Spaces:
Build error
Build error
| import threading | |
| import re | |
| from modules.patch import PatchSettings, patch_settings, patch_all | |
| patch_all() | |
| class AsyncTask: | |
| def __init__(self, args): | |
| self.args = args | |
| self.yields = [] | |
| self.results = [] | |
| self.last_stop = False | |
| self.processing = False | |
| async_tasks = [] | |
| def worker(): | |
| global async_tasks | |
| import os | |
| import traceback | |
| import math | |
| import numpy as np | |
| import cv2 | |
| import torch | |
| import time | |
| import shared | |
| import random | |
| import copy | |
| import modules.default_pipeline as pipeline | |
| import modules.core as core | |
| import modules.flags as flags | |
| import modules.config | |
| 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 | