import os import json import math import time import hashlib import random from contextlib import nullcontext from typing import Any, Dict, List import torch import numpy as np import cv2 from PIL import Image, ImageFilter, ImageOps from skimage import exposure from ldm.data.util import AddMiDaS from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion from einops import repeat, rearrange from blendmodes.blend import blendLayers, BlendType from installer import git_commit from modules import shared, devices import modules.memstats import modules.lowvram import modules.masking import modules.paths import modules.scripts import modules.prompt_parser import modules.extra_networks import modules.face_restoration import modules.images as images import modules.styles import modules.sd_hijack import modules.sd_samplers import modules.sd_samplers_common import modules.sd_models import modules.sd_vae import modules.sd_vae_approx import modules.generation_parameters_copypaste opt_C = 4 opt_f = 8 def setup_color_correction(image): shared.log.debug("Calibrating color correction.") correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB) return correction_target def apply_color_correction(correction, original_image): shared.log.debug("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(2, 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 in {'hybrid', 'concat'}: # Inpainting models # 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 elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device) else: # Dummy zero conditioning if we're not using inpainting or unclip models. # 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) 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, latent_sampler: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, image_cfg_scale: float = None, clip_skip: int = 1, width: int = 512, height: int = 512, full_quality: bool = True, 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, diffusers_guidance_rescale: float = 0.7, ddim_discretize: str = None, s_min_uncond: float = 0.0, 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): # pylint: disable=unused-argument 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.latent_sampler: str = latent_sampler self.batch_size: int = batch_size self.n_iter: int = n_iter self.steps: int = steps self.hr_second_pass_steps = 0 self.cfg_scale: float = cfg_scale self.image_cfg_scale = image_cfg_scale self.diffusers_guidance_rescale = diffusers_guidance_rescale self.width: int = width self.height: int = height self.full_quality: bool = full_quality 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 shared.opts.ddim_discretize self.s_min_uncond = s_min_uncond or shared.opts.s_min_uncond self.s_churn = s_churn or shared.opts.s_churn self.s_tmin = s_tmin or shared.opts.s_tmin self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option self.s_noise = s_noise or shared.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 self.token_merging_ratio = 0 self.token_merging_ratio_hr = 0 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 or [] self.per_script_args = {} self.all_prompts = None self.all_negative_prompts = None self.all_seeds = None self.all_subseeds = None self.clip_skip = clip_skip self.iteration = 0 self.is_hr_pass = False self.hr_force = False self.enable_hr = None self.refiner_steps = 5 self.refiner_start = 0 self.ops = [] shared.opts.data['clip_skip'] = clip_skip @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 unclip_image_conditioning(self, source_image): c_adm = self.sd_model.embedder(source_image) if self.sd_model.noise_augmentor is not None: noise_level = 0 c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0])) c_adm = torch.cat((c_adm, noise_level_emb), 1) return c_adm 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(device=shared.device, dtype=source_image.dtype) return image_conditioning def diffusers_image_conditioning(self, _source_image, latent_image, _image_mask=None): # shared.log.warning('Diffusers not implemented: img2img_image_conditioning') return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1) 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 shared.backend == shared.Backend.DIFFUSERS: return self.diffusers_image_conditioning(source_image, latent_image, image_mask) if isinstance(self.sd_model, LatentDepth2ImageDiffusion): return self.depth2img_image_conditioning(source_image) if hasattr(self.sd_model, 'cond_stage_key') and self.sd_model.cond_stage_key == "edit": return self.edit_image_conditioning(source_image) if hasattr(self.sampler, 'conditioning_key') and self.sampler.conditioning_key in {'hybrid', 'concat'}: return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask) if hasattr(self.sampler, 