From d1f098540ad1dbc2abb8d04322634efba650b631 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 30 Sep 2022 11:42:40 +0300 Subject: [PATCH] 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(-) diff --git a/launch.py b/launch.py index 3b8d8f233..d2793ed20 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 ce841aa4f..ea91abfe8 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 1d4e9fa8d..1bff58749 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 2bf8a1ee0..bb30d7330 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 6430cfec2..e89c44b25 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 f8f3c3d38..7faac6e18 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 b3e6dc362..1106aeb7f 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 0a2eb8966..dc0123e02 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 4b9000a4e..caa85d5e3 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 690021589..03a1a4d3f 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 5fd65edc3..b8cccd546 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())