)
+
+    def get_block_name(self):
+        return "button"
+
+
+class FormRow(gr.Row, gr.components.FormComponent):
+    """Same as gr.Row but fits inside gradio forms"""
+
+    def get_block_name(self):
+        return "row"
-- 
cgit v1.2.3


From 5f12b23b8bb7fca585a3a1e844881d06f171364e Mon Sep 17 00:00:00 2001
From: AlUlkesh <99896447+AlUlkesh@users.noreply.github.com>
Date: Wed, 28 Dec 2022 22:18:19 +0100
Subject: Adding image numbers on grids

New grid option in settings enables adding of image numbers on grids. This makes identifying the images, especially in larger batches, much easier.

Revert "Adding image numbers on grids"

This reverts commit 3530c283b4b1d3a3cab40efbffe4cf2697938b6f.

Implements Callback for image grid loop

Necessary to make "Add image's number to its picture in the grid" extension possible.
---
 modules/images.py           |  1 +
 modules/script_callbacks.py | 20 ++++++++++++++++++++
 2 files changed, 21 insertions(+)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 31d4528d..5afd3891 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -43,6 +43,7 @@ def image_grid(imgs, batch_size=1, rows=None):
     grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
 
     for i, img in enumerate(imgs):
+        script_callbacks.image_grid_loop_callback(img)
         grid.paste(img, box=(i % cols * w, i // cols * h))
 
     return grid
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 8e22f875..0c854407 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -51,6 +51,11 @@ class UiTrainTabParams:
         self.txt2img_preview_params = txt2img_preview_params
 
 
+class ImageGridLoopParams:
+    def __init__(self, img):
+        self.img = img
+
+
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
 callback_map = dict(
     callbacks_app_started=[],
@@ -63,6 +68,7 @@ callback_map = dict(
     callbacks_cfg_denoiser=[],
     callbacks_before_component=[],
     callbacks_after_component=[],
+    callbacks_image_grid_loop=[],
 )
 
 
@@ -154,6 +160,12 @@ def after_component_callback(component, **kwargs):
         except Exception:
             report_exception(c, 'after_component_callback')
 
+def image_grid_loop_callback(component, **kwargs):
+    for c in callback_map['callbacks_image_grid_loop']:
+        try:
+            c.callback(component, **kwargs)
+        except Exception:
+            report_exception(c, 'image_grid_loop')
 
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
@@ -255,3 +267,11 @@ def on_before_component(callback):
 def on_after_component(callback):
     """register a function to be called after a component is created. See on_before_component for more."""
     add_callback(callback_map['callbacks_after_component'], callback)
+
+
+def on_image_grid_loop(callback):
+    """register a function to be called inside the image grid loop.
+    The callback is called with one argument:
+       - params: ImageGridLoopParams - parameters to be used inside the image grid loop.
+    """
+    add_callback(callback_map['callbacks_image_grid_loop'], callback)
-- 
cgit v1.2.3


From 524d532b387732d4d32f237e792c7f201a934400 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 14:07:40 +0300
Subject: moved roll artist to built-in extensions

---
 modules/ui.py | 37 +++----------------------------------
 1 file changed, 3 insertions(+), 34 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 32fa80d1..27da2c2c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -80,7 +80,6 @@ css_hide_progressbar = """
 # Important that they exactly match script.js for tooltip to work.
 random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
 reuse_symbol = '\u267b\ufe0f'  # ♻️
-art_symbol = '\U0001f3a8'  # 🎨
 paste_symbol = '\u2199\ufe0f'  # ↙
 folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
@@ -234,13 +233,6 @@ def check_progress_call_initial(id_part):
     return check_progress_call(id_part)
 
 
-def roll_artist(prompt):
-    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
-    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
-
-    return prompt + ", " + artist.name if prompt != '' else artist.name
-
-
 def visit(x, func, path=""):
     if hasattr(x, 'children'):
         for c in x.children:
@@ -403,7 +395,6 @@ def create_toprow(is_img2img):
                         )
 
         with gr.Column(scale=1, elem_id="roll_col"):
-            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
@@ -452,7 +443,7 @@ def create_toprow(is_img2img):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
                     prompt_style2.save_to_config = True
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+    return prompt, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -668,7 +659,7 @@ def create_ui():
     modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
+        txt2img_prompt, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -771,16 +762,6 @@ def create_ui():
                 outputs=[hr_options],
             )
 
-            roll.click(
-                fn=roll_artist,
-                _js="update_txt2img_tokens",
-                inputs=[
-                    txt2img_prompt,
-                ],
-                outputs=[
-                    txt2img_prompt,
-                ]
-            )
 
             txt2img_paste_fields = [
                 (txt2img_prompt, "Prompt"),
@@ -823,7 +804,7 @@ def create_ui():
     modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True)
+        img2img_prompt, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -999,18 +980,6 @@ def create_ui():
                 outputs=[img2img_prompt],
             )
 
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_img2img_tokens",
-                inputs=[
-                    img2img_prompt,
-                ],
-                outputs=[
-                    img2img_prompt,
-                ]
-            )
-
             prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
             style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
             style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-- 
cgit v1.2.3


From e672cfb07418a1a3130d3bf21c14a0d3819f81fb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 18:37:37 +0300
Subject: rework of callback for #6094

---
 modules/images.py           | 10 ++++++----
 modules/script_callbacks.py | 26 +++++++++++++++-----------
 2 files changed, 21 insertions(+), 15 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 719aaf3b..f84fd485 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -39,12 +39,14 @@ def image_grid(imgs, batch_size=1, rows=None):
 
     cols = math.ceil(len(imgs) / rows)
 
+    params = script_callbacks.ImageGridLoopParams(imgs, cols, rows)
+    script_callbacks.image_grid_callback(params)
+
     w, h = imgs[0].size
-    grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
+    grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color='black')
 
-    for i, img in enumerate(imgs):
-        script_callbacks.image_grid_loop_callback(img)
-        grid.paste(img, box=(i % cols * w, i // cols * h))
+    for i, img in enumerate(params.imgs):
+        grid.paste(img, box=(i % params.cols * w, i // params.cols * h))
 
     return grid
 
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 0c854407..de69fd9f 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -52,8 +52,10 @@ class UiTrainTabParams:
 
 
 class ImageGridLoopParams:
-    def __init__(self, img):
-        self.img = img
+    def __init__(self, imgs, cols, rows):
+        self.imgs = imgs
+        self.cols = cols
+        self.rows = rows
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
@@ -68,7 +70,7 @@ callback_map = dict(
     callbacks_cfg_denoiser=[],
     callbacks_before_component=[],
     callbacks_after_component=[],
-    callbacks_image_grid_loop=[],
+    callbacks_image_grid=[],
 )
 
 
@@ -160,12 +162,14 @@ def after_component_callback(component, **kwargs):
         except Exception:
             report_exception(c, 'after_component_callback')
 
-def image_grid_loop_callback(component, **kwargs):
-    for c in callback_map['callbacks_image_grid_loop']:
+
+def image_grid_callback(params: ImageGridLoopParams):
+    for c in callback_map['callbacks_image_grid']:
         try:
-            c.callback(component, **kwargs)
+            c.callback(params)
         except Exception:
-            report_exception(c, 'image_grid_loop')
+            report_exception(c, 'image_grid')
+
 
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
@@ -269,9 +273,9 @@ def on_after_component(callback):
     add_callback(callback_map['callbacks_after_component'], callback)
 
 
-def on_image_grid_loop(callback):
-    """register a function to be called inside the image grid loop.
+def on_image_grid(callback):
+    """register a function to be called before making an image grid.
     The callback is called with one argument:
-       - params: ImageGridLoopParams - parameters to be used inside the image grid loop.
+       - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.
     """
-    add_callback(callback_map['callbacks_image_grid_loop'], callback)
+    add_callback(callback_map['callbacks_image_grid'], callback)
-- 
cgit v1.2.3


From a005fccddd5a37c57f1afe5234660b59b9a41508 Mon Sep 17 00:00:00 2001
From: me <25877290+Kryptortio@users.noreply.github.com>
Date: Sun, 1 Jan 2023 14:51:12 +0100
Subject: Add a lot more elem_id/HTML id, modified some that were duplicates
 for seed section

---
 modules/generation_parameters_copypaste.py |   2 +-
 modules/ui.py                              | 254 ++++++++++++++---------------
 2 files changed, 128 insertions(+), 128 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 54b3372d..8e7f0df0 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -93,7 +93,7 @@ def integrate_settings_paste_fields(component_dict):
 def create_buttons(tabs_list):
     buttons = {}
     for tab in tabs_list:
-        buttons[tab] = gr.Button(f"Send to {tab}")
+        buttons[tab] = gr.Button(f"Send to {tab}", elem_id=f"{tab}_tab")
     return buttons
 
 
diff --git a/modules/ui.py b/modules/ui.py
index 27da2c2c..7070ea15 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -272,17 +272,17 @@ def interrogate_deepbooru(image):
     return gr_show(True) if prompt is None else prompt
 
 
-def create_seed_inputs():
+def create_seed_inputs(target_interface):
     with gr.Row():
         with gr.Box():
-            with gr.Row(elem_id='seed_row'):
-                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
+            with gr.Row(elem_id=target_interface + '_seed_row'):
+                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
                 seed.style(container=False)
-                random_seed = gr.Button(random_symbol, elem_id='random_seed')
-                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
+                random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
+                reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
 
-        with gr.Box(elem_id='subseed_show_box'):
-            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
+        with gr.Box(elem_id=target_interface + '_subseed_show_box'):
+            seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
 
     # Components to show/hide based on the 'Extra' checkbox
     seed_extras = []
@@ -290,17 +290,17 @@ def create_seed_inputs():
     with gr.Row(visible=False) as seed_extra_row_1:
         seed_extras.append(seed_extra_row_1)
         with gr.Box():
-            with gr.Row(elem_id='subseed_row'):
-                subseed = gr.Number(label='Variation seed', value=-1)
+            with gr.Row(elem_id=target_interface + '_subseed_row'):
+                subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
                 subseed.style(container=False)
-                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
-                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
-        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
+                random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
+                reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
 
     with gr.Row(visible=False) as seed_extra_row_2:
         seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w')
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h')
 
     random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
     random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
@@ -678,28 +678,28 @@ def create_ui():
                 steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
+                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
 
                 with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
+                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
+                    enable_hr = gr.Checkbox(label='Highres. fix', value=False, elem_id="txt2img_enable_hr")
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0, elem_id="txt2img_firstphase_width")
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0, elem_id="txt2img_firstphase_height")
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
 
                 with gr.Row(equal_height=True):
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
 
-                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
 
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
 
-                with gr.Group():
+                with gr.Group(elem_id="txt2img_script_container"):
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
             txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
@@ -821,10 +821,10 @@ def create_ui():
             with gr.Column(variant='panel', elem_id="img2img_settings"):
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
-                    with gr.TabItem('img2img', id='img2img'):
+                    with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab"):
                         init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool, image_mode="RGBA").style(height=480)
 
-                    with gr.TabItem('Inpaint', id='inpaint'):
+                    with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab"):
                         init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
                         init_img_with_mask_orig = gr.State(None)
 
@@ -843,24 +843,24 @@ def create_ui():
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
 
                         with gr.Row():
-                            mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
-                            mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch)
+                            mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+                            mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch, elem_id="img2img_mask_alpha")
 
                         with gr.Row():
                             mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
+                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
 
-                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
+                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
 
                         with gr.Row():
-                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False, elem_id="img2img_inpaint_full_res")
+                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
 
-                    with gr.TabItem('Batch img2img', id='batch'):
+                    with gr.TabItem('Batch img2img', id='batch', elem_id="img2img_batch_tab"):
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
                         gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
-                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
-                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
+                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
+                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
 
                 with gr.Row():
                     resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
@@ -872,20 +872,20 @@ def create_ui():
                     height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
 
                 with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
+                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
 
                 with gr.Row():
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
 
                 with gr.Group():
-                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
+                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
 
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
 
-                with gr.Group():
+                with gr.Group(elem_id="img2img_script_container"):
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui()
 
             img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
@@ -1032,45 +1032,45 @@ def create_ui():
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
                 with gr.Tabs(elem_id="mode_extras"):
-                    with gr.TabItem('Single Image'):
-                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
+                    with gr.TabItem('Single Image', elem_id="extras_single_tab"):
+                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
 
-                    with gr.TabItem('Batch Process'):
-                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
+                    with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab"):
+                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
 
