diff --git a/README.md b/README.md index a3c36aac..0274f4f9 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # Stable Diffusion web UI A browser interface based on Gradio library for Stable Diffusion. -Original script with Gradio UI was written by a kind anonymopus user. This is a modification. +Original script with Gradio UI was written by a kind anonymous user. This is a modification. ![](screenshot.png) ## Installing and running @@ -128,7 +128,7 @@ Example: Gradio's loading graphic has a very negative effect on the processing speed of the neural network. My RTX 3090 makes images about 10% faster when the tab with gradio is not active. By default, the UI now hides loading progress animation and replaces it with static "Loading..." text, which achieves -the same effect. Use the --no-progressbar-hiding commandline option to revert this and show loading animations. +the same effect. Use the `--no-progressbar-hiding` commandline option to revert this and show loading animations. ### Prompt validation Stable Diffusion has a limit for input text length. If your prompt is too long, you will get a @@ -152,6 +152,28 @@ Adds information about generation parameters to PNG as a text chunk. You can view this information later using any software that supports viewing PNG chunk info, for example: https://www.nayuki.io/page/png-file-chunk-inspector -This can be disabled using the `--disable-pnginfo` command line option. - ![](images/pnginfo.png) + +### Textual Inversion +Allows you to use pretrained textual inversion embeddings. +See originial site for details: https://textual-inversion.github.io/. +I used lstein's repo for training embdedding: https://github.com/lstein/stable-diffusion; if +you want to train your own, I recommend following the guide on his site. + +No additional libraries/repositories are required to use pretrained embeddings. + +To make use of pretrained embeddings, create `embeddings` directory in the root dir of Stable +Diffusion and put your embeddings into it. They must be .pt files about 5Kb in size, each with only +one trained embedding, and the filename (without .pt) will be the term you'd use in prompt +to get that embedding. + +As an example, I trained one for about 5000 steps: https://files.catbox.moe/e2ui6r.pt; it does +not produce very good results, but it does work. Download and rename it to `Usada Pekora.pt`, +and put it into `embeddings` dir and use Usada Pekora in prompt. + +![](images/inversion.png) + +### Settings +A tab with settings, allowing you to use UI to edit more than half of parameters that previously +were commandline. Settings are saved to config.js file. Settings that remain as commandline +options are ones that are required at startup. diff --git a/images/inversion.png b/images/inversion.png new file mode 100644 index 00000000..4105de5e Binary files /dev/null and b/images/inversion.png differ diff --git a/webui.py b/webui.py index 55654f55..4e586c02 100644 --- a/webui.py +++ b/webui.py @@ -8,17 +8,19 @@ from PIL import Image, ImageFont, ImageDraw, PngImagePlugin from itertools import islice from einops import rearrange, repeat from torch import autocast -from contextlib import contextmanager, nullcontext import mimetypes import random import math import html import time +import json +import traceback import k_diffusion as K from ldm.util import instantiate_from_config from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.plms import PLMSSampler +import ldm.modules.encoders.modules try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. @@ -38,30 +40,18 @@ opt_f = 8 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) invalid_filename_chars = '<>:"/\|?*\n' +config_filename = "config.json" parser = argparse.ArgumentParser() -parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None) -parser.add_argument("--skip_grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",) -parser.add_argument("--skip_save", action='store_true', help="do not save indiviual samples. For speed measurements.",) -parser.add_argument("--n_rows", type=int, default=-1, help="rows in the grid; use -1 for autodetect and 0 for n_rows to be same as batch_size (default: -1)",) parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",) parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",) -parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast") parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) # i disagree with where you're putting it but since all guidefags are doing it this way, there you go -parser.add_argument("--no-verify-input", action='store_true', help="do not verify input to check if it's too long") parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats") parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware accleration in