From 0dce0df1ee63b2f158805c1a1f1a3743cc4a104b Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Thu, 29 Sep 2022 17:46:23 -0500
Subject: Holy $hit.

Yep.

Fix gfpgan_model_arch requirement(s).
Add Upscaler base class, move from images.
Add a lot of methods to Upscaler.
Re-work all the child upscalers to be proper classes.
Add BSRGAN scaler.
Add ldsr_model_arch class, removing the dependency for another repo that just uses regular latent-diffusion stuff.
Add one universal method that will always find and load new upscaler models without having to add new "setup_model" calls. Still need to add command line params, but that could probably be automated.
Add a "self.scale" property to all Upscalers so the scalers themselves can do "things" in response to the requested upscaling size.
Ensure LDSR doesn't get stuck in a longer loop of "upscale/downscale/upscale" as we try to reach the target upscale size.
Add typehints for IDE sanity.
PEP-8 improvements.
Moar.
---
 modules/ldsr_model_arch.py | 223 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 223 insertions(+)
 create mode 100644 modules/ldsr_model_arch.py

(limited to 'modules/ldsr_model_arch.py')

diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
new file mode 100644
index 00000000..8fe87c6a
--- /dev/null
+++ b/modules/ldsr_model_arch.py
@@ -0,0 +1,223 @@
+import gc
+import time
+import warnings
+
+import numpy as np
+import torch
+import torchvision
+from PIL import Image
+from einops import rearrange, repeat
+from omegaconf import OmegaConf
+
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.util import instantiate_from_config, ismap
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+# Create LDSR Class
+class LDSR:
+    def load_model_from_config(self, half_attention):
+        print(f"Loading model from {self.modelPath}")
+        pl_sd = torch.load(self.modelPath, map_location="cpu")
+        sd = pl_sd["state_dict"]
+        config = OmegaConf.load(self.yamlPath)
+        model = instantiate_from_config(config.model)
+        model.load_state_dict(sd, strict=False)
+        model.cuda()
+        if half_attention:
+            model = model.half()
+
+        model.eval()
+        return {"model": model}
+
+    def __init__(self, model_path, yaml_path):
+        self.modelPath = model_path
+        self.yamlPath = yaml_path
+
+    @staticmethod
+    def run(model, selected_path, custom_steps, eta):
+        example = get_cond(selected_path)
+
+        n_runs = 1
+        guider = None
+        ckwargs = None
+        ddim_use_x0_pred = False
+        temperature = 1.
+        eta = eta
+        custom_shape = None
+
+        height, width = example["image"].shape[1:3]
+        split_input = height >= 128 and width >= 128
+
+        if split_input:
+            ks = 128
+            stride = 64
+            vqf = 4  #
+            model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
+                                        "vqf": vqf,
+                                        "patch_distributed_vq": True,
+                                        "tie_braker": False,
+                                        "clip_max_weight": 0.5,
+                                        "clip_min_weight": 0.01,
+                                        "clip_max_tie_weight": 0.5,
+                                        "clip_min_tie_weight": 0.01}
+        else:
+            if hasattr(model, "split_input_params"):
+                delattr(model, "split_input_params")
+
+        x_t = None
+        logs = None
+        for n in range(n_runs):
+            if custom_shape is not None:
+                x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
+                x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
+
+            logs = make_convolutional_sample(example, model,
+                                             custom_steps=custom_steps,
+                                             eta=eta, quantize_x0=False,
+                                             custom_shape=custom_shape,
+                                             temperature=temperature, noise_dropout=0.,
+                                             corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
+                                             ddim_use_x0_pred=ddim_use_x0_pred
+                                             )
+        return logs
+
+    def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
+        model = self.load_model_from_config(half_attention)
+
+        # Run settings
+        diffusion_steps = int(steps)
+        eta = 1.0
+
+        down_sample_method = 'Lanczos'
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        im_og = image
+        width_og, height_og = im_og.size
+        # If we can adjust the max upscale size, then the 4 below should be our variable
