import argparse import os import sys from collections import namedtuple 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 torch import autocast import mimetypes import random import math import html import time import json import traceback import k_diffusion.sampling 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 Exception: 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' config_filename = "config.json" parser = argparse.ArgumentParser() 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("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) 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("--embeddings-dir", type=str, default='embeddings', help="embeddings dirtectory for textual inversion (default: embeddings)") cmd_opts = parser.parse_args() 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; } """ SamplerData = namedtuple('SamplerData', ['name', 'constructor']) samplers = [ *[SamplerData(x[0], lambda funcname=x[1]: KDiffusionSampler(funcname)) for x in [ ('LMS', 'sample_lms'), ('Heun', 'sample_heun'), ('Euler', 'sample_euler'), ('Euler ancestral', 'sample_euler_ancestral'), ('DPM 2', 'sample_dpm_2'), ('DPM 2 Ancestral', 'sample_dpm_2_ancestral'), ] if hasattr(k_diffusion.sampling, x[1])], SamplerData('DDIM', lambda: VanillaStableDiffusionSampler(DDIMSampler)), SamplerData('PLMS', lambda: VanillaStableDiffusionSampler(PLMSSampler)), ] samplers_for_img2img = [x for x in samplers if x.name != 'DDIM' and x.name != 'PLMS'] RealesrganModelInfo = namedtuple("RealesrganModelInfo", ["name", "location", "model", "netscale"]) try: from basicsr.archs.rrdbnet_arch import RRDBNet from realesrgan import RealESRGANer from realesrgan.archs.srvgg_arch import SRVGGNetCompact realesrgan_models = [ RealesrganModelInfo( name="Real-ESRGAN 4x plus", location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth", netscale=4, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) ), RealesrganModelInfo( name="Real-ESRGAN 4x plus anime 6B", location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth", netscale=4, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) ), RealesrganModelInfo( name="Real-ESRGAN 2x plus", location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth", netscale=2, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) ), ] have_realesrgan = True except Exception: print("Error loading Real-ESRGAN:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) realesrgan_models = [RealesrganModelInfo('None', '', 0, None)] have_realesrgan = False sd_upscalers = { "RealESRGAN": lambda img: upscale_with_realesrgan(img, 2, 0), "Lanczos": lambda img: img.resize((img.width*2, img.height*2), resample=LANCZOS), "None": lambda img: img } class Options: class OptionInfo: def __init__(self, default=None, label="", component=None, component_args=None): self.default = default self.label = label self.component = component self.component_args = component_args data = None data_labels = { "outdir": OptionInfo("", "Output dictectory; if empty, defaults to 'outputs/*'"), "samples_save": OptionInfo(True, "Save indiviual samples"), "samples_format": OptionInfo('png', 'File format for indiviual samples'), "grid_save": OptionInfo(True, "Save image grids"), "grid_format": OptionInfo('png', 'File format for grids'), "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"), "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}), "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}), "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"), "prompt_matrix_add_to_start": OptionInfo(True, "In prompt matrix, add the variable combination of text to the start of the prompt, rather than the end"), "sd_upscale_upscaler_index": OptionInfo("RealESRGAN", "Upscaler to use for SD upscale", gr.Radio, {"choices": list(sd_upscalers.keys())}), "sd_upscale_overlap": OptionInfo(64, "Overlap for tiles for SD upscale. The smaller it is, the less smooth transition from one tile to another", gr.Slider, {"minimum": 0, "maximum": 256, "step": 16}), } def __init__(self): self.data = {k: v.default 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] if item in self.data_labels: return self.data_labels[item].default 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 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 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 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 opts.enable_pnginfo and info is not None: pnginfo = PngImagePlugin.PngInfo() pnginfo.add_text("parameters", info) else: pnginfo = None image.save(os.path.join(path, filename), quality=opts.jpeg_quality, pnginfo=pnginfo) def sanitize_filename_part(text): return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128] 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(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(cmd_opts.gfpgan_dir)) from