-                    with gr.TabItem('Batch from Directory'):
-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
-                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
+                    with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab"):
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
+                        show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
 
                 submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
 
                 with gr.Tabs(elem_id="extras_resize_mode"):
-                    with gr.TabItem('Scale by'):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
-                    with gr.TabItem('Scale to'):
+                    with gr.TabItem('Scale by', elem_id="extras_scale_by_tab"):
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
+                    with gr.TabItem('Scale to', elem_id="extras_scale_to_tab"):
                         with gr.Group():
                             with gr.Row():
-                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
-                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
+                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
+                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
 
                 with gr.Group():
                     extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
+                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1, elem_id="extras_upscaler_2_visibility")
 
                 with gr.Group():
-                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
+                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan, elem_id="extras_gfpgan_visibility")
 
                 with gr.Group():
-                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
-                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
+                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer, elem_id="extras_codeformer_visibility")
+                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer, elem_id="extras_codeformer_weight")
 
                 with gr.Group():
-                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
+                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False, elem_id="extras_upscale_before_face_fix")
 
             result_images, html_info_x, html_info, html_log = create_output_panel("extras", opts.outdir_extras_samples)
 
@@ -1117,7 +1117,7 @@ def create_ui():
 
             with gr.Column(variant='panel'):
                 html = gr.HTML()
-                generation_info = gr.Textbox(visible=False)
+                generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
                 html2 = gr.HTML()
                 with gr.Row():
                     buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
@@ -1144,13 +1144,13 @@ def create_ui():
                     tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
                     create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
 
-                custom_name = gr.Textbox(label="Custom Name (Optional)")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
-                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
+                custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
+                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
 
                 with gr.Row():
-                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format")
-                    save_as_half = gr.Checkbox(value=False, label="Save as float16")
+                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+                    save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
 
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
@@ -1165,58 +1165,58 @@ def create_ui():
             with gr.Tabs(elem_id="train_tabs"):
 
                 with gr.Tab(label="Create embedding"):
-                    new_embedding_name = gr.Textbox(label="Name")
-                    initialization_text = gr.Textbox(label="Initialization text", value="*")
-                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
-                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
+                    new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
+                    initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
 
                     with gr.Row():
                         with gr.Column(scale=3):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            create_embedding = gr.Button(value="Create embedding", variant='primary')
+                            create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
 
                 with gr.Tab(label="Create hypernetwork"):
-                    new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"])
-                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
-                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
-                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
-                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
+                    new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
 
                     with gr.Row():
                         with gr.Column(scale=3):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
 
                 with gr.Tab(label="Preprocess images"):
-                    process_src = gr.Textbox(label='Source directory')
-                    process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
-                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
+                    process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
+                    process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
 
                     with gr.Row():
-                        process_flip = gr.Checkbox(label='Create flipped copies')
-                        process_split = gr.Checkbox(label='Split oversized images')
-                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
-                        process_caption = gr.Checkbox(label='Use BLIP for caption')
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True)
+                        process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
+                        process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
+                        process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
 
                     with gr.Row(visible=False) as process_split_extra_row:
-                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
 
                     with gr.Row(visible=False) as process_focal_crop_row:
-                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
+                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1224,8 +1224,8 @@ def create_ui():
 
                         with gr.Column():
                             with gr.Row():
-                                interrupt_preprocessing = gr.Button("Interrupt")
-                            run_preprocess = gr.Button(value="Preprocess", variant='primary')
+                                interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
+                            run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
 
                     process_split.change(
                         fn=lambda show: gr_show(show),
@@ -1248,31 +1248,31 @@ def create_ui():
                         train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
                         create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
                     with gr.Row():
-                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
-                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
-
-                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
-                    gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
-                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
-                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
-                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
-                    steps = gr.Number(label='Max steps', value=100000, precision=0)
-                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
-                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
+
+                    batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
+                    gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
+                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"), elem_id="train_template_file")
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
+                    steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
                     with gr.Row():
-                        shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False)
-                        tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0)
+                        shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
+                        tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
                     with gr.Row():
-                        latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
+                        latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
 
                     with gr.Row():
-                        interrupt_training = gr.Button(value="Interrupt")
-                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
-                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
+                        interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
+                        train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
 
                 params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
 
@@ -1490,7 +1490,7 @@ def create_ui():
         return gr.update(value=value), opts.dumpjson()
 
     with gr.Blocks(analytics_enabled=False) as settings_interface:
-        settings_submit = gr.Button(value="Apply settings", variant='primary')
+        settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
         result = gr.HTML()
 
         settings_cols = 3
@@ -1541,8 +1541,8 @@ def create_ui():
             download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
 
         with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary', elem_id="settings_restart_gradio")
 
         request_notifications.click(
             fn=lambda: None,
-- 
cgit v1.2.3


From 311354c0bb8930ea939d6aa6b3edd50c69301320 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 00:38:09 +0300
Subject: fix the issue with training on SD2.0

---
 modules/sd_models.py                           | 2 ++
 modules/textual_inversion/textual_inversion.py | 3 +--
 2 files changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ebd4dff7..bff8d6c9 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -228,6 +228,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    model.logvar = model.logvar.to(devices.device)  # fix for training
+
     sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 66f40367..1e5722e7 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -282,7 +282,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
         return embedding, filename
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
 
-   # dataset loading may take a while, so input validations and early returns should be done before this
+    # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     old_parallel_processing_allowed = shared.parallel_processing_allowed
 
@@ -310,7 +310,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     loss_step = 0
     _loss_step = 0 #internal
 
-
     last_saved_file = "<none>"
     last_saved_image = "<none>"
     forced_filename = "<none>"
-- 
cgit v1.2.3


From b5819d9bf1794071139c640b5f1e72c84a0e051a Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Mon, 2 Jan 2023 10:17:33 +1100
Subject: feat(api): add /sdapi/v1/embeddings

---
 modules/api/api.py    | 8 ++++++++
 modules/api/models.py | 3 +++
 2 files changed, 11 insertions(+)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 11daff0d..30bf3dac 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -100,6 +100,7 @@ class Api:
         self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
         self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
         self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
+        self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse)
         self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
         self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
         self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
@@ -327,6 +328,13 @@ class Api:
     def get_artists(self):
         return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
 
+    def get_embeddings(self):
+        db = sd_hijack.model_hijack.embedding_db
+        return {
+            "loaded": sorted(db.word_embeddings.keys()),
+            "skipped": sorted(db.skipped_embeddings),
+        }
+
     def refresh_checkpoints(self):
         shared.refresh_checkpoints()
 
diff --git a/modules/api/models.py b/modules/api/models.py
index c446ce7a..a8472dc9 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -249,3 +249,6 @@ class ArtistItem(BaseModel):
     score: float = Field(title="Score")
     category: str = Field(title="Category")
 
+class EmbeddingsResponse(BaseModel):
+    loaded: List[str] = Field(title="Loaded", description="Embeddings loaded for the current model")
+    skipped: List[str] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
\ No newline at end of file
-- 
cgit v1.2.3


From c65909ad16a1962129114c6251de092f49479b06 Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Mon, 2 Jan 2023 12:21:22 +1100
Subject: feat(api): return more data for embeddings

---
 modules/api/api.py                             | 17 +++++++++++++++--
 modules/api/models.py                          | 11 +++++++++--
 modules/textual_inversion/textual_inversion.py |  8 ++++----
 3 files changed, 28 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 30bf3dac..9c670f00 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -330,9 +330,22 @@ class Api:
 
     def get_embeddings(self):
         db = sd_hijack.model_hijack.embedding_db
+
+        def convert_embedding(embedding):
+            return {
+                "step": embedding.step,
+                "sd_checkpoint": embedding.sd_checkpoint,
+                "sd_checkpoint_name": embedding.sd_checkpoint_name,
+                "shape": embedding.shape,
+                "vectors": embedding.vectors,
+            }
+
+        def convert_embeddings(embeddings):
+            return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()}
+
         return {
-            "loaded": sorted(db.word_embeddings.keys()),
-            "skipped": sorted(db.skipped_embeddings),
+            "loaded": convert_embeddings(db.word_embeddings),
+            "skipped": convert_embeddings(db.skipped_embeddings),
         }
 
     def refresh_checkpoints(self):
diff --git a/modules/api/models.py b/modules/api/models.py
index a8472dc9..4a632c68 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -249,6 +249,13 @@ class ArtistItem(BaseModel):
     score: float = Field(title="Score")
     category: str = Field(title="Category")
 
+class EmbeddingItem(BaseModel):
+    step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
+    sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
+    sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead")
+    shape: int = Field(title="Shape", description="The length of each individual vector in the embedding")
+    vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
+
 class EmbeddingsResponse(BaseModel):
-    loaded: List[str] = Field(title="Loaded", description="Embeddings loaded for the current model")
-    skipped: List[str] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
\ No newline at end of file
+    loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
+    skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
\ No newline at end of file
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1e5722e7..fd253477 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -59,7 +59,7 @@ class EmbeddingDatabase:
     def __init__(self, embeddings_dir):
         self.ids_lookup = {}
         self.word_embeddings = {}
-        self.skipped_embeddings = []
+        self.skipped_embeddings = {}
         self.dir_mtime = None
         self.embeddings_dir = embeddings_dir
         self.expected_shape = -1
@@ -91,7 +91,7 @@ class EmbeddingDatabase:
         self.dir_mtime = mt
         self.ids_lookup.clear()
         self.word_embeddings.clear()
-        self.skipped_embeddings = []
+        self.skipped_embeddings.clear()
         self.expected_shape = self.get_expected_shape()
 
         def process_file(path, filename):
@@ -136,7 +136,7 @@ class EmbeddingDatabase:
             if self.expected_shape == -1 or self.expected_shape == embedding.shape:
                 self.register_embedding(embedding, shared.sd_model)
             else:
-                self.skipped_embeddings.append(name)
+                self.skipped_embeddings[name] = embedding
 
         for fn in os.listdir(self.embeddings_dir):
             try:
@@ -153,7 +153,7 @@ class EmbeddingDatabase:
 
         print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
         if len(self.skipped_embeddings) > 0:
-            print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings)}")
+            print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
-- 
cgit v1.2.3


From ef27a18b6b7cb1a8eebdc9b2e88d25baf2c2414d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 19:42:10 +0300
Subject: Hires fix rework

---
 modules/generation_parameters_copypaste.py | 32 ++++++++++++++
 modules/images.py                          | 24 +++++++++--
 modules/processing.py                      | 68 ++++++++++++------------------
 modules/shared.py                          |  7 ++-
 modules/txt2img.py                         |  6 +--
 modules/ui.py                              | 15 +++----
 6 files changed, 95 insertions(+), 57 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 8e7f0df0..d6fa822b 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -1,5 +1,6 @@
 import base64
 import io
+import math
 import os
 import re
 from pathlib import Path
@@ -164,6 +165,35 @@ def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
     return None
 
 
+def restore_old_hires_fix_params(res):
+    """for infotexts that specify old First pass size parameter, convert it into
+    width, height, and hr scale"""
+
+    firstpass_width = res.get('First pass size-1', None)
+    firstpass_height = res.get('First pass size-2', None)
+
+    if firstpass_width is None or firstpass_height is None:
+        return
+
+    firstpass_width, firstpass_height = int(firstpass_width), int(firstpass_height)
+    width = int(res.get("Size-1", 512))
+    height = int(res.get("Size-2", 512))
+
+    if firstpass_width == 0 or firstpass_height == 0:
+        # old algorithm for auto-calculating first pass size
+        desired_pixel_count = 512 * 512
+        actual_pixel_count = width * height
+        scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+        firstpass_width = math.ceil(scale * width / 64) * 64
+        firstpass_height = math.ceil(scale * height / 64) * 64
+
+    hr_scale = width / firstpass_width if firstpass_width > 0 else height / firstpass_height
+
+    res['Size-1'] = firstpass_width
+    res['Size-2'] = firstpass_height
+    res['Hires upscale'] = hr_scale
+
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
@@ -221,6 +251,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         hypernet_hash = res.get("Hypernet hash", None)
         res["Hypernet"] = find_hypernetwork_key(hypernet_name, hypernet_hash)
 
+    restore_old_hires_fix_params(res)
+
     return res
 
 
diff --git a/modules/images.py b/modules/images.py
index f84fd485..c3a5fc8b 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -230,16 +230,32 @@ def draw_prompt_matrix(im, width, height, all_prompts):
     return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
 