+        print("Foo")
+        down_sample_rate = target_scale / 4
+        print(f"Downsample rate is {down_sample_rate}")
+        width_downsampled_pre = width_og * down_sample_rate
+        height_downsampled_pre = height_og * down_sample_method
+
+        if down_sample_rate != 1:
+            print(
+                f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
+            im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
+        else:
+            print(f"Down sample rate is 1 from {target_scale} / 4")
+        logs = self.run(model["model"], im_og, diffusion_steps, eta)
+
+        sample = logs["sample"]
+        sample = sample.detach().cpu()
+        sample = torch.clamp(sample, -1., 1.)
+        sample = (sample + 1.) / 2. * 255
+        sample = sample.numpy().astype(np.uint8)
+        sample = np.transpose(sample, (0, 2, 3, 1))
+        a = Image.fromarray(sample[0])
+
+        del model
+        gc.collect()
+        torch.cuda.empty_cache()
+        print(f'Processing finished!')
+        return a
+
+
+def get_cond(selected_path):
+    example = dict()
+    up_f = 4
+    c = selected_path.convert('RGB')
+    c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
+    c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
+                                                    antialias=True)
+    c_up = rearrange(c_up, '1 c h w -> 1 h w c')
+    c = rearrange(c, '1 c h w -> 1 h w c')
+    c = 2. * c - 1.
+
+    c = c.to(torch.device("cuda"))
+    example["LR_image"] = c
+    example["image"] = c_up
+
+    return example
+
+
+@torch.no_grad()
+def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
+                    mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
+                    corrector_kwargs=None, x_t=None
+                    ):
+    ddim = DDIMSampler(model)
+    bs = shape[0]
+    shape = shape[1:]
+    print(f"Sampling with eta = {eta}; steps: {steps}")
+    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
+                                         normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
+                                         mask=mask, x0=x0, temperature=temperature, verbose=False,
+                                         score_corrector=score_corrector,
+                                         corrector_kwargs=corrector_kwargs, x_t=x_t)
+
+    return samples, intermediates
+
+
+@torch.no_grad()
+def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
+                              corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
+    log = dict()
+
+    z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
+                                        return_first_stage_outputs=True,
+                                        force_c_encode=not (hasattr(model, 'split_input_params')
+                                                            and model.cond_stage_key == 'coordinates_bbox'),
+                                        return_original_cond=True)
+
+    if custom_shape is not None:
+        z = torch.randn(custom_shape)
+        print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
+
+    z0 = None
+
+    log["input"] = x
+    log["reconstruction"] = xrec
+
+    if ismap(xc):
+        log["original_conditioning"] = model.to_rgb(xc)
+        if hasattr(model, 'cond_stage_key'):
+            log[model.cond_stage_key] = model.to_rgb(xc)
+
+    else:
+        log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
+        if model.cond_stage_model:
+            log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
+            if model.cond_stage_key == 'class_label':
+                log[model.cond_stage_key] = xc[model.cond_stage_key]
+
+    with model.ema_scope("Plotting"):
+        t0 = time.time()
+
+        sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
+                                                eta=eta,
+                                                quantize_x0=quantize_x0, mask=None, x0=z0,
+                                                temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
+                                                x_t=x_T)
+        t1 = time.time()
+
+        if ddim_use_x0_pred:
+            sample = intermediates['pred_x0'][-1]
+
+    x_sample = model.decode_first_stage(sample)
+
+    try:
+        x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
+        log["sample_noquant"] = x_sample_noquant
+        log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
+    except:
+        pass
+
+    log["sample"] = x_sample
+    log["time"] = t1 - t0
+
+    return log
-- 
cgit v1.2.3


From 435fd2112aee9a0e61408ac56663e41beea1e446 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Thu, 29 Sep 2022 19:59:53 -0500
Subject: Fixes, cleanup.