gfpgan import GFPGANer return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None) def image_grid(imgs, batch_size, force_n_rows=None): if force_n_rows is not None: rows = force_n_rows elif opts.n_rows > 0: rows = opts.n_rows elif opts.n_rows == 0: rows = batch_size else: rows = math.sqrt(len(imgs)) rows = 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 Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"]) def split_grid(image, tile_w=512, tile_h=512, overlap=64): w = image.width h = image.height now = tile_w - overlap # non-overlap width noh = tile_h - overlap cols = math.ceil((w - overlap) / now) rows = math.ceil((h - overlap) / noh) grid = Grid([], tile_w, tile_h, w, h, overlap) for row in range(rows): row_images = [] y = row * noh if y + tile_h >= h: y = h - tile_h for col in range(cols): x = col * now if x+tile_w >= w: x = w - tile_w tile = image.crop((x, y, x + tile_w, y + tile_h)) row_images.append([x, tile_w, tile]) grid.tiles.append([y, tile_h, row_images]) return grid def combine_grid(grid): def make_mask_image(r): r = r * 255 / grid.overlap r = r.astype(np.uint8) return Image.fromarray(r, 'L') mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0)) mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float).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: combined_row = Image.new("RGB", (grid.image_w, h)) for x, w, tile in row: if x == 0: combined_row.paste(tile, (0, 0)) continue combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w) combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0)) if y == 0: combined_image.paste(combined_row, (0, 0)) continue combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h) combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap)) return combined_image def draw_grid_annotations(im, width, height, hor_texts, ver_texts, hor_crossed_texts, ver_crossed_texts): def wrap(text, 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, draw_x, draw_y, texts, sizes, active): for i, (text, size) in enumerate(zip(texts, sizes)): if not active: text = '\u0336'.join(text) + '\u0336' d.multiline_text((draw_x, draw_y + size[1] / 2), text, font=fnt, fill=color_active if active else color_inactive, anchor="mm", align="center") draw_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(hor_texts) > 1 else 0 cols = im.width // width rows = im.height // height result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white") result.paste(im, (pad_left, pad_top)) d = ImageDraw.Draw(result) prompts_horiz = [wrap(x, fnt, width) for x in hor_texts] prompts_vert = [wrap(x, fnt, pad_left) for x in ver_texts] 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 draw_prompt_matrix(im, width, height, all_prompts): def wrap(text, 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, draw_x, draw_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((draw_x, draw_y + size[1] / 2), text, font=fnt, fill=color_active if active else color_inactive, anchor="mm", align="center") draw_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, fnt, width) for x in prompts[:boundary]] prompts_vert = [wrap(x, 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 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 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 StableDiffuionModelHijack: ids_lookup = {} word_embeddings = {} word_embeddings_checksums = {} fixes = None comments = None dir_mtime = None def load_textual_inversion_embeddings(self, dirname, model): mt = os.path.getmtime(dirname) 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(dirname): try: process_file(os.path.join(dirname, fn), fn) except Exception: 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, hijack): super().__init__() self.wrapped = wrapped self.hijack = hijack self.tokenizer = wrapped.tokenizer self.max_length = wrapped.max_length self.token_mults = {} tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k] for text, ident in tokens_with_parens: mult = 1.0 for c in text: if c == '[': mult /= 1.1 if c == ']': mult *= 1.1 if c == '(': mult *= 1.1 if c == ')': mult /= 1.1 if mult != 1.0: self.token_mults[ident] = mult def forward(self, text): self.hijack.fixes = [] self.hijack.comments = [] 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 used_custom_terms = [] cache = {} batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"] batch_multipliers = [] for tokens in batch_tokens: tuple_tokens = tuple(tokens) if tuple_tokens in cache: remade_tokens, fixes, multipliers = cache[tuple_tokens] else: fixes = [] remade_tokens = [] multipliers = [] mult = 1.0 i = 0 while i < len(tokens): token = tokens[i] possible_matches = self.hijack.ids_lookup.get(token, None) mult_change = self.token_mults.get(token) if mult_change is not