'conditioning_key') and self.sampler.conditioning_key == "crossattn-adm": return self.unclip_image_conditioning(source_image) # 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 # pylint: disable=attribute-defined-outside-init def get_token_merging_ratio(self, for_hr=False): if for_hr: return self.token_merging_ratio_hr or shared.opts.token_merging_ratio_hr or self.token_merging_ratio or shared.opts.token_merging_ratio return self.token_merging_ratio or shared.opts.token_merging_ratio 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 = p.image_cfg_scale self.steps = p.steps self.batch_size = p.batch_size self.restore_faces = p.restore_faces self.face_restoration_model = shared.opts.face_restoration_model if p.restore_faces else None self.sd_model_hash = getattr(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 = p.denoising_strength self.extra_generation_params = p.extra_generation_params self.index_of_first_image = index_of_first_image self.styles = p.styles self.job_timestamp = shared.state.job_timestamp self.clip_skip = p.clip_skip 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.s_min_uncond = p.s_min_uncond 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.token_merging_ratio = p.token_merging_ratio self.token_merging_ratio_hr = p.token_merging_ratio_hr 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) def get_token_merging_ratio(self, for_hr=False): return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio # 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 = shared.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 shared.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): if hasattr(model, 'decode_first_stage'): x = model.decode_first_stage(x) elif hasattr(model, 'vae'): x = model.vae(x) else: shared.log.warning('Cannot decode first stage') 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: StableDiffusionProcessing, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, index=None, all_negative_prompts=None): if not hasattr(shared.sd_model, 'sd_checkpoint_info'): return '' if index is None: index = position_in_batch + iteration * p.batch_size if all_negative_prompts is None: all_negative_prompts = p.all_negative_prompts comment = ', '.join(comments) if comments is not None and type(comments) is list else None args = { # basic "Steps": p.steps, "Seed": all_seeds[index], "Sampler": p.sampler_name, "CFG scale": p.cfg_scale, "Size": f"{p.width}x{p.height}", "Batch": f'{p.n_iter}x{p.batch_size}' if p.n_iter > 1 or p.batch_size > 1 else None, "Parser": shared.opts.prompt_attention, "Model": None if (not shared.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(':', ''), "Model hash": getattr(p, 'sd_model_hash', None if (not shared.opts.add_model_hash_to_info) or (not shared.sd_model.sd_model_hash) else shared.sd_model.sd_model_hash), "VAE": (None if not shared.opts.add_model_name_to_info or modules.sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(modules.sd_vae.loaded_vae_file))[0]) if p.full_quality else 'TAESD', "Variation seed": None if p.subseed_strength == 0 else all_subseeds[index], "Variation 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}", "Clip skip": p.clip_skip if p.clip_skip > 1 else None, "Prompt2": p.refiner_prompt if len(p.refiner_prompt) > 0 else None, "Negative2": p.refiner_negative if len(p.refiner_negative) > 0 else None, # other "ENSD": shared.opts.eta_noise_seed_delta if shared.opts.eta_noise_seed_delta != 0 and modules.sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p) else None, "Tiling": p.tiling if p.tiling else None, # sdnext "Backend": 'Diffusers' if shared.backend == shared.Backend.DIFFUSERS else 'Original', "Version": git_commit, "Comment": comment, "Operations": '; '.join(p.ops).replace('"', '') if len(p.ops) > 0 else 'none', } if 'txt2img' in p.ops: pass if 'hires' in p.ops or 'upscale' in p.ops: args["Hires steps"] = p.hr_second_pass_steps args["Hires upscaler"] = p.hr_upscaler args["Hires upscale"] = p.hr_scale args["Hires resize"] = f"{p.hr_resize_x}x{p.hr_resize_y}" args["Hires size"] = f"{p.hr_upscale_to_x}x{p.hr_upscale_to_y}" args["Denoising strength"] = p.denoising_strength args["Latent sampler"] = p.latent_sampler args["Image CFG scale"] = p.image_cfg_scale args["CFG rescale"] = p.diffusers_guidance_rescale if shared.backend == shared.Backend.DIFFUSERS else None if 'refine' in p.ops: args["Refiner"] = None if (not shared.opts.add_model_name_to_info) or (not shared.sd_refiner) or (not shared.sd_refiner.sd_checkpoint_info.model_name) else shared.sd_refiner.sd_checkpoint_info.model_name.replace(',', '').replace(':', '') args['Image CFG scale'] = p.image_cfg_scale args['Refiner steps'] = p.refiner_steps args['Refiner start'] = p.refiner_start args["Hires