---
 modules/ldsr_model_arch.py |  6 ++++--
 modules/modelloader.py     | 14 --------------
 modules/swinir_model.py    |  2 +-
 3 files changed, 5 insertions(+), 17 deletions(-)

(limited to 'modules/ldsr_model_arch.py')

diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
index 8fe87c6a..f8f3c3d3 100644
--- a/modules/ldsr_model_arch.py
+++ b/modules/ldsr_model_arch.py
@@ -101,8 +101,10 @@ class LDSR:
         print("Foo")
         down_sample_rate = target_scale / 4
         print(f"Downsample rate is {down_sample_rate}")
-        width_downsampled_pre = width_og * down_sample_rate
-        height_downsampled_pre = height_og * down_sample_method
+        wd = width_og * down_sample_rate
+        hd = height_og * down_sample_rate
+        width_downsampled_pre = int(wd)
+        height_downsampled_pre = int(hd)
 
         if down_sample_rate != 1:
             print(
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 6de65c69..51b3ecd5 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -118,11 +118,9 @@ def load_upscalers():
     for cls in Upscaler.__subclasses__():
         name = cls.__name__
         module_name = cls.__module__
-        print(f"Class: {name} and {module_name}")
         module = importlib.import_module(module_name)
         class_ = getattr(module, name)
         cmd_name = f"{name.lower().replace('upscaler', '')}-models-path"
-        print(f"CMD Name: {cmd_name}")
         opt_string = None
         try:
             opt_string = shared.opts.__getattr__(cmd_name)
@@ -130,18 +128,6 @@ def load_upscalers():
             pass
         scaler = class_(opt_string)
         for child in scaler.scalers:
-            print(f"Appending {child.name}")
             datas.append(child)
 
     shared.sd_upscalers = datas
-
-    # for scaler in subclasses:
-    #     print(f"Found scaler: {type(scaler).__name__}")
-    #     try:
-    #         scaler = scaler()
-    #         for child in scaler.scalers:
-    #             print(f"Appending {child.name}")
-    #             datas.append[child]
-    #     except:
-    #         pass
-    # shared.sd_upscalers = datas
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index ea7b6301..41fda5a7 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -52,7 +52,7 @@ class UpscalerSwinIR(Upscaler):
 
     def load_model(self, path, scale=4):
         if "http" in path:
-            dl_name = "%s%s" % (self.name.replace(" ", "_"), ".pth")
+            dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
             filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
         else:
             filename = path
-- 
cgit v1.2.3


From d1f098540ad1dbc2abb8d04322634efba650b631 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 30 Sep 2022 11:42:40 +0300
Subject: remove unwanted formatting/functionality from the PR

---
 launch.py                   |   7 +--
 modules/esrgan_model.py     | 123 ++++++++++++++++++++++----------------------
 modules/extras.py           |  35 +++++++------
 modules/gfpgan_model.py     |  12 ++---
 modules/images.py           |  37 +++++--------
 modules/ldsr_model_arch.py  |   1 -
 modules/modelloader.py      |   9 +++-
 modules/realesrgan_model.py |  12 ++---
 modules/sd_models.py        |  56 ++++++--------------
 modules/shared.py           |   8 +--
 webui.py                    |   2 +-
 11 files changed, 127 insertions(+), 175 deletions(-)

(limited to 'modules/ldsr_model_arch.py')

diff --git a/launch.py b/launch.py
index 3b8d8f23..d2793ed2 100644
--- a/launch.py
+++ b/launch.py
@@ -1,5 +1,4 @@
 # this scripts installs necessary requirements and launches main program in webui.py
-import shutil
 import subprocess
 import os
 import sys
@@ -119,11 +118,7 @@ git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming
 git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
 git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
 git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
-if os.path.isdir(repo_dir('latent-diffusion')):
-    try:
-        shutil.rmtree(repo_dir('latent-diffusion'))
-    except:
-        pass
+
 if not is_installed("lpips"):
     run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
 
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index ce841aa4..ea91abfe 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -13,6 +13,63 @@ from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
 