browser)") parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") -parser.add_argument("--save-format", type=str, default='png', help="file format for saved indiviual samples; can be png or jpg") -parser.add_argument("--grid-format", type=str, default='png', help="file format for saved grids; can be png or jpg") -parser.add_argument("--grid-extended-filename", action='store_true', help="save grid images to filenames with extended info: seed, prompt") -parser.add_argument("--jpeg-quality", type=int, default=80, help="quality for saved jpeg images") -parser.add_argument("--disable-pnginfo", action='store_true', help="disable saving text information about generation parameters as chunks to png files") +parser.add_argument("--embeddings-dir", type=str, default='embeddings', help="embeddings dirtectory for textual inversion (default: embeddings)") -parser.add_argument("--inversion", action='store_true', help="switch to stable inversion version; allows for uploading embeddings; this option should be used only with textual inversion repo") -opt = parser.parse_args() - -GFPGAN_dir = opt.gfpgan_dir +cmd_opts = parser.parse_args() css_hide_progressbar = """ .wrap .m-12 svg { display:none!important; } @@ -70,6 +60,49 @@ css_hide_progressbar = """ .meta-text { display:none!important; } """ + + +class Options: + data = None + data_labels = { + "outdir": ("", "Output dictectory; if empty, defaults to 'outputs/*'"), + "samples_save": (True, "Save indiviual samples"), + "samples_format": ('png', 'File format for indiviual samples'), + "grid_save": (True, "Save image grids"), + "grid_format": ('png', 'File format for grids'), + "grid_extended_filename": (False, "Add extended info (seed, prompt) to filename when saving grid"), + "n_rows": (-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", -1, 16), + "jpeg_quality": (80, "Quality for saved jpeg images", 1, 100), + "verify_input": (True, "Check input, and produce warning if it's too long"), + "enable_pnginfo": (True, "Save text information about generation parameters as chunks to png files"), + } + + def __init__(self): + self.data = {k: v[0] for k, v in self.data_labels.items()} + + def __setattr__(self, key, value): + if self.data is not None: + if key in self.data: + self.data[key] = value + + return super(Options, self).__setattr__(key, value) + + def __getattr__(self, item): + if self.data is not None: + if item in self.data: + return self.data[item] + + return super(Options, self).__getattribute__(item) + + def save(self, filename): + with open(filename, "w", encoding="utf8") as file: + json.dump(self.data, file) + + def load(self, filename): + with open(filename, "r", encoding="utf8") as file: + self.data = json.load(file) + + def chunk(it, size): it = iter(it) return iter(lambda: tuple(islice(it, size)), ()) @@ -154,13 +187,13 @@ def save_image(image, path, basename, seed, prompt, extension, info=None, short_ else: filename = f"{basename}-{seed}-{prompt[:128]}.{extension}" - if extension == 'png' and not opt.disable_pnginfo: + if extension == 'png' and opts.enable_pnginfo: pnginfo = PngImagePlugin.PngInfo() pnginfo.add_text("parameters", info) else: pnginfo = None - image.save(os.path.join(path, filename), quality=opt.jpeg_quality, pnginfo=pnginfo) + image.save(os.path.join(path, filename), quality=opts.jpeg_quality, pnginfo=pnginfo) def plaintext_to_html(text): @@ -170,39 +203,22 @@ def plaintext_to_html(text): def load_GFPGAN(): model_name = 'GFPGANv1.3' - model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth') + model_path = os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models', model_name + '.pth') if not os.path.isfile(model_path): raise Exception("GFPGAN model not found at path "+model_path) - sys.path.append(os.path.abspath(GFPGAN_dir)) + sys.path.append(os.path.abspath(cmd_opts.gfpgan_dir)) from gfpgan import GFPGANer return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None) -GFPGAN = None -if os.path.exists(GFPGAN_dir): - try: - GFPGAN = load_GFPGAN() - print("Loaded GFPGAN") - except Exception: - import traceback - print("Error loading GFPGAN:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - -config = OmegaConf.load(opt.config) -model = load_model_from_config(config, opt.ckpt) - -device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") -model = (model if opt.no_half else model.half()).to(device) - - def image_grid(imgs, batch_size, round_down=False, force_n_rows=None): if force_n_rows is not None: rows = force_n_rows - elif opt.n_rows > 0: - rows = opt.n_rows - elif opt.n_rows == 0: + elif opts.n_rows > 0: + rows = opts.n_rows + elif opts.n_rows == 0: rows = batch_size else: rows = math.sqrt(len(imgs)) @@ -353,6 +369,163 @@ def wrap_gradio_call(func): return f +GFPGAN = None +if