import threading from extras.inpaint_mask import generate_mask_from_image, SAMOptions from modules.patch import PatchSettings, patch_settings, patch_all import modules.config patch_all() class AsyncTask: def __init__(self, args): from modules.flags import Performance, MetadataScheme, ip_list, disabled from modules.util import get_enabled_loras from modules.config import default_max_lora_number import args_manager self.args = args.copy() self.yields = [] self.results = [] self.last_stop = False self.processing = False self.performance_loras = [] if len(args) == 0: return args.reverse() self.generate_image_grid = args.pop() self.prompt = args.pop() self.negative_prompt = args.pop() self.style_selections = args.pop() self.performance_selection = Performance(args.pop()) self.steps = self.performance_selection.steps() self.original_steps = self.steps self.aspect_ratios_selection = args.pop() self.image_number = args.pop() self.output_format = args.pop() self.seed = int(args.pop()) self.read_wildcards_in_order = args.pop() self.sharpness = args.pop() self.cfg_scale = args.pop() self.base_model_name = args.pop() self.refiner_model_name = args.pop() self.refiner_switch = args.pop() self.loras = get_enabled_loras([(bool(args.pop()), str(args.pop()), float(args.pop())) for _ in range(default_max_lora_number)]) self.input_image_checkbox = args.pop() self.current_tab = args.pop() self.uov_method = args.pop() self.uov_input_image = args.pop() self.outpaint_selections = args.pop() self.inpaint_input_image = args.pop() self.inpaint_additional_prompt = args.pop() self.inpaint_mask_image_upload = args.pop() self.disable_preview = args.pop() self.disable_intermediate_results = args.pop() self.disable_seed_increment = args.pop() self.black_out_nsfw = args.pop() self.adm_scaler_positive = args.pop() self.adm_scaler_negative = args.pop() self.adm_scaler_end = args.pop() self.adaptive_cfg = args.pop() self.clip_skip = args.pop() self.sampler_name = args.pop() self.scheduler_name = args.pop() self.vae_name = args.pop() self.overwrite_step = args.pop() self.overwrite_switch = args.pop() self.overwrite_width = args.pop() self.overwrite_height = args.pop() self.overwrite_vary_strength = args.pop() self.overwrite_upscale_strength = args.pop() self.mixing_image_prompt_and_vary_upscale = args.pop() self.mixing_image_prompt_and_inpaint = args.pop() self.debugging_cn_preprocessor = args.pop() self.skipping_cn_preprocessor = args.pop() self.canny_low_threshold = args.pop() self.canny_high_threshold = args.pop() self.refiner_swap_method = args.pop() self.controlnet_softness = args.pop() self.freeu_enabled = args.pop() self.freeu_b1 = args.pop() self.freeu_b2 = args.pop() self.freeu_s1 = args.pop() self.freeu_s2 = args.pop() self.debugging_inpaint_preprocessor = args.pop() self.inpaint_disable_initial_latent = args.pop() self.inpaint_engine = args.pop() self.inpaint_strength = args.pop() self.inpaint_respective_field = args.pop() self.inpaint_advanced_masking_checkbox = args.pop() self.invert_mask_checkbox = args.pop() self.inpaint_erode_or_dilate = args.pop() self.save_final_enhanced_image_only = args.pop() if not args_manager.args.disable_image_log else False self.save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False self.metadata_scheme = MetadataScheme( args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS self.cn_tasks = {x: [] for x in ip_list} for _ in range(modules.config.default_controlnet_image_count): cn_img = args.pop() cn_stop = args.pop() cn_weight = args.pop() cn_type = args.pop() if cn_img is not None: self.cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight]) self.debugging_dino = args.pop() self.dino_erode_or_dilate = args.pop() self.debugging_enhance_masks_checkbox = args.pop() self.enhance_input_image = args.pop() self.enhance_checkbox = args.pop() self.enhance_uov_method = args.pop() self.enhance_uov_processing_order = args.pop() self.enhance_uov_prompt_type = args.pop() self.enhance_ctrls = [] for _ in range(modules.config.default_enhance_tabs): enhance_enabled = args.pop() enhance_mask_dino_prompt_text = args.pop() enhance_prompt = args.pop() enhance_negative_prompt = args.pop() enhance_mask_model = args.pop() enhance_mask_cloth_category = args.pop() enhance_mask_sam_model = args.pop() enhance_mask_text_threshold = args.pop() enhance_mask_box_threshold = args.pop() enhance_mask_sam_max_detections = args.pop() enhance_inpaint_disable_initial_latent = args.pop() enhance_inpaint_engine = args.pop() enhance_inpaint_strength = args.pop() enhance_inpaint_respective_field = args.pop() enhance_inpaint_erode_or_dilate = args.pop() enhance_mask_invert = args.pop() if enhance_enabled: self.enhance_ctrls.append([ enhance_mask_dino_prompt_text, enhance_prompt, enhance_negative_prompt, enhance_mask_model, enhance_mask_cloth_category, enhance_mask_sam_model, enhance_mask_text_threshold, enhance_mask_box_threshold, enhance_mask_sam_max_detections, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field, enhance_inpaint_erode_or_dilate, enhance_mask_invert ]) self.should_enhance = self.enhance_checkbox and (self.enhance_uov_method != disabled.casefold() or len(self.enhance_ctrls) > 0) self.images_to_enhance_count = 0 self.enhance_stats = {} async_tasks = [] class EarlyReturnException(BaseException): pass def worker(): global async_tasks import os import traceback import math import numpy as np import torch import time import shared import random import copy import cv2 import modules.default_pipeline as pipeline import modules.core as core import modules.flags as flags import modules.patch import ldm_patched.modules.model_management import extras.preprocessors as preprocessors import modules.inpaint_worker as inpaint_worker import modules.constants as constants import extras.ip_adapter as ip_adapter import extras.face_crop import fooocus_version from extras.censor import default_censor from modules.sdxl_styles import apply_style, get_random_style, fooocus_expansion, apply_arrays, random_style_name from modules.private_logger import log from extras.expansion import safe_str from modules.util import (remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate, parse_lora_references_from_prompt, apply_wildcards) from modules.upscaler import perform_upscale from modules.flags import Performance from modules.meta_parser import get_metadata_parser pid = os.getpid() print(f'Started worker with PID {pid}') try: async_gradio_app = shared.gradio_root flag = f'''App started successful. Use the app with {str(async_gradio_app.local_url)} or {str(async_gradio_app.server_name)}:{str(async_gradio_app.server_port)}''' if async_gradio_app.share: flag += f''' or {async_gradio_app.share_url}''' print(flag) except Exception as e: print(e) def progressbar(async_task, number, text): print(f'[Fooocus] {text}') async_task.yields.append(['preview', (number, text, None)]) def yield_result(async_task, imgs, progressbar_index, black_out_nsfw, censor=True, do_not_show_finished_images=False): if not isinstance(imgs, list): imgs = [imgs] if censor and (modules.config.default_black_out_nsfw or black_out_nsfw): progressbar(async_task, progressbar_index, 'Checking for NSFW content ...') imgs = default_censor(imgs) async_task.results = async_task.results + imgs if