None: mult *= mult_change elif possible_matches is None: remade_tokens.append(token) multipliers.append(mult) 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) multipliers.append(mult) i += len(ids) - 1 found = True used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word])) break if not found: remade_tokens.append(token) multipliers.append(mult) i += 1 if len(remade_tokens) > maxlen - 2: vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()} ovf = remade_tokens[maxlen - 2:] overflowing_words = [vocab.get(int(x), "") for x in ovf] overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words)) self.hijack.comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") 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, multipliers) multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers)) multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0] remade_batch_tokens.append(remade_tokens) self.hijack.fixes.append(fixes) batch_multipliers.append(multipliers) if len(used_custom_terms) > 0: self.hijack.comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) tokens = torch.asarray(remade_batch_tokens).to(self.wrapped.device) outputs = self.wrapped.transformer(input_ids=tokens) z = outputs.last_hidden_state # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise batch_multipliers = torch.asarray(np.array(batch_multipliers)).to(device) original_mean = z.mean() z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape) new_mean = z.mean() z *= original_mean / new_mean 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 = None inputs_embeds = self.wrapped(input_ids) if batch_fixes is not None: for fixes, tensor in zip(batch_fixes, inputs_embeds): for offset, word in fixes: tensor[offset] = self.embeddings.word_embeddings[word] return inputs_embeds class StableDiffusionProcessing: def __init__(self, outpath=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_grid=False, extra_generation_params=None): self.outpath: str = outpath self.prompt: str = prompt self.seed: int = seed self.sampler_index: int = sampler_index self.batch_size: int = batch_size self.n_iter: int = n_iter self.steps: int = steps self.cfg_scale: float = cfg_scale self.width: int = width self.height: int = height self.prompt_matrix: bool = prompt_matrix self.use_GFPGAN: bool = use_GFPGAN self.do_not_save_grid: bool = do_not_save_grid self.extra_generation_params: dict = extra_generation_params def init(self): pass def sample(self, x, conditioning, unconditional_conditioning): raise NotImplementedError() class VanillaStableDiffusionSampler: def __init__(self, constructor): self.sampler = constructor(sd_model) def sample(self, p: StableDiffusionProcessing, x, conditioning, unconditional_conditioning): samples_ddim, _ = self.sampler.sample(S=p.steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x) return samples_ddim 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, funcname): self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model) self.funcname = funcname self.func = getattr(k_diffusion.sampling, self.funcname) self.model_wrap_cfg = CFGDenoiser(self.model_wrap) def sample(self, p: StableDiffusionProcessing, x, conditioning, unconditional_conditioning): sigmas = self.model_wrap.get_sigmas(p.steps) x = x * sigmas[0] samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False) return samples_ddim def process_images(p: StableDiffusionProcessing): """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""" prompt = p.prompt model = sd_model assert p.prompt is not None torch_gc() seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed) os.makedirs(p.outpath, exist_ok=True) sample_path = os.path.join(p.outpath, "samples") os.makedirs(sample_path, exist_ok=True) base_count = len(os.listdir(sample_path)) grid_count = len(os.listdir(p.outpath)) - 1 comments = [] prompt_matrix_parts = [] if p.prompt_matrix: all_prompts = [] prompt_matrix_parts = prompt.split("|") combination_count = 2 ** (len(prompt_matrix_parts) - 1) for combination_num in range(combination_count): selected_prompts = [text.strip().strip(',') for n, text in enumerate(prompt_matrix_parts[1:]) if combination_num & (1 << n)] if opts.prompt_matrix_add_to_start: selected_prompts = selected_prompts + [prompt_matrix_parts[0]] else: selected_prompts = [prompt_matrix_parts[0]] + selected_prompts all_prompts.append(", ".join(selected_prompts)) p.n_iter = math.ceil(len(all_prompts) / p.batch_size) all_seeds = len(all_prompts) * [seed] print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.") else: all_prompts = p.batch_size * p.n_iter * [prompt] all_seeds = [seed + x for x in range(len(all_prompts))] generation_params = { "Steps": p.steps, "Sampler": samplers[p.sampler_index].name, "CFG scale": p.cfg_scale, "Seed": seed, "GFPGAN": ("GFPGAN" if p.use_GFPGAN