import json import math import os import sys import warnings 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, script_callbacks, extra_networks, sd_vae_approx, scripts from modules.sd_hijack import model_hijack from modules.shared import opts, cmd_opts, state import modules.shared as shared import modules.paths as paths import modules.face_restoration import modules.images as images import modules.styles import modules.sd_models as sd_models import modules.sd_vae as sd_vae import logging from ldm.data.util import AddMiDaS from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion from einops import repeat, rearrange from blendmodes.blend import blendLayers, BlendType # 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, original_image): logging.info("Applying color correction.") image = Image.fromarray(cv2.cvtColor(exposure.match_histograms( cv2.cvtColor( np.asarray(original_image), cv2.COLOR_RGB2LAB ), correction, channel_axis=2 ), cv2.COLOR_LAB2RGB).astype("uint8")) image = blendLayers(image, original_image, BlendType.LUMINOSITY) return image def apply_overlay(image, paste_loc, index, overlays): if overlays is None or index >= len(overlays): return image overlay = overlays[index] 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 txt2img_image_conditioning(sd_model, x, width, height): if sd_model.model.conditioning_key not in {'hybrid', 'concat'}: # 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. return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) # 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 = sd_model.get_first_stage_encoding(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) return image_conditioning 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_name: str = None, 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 = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None): if sampler_index is not None: print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr) 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_name: str = sampler_name 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 = denoising_strength self.sampler_noise_scheduler_override = None self.ddim_discretize = ddim_discretize or 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 self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts} self.override_settings_restore_afterwards = override_settings_restore_afterwards self.is_using_inpainting_conditioning = False self.disable_extra_networks = False 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 = script_args self.all_prompts = None self.all_negative_prompts = None self.all_seeds = None self.all_subseeds = None self.iteration = 0 @property def sd_model(self): return shared.sd_model def txt2img_image_conditioning(self, x, width=None, height=None): self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'} return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height) def depth2img_image_conditioning(self, source_image): # Use the AddMiDaS helper to Format our source image to suit the MiDaS model transformer = AddMiDaS(model_type="dpt_hybrid") transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")}) midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device) midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size) conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image)) conditioning = torch.nn.functional.interpolate( self.sd_model.depth_model(midas_in), size=conditioning_image.shape[2:], mode="bicubic", align_corners=False, ) (depth_min, depth_max) = torch.aminmax(conditioning) conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1. return conditioning def edit_image_conditioning(self, source_image): conditioning_image = self.sd_model.encode_first_stage(source_image).mode() return conditioning_image def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None): self.is_using_inpainting_conditioning = True # Handle the different mask inputs if image_mask is not None: if torch.is_tensor(image_mask): conditioning_mask = image_mask else: conditioning_mask = np.array(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 = source_image.new_ones(1, 1, *source_image.shape[-2:]) # Create another latent image, this time with a masked version of the original input. # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter. conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype) conditioning_image = torch.lerp( source_image, source_image * (1.0 - conditioning_mask), getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) ) # Encode the new masked image using first stage of network. 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=latent_image.shape[-2:]) conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype) return image_conditioning def img2img_image_conditioning(self, source_image, latent_image, image_mask=None): source_image = devices.cond_cast_float(source_image) # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely # identify itself with a field common to all models. The conditioning_key is also hybrid. if isinstance(self.sd_model, LatentDepth2ImageDiffusion): return self.depth2img_image_conditioning(source_image) if self.sd_model.cond_stage_key == "edit": return self.edit_image_conditioning(source_image) if self.sampler.conditioning_key in {'hybrid', 'concat'}: return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask) # Dummy zero conditioning if we're not using inpainting or depth model. return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1) def init(self, all_prompts, all_seeds, all_subseeds): pass def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): raise NotImplementedError() def close(self): self.sampler = None class Processed: def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""): 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.comments = comments self.width = p.width self.height = p.height self.sampler_name = p.sampler_name self.cfg_scale = p.cfg_scale self.image_cfg_scale = getattr(p, 'image_cfg_scale', None) 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.