do_not_show_finished_images: return async_task.yields.append(['results', async_task.results]) return def build_image_wall(async_task): results = [] if len(async_task.results) < 2: return for img in async_task.results: if isinstance(img, str) and os.path.exists(img): img = cv2.imread(img) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) if not isinstance(img, np.ndarray): return if img.ndim != 3: return results.append(img) H, W, C = results[0].shape for img in results: Hn, Wn, Cn = img.shape if H != Hn: return if W != Wn: return if C != Cn: return cols = float(len(results)) ** 0.5 cols = int(math.ceil(cols)) rows = float(len(results)) / float(cols) rows = int(math.ceil(rows)) wall = np.zeros(shape=(H * rows, W * cols, C), dtype=np.uint8) for y in range(rows): for x in range(cols): if y * cols + x < len(results): img = results[y * cols + x] wall[y * H:y * H + H, x * W:x * W + W, :] = img # must use deep copy otherwise gradio is super laggy. Do not use list.append() . async_task.results = async_task.results + [wall] return def process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, final_scheduler_name, goals, initial_latent, steps, switch, positive_cond, negative_cond, task, loras, tiled, use_expansion, width, height, base_progress, preparation_steps, total_count, show_intermediate_results, persist_image=True): if async_task.last_stop is not False: ldm_patched.modules.model_management.interrupt_current_processing() if 'cn' in goals: for cn_flag, cn_path in [ (flags.cn_canny, controlnet_canny_path), (flags.cn_cpds, controlnet_cpds_path) ]: for cn_img, cn_stop, cn_weight in async_task.cn_tasks[cn_flag]: positive_cond, negative_cond = core.apply_controlnet( positive_cond, negative_cond, pipeline.loaded_ControlNets[cn_path], cn_img, cn_weight, 0, cn_stop) imgs = pipeline.process_diffusion( positive_cond=positive_cond, negative_cond=negative_cond, steps=steps, switch=switch, width=width, height=height, image_seed=task['task_seed'], callback=callback, sampler_name=async_task.sampler_name, scheduler_name=final_scheduler_name, latent=initial_latent, denoise=denoising_strength, tiled=tiled, cfg_scale=async_task.cfg_scale, refiner_swap_method=async_task.refiner_swap_method, disable_preview=async_task.disable_preview ) del positive_cond, negative_cond # Save memory if inpaint_worker.current_task is not None: imgs = [inpaint_worker.current_task.post_process(x) for x in imgs] current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * steps) if modules.config.default_black_out_nsfw or async_task.black_out_nsfw: progressbar(async_task, current_progress, 'Checking for NSFW content ...') imgs = default_censor(imgs) progressbar(async_task, current_progress, f'Saving image {current_task_id + 1}/{total_count} to system ...') img_paths = save_and_log(async_task, height, imgs, task, use_expansion, width, loras, persist_image) yield_result(async_task, img_paths, current_progress, async_task.black_out_nsfw, False, do_not_show_finished_images=not show_intermediate_results or async_task.disable_intermediate_results) return imgs, img_paths, current_progress def apply_patch_settings(async_task): patch_settings[pid] = PatchSettings( async_task.sharpness, async_task.adm_scaler_end, async_task.adm_scaler_positive, async_task.adm_scaler_negative, async_task.controlnet_softness, async_task.adaptive_cfg ) def save_and_log(async_task, height, imgs, task, use_expansion, width, loras, persist_image=True) -> list: img_paths = [] for x in imgs: d = [('Prompt', 'prompt', task['log_positive_prompt']), ('Negative Prompt', 'negative_prompt', task['log_negative_prompt']), ('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']), ('Styles', 'styles', str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])), ('Performance', 'performance', async_task.performance_selection.value), ('Steps', 'steps', async_task.steps), ('Resolution', 'resolution', str((width, height))), ('Guidance Scale', 'guidance_scale', async_task.cfg_scale), ('Sharpness', 'sharpness', async_task.sharpness), ('ADM Guidance', 'adm_guidance', str(( modules.patch.patch_settings[pid].positive_adm_scale, modules.patch.patch_settings[pid].negative_adm_scale, modules.patch.patch_settings[pid].adm_scaler_end))), ('Base Model', 'base_model', async_task.base_model_name), ('Refiner Model', 'refiner_model', async_task.refiner_model_name), ('Refiner Switch', 'refiner_switch', async_task.refiner_switch)] if async_task.refiner_model_name != 'None': if async_task.overwrite_switch > 0: d.append(('Overwrite Switch', 'overwrite_switch', async_task.overwrite_switch)) if async_task.refiner_swap_method != flags.refiner_swap_method: d.append(('Refiner Swap Method', 'refiner_swap_method', async_task.refiner_swap_method)) if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr: d.append( ('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg)) if async_task.clip_skip > 1: d.append(('CLIP Skip', 'clip_skip', async_task.clip_skip)) d.append(('Sampler', 'sampler', async_task.sampler_name)) d.append(('Scheduler', 'scheduler', async_task.scheduler_name)) d.append(('VAE', 'vae', async_task.vae_name)) d.append(('Seed', 'seed', str(task['task_seed']))) if async_task.freeu_enabled: d.append(('FreeU', 'freeu', str((async_task.freeu_b1, async_task.freeu_b2, async_task.freeu_s1, async_task.freeu_s2)))) for li, (n, w) in enumerate(loras): if n != 'None': d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}')) metadata_parser = None if async_task.save_metadata_to_images: metadata_parser = modules.meta_parser.get_metadata_parser(async_task.metadata_scheme) metadata_parser.set_data(task['log_positive_prompt'], task['positive'], task['log_negative_prompt'], task['negative'], async_task.steps, async_task.base_model_name, async_task.refiner_model_name, loras, async_task.vae_name) d.append(('Metadata Scheme', 'metadata_scheme', async_task.metadata_scheme.value if async_task.save_metadata_to_images else async_task.save_metadata_to_images)) d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version)) img_paths.append(log(x, d, metadata_parser, async_task.output_format, task, persist_image)) return img_paths def apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width, current_progress): for task in async_task.cn_tasks[flags.cn_canny]: cn_img, cn_stop, cn_weight = task cn_img = resize_image(HWC3(cn_img), width=width, height=height) if not async_task.skipping_cn_preprocessor: cn_img = preprocessors.canny_pyramid(cn_img, async_task.canny_low_threshold, async_task.canny_high_threshold) cn_img = HWC3(cn_img) task[0] = core.numpy_to_pytorch(cn_img) if async_task.debugging_cn_preprocessor: yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True) for task in async_task.cn_tasks[flags.cn_cpds]: cn_img, cn_stop, cn_weight = task cn_img = resize_image(HWC3(cn_img), width=width, height=height) if not async_task.skipping_cn_preprocessor: cn_img = preprocessors.cpds(cn_img) cn_img = HWC3(cn_img) task[0] = core.numpy_to_pytorch(cn_img) if async_task.debugging_cn_preprocessor: yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True) for task in async_task.cn_tasks[flags.cn_ip]: cn_img, cn_stop, cn_weight = task cn_img = HWC3(cn_img) # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75 cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0) task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path) if async_task.debugging_cn_preprocessor: yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True) for task in async_task.cn_tasks[flags.cn_ip_face]: cn_img, cn_stop, cn_weight = task cn_img = HWC3(cn_img) if not async_task.skipping_cn_preprocessor: cn_img = extras.face_crop.crop_image(cn_img) # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75 cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0) task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path) if async_task.debugging_cn_preprocessor: yield_result(async_task, cn_img, current_progress, async_task.black_out_nsfw, do_not_show_finished_images=True) all_ip_tasks = async_task.cn_tasks[flags.cn_ip] + async_task.cn_tasks[flags.cn_ip_face] if len(all_ip_tasks) > 0: pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks) def apply_vary(async_task, uov_method, denoising_strength, uov_input_image, switch, current_progress, advance_progress=False): if 'subtle' in uov_method: denoising_strength = 0.5 if 'strong' in uov_method: denoising_strength = 0.85 if async_task.overwrite_vary_strength > 0: denoising_strength = async_task.overwrite_vary_strength shape_ceil = get_image_shape_ceil(uov_input_image) if shape_ceil < 1024: print(f'[Vary] Image is resized because it is too small.') shape_ceil = 1024 elif shape_ceil > 2048: print(f'[Vary] Image is resized because it is too big.') shape_ceil = 2048 uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil) initial_pixels = core.numpy_to_pytorch(uov_input_image) if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'VAE encoding ...') candidate_vae, _ = pipeline.get_candidate_vae( steps=async_task.steps, switch=switch, denoise=denoising_strength, refiner_swap_method=async_task.refiner_swap_method ) initial_latent = core.encode_vae(vae=candidate_vae, pixels=initial_pixels) B, C, H, W = initial_latent['samples'].shape width = W * 8 height = H * 8 print(f'Final resolution is {str((width, height))}.') return uov_input_image, denoising_strength, initial_latent, width, height, current_progress def apply_inpaint(async_task, initial_latent, inpaint_head_model_path, inpaint_image, inpaint_mask, inpaint_parameterized, denoising_strength, inpaint_respective_field, switch, inpaint_disable_initial_latent, current_progress, skip_apply_outpaint=False, advance_progress=False): if not skip_apply_outpaint: inpaint_image, inpaint_mask = apply_outpaint(async_task, inpaint_image, inpaint_mask) inpaint_worker.current_task = inpaint_worker.InpaintWorker( image=inpaint_image, mask=inpaint_mask, use_fill=denoising_strength > 0.99, k=inpaint_respective_field ) if async_task.debugging_inpaint_preprocessor: yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), 100, async_task.black_out_nsfw, do_not_show_finished_images=True) raise EarlyReturnException if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'VAE Inpaint encoding ...') inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill) inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image) inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask) candidate_vae, candidate_vae_swap = pipeline.get_candidate_vae( steps=async_task.steps, switch=switch, denoise=denoising_strength, refiner_swap_method=async_task.refiner_swap_method ) latent_inpaint, latent_mask = core.encode_vae_inpaint( mask=inpaint_pixel_mask, vae=candidate_vae, pixels=inpaint_pixel_image) latent_swap = None if candidate_vae_swap is not None: if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'VAE SD15 encoding ...') latent_swap = core.encode_vae( vae=candidate_vae_swap, pixels=inpaint_pixel_fill)['samples'] if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'VAE encoding ...') latent_fill = core.encode_vae( vae=candidate_vae, pixels=inpaint_pixel_fill)['samples'] inpaint_worker.current_task.load_latent( latent_fill=latent_fill, latent_mask=latent_mask, latent_swap=latent_swap) if inpaint_parameterized: pipeline.final_unet = inpaint_worker.current_task.patch( inpaint_head_model_path=inpaint_head_model_path, inpaint_latent=latent_inpaint, inpaint_latent_mask=latent_mask, model=pipeline.final_unet ) if not inpaint_disable_initial_latent: initial_latent = {'samples': latent_fill} B, C, H, W = latent_fill.shape height, width = H * 8, W * 8 final_height, final_width = inpaint_worker.current_task.image.shape[:2] print(f'Final resolution is {str((final_width, final_height))}, latent is {str((width, height))}.') return denoising_strength, initial_latent, width, height, current_progress def apply_outpaint(async_task, inpaint_image, inpaint_mask): if len(async_task.outpaint_selections) > 0: H, W, C = inpaint_image.shape if 'top' in async_task.outpaint_selections: inpaint_image = np.pad(inpaint_image, [[int(H * 0.3), 0], [0, 0], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[int(H * 0.3), 0], [0, 0]], mode='constant', constant_values=255) if 'bottom' in async_task.outpaint_selections: inpaint_image = np.pad(inpaint_image, [[0, int(H * 0.3)], [0, 0], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[0, int(H * 0.3)], [0, 0]], mode='constant', constant_values=255) H, W, C = inpaint_image.shape if 'left' in async_task.outpaint_selections: inpaint_image = np.pad(inpaint_image, [[0, 0], [int(W * 0.3), 0], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(W * 0.3), 0]], mode='constant', constant_values=255) if 'right' in async_task.outpaint_selections: inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(W * 0.3)], [0, 0]], mode='edge') inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(W * 0.3)]], mode='constant', constant_values=255) inpaint_image = np.ascontiguousarray(inpaint_image.copy()) inpaint_mask = np.ascontiguousarray(inpaint_mask.copy()) async_task.inpaint_strength = 1.0 async_task.inpaint_respective_field = 1.0 return inpaint_image, inpaint_mask def apply_upscale(async_task, uov_input_image, uov_method, switch, current_progress, advance_progress=False): H, W, C = uov_input_image.shape if advance_progress: current_progress += 1 progressbar(async_task, current_progress, f'Upscaling image from {str((W, H))} ...') uov_input_image = perform_upscale(uov_input_image) print(f'Image upscaled.') if '1.5x' in uov_method: f = 1.5 elif '2x' in uov_method: f = 2.0 else: f = 1.0 shape_ceil = get_shape_ceil(H * f, W * f) if shape_ceil < 1024: print(f'[Upscale] Image is resized because it is too small.') uov_input_image = set_image_shape_ceil(uov_input_image, 1024) shape_ceil = 1024 else: uov_input_image = resample_image(uov_input_image, width=W * f, height=H * f) image_is_super_large = shape_ceil > 2800 if 'fast' in uov_method: direct_return = True elif image_is_super_large: print('Image is too large. Directly returned the SR image. ' 'Usually directly return SR image at 4K resolution ' 'yields better results than SDXL diffusion.') direct_return = True else: direct_return = False if direct_return: return direct_return, uov_input_image, None, None, None, None, None, current_progress tiled = True denoising_strength = 0.382 if async_task.overwrite_upscale_strength > 0: denoising_strength = async_task.overwrite_upscale_strength initial_pixels = core.numpy_to_pytorch(uov_input_image) if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'VAE