and GFPGAN is not None else None) } if p.extra_generation_params is not None: generation_params.update(p.extra_generation_params) generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None]) def infotext(): return f"{prompt}\n{generation_params_text}".strip() + "".join(["\n\n" + x for x in comments]) if os.path.exists(cmd_opts.embeddings_dir): model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, model) output_images = [] with torch.no_grad(), autocast("cuda"), model.ema_scope(): p.init() for n in range(p.n_iter): prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size] seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size] uc = model.get_learned_conditioning(len(prompts) * [""]) c = model.get_learned_conditioning(prompts) if len(model_hijack.comments) > 0: comments += model_hijack.comments # we manually generate all input noises because each one should have a specific seed x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds) samples_ddim = p.sample(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 p.prompt_matrix or opts.samples_save or opts.grid_save: for i, x_sample in enumerate(x_samples_ddim): x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) if p.use_GFPGAN and GFPGAN is not None: torch_gc() 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], opts.samples_format, info=infotext()) output_images.append(image) base_count += 1 if (p.prompt_matrix or opts.grid_save) and not p.do_not_save_grid: if p.prompt_matrix: grid = image_grid(output_images, p.batch_size, force_n_rows=1 << ((len(prompt_matrix_parts)-1)//2)) try: grid = draw_prompt_matrix(grid, p.width, p.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) else: grid = image_grid(output_images, p.batch_size) save_image(grid, p.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, infotext() class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None def init(self): self.sampler = samplers[self.sampler_index].constructor() def sample(self, x, conditioning, unconditional_conditioning): samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning) return samples_ddim def txt2img(prompt: str, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int): outpath = opts.outdir or "outputs/txt2img-samples" p = StableDiffusionProcessingTxt2Img( outpath=outpath, prompt=prompt, seed=seed, sampler_index=sampler_index, 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 ) output_images, seed, info = process_images(p) 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=[x.name for x in samplers], value=samplers[0].name, type="index"), 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=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), ], outputs=[ gr.Gallery(label="Images"), gr.Number(label='Seed'), gr.HTML(), ], title="Stable Diffusion Text-to-Image", flagging_callback=Flagging() ) class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, **kwargs): super().__init__(**kwargs) self.init_images = init_images self.resize_mode: int = resize_mode self.denoising_strength: float = denoising_strength self.init_latent = None def init(self): self.sampler = samplers_for_img2img[self.sampler_index].constructor() imgs = [] for img in self.init_images: image = img.convert("RGB") image = resize_image(self.resize_mode, image, self.width, self.height) image = np.array(image).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) imgs.append(image) if len(imgs) == 1: batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0) elif len(imgs) <= self.batch_size: self.batch_size = len(imgs) batch_images = np.array(imgs) else: raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less") image = torch.from_numpy(batch_images) image = 2. * image - 1. image = image.to(device) self.init_latent = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image)) def sample(self, x, conditioning, unconditional_conditioning): t_enc = int(self.denoising_strength * self.steps) sigmas = self.sampler.model_wrap.get_sigmas(self.steps) noise = x * sigmas[self.steps - t_enc - 1] xi = self.init_latent + noise sigma_sched = sigmas[self.steps - t_enc - 1:] samples_ddim = self.sampler.func(self.sampler.