import contextlib import json import math import os import sys import torch import numpy as np from PIL import Image, ImageFilter, ImageOps import random import cv2 from skimage import exposure import modules.sd_hijack from modules import devices, prompt_parser, masking from modules.sd_hijack import model_hijack from modules.sd_samplers import samplers, samplers_for_img2img from modules.shared import opts, cmd_opts, state import modules.shared as shared import modules.face_restoration import modules.images as images import modules.styles # 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 def setup_color_correction(image): correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB) return correction_target def apply_color_correction(correction, image): image = Image.fromarray(cv2.cvtColor(exposure.match_histograms( cv2.cvtColor( np.asarray(image), cv2.COLOR_RGB2LAB ), correction, channel_axis=2 ), cv2.COLOR_LAB2RGB).astype("uint8")) return image class StableDiffusionProcessing: def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None): self.sd_model = sd_model self.outpath_samples: str = outpath_samples self.outpath_grids: str = outpath_grids self.prompt: str = prompt self.prompt_for_display: str = None self.negative_prompt: str = (negative_prompt or "") self.styles: str = styles self.seed: int = seed self.subseed: int = subseed self.subseed_strength: float = subseed_strength self.seed_resize_from_h: int = seed_resize_from_h self.seed_resize_from_w: int = seed_resize_from_w 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.restore_faces: bool = restore_faces self.tiling: bool = tiling self.do_not_save_samples: bool = do_not_save_samples self.do_not_save_grid: bool = do_not_save_grid self.extra_generation_params: dict = extra_generation_params self.overlay_images = overlay_images self.paste_to = None self.color_corrections = None def init(self, seed): pass def sample(self, x, conditioning, unconditional_conditioning): raise NotImplementedError() class Processed: def __init__(self, p: StableDiffusionProcessing, images_list, seed, info): self.images = images_list self.prompt = p.prompt self.negative_prompt = p.negative_prompt self.seed = seed self.info = info self.width = p.width self.height = p.height self.sampler = samplers[p.sampler_index].name self.cfg_scale = p.cfg_scale self.steps = p.steps def js(self): obj = { "prompt": self.prompt if type(self.prompt) != list else self.prompt[0], "negative_prompt": self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0], "seed": int(self.seed if type(self.seed) != list else self.seed[0]), "width": self.width, "height": self.height, "sampler": self.sampler, "cfg_scale": self.cfg_scale, "steps": self.steps, } return json.dumps(obj) # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3 def slerp(val, low, high): low_norm = low/torch.norm(low, dim=1, keepdim=True) high_norm = high/torch.norm(high, dim=1, keepdim=True) omega = torch.acos((low_norm*high_norm).sum(1)) so = torch.sin(omega) res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high return res def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None): xs = [] # if we have multiple seeds, this means we are working with batch size>1; this then # enables the generation of additional tensors with noise that the sampler will use during its processing. # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to # produce the same images as with two batches [100], [101]. if p is not None and p.sampler is not None and len(seeds) > 1 and opts.enable_batch_seeds: sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))] else: sampler_noises = None for i, seed in enumerate(seeds): noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8) subnoise = None if subseeds is not None: subseed = 0 if i >= len(subseeds) else subseeds[i] subnoise = devices.randn(subseed, noise_shape) # 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. noise = devices.randn(seed, noise_shape) if subnoise is not None: #noise = subnoise * subseed_strength + noise * (1 - subseed_strength) noise = slerp(subseed_strength, noise, subnoise) if noise_shape != shape: #noise = torch.nn.functional.interpolate(noise.unsqueeze(1), size=shape[1:], mode="bilinear").squeeze() x = devices.randn(seed, shape) dx = (shape[2] - noise_shape[2]) // 2 dy = (shape[1] - noise_shape[1]) // 2 w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy tx = 0 if dx < 0 else dx ty = 0 if dy < 0 else dy dx = max(-dx, 0) dy = max(-dy, 0) x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w] noise = x if sampler_noises is not None: cnt = p.sampler.number_of_needed_noises(p) for j in range(cnt): sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape))) xs.append(noise) if sampler_noises is not None: p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises] x = torch.stack(xs).to(shared.device) return x def fix_seed(p): p.seed = int(random.randrange(4294967294)) if p.seed is None or p.seed == -1 else p.seed p.subseed = int(random.randrange(4294967294)) if p.subseed is None or p.subseed == -1 else p.subseed def process_images(p: StableDiffusionProcessing) -> Processed: """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""" if type(p.prompt) == list: assert(len(p.prompt) > 0) else: assert p.prompt is not None devices.torch_gc() fix_seed(p) os.makedirs(p.outpath_samples, exist_ok=True) os.makedirs(p.outpath_grids, exist_ok=True) modules.sd_hijack.model_hijack.apply_circular(p.tiling) comments = {} shared.prompt_styles.apply_styles(p) if type(p.prompt) == list: all_prompts = p.prompt else: all_prompts = p.batch_size * p.n_iter * [p.prompt] if type(p.seed) == list: all_seeds = p.seed else: all_seeds = [int(p.seed + (x if p.subseed_strength == 0 else 0)) for x in range(len(all_prompts))] if type(p.subseed) == list: all_subseeds = p.subseed else: all_subseeds = [int(p.subseed + x) for x in range(len(all_prompts))] def infotext(iteration=0, position_in_batch=0): index = position_in_batch + iteration * p.batch_size generation_params = { "Steps": p.steps, "Sampler": samplers[p.sampler_index].name, "CFG scale": p.cfg_scale, "Seed": all_seeds[index], "Face restoration": (opts.face_restoration_model if p.restore_faces else None), "Size": f"{p.width}x{p.height}", "Model hash": (None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength), "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Denoising strength": getattr(p, 'denoising_strength', 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]) negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else "" return f"{all_prompts[index]}{negative_prompt_text}\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, p.sd_model) output_images = [] precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope) with torch.no_grad(), precision_scope("cuda"), ema_scope(): p.init(seed=all_seeds[0]) if state.job_count == -1: state.job_count = p.n_iter for n in range(p.n_iter): if state.interrupted: break prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size] seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size] subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] if (len(prompts) == 0): break #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) #c = p.sd_model.get_learned_conditioning(prompts) uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps) c = prompt_parser.get_learned_conditioning(prompts, p.steps) if len(model_hijack.comments) > 0: for comment in model_hijack.comments: comments[comment] = 1 # 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, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p) if p.n_iter > 1: shared.state.job = f"Batch {n+1} out of {p.n_iter}" samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc) if state.interrupted: # if we are interruped, sample returns just noise # use the image collected previously in sampler loop samples_ddim = shared.state.current_latent x_samples_ddim = p.sd_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 opts.filter_nsfw: import modules.safety as safety x_samples_ddim = modules.safety.censor_batch(x_samples_ddim) 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.restore_faces: if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration: images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p) devices.torch_gc() x_sample = modules.face_restoration.restore_faces(x_sample) image = Image.fromarray(x_sample) if p.color_corrections is not None and i < len(p.color_corrections): image = apply_color_correction(p.color_corrections[i], image) if p.overlay_images is not None and i < len(p.overlay_images): overlay = p.overlay_images[i] if p.paste_to is not None: x, y, w, h = p.paste_to base_image = Image.new('RGBA', (overlay.width, overlay.height)) image = images.resize_image(1, image, w, h) base_image.paste(image, (x, y)) image = base_image image = image.convert('RGBA') image.alpha_composite(overlay) image = image.convert('RGB') if opts.samples_save and not p.do_not_save_samples: images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p) output_images.append(image) state.nextjob() p.color_corrections = None unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count: grid = images.image_grid(output_images, p.batch_size) if opts.return_grid: output_images.insert(0, grid) if opts.grid_save: images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p) devices.torch_gc() return Processed(p, output_images, all_seeds[0], infotext()) class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None def init(self, seed): self.sampler = samplers[self.sampler_index].constructor(self.sd_model) def sample(self, x, conditioning, unconditional_conditioning): samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning) return samples_ddim class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpainting_mask_invert=0, **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 self.image_mask = mask #self.image_unblurred_mask = None self.latent_mask = None self.mask_for_overlay = None self.mask_blur = mask_blur self.inpainting_fill = inpainting_fill self.inpaint_full_res = inpaint_full_res self.inpainting_mask_invert = inpainting_mask_invert self.mask = None self.nmask = None def init(self, seed): self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model) crop_region = None if self.image_mask is not None: self.image_mask = self.image_mask.convert('L') if self.inpainting_mask_invert: self.image_mask = ImageOps.invert(self.image_mask) #self.image_unblurred_mask = self.image_mask if self.mask_blur > 0: self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)) if self.inpaint_full_res: self.mask_for_overlay = self.image_mask mask = self.image_mask.convert('L') crop_region = masking.get_crop_region(np.array(mask), opts.upscale_at_full_resolution_padding) crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height) x1, y1, x2, y2 = crop_region mask = mask.crop(crop_region) self.image_mask = images.resize_image(2, mask, self.width, self.height) self.paste_to = (x1, y1, x2-x1, y2-y1) else: self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height) np_mask = np.array(self.image_mask) np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8) self.mask_for_overlay = Image.fromarray(np_mask) self.overlay_images = [] latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask add_color_corrections = opts.img2img_color_correction and self.color_corrections is None if add_color_corrections: self.color_corrections = [] imgs = [] for img in self.init_images: image = img.convert("RGB") if crop_region is None: image = images.resize_image(self.resize_mode, image, self.width, self.height) if self.image_mask is not None: image_masked = Image.new('RGBa', (image.width, image.height)) image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L'))) self.overlay_images.append(image_masked.convert('RGBA')) if crop_region is not None: image = image.crop(crop_region) image = images.resize_image(2, image, self.width, self.height) if self.image_mask is not None: if self.inpainting_fill != 1: image = masking.fill(image, latent_mask) if add_color_corrections: self.color_corrections.append(setup_color_correction(image)) 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) if self.overlay_images is not None: self.overlay_images = self.overlay_images * self.batch_size 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(shared.device) self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image)) if self.image_mask is not None: init_mask = latent_mask latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2])) latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255 latmask = latmask[0] latmask = np.around(latmask) latmask = np.tile(latmask[None], (4, 1, 1)) self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype) self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype) if self.inpainting_fill == 2: self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], [seed + x + 1 for x in range(self.init_latent.shape[0])]) * self.nmask elif self.inpainting_fill == 3: self.init_latent = self.init_latent * self.mask def sample(self, x, conditioning, unconditional_conditioning): samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning) if self.mask is not None: samples = samples * self.nmask + self.init_latent * self.mask return samples