 
-def resize_image(resize_mode, im, width, height):
+def resize_image(resize_mode, im, width, height, upscaler_name=None):
+    """
+    Resizes an image with the specified resize_mode, width, and height.
+
+    Args:
+        resize_mode: The mode to use when resizing the image.
+            0: Resize the image to the specified width and height.
+            1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess.
+            2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image.
+        im: The image to resize.
+        width: The width to resize the image to.
+        height: The height to resize the image to.
+        upscaler_name: The name of the upscaler to use. If not provided, defaults to opts.upscaler_for_img2img.
+    """
+
+    upscaler_name = upscaler_name or opts.upscaler_for_img2img
+
     def resize(im, w, h):
-        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None" or im.mode == 'L':
+        if upscaler_name is None or upscaler_name == "None" or im.mode == 'L':
             return im.resize((w, h), resample=LANCZOS)
 
         scale = max(w / im.width, h / im.height)
 
         if scale > 1.0:
-            upscalers = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img]
-            assert len(upscalers) > 0, f"could not find upscaler named {opts.upscaler_for_img2img}"
+            upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
+            assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}"
 
             upscaler = upscalers[0]
             im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
diff --git a/modules/processing.py b/modules/processing.py
index 42dc19ea..4654570c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -658,14 +658,18 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
+    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.denoising_strength = denoising_strength
-        self.firstphase_width = firstphase_width
-        self.firstphase_height = firstphase_height
-        self.truncate_x = 0
-        self.truncate_y = 0
+        self.hr_scale = hr_scale
+        self.hr_upscaler = hr_upscaler
+
+        if firstphase_width != 0 or firstphase_height != 0:
+            print("firstphase_width/firstphase_height no longer supported; use hr_scale", file=sys.stderr)
+            self.hr_scale = self.width / firstphase_width
+            self.width = firstphase_width
+            self.height = firstphase_height
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
@@ -674,47 +678,29 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
-            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
-
-            if self.firstphase_width == 0 or self.firstphase_height == 0:
-                desired_pixel_count = 512 * 512
-                actual_pixel_count = self.width * self.height
-                scale = math.sqrt(desired_pixel_count / actual_pixel_count)
-                self.firstphase_width = math.ceil(scale * self.width / 64) * 64
-                self.firstphase_height = math.ceil(scale * self.height / 64) * 64
-                firstphase_width_truncated = int(scale * self.width)
-                firstphase_height_truncated = int(scale * self.height)
-
-            else:
-
-                width_ratio = self.width / self.firstphase_width
-                height_ratio = self.height / self.firstphase_height
-
-                if width_ratio > height_ratio:
-                    firstphase_width_truncated = self.firstphase_width
-                    firstphase_height_truncated = self.firstphase_width * self.height / self.width
-                else:
-                    firstphase_width_truncated = self.firstphase_height * self.width / self.height
-                    firstphase_height_truncated = self.firstphase_height
-
-            self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
-            self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
+            self.extra_generation_params["Hires upscale"] = self.hr_scale
+            if self.hr_upscaler is not None:
+                self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
+        latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_default_mode
+        if self.enable_hr and latent_scale_mode is None:
+            assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}"
+
+        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+
         if not self.enable_hr:
-            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
             return samples
 
-        x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height))
-
-        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
+        target_width = int(self.width * self.hr_scale)
+        target_height = int(self.height * self.hr_scale)
 
-        """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
         def save_intermediate(image, index):
+            """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
+
             if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
                 return
 
@@ -723,11 +709,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
 
-        if opts.use_scale_latent_for_hires_fix:
+        if latent_scale_mode is not None:
             for i in range(samples.shape[0]):
                 save_intermediate(samples, i)
 
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode)
 
             # Avoid making the inpainting conditioning unless necessary as
             # this does need some extra compute to decode / encode the image again.
@@ -747,7 +733,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
                 save_intermediate(image, i)
 
-                image = images.resize_image(0, image, self.width, self.height)
+                image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler)
                 image = np.array(image).astype(np.float32) / 255.0
                 image = np.moveaxis(image, 2, 0)
                 batch_images.append(image)
@@ -764,7 +750,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
-        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
         x = None
diff --git a/modules/shared.py b/modules/shared.py
index 7f430b93..b65559ee 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -327,7 +327,6 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
-    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -545,6 +544,12 @@ opts = Options()
 if os.path.exists(config_filename):
     opts.load(config_filename)
 
+latent_upscale_default_mode = "Latent"
+latent_upscale_modes = {
+    "Latent": "bilinear",
+    "Latent (nearest)": "nearest",
+}
+
 sd_upscalers = []
 
 sd_model = None
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 7f61e19a..e189a899 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -8,7 +8,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -33,8 +33,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         tiling=tiling,
         enable_hr=enable_hr,
         denoising_strength=denoising_strength if enable_hr else None,
-        firstphase_width=firstphase_width if enable_hr else None,
-        firstphase_height=firstphase_height if enable_hr else None,
+        hr_scale=hr_scale,
+        hr_upscaler=hr_upscaler,
     )
 
     p.scripts = modules.scripts.scripts_txt2img
diff --git a/modules/ui.py b/modules/ui.py
index 7070ea15..27cd9ddd 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -684,11 +684,11 @@ def create_ui():
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
                     tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
-                    enable_hr = gr.Checkbox(label='Highres. fix', value=False, elem_id="txt2img_enable_hr")
+                    enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0, elem_id="txt2img_firstphase_width")
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0, elem_id="txt2img_firstphase_height")
+                    hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+                    hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
 
                 with gr.Row(equal_height=True):
@@ -729,8 +729,8 @@ def create_ui():
                     width,
                     enable_hr,
                     denoising_strength,
-                    firstphase_width,
-                    firstphase_height,
+                    hr_scale,
+                    hr_upscaler,
                 ] + custom_inputs,
 
                 outputs=[
@@ -762,7 +762,6 @@ def create_ui():
                 outputs=[hr_options],
             )
 
-
             txt2img_paste_fields = [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
@@ -781,8 +780,8 @@ def create_ui():
                 (denoising_strength, "Denoising strength"),
                 (enable_hr, lambda d: "Denoising strength" in d),
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (firstphase_width, "First pass size-1"),
-                (firstphase_height, "First pass size-2"),
+                (hr_scale, "Hires upscale"),
+                (hr_upscaler, "Hires upscaler"),
                 *modules.scripts.scripts_txt2img.infotext_fields
             ]
             parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
-- 
cgit v1.2.3


From 4dbde228ff48dbb105241b1ed25c21ce3f87d182 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 20:01:16 +0300
Subject: make it possible to use fractional values for SD upscale.

---
 modules/upscaler.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules')

diff --git a/modules/upscaler.py b/modules/upscaler.py
index c4e6e6bd..231680cb 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -53,10 +53,10 @@ class Upscaler:
     def do_upscale(self, img: PIL.Image, selected_model: str):
         return img
 
-    def upscale(self, img: PIL.Image, scale: int, selected_model: str = None):
+    def upscale(self, img: PIL.Image, scale, selected_model: str = None):
         self.scale = scale
-        dest_w = img.width * scale
-        dest_h = img.height * scale
+        dest_w = int(img.width * scale)
+        dest_h = int(img.height * scale)
 
         for i in range(3):
             shape = (img.width, img.height)
-- 
cgit v1.2.3


From 84dd7e8e2495c4fc2997e97f8267aa831eb90d11 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 20:30:02 +0300
Subject: error out with a readable message in chwewckpoint merger for
 incompatible tensor shapes (ie when trying to merge SD1.5 with SD2.0)

---
 modules/extras.py | 2 ++
 modules/ui.py     | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 68939dea..5e270250 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -303,6 +303,8 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
                 theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
                 result_is_inpainting_model = True
             else:
+                assert a.shape == b.shape, f'Incompatible shapes for layer {key}: A is {a.shape}, and B is {b.shape}'
+
                 theta_0[key] = theta_func2(a, b, multiplier)
 
             if save_as_half:
diff --git a/modules/ui.py b/modules/ui.py
index 27cd9ddd..67a51888 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1663,7 +1663,7 @@ def create_ui():
                 print("Error loading/saving model file:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
                 modules.sd_models.list_models()  # to remove the potentially missing models from the list
-                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
+                return [f"Error merging checkpoints: {e}"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)]
             return results
 
         modelmerger_merge.click(
-- 
cgit v1.2.3


From 8d12a729b8b036cb765cf2d87576d5ae256135c8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 20:46:51 +0300
Subject: fix possible error with accessing nonexistent setting

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 67a51888..9350a80f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -491,7 +491,7 @@ def apply_setting(key, value):
         return
 
     valtype = type(opts.data_labels[key].default)
-    oldval = opts.data[key]
+    oldval = opts.data.get(key, None)
     opts.data[key] = valtype(value) if valtype != type(None) else value
     if oldval != value and opts.data_labels[key].onchange is not None:
         opts.data_labels[key].onchange()
-- 
cgit v1.2.3


From 251ecee6949c36e9df1d99a950b3e1af2b5fa2b6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 22:44:46 +0300
Subject: make "send to" buttons send actual dimension of the sent image rather
 than fields

---
 modules/generation_parameters_copypaste.py | 58 ++++++++++++++++++++----------
 1 file changed, 40 insertions(+), 18 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index d6fa822b..ec60319a 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -103,35 +103,57 @@ def bind_buttons(buttons, send_image, send_generate_info):
     bind_list.append([buttons, send_image, send_generate_info])
 
 
+def send_image_and_dimensions(x):
+    if isinstance(x, Image.Image):
+        img = x
+    else:
+        img = image_from_url_text(x)
+
+    if shared.opts.send_size and isinstance(img, Image.Image):
+        w = img.width
+        h = img.height
+    else:
+        w = gr.update()
+        h = gr.update()
+
+    return img, w, h
+
+
 def run_bind():
-    for buttons, send_image, send_generate_info in bind_list:
+    for buttons, source_image_component, send_generate_info in bind_list:
         for tab in buttons:
             button = buttons[tab]
-            if send_image and paste_fields[tab]["init_img"]:
-                if type(send_image) == gr.Gallery:
-                    button.click(
-                        fn=lambda x: image_from_url_text(x),
-                        _js="extract_image_from_gallery",
-                        inputs=[send_image],
-                        outputs=[paste_fields[tab]["init_img"]],
-                    )
+            destination_image_component = paste_fields[tab]["init_img"]
+            fields = paste_fields[tab]["fields"]
+
+            destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
+            destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
+
+            if source_image_component and destination_image_component:
+                if isinstance(source_image_component, gr.Gallery):
+                    func = send_image_and_dimensions if destination_width_component else image_from_url_text
+                    jsfunc = "extract_image_from_gallery"
                 else:
-                    button.click(
-                        fn=lambda x: x,
-                        inputs=[send_image],
-                        outputs=[paste_fields[tab]["init_img"]],
-                    )
+                    func = send_image_and_dimensions if destination_width_component else lambda x: x
+                    jsfunc = None
+
+                button.click(
+                    fn=func,
+                    _js=jsfunc,
+                    inputs=[source_image_component],
+                    outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
+                )
 
-            if send_generate_info and paste_fields[tab]["fields"] is not None:
+            if send_generate_info and fields is not None:
                 if send_generate_info in paste_fields:
-                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] +  (['Size-1', 'Size-2'] if shared.opts.send_size else []) + (["Seed"] if shared.opts.send_seed else [])
+                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
                     button.click(
                         fn=lambda *x: x,
                         inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
-                        outputs=[field for field, name in paste_fields[tab]["fields"] if name in paste_field_names],
+                        outputs=[field for field, name in fields if name in paste_field_names],
                     )
                 else:
-                    connect_paste(button, paste_fields[tab]["fields"], send_generate_info)
+                    connect_paste(button, fields, send_generate_info)
 
             button.click(
                 fn=None,
-- 
cgit v1.2.3


From 269f6e867651cadef40d2c939a79d13291280bcd Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 07:20:20 +0300
Subject: change settings UI to use vertical tabs

---
 modules/ui.py | 45 +++++++++++++++++----------------------------
 1 file changed, 17 insertions(+), 28 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index 9350a80f..f8c973ba 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1489,41 +1489,34 @@ def create_ui():
         return gr.update(value=value), opts.dumpjson()
 
     with gr.Blocks(analytics_enabled=False) as settings_interface:
-        settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
-        result = gr.HTML()
+        with gr.Row():
+            settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
+            restart_gradio = gr.Button(value='Restart UI', variant='primary', elem_id="settings_restart_gradio")
 
-        settings_cols = 3
-        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
+        result = gr.HTML(elem_id="settings_result")
 
         quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
         quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
 
         quicksettings_list = []
 
-        cols_displayed = 0
-        items_displayed = 0
         previous_section = None
-        column = None
-        with gr.Row(elem_id="settings").style(equal_height=False):
+        current_tab = None
+        with gr.Tabs(elem_id="settings"):
             for i, (k, item) in enumerate(opts.data_labels.items()):
                 section_must_be_skipped = item.section[0] is None
 
                 if previous_section != item.section and not section_must_be_skipped:
-                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
-                        if column is not None:
-                            column.__exit__()
+                    elem_id, text = item.section
 
-                        column = gr.Column(variant='panel')
-                        column.__enter__()
+                    if current_tab is not None:
+                        current_tab.__exit__()
 
-                        items_displayed = 0
-                        cols_displayed += 1
+                    current_tab = gr.TabItem(elem_id="settings_{}".format(elem_id), label=text)
+                    current_tab.__enter__()
 
                     previous_section = item.section
 
-                    elem_id, text = item.section
-                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
-
                 if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
                     quicksettings_list.append((i, k, item))
                     components.append(dummy_component)
@@ -1533,15 +1526,14 @@ def create_ui():
                     component = create_setting_component(k)
                     component_dict[k] = component
                     components.append(component)
-                    items_displayed += 1
 