encoding ...') candidate_vae, _ = pipeline.get_candidate_vae( steps=async_task.steps, switch=switch, denoise=denoising_strength, refiner_swap_method=async_task.refiner_swap_method ) initial_latent = core.encode_vae( vae=candidate_vae, pixels=initial_pixels, tiled=True) B, C, H, W = initial_latent['samples'].shape width = W * 8 height = H * 8 print(f'Final resolution is {str((width, height))}.') return direct_return, uov_input_image, denoising_strength, initial_latent, tiled, width, height, current_progress def apply_overrides(async_task, steps, height, width): if async_task.overwrite_step > 0: steps = async_task.overwrite_step switch = int(round(async_task.steps * async_task.refiner_switch)) if async_task.overwrite_switch > 0: switch = async_task.overwrite_switch if async_task.overwrite_width > 0: width = async_task.overwrite_width if async_task.overwrite_height > 0: height = async_task.overwrite_height return steps, switch, width, height def process_prompt(async_task, prompt, negative_prompt, base_model_additional_loras, image_number, disable_seed_increment, use_expansion, use_style, use_synthetic_refiner, current_progress, advance_progress=False): prompts = remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='') negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.splitlines()], default='') prompt = prompts[0] negative_prompt = negative_prompts[0] if prompt == '': # disable expansion when empty since it is not meaningful and influences image prompt use_expansion = False extra_positive_prompts = prompts[1:] if len(prompts) > 1 else [] extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else [] if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'Loading models ...') lora_filenames = modules.util.remove_performance_lora(modules.config.lora_filenames, async_task.performance_selection) loras, prompt = parse_lora_references_from_prompt(prompt, async_task.loras, modules.config.default_max_lora_number, lora_filenames=lora_filenames) loras += async_task.performance_loras pipeline.refresh_everything(refiner_model_name=async_task.refiner_model_name, base_model_name=async_task.base_model_name, loras=loras, base_model_additional_loras=base_model_additional_loras, use_synthetic_refiner=use_synthetic_refiner, vae_name=async_task.vae_name) pipeline.set_clip_skip(async_task.clip_skip) if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'Processing prompts ...') tasks = [] for i in range(image_number): if disable_seed_increment: task_seed = async_task.seed % (constants.MAX_SEED + 1) else: task_seed = (async_task.seed + i) % (constants.MAX_SEED + 1) # randint is inclusive, % is not task_rng = random.Random(task_seed) # may bind to inpaint noise in the future task_prompt = apply_wildcards(prompt, task_rng, i, async_task.read_wildcards_in_order) task_prompt = apply_arrays(task_prompt, i) task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, async_task.read_wildcards_in_order) task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, async_task.read_wildcards_in_order) for pmt in extra_positive_prompts] task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, async_task.read_wildcards_in_order) for pmt in extra_negative_prompts] positive_basic_workloads = [] negative_basic_workloads = [] task_styles = async_task.style_selections.copy() if use_style: placeholder_replaced = False for j, s in enumerate(task_styles): if s == random_style_name: s = get_random_style(task_rng) task_styles[j] = s p, n, style_has_placeholder = apply_style(s, positive=task_prompt) if style_has_placeholder: placeholder_replaced = True positive_basic_workloads = positive_basic_workloads + p negative_basic_workloads = negative_basic_workloads + n if not placeholder_replaced: positive_basic_workloads = [task_prompt] + positive_basic_workloads else: positive_basic_workloads.append(task_prompt) negative_basic_workloads.append(task_negative_prompt) # Always use independent workload for negative. positive_basic_workloads = positive_basic_workloads + task_extra_positive_prompts negative_basic_workloads = negative_basic_workloads + task_extra_negative_prompts positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=task_prompt) negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=task_negative_prompt) tasks.append(dict( task_seed=task_seed, task_prompt=task_prompt, task_negative_prompt=task_negative_prompt, positive=positive_basic_workloads, negative=negative_basic_workloads, expansion='', c=None, uc=None, positive_top_k=len(positive_basic_workloads), negative_top_k=len(negative_basic_workloads), log_positive_prompt='\n'.join([task_prompt] + task_extra_positive_prompts), log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts), styles=task_styles )) if use_expansion: if advance_progress: current_progress += 1 for i, t in enumerate(tasks): progressbar(async_task, current_progress, f'Preparing Fooocus text #{i + 1} ...') expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed']) print(f'[Prompt Expansion] {expansion}') t['expansion'] = expansion t['positive'] = copy.deepcopy(t['positive']) + [expansion] # Deep copy. if advance_progress: current_progress += 1 for i, t in enumerate(tasks): progressbar(async_task, current_progress, f'Encoding positive #{i + 1} ...') t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k']) if advance_progress: current_progress += 1 for i, t in enumerate(tasks): if abs(float(async_task.cfg_scale) - 1.0) < 1e-4: t['uc'] = pipeline.clone_cond(t['c']) else: progressbar(async_task, current_progress, f'Encoding negative #{i + 1} ...') t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k']) return tasks, use_expansion, loras, current_progress def apply_freeu(async_task): print(f'FreeU is enabled!') pipeline.final_unet = core.apply_freeu( pipeline.final_unet, async_task.freeu_b1, async_task.freeu_b2, async_task.freeu_s1, async_task.freeu_s2 ) def patch_discrete(unet, scheduler_name): return core.opModelSamplingDiscrete.patch(unet, scheduler_name, False)[0] def patch_edm(unet, scheduler_name): return core.opModelSamplingContinuousEDM.patch(unet, scheduler_name, 120.0, 0.002)[0] def patch_samplers(async_task): final_scheduler_name = async_task.scheduler_name if async_task.scheduler_name in ['lcm', 'tcd']: final_scheduler_name = 'sgm_uniform' if pipeline.final_unet is not None: pipeline.final_unet = patch_discrete(pipeline.final_unet, async_task.scheduler_name) if pipeline.final_refiner_unet is not None: pipeline.final_refiner_unet = patch_discrete(pipeline.final_refiner_unet, async_task.scheduler_name) elif async_task.scheduler_name == 'edm_playground_v2.5': final_scheduler_name = 'karras' if pipeline.final_unet is not None: pipeline.final_unet = patch_edm(pipeline.final_unet, async_task.scheduler_name) if pipeline.final_refiner_unet is not None: pipeline.final_refiner_unet = patch_edm(pipeline.final_refiner_unet, async_task.scheduler_name) return final_scheduler_name def set_hyper_sd_defaults(async_task, current_progress, advance_progress=False): print('Enter Hyper-SD mode.') if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'Downloading Hyper-SD components ...') async_task.performance_loras += [(modules.config.downloading_sdxl_hyper_sd_lora(), 0.8)] if async_task.refiner_model_name != 'None': print(f'Refiner