+def fix_model_layers(crt_model, pretrained_net):
+    # this code is adapted from https://github.com/xinntao/ESRGAN
+    if 'conv_first.weight' in pretrained_net:
+        return pretrained_net
+
+    if 'model.0.weight' not in pretrained_net:
+        is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
+        if is_realesrgan:
+            raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
+        else:
+            raise Exception("The file is not a ESRGAN model.")
+
+    crt_net = crt_model.state_dict()
+    load_net_clean = {}
+    for k, v in pretrained_net.items():
+        if k.startswith('module.'):
+            load_net_clean[k[7:]] = v
+        else:
+            load_net_clean[k] = v
+    pretrained_net = load_net_clean
+
+    tbd = []
+    for k, v in crt_net.items():
+        tbd.append(k)
+
+    # directly copy
+    for k, v in crt_net.items():
+        if k in pretrained_net and pretrained_net[k].size() == v.size():
+            crt_net[k] = pretrained_net[k]
+            tbd.remove(k)
+
+    crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
+    crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
+
+    for k in tbd.copy():
+        if 'RDB' in k:
+            ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
+            if '.weight' in k:
+                ori_k = ori_k.replace('.weight', '.0.weight')
+            elif '.bias' in k:
+                ori_k = ori_k.replace('.bias', '.0.bias')
+            crt_net[k] = pretrained_net[ori_k]
+            tbd.remove(k)
+
+    crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
+    crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
+    crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
+    crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
+    crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
+    crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
+    crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
+    crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
+    crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
+    crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
+
+    return crt_net
+
 class UpscalerESRGAN(Upscaler):
     def __init__(self, dirname):
         self.name = "ESRGAN"
@@ -28,14 +85,12 @@ class UpscalerESRGAN(Upscaler):
             scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
             scalers.append(scaler_data)
         for file in model_paths:
-            print(f"File: {file}")
             if "http" in file:
                 name = self.model_name
             else:
                 name = modelloader.friendly_name(file)
 
             scaler_data = UpscalerData(name, file, self, 4)
-            print(f"ESRGAN: Adding scaler {name}")
             self.scalers.append(scaler_data)
 
     def do_upscale(self, img, selected_model):
@@ -56,67 +111,14 @@ class UpscalerESRGAN(Upscaler):
         if not os.path.exists(filename) or filename is None:
             print("Unable to load %s from %s" % (self.model_path, filename))
             return None
-        # this code is adapted from https://github.com/xinntao/ESRGAN
+
         pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
         crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
 
-        if 'conv_first.weight' in pretrained_net:
-            crt_model.load_state_dict(pretrained_net)
-            return crt_model
-
-        if 'model.0.weight' not in pretrained_net:
-            is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net[
-                "params_ema"]
-            if is_realesrgan:
-                raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
-            else:
-                raise Exception("The file is not a ESRGAN model.")
-
-        crt_net = crt_model.state_dict()
-        load_net_clean = {}
-        for k, v in pretrained_net.items():
-            if k.startswith('module.'):
-                load_net_clean[k[7:]] = v
-            else:
-                load_net_clean[k] = v
-        pretrained_net = load_net_clean
-
-        tbd = []
-        for k, v in crt_net.items():
-            tbd.append(k)
-
-        # directly copy
-        for k, v in crt_net.items():
-            if k in pretrained_net and pretrained_net[k].size() == v.size():
-                crt_net[k] = pretrained_net[k]
-                tbd.remove(k)
-
-        crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
-        crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
-
-        for k in tbd.copy():
-            if 'RDB' in k:
-                ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
-                if '.weight' in k:
-                    ori_k = ori_k.replace('.weight', '.0.weight')
-                elif '.bias' in k:
-                    ori_k = ori_k.replace('.bias', '.0.bias')
-                crt_net[k] = pretrained_net[ori_k]
-                tbd.remove(k)
-
-        crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
-        crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
-        crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
-        crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
-        crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
-        crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
-        crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
-        crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
-        crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
-        crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
-
-        crt_model.load_state_dict(crt_net)
+        pretrained_net = fix_model_layers(crt_model, pretrained_net)
+        crt_model.load_state_dict(pretrained_net)
         crt_model.eval()
+
         return crt_model
 
 
@@ -154,7 +156,6 @@ def esrgan_upscale(model, img):
             newrow.append([x * scale_factor, w * scale_factor, output])
         newtiles.append([y * scale_factor, h * scale_factor, newrow])
 
-    newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor,
-                                  grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
+    newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
     output = images.combine_grid(newgrid)
     return output
diff --git a/modules/extras.py b/modules/extras.py
index 1d4e9fa8..1bff5874 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -67,28 +67,29 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
             info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
             image = res
 
-        def upscale(image, scaler_index, resize):
-            small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
-            pixels = tuple(np.array(small).flatten().tolist())
-            key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
+        if upscaling_resize != 1.0:
+            def upscale(image, scaler_index, resize):
+                small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
+                pixels = tuple(np.array(small).flatten().tolist())
+                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
 
-            c = cached_images.get(key)
-            if c is None:
-                upscaler = shared.sd_upscalers[scaler_index]
-                c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
-                cached_images[key] = c
+                c = cached_images.get(key)
+                if c is None:
+                    upscaler = shared.sd_upscalers[scaler_index]
+                    c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+                    cached_images[key] = c
 
-            return c
+                return c
 
-        info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
-        res = upscale(image, extras_upscaler_1, upscaling_resize)
+            info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
+            res = upscale(image, extras_upscaler_1, upscaling_resize)
 
-        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-            res2 = upscale(image, extras_upscaler_2, upscaling_resize)
-            info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
-            res = Image.blend(res, res2, extras_upscaler_2_visibility)
+            if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
+                res2 = upscale(image, extras_upscaler_2, upscaling_resize)
+                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
+                res = Image.blend(res, res2, extras_upscaler_2_visibility)
 
-        image = res
+            image = res
 
         while len(cached_images) > 2:
             del cached_images[next(iter(cached_images.keys()))]
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index 2bf8a1ee..bb30d733 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -36,8 +36,7 @@ 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_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
     model.gfpgan.to(shared.device)
     loaded_gfpgan_model = model
 
@@ -49,8 +48,7 @@ def gfpgan_fix_faces(np_image):
     if model is None:
         return np_image
     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)
+    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]
 
     if shared.opts.face_restoration_unload:
@@ -79,7 +77,6 @@ def setup_model(dirname):
         facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
 
         def my_load_file_from_url(**kwargs):
-            print("Setting model_dir to " + model_path)
             return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
 
         def facex_load_file_from_url(**kwargs):
@@ -92,7 +89,6 @@ def setup_model(dirname):
         facexlib.detection.load_file_from_url = facex_load_file_from_url
         facexlib.parsing.load_file_from_url = facex_load_file_from_url2
         user_path = dirname
-        print("Have gfpgan should be true?")
         have_gfpgan = True
         gfpgan_constructor = GFPGANer
 
@@ -102,9 +98,7 @@ def setup_model(dirname):
 
             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)
+                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
diff --git a/modules/images.py b/modules/images.py
index 6430cfec..e89c44b2 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -84,10 +84,8 @@ def combine_grid(grid):
         r = r.astype(np.uint8)
         return Image.fromarray(r, 'L')
 
-    mask_w = make_mask_image(
-        np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
-    mask_h = make_mask_image(
-        np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
+    mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
+    mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
 
     combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
     for y, h, row in grid.tiles:
@@ -130,12 +128,10 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
 
     def draw_texts(drawing, draw_x, draw_y, lines):
         for i, line in enumerate(lines):
-            drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt,
-                                   fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
+            drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
 
             if not line.is_active:
-                drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2,
-                              draw_y + line.size[1] // 2), fill=color_inactive, width=4)
+                drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2, draw_y + line.size[1] // 2), fill=color_inactive, width=4)
 
             draw_y += line.size[1] + line_spacing
 
@@ -206,10 +202,8 @@ def draw_prompt_matrix(im, width, height, all_prompts):
     prompts_horiz = prompts[:boundary]
     prompts_vert = prompts[boundary:]
 