-        with gr.Row():
-            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
-            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+            if current_tab is not None:
+                current_tab.__exit__()
 
-        with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary', elem_id="settings_restart_gradio")
+            with gr.TabItem("Actions"):
+                request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+                download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+                reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
 
         request_notifications.click(
             fn=lambda: None,
@@ -1578,9 +1570,6 @@ def create_ui():
             outputs=[],
         )
 
-        if column is not None:
-            column.__exit__()
-
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
         (img2img_interface, "img2img", "img2img"),
-- 
cgit v1.2.3


From 18c03cdeac6272734b0c09afd3fbe47d1372dd07 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 09:04:29 +0300
Subject: styling rework to make things more compact

---
 modules/ui.py            | 121 ++++++++++++++++++++++++-----------------------
 modules/ui_components.py |   7 +++
 2 files changed, 68 insertions(+), 60 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index f8c973ba..f787b518 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -19,7 +19,8 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, ui_components
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru
+from modules.ui_components import FormRow, FormGroup, ToolButton
 from modules.paths import script_path
 
 from modules.shared import opts, cmd_opts, restricted_opts
@@ -273,31 +274,27 @@ def interrogate_deepbooru(image):
 
 
 def create_seed_inputs(target_interface):
-    with gr.Row():
-        with gr.Box():
-            with gr.Row(elem_id=target_interface + '_seed_row'):
-                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
-                seed.style(container=False)
-                random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
-                reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
-
-        with gr.Box(elem_id=target_interface + '_subseed_show_box'):
+    with FormRow(elem_id=target_interface + '_seed_row'):
+        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
+        seed.style(container=False)
+        random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
+        reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
+
+        with gr.Group(elem_id=target_interface + '_subseed_show_box'):
             seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
 
     # Components to show/hide based on the 'Extra' checkbox
     seed_extras = []
 
-    with gr.Row(visible=False) as seed_extra_row_1:
+    with FormRow(visible=False, elem_id=target_interface + '_subseed_row') as seed_extra_row_1:
         seed_extras.append(seed_extra_row_1)
-        with gr.Box():
-            with gr.Row(elem_id=target_interface + '_subseed_row'):
-                subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
-                subseed.style(container=False)
-                random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
-                reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
+        subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
+        subseed.style(container=False)
+        random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
+        reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
         subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
 
-    with gr.Row(visible=False) as seed_extra_row_2:
+    with FormRow(visible=False) as seed_extra_row_2:
         seed_extras.append(seed_extra_row_2)
         seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w')
         seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h')
@@ -523,7 +520,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
 
         return gr.update(**(args or {}))
 
-    refresh_button = ui_components.ToolButton(value=refresh_symbol, elem_id=elem_id)
+    refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
     refresh_button.click(
         fn=refresh,
         inputs=[],
@@ -636,11 +633,11 @@ Requested path was: {f}
 
 def create_sampler_and_steps_selection(choices, tabname):
     if opts.samplers_in_dropdown:
-        with gr.Row(elem_id=f"sampler_selection_{tabname}"):
+        with FormRow(elem_id=f"sampler_selection_{tabname}"):
             sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
             steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)
     else:
-        with gr.Group(elem_id=f"sampler_selection_{tabname}"):
+        with FormGroup(elem_id=f"sampler_selection_{tabname}"):
             steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)
             sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
 
@@ -677,29 +674,29 @@ def create_ui():
             with gr.Column(variant='panel', elem_id="txt2img_settings"):
                 steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
 
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+                with FormRow():
+                    with gr.Column(elem_id="txt2img_column_size", scale=4):
+                        width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+                        height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+                    with gr.Column(elem_id="txt2img_column_batch"):
+                        batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                        batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
 
-                with gr.Row():
+                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
+
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
+
+                with FormRow(elem_id="txt2img_checkboxes"):
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
                     tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
                     enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
 
-                with gr.Row(visible=False) as hr_options:
+                with FormRow(visible=False) as hr_options:
                     hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
                     hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
 
-                with gr.Row(equal_height=True):
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
-
-                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
-
-                with gr.Group(elem_id="txt2img_script_container"):
+                with FormGroup(elem_id="txt2img_script_container"):
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
             txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
@@ -816,7 +813,7 @@ def create_ui():
                 img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
                 setup_progressbar(progressbar, img2img_preview, 'img2img')
 
-        with gr.Row().style(equal_height=False):
+        with FormRow().style(equal_height=False):
             with gr.Column(variant='panel', elem_id="img2img_settings"):
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
@@ -841,19 +838,23 @@ def create_ui():
                         init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
 
-                        with gr.Row():
+                        with FormRow():
                             mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
                             mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch, elem_id="img2img_mask_alpha")
 
-                        with gr.Row():
-                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
+                        with FormRow():
+                            mask_mode = gr.Radio(label="Mask source", choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
+                            inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
 
-                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
+                        with FormRow():
+                            inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
 
-                        with gr.Row():
-                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False, elem_id="img2img_inpaint_full_res")
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+                        with FormRow():
+                            with gr.Column():
+                                inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
+
+                            with gr.Column(scale=4):
+                                inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
 
                     with gr.TabItem('Batch img2img', id='batch', elem_id="img2img_batch_tab"):
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
@@ -861,30 +862,30 @@ def create_ui():
                         img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
                         img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
 
-                with gr.Row():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
+                with FormRow():
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
                 steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
 
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
-                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
+                with FormRow():
+                    with gr.Column(elem_id="img2img_column_size", scale=4):
+                        width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+                        height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+                    with gr.Column(elem_id="img2img_column_batch"):
+                        batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                        batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
 
-                with gr.Row():
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
-                with gr.Group():
+                with FormGroup():
                     cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
 
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
 
-                with gr.Group(elem_id="img2img_script_container"):
+                with FormRow(elem_id="img2img_checkboxes"):
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
+                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
+
+                with FormGroup(elem_id="img2img_script_container"):
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui()
 
             img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
@@ -1444,7 +1445,7 @@ def create_ui():
                 res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                 create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
             else:
-                with ui_components.FormRow():
+                with FormRow():
                     res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                     create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
         else:
diff --git a/modules/ui_components.py b/modules/ui_components.py
index d0519d2d..91eb0e3d 100644
--- a/modules/ui_components.py
+++ b/modules/ui_components.py
@@ -16,3 +16,10 @@ class FormRow(gr.Row, gr.components.FormComponent):
 
     def get_block_name(self):
         return "row"
+
+
+class FormGroup(gr.Group, gr.components.FormComponent):
+    """Same as gr.Row but fits inside gradio forms"""
+
+    def get_block_name(self):
+        return "group"
-- 
cgit v1.2.3


From 2bc86712ec16cada01a2353f1d978c1aabc84dbb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 09:13:35 +0300
Subject: make quicksettings UI elements appear in same order as they are
 listed in the setting

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index f787b518..d7b911da 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1497,7 +1497,7 @@ def create_ui():
         result = gr.HTML(elem_id="settings_result")
 
         quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
-        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
+        quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'}
 
         quicksettings_list = []
 
@@ -1604,7 +1604,7 @@ def create_ui():
 
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings"):
-            for i, k, item in quicksettings_list:
+            for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
                 component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
-- 
cgit v1.2.3


From 9d4eff097deff6153c4023f158bd9fbd4f3e88b3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 10:01:06 +0300
Subject: add a button to show all setting pages

---
 modules/ui.py | 2 ++
 1 file changed, 2 insertions(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index d7b911da..2c92c422 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1536,6 +1536,8 @@ def create_ui():
                 download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
                 reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
 
+            gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
+
         request_notifications.click(
             fn=lambda: None,
             inputs=[],
-- 
cgit v1.2.3


From a1cf55a9d1c82f8e56c00d549bca5c8fa069f412 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 10:39:21 +0300
Subject: add option to reorder items in main UI

---
 modules/shared.py |  13 ++++++
 modules/ui.py     | 130 +++++++++++++++++++++++++++++++++++-------------------
 2 files changed, 97 insertions(+), 46 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index b65559ee..23657a93 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -109,6 +109,17 @@ restricted_opts = {
     "outdir_save",
 }
 
+ui_reorder_categories = [
+    "sampler",
+    "dimensions",
+    "cfg",
+    "seed",
+    "checkboxes",
+    "hires_fix",
+    "batch",
+    "scripts",
+]
+
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
@@ -410,7 +421,9 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
     "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
+    "dimensions_and_batch_together": OptionInfo(True, "Show Witdth/Height and Batch sliders in same row"),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
+    'ui_reorder': OptionInfo(", ".join(ui_reorder_categories), "txt2img/ing2img UI item order"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
 
diff --git a/modules/ui.py b/modules/ui.py
index 2c92c422..f2e7c0d6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -644,6 +644,13 @@ def create_sampler_and_steps_selection(choices, tabname):
     return steps, sampler_index
 
 
+def ordered_ui_categories():
+    user_order = {x.strip(): i for i, x in enumerate(shared.opts.ui_reorder.split(","))}
+
+    for i, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] + 1000)):
+        yield category
+
+
 def create_ui():
     import modules.img2img
     import modules.txt2img
@@ -672,32 +679,48 @@ def create_ui():
 
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel', elem_id="txt2img_settings"):
-                steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
-
-                with FormRow():
-                    with gr.Column(elem_id="txt2img_column_size", scale=4):
-                        width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
-                        height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
-                    with gr.Column(elem_id="txt2img_column_batch"):
-                        batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
-                        batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
-
-                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
-
-                with FormRow(elem_id="txt2img_checkboxes"):
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
-                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
-                    enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
+                for category in ordered_ui_categories():
+                    if category == "sampler":
+                        steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
 
-                with FormRow(visible=False) as hr_options:
-                    hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
-                    hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
-
-                with FormGroup(elem_id="txt2img_script_container"):
-                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+                    elif category == "dimensions":
+                        with FormRow():
+                            with gr.Column(elem_id="txt2img_column_size", scale=4):
+                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+
+                            if opts.dimensions_and_batch_together:
+                                with gr.Column(elem_id="txt2img_column_batch"):
+                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+                    elif category == "cfg":
+                        cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
+
+                    elif category == "seed":
+                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
+
+                    elif category == "checkboxes":
+                        with FormRow(elem_id="txt2img_checkboxes"):
+                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
+                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
+                            enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
+
+                    elif category == "hires_fix":
+                        with FormRow(visible=False, elem_id="txt2img_hires_fix") as hr_options:
+                            hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+                            hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
+                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
+
+                    elif category == "batch":
+                        if not opts.dimensions_and_batch_together:
+                            with FormRow(elem_id="txt2img_column_batch"):
+                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+                    elif category == "scripts":
+                        with FormGroup(elem_id="txt2img_script_container"):
+                            custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
             txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
             parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
@@ -865,28 +888,43 @@ def create_ui():
                 with FormRow():
                     resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
-                steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
-
-                with FormRow():
-                    with gr.Column(elem_id="img2img_column_size", scale=4):
-                        width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
-                        height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
-                    with gr.Column(elem_id="img2img_column_batch"):
-                        batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
-                        batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
-                with FormGroup():
-                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
+                for category in ordered_ui_categories():
+                    if category == "sampler":
+                        steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
 
-                with FormRow(elem_id="img2img_checkboxes"):
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
-                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
-
-                with FormGroup(elem_id="img2img_script_container"):
-                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+                    elif category == "dimensions":
+                        with FormRow():
+                            with gr.Column(elem_id="img2img_column_size", scale=4):
+                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+
+                            if opts.dimensions_and_batch_together:
+                                with gr.Column(elem_id="img2img_column_batch"):
+                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+                    elif category == "cfg":
+                        with FormGroup():
+                            cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
+
+                    elif category == "seed":
+                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
+
+                    elif category == "checkboxes":
+                        with FormRow(elem_id="img2img_checkboxes"):
+                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
+                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
+
+                    elif category == "batch":
+                        if not opts.dimensions_and_batch_together:
+                            with FormRow(elem_id="img2img_column_batch"):
+                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+                    elif category == "scripts":
+                        with FormGroup(elem_id="img2img_script_container"):
+                            custom_inputs = modules.scripts.scripts_img2img.setup_ui()
 
             img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
             parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
-- 
cgit v1.2.3


From c0ee1488702d5a6ae35fbf7e0422f9f685394920 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 14:18:48 +0300
Subject: add support for running with gradio 3.9 installed

---
 modules/generation_parameters_copypaste.py |  4 ++--
 modules/ui_tempdir.py                      | 23 +++++++++++++++++++++--
 2 files changed, 23 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index ec60319a..d94f11a3 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -7,7 +7,7 @@ from pathlib import Path
 
 import gradio as gr
 from modules.shared import script_path
-from modules import shared
+from modules import shared, ui_tempdir
 import tempfile
 from PIL import Image
 
