| 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 |
|
|
| |
| 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 |
| 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) |
|
|
| |
| 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) |
|
|
| |
| 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 == '': |
| |
| 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) |
|
|
| task_rng = random.Random(task_seed) |
| 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) |
|
|
| 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] |
| 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' |
| 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 ...') |
| |
| 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] |
| |
| |
| |
| 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'] |
| 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): |
| |
| 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 |
| |
| 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) |
|
|
| |
| 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) |
|
|
| |
| 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 |
| |
| steps = 0 |
|
|
| 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'] |
| 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 |
|
|
| |
| 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 |
| |
| 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() |
|
|
