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 98cc6c6e744f454cbbd763033c93a0dceb40c343 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 14:16:26 +0300
Subject: add embeddings dir

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

(limited to 'modules')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 5945b7c2..fa7eaeb8 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -232,7 +232,12 @@ class StableDiffusionModelHijack:
 
         for fn in os.listdir(dirname):
             try:
-                process_file(os.path.join(dirname, fn), fn)
+                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)
-- 
cgit v1.2.3


From 980cd1697ae980f57399da2b90462c07d102d935 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 14:23:41 +0300
Subject: prevent neural network resizing when it is not necessary #1109

---
 modules/images.py | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

(limited to 'modules')

diff --git a/modules/images.py b/modules/images.py
index 2e8305ed..8f34dcc1 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -213,17 +213,19 @@ def resize_image(resize_mode, im, width, height):
         if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None" or im.mode == 'L':
             return im.resize((w, h), resample=LANCZOS)
 
-        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}"
-
-        upscaler = upscalers[0]
         scale = max(w / im.width, h / im.height)
-        upscaled = upscaler.scaler.upscale(im, scale, upscaler.data_path)
 
-        if upscaled.width != w or upscaled.height != h:
-            upscaled = im.resize((w, h), resample=LANCZOS)
+        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}"
+
+            upscaler = upscalers[0]
+            im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
+
+        if im.width != w or im.height != h:
+            im = im.resize((w, h), resample=LANCZOS)
 
-        return upscaled
+        return im
 
     if resize_mode == 0:
         res = resize(im, width, height)
-- 
cgit v1.2.3


From 41f9449d96488017092d229d65f674c2e8252697 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 08:33:06 -0500
Subject: Fix LDSR YAML path

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

(limited to 'modules')

diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
index 969d1a0d..877e7e73 100644
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@@ -24,7 +24,7 @@ class UpscalerLDSR(Upscaler):
     def load_model(self, path: str):
         model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
                                    file_name="model.pth", progress=True)
-        yaml = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+        yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
                                   file_name="project.yaml", progress=True)
 
         try:
-- 
cgit v1.2.3


From 64c6b13312ff3a20f48781c4c3780355c4b7b2af Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 08:41:25 -0500
Subject: Remove LDSR project.yaml if too large.

If we accidentally downloaded the wrong file and saved it as project.yaml, this will delete it so it can be re-downloaded.
---
 modules/ldsr_model.py | 7 +++++++
 1 file changed, 7 insertions(+)

(limited to 'modules')

diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
index 877e7e73..4d8687c2 100644
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@@ -22,6 +22,13 @@ class UpscalerLDSR(Upscaler):
         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")
+        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)
         model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
                                    file_name="model.pth", progress=True)
         yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
-- 
cgit v1.2.3


From 8d60645106d7e2daa0da89c5b21d7ffdac61cf9e Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 08:55:04 -0500
Subject: Fix model paths, ensure we have the right files.

Also, clean up logging in the ldsr arch file.
---
 modules/ldsr_model.py      | 9 +++++++--
 modules/ldsr_model_arch.py | 3 +--
 2 files changed, 8 insertions(+), 4 deletions(-)

(limited to 'modules')

diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
index 4d8687c2..7dff0a9c 100644
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@@ -24,13 +24,18 @@ class UpscalerLDSR(Upscaler):
     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:
+            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.pth", progress=True)
+                                   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)
 
diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
index 7faac6e1..093a3210 100644
--- a/modules/ldsr_model_arch.py
+++ b/modules/ldsr_model_arch.py
@@ -100,7 +100,6 @@ class LDSR:
         # If we can adjust the max upscale size, then the 4 below should be our variable
         print("Foo")
         down_sample_rate = target_scale / 4
-        print(f"Downsample rate is {down_sample_rate}")
         wd = width_og * down_sample_rate
         hd = height_og * down_sample_rate
         width_downsampled_pre = int(wd)
@@ -111,7 +110,7 @@ class LDSR:
                 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")
+            print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
         logs = self.run(model["model"], im_og, diffusion_steps, eta)
 
         sample = logs["sample"]
-- 
cgit v1.2.3


From 99aa132df7045077a420918d276fcca877fdc9e3 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 08:56:39 -0500
Subject: Remove useless print message

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

(limited to 'modules')

diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
index 093a3210..14db5076 100644
--- a/modules/ldsr_model_arch.py
+++ b/modules/ldsr_model_arch.py
@@ -98,7 +98,6 @@ class LDSR:
         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
-        print("Foo")
         down_sample_rate = target_scale / 4
         wd = width_og * down_sample_rate
         hd = height_og * down_sample_rate
-- 
cgit v1.2.3


From 19eb1467f1acbb45d3b416ad7887ba9ab8a94e75 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 09:05:00 -0500
Subject: Fix BSRGAN variable not found.