@@ -39,7 +39,7 @@ def quote(text):
 def image_from_url_text(filedata):
     if type(filedata) == dict and filedata["is_file"]:
         filename = filedata["name"]
-        is_in_right_dir = any([filename in fileset for fileset in shared.demo.temp_file_sets])
+        is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
         assert is_in_right_dir, 'trying to open image file outside of allowed directories'
 
         return Image.open(filename)
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
index 363d449d..21945235 100644
--- a/modules/ui_tempdir.py
+++ b/modules/ui_tempdir.py
@@ -1,6 +1,7 @@
 import os
 import tempfile
 from collections import namedtuple
+from pathlib import Path
 
 import gradio as gr
 
@@ -12,10 +13,28 @@ from modules import shared
 Savedfile = namedtuple("Savedfile", ["name"])
 
 
+def register_tmp_file(gradio, filename):
+    if hasattr(gradio, 'temp_file_sets'):  # gradio 3.15
+        gradio.temp_file_sets[0] = gradio.temp_file_sets[0] | {os.path.abspath(filename)}
+
+    if hasattr(gradio, 'temp_dirs'):  # gradio 3.9
+        gradio.temp_dirs = gradio.temp_dirs | {os.path.abspath(os.path.dirname(filename))}
+
+
+def check_tmp_file(gradio, filename):
+    if hasattr(gradio, 'temp_file_sets'):
+        return any([filename in fileset for fileset in gradio.temp_file_sets])
+
+    if hasattr(gradio, 'temp_dirs'):
+        return any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in gradio.temp_dirs)
+
+    return False
+
+
 def save_pil_to_file(pil_image, dir=None):
     already_saved_as = getattr(pil_image, 'already_saved_as', None)
     if already_saved_as and os.path.isfile(already_saved_as):
-        shared.demo.temp_file_sets[0] = shared.demo.temp_file_sets[0] | {os.path.abspath(already_saved_as)}
+        register_tmp_file(shared.demo, already_saved_as)
 
         file_obj = Savedfile(already_saved_as)
         return file_obj
@@ -45,7 +64,7 @@ def on_tmpdir_changed():
 
     os.makedirs(shared.opts.temp_dir, exist_ok=True)
 
-    shared.demo.temp_file_sets[0] = shared.demo.temp_file_sets[0] | {os.path.abspath(shared.opts.temp_dir)}
+    register_tmp_file(shared.demo, os.path.join(shared.opts.temp_dir, "x"))
 
 
 def cleanup_tmpdr():
-- 
cgit v1.2.3


From bddebe09edeb6a18f2c06986d5658a7be3a563ea Mon Sep 17 00:00:00 2001
From: Shondoit <shondoit@gmail.com>
Date: Tue, 3 Jan 2023 10:26:37 +0100
Subject: Save Optimizer next to TI embedding

Also add check to load only .PT and .BIN files as embeddings. (since we add .optim files in the same directory)
---
 modules/shared.py                              |  2 +-
 modules/textual_inversion/textual_inversion.py | 40 ++++++++++++++++++++------
 2 files changed, 33 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 23657a93..c541d18c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -355,7 +355,7 @@ options_templates.update(options_section(('system', "System"), {
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fd253477..16176e90 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -28,6 +28,7 @@ class Embedding:
         self.cached_checksum = None
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
+        self.optimizer_state_dict = None
 
     def save(self, filename):
         embedding_data = {
@@ -41,6 +42,13 @@ class Embedding:
 
         torch.save(embedding_data, filename)
 
+        if shared.opts.save_optimizer_state and self.optimizer_state_dict is not None:
+            optimizer_saved_dict = {
+                'hash': self.checksum(),
+                'optimizer_state_dict': self.optimizer_state_dict,
+            }
+            torch.save(optimizer_saved_dict, filename + '.optim')
+
     def checksum(self):
         if self.cached_checksum is not None:
             return self.cached_checksum
@@ -95,9 +103,10 @@ class EmbeddingDatabase:
         self.expected_shape = self.get_expected_shape()
 
         def process_file(path, filename):
-            name = os.path.splitext(filename)[0]
+            name, ext = os.path.splitext(filename)
+            ext = ext.upper()
 
-            if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
+            if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
                 embed_image = Image.open(path)
                 if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
                     data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
@@ -105,8 +114,10 @@ class EmbeddingDatabase:
                 else:
                     data = extract_image_data_embed(embed_image)
                     name = data.get('name', name)
-            else:
+            elif ext in ['.BIN', '.PT']:
                 data = torch.load(path, map_location="cpu")
+            else:
+                return
 
             # textual inversion embeddings
             if 'string_to_param' in data:
@@ -300,6 +311,20 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 
     embedding.vec.requires_grad = True
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
+    if shared.opts.save_optimizer_state:
+        optimizer_state_dict = None
+        if os.path.exists(filename + '.optim'):
+            optimizer_saved_dict = torch.load(filename + '.optim', map_location='cpu')
+            if embedding.checksum() == optimizer_saved_dict.get('hash', None):
+                optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+    
+        if optimizer_state_dict is not None:
+            optimizer.load_state_dict(optimizer_state_dict)
+            print("Loaded existing optimizer from checkpoint")
+        else:
+            print("No saved optimizer exists in checkpoint")
+
+
     scaler = torch.cuda.amp.GradScaler()
 
     batch_size = ds.batch_size
@@ -366,9 +391,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     # Before saving, change name to match current checkpoint.
                     embedding_name_every = f'{embedding_name}-{steps_done}'
                     last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
-                    #if shared.opts.save_optimizer_state:
-                        #embedding.optimizer_state_dict = optimizer.state_dict()
-                    save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
+                    save_embedding(embedding, optimizer, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
                     embedding_yet_to_be_embedded = True
 
                 write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, {
@@ -458,7 +481,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
         filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
-        save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+        save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
     except Exception:
         print(traceback.format_exc(), file=sys.stderr)
         pass
@@ -470,7 +493,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     return embedding, filename
 
-def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True):
+def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True):
     old_embedding_name = embedding.name
     old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
     old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
@@ -481,6 +504,7 @@ def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cache
         if remove_cached_checksum:
             embedding.cached_checksum = None
         embedding.name = embedding_name
+        embedding.optimizer_state_dict = optimizer.state_dict()
         embedding.save(filename)
     except:
         embedding.sd_checkpoint = old_sd_checkpoint
-- 
cgit v1.2.3


From e9fb9bb0c25f59109a816fc53c385bed58965c24 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 17:40:20 +0300
Subject: fix hires fix not working in API when user does not specify upscaler

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index 4654570c..a172af0b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -685,7 +685,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
-        latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_default_mode
+        latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
         if self.enable_hr and latent_scale_mode is None:
             assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}"
 
-- 
cgit v1.2.3


From aaa4c2aacbb6523077334093c81bd475d757f7a1 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 3 Jan 2023 09:45:16 -0500
Subject: add api logging

---
 modules/api/api.py | 24 +++++++++++++++++++++++-
 modules/shared.py  |  1 +
 2 files changed, 24 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 9c670f00..53135470 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,11 +1,12 @@
 import base64
 import io
 import time
+import datetime
 import uvicorn
 from threading import Lock
 from io import BytesIO
 from gradio.processing_utils import decode_base64_to_file
-from fastapi import APIRouter, Depends, FastAPI, HTTPException
+from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
 
@@ -67,6 +68,26 @@ def encode_pil_to_base64(image):
         bytes_data = output_bytes.getvalue()
     return base64.b64encode(bytes_data)
 
+def init_api_middleware(app: FastAPI):
+    @app.middleware("http")
+    async def log_and_time(req: Request, call_next):
+        ts = time.time()
+        res: Response = await call_next(req)
+        duration = str(round(time.time() - ts, 4))
+        res.headers["X-Process-Time"] = duration
+        if shared.cmd_opts.api_log:
+            print('API {t} {code} {prot}/{ver} {method} {p} {cli} {duration}'.format(
+                t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
+                code = res.status_code,
+                ver = req.scope.get('http_version', '0.0'),
+                cli = req.scope.get('client', ('0:0.0.0', 0))[0],
+                prot = req.scope.get('scheme', 'err'),
+                method = req.scope.get('method', 'err'),
+                p = req.scope.get('path', 'err'),
+                duration = duration,
+            ))
+        return res
+
 
 class Api:
     def __init__(self, app: FastAPI, queue_lock: Lock):
@@ -78,6 +99,7 @@ class Api:
 
         self.router = APIRouter()
         self.app = app
+        init_api_middleware(self.app)
         self.queue_lock = queue_lock
         self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
diff --git a/modules/shared.py b/modules/shared.py
index 23657a93..2a03d716 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -82,6 +82,7 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
 parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-- 
cgit v1.2.3


From 1d9dc48efda2e8da6d13fc62e65500198a9b041c Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 3 Jan 2023 10:21:51 -0500
Subject: init job and add info to model merge

---
 modules/extras.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 5e270250..7e222313 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -242,6 +242,9 @@ def run_pnginfo(image):
 
 
 def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
+    shared.state.begin()
+    shared.state.job = 'model-merge'
+
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
@@ -263,8 +266,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
     theta_func1, theta_func2 = theta_funcs[interp_method]
 
     if theta_func1 and not tertiary_model_info:
+        shared.state.textinfo = "Failed: Interpolation method requires a tertiary model."
+        shared.state.end()
         return ["Failed: Interpolation method requires a tertiary model."] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
 
+    shared.state.textinfo = f"Loading {secondary_model_info.filename}..."
     print(f"Loading {secondary_model_info.filename}...")
     theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
 
@@ -281,6 +287,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
                     theta_1[key] = torch.zeros_like(theta_1[key])
         del theta_2
 
+    shared.state.textinfo = f"Loading {primary_model_info.filename}..."
     print(f"Loading {primary_model_info.filename}...")
     theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
 
@@ -291,6 +298,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
             a = theta_0[key]
             b = theta_1[key]
 
+            shared.state.textinfo = f'Merging layer {key}'
             # this enables merging an inpainting model (A) with another one (B);
             # where normal model would have 4 channels, for latenst space, inpainting model would
             # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
@@ -303,8 +311,6 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
                 theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
                 result_is_inpainting_model = True
             else:
-                assert a.shape == b.shape, f'Incompatible shapes for layer {key}: A is {a.shape}, and B is {b.shape}'
-
                 theta_0[key] = theta_func2(a, b, multiplier)
 
             if save_as_half:
@@ -332,6 +338,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
 
     output_modelname = os.path.join(ckpt_dir, filename)
 
+    shared.state.textinfo = f"Saving to {output_modelname}..."
     print(f"Saving to {output_modelname}...")
 
     _, extension = os.path.splitext(output_modelname)
@@ -343,4 +350,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
     sd_models.list_models()
 
     print("Checkpoint saved.")
+    shared.state.textinfo = "Checkpoint saved to " + output_modelname
+    shared.state.end()
+
     return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
-- 
cgit v1.2.3


From 192ddc04d6de0d780f73aa5fbaa8c66cd4642e1c Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 3 Jan 2023 10:34:51 -0500
Subject: add job info to modules

---
 modules/extras.py                              | 17 +++++++++++++----
 modules/hypernetworks/hypernetwork.py          |  1 +
 modules/textual_inversion/preprocess.py        |  1 +
 modules/textual_inversion/textual_inversion.py |  1 +
 4 files changed, 16 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 7e222313..d665440a 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -58,6 +58,9 @@ cached_images: LruCache = LruCache(max_size=5)
 def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
     devices.torch_gc()
 
+    shared.state.begin()
+    shared.state.job = 'extras'
+
     imageArr = []
     # Also keep track of original file names
     imageNameArr = []
@@ -94,6 +97,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     # Extra operation definitions
 
     def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        shared.state.job = 'extras-gfpgan'
         restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
         res = Image.fromarray(restored_img)
 
@@ -104,6 +108,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         return (res, info)
 
     def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        shared.state.job = 'extras-codeformer'
         restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
         res = Image.fromarray(restored_img)
 
@@ -114,6 +119,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         return (res, info)
 
     def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
+        shared.state.job = 'extras-upscale'
         upscaler = shared.sd_upscalers[scaler_index]
         res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
         if mode == 1 and crop:
@@ -180,6 +186,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
+
+        shared.state.textinfo = f'Processing image {image_name}'
+        
         existing_pnginfo = image.info or {}
 
         image = image.convert("RGB")
@@ -193,6 +202,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         else:
             basename = ''
 
+        if opts.enable_pnginfo: # append info before save
+            image.info = existing_pnginfo
+            image.info["extras"] = info
+
         if save_output:
             # Add upscaler name as a suffix.
             suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
@@ -203,10 +216,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
                             no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
 
-        if opts.enable_pnginfo:
-            image.info = existing_pnginfo
-            image.info["extras"] = info
-
         if extras_mode != 2 or show_extras_results :
             outputs.append(image)
 
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 109e8078..450fecac 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -417,6 +417,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
     shared.loaded_hypernetwork = Hypernetwork()
     shared.loaded_hypernetwork.load(path)
 
+    shared.state.job = "train-hypernetwork"
     shared.state.textinfo = "Initializing hypernetwork training..."
     shared.state.job_count = steps
 
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 56b9b2eb..feb876c6 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -124,6 +124,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
 
     files = listfiles(src)
 
+    shared.state.job = "preprocess"
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fd253477..2c1251d6 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -245,6 +245,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     create_image_every = create_image_every or 0
     validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
 
+    shared.state.job = "train-embedding"
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
 
-- 
cgit v1.2.3


From 2d5a5076bb2a0c05cc27d75a1bcadab7f32a46d0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 18:38:21 +0300
Subject: Make it so that upscalers are not repeated when restarting UI.

---
 modules/modelloader.py | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

(limited to 'modules')

diff --git a/modules/modelloader.py b/modules/modelloader.py
index e647f6fa..6a1a7ac8 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -123,6 +123,23 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
         pass
 
 
+builtin_upscaler_classes = []
+forbidden_upscaler_classes = set()
+
+
+def list_builtin_upscalers():
+    load_upscalers()
+
+    builtin_upscaler_classes.clear()
+    builtin_upscaler_classes.extend(Upscaler.__subclasses__())
+
+
+def forbid_loaded_nonbuiltin_upscalers():
+    for cls in Upscaler.__subclasses__():
+        if cls not in builtin_upscaler_classes:
+            forbidden_upscaler_classes.add(cls)
+
+
 def load_upscalers():
     # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
     # so we'll try to import any _model.py files before looking in __subclasses__
@@ -139,6 +156,9 @@ def load_upscalers():
     datas = []
     commandline_options = vars(shared.cmd_opts)
     for cls in Upscaler.__subclasses__():
+        if cls in forbidden_upscaler_classes:
+            continue
+
         name = cls.__name__
         cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
         scaler = cls(commandline_options.get(cmd_name, None))
-- 
cgit v1.2.3


From 8f96f9289981a66741ba770d14f3d27ce335a0fb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 18:39:14 +0300
Subject: call script callbacks for reloaded model after loading embeddings

---
 modules/sd_models.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index bff8d6c9..b98b05fc 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -324,12 +324,12 @@ def load_model(checkpoint_info=None):
     sd_model.eval()
     shared.sd_model = sd_model
 
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
+
     script_callbacks.model_loaded_callback(sd_model)
 
     print("Model loaded.")
 
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload = True) # Reload embeddings after model load as they may or may not fit the model
-
     return sd_model
 
 
-- 
cgit v1.2.3


From cec209981ee988536c2521297baf9bc1b256005f Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 3 Jan 2023 10:58:52 -0500
Subject: log only sdapi

---
 modules/api/api.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 53135470..78751c57 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -68,22 +68,23 @@ def encode_pil_to_base64(image):
         bytes_data = output_bytes.getvalue()
     return base64.b64encode(bytes_data)
 
-def init_api_middleware(app: FastAPI):
+def api_middleware(app: FastAPI):
     @app.middleware("http")
     async def log_and_time(req: Request, call_next):
         ts = time.time()
         res: Response = await call_next(req)
         duration = str(round(time.time() - ts, 4))
         res.headers["X-Process-Time"] = duration
-        if shared.cmd_opts.api_log:
-            print('API {t} {code} {prot}/{ver} {method} {p} {cli} {duration}'.format(
+        endpoint = req.scope.get('path', 'err')
+        if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'):
+            print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format(
                 t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
                 code = res.status_code,
                 ver = req.scope.get('http_version', '0.0'),
                 cli = req.scope.get('client', ('0:0.0.0', 0))[0],
                 prot = req.scope.get('scheme', 'err'),
                 method = req.scope.get('method', 'err'),
-                p = req.scope.get('path', 'err'),
+                endpoint = endpoint,
                 duration = duration,
             ))
         return res
-- 
cgit v1.2.3


From d8d206c1685d1e7027d4af82ed18d106f41d1cc4 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 3 Jan 2023 11:01:04 -0500
Subject: add state to interrogate

---
 modules/interrogate.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 6f761c5a..738d8ff7 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -136,7 +136,8 @@ class InterrogateModels:
 
     def interrogate(self, pil_image):
         res = ""
-
+        shared.state.begin()
+        shared.state.job = 'interrogate'
         try:
 
             if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -177,5 +178,6 @@ class InterrogateModels:
             res += "<error>"
 
         self.unload()
+        shared.state.end()
 
         return res
-- 
cgit v1.2.3


From 82cfc227d735c140447d5b8dca29a71ee9bde127 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 20:23:17 +0300
Subject: added licenses screen to settings added footer removed unused
 inpainting code

---
 modules/sd_hijack_inpainting.py | 232 ----------------------------------------
 modules/ui.py                   |  15 ++-
 2 files changed, 13 insertions(+), 234 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 06b75772..3c214a35 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -12,191 +12,6 @@ from ldm.models.diffusion.ddpm import LatentDiffusion
 from ldm.models.diffusion.plms import PLMSSampler
 from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 
-# =================================================================================================
-# Monkey patch DDIMSampler methods from RunwayML repo directly.
-# Adapted from:
-# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
-# =================================================================================================
-@torch.no_grad()
-def sample_ddim(self,
-            S,
-            batch_size,
-            shape,
-            conditioning=None,
-            callback=None,
-            normals_sequence=None,
-            img_callback=None,
-            quantize_x0=False,
-            eta=0.,
-            mask=None,
-            x0=None,
-            temperature=1.,
-            noise_dropout=0.,
-            score_corrector=None,
-            corrector_kwargs=None,
-            verbose=True,
-            x_T=None,
-            log_every_t=100,
-            unconditional_guidance_scale=1.,
-            unconditional_conditioning=None,
-            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-            **kwargs
-            ):
-    if conditioning is not None:
-        if isinstance(conditioning, dict):
-            ctmp = conditioning[list(conditioning.keys())[0]]
-            while isinstance(ctmp, list):
-                ctmp = ctmp[0]
-            cbs = ctmp.shape[0]
-            if cbs != batch_size:
-                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-        else:
-            if conditioning.shape[0] != batch_size:
-                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-    # sampling
-    C, H, W = shape
-    size = (batch_size, C, H, W)
-    print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-
-    samples, intermediates = self.ddim_sampling(conditioning, size,
-                                                callback=callback,
-                                                img_callback=img_callback,
-                                                quantize_denoised=quantize_x0,
-                                                mask=mask, x0=x0,
-                                                ddim_use_original_steps=False,
-                                                noise_dropout=noise_dropout,
-                                                temperature=temperature,
-                                                score_corrector=score_corrector,
-                                                corrector_kwargs=corrector_kwargs,
-                                                x_T=x_T,
-                                                log_every_t=log_every_t,
-                                                unconditional_guidance_scale=unconditional_guidance_scale,
-                                                unconditional_conditioning=unconditional_conditioning,
-                                                )
-    return samples, intermediates
-
-@torch.no_grad()
-def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                    unconditional_guidance_scale=1., unconditional_conditioning=None):
-    b, *_, device = *x.shape, x.device
-
-    if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-        e_t = self.model.apply_model(x, t, c)
-    else:
-        x_in = torch.cat([x] * 2)
-        t_in = torch.cat([t] * 2)
-        if isinstance(c, dict):
-            assert isinstance(unconditional_conditioning, dict)
-            c_in = dict()
-            for k in c:
-                if isinstance(c[k], list):
-                    c_in[k] = [
-                        torch.cat([unconditional_conditioning[k][i], c[k][i]])
-                        for i in range(len(c[k]))
-                    ]
-                else:
-                    c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
-        else:
-            c_in = torch.cat([unconditional_conditioning, c])
-        e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-        e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-    if score_corrector is not None:
-        assert self.model.parameterization == "eps"
-        e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-    # select parameters corresponding to the currently considered timestep
-    a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-    a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-    sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-    sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-    # current prediction for x_0
-    pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-    if quantize_denoised:
-        pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-    # direction pointing to x_t
-    dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-    noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-    if noise_dropout > 0.:
-        noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-    x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-    return x_prev, pred_x0
-
-
-# =================================================================================================
-# Monkey patch PLMSSampler methods.
-# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes.
-# Adapted from:
-# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py
-# =================================================================================================
-@torch.no_grad()
-def sample_plms(self,
-            S,
-            batch_size,
-            shape,
-            conditioning=None,
-            callback=None,
-            normals_sequence=None,
-            img_callback=None,
-            quantize_x0=False,
-            eta=0.,
-            mask=None,
-            x0=None,
-            temperature=1.,
-            noise_dropout=0.,
-            score_corrector=None,
-            corrector_kwargs=None,
-            verbose=True,
-            x_T=None,
-            log_every_t=100,
-            unconditional_guidance_scale=1.,
-            unconditional_conditioning=None,
-            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-            **kwargs
-            ):
-    if conditioning is not None:
-        if isinstance(conditioning, dict):
-            ctmp = conditioning[list(conditioning.keys())[0]]
-            while isinstance(ctmp, list):
-                ctmp = ctmp[0]
-            cbs = ctmp.shape[0]
-            if cbs != batch_size:
-                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-        else:
-            if conditioning.shape[0] != batch_size:
-                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-    # sampling
-    C, H, W = shape
-    size = (batch_size, C, H, W)
-    # print(f'Data shape for PLMS sampling is {size}') # remove unnecessary message
-
-    samples, intermediates = self.plms_sampling(conditioning, size,
-                                                callback=callback,
-                                                img_callback=img_callback,
-                                                quantize_denoised=quantize_x0,
-                                                mask=mask, x0=x0,
-                                                ddim_use_original_steps=False,
-                                                noise_dropout=noise_dropout,
-                                                temperature=temperature,
-                                                score_corrector=score_corrector,
-                                                corrector_kwargs=corrector_kwargs,
-                                                x_T=x_T,
-                                                log_every_t=log_every_t,
-                                                unconditional_guidance_scale=unconditional_guidance_scale,
-                                                unconditional_conditioning=unconditional_conditioning,
-                                                )
-    return samples, intermediates
-
 
 @torch.no_grad()
 def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
@@ -280,44 +95,6 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
 
     return x_prev, pred_x0, e_t
 
-# =================================================================================================
-# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
-# Adapted from:
-# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
-# =================================================================================================
-
-@torch.no_grad()
-def get_unconditional_conditioning(self, batch_size, null_label=None):
-    if null_label is not None:
-        xc = null_label
-        if isinstance(xc, ListConfig):
-            xc = list(xc)
-        if isinstance(xc, dict) or isinstance(xc, list):
-            c = self.get_learned_conditioning(xc)
-        else:
-            if hasattr(xc, "to"):
-                xc = xc.to(self.device)
-            c = self.get_learned_conditioning(xc)
-    else:
-        # todo: get null label from cond_stage_model
-        raise NotImplementedError()
-    c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
-    return c
-
-
-class LatentInpaintDiffusion(LatentDiffusion):
-    def __init__(
-        self,
-        concat_keys=("mask", "masked_image"),
-        masked_image_key="masked_image",
-        *args,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-        self.masked_image_key = masked_image_key
-        assert self.masked_image_key in concat_keys
-        self.concat_keys = concat_keys
-
 
 def should_hijack_inpainting(checkpoint_info):
     ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
@@ -326,15 +103,6 @@ def should_hijack_inpainting(checkpoint_info):
 
 
 def do_inpainting_hijack():
-    # most of this stuff seems to no longer be needed because it is already included into SD2.0
     # p_sample_plms is needed because PLMS can't work with dicts as conditionings
-    # this file should be cleaned up later if everything turns out to work fine
-
-    # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
-    # ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
-
-    # ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
-    # ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
 
     ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
-    # ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
diff --git a/modules/ui.py b/modules/ui.py
index f2e7c0d6..d941cb5f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1529,8 +1529,10 @@ def create_ui():
 
     with gr.Blocks(analytics_enabled=False) as settings_interface:
         with gr.Row():
-            settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
-            restart_gradio = gr.Button(value='Restart UI', variant='primary', elem_id="settings_restart_gradio")
+            with gr.Column(scale=6):
+                settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
+            with gr.Column():
+                restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
 
         result = gr.HTML(elem_id="settings_result")
 
@@ -1574,6 +1576,11 @@ def create_ui():
                 download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
                 reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
 
+            if os.path.exists("html/licenses.html"):
+                with open("html/licenses.html", encoding="utf8") as file:
+                    with gr.TabItem("Licenses"):
+                        gr.HTML(file.read(), elem_id="licenses")
+
             gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
 
         request_notifications.click(
@@ -1659,6 +1666,10 @@ def create_ui():
         if os.path.exists(os.path.join(script_path, "notification.mp3")):
             audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
 
