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 from typing import Any, Dict, List, Optional import modules.sd_hijack from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste from modules.sd_hijack import model_hijack 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 def apply_overlay(overlay_exists, overlay, paste_loc, image): if overlay_exists: if paste_loc is not None: x, y, w, h = paste_loc 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') return image def get_correct_sampler(p): if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img): return sd_samplers.samplers elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img): return sd_samplers.samplers_for_img2img elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI): return sd_samplers.samplers class StableDiffusionProcessing(): """ The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing """ def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str="", styles: List[str]=None, seed: int=-1, subseed: int=-1, subseed_strength: float=0, seed_resize_from_h: int=-1, seed_resize_from_w: int=-1, seed_enable_extras: bool=True, sampler_index: int=0, batch_size: int=1, n_iter: int=1, steps:int =50, cfg_scale:float=7.0, width:int=512, height:int=512, restore_faces:bool=False, tiling:bool=False, do_not_save_samples:bool=False, do_not_save_grid:bool=False, extra_generation_params: Dict[Any,Any]=None, overlay_images: Any=None, negative_prompt: str=None, eta: float =None, do_not_reload_embeddings: bool=False, denoising_strength: float = 0, ddim_discretize: str = "uniform", s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0): 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.do_not_reload_embeddings = do_not_reload_embeddings 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 = s_churn or opts.s_churn self.s_tmin = s_tmin or opts.s_tmin self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option self.s_noise = s_noise or 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 self.scripts = None self.script_args = None self.all_prompts = None self.all_seeds = None self.all_subseeds = None 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 = sd_samplers.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.styles = p.styles self.job_timestamp = state.job_timestamp self.clip_skip = opts.CLIP_stop_at_last_layers 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]) if self.seed is not None else -1 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, "styles": self.styles, "job_timestamp": self.job_timestamp, "clip_skip": self.clip_skip, } 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 or opts.eta_noise_seed_delta > 0): 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) if opts.eta_noise_seed_delta > 0: torch.manual_seed(seed + opts.eta_noise_seed_delta) 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 decode_first_stage(model, x): with devices.autocast(disable=x.dtype == devices.dtype_vae): x = model.decode_first_stage(x) 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 clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers) generation_params = { "Steps": p.steps, "Sampler": get_correct_sampler(p)[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), "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name), "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), "Clip skip": None if clip_skip <= 1 else clip_skip, "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta, } generation_params.update(p.extra_generation_params) generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(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 with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file: processed = Processed(p, [], p.seed, "") file.write(processed.infotext(p, 0)) devices.torch_gc() seed = get_fixed_seed(p.seed) subseed = get_fixed_seed(p.subseed) modules.sd_hijack.model_hijack.apply_circular(p.tiling) modules.sd_hijack.model_hijack.clear_comments() comments = {} shared.prompt_styles.apply_styles(p) if type(p.prompt) == list: p.all_prompts = p.prompt else: p.all_prompts = p.batch_size * p.n_iter * [p.prompt] if type(seed) == list: p.all_seeds = seed else: p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))] if type(subseed) == list: p.all_subseeds = subseed else: p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))] def infotext(iteration=0, position_in_batch=0): return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch) if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings: model_hijack.embedding_db.load_textual_inversion_embeddings() if p.scripts is not None: p.scripts.run_alwayson_scripts(p) infotexts = [] output_images = [] with torch.no_grad(), p.sd_model.ema_scope(): with devices.autocast(): p.init(p.all_prompts, p.all_seeds, p.all_subseeds) if state.job_count == -1: state.job_count = p.n_iter for n in range(p.n_iter): if state.skipped: state.skipped = False if state.interrupted: break prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size] seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size] subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] if (len(prompts) == 0): break with devices.autocast(): uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) c = prompt_parser.get_multicond_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) samples_ddim = samples_ddim.to(devices.dtype_vae) x_samples_ddim = decode_first_stage(p.sd_model, 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): 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, suffix="-before-face-restoration") devices.torch_gc() x_sample = modules.face_restoration.restore_faces(x_sample) devices.torch_gc() 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: image_without_cc = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image) images.save_image(image_without_cc, 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) image = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image) 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) text = infotext(n, i) infotexts.append(text) if opts.enable_pnginfo: image.info["parameters"] = text output_images.append(image) del x_samples_ddim devices.torch_gc() state.nextjob() 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: text = infotext() infotexts.insert(0, text) if opts.enable_pnginfo: grid.info["parameters"] = text output_images.insert(0, grid) index_of_first_image = 1 if opts.grid_save: images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.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, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts) class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.denoising_strength = denoising_strength self.firstphase_width = firstphase_width self.firstphase_height = firstphase_height self.truncate_x = 0 self.truncate_y = 0 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 self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}" if self.firstphase_width == 0 or self.firstphase_height == 0: 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 firstphase_width_truncated = int(scale * self.width) firstphase_height_truncated = int(scale * self.height) else: width_ratio = self.width / self.firstphase_width height_ratio = self.height / self.firstphase_height if width_ratio > height_ratio: firstphase_width_truncated = self.firstphase_width firstphase_height_truncated = self.firstphase_width * self.height / self.width else: firstphase_width_truncated = self.firstphase_height * self.width / self.height firstphase_height_truncated = self.firstphase_height self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f def create_dummy_mask(self, x, width=None, height=None): if self.sampler.conditioning_key in {'hybrid', 'concat'}: height = height or self.height width = width or self.width # The "masked-image" in this case will just be all zeros since the entire image is masked. image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) # Add the fake full 1s mask to the first dimension. image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) image_conditioning = image_conditioning.to(x.dtype) else: # Dummy zero conditioning if we're not using inpainting model. # Still takes up a bit of memory, but no encoder call. # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) return image_conditioning def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, 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, image_conditioning=self.create_dummy_mask(x)) 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, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height)) samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] if opts.use_scale_latent_for_hires_fix: samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") else: decoded_samples = decode_first_stage(self.sd_model, samples) 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 = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, 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, image_conditioning=self.create_dummy_mask(samples)) return samples class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: Any=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=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 self.image_conditioning = None def init(self, all_prompts, all_seeds, all_subseeds): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, 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 if self.color_corrections is not None and len(self.color_corrections) == 1: self.color_corrections = self.color_corrections * 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 if self.sampler.conditioning_key in {'hybrid', 'concat'}: if self.image_mask is not None: conditioning_mask = np.array(self.image_mask.convert("L")) conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 conditioning_mask = torch.round(conditioning_mask) else: conditioning_mask = torch.ones(1, 1, *image.shape[-2:]) # Create another latent image, this time with a masked version of the original input. conditioning_mask = conditioning_mask.to(image.device) conditioning_image = image * (1.0 - conditioning_mask) conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) # Create the concatenated conditioning tensor to be fed to `c_concat` conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:]) conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) else: self.image_conditioning = torch.zeros( self.init_latent.shape[0], 5, 1, 1, dtype=self.init_latent.dtype, device=self.init_latent.device ) 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, image_conditioning=self.image_conditioning) if self.mask is not None: samples = samples * self.nmask + self.init_latent * self.mask del x devices.torch_gc() return samples