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': self.cfg_scale}, disable=False) return samples_ddim def img2img(prompt: str, init_img, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, sd_upscale: 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" assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]' p = StableDiffusionProcessingImg2Img( outpath=outpath, prompt=prompt, seed=seed, sampler_index=sampler_index, 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, init_images=[init_img], resize_mode=resize_mode, denoising_strength=denoising_strength, extra_generation_params={"Denoising Strength": denoising_strength} ) if loopback: output_images, info = None, None history = [] initial_seed = None for i in range(n_iter): p.n_iter = 1 p.batch_size = 1 p.do_not_save_grid = True output_images, seed, info = process_images(p) if initial_seed is None: initial_seed = seed p.init_img = output_images[0] p.seed = seed + 1 p.denoising_strength = max(p.denoising_strength * 0.95, 0.1) history.append(output_images[0]) 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, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename) output_images = history seed = initial_seed elif sd_upscale: initial_seed = None initial_info = None upscaler = sd_upscalers[opts.sd_upscale_upscaler_index] img = upscaler(init_img) torch_gc() grid = split_grid(img, tile_w=width, tile_h=height, overlap=opts.sd_upscale_overlap) p.n_iter = 1 p.do_not_save_grid = True work = [] work_results = [] for y, h, row in grid.tiles: for tiledata in row: work.append(tiledata[2]) batch_count = math.ceil(len(work) / p.batch_size) print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} in a total of {batch_count} batches.") for i in range(batch_count): p.init_images = work[i*p.batch_size:(i+1)*p.batch_size] output_images, seed, info = process_images(p) if initial_seed is None: initial_seed = seed initial_info = info p.seed = seed + 1 work_results += output_images image_index = 0 for y, h, row in grid.tiles: for tiledata in row: tiledata[2] = work_results[image_index] image_index += 1 combined_image = combine_grid(grid) grid_count = len(os.listdir(outpath)) - 1 save_image(combined_image, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=initial_info, short_filename=not opts.grid_extended_filename) output_images = [combined_image] seed = initial_seed info = initial_info else: output_images, seed, info = process_images(p) 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.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index"), 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.Checkbox(label='Stable Diffusion upscale', value=False), 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") ], outputs=[ gr.Gallery(), gr.Number(label='Seed'), gr.HTML(), ], allow_flagging="never", ) def upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index): info = realesrgan_models[RealESRGAN_model_index] model = info.model() upsampler = RealESRGANer( scale=info.netscale, model_path=info.location, model=model, half=True ) upsampled = upsampler.enhance(np.array(image), outscale=RealESRGAN_upscaling)[0] image = Image.fromarray(upsampled) return image def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_index): torch_gc() image = image.convert("RGB") outpath = opts.outdir or "outputs/extras-samples" if GFPGAN is not None and GFPGAN_strength > 0: 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 GFPGAN_strength < 1.0: res = Image.blend(image, res, GFPGAN_strength) image = res if have_realesrgan and RealESRGAN_upscaling != 1.0: image = upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index) os.makedirs(outpath, exist_ok=True) base_count = len(os.listdir(outpath)) save_image(image, outpath, f"{base_count:05}", None, '', opts.samples_format, short_filename=True) return image, 0, '' extras_interface = gr.Interface( wrap_gradio_call(run_extras), inputs=[ gr.Image(label="Source", source="upload", interactive=True, type="pil"), gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN strength", value=1, interactive=GFPGAN is not None), gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Real-ESRGAN upscaling", value=2, interactive=have_realesrgan), gr.Radio(label='Real-ESRGAN model', choices=[x.name for x in realesrgan_models], value=realesrgan_models[0].name, type="index", interactive=have_realesrgan), ], outputs=[ gr.Image(label="Result"), gr.Number(label='Seed', visible=False), gr.HTML(), ], 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].default info = opts.data_labels[key] t = type(info.default) if info.component is not None: item = info.component(label=info.label, value=fun, **(info.component_args or {})) elif t == str: item = gr.Textbox(label=info.label, value=fun, lines=1) elif t == int: item = gr.Number(label=info.label, value=fun) elif t == bool: item = gr.Checkbox(label=info.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"), (extras_interface, "Extras"), (settings_interface, "Settings"), ] sd_config = OmegaConf.load(cmd_opts.config) sd_model = load_model_from_config(sd_config, cmd_opts.ckpt) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") sd_model = (sd_model if cmd_opts.no_half else sd_model.half()).to(device) model_hijack = StableDiffuionModelHijack() model_hijack.hijack(sd_model) demo = gr.TabbedInterface( interface_list=[x[0] for x in interfaces], tab_names=[x[1] for x in interfaces], 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()