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

(limited to 'modules')

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
index 77141545..339f402b 100644
--- a/modules/bsrgan_model.py
+++ b/modules/bsrgan_model.py
@@ -67,9 +67,8 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
         else:
             filename = path
         if not os.path.exists(filename) or filename is None:
-            print("Unable to load %s from %s" % (self.model_dir, filename))
+            print("Unable to load %s from %s" % (self.model_path, filename))
             return None
-        print("Loading %s from %s" % (self.model_dir, filename))
         model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=2)  # define network
         model.load_state_dict(torch.load(filename), strict=True)
         model.eval()
-- 
cgit v1.2.3


From ca87c09c0ec9f6d3fbfda5d8579907c7017eb629 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 09:28:28 -0500
Subject: Fix recursive model loading

Ensure we find checkpoints within subdirectories.
---
 modules/modelloader.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

(limited to 'modules')

diff --git a/modules/modelloader.py b/modules/modelloader.py
index 1106aeb7..df29b6ef 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -1,3 +1,4 @@
+import glob
 import os
 import shutil
 import importlib
@@ -41,7 +42,7 @@ 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 os.listdir(place):
+                for file in glob.iglob(place + '**/**', recursive=True):
                     full_path = os.path.join(place, file)
                     if os.path.isdir(full_path):
                         continue
@@ -50,6 +51,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                         if extension not in ext_filter:
                             continue
                     if file not in output:
+                        print(f"FILE: {full_path}")
                         output.append(full_path)
 
         if model_url is not None and len(output) == 0:
-- 
cgit v1.2.3


From 5d52231bcb9fc21953b3d3495b577dc0bd82aa6a Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 09:28:48 -0500
Subject: Logging....

Cleanup. Sorry.
---
 modules/modelloader.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'modules')

diff --git a/modules/modelloader.py b/modules/modelloader.py
index df29b6ef..8c862b42 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -51,7 +51,6 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                         if extension not in ext_filter:
                             continue
                     if file not in output:
-                        print(f"FILE: {full_path}")
                         output.append(full_path)
 
         if model_url is not None and len(output) == 0:
-- 
cgit v1.2.3


From f71d02b85feacf52d03d69d5caca97cd7d90b481 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 09:29:31 -0500
Subject: Remove unused LDSR opt

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

(limited to 'modules')

diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
index 7dff0a9c..1c1070fc 100644
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@@ -53,5 +53,4 @@ class UpscalerLDSR(Upscaler):
             print("NO LDSR!")
             return img
         ddim_steps = shared.opts.ldsr_steps
-        pre_scale = shared.opts.ldsr_pre_down
         return ldsr.super_resolution(img, ddim_steps, self.scale)
-- 
cgit v1.2.3


From 9fc1e49bd2674f8c30dd3545c1c271472ff6d3c2 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 09:38:57 -0500
Subject: Set default value for ckpt-dir

---
 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 8428c7a3..76233ea0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -21,7 +21,7 @@ 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; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
-parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
+parser.add_argument("--ckpt-dir", type=str, default=os.path.join(model_path, "Stable-diffusion"), 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'))
 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")
-- 
cgit v1.2.3


From 43c87ef0fcf1771d5511004968e70f804cfd95b8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 18:07:49 +0300
Subject: change default inpaint mode to original

---
 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 ada9a38e..249b3eea 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -599,7 +599,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                             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='fill', 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)
-- 
cgit v1.2.3


From ac21d308cd0144b8e02e4cfa1fc36722b2d909d5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 18:55:46 +0300
Subject: fix for one error with BSRGAN, though it fails to work anyway #1109

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

(limited to 'modules')

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
index 77141545..47346f31 100644
--- a/modules/bsrgan_model.py
+++ b/modules/bsrgan_model.py
@@ -67,9 +67,8 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
         else:
             filename = path
         if not os.path.exists(filename) or filename is None:
-            print("Unable to load %s from %s" % (self.model_dir, filename))
+            print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
             return None
-        print("Loading %s from %s" % (self.model_dir, filename))
         model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=2)  # define network
         model.load_state_dict(torch.load(filename), strict=True)
         model.eval()
-- 
cgit v1.2.3


From 4794202ebc1f5184e0a15cc11d91ea9a23640f27 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 18:56:10 +0300
Subject: hello #1109

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

(limited to 'modules')

diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
index 969d1a0d..877e7e73 100644
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@@ -24,7 +24,7 @@ class UpscalerLDSR(Upscaler):
     def load_model(self, path: str):
         model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
                                    file_name="model.pth", progress=True)
-        yaml = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+        yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
                                   file_name="project.yaml", progress=True)
 
         try:
-- 
cgit v1.2.3


From 8f1d412e7bcd279c3c49b0153def0e3d6d941e5a Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 12:51:54 -0500
Subject: Fix BSRGAN Model loading.