steps"] = p.hr_second_pass_steps args["Latent sampler"] = p.latent_sampler args["CFG rescale"] = p.diffusers_guidance_rescale if shared.backend == shared.Backend.DIFFUSERS else None if 'img2img' in p.ops or 'inpaint' in p.ops: args["Init image size"] = f"{getattr(p, 'init_img_width', 0)}x{getattr(p, 'init_img_height', 0)}" args["Init image hash"] = getattr(p, 'init_img_hash', None) args["Conditional mask weight"] = getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None args['Resize mode'] = p.resize_mode args["Mask blur"] = p.mask_blur if p.mask is not None and p.mask_blur > 0 else None args["Noise multiplier"] = p.initial_noise_multiplier if p.initial_noise_multiplier != 1.0 else None args["Denoising strength"] = p.denoising_strength if 'face' in p.ops: args["Face restoration"] = shared.opts.face_restoration_model if 'color' in p.ops: args["Color correction"] = True if hasattr(modules.sd_hijack.model_hijack, 'embedding_db') and len(modules.sd_hijack.model_hijack.embedding_db.embeddings_used) > 0: # this is for original hijaacked models only, diffusers are handled separately args["Embeddings"] = ', '.join(modules.sd_hijack.model_hijack.embedding_db.embeddings_used) # tome token_merging_ratio = p.get_token_merging_ratio() token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True) if p.enable_hr else None args['Token merging ratio'] = token_merging_ratio if token_merging_ratio != 0 else None args['Token merging ratio hr'] = token_merging_ratio_hr if token_merging_ratio_hr != 0 else None args.update(p.extra_generation_params) params_text = ", ".join([k if k == v else f'{k}: {modules.generation_parameters_copypaste.quote(v)}' for k, v in args.items() if v is not None]) negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else "" infotext = f"{all_prompts[index]}{negative_prompt_text}\n{params_text}".strip() return infotext """ def print_profile(profile, msg: str): try: from rich import print # pylint: disable=redefined-builtin except Exception: pass lines = profile.key_averages().table(sort_by="cuda_time_total", row_limit=20) lines = lines.split('\n') lines = [l for l in lines if '/profiler' not in l] print(f'Profile {msg}:', '\n'.join(lines)) """ def print_profile(profile, msg: str): import io import pstats try: from rich import print # pylint: disable=redefined-builtin except Exception: pass profile.disable() stream = io.StringIO() # pylint: disable=abstract-class-instantiated ps = pstats.Stats(profile, stream=stream) ps.sort_stats(pstats.SortKey.CUMULATIVE).print_stats(15) profile = None lines = stream.getvalue().split('\n') lines = [line for line in lines if ' Processed: if not hasattr(p.sd_model, 'sd_checkpoint_info'): return None stored_opts = {} for k, v in p.override_settings.copy().items(): orig = shared.opts.data.get(k, None) or shared.opts.data_labels[k].default if orig == v or os.path.splitext(orig)[0] == v: p.override_settings.pop(k, None) for k in p.override_settings.keys(): stored_opts[k] = shared.opts.data.get(k, None) or shared.opts.data_labels[k].default try: # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint if p.override_settings.get('sd_model_checkpoint', None) is not None and modules.sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None: shared.log.warning(f"Override not found: checkpoint={p.override_settings.get('sd_model_checkpoint', None)}") p.override_settings.pop('sd_model_checkpoint', None) modules.sd_models.reload_model_weights() if p.override_settings.get('sd_model_refiner', None) is not None and modules.sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_refiner')) is None: shared.log.warning(f"Override not found: refiner={p.override_settings.get('sd_model_refiner', None)}") p.override_settings.pop('sd_model_refiner', None) modules.sd_models.reload_model_weights() if p.override_settings.get('sd_vae', None) is not None: if p.override_settings.get('sd_vae', None) == 'TAESD': p.full_quality = False # p.override_settings.pop('sd_vae', None) if len(p.override_settings.keys()) > 0: shared.log.debug(f'Override: {p.override_settings}') for k, v in p.override_settings.items(): setattr(shared.opts, k, v) if k == 'sd_model_checkpoint': modules.sd_models.reload_model_weights() if k == 'sd_vae': modules.sd_vae.reload_vae_weights() if not shared.opts.cuda_compile: modules.sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio()) if shared.cmd_opts.profile: """ import torch.profiler # pylint: disable=redefined-outer-name with torch.profiler.profile(profile_memory=True, with_modules=True) as prof: with torch.profiler.record_function("process_images"): res = process_images_inner(p) print_profile(prof, 'process_images') """ import cProfile pr = cProfile.Profile() pr.enable() res = process_images_inner(p) print_profile(pr, 