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, lowvram 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 import logging # 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): logging.info("Calibrating color correction.") correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB) return correction_target def apply_color_correction(correction, image): logging.info("Applying color correction.") 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, seed_enable_extras=True, 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, eta=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: list = styles or [] 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 or {} self.overlay_images = overlay_images self.eta = eta self.paste_to = None self.color_corrections = None self.denoising_strength: float = 0 self.sampler_noise_scheduler_override = None self.ddim_discretize = opts.ddim_discretize self.s_churn = opts.s_churn self.s_tmin = opts.s_tmin self.s_tmax = float('inf') # not representable as a standard ui option self.s_noise = opts.s_noise if not seed_enable_extras: self.subseed = -1 self.subseed_strength = 0 self.seed_resize_from_h = 0 self.seed_resize_from_w = 0 def init(self, all_prompts, all_seeds, all_subseeds): pass def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): raise NotImplementedError() class Processed: def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None): self.images = images_list self.prompt = p.prompt self.negative_prompt = p.negative_prompt self.seed = seed self.subseed = subseed self.subseed_strength = p.subseed_strength self.info = info self.width = p.width self.height = p.height self.sampler_index = p.sampler_index self.sampler = samplers[p.sampler_index].name self.cfg_scale = p.cfg_scale self.steps = p.steps self.batch_size = p.batch_size self.restore_faces = p.restore_faces self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None self.sd_model_hash = shared.sd_model.sd_model_hash self.seed_resize_from_w = p.seed_resize_from_w self.seed_resize_from_h = p.seed_resize_from_h self.denoising_strength = getattr(p, 'denoising_strength', None) self.extra_generation_params = p.extra_generation_params self.index_of_first_image = index_of_first_image self.eta = p.eta self.ddim_discretize = p.ddim_discretize self.s_churn = p.s_churn self.s_tmin = p.s_tmin self.s_tmax = p.s_tmax self.s_noise = p.s_noise self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0] self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0] self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1 self.all_prompts = all_prompts or [self.prompt] self.all_seeds = all_seeds or [self.seed] self.all_subseeds = all_subseeds or [self.subseed] self.infotexts = infotexts or [info] def js(self): obj = { "prompt": self.prompt, "all_prompts": self.all_prompts, "negative_prompt": self.negative_prompt, "seed": self.seed, "all_seeds": self.all_seeds, "subseed": self.subseed, "all_subseeds": self.all_subseeds, "subseed_strength": self.subseed_strength, "width": self.width, "height": self.height, "sampler_index": self.sampler_index, "sampler": self.sampler, "cfg_scale": self.cfg_scale, "steps": self.steps, "batch_size": self.batch_size, "restore_faces": self.restore_faces, "face_restoration_model": self.face_restoration_model, "sd_model_hash": self.sd_model_hash, "seed_resize_from_w": self.seed_resize_from_w, "seed_resize_from_h": self.seed_resize_from_h, "denoising_strength": self.denoising_strength, "extra_generation_params": self.extra_generation_params, "index_of_first_image": self.index_of_first_image, "infotexts": self.infotexts, } return json.dumps(obj) def infotext(self, p: StableDiffusionProcessing, index): return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size) # 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) dot = (low_norm*high_norm).sum(1) if dot.mean() > 0.9995: return low * val + high * (1 - val) omega = torch.acos(dot) 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 = slerp(subseed_strength, noise, subnoise) if noise_shape != shape: 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 get_fixed_seed(seed): if seed is None or seed == '' or seed == -1: return int(random.randrange(4294967294)) return seed def fix_seed(p): p.seed = get_fixed_seed(p.seed) p.subseed = get_fixed_seed(p.subseed) def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, 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": getattr(p, 'sd_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), "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta), } 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() 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() seed = get_fixed_seed(p.seed) subseed = get_fixed_seed(p.subseed) if p.outpath_samples is not None: os.makedirs(p.outpath_samples, exist_ok=True) if p.outpath_grids is not None: 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(seed) == list: all_seeds = seed else: all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))] if type(subseed) == list: all_subseeds = subseed else: all_subseeds = [int(subseed) + x for x in range(len(all_prompts))] def infotext(iteration=0, position_in_batch=0): return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch) if os.path.exists(cmd_opts.embeddings_dir): model_hijack.embedding_db.load_textual_inversion_embeddings() infotexts = [] output_images = [] with torch.no_grad(): with