remove unwanted formatting/functionality from the PR (d1f09854) · Commits · github_fork / Stable Diffusion Webui

launch.py

+1 −6

Original line number	Diff line number	Diff line
		# 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")

modules/esrgan_model.py

+62 −61

Original line number	Diff line number	Diff line
		@@ -13,60 +13,13 @@ from modules.upscaler import Upscaler, UpscalerData
		from modules.shared import opts


		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.scalers = []
		self.user_path = dirname
		self.model_path = os.path.join(models_path, self.name)
		super().__init__()
		model_paths = self.find_models(ext_filter=[".pt", ".pth"])
		scalers = []
		if len(model_paths) == 0:
		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):
		model = self.load_model(selected_model)
		if model is None:
		return img
		model.to(shared.device)
		img = esrgan_upscale(model, img)
		return img

		def load_model(self, path: str):
		if "http" in path:
		filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
		file_name="%s.pth" % self.model_name,
		progress=True)
		else:
		filename = path
		if not os.path.exists(filename) or filename is None:
		print("Unable to load %s from %s" % (self.model_path, filename))
		return None
		def fix_model_layers(crt_model, pretrained_net):
		# 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
		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"]
		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:
		@@ -115,8 +68,57 @@ class UpscalerESRGAN(Upscaler):
		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)
		return crt_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.scalers = []
		self.user_path = dirname
		self.model_path = os.path.join(models_path, self.name)
		super().__init__()
		model_paths = self.find_models(ext_filter=[".pt", ".pth"])
		scalers = []
		if len(model_paths) == 0:
		scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
		scalers.append(scaler_data)
		for file in model_paths:
		if "http" in file:
		name = self.model_name
		else:
		name = modelloader.friendly_name(file)

		scaler_data = UpscalerData(name, file, self, 4)
		self.scalers.append(scaler_data)

		def do_upscale(self, img, selected_model):
		model = self.load_model(selected_model)
		if model is None:
		return img
		model.to(shared.device)
		img = esrgan_upscale(model, img)
		return img

		def load_model(self, path: str):
		if "http" in path:
		filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
		file_name="%s.pth" % self.model_name,
		progress=True)
		else:
		filename = path
		if not os.path.exists(filename) or filename is None:
		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)
		crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)

		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

modules/extras.py

+18 −17

Original line number	Diff line number	Diff line
		@@ -67,6 +67,7 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
		info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
		image = res

		if 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())

modules/gfpgan_model.py

+3 −9

Original line number	Diff line number	Diff line
		@@ -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

modules/images.py

+12 −25

Original line number	Diff line number	Diff line
		@@ -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:

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