'Torch') else: res = process_images_inner(p) finally: if not shared.opts.cuda_compile: modules.sd_models.apply_token_merging(p.sd_model, 0) if p.override_settings_restore_afterwards: # restore opts to original state for k, v in stored_opts.items(): setattr(shared.opts, k, v) if k == 'sd_model_checkpoint': modules.sd_models.reload_model_weights() if k == 'sd_model_refiner': modules.sd_models.reload_model_weights() if k == 'sd_vae': modules.sd_vae.reload_vae_weights() return res def validate_sample(sample): ok = True try: sample = sample.astype(np.uint8) return sample except (Exception, Warning, RuntimeWarning) as e: shared.log.error(f'Failed to validate sample values: {e}') ok = False if not ok: try: sample = np.nan_to_num(sample, nan=0, posinf=255, neginf=0) sample = sample.astype(np.uint8) shared.log.debug('Corrected sample values') except (Exception, Warning, RuntimeWarning) as e: shared.log.error(f'Failed to correct sample values: {e}') sample = np.zeros_like(sample) sample = sample.astype(np.uint8) return sample 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 seed = get_fixed_seed(p.seed) subseed = get_fixed_seed(p.subseed) if shared.backend == shared.Backend.ORIGINAL: 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))] if os.path.exists(shared.opts.embeddings_dir) and not p.do_not_reload_embeddings: modules.sd_hijack.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): 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] def infotext(_inxex=0): # dummy function overriden if there are iterations return '' ema_scope_context = p.sd_model.ema_scope if shared.backend == shared.Backend.ORIGINAL else nullcontext with devices.inference_context(), ema_scope_context(): t0 = time.time() with devices.autocast(): p.init(p.all_prompts, p.all_seeds, p.all_subseeds) if shared.opts.live_previews_enable and shared.opts.show_progress_type == "Approximate NN" and shared.backend == shared.Backend.ORIGINAL: modules.sd_vae_approx.model() if shared.state.job_count == -1: shared.state.job_count = p.n_iter extra_network_data = None for n in range(p.n_iter): p.iteration = n if shared.state.skipped: shared.log.debug(f'Process skipped: {n}/{p.n_iter}') shared.state.skipped = False continue if shared.state.interrupted: shared.log.debug(f'Process interrupted: {n}/{p.n_iter}') break p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size] p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size] p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size] p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] if p.scripts is not None: p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds) if len(p.prompts) == 0: break p.prompts, extra_network_data = modules.extra_networks.parse_prompts(p.prompts) if not p.disable_extra_networks: with devices.autocast(): modules.extra_networks.activate(p, extra_network_data) if p.scripts is not None: p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds) if n == 0: with open(os.path.join(modules.paths.data_path, "params.txt"), "w", encoding="utf8") as file: processed = Processed(p, [], p.seed, "") file.write(processed.infotext(p, 0)) step_multiplier = 1 sampler_config = modules.sd_samplers.find_sampler_config(p.sampler_name) step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1 if p.n_iter > 1: shared.state.job = f"Batch {n+1} out of {p.n_iter}" if shared.backend == shared.Backend.ORIGINAL: uc = get_conds_with_caching(modules.prompt_parser.get_learned_conditioning, p.negative_prompts, p.steps * step_multiplier, cached_uc) c = get_conds_with_caching(modules.prompt_parser.get_multicond_learned_conditioning, p.prompts, p.steps * step_multiplier, cached_c) if len(modules.sd_hijack.model_hijack.comments) > 0: for comment in modules.sd_hijack.model_hijack.comments: comments[comment] = 1 with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast(): samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.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))] try: for x in x_samples_ddim: devices.test_for_nans(x, "vae") except devices.NansException as e: if not shared.opts.no_half and not shared.opts.no_half_vae and shared.cmd_opts.rollback_vae: shared.log.warning('Tensor with all NaNs was produced in VAE') devices.dtype_vae = torch.bfloat16 vae_file, vae_source = modules.sd_vae.resolve_vae(p.sd_model.sd_model_checkpoint) modules.sd_vae.load_vae(p.sd_model, vae_file, vae_source) 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") else: raise e 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 elif shared.backend == shared.Backend.DIFFUSERS: from modules.processing_diffusers import process_diffusers x_samples_ddim = process_diffusers(p, p.seeds, p.prompts, p.negative_prompts) else: raise ValueError(f"Unknown backend {shared.backend}") if shared.cmd_opts.lowvram or shared.cmd_opts.medvram and shared.backend == shared.Backend.ORIGINAL: modules.