remove unwanted formatting/functionality from the PR

11 files changed, 127 insertions, 175 deletions

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())