devices.autocast(): p.init(all_prompts, all_seeds, all_subseeds) if state.job_count == -1: state.job_count = p.n_iter for n in range(p.n_iter): with torch.no_grad(), precision_scope("cuda"), ema_scope(): 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) with devices.autocast(): uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) c = prompt_parser.get_learned_conditioning(shared.sd_model, prompts, p.steps) if len(model_hijack.comments) > 0: for comment in model_hijack.comments: comments[comment] = 1 if p.n_iter > 1: shared.state.job = f"Batch {n+1} out of {p.n_iter}" with devices.autocast(): samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) 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 samples_ddim = samples_ddim.to(devices.dtype) 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) del samples_ddim if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.send_everything_to_cpu() devices.torch_gc() 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): with torch.no_grad(), precision_scope("cuda"), ema_scope(): x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) if p.restore_faces: with torch.no_grad(), precision_scope("cuda"), ema_scope(): 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, suffix="-before-face-restoration") x_sample = modules.face_restoration.restore_faces(x_sample) devices.torch_gc() devices.torch_gc() with torch.no_grad(), precision_scope("cuda"), ema_scope(): image = Image.fromarray(x_sample) if p.color_corrections is not None and i < len(p.color_corrections): if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction: images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction") 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) infotexts.append(infotext(n, i)) output_images.append(image) del x_samples_ddim devices.torch_gc() state.nextjob() with torch.no_grad(), precision_scope("cuda"), ema_scope(): p.color_corrections = None index_of_first_image = 0 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: infotexts.insert(0, infotext()) output_images.insert(0, grid) index_of_first_image = 1 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, grid=True) devices.torch_gc() return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts) class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None firstphase_width = 0 firstphase_height = 0 firstphase_width_truncated = 0 firstphase_height_truncated = 0 def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.scale_latent = scale_latent self.denoising_strength = denoising_strength def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: if state.job_count == -1: state.job_count = self.n_iter * 2 else: state.job_count = state.job_count * 2 desired_pixel_count = 512 * 512 actual_pixel_count = self.width * self.height scale = math.sqrt(desired_pixel_count / actual_pixel_count) self.firstphase_width = math.ceil(scale * self.width / 64) * 64 self.firstphase_height = math.ceil(scale * self.height / 64) * 64 self.firstphase_width_truncated = int(scale * self.width) self.firstphase_height_truncated = int(scale * self.height) def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): self.sampler = samplers[self.sampler_index].constructor(self.sd_model) if not self.enable_hr: x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) return samples x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] if self.scale_latent: samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") else: decoded_samples = self.sd_model.decode_first_stage(samples) if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None": decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear") else: lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) batch_images = [] for i, x_sample in enumerate(lowres_samples): x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) image = Image.fromarray(x_sample) image = images.resize_image(0, image, self.width, self.height) image = np.array(image).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) batch_images.append(image) decoded_samples = torch.from_numpy(np.array(batch_images)) decoded_samples = decoded_samples.to(shared.device) decoded_samples = 2. * decoded_samples - 1. samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) shared.state.nextjob() self.sampler = samplers[self.sampler_index].constructor(self.sd_model) noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) # GC now before running the next img2img to prevent running out of memory x = None devices.torch_gc() samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) return samples 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, inpaint_full_res_padding=0, 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.inpaint_full_res_padding = inpaint_full_res_padding self.inpainting_mask_invert = inpainting_mask_invert self.mask = None self.nmask = None def init(self, all_prompts, all_seeds, all_subseeds): 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), self.inpaint_full_res_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) # this needs to be fixed to be done in sample() using actual seeds for batches if self.inpainting_fill == 2: self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask elif self.inpainting_fill == 3: self.init_latent = self.init_latent * self.mask def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) 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 del x devices.torch_gc() return samples