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning self.all_prompts = all_prompts or p.all_prompts or [self.prompt] self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt] self.all_seeds = all_seeds or p.all_seeds or [self.seed] self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed] self.infotexts = infotexts or [info] def js(self): obj = { "prompt": self.all_prompts[0], "all_prompts": self.all_prompts, "negative_prompt": self.all_negative_prompts[0], "all_negative_prompts": self.all_negative_prompts, "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_name": self.sampler_name, "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, "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning, } 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): eta_noise_seed_delta = opts.eta_noise_seed_delta or 0 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 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 eta_noise_seed_delta > 0: torch.manual_seed(seed + 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=None, 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": p.sampler_name, "CFG scale": p.cfg_scale, "Image CFG scale": getattr(p, 'image_cfg_scale', None), "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(':', '')), "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), "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None, "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.all_negative_prompts[index] if p.all_negative_prompts[index] else "" return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip() def process_images(p: StableDiffusionProcessing) -> Processed: stored_opts = {k: opts.data[k] for k in p.override_settings.keys()} try: for k, v in p.override_settings.items(): setattr(opts, k, v) if k == 'sd_model_checkpoint': sd_models.reload_model_weights() if k == 'sd_vae': sd_vae.reload_vae_weights() res = process_images_inner(p) finally: # restore opts to original state if p.override_settings_restore_afterwards: for k, v in stored_opts.items(): setattr(opts, k, v) if k == 'sd_model_checkpoint': sd_models.reload_model_weights() if k == 'sd_vae': sd_vae.reload_vae_weights() return res def process_images_inner(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) modules.sd_hijack.model_hijack.apply_circular(p.tiling) modules.sd_hijack.model_hijack.clear_comments() comments = {} if type(p.prompt) == list: p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt] else: p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)] if type(p.negative_prompt) == list: p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt] else: p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)] 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.process(p) infotexts = [] output_images = [] cached_uc = [None, None] cached_c = [None, None] def get_conds_with_caching(function, required_prompts, steps, cache): """ Returns the result of calling function(shared.sd_model, required_prompts, steps) using a cache to store the result if the same arguments have been used before. cache is an array containing two elements. The first element is a tuple representing the previously used arguments, or None if no arguments have been used before. The second element is where the previously computed result is stored. """ if cache[0] is not None and (required_prompts, steps) == cache[0]: return cache[1] with devices.autocast(): cache[1] = function(shared.sd_model, required_prompts, steps) cache[0] = (required_prompts, steps) return cache[1] with torch.no_grad(), p.sd_model.ema_scope(): with devices.autocast(): p.init(p.all_prompts, p.all_seeds, p.all_subseeds) # for OSX, loading the model during sampling changes the generated picture, so it is loaded here if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN": sd_vae_approx.model() if state.job_count == -1: state.job_count = p.n_iter for n in range(p.n_iter): p.iteration = n if state.skipped: state.skipped = False if state.interrupted: break prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size] negative_prompts = p.all_negative_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 prompts, extra_network_data = extra_networks.parse_prompts(prompts) if not p.disable_extra_networks: with devices.autocast(): extra_networks.activate(p, extra_network_data) if p.scripts is not None: p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds) # params.txt should be saved after scripts.process_batch, since the # infotext could be modified by that callback # Example: a wildcard processed by process_batch sets an extra model # strength, which is saved as "Model Strength: 1.0" in the infotext if n == 0: with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file: processed = Processed(p, [], p.seed, "") file.write(processed.infotext(p, 0)) uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc) c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c) 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.without_autocast() if devices.unet_needs_upcast else devices.autocast(): samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts) x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))] for x in x_samples_ddim: devices.test_for_nans(x, "vae") x_samples_ddim = torch.stack(x_samples_ddim).float() 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 p.scripts is not None: p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n) 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.scripts is not None: pp = scripts.PostprocessImageArgs(image) p.scripts.postprocess_image(p, pp) image = pp.image 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(image, p.paste_to, i, p.overlay_images) 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(image, p.paste_to, i, p.overlay_images) 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) if not p.disable_extra_networks: extra_networks.deactivate(p, extra_network_data) devices.torch_gc() res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts) if p.scripts is not None: p.scripts.postprocess(p, res) return res def old_hires_fix_first_pass_dimensions(width, height): """old algorithm for auto-calculating first pass size""" desired_pixel_count = 512 * 512 actual_pixel_count = width * height scale = math.sqrt(desired_pixel_count / actual_pixel_count) width = math.ceil(scale * width / 64) * 64 height = math.ceil(scale * height / 64) * 64 return width, height 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, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.denoising_strength = denoising_strength self.hr_scale = hr_scale self.hr_upscaler = hr_upscaler self.hr_second_pass_steps = hr_second_pass_steps self.hr_resize_x = hr_resize_x self.hr_resize_y = hr_resize_y self.hr_upscale_to_x = hr_resize_x self.hr_upscale_to_y = hr_resize_y if firstphase_width != 0 or firstphase_height != 0: self.hr_upscale_to_x = self.width self.hr_upscale_to_y = self.height self.width = firstphase_width self.height = firstphase_height self.truncate_x = 0 self.truncate_y = 0 self.applied_old_hires_behavior_to = None def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height): self.hr_resize_x = self.width self.hr_resize_y = self.height self.hr_upscale_to_x = self.width self.hr_upscale_to_y = self.height self.width, self.height = old_hires_fix_first_pass_dimensions(self.width, self.height) self.applied_old_hires_behavior_to = (self.width, self.height) if self.hr_resize_x == 0 and self.hr_resize_y == 0: self.extra_generation_params["Hires upscale"] = self.hr_scale self.hr_upscale_to_x = int(self.width * self.hr_scale) self.hr_upscale_to_y = int(self.height * self.hr_scale) else: self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}" if self.hr_resize_y == 0: self.hr_upscale_to_x = self.hr_resize_x self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width elif self.hr_resize_x == 0: self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height self.hr_upscale_to_y = self.hr_resize_y else: target_w = self.hr_resize_x target_h = self.hr_resize_y src_ratio = self.width / self.height dst_ratio = self.hr_resize_x / self.hr_resize_y if src_ratio < dst_ratio: self.hr_upscale_to_x = self.hr_resize_x self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width else: self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height self.hr_upscale_to_y = self.hr_resize_y self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f # special case: the user has chosen to do nothing if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height: self.enable_hr = False self.denoising_strength = None self.extra_generation_params.pop("Hires upscale", None) self.extra_generation_params.pop("Hires resize", None) return if not state.processing_has_refined_job_count: if state.job_count == -1: state.job_count = self.n_iter shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count) state.job_count = state.job_count * 2 state.processing_has_refined_job_count = True if self.hr_second_pass_steps: self.extra_generation_params["Hires steps"] = self.hr_second_pass_steps if self.hr_upscaler is not None: self.extra_generation_params["Hires upscaler"] = self.hr_upscaler def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest") if self.enable_hr and latent_scale_mode is None: assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}" 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.txt2img_image_conditioning(x)) if not self.enable_hr: return samples target_width = self.hr_upscale_to_x target_height = self.hr_upscale_to_y def save_intermediate(image, index): """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images""" if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix: return if not isinstance(image, Image.Image): image = sd_samplers.sample_to_image(image, index, approximation=0) info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index) images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix") if latent_scale_mode is not None: for i in range(samples.shape[0]): save_intermediate(samples, i) samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"]) # Avoid making the inpainting conditioning unless necessary as # this does need some extra compute to decode / encode the image again. if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0: image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples) else: image_conditioning = self.txt2img_image_conditioning(samples) 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) save_intermediate(image, i) image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler) 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)) image_conditioning = self.img2img_image_conditioning(decoded_samples, samples) shared.state.nextjob() img2img_sampler_name = self.sampler_name if self.sampler_name != 'PLMS' else 'DDIM' # PLMS does not support img2img so we just silently switch ot DDIM self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model) samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2] noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, 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.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning) return samples class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, 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, initial_noise_multiplier: float = None, **kwargs): super().__init__(**kwargs) self.init_images = init_images self.resize_mode: int = resize_mode self.denoising_strength: float = denoising_strength self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None self.init_latent = None self.image_mask = mask 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.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier 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(self.sampler_name, self.sd_model) crop_region = None image_mask = self.image_mask if image_mask is not None: image_mask = image_mask.convert('L') if self.inpainting_mask_invert: image_mask = ImageOps.invert(image_mask) if self.mask_blur > 0: image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)) if self.inpaint_full_res: self.mask_for_overlay = image_mask mask = 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) image_mask = images.resize_image(2, mask, self.width, self.height) self.paste_to = (x1, y1, x2-x1, y2-y1) else: image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height) np_mask = np.array(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 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 = images.flatten(img, opts.img2img_background_color) if crop_region is None and self.resize_mode != 3: image = images.resize_image(self.resize_mode, image, self.width, self.height) if 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')) # crop_region is not None if we are doing inpaint full res if crop_region is not None: image = image.crop(crop_region) image = images.resize_image(2, image, self.width, self.height) if 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.resize_mode == 3: self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") if 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 self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask) def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): 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) if self.initial_noise_multiplier != 1.0: self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier x *= self.initial_noise_multiplier 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