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) if p.scripts is not None: p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size] p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size] batch_params = modules.scripts.PostprocessBatchListArgs(list(x_samples_ddim)) p.scripts.postprocess_batch_list(p, batch_params, batch_number=n) x_samples_ddim = batch_params.images def infotext(index=0): # pylint: disable=function-redefined # noqa: F811 return create_infotext(p, p.prompts, p.seeds, p.subseeds, index=index, all_negative_prompts=p.negative_prompts) for i, x_sample in enumerate(x_samples_ddim): p.batch_index = i if type(x_sample) == Image.Image: image = x_sample x_sample = np.array(x_sample) else: x_sample = 255. * (np.moveaxis(x_sample.cpu().numpy(), 0, 2) if shared.backend == shared.Backend.ORIGINAL else x_sample) x_sample = validate_sample(x_sample) image = Image.fromarray(x_sample) if p.restore_faces: if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_face_restoration: orig = p.restore_faces p.restore_faces = False info = infotext(i) p.restore_faces = orig images.save_image(Image.fromarray(x_sample), path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-face-restoration") p.ops.append('face') x_sample = modules.face_restoration.restore_faces(x_sample) image = Image.fromarray(x_sample) if p.scripts is not None: pp = modules.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 shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_color_correction: orig = p.color_corrections p.color_corrections = None info = infotext(i) p.color_corrections = orig image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images) images.save_image(image_without_cc, path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-color-correction") p.ops.append('color') image = apply_color_correction(p.color_corrections[i], image) image = apply_overlay(image, p.paste_to, i, p.overlay_images) text = infotext(i) infotexts.append(text) image.info["parameters"] = text output_images.append(image) if shared.opts.samples_save and not p.do_not_save_samples: images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p) if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([shared.opts.save_mask, shared.opts.save_mask_composite, shared.opts.return_mask, shared.opts.return_mask_composite]): image_mask = p.mask_for_overlay.convert('RGB') image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(3, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA') if shared.opts.save_mask: images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p, suffix="-mask") if shared.opts.save_mask_composite: images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p, suffix="-mask-composite") if shared.opts.return_mask: output_images.append(image_mask) if shared.opts.return_mask_composite: output_images.append(image_mask_composite) del x_samples_ddim devices.torch_gc() shared.state.nextjob() t1 = time.time() shared.log.info(f'Processed: images={len(output_images)} time={t1 - t0:.2f}s its={(p.steps * len(output_images)) / (t1 - t0):.2f} memory={modules.memstats.memory_stats()}') p.color_corrections = None index_of_first_image = 0 if (shared.opts.return_grid or shared.opts.grid_save) and not p.do_not_save_grid and len(output_images) > 1: if images.check_grid_size(output_images): grid = images.image_grid(output_images, p.batch_size) if shared.opts.return_grid: text = infotext() infotexts.insert(0, text) grid.info["parameters"] = text output_images.insert(0, grid) index_of_first_image = 1 if shared.opts.grid_save: images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], shared.opts.grid_format, info=infotext(), short_filename=not shared.opts.grid_extended_filename, p=p, grid=True) if not p.disable_extra_networks and extra_network_data: modules.extra_networks.deactivate(p, extra_network_data) res = Processed( p, images_list=output_images, seed=p.all_seeds[0], info=infotext(), comments="\n".join(comments), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts, ) if p.scripts is not None and not shared.state.interrupted: 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): 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_force: bool = False, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 5, refiner_start: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **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_force = hr_force 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 self.refiner_steps = refiner_steps self.refiner_start = refiner_start self.refiner_prompt = refiner_prompt self.refiner_negative = refiner_negative self.sampler = None def init(self, all_prompts, all_seeds, all_subseeds): if shared.backend == shared.Backend.DIFFUSERS: modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.TEXT_2_IMAGE) self.width = self.width or 512 self.height = self.height or 512 def init_hr(self): if self.hr_resize_x == 0 and self.hr_resize_y == 0: self.hr_upscale_to_x = int(self.width * self.hr_scale) self.hr_upscale_to_y = int(self.height * self.hr_scale) else: 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) // 8 self.truncate_y = (self.hr_upscale_to_y - target_h) // 8 # 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) or self.hr_upscaler is None or self.hr_upscaler == 'None': self.is_hr_pass = False return self.is_hr_pass = True if not shared.state.processing_has_refined_job_count: if shared.state.job_count == -1: shared.state.job_count = self.n_iter shared.state.job_count = shared.state.job_count * 2 shared.state.processing_has_refined_job_count = True shared.log.debug(f'Init hires: upscaler={self.hr_upscaler} sampler={self.latent_sampler} resize={self.hr_resize_x}x{self.hr_resize_y} upscale={self.hr_upscale_to_x}x{self.hr_upscale_to_y}') def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): 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 shared.opts.save or self.do_not_save_samples or not shared.opts.save_images_before_highres_fix: return if not isinstance(image, Image.Image): image = modules.sd_samplers.sample_to_image(image, index, approximation=0) orig1 = self.extra_generation_params orig2 = self.restore_faces self.extra_generation_params = {} self.restore_faces = False info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index) self.extra_generation_params = orig1 self.restore_faces = orig2 images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], shared.opts.samples_format, info=info, suffix="-before-highres-fix") if shared.backend == shared.Backend.DIFFUSERS: modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.TEXT_2_IMAGE) 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, "None") if self.enable_hr and (latent_scale_mode is None or self.hr_force): if len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) == 0: shared.log.warning(f"Cannot find upscaler for hires: {self.hr_upscaler}") self.enable_hr = False self.ops.append('txt2img') self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model) x = create_random_tensors([4, self.height // 8, self.width // 8], 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 or shared.state.interrupted or shared.state.skipped: return samples self.init_hr() if self.is_hr_pass: self.ops.append('hires') target_width = self.hr_upscale_to_x target_height = self.hr_upscale_to_y for i in range(samples.shape[0]): save_intermediate(samples, i) if latent_scale_mode is None or self.hr_force: # non-latent upscaling decoded_samples = decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae)) 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 = validate_sample(x_sample) image = Image.fromarray(x_sample) save_intermediate(image, i) image = images.resize_image(1, 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(device=shared.device, dtype=devices.dtype_vae) decoded_samples = 2. * decoded_samples - 1. if shared.opts.sd_vae_sliced_encode and len(decoded_samples) > 1: samples = torch.stack([ self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(torch.unsqueeze(decoded_sample, 0)))[0] for decoded_sample in decoded_samples ]) else: 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) else: samples = torch.nn.functional.interpolate(samples, size=(target_height // 8, target_width // 8), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"]) 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.to(dtype=devices.dtype_vae)), samples) else: image_conditioning = self.txt2img_image_conditioning(samples.to(dtype=devices.dtype_vae)) if self.latent_sampler == "PLMS": self.latent_sampler = 'UniPC' if self.hr_force or latent_scale_mode is not None: devices.torch_gc() # GC now before running the next img2img to prevent running out of memory self.sampler = modules.sd_samplers.create_sampler(self.latent_sampler or self.