import argparse, os, sys, glob import torch import torch.nn as nn import numpy as np import gradio as gr from omegaconf import OmegaConf 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 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 try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. from transformers import logging logging.set_verbosity_error() except: pass # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI mimetypes.init() mimetypes.add_type('application/javascript', '.js') # some of those options should not be changed at all because they would break the model, so I removed them from options. opt_C = 4 opt_f = 8 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) invalid_filename_chars = '<>:"/\|?*\n' 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("--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 css_hide_progressbar = """ .wrap .m-12 svg { display:none!important; } .wrap .m-12::before { content:"Loading..." } .progress-bar { display:none!important; } .meta-text { display:none!important; } """ def chunk(it, size): it = iter(it) return iter(lambda: tuple(islice(it, size)), ()) def load_model_from_config(config, ckpt, verbose=False): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") print(m) if len(u) > 0 and verbose: print("unexpected keys:") print(u) model.cuda() model.eval() return model class CFGDenoiser(nn.Module): def __init__(self, model): super().__init__() self.inner_model = model def forward(self, x, sigma, uncond, cond, cond_scale): x_in = torch.cat([x] * 2) sigma_in = torch.cat([sigma] * 2) cond_in = torch.cat([uncond, cond]) uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2) return uncond + (cond - uncond) * cond_scale class KDiffusionSampler: def __init__(self, m): self.model = m self.model_wrap = K.external.CompVisDenoiser(m) def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T): sigmas = self.model_wrap.get_sigmas(S) x = x_T * sigmas[0] model_wrap_cfg = CFGDenoiser(self.model_wrap) samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}, disable=False) return samples_ddim, None def create_random_tensors(shape, seeds): xs = [] for seed in seeds: torch.manual_seed(seed) # randn results depend on device; gpu and cpu get different results for same seed; # the way I see it, it's better to do this on CPU, so that everyone gets same result; # but the original script had it like this so i do not dare change it for now because # it will break everyone's seeds. xs.append(torch.randn(shape, device=device)) x = torch.stack(xs) return x def torch_gc(): torch.cuda.empty_cache() torch.cuda.ipc_collect() def sanitize_filename_part(text): return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128] def save_image(image, path, basename, seed, prompt, extension, info=None, short_filename=False): prompt = sanitize_filename_part(prompt) if short_filename: filename = f"{basename}.{extension}" else: filename = f"{basename}-{seed}-{prompt[:128]}.{extension}" if extension == 'png' and not opt.disable_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) def plaintext_to_html(text): text = "".join([f"

{html.escape(x)}

\n" for x in text.split('\n')]) return text def load_GFPGAN(): model_name = 'GFPGANv1.3' model_path = os.path.join(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)) 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: rows = batch_size else: rows = math.sqrt(len(imgs)) rows = int(rows) if round_down else round(rows) cols = math.ceil(len(imgs) / rows) w, h = imgs[0].size grid = Image.new('RGB', size=(cols * w, rows * h), color='black') for i, img in enumerate(imgs): grid.paste(img, box=(i % cols * w, i // cols * h)) return grid def draw_prompt_matrix(im, width, height, all_prompts): def wrap(text, d, font, line_length): lines = [''] for word in text.split(): line = f'{lines[-1]} {word}'.strip() if d.textlength(line, font=font) <= line_length: lines[-1] = line else: lines.append(word) return '\n'.join(lines) def draw_texts(pos, x, y, texts, sizes): for i, (text, size) in enumerate(zip(texts, sizes)): active = pos & (1 << i) != 0 if not active: text = '\u0336'.join(text) + '\u0336' d.multiline_text((x, y + size[1] / 2), text, font=fnt, fill=color_active if active else color_inactive, anchor="mm", align="center") y += size[1] + line_spacing fontsize = (width + height) // 25 line_spacing = fontsize // 2 fnt = ImageFont.truetype("arial.ttf", fontsize) color_active = (0, 0, 0) color_inactive = (153, 153, 153) pad_top = height // 4 pad_left = width * 3 // 4 if len(all_prompts) > 2 else 0 cols = im.width // width rows = im.height // height prompts = all_prompts[1:] result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white") result.paste(im, (pad_left, pad_top)) d = ImageDraw.Draw(result) boundary = math.ceil(len(prompts) / 2) prompts_horiz = [wrap(x, d, fnt, width) for x in prompts[:boundary]] prompts_vert = [wrap(x, d, fnt, pad_left) for x in prompts[boundary:]] sizes_hor = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_horiz]] sizes_ver = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_vert]] hor_text_height = sum([x[1] + line_spacing for x in sizes_hor]) - line_spacing ver_text_height = sum([x[1] + line_spacing for x in sizes_ver]) - line_spacing for col in range(cols): x = pad_left + width * col + width / 2 y = pad_top / 2 - hor_text_height / 2 draw_texts(col, x, y, prompts_horiz, sizes_hor) for row in range(rows): x = pad_left / 2 y = pad_top + height * row + height / 2 - ver_text_height / 2 draw_texts(row, x, y, prompts_vert, sizes_ver) return result def resize_image(resize_mode, im, width, height): if resize_mode == 0: res = im.resize((width, height), resample=LANCZOS) elif resize_mode == 1: ratio = width / height src_ratio = im.width / im.height src_w = width if ratio > src_ratio else im.width * height // im.height src_h = height if ratio <= src_ratio else im.height * width // im.width resized = im.resize((src_w, src_h), resample=LANCZOS) res = Image.new("RGB", (width, height)) res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) else: ratio = width / height src_ratio = im.width / im.height src_w = width if ratio < src_ratio else im.width * height // im.height src_h = height if ratio >= src_ratio else im.height * width // im.width resized = im.resize((src_w, src_h), resample=LANCZOS) res = Image.new("RGB", (width, height)) res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) 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)) 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)) return res def check_prompt_length(prompt, comments): """this function tests if prompt is too long, and if so, adds a message to comments""" tokenizer = model.cond_stage_model.tokenizer max_length = model.cond_stage_model.max_length info = model.cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length, return_overflowing_tokens=True, padding="max_length", return_tensors="pt") ovf = info['overflowing_tokens'][0] overflowing_count = ovf.shape[0] if overflowing_count == 0: return vocab = {v: k for k, v in tokenizer.get_vocab().items()} overflowing_words = [vocab.get(int(x), "") for x in ovf] overflowing_text = tokenizer.convert_tokens_to_string(''.join(overflowing_words)) comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") def wrap_gradio_call(func): def f(*p1, **p2): t = time.perf_counter() res = list(func(*p1, **p2)) elapsed = time.perf_counter() - t # last item is always HTML res[-1] = res[-1] + f"

Time taken: {elapsed:.2f}s

" return tuple(res) return f 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""" assert prompt is not None torch_gc() if seed == -1: seed = random.randrange(4294967294) seed = int(seed) os.makedirs(outpath, exist_ok=True) sample_path = os.path.join(outpath, "samples") os.makedirs(sample_path, exist_ok=True) base_count = len(os.listdir(sample_path)) grid_count = len(os.listdir(outpath)) - 1 comments = [] prompt_matrix_parts = [] if prompt_matrix: all_prompts = [] prompt_matrix_parts = prompt.split("|") combination_count = 2 ** (len(prompt_matrix_parts) - 1) for combination_num in range(combination_count): current = prompt_matrix_parts[0] for n, text in enumerate(prompt_matrix_parts[1:]): if combination_num & (2 ** n) > 0: current += ("" if text.strip().startswith(",") else ", ") + text all_prompts.append(current) n_iter = math.ceil(len(all_prompts) / batch_size) all_seeds = len(all_prompts) * [seed] print(f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches.") else: if not opt.no_verify_input: try: check_prompt_length(prompt, comments) except: import traceback print("Error verifying input:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) 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]) precision_scope = autocast if opt.precision == "autocast" else nullcontext output_images = [] with torch.no_grad(), precision_scope("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) c = model.get_learned_conditioning(prompts) # 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) samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc) 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: 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) if use_GFPGAN and GFPGAN is not None: cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True) 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) output_images.append(image) base_count += 1 if (prompt_matrix or not opt.skip_grid) and not do_not_save_grid: grid = image_grid(output_images, batch_size, round_down=prompt_matrix) if prompt_matrix: try: grid = draw_prompt_matrix(grid, width, height, prompt_matrix_parts) except Exception: import traceback print("Error creating prompt_matrix text:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) 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) grid_count += 1 torch_gc() return output_images, seed, info 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) if sampler_name == 'PLMS': sampler = PLMSSampler(model) elif sampler_name == 'DDIM': sampler = DDIMSampler(model) elif sampler_name == 'k-diffusion': sampler = KDiffusionSampler(model) else: raise Exception("Unknown sampler: " + sampler_name) def init(): pass def