+        if os.path.exists("html/footer.html"):
+            with open("html/footer.html", encoding="utf8") as file:
+                gr.HTML(file.read(), elem_id="footer")
+
         text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
         settings_submit.click(
             fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
-- 
cgit v1.2.3


From 3e22e294135ed0327ce9d9738655ff03c53df3c0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 21:49:24 +0300
Subject: fix broken send to extras button

---
 modules/generation_parameters_copypaste.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index d94f11a3..4baf4d9a 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -37,7 +37,10 @@ def quote(text):
 
 
 def image_from_url_text(filedata):
-    if type(filedata) == dict and filedata["is_file"]:
+    if type(filedata) == list and len(filedata) > 0 and type(filedata[0]) == dict and filedata[0].get("is_file", False):
+        filedata = filedata[0]
+
+    if type(filedata) == dict and filedata.get("is_file", False):
         filename = filedata["name"]
         is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
         assert is_in_right_dir, 'trying to open image file outside of allowed directories'
-- 
cgit v1.2.3


From 917b5bd8d0cd47c9dc241c1852ccd440a8c61668 Mon Sep 17 00:00:00 2001
From: Max Weber <mii7303@gmail.com>
Date: Tue, 3 Jan 2023 18:19:56 -0700
Subject: ui: save dropdown sampling method to the ui-config

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index d941cb5f..bfc93634 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -635,6 +635,7 @@ def create_sampler_and_steps_selection(choices, tabname):
     if opts.samplers_in_dropdown:
         with FormRow(elem_id=f"sampler_selection_{tabname}"):
             sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+            sampler_index.save_to_config = True
             steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)
     else:
         with FormGroup(elem_id=f"sampler_selection_{tabname}"):
-- 
cgit v1.2.3


From e5b7ee910e7bb88f08e8876b5732cb034c6fe529 Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Wed, 4 Jan 2023 04:22:01 -0500
Subject: fix: Save full res of intermediate step

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index a172af0b..93e75ba6 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -705,7 +705,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 return
 
             if not isinstance(image, Image.Image):
-                image = sd_samplers.sample_to_image(image, index)
+                image = sd_samplers.sample_to_image(image, index, approximation=0)
 
             images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
 
-- 
cgit v1.2.3


From 02d7abf5141431b9a3a8a189bb3136c71abd5e79 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 12:35:07 +0300
Subject: helpful error message when trying to load 2.0 without config failing
 to load model weights from settings won't break generation for currently
 loaded model anymore

---
 modules/errors.py    | 25 +++++++++++++++++++++++--
 modules/sd_models.py | 26 ++++++++++++++++++--------
 modules/shared.py    |  9 +++++++--
 3 files changed, 48 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/errors.py b/modules/errors.py
index 372dc51a..a668c014 100644
--- a/modules/errors.py
+++ b/modules/errors.py
@@ -2,9 +2,30 @@ import sys
 import traceback
 
 
+def print_error_explanation(message):
+    lines = message.strip().split("\n")
+    max_len = max([len(x) for x in lines])
+
+    print('=' * max_len, file=sys.stderr)
+    for line in lines:
+        print(line, file=sys.stderr)
+    print('=' * max_len, file=sys.stderr)
+
+
+def display(e: Exception, task):
+    print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
+    print(traceback.format_exc(), file=sys.stderr)
+
+    message = str(e)
+    if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
+        print_error_explanation("""
+The most likely cause of this is you are trying to load Stable Diffusion 2.0 model without specifying its connfig file.
+See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20 for how to solve this.
+        """)
+
+
 def run(code, task):
     try:
         code()
     except Exception as e:
-        print(f"{task}: {type(e).__name__}", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        display(task, e)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index b98b05fc..6846b74a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -278,6 +278,7 @@ def enable_midas_autodownload():
 
     midas.api.load_model = load_model_wrapper
 
+
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
@@ -312,6 +313,7 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.unet_config.params.use_fp16 = False
 
     sd_model = instantiate_from_config(sd_config.model)
+
     load_model_weights(sd_model, checkpoint_info)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -336,10 +338,12 @@ def load_model(checkpoint_info=None):
 def reload_model_weights(sd_model=None, info=None):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
- 
+
     if not sd_model:
         sd_model = shared.sd_model
 
+    current_checkpoint_info = sd_model.sd_checkpoint_info
+
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
@@ -356,13 +360,19 @@ def reload_model_weights(sd_model=None, info=None):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info)
-
-    sd_hijack.model_hijack.hijack(sd_model)
-    script_callbacks.model_loaded_callback(sd_model)
-
-    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
-        sd_model.to(devices.device)
+    try:
+        load_model_weights(sd_model, checkpoint_info)
+    except Exception as e:
+        print("Failed to load checkpoint, restoring previous")
+        load_model_weights(sd_model, current_checkpoint_info)
+        raise
+    finally:
+        sd_hijack.model_hijack.hijack(sd_model)
+        script_callbacks.model_loaded_callback(sd_model)
+
+        if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+            sd_model.to(devices.device)
 
     print("Weights loaded.")
+
     return sd_model
diff --git a/modules/shared.py b/modules/shared.py
index 23657a93..7588c47b 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -14,7 +14,7 @@ import modules.interrogate
 import modules.memmon
 import modules.styles
 import modules.devices as devices
-from modules import localization, sd_vae, extensions, script_loading
+from modules import localization, sd_vae, extensions, script_loading, errors
 from modules.paths import models_path, script_path, sd_path
 
 
@@ -494,7 +494,12 @@ class Options:
             return False
 
         if self.data_labels[key].onchange is not None:
-            self.data_labels[key].onchange()
+            try:
+                self.data_labels[key].onchange()
+            except Exception as e:
+                errors.display(e, f"changing setting {key} to {value}")
+                setattr(self, key, oldval)
+                return False
 
         return True
 
-- 
cgit v1.2.3


From 8d8a05a3bbb50fdfeab51679a919d2487bd97976 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 12:47:42 +0300
Subject: find configs for models at runtime rather than when starting

---
 modules/sd_hijack_inpainting.py |  5 ++++-
 modules/sd_models.py            | 31 ++++++++++++++++++-------------
 2 files changed, 22 insertions(+), 14 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 3c214a35..31d2c898 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -97,8 +97,11 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
 
 
 def should_hijack_inpainting(checkpoint_info):
+    from modules import sd_models
+
     ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
-    cfg_basename = os.path.basename(checkpoint_info.config).lower()
+    cfg_basename = os.path.basename(sd_models.find_checkpoint_config(checkpoint_info)).lower()
+
     return "inpainting" in ckpt_basename and not "inpainting" in cfg_basename
 
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6846b74a..6dca4ddf 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -20,7 +20,7 @@ from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inp
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
 checkpoints_list = {}
 checkpoints_loaded = collections.OrderedDict()
 
@@ -48,6 +48,14 @@ def checkpoint_tiles():
     return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
 
 
+def find_checkpoint_config(info):
+    config = os.path.splitext(info.filename)[0] + ".yaml"
+    if os.path.exists(config):
+        return config
+
+    return shared.cmd_opts.config
+
+
 def list_models():
     checkpoints_list.clear()
     model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"])
@@ -73,7 +81,7 @@ def list_models():
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
         shared.opts.data['sd_model_checkpoint'] = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
@@ -81,12 +89,7 @@ def list_models():
         h = model_hash(filename)
         title, short_model_name = modeltitle(filename, h)
 
-        basename, _ = os.path.splitext(filename)
-        config = basename + ".yaml"
-        if not os.path.exists(config):
-            config = shared.cmd_opts.config
-
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
 
 
 def get_closet_checkpoint_match(searchString):
@@ -282,9 +285,10 @@ def enable_midas_autodownload():
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
+    checkpoint_config = find_checkpoint_config(checkpoint_info)
 
-    if checkpoint_info.config != shared.cmd_opts.config:
-        print(f"Loading config from: {checkpoint_info.config}")
+    if checkpoint_config != shared.cmd_opts.config:
+        print(f"Loading config from: {checkpoint_config}")
 
     if shared.sd_model:
         sd_hijack.model_hijack.undo_hijack(shared.sd_model)
@@ -292,7 +296,7 @@ def load_model(checkpoint_info=None):
         gc.collect()
         devices.torch_gc()
 
-    sd_config = OmegaConf.load(checkpoint_info.config)
+    sd_config = OmegaConf.load(checkpoint_config)
     
     if should_hijack_inpainting(checkpoint_info):
         # Hardcoded config for now...
@@ -302,7 +306,7 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.finetune_keys = None
 
         # Create a "fake" config with a different name so that we know to unload it when switching models.
-        checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
+        checkpoint_info = checkpoint_info._replace(config=checkpoint_config.replace(".yaml", "-inpainting.yaml"))
 
     if not hasattr(sd_config.model.params, "use_ema"):
         sd_config.model.params.use_ema = False
@@ -343,11 +347,12 @@ def reload_model_weights(sd_model=None, info=None):
         sd_model = shared.sd_model
 
     current_checkpoint_info = sd_model.sd_checkpoint_info
+    checkpoint_config = find_checkpoint_config(current_checkpoint_info)
 
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
-    if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+    if checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         del sd_model
         checkpoints_loaded.clear()
         load_model(checkpoint_info)
-- 
cgit v1.2.3


From 96cf15bedecbed97ef9b70b8413d543a9aee5adf Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Wed, 4 Jan 2023 05:12:06 -0500
Subject: Add new latent upscale modes

---
 modules/shared.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 7588c47b..a10f69a9 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -564,8 +564,11 @@ if os.path.exists(config_filename):
 
 latent_upscale_default_mode = "Latent"
 latent_upscale_modes = {
-    "Latent": "bilinear",
-    "Latent (nearest)": "nearest",
+    "Latent": {"mode": "bilinear", "antialias": False},
+    "Latent (antialiased)": {"mode": "bilinear", "antialias": True},
+    "Latent (bicubic)": {"mode": "bicubic", "antialias": False},
+    "Latent (bicubic, antialiased)": {"mode": "bicubic", "antialias": True},
+    "Latent (nearest)": {"mode": "nearest", "antialias": False},
 }
 
 sd_upscalers = []
-- 
cgit v1.2.3


From 15fd0b8bc4734ea85bca1acfb12b51465ab9817d Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Wed, 4 Jan 2023 05:12:54 -0500
Subject: Update processing.py

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index a172af0b..7c72b56a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -713,7 +713,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             for i in range(samples.shape[0]):
                 save_intermediate(samples, i)
 
-            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode)
+            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
 
             # Avoid making the inpainting conditioning unless necessary as
             # this does need some extra compute to decode / encode the image again.
-- 
cgit v1.2.3


From 4ec6470a1a2d9430b91266426f995e48f59564e1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 13:26:23 +0300
Subject: fix checkpoint list API

---
 modules/api/api.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 9c670f00..2b1f180c 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -18,7 +18,7 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
-from modules.sd_models import checkpoints_list
+from modules.sd_models import checkpoints_list, find_checkpoint_config
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
 from typing import List
@@ -303,7 +303,7 @@ class Api:
         return upscalers
 
     def get_sd_models(self):
-        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": x.config} for x in checkpoints_list.values()]
+        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
 
     def get_hypernetworks(self):
         return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
-- 
cgit v1.2.3


From b2151b934fe0a3613570c6abd7615d3788fd1c8f Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Wed, 4 Jan 2023 05:36:18 -0500
Subject: Rename bicubic antialiased option

Comma was causing the the value in PNG info to be quoted, which causes the upscaler dropdown option to be blank when sending to UI
---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index a10f69a9..c1b20081 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -567,7 +567,7 @@ latent_upscale_modes = {
     "Latent": {"mode": "bilinear", "antialias": False},
     "Latent (antialiased)": {"mode": "bilinear", "antialias": True},
     "Latent (bicubic)": {"mode": "bicubic", "antialias": False},
-    "Latent (bicubic, antialiased)": {"mode": "bicubic", "antialias": True},
+    "Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
     "Latent (nearest)": {"mode": "nearest", "antialias": False},
 }
 
-- 
cgit v1.2.3


From 3bd737767b071878ea980e94b8705f603bcf545e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 14:20:32 +0300
Subject: disable broken API logging

---
 modules/api/api.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index a6c1d6ed..6267afdc 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -100,7 +100,6 @@ class Api:
 
         self.router = APIRouter()
         self.app = app
-        init_api_middleware(self.app)
         self.queue_lock = queue_lock
         self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
-- 
cgit v1.2.3


From 0cd6399b8b1699b8b7acad6f0ad2988111fe618e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 14:29:13 +0300
Subject: fix broken inpainting model

---
 modules/sd_models.py | 3 ---
 1 file changed, 3 deletions(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6dca4ddf..a568823d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -305,9 +305,6 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.unet_config.params.in_channels = 9
         sd_config.model.params.finetune_keys = None
 