-    hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in
-                 range(1 << len(prompts_horiz))]
-    ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in
-                 range(1 << len(prompts_vert))]
+    hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]
+    ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]
 
     return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
 
@@ -259,13 +253,11 @@ def resize_image(resize_mode, im, width, height):
         if ratio < src_ratio:
             fill_height = height // 2 - src_h // 2
             res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
-            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)),
-                      box=(0, fill_height + src_h))
+            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
         elif ratio > src_ratio:
             fill_width = width // 2 - src_w // 2
             res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
-            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)),
-                      box=(fill_width + src_w, 0))
+            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
 
     return res
 
@@ -300,8 +292,7 @@ def apply_filename_pattern(x, p, seed, prompt):
             words = [x for x in re_nonletters.split(prompt or "") if len(x) > 0]
             if len(words) == 0:
                 words = ["empty"]
-            x = x.replace("[prompt_words]",
-                          sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))
+            x = 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))
@@ -309,8 +300,7 @@ def apply_filename_pattern(x, p, seed, prompt):
         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))
-        x = x.replace("[sampler]",
-                      sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, 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())
@@ -336,8 +326,7 @@ def get_next_sequence_number(path, basename):
     prefix_length = len(basename)
     for p in os.listdir(path):
         if p.startswith(basename):
-            l = os.path.splitext(p[prefix_length:])[0].split(
-                '-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
+            l = os.path.splitext(p[prefix_length:])[0].split('-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
             try:
                 result = max(int(l[0]), result)
             except ValueError:
@@ -346,9 +335,7 @@ 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=""):
     if short_filename or prompt is None or seed is None:
         file_decoration = ""
     elif opts.save_to_dirs:
diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
index f8f3c3d3..7faac6e1 100644
--- a/modules/ldsr_model_arch.py
+++ b/modules/ldsr_model_arch.py
@@ -125,7 +125,6 @@ class LDSR:
         del model
         gc.collect()
         torch.cuda.empty_cache()
-        print(f'Processing finished!')
         return a
 
 
diff --git a/modules/modelloader.py b/modules/modelloader.py
index b3e6dc36..1106aeb7 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -25,8 +25,10 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
 
     if ext_filter is None:
         ext_filter = []
+
     try:
         places = []
+
         if command_path is not None and command_path != model_path:
             pretrained_path = os.path.join(command_path, 'experiments/pretrained_models')
             if os.path.exists(pretrained_path):
@@ -34,7 +36,9 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                 places.append(pretrained_path)
             elif os.path.exists(command_path):
                 places.append(command_path)
+
         places.append(model_path)
+
         for place in places:
             if os.path.exists(place):
                 for file in os.listdir(place):
@@ -47,14 +51,17 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                             continue
                     if file not in output:
                         output.append(full_path)
+
         if model_url is not None and len(output) == 0:
             if download_name is not None:
                 dl = load_file_from_url(model_url, model_path, True, download_name)
                 output.append(dl)
             else:
                 output.append(model_url)
-    except:
+
+    except Exception:
         pass
+
     return output
 
 
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py
index 0a2eb896..dc0123e0 100644
--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@@ -88,28 +88,24 @@ def get_realesrgan_models(scaler):
         models = [
             UpscalerData(
                 name="R-ESRGAN General 4xV3",
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3"
-                     ".pth",
+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
                 scale=4,
                 upscaler=scaler,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4,
-                                              act_type='prelu')
+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
             ),
             UpscalerData(
                 name="R-ESRGAN General WDN 4xV3",
                 path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
                 scale=4,
                 upscaler=scaler,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4,
-                                              act_type='prelu')
+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
             ),
             UpscalerData(
                 name="R-ESRGAN AnimeVideo",
                 path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
                 scale=4,
                 upscaler=scaler,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4,
-                                              act_type='prelu')
+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
             ),
             UpscalerData(
                 name="R-ESRGAN 4x+",
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4b9000a4..caa85d5e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -12,10 +12,10 @@ from modules import shared, modelloader
 from modules.paths import models_path
 
 model_dir = "Stable-diffusion"
-model_path = os.path.join(models_path, model_dir)
+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
+user_dir: (str | None) = None
 
 CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
 checkpoints_list = {}
@@ -30,26 +30,8 @@ except Exception:
     pass
 
 
-def modeltitle(path, h):
-    abspath = os.path.abspath(path)
-
-    if abspath.startswith(model_dir):
-        name = abspath.replace(model_dir, '')
-    else:
-        name = os.path.basename(path)
-
-    if name.startswith("\\") or name.startswith("/"):
-        name = name[1:]
-
-    return f'{name} [{h}]'
-
-
 def setup_model(dirname):
-    global model_path
-    global model_name
-    global model_url
     global user_dir
-    global model_list
     user_dir = dirname
     if not os.path.exists(model_path):
         os.makedirs(model_path)
@@ -62,21 +44,16 @@ def checkpoint_tiles():
 
 
 def list_models():
-    global model_path
-    global model_url
-    global model_name
-    global user_dir
     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)
-    print(f"Model list: {model_list}")
-    model_dir = os.path.abspath(model_path)
+    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=user_dir, ext_filter=[".ckpt"], download_name=model_name)
 
-    def modeltitle(path, h):
+    def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
 
-        if abspath.startswith(model_dir):
-            name = abspath.replace(model_dir, '')
+        if user_dir is not None and abspath.startswith(user_dir):
+            name = abspath.replace(user_dir, '')
+        elif abspath.startswith(model_path):
+            name = abspath.replace(model_path, '')
         else:
             name = os.path.basename(path)
 
@@ -85,29 +62,30 @@ def list_models():
 
         shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
 
-        return f'{name} [{h}]', shortname
+        return f'{name} [{shorthash}]', shortname
 
     cmd_ckpt = shared.cmd_opts.ckpt
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
-        title, model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, model_name)
+        title, short_model_name = modeltitle(cmd_ckpt, h)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
     for filename in model_list:
         h = model_hash(filename)
-        title, model_name = modeltitle(filename, h)
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, model_name)
+        title, short_model_name = modeltitle(filename, h)
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
+
 
 def get_closet_checkpoint_match(searchString):
     applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title))
-    if len(applicable)>0:
+    if len(applicable) > 0:
         return applicable[0]
     return None
 
+
 def model_hash(filename):
     try:
-        print(f"Opening: {filename}")
         with open(filename, "rb") as file:
             import hashlib
             m = hashlib.sha256()
@@ -128,7 +106,7 @@ 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.stablediffusion_models_path)}", file=sys.stderr)
+        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/shared.py b/modules/shared.py
index 69002158..03a1a4d3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -21,8 +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",)
-# This should be deprecated, but we'll leave it for a few iterations
-parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints (Deprecated, use '--stablediffusion-models-path'", )
+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)
 parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
@@ -41,7 +40,6 @@ parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory wi
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(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("--stablediffusion-models-path", type=str, help="Path to directory with Stable-diffusion checkpoints.", default=os.path.join(model_path, 'SwinIR'))
 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.")
@@ -61,10 +59,6 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 
 cmd_opts = parser.parse_args()
-if cmd_opts.ckpt_dir is not None:
-    print("The 'ckpt-dir' arg is deprecated in favor of the 'stablediffusion-models-path' argument and will be "
-          "removed in a future release. Please use the new option if you wish to use a custom checkpoint directory.")
-    cmd_opts.__setattr__("stablediffusion-models-path", cmd_opts.ckpt_dir)
 device = get_optimal_device()
 
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
diff --git a/webui.py b/webui.py
index 5fd65edc..b8cccd54 100644
--- a/webui.py
+++ b/webui.py
@@ -28,7 +28,7 @@ from modules.paths import script_path
 from modules.shared import cmd_opts
 
 modelloader.cleanup_models()
-modules.sd_models.setup_model(cmd_opts.stablediffusion_models_path)
+modules.sd_models.setup_model(cmd_opts.ckpt_dir)
 codeformer.setup_model(cmd_opts.codeformer_models_path)
 gfpgan.setup_model(cmd_opts.gfpgan_models_path)
 shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-- 
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/ldsr_model_arch.py')

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/ldsr_model_arch.py')

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