os.path.exists(cmd_opts.gfpgan_dir): + try: + GFPGAN = load_GFPGAN() + print("Loaded GFPGAN") + except Exception: + print("Error loading GFPGAN:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + + +class TextInversionEmbeddings: + ids_lookup = {} + word_embeddings = {} + word_embeddings_checksums = {} + fixes = [] + used_custom_terms = [] + dir_mtime = None + + def load(self, dir, model): + mt = os.path.getmtime(dir) + if self.dir_mtime is not None and mt <= self.dir_mtime: + return + + self.dir_mtime = mt + self.ids_lookup.clear() + self.word_embeddings.clear() + + tokenizer = model.cond_stage_model.tokenizer + + def const_hash(a): + r = 0 + for v in a: + r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF + return r + + def process_file(path, filename): + name = os.path.splitext(filename)[0] + + data = torch.load(path) + param_dict = data['string_to_param'] + assert len(param_dict) == 1, 'embedding file has multiple terms in it' + emb = next(iter(param_dict.items()))[1].reshape(768) + self.word_embeddings[name] = emb + self.word_embeddings_checksums[name] = f'{const_hash(emb)&0xffff:04x}' + + ids = tokenizer([name], add_special_tokens=False)['input_ids'][0] + first_id = ids[0] + if first_id not in self.ids_lookup: + self.ids_lookup[first_id] = [] + self.ids_lookup[first_id].append((ids, name)) + + for fn in os.listdir(dir): + try: + process_file(os.path.join(dir, fn), fn) + except: + print(f"Error loading emedding {fn}:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + continue + + print(f"Loaded a total of {len(self.word_embeddings)} text inversion embeddings.") + + def hijack(self, m): + model_embeddings = m.cond_stage_model.transformer.text_model.embeddings + + model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self) + m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self) + +class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): + def __init__(self, wrapped, embeddings): + super().__init__() + self.wrapped = wrapped + self.embeddings = embeddings + self.tokenizer = wrapped.tokenizer + self.max_length = wrapped.max_length + + def forward(self, text): + self.embeddings.fixes = [] + self.embeddings.used_custom_terms = [] + remade_batch_tokens = [] + id_start = self.wrapped.tokenizer.bos_token_id + id_end = self.wrapped.tokenizer.eos_token_id + maxlen = self.wrapped.max_length - 2 + + cache = {} + batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"] + for tokens in batch_tokens: + tuple_tokens = tuple(tokens) + + if tuple_tokens in cache: + remade_tokens, fixes = cache[tuple_tokens] + else: + fixes = [] + remade_tokens = [] + + i = 0 + while i < len(tokens): + token = tokens[i] + + possible_matches = self.embeddings.ids_lookup.get(token, None) + + if possible_matches is None: + remade_tokens.append(token) + else: + found = False + for ids, word in possible_matches: + if tokens[i:i+len(ids)] == ids: + fixes.append((len(remade_tokens), word)) + remade_tokens.append(777) + i += len(ids) - 1 + found = True + self.embeddings.used_custom_terms.append((word, self.embeddings.word_embeddings_checksums[word])) + break + + if not found: + remade_tokens.append(token) + + i += 1 + + remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens)) + remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end] + cache[tuple_tokens] = (remade_tokens, fixes) + + remade_batch_tokens.append(remade_tokens) + self.embeddings.fixes.append(fixes) + + tokens = torch.asarray(remade_batch_tokens).to(self.wrapped.device) + outputs = self.wrapped.transformer(input_ids=tokens) + z = outputs.last_hidden_state + return z + + +class EmbeddingsWithFixes(nn.Module): + def __init__(self, wrapped, embeddings): + super().__init__() + self.wrapped = wrapped + self.embeddings = embeddings + + def forward(self, input_ids): + batch_fixes = self.embeddings.fixes + self.embeddings.fixes = [] + + inputs_embeds = self.wrapped(input_ids) + + for fixes, tensor in zip(batch_fixes, inputs_embeds): + for offset, word in fixes: + tensor[offset] = self.embeddings.word_embeddings[word] + + return inputs_embeds + + +def get_learned_conditioning_with_embeddings(model, prompts): + if os.path.exists(cmd_opts.embeddings_dir): + text_inversion_embeddings.load(cmd_opts.embeddings_dir, model) + + return model.get_learned_conditioning(prompts) + + def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False): """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch""" @@ -392,7 +565,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, print(f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches.") else: - if not opt.no_verify_input: + if opts.verify_input: try: check_prompt_length(prompt, comments) except: @@ -403,27 +576,29 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, all_prompts = batch_size * n_iter * [prompt] all_seeds = [seed + x for x in range(len(all_prompts))] - info = f""" - {prompt} - Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''} - """.strip() + "".join(["\n\n" + x for x in comments]) + def infotext(): + return f""" +{prompt} +Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''} + """.strip() + "".join(["\n\n" + x for x in comments]) + + if os.path.exists(cmd_opts.embeddings_dir): + text_inversion_embeddings.load(cmd_opts.embeddings_dir, model) - precision_scope = autocast if opt.precision == "autocast" else nullcontext output_images = [] - with torch.no_grad(), precision_scope("cuda"), model.ema_scope(): + with torch.no_grad(), autocast("cuda"), model.ema_scope(): init_data = func_init() for n in range(n_iter): prompts = all_prompts[n * batch_size:(n + 1) * batch_size] seeds = all_seeds[n * batch_size:(n + 1) * batch_size] - uc = None - if cfg_scale != 1.0: - uc = model.get_learned_conditioning(len(prompts) * [""]) - if isinstance(prompts, tuple): - prompts = list(prompts) + uc = model.get_learned_conditioning(len(prompts) * [""]) c = model.get_learned_conditioning(prompts) + if len(text_inversion_embeddings.used_custom_terms) > 0: + comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in text_inversion_embeddings.used_custom_terms])) + # we manually generate all input noises because each one should have a specific seed x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=seeds) @@ -432,7 +607,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) - if prompt_matrix or not opt.skip_save or not opt.skip_grid: + if prompt_matrix or opts.samples_save or opts.grid_save: for i, x_sample in enumerate(x_samples_ddim): x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') x_sample = x_sample.astype(np.uint8) @@ -442,12 +617,12 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, x_sample = restored_img image = Image.fromarray(x_sample) - save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opt.save_format, info=info) + save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opts.samples_format, info=infotext()) output_images.append(image) base_count += 1 - if (prompt_matrix or not opt.skip_grid) and not do_not_save_grid: + if (prompt_matrix or opts.grid_save) and not do_not_save_grid: grid = image_grid(output_images, batch_size, round_down=prompt_matrix) if prompt_matrix: @@ -461,23 +636,17 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, output_images.insert(0, grid) - save_image(grid, outpath, f"grid-{grid_count:04}", seed, prompt, opt.grid_format, info=info, short_filename=not opt.grid_extended_filename) + save_image(grid, outpath, f"grid-{grid_count:04}", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename) grid_count += 1 + + torch_gc() - return output_images, seed, info + return output_images, seed, infotext() -def load_embeddings(fp): - if fp is not None and hasattr(model, "embedding_manager"): - # load the file - model.embedding_manager.load(fp.name) - - -def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, embeddings_fp): - outpath = opt.outdir or "outputs/txt2img-samples" - - load_embeddings(embeddings_fp) +def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int): + outpath = opts.outdir or "outputs/txt2img-samples" if sampler_name == 'PLMS': sampler = PLMSSampler(model) @@ -567,29 +736,25 @@ txt2img_interface = gr.Interface( gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None), gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False), gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False), - gr.Slider(minimum=1, maximum=opt.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1), + gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1), gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1), gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0), gr.Number(label='Seed', value=-1), gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512), gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512), - gr.File(label = "Embeddings file for textual inversion", visible=opt.inversion) ], outputs=[ gr.Gallery(label="Images"), gr.Number(label='Seed'), gr.HTML(), ], - title="Stable Diffusion Text-to-Image K", - description="Generate images from text with Stable Diffusion (using K-LMS)", + title="Stable Diffusion Text-to-Image", flagging_callback=Flagging() ) -def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, embeddings_fp): - outpath = opt.outdir or "outputs/img2img-samples" - - load_embeddings(embeddings_fp) +def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int): + outpath = opts.outdir or "outputs/img2img-samples" sampler = KDiffusionSampler(model) @@ -658,7 +823,7 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat grid_count = len(os.listdir(outpath)) - 1 grid = image_grid(history, batch_size, force_n_rows=1) - save_image(grid, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opt.grid_format, info=info, short_filename=not opt.grid_extended_filename) + save_image(grid, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename) output_images = history seed = initial_seed @@ -698,15 +863,14 @@ img2img_interface = gr.Interface( gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None), gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False), gr.