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

(limited to 'modules')

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
index 339f402b..165f77b5 100644
--- a/modules/bsrgan_model.py
+++ b/modules/bsrgan_model.py
@@ -69,7 +69,8 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
         if not os.path.exists(filename) or filename is None:
             print("Unable to load %s from %s" % (self.model_path, filename))
             return None
-        model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=2)  # define network
+        print("Loading %s from %s" % (self.model_path, filename))
+        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():
-- 
cgit v1.2.3


From 7ab91d9e1bf3abc11dc0d90e270b199ae8c3c84a Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 13:29:33 -0500
Subject: Update bsrgan_model.py

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

(limited to 'modules')

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
index 827a50a5..e62c6657 100644
--- a/modules/bsrgan_model.py
+++ b/modules/bsrgan_model.py
@@ -69,7 +69,6 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
         if not os.path.exists(filename) or filename is None:
             print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
             return None
-        print("Loading %s from %s" % (self.model_path, filename))
         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()
-- 
cgit v1.2.3


From 2b03f0bbda1229dff6e7ab6f656b28587eba8308 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 22:16:03 +0300
Subject: if --ckpt option is specified, load that model

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

(limited to 'modules')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ab014efb..2539f14c 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -69,6 +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
     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:
diff --git a/modules/shared.py b/modules/shared.py
index 8428c7a3..ac968b2d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -20,7 +20,7 @@ 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; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
+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'))
 parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
-- 
cgit v1.2.3


From 3f64e23f81618395428da0a132bf3a6cfd5c42c9 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 22:57:25 +0300
Subject: fix model checkpoint merger tab failing to save model

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

(limited to 'modules')

diff --git a/modules/extras.py b/modules/extras.py
index 1bff5874..6a0d5cb0 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -191,9 +191,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
 
+    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 = filename if custom_name == '' else (custom_name + '.ckpt')
-    output_modelname = os.path.join(shared.cmd_opts.ckpt_dir, filename)
+    output_modelname = os.path.join(ckpt_dir, filename)
 
     print(f"Saving to {output_modelname}...")
     torch.save(primary_model, output_modelname)
-- 
cgit v1.2.3


From 89f0acc6bf3726e40bd9a3c634716debe3ece87d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 23:18:47 +0300
Subject: remove extra print from BSRGAN

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

(limited to 'modules')

diff --git a/modules/bsrgan_model_arch.py b/modules/bsrgan_model_arch.py
index d72647db..cb4d1c13 100644
--- a/modules/bsrgan_model_arch.py
+++ b/modules/bsrgan_model_arch.py
@@ -76,7 +76,6 @@ class RRDBNet(nn.Module):
         super(RRDBNet, self).__init__()
         RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
         self.sf = sf
-        print([in_nc, out_nc, nf, nb, gc, sf])
 
         self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
         self.RRDB_trunk = make_layer(RRDB_block_f, nb)
-- 
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 d1db330010de31d48975bb3ec86e9e60d56ae9e9 Mon Sep 17 00:00:00 2001
From: Trung Ngo <codem01@gmail.com>
Date: Fri, 30 Sep 2022 15:31:00 -0500
Subject: ALT + return will submit prompt when it's in focus

---
 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 249b3eea..15572bb0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -380,7 +380,7 @@ def create_toprow(is_img2img):
         with gr.Column(scale=1):
             with gr.Row():
                 interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
-                submit = gr.Button('Generate', elem_id="generate", variant='primary')
+                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
 
                 interrupt.click(
                     fn=lambda: shared.state.interrupt(),
-- 
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 9de1e56e2dbb405213da9c221e0329d27f411691 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 30 Sep 2022 01:44:38 +0100
Subject: add sampler_noise_scheduler_override property

---
 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 7eeb5191..1da753a2 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -79,7 +79,7 @@ class StableDiffusionProcessing:
         self.paste_to = None
         self.color_corrections = None
         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
@@ -130,7 +130,7 @@ class Processed:
         self.s_tmin = p.s_tmin
         self.s_tmax = p.s_tmax
         self.s_noise = p.s_noise
-        
+        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])
-- 
cgit v1.2.3


From bc38c80cfc83d4e2fc09c02dd49355664c05d15c Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 30 Sep 2022 01:46:06 +0100
Subject: add sampler_noise_scheduler_override switch

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

(limited to 'modules')

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index dff89c09..92522214 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -290,7 +290,10 @@ class KDiffusionSampler:
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
         steps, t_enc = setup_img2img_steps(p, steps)
 
-        sigmas = self.model_wrap.get_sigmas(steps)
+        if p.sampler_noise_scheduler_override:
+          sigmas = p.sampler_noise_scheduler_override(steps)
+        else:
+          sigmas = self.model_wrap.get_sigmas(steps)
 
         noise = noise * sigmas[steps - t_enc - 1]
         xi = x + noise
@@ -306,7 +309,10 @@ class KDiffusionSampler:
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
         steps = steps or p.steps
 
-        sigmas = self.model_wrap.get_sigmas(steps)
+        if p.sampler_noise_scheduler_override:
+          sigmas = p.sampler_noise_scheduler_override(steps)
+        else:
+          sigmas = self.model_wrap.get_sigmas(steps)
         x = x * sigmas[0]
 
         extra_params_kwargs = self.initialize(p)
-- 
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 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 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