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) modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True)) samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning) modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio()) x = None shared.state.nextjob() self.is_hr_pass = False return samples class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.3, 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, refiner_steps: int = 5, refiner_start: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **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 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 = shared.opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier self.mask = None self.nmask = None self.image_conditioning = None self.refiner_steps = refiner_steps self.refiner_start = refiner_start self.refiner_prompt = refiner_prompt self.refiner_negative = refiner_negative self.enable_hr = None self.is_batch = False self.scale_by = 1.0 self.sampler = None def init(self, all_prompts, all_seeds, all_subseeds): if shared.backend == shared.Backend.DIFFUSERS and self.image_mask is None: modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.IMAGE_2_IMAGE) elif shared.backend == shared.Backend.DIFFUSERS and self.image_mask is not None: modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.INPAINTING) self.sd_model.dtype = self.sd_model.unet.dtype if self.sampler_name == "PLMS": self.sampler_name = 'UniPC' self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model) if self.image_mask is not None: self.ops.append('inpaint') else: self.ops.append('img2img') 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 = modules.masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding) crop_region = modules.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(3, 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 = shared.opts.img2img_color_correction and self.color_corrections is None if add_color_corrections: self.color_corrections = [] imgs = [] unprocessed = [] for img in self.init_images: self.init_img_hash = hashlib.sha256(img.tobytes()).hexdigest()[0:8] # pylint: disable=attribute-defined-outside-init self.init_img_width = img.width # pylint: disable=attribute-defined-outside-init self.init_img_height = img.height # pylint: disable=attribute-defined-outside-init if shared.opts.save_init_img: images.save_image(img, path=shared.opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False) image = images.flatten(img, shared.opts.img2img_background_color) if crop_region is None and self.resize_mode != 4: image = images.resize_image(self.resize_mode, image, self.width, self.height) self.width = image.width self.height = image.height if image_mask is not None: try: image_masked = Image.new('RGBa', (image.width, image.height)) image_to_paste = image.convert("RGBA").convert("RGBa") image_to_mask = ImageOps.invert(self.mask_for_overlay.convert('L')) if self.mask_for_overlay is not None else None image_masked.paste(image_to_paste, mask=image_to_mask) self.overlay_images.append(image_masked.convert('RGBA')) except Exception as e: shared.log.error(f"Failed to apply mask to image: {e}") self.mask = image_mask # assign early for diffusers # 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(3, image, self.width, self.height) if shared.backend == shared.Backend.DIFFUSERS: unprocessed.append(image) self.init_images = [image] # assign early for diffusers if image_mask is not None: if self.inpainting_fill != 1: image = modules.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) self.init_images = unprocessed if shared.backend == shared.Backend.DIFFUSERS else imgs 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") if shared.backend == shared.Backend.DIFFUSERS: # we've already set self.init_images and self.mask and we dont need any more processing return image = torch.from_numpy(batch_images) image = 2. * image - 1. image = image.to(device=shared.device, dtype=devices.dtype_vae) self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image)) if self.resize_mode == 4: self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // 8, self.width // 8), 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.tile(latmask[None], (4, 1, 1)) latmask = np.around(latmask) self.mask = torch.asarray(1.0 - latmask).to(device=shared.device, dtype=self.sd_model.dtype) self.nmask = torch.asarray(latmask).to(device=shared.device, dtype=self.sd_model.dtype) if self.inpainting_fill == 2: self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], 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): if shared.backend == shared.Backend.DIFFUSERS: if self.init_mask is None: # pylint: disable=no-member modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.IMAGE_2_IMAGE) else: modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.INPAINTING) self.sd_model.dtype = self.sd_model.unet.dtype x = create_random_tensors([4, self.height // 8, self.width // 8], 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) 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 def get_token_merging_ratio(self, for_hr=False): return self.token_merging_ratio or ("token_merging_ratio" in self.override_settings and shared.opts.token_merging_ratio) or shared.opts.token_merging_ratio_img2img or shared.opts.token_merging_ratio