disabled in Hyper-SD mode.') async_task.refiner_model_name = 'None' async_task.sampler_name = 'dpmpp_sde_gpu' async_task.scheduler_name = 'karras' async_task.sharpness = 0.0 async_task.cfg_scale = 1.0 async_task.adaptive_cfg = 1.0 async_task.refiner_switch = 1.0 async_task.adm_scaler_positive = 1.0 async_task.adm_scaler_negative = 1.0 async_task.adm_scaler_end = 0.0 return current_progress def set_lightning_defaults(async_task, current_progress, advance_progress=False): print('Enter Lightning mode.') if advance_progress: current_progress += 1 progressbar(async_task, 1, 'Downloading Lightning components ...') async_task.performance_loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)] if async_task.refiner_model_name != 'None': print(f'Refiner disabled in Lightning mode.') async_task.refiner_model_name = 'None' async_task.sampler_name = 'euler' async_task.scheduler_name = 'sgm_uniform' async_task.sharpness = 0.0 async_task.cfg_scale = 1.0 async_task.adaptive_cfg = 1.0 async_task.refiner_switch = 1.0 async_task.adm_scaler_positive = 1.0 async_task.adm_scaler_negative = 1.0 async_task.adm_scaler_end = 0.0 return current_progress def set_lcm_defaults(async_task, current_progress, advance_progress=False): print('Enter LCM mode.') if advance_progress: current_progress += 1 progressbar(async_task, 1, 'Downloading LCM components ...') async_task.performance_loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)] if async_task.refiner_model_name != 'None': print(f'Refiner disabled in LCM mode.') async_task.refiner_model_name = 'None' async_task.sampler_name = 'lcm' async_task.scheduler_name = 'lcm' async_task.sharpness = 0.0 async_task.cfg_scale = 1.0 async_task.adaptive_cfg = 1.0 async_task.refiner_switch = 1.0 async_task.adm_scaler_positive = 1.0 async_task.adm_scaler_negative = 1.0 async_task.adm_scaler_end = 0.0 return current_progress def apply_image_input(async_task, base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, goals, inpaint_head_model_path, inpaint_image, inpaint_mask, inpaint_parameterized, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner): if (async_task.current_tab == 'uov' or ( async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_vary_upscale)) \ and async_task.uov_method != flags.disabled.casefold() and async_task.uov_input_image is not None: async_task.uov_input_image, skip_prompt_processing, async_task.steps = prepare_upscale( async_task, goals, async_task.uov_input_image, async_task.uov_method, async_task.performance_selection, async_task.steps, 1, skip_prompt_processing=skip_prompt_processing) if (async_task.current_tab == 'inpaint' or ( async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_inpaint)) \ and isinstance(async_task.inpaint_input_image, dict): inpaint_image = async_task.inpaint_input_image['image'] inpaint_mask = async_task.inpaint_input_image['mask'][:, :, 0] if async_task.inpaint_advanced_masking_checkbox: if isinstance(async_task.inpaint_mask_image_upload, dict): if (isinstance(async_task.inpaint_mask_image_upload['image'], np.ndarray) and isinstance(async_task.inpaint_mask_image_upload['mask'], np.ndarray) and async_task.inpaint_mask_image_upload['image'].ndim == 3): async_task.inpaint_mask_image_upload = np.maximum( async_task.inpaint_mask_image_upload['image'], async_task.inpaint_mask_image_upload['mask']) if isinstance(async_task.inpaint_mask_image_upload, np.ndarray) and async_task.inpaint_mask_image_upload.ndim == 3: H, W, C = inpaint_image.shape async_task.inpaint_mask_image_upload = resample_image(async_task.inpaint_mask_image_upload, width=W, height=H) async_task.inpaint_mask_image_upload = np.mean(async_task.inpaint_mask_image_upload, axis=2) async_task.inpaint_mask_image_upload = (async_task.inpaint_mask_image_upload > 127).astype( np.uint8) * 255 inpaint_mask = np.maximum(inpaint_mask, async_task.inpaint_mask_image_upload) if int(async_task.inpaint_erode_or_dilate) != 0: inpaint_mask = erode_or_dilate(inpaint_mask, async_task.inpaint_erode_or_dilate) if async_task.invert_mask_checkbox: inpaint_mask = 255 - inpaint_mask inpaint_image = HWC3(inpaint_image) if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \ and (np.any(inpaint_mask > 127) or len(async_task.outpaint_selections) > 0): progressbar(async_task, 1, 'Downloading upscale models ...') modules.config.downloading_upscale_model() if inpaint_parameterized: progressbar(async_task, 1, 'Downloading inpainter ...') inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models( async_task.inpaint_engine) base_model_additional_loras += [(inpaint_patch_model_path, 1.0)] print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}') if async_task.refiner_model_name == 'None': use_synthetic_refiner = True async_task.refiner_switch = 0.8 else: inpaint_head_model_path, inpaint_patch_model_path = None, None print(f'[Inpaint] Parameterized inpaint is disabled.') if async_task.inpaint_additional_prompt != '': if async_task.prompt == '': async_task.prompt = async_task.inpaint_additional_prompt else: async_task.prompt = async_task.inpaint_additional_prompt + '\n' + async_task.prompt goals.append('inpaint') if async_task.current_tab == 'ip' or \ async_task.mixing_image_prompt_and_vary_upscale or \ async_task.mixing_image_prompt_and_inpaint: goals.append('cn') progressbar(async_task, 1, 'Downloading control models ...') if len(async_task.cn_tasks[flags.cn_canny]) > 0: controlnet_canny_path = modules.config.downloading_controlnet_canny() if len(async_task.cn_tasks[flags.cn_cpds]) > 0: controlnet_cpds_path = modules.config.downloading_controlnet_cpds() if len(async_task.cn_tasks[flags.cn_ip]) > 0: clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip') if len(async_task.cn_tasks[flags.cn_ip_face]) > 0: clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters( 'face') if async_task.current_tab == 'enhance' and async_task.enhance_input_image is not None: goals.append('enhance') skip_prompt_processing = True async_task.enhance_input_image = HWC3(async_task.enhance_input_image) return base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, inpaint_head_model_path, inpaint_image, inpaint_mask, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner def prepare_upscale(async_task, goals, uov_input_image, uov_method, performance, steps, current_progress, advance_progress=False, skip_prompt_processing=False): uov_input_image = HWC3(uov_input_image) if 'vary' in uov_method: goals.append('vary') elif 'upscale' in uov_method: goals.append('upscale') if 'fast' in uov_method: skip_prompt_processing = True steps = 0 else: steps = performance.steps_uov() if advance_progress: current_progress += 1 progressbar(async_task, current_progress, 'Downloading upscale models ...') modules.config.downloading_upscale_model() return uov_input_image, skip_prompt_processing, steps def prepare_enhance_prompt(prompt: str, fallback_prompt: str): if safe_str(prompt) == '' or len(remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='')) == 0: prompt = fallback_prompt return prompt def stop_processing(async_task, processing_start_time): async_task.processing = False processing_time = time.perf_counter() - processing_start_time print(f'Processing time (total): {processing_time:.2f} seconds') def