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False), - gr.Slider(minimum=1, maximum=opt.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1), + gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1), gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1), gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0), gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75), gr.Number(label='Seed', value=-1), gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512), gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512), - gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize"), - gr.File(label = "Embeddings file for textual inversion", visible=opt.inversion) + gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize") ], outputs=[ gr.Gallery(), @@ -714,15 +878,9 @@ img2img_interface = gr.Interface( gr.HTML(), ], title="Stable Diffusion Image-to-Image", - description="Generate images from images with Stable Diffusion", allow_flagging="never", ) -interfaces = [ - (txt2img_interface, "txt2img"), - (img2img_interface, "img2img") -] - def run_GFPGAN(image, strength): image = image.convert("RGB") @@ -735,31 +893,100 @@ def run_GFPGAN(image, strength): return res, 0, '' -if GFPGAN is not None: - interfaces.append((gr.Interface( - run_GFPGAN, - inputs=[ - gr.Image(label="Source", source="upload", interactive=True, type="pil"), - gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength", value=100), - ], - outputs=[ - gr.Image(label="Result"), - gr.Number(label='Seed', visible=False), - gr.HTML(), - ], - title="GFPGAN", - description="Fix faces on images", - allow_flagging="never", - ), "GFPGAN")) +gfpgan_interface = gr.Interface( + run_GFPGAN, + inputs=[ + gr.Image(label="Source", source="upload", interactive=True, type="pil"), + gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength", value=100), + ], + outputs=[ + gr.Image(label="Result"), + gr.Number(label='Seed', visible=False), + gr.HTML(), + ], + title="GFPGAN", + description="Fix faces on images", + allow_flagging="never", +) +opts = Options() +if os.path.exists(config_filename): + opts.load(config_filename) + + +def run_settings(*args): + up = [] + + for key, value, comp in zip(opts.data_labels.keys(), args, settings_interface.input_components): + opts.data[key] = value + up.append(comp.update(value=value)) + + opts.save(config_filename) + + return 'Settings saved.', '' + + +def create_setting_component(key): + def fun(): + return opts.data[key] if key in opts.data else opts.data_labels[key][0] + + labelinfo = opts.data_labels[key] + t = type(labelinfo[0]) + label = labelinfo[1] + if t == str: + item = gr.Textbox(label=label, value=fun, lines=1) + elif t == int: + if len(labelinfo) == 4: + item = gr.Slider(minimum=labelinfo[2], maximum=labelinfo[3], step=1, label=label, value=fun) + else: + item = gr.Number(label=label, value=fun) + elif t == bool: + item = gr.Checkbox(label=label, value=fun) + else: + raise Exception(f'bad options item type: {str(t)} for key {key}') + + return item + + +settings_interface = gr.Interface( + run_settings, + inputs=[create_setting_component(key) for key in opts.data_labels.keys()], + outputs=[ + gr.Textbox(label='Result'), + gr.HTML(), + ], + title=None, + description=None, + allow_flagging="never", +) + +interfaces = [ + (txt2img_interface, "txt2img"), + (img2img_interface, "img2img"), + (gfpgan_interface, "GFPGAN"), + (settings_interface, "Settings"), +] + +config = OmegaConf.load(cmd_opts.config) +model = load_model_from_config(config, cmd_opts.ckpt) + +device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") +model = (model if cmd_opts.no_half else model.half()).to(device) +text_inversion_embeddings = TextInversionEmbeddings() + +if os.path.exists(cmd_opts.embeddings_dir): + text_inversion_embeddings.hijack(model) + +if GFPGAN is None: + interfaces = [x for x in interfaces if x[0] != gfpgan_interface] demo = gr.TabbedInterface( interface_list=[x[0] for x in interfaces], tab_names=[x[1] for x in interfaces], - css=("" if opt.no_progressbar_hiding else css_hide_progressbar) + """ + css=("" if cmd_opts.no_progressbar_hiding else css_hide_progressbar) + """ .output-html p {margin: 0 0.5em;} .performance { font-size: 0.85em; color: #444; } """ ) -demo.launch() \ No newline at end of file +demo.launch()