-        # Create a "fake" config with a different name so that we know to unload it when switching models.
-        checkpoint_info = checkpoint_info._replace(config=checkpoint_config.replace(".yaml", "-inpainting.yaml"))
-
     if not hasattr(sd_config.model.params, "use_ema"):
         sd_config.model.params.use_ema = False
 
-- 
cgit v1.2.3


From 11b8160a086c434d5baf4971edda46e6d2126800 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 4 Jan 2023 06:36:57 -0500
Subject: fix typo

---
 modules/api/api.py | 1 +
 1 file changed, 1 insertion(+)

(limited to 'modules')

diff --git a/modules/api/api.py b/modules/api/api.py
index 6267afdc..48a70a44 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -101,6 +101,7 @@ class Api:
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
+        api_middleware(self.app)
         self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
-- 
cgit v1.2.3


From 642142556d8ecdea9beb86d7618b628b1803ab98 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 15:09:53 +0300
Subject: use commandline-supplied cuda device name instead of cuda:0 for
 safetensors PR that doesn't fix anything

---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ee918f24..76a89e88 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -173,7 +173,7 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
     if extension.lower() == ".safetensors":
         device = map_location or shared.weight_load_location
         if device is None:
-            device = "cuda:0" if torch.cuda.is_available() else "cpu"
+            device = devices.get_cuda_device_string() if torch.cuda.is_available() else "cpu"
         pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
     else:
         pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
-- 
cgit v1.2.3


From 21ee77db314ede7ccbb18787962347c09a4df0c7 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 4 Jan 2023 08:04:38 -0500
Subject: add cross-attention info

---
 modules/sd_hijack.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index edcbaf52..fa2cd4bb 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -35,26 +35,35 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
+    
+    optimization_method = None
 
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+        optimization_method = 'xformers'
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+        optimization_method = 'V1'
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
         if not invokeAI_mps_available and shared.device.type == 'mps':
             print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
             print("Applying v1 cross attention optimization.")
             ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+            optimization_method = 'V1'
         else:
             print("Applying cross attention optimization (InvokeAI).")
             ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
+            optimization_method = 'InvokeAI'
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
         print("Applying cross attention optimization (Doggettx).")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
+        optimization_method = 'Doggettx'
+
+    return optimization_method
 
 
 def undo_optimizations():
@@ -75,6 +84,7 @@ class StableDiffusionModelHijack:
     layers = None
     circular_enabled = False
     clip = None
+    optimization_method = None
 
     embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
 
@@ -94,7 +104,7 @@ class StableDiffusionModelHijack:
             m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
             m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
 
-        apply_optimizations()
+        self.optimization_method = apply_optimizations()
 
         self.clip = m.cond_stage_model
         
-- 
cgit v1.2.3


From 1cfd8aec4ae5a6ca1afd67b44cb4ef6dd14d8c34 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 16:05:42 +0300
Subject: make it possible to work with opts.show_progress_every_n_steps = -1
 with medvram

---
 modules/shared.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

(limited to 'modules')

diff --git a/modules/shared.py b/modules/shared.py
index 4fcc6edd..54a6ba23 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -214,12 +214,13 @@ class State:
 
     """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
     def set_current_image(self):
+        if not parallel_processing_allowed:
+            return
+
         if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
             self.do_set_current_image()
 
     def do_set_current_image(self):
-        if not parallel_processing_allowed:
-            return
         if self.current_latent is None:
             return
 
@@ -231,6 +232,7 @@ class State:
 
         self.current_image_sampling_step = self.sampling_step
 
+
 state = State()
 
 artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
-- 
cgit v1.2.3


From 4d66bf2c0d27702cc83b9cc57ebb1f359d18d938 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 17:24:46 +0300
Subject: add infotext to "-before-highres-fix" images

---
 modules/processing.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index fd7c7015..c03e77e7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -136,6 +136,7 @@ class StableDiffusionProcessing():
         self.all_negative_prompts = None
         self.all_seeds = None
         self.all_subseeds = None
+        self.iteration = 0
 
     def txt2img_image_conditioning(self, x, width=None, height=None):
         if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
@@ -544,6 +545,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             state.job_count = p.n_iter
 
         for n in range(p.n_iter):
+            p.iteration = n
+
             if state.skipped:
                 state.skipped = False
 
@@ -707,7 +710,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             if not isinstance(image, Image.Image):
                 image = sd_samplers.sample_to_image(image, index, approximation=0)
 
-            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
+            info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
+            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
 
         if latent_scale_mode is not None:
             for i in range(samples.shape[0]):
-- 
cgit v1.2.3


From 184e670126f5fc50ba56fa0fedcf0cf60e45ed7e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 17:45:01 +0300
Subject: fix the merge

---
 modules/textual_inversion/textual_inversion.py | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

(limited to 'modules')

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5421a758..8731ea5d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -251,6 +251,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 
+
 def create_dummy_mask(x, width=None, height=None):
     if shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}:
 
@@ -380,17 +381,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     break
 
                 with devices.autocast():
-                    # c = stack_conds(batch.cond).to(devices.device)
-                    # mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
-                    # print(mask)
-                    # c[:, 1:1+embedding.vec.shape[0]] = embedding.vec.to(devices.device, non_blocking=pin_memory)
-                    
-                    
-                    if img_c is None:
-                        img_c = create_dummy_mask(c, training_width, training_height)
-                    
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                     c = shared.sd_model.cond_stage_model(batch.cond_text)
+
+                    if img_c is None:
+                        img_c = create_dummy_mask(c, training_width, training_height)
+
                     cond = {"c_concat": [img_c], "c_crossattn": [c]}
                     loss = shared.sd_model(x, cond)[0] / gradient_step
                     del x
-- 
cgit v1.2.3


From 525cea924562afd676f55470095268a0f6fca59e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 17:58:07 +0300
Subject: use shared function from processing for creating dummy mask when
 training inpainting model

---
 modules/processing.py                          | 39 +++++++++++++-------------
 modules/textual_inversion/textual_inversion.py | 33 ++++++----------------
 2 files changed, 29 insertions(+), 43 deletions(-)

(limited to 'modules')

diff --git a/modules/processing.py b/modules/processing.py
index c03e77e7..c7264aff 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -76,6 +76,24 @@ def apply_overlay(image, paste_loc, index, overlays):
     return image
 
 
+def txt2img_image_conditioning(sd_model, x, width, height):
+    if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
+        # Dummy zero conditioning if we're not using inpainting model.
+        # Still takes up a bit of memory, but no encoder call.
+        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+        return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+
+    # The "masked-image" in this case will just be all zeros since the entire image is masked.
+    image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+    image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+
+    # Add the fake full 1s mask to the first dimension.
+    image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+    image_conditioning = image_conditioning.to(x.dtype)
+
+    return image_conditioning
+
+
 class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
@@ -139,26 +157,9 @@ class StableDiffusionProcessing():
         self.iteration = 0
 
     def txt2img_image_conditioning(self, x, width=None, height=None):
-        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
-            # Dummy zero conditioning if we're not using inpainting model.
-            # Still takes up a bit of memory, but no encoder call.
-            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-            return x.new_zeros(x.shape[0], 5, 1, 1)
+        self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
 
-        self.is_using_inpainting_conditioning = True
-
-        height = height or self.height
-        width = width or self.width
-
-        # The "masked-image" in this case will just be all zeros since the entire image is masked.
-        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
-
-        # Add the fake full 1s mask to the first dimension.
-        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-        image_conditioning = image_conditioning.to(x.dtype)
-
-        return image_conditioning
+        return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
 
     def depth2img_image_conditioning(self, source_image):
         # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 8731ea5d..2250e41b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -252,26 +252,6 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
         assert log_directory, "Log directory is empty"
 
 
-def create_dummy_mask(x, width=None, height=None):
-    if shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}:
-
-        # The "masked-image" in this case will just be all zeros since the entire image is masked.
-        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = shared.sd_model.get_first_stage_encoding(shared.sd_model.encode_first_stage(image_conditioning))
-
-        # Add the fake full 1s mask to the first dimension.
-        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-        image_conditioning = image_conditioning.to(x.dtype)
-
-    else:
-        # Dummy zero conditioning if we're not using inpainting model.
-        # Still takes up a bit of memory, but no encoder call.
-        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-        image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
-
-    return image_conditioning
-
-
 def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
@@ -346,7 +326,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
         else:
             print("No saved optimizer exists in checkpoint")
 
-
     scaler = torch.cuda.amp.GradScaler()
 
     batch_size = ds.batch_size
@@ -362,7 +341,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
 
+    is_training_inpainting_model = shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}
     img_c = None
+
     pbar = tqdm.tqdm(total=steps - initial_step)
     try:
         for i in range((steps-initial_step) * gradient_step):
@@ -384,10 +365,14 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                     c = shared.sd_model.cond_stage_model(batch.cond_text)
 
-                    if img_c is None:
-                        img_c = create_dummy_mask(c, training_width, training_height)
+                    if is_training_inpainting_model:
+                        if img_c is None:
+                            img_c = processing.txt2img_image_conditioning(shared.sd_model, c, training_width, training_height)
+
+                        cond = {"c_concat": [img_c], "c_crossattn": [c]}
+                    else:
+                        cond = c
 
-                    cond = {"c_concat": [img_c], "c_crossattn": [c]}
                     loss = shared.sd_model(x, cond)[0] / gradient_step
                     del x
 
-- 
cgit v1.2.3


From a8eb9e3bf814f72293e474c11e9ff0098859a942 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 18:20:38 +0300
Subject: Revert "Merge pull request #3791 from shirayu/fix/filename"

This reverts commit eed58279e7cb0e873ebd88a29609f9bab0f1f3af, reversing
changes made to 4ae960b01c6711c66985479f14809dc7fa549fc2.
---
 modules/images.py | 16 ++++------------
 1 file changed, 4 insertions(+), 12 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 2967fa9a..c3a5fc8b 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -447,14 +447,6 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
-def truncate_fullpath(full_path, encoding='utf-8'):
-    dir_name, full_name = os.path.split(full_path)
-    file_name, file_ext = os.path.splitext(full_name)
-    max_length = os.statvfs(dir_name).f_namemax
-    file_name_truncated = file_name.encode(encoding)[:max_length - len(file_ext)].decode(encoding, 'ignore')
-    return os.path.join(dir_name , file_name_truncated + file_ext)
-
-
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
     """Save an image.
 
@@ -495,7 +487,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
     if save_to_dirs:
         dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')
-        path = truncate_fullpath(os.path.join(path, dirname))
+        path = os.path.join(path, dirname)
 
     os.makedirs(path, exist_ok=True)
 
@@ -519,13 +511,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             fullfn = None
             for i in range(500):
                 fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
-                fullfn = truncate_fullpath(os.path.join(path, f"{fn}{file_decoration}.{extension}"))
+                fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
                 if not os.path.exists(fullfn):
                     break
         else:
-            fullfn = truncate_fullpath(os.path.join(path, f"{file_decoration}.{extension}"))
+            fullfn = os.path.join(path, f"{file_decoration}.{extension}")
     else:
-        fullfn = truncate_fullpath(os.path.join(path, f"{forced_filename}.{extension}"))
+        fullfn = os.path.join(path, f"{forced_filename}.{extension}")
 
     pnginfo = existing_info or {}
     if info is not None:
-- 
cgit v1.2.3


From 3dae545a03f5102ba5d9c3f27bb6241824c5a916 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 18:42:51 +0300
Subject: rename weirdly named variables from #3176

---
 modules/ui.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

(limited to 'modules')

diff --git a/modules/ui.py b/modules/ui.py
index e4859020..184af7ad 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -162,16 +162,14 @@ def save_files(js_data, images, do_make_zip, index):
     return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
 
 
-
-
-def calc_time_left(progress, threshold, label, force_display, showTime):
+def calc_time_left(progress, threshold, label, force_display, show_eta):
     if progress == 0:
         return ""
     else:
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
         eta_relative = eta-time_since_start
-        if (eta_relative > threshold and showTime) or force_display:
+        if (eta_relative > threshold and show_eta) or force_display:
             if eta_relative > 3600:
                 return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
             elif eta_relative > 60:
@@ -194,9 +192,9 @@ def check_progress_call(id_part):
         progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
 
     # Show progress percentage and time left at the same moment, and base it also on steps done
-    showPBText = progress >= 0.01 or shared.state.sampling_step >= 10
+    show_eta = progress >= 0.01 or shared.state.sampling_step >= 10
 
-    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display, showPBText )
+    time_left = calc_time_left(progress, 1, " ETA: ", shared.state.time_left_force_display, show_eta)
     if time_left != "":
         shared.state.time_left_force_display = True
 
@@ -204,7 +202,7 @@ def check_progress_call(id_part):
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if showPBText else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if show_eta else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
-- 
cgit v1.2.3