sample(init_data, x, conditioning, unconditional_conditioning): samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x) return samples_ddim output_images, seed, info = process_images( outpath=outpath, func_init=init, func_sample=sample, prompt=prompt, seed=seed, sampler_name=sampler_name, batch_size=batch_size, n_iter=n_iter, steps=ddim_steps, cfg_scale=cfg_scale, width=width, height=height, prompt_matrix=prompt_matrix, use_GFPGAN=use_GFPGAN ) del sampler return output_images, seed, plaintext_to_html(info) class Flagging(gr.FlaggingCallback): def setup(self, components, flagging_dir: str): pass def flag(self, flag_data, flag_option=None, flag_index=None, username=None): import csv os.makedirs("log/images", exist_ok=True) # those must match the "txt2img" function prompt, ddim_steps, sampler_name, use_GFPGAN, prompt_matrix, ddim_eta, n_iter, n_samples, cfg_scale, request_seed, height, width, images, seed, comment = flag_data filenames = [] with open("log/log.csv", "a", encoding="utf8", newline='') as file: import time import base64 at_start = file.tell() == 0 writer = csv.writer(file) if at_start: writer.writerow(["prompt", "seed", "width", "height", "cfgs", "steps", "filename"]) filename_base = str(int(time.time() * 1000)) for i, filedata in enumerate(images): filename = "log/images/"+filename_base + ("" if len(images) == 1 else "-"+str(i+1)) + ".png" if filedata.startswith("data:image/png;base64,"): filedata = filedata[len("data:image/png;base64,"):] with open(filename, "wb") as imgfile: imgfile.write(base64.decodebytes(filedata.encode('utf-8'))) filenames.append(filename) writer.writerow([prompt, seed, width, height, cfg_scale, ddim_steps, filenames[0]]) print("Logged:", filenames[0]) txt2img_interface = gr.Interface( wrap_gradio_call(txt2img), inputs=[ gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1), gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50), gr.Radio(label='Sampling method', choices=["DDIM", "PLMS", "k-diffusion"], value="k-diffusion"), 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=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)", 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) sampler = KDiffusionSampler(model) assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]' def init(): image = init_img.convert("RGB") image = resize_image(resize_mode, image, width, height) image = np.array(image).astype(np.float32) / 255.0 image = image[None].transpose(0, 3, 1, 2) image = torch.from_numpy(image) init_image = 2. * image - 1. init_image = init_image.to(device) init_image = repeat(init_image, '1 ... -> b ...', b=batch_size) init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space return init_latent, def sample(init_data, x, conditioning, unconditional_conditioning): t_enc = int(denoising_strength * ddim_steps) x0, = init_data sigmas = sampler.model_wrap.get_sigmas(ddim_steps) noise = x * sigmas[ddim_steps - t_enc - 1] xi = x0 + noise sigma_sched = sigmas[ddim_steps - t_enc - 1:] model_wrap_cfg = CFGDenoiser(sampler.model_wrap) samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale}, disable=False) return samples_ddim if loopback: output_images, info = None, None history = [] initial_seed = None for i in range(n_iter): output_images, seed, info = process_images( outpath=outpath, func_init=init, func_sample=sample, prompt=prompt, seed=seed, sampler_name='k-diffusion', batch_size=1, n_iter=1, steps=ddim_steps, cfg_scale=cfg_scale, width=width, height=height, prompt_matrix=prompt_matrix, use_GFPGAN=use_GFPGAN, do_not_save_grid=True ) if initial_seed is None: initial_seed = seed init_img = output_images[0] seed = seed + 1 denoising_strength = max(denoising_strength * 0.95, 0.1) history.append(init_img) 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) output_images = history seed = initial_seed else: output_images, seed, info = process_images( outpath=outpath, func_init=init, func_sample=sample, prompt=prompt, seed=seed, sampler_name='k-diffusion', batch_size=batch_size, n_iter=n_iter, steps=ddim_steps, cfg_scale=cfg_scale, width=width, height=height, prompt_matrix=prompt_matrix, use_GFPGAN=use_GFPGAN ) del sampler return output_images, seed, plaintext_to_html(info) sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg" sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None img2img_interface = gr.Interface( wrap_gradio_call(img2img), inputs=[ gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1), gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil"), gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50), 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=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) ], outputs=[ gr.Gallery(), gr.Number(label='Seed'), 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") cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True) res = Image.fromarray(restored_img) if strength < 1.0: res = Image.blend(image, res, 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")) 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) + """ .output-html p {margin: 0 0.5em;} .performance { font-size: 0.85em; color: #444; } """ ) demo.launch()