process_enhance(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_progress, current_task_id, denoising_strength, inpaint_disable_initial_latent, inpaint_engine, inpaint_respective_field, inpaint_strength, prompt, negative_prompt, final_scheduler_name, goals, height, img, mask, preparation_steps, steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, show_intermediate_results=True, persist_image=True): base_model_additional_loras = [] inpaint_head_model_path = None inpaint_parameterized = inpaint_engine != 'None' # inpaint_engine = None, improve detail initial_latent = None prompt = prepare_enhance_prompt(prompt, async_task.prompt) negative_prompt = prepare_enhance_prompt(negative_prompt, async_task.negative_prompt) if 'vary' in goals: img, denoising_strength, initial_latent, width, height, current_progress = apply_vary( async_task, async_task.enhance_uov_method, denoising_strength, img, switch, current_progress) if 'upscale' in goals: direct_return, img, denoising_strength, initial_latent, tiled, width, height, current_progress = apply_upscale( async_task, img, async_task.enhance_uov_method, switch, current_progress) if direct_return: d = [('Upscale (Fast)', 'upscale_fast', '2x')] if modules.config.default_black_out_nsfw or async_task.black_out_nsfw: progressbar(async_task, current_progress, 'Checking for NSFW content ...') img = default_censor(img) progressbar(async_task, current_progress, f'Saving image {current_task_id + 1}/{total_count} to system ...') uov_image_path = log(img, d, output_format=async_task.output_format, persist_image=persist_image) yield_result(async_task, uov_image_path, current_progress, async_task.black_out_nsfw, False, do_not_show_finished_images=not show_intermediate_results or async_task.disable_intermediate_results) return current_progress, img, prompt, negative_prompt if 'inpaint' in goals and inpaint_parameterized: progressbar(async_task, current_progress, 'Downloading inpainter ...') inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models( inpaint_engine) if inpaint_patch_model_path not in base_model_additional_loras: base_model_additional_loras += [(inpaint_patch_model_path, 1.0)] progressbar(async_task, current_progress, 'Preparing enhance prompts ...') # positive and negative conditioning aren't available here anymore, process prompt again tasks_enhance, use_expansion, loras, current_progress = process_prompt( async_task, prompt, negative_prompt, base_model_additional_loras, 1, True, use_expansion, use_style, use_synthetic_refiner, current_progress) task_enhance = tasks_enhance[0] # TODO could support vary, upscale and CN in the future # if 'cn' in goals: # apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width) if async_task.freeu_enabled: apply_freeu(async_task) patch_samplers(async_task) if 'inpaint' in goals: denoising_strength, initial_latent, width, height, current_progress = apply_inpaint( async_task, None, inpaint_head_model_path, img, mask, inpaint_parameterized, inpaint_strength, inpaint_respective_field, switch, inpaint_disable_initial_latent, current_progress, True) imgs, img_paths, current_progress = process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, final_scheduler_name, goals, initial_latent, steps, switch, task_enhance['c'], task_enhance['uc'], task_enhance, loras, tiled, use_expansion, width, height, current_progress, preparation_steps, total_count, show_intermediate_results, persist_image) del task_enhance['c'], task_enhance['uc'] # Save memory return current_progress, imgs[0], prompt, negative_prompt def enhance_upscale(all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps, prompt, negative_prompt, final_scheduler_name, height, img, preparation_steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image=True): # reset inpaint worker to prevent tensor size issues and not mix upscale and inpainting inpaint_worker.current_task = None current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * (done_steps_upscaling + done_steps_inpainting)) goals_enhance = [] img, skip_prompt_processing, steps = prepare_upscale( async_task, goals_enhance, img, async_task.enhance_uov_method, async_task.performance_selection, enhance_steps, current_progress) steps, _, _, _ = apply_overrides(async_task, steps, height, width) exception_result = '' if len(goals_enhance) > 0: try: current_progress, img, prompt, negative_prompt = process_enhance( all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_progress, current_task_id, denoising_strength, False, 'None', 0.0, 0.0, prompt, negative_prompt, final_scheduler_name, goals_enhance, height, img, None, preparation_steps, steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image=persist_image) except ldm_patched.modules.model_management.InterruptProcessingException: if async_task.last_stop == 'skip': print('User skipped') async_task.last_stop = False # also skip all enhance steps for this image, but add the steps to the progress bar if async_task.enhance_uov_processing_order == flags.enhancement_uov_before: done_steps_inpainting += len(async_task.enhance_ctrls) * enhance_steps exception_result = 'continue' else: print('User stopped') exception_result = 'break' finally: done_steps_upscaling += steps return current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result @torch.no_grad() @torch.inference_mode() def handler(async_task: AsyncTask): preparation_start_time = time.perf_counter() async_task.processing = True async_task.outpaint_selections = [o.lower() for o in async_task.outpaint_selections] base_model_additional_loras = [] async_task.uov_method = async_task.uov_method.casefold() async_task.enhance_uov_method = async_task.enhance_uov_method.casefold() if fooocus_expansion in async_task.style_selections: use_expansion = True async_task.style_selections.remove(fooocus_expansion) else: use_expansion = False use_style = len(async_task.style_selections) > 0 if async_task.base_model_name == async_task.refiner_model_name: print(f'Refiner disabled because base model and refiner are same.') async_task.refiner_model_name = 'None' current_progress = 0 if async_task.performance_selection == Performance.EXTREME_SPEED: set_lcm_defaults(async_task, current_progress, advance_progress=True) elif async_task.performance_selection == Performance.LIGHTNING: set_lightning_defaults(async_task, current_progress, advance_progress=True) elif async_task.performance_selection == Performance.HYPER_SD: set_hyper_sd_defaults(async_task, current_progress, advance_progress=True) print(f'[Parameters] Adaptive CFG = {async_task.adaptive_cfg}') print(f'[Parameters] CLIP Skip = {async_task.clip_skip}') print(f'[Parameters] Sharpness = {async_task.sharpness}') print(f'[Parameters] ControlNet Softness = {async_task.controlnet_softness}') print(f'[Parameters] ADM Scale = ' f'{async_task.adm_scaler_positive} : ' f'{async_task.adm_scaler_negative} : ' f'{async_task.adm_scaler_end}') print(f'[Parameters] Seed = {async_task.seed}') apply_patch_settings(async_task) print(f'[Parameters] CFG = {async_task.cfg_scale}') initial_latent = None denoising_strength = 1.0 tiled = False width, height = async_task.aspect_ratios_selection.replace('×', ' ').split(' ')[:2] width, height = int(width), int(height) skip_prompt_processing = False inpaint_worker.current_task = None inpaint_parameterized = async_task.inpaint_engine != 'None' inpaint_image = None inpaint_mask = None inpaint_head_model_path = None use_synthetic_refiner = False controlnet_canny_path = None controlnet_cpds_path = None clip_vision_path, ip_negative_path, ip_adapter_path, ip_adapter_face_path = None, None, None, None goals = [] tasks = [] current_progress = 1 if async_task.input_image_checkbox: base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, inpaint_head_model_path, inpaint_image, inpaint_mask, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner = apply_image_input( async_task, base_model_additional_loras, clip_vision_path, controlnet_canny_path, controlnet_cpds_path, goals, inpaint_head_model_path, inpaint_image, inpaint_mask, inpaint_parameterized, ip_adapter_face_path, ip_adapter_path, ip_negative_path, skip_prompt_processing, use_synthetic_refiner) # Load or unload CNs progressbar(async_task, current_progress, 'Loading control models ...') pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path]) ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path) ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path) async_task.steps, switch, width, height = apply_overrides(async_task, async_task.steps, height, width) print(f'[Parameters] Sampler = {async_task.sampler_name} - {async_task.scheduler_name}') print(f'[Parameters] Steps = {async_task.steps} - {switch}') progressbar(async_task, current_progress, 'Initializing ...') loras = async_task.loras if not skip_prompt_processing: tasks, use_expansion, loras, current_progress = process_prompt(async_task, async_task.prompt, async_task.negative_prompt, base_model_additional_loras, async_task.image_number, async_task.disable_seed_increment, use_expansion, use_style, use_synthetic_refiner, current_progress, advance_progress=True) if len(goals) > 0: current_progress += 1 progressbar(async_task, current_progress, 'Image processing ...') should_enhance = async_task.enhance_checkbox and (async_task.enhance_uov_method != flags.disabled.casefold() or len(async_task.enhance_ctrls) > 0) if 'vary' in goals: async_task.uov_input_image, denoising_strength, initial_latent, width, height, current_progress = apply_vary( async_task, async_task.uov_method, denoising_strength, async_task.uov_input_image, switch, current_progress) if 'upscale' in goals: direct_return, async_task.uov_input_image, denoising_strength, initial_latent, tiled, width, height, current_progress = apply_upscale( async_task, async_task.uov_input_image, async_task.uov_method, switch, current_progress, advance_progress=True) if direct_return: d = [('Upscale (Fast)', 'upscale_fast', '2x')] if modules.config.default_black_out_nsfw or async_task.black_out_nsfw: progressbar(async_task, 100, 'Checking for NSFW content ...') async_task.uov_input_image = default_censor(async_task.uov_input_image) progressbar(async_task, 100, 'Saving image to system ...') uov_input_image_path = log(async_task.uov_input_image, d, output_format=async_task.output_format) yield_result(async_task, uov_input_image_path, 100, async_task.black_out_nsfw, False, do_not_show_finished_images=True) return if 'inpaint' in goals: try: denoising_strength, initial_latent, width, height, current_progress = apply_inpaint(async_task, initial_latent, inpaint_head_model_path, inpaint_image, inpaint_mask, inpaint_parameterized, async_task.inpaint_strength, async_task.inpaint_respective_field, switch, async_task.inpaint_disable_initial_latent, current_progress, advance_progress=True) except EarlyReturnException: return if 'cn' in goals: apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width, current_progress) if async_task.debugging_cn_preprocessor: return if async_task.freeu_enabled: apply_freeu(async_task) # async_task.steps can have value of uov steps here when upscale has been applied steps, _, _, _ = apply_overrides(async_task, async_task.steps, height, width) images_to_enhance = [] if 'enhance' in goals: async_task.image_number = 1 images_to_enhance += [async_task.enhance_input_image] height, width, _ = async_task.enhance_input_image.shape # input image already provided, processing is skipped steps = 0 yield_result(async_task, async_task.enhance_input_image, current_progress, async_task.black_out_nsfw, False, async_task.disable_intermediate_results) all_steps = steps * async_task.image_number if async_task.enhance_checkbox and async_task.enhance_uov_method != flags.disabled.casefold(): enhance_upscale_steps = async_task.performance_selection.steps() if 'upscale' in async_task.enhance_uov_method: if 'fast' in async_task.enhance_uov_method: enhance_upscale_steps = 0 else: enhance_upscale_steps = async_task.performance_selection.steps_uov() enhance_upscale_steps, _, _, _ = apply_overrides(async_task, enhance_upscale_steps, height, width) enhance_upscale_steps_total = async_task.image_number * enhance_upscale_steps all_steps += enhance_upscale_steps_total if async_task.enhance_checkbox and len(async_task.enhance_ctrls) != 0: enhance_steps, _, _, _ = apply_overrides(async_task, async_task.original_steps, height, width) all_steps += async_task.image_number * len(async_task.enhance_ctrls) * enhance_steps all_steps = max(all_steps, 1) print(f'[Parameters] Denoising Strength = {denoising_strength}') if isinstance(initial_latent, dict) and 'samples' in initial_latent: log_shape = initial_latent['samples'].shape else: log_shape = f'Image Space {(height, width)}' print(f'[Parameters] Initial Latent shape: {log_shape}') preparation_time = time.perf_counter() - preparation_start_time print(f'Preparation time: {preparation_time:.2f} seconds') final_scheduler_name = patch_samplers(async_task) print(f'Using {final_scheduler_name} scheduler.') async_task.yields.append(['preview', (current_progress, 'Moving model to GPU ...', None)]) processing_start_time = time.perf_counter() preparation_steps = current_progress total_count = async_task.image_number def callback(step, x0, x, total_steps, y): if step == 0: async_task.callback_steps = 0 async_task.callback_steps += (100 - preparation_steps) / float(all_steps) async_task.yields.append(['preview', ( int(current_progress + async_task.callback_steps), f'Sampling step {step + 1}/{total_steps}, image {current_task_id + 1}/{total_count} ...', y)]) show_intermediate_results = len(tasks) > 1 or async_task.should_enhance persist_image = not async_task.should_enhance or not async_task.save_final_enhanced_image_only for current_task_id, task in enumerate(tasks): progressbar(async_task, current_progress, f'Preparing task {current_task_id + 1}/{async_task.image_number} ...') execution_start_time = time.perf_counter() try: imgs, img_paths, current_progress = process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, final_scheduler_name, goals, initial_latent, async_task.steps, switch, task['c'], task['uc'], task, loras, tiled, use_expansion, width, height, current_progress, preparation_steps, async_task.image_number, show_intermediate_results, persist_image) current_progress = int(preparation_steps + (100 - preparation_steps) / float(all_steps) * async_task.steps * (current_task_id + 1)) images_to_enhance += imgs except ldm_patched.modules.model_management.InterruptProcessingException: if async_task.last_stop == 'skip': print('User skipped') async_task.last_stop = False continue else: print('User stopped') break del task['c'], task['uc'] # Save memory execution_time = time.perf_counter() - execution_start_time print(f'Generating and saving time: {execution_time:.2f} seconds') if not async_task.should_enhance: print(f'[Enhance] Skipping, preconditions aren\'t met') stop_processing(async_task, processing_start_time) return progressbar(async_task, current_progress, 'Processing enhance ...') active_enhance_tabs = len(async_task.enhance_ctrls) should_process_enhance_uov = async_task.enhance_uov_method != flags.disabled.casefold() enhance_uov_before = False enhance_uov_after = False if should_process_enhance_uov: active_enhance_tabs += 1 enhance_uov_before = async_task.enhance_uov_processing_order == flags.enhancement_uov_before enhance_uov_after = async_task.enhance_uov_processing_order == flags.enhancement_uov_after total_count = len(images_to_enhance) * active_enhance_tabs async_task.images_to_enhance_count = len(images_to_enhance) base_progress = current_progress current_task_id = -1 done_steps_upscaling = 0 done_steps_inpainting = 0 enhance_steps, _, _, _ = apply_overrides(async_task, async_task.original_steps, height, width) exception_result = None for index, img in enumerate(images_to_enhance): async_task.enhance_stats[index] = 0 enhancement_image_start_time = time.perf_counter() last_enhance_prompt = async_task.prompt last_enhance_negative_prompt = async_task.negative_prompt if enhance_uov_before: current_task_id += 1 persist_image = not async_task.save_final_enhanced_image_only or active_enhance_tabs == 0 current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result = enhance_upscale( all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps, async_task.prompt, async_task.negative_prompt, final_scheduler_name, height, img, preparation_steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image) async_task.enhance_stats[index] += 1 if exception_result == 'continue': continue elif exception_result == 'break': break # inpaint for all other tabs for enhance_mask_dino_prompt_text, enhance_prompt, enhance_negative_prompt, enhance_mask_model, enhance_mask_cloth_category, enhance_mask_sam_model, enhance_mask_text_threshold, enhance_mask_box_threshold, enhance_mask_sam_max_detections, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field, enhance_inpaint_erode_or_dilate, enhance_mask_invert in async_task.enhance_ctrls: current_task_id += 1 current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * (done_steps_upscaling + done_steps_inpainting)) progressbar(async_task, current_progress, f'Preparing enhancement {current_task_id + 1}/{total_count} ...') enhancement_task_start_time = time.perf_counter() is_last_enhance_for_image = (current_task_id + 1) % active_enhance_tabs == 0 and not enhance_uov_after persist_image = not async_task.save_final_enhanced_image_only or is_last_enhance_for_image extras = {} if enhance_mask_model == 'sam': print(f'[Enhance] Searching for "{enhance_mask_dino_prompt_text}"') elif enhance_mask_model == 'u2net_cloth_seg': extras['cloth_category'] = enhance_mask_cloth_category mask, dino_detection_count, sam_detection_count, sam_detection_on_mask_count = generate_mask_from_image( img, mask_model=enhance_mask_model, extras=extras, sam_options=SAMOptions( dino_prompt=enhance_mask_dino_prompt_text, dino_box_threshold=enhance_mask_box_threshold, dino_text_threshold=enhance_mask_text_threshold, dino_erode_or_dilate=async_task.dino_erode_or_dilate, dino_debug=async_task.debugging_dino, max_detections=enhance_mask_sam_max_detections, model_type=enhance_mask_sam_model, )) if len(mask.shape) == 3: mask = mask[:, :, 0] if int(enhance_inpaint_erode_or_dilate) != 0: mask = erode_or_dilate(mask, enhance_inpaint_erode_or_dilate) if enhance_mask_invert: mask = 255 - mask if async_task.debugging_enhance_masks_checkbox: async_task.yields.append(['preview', (current_progress, 'Loading ...', mask)]) yield_result(async_task, mask, current_progress, async_task.black_out_nsfw, False, async_task.disable_intermediate_results) async_task.enhance_stats[index] += 1 print(f'[Enhance] {dino_detection_count} boxes detected') print(f'[Enhance] {sam_detection_count} segments detected in boxes') print(f'[Enhance] {sam_detection_on_mask_count} segments applied to mask') if enhance_mask_model == 'sam' and (dino_detection_count == 0 or not async_task.debugging_dino and sam_detection_on_mask_count == 0): print(f'[Enhance] No "{enhance_mask_dino_prompt_text}" detected, skipping') continue goals_enhance = ['inpaint'] try: current_progress, img, enhance_prompt_processed, enhance_negative_prompt_processed = process_enhance( all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_progress, current_task_id, denoising_strength, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_respective_field, enhance_inpaint_strength, enhance_prompt, enhance_negative_prompt, final_scheduler_name, goals_enhance, height, img, mask, preparation_steps, enhance_steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image=persist_image) async_task.enhance_stats[index] += 1 if (should_process_enhance_uov and async_task.enhance_uov_processing_order == flags.enhancement_uov_after and async_task.enhance_uov_prompt_type == flags.enhancement_uov_prompt_type_last_filled): if enhance_prompt_processed != '': last_enhance_prompt = enhance_prompt_processed if enhance_negative_prompt_processed != '': last_enhance_negative_prompt = enhance_negative_prompt_processed except ldm_patched.modules.model_management.InterruptProcessingException: if async_task.last_stop == 'skip': print('User skipped') async_task.last_stop = False continue else: print('User stopped') exception_result = 'break' break finally: done_steps_inpainting += enhance_steps enhancement_task_time = time.perf_counter() - enhancement_task_start_time print(f'Enhancement time: {enhancement_task_time:.2f} seconds') if exception_result == 'break': break if enhance_uov_after: current_task_id += 1 # last step in enhance, always save persist_image = True current_task_id, done_steps_inpainting, done_steps_upscaling, img, exception_result = enhance_upscale( all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id, denoising_strength, done_steps_inpainting, done_steps_upscaling, enhance_steps, last_enhance_prompt, last_enhance_negative_prompt, final_scheduler_name, height, img, preparation_steps, switch, tiled, total_count, use_expansion, use_style, use_synthetic_refiner, width, persist_image) async_task.enhance_stats[index] += 1 if exception_result == 'continue': continue elif exception_result == 'break': break enhancement_image_time = time.perf_counter() - enhancement_image_start_time print(f'Enhancement image time: {enhancement_image_time:.2f} seconds') stop_processing(async_task, processing_start_time) return while True: time.sleep(0.01) if len(async_tasks) > 0: task = async_tasks.pop(0) try: handler(task) if task.generate_image_grid: build_image_wall(task) task.yields.append(['finish', task.results]) pipeline.prepare_text_encoder(async_call=True) except: traceback.print_exc() task.yields.append(['finish', task.results]) finally: if pid in modules.patch.patch_settings: del modules.patch.patch_settings[pid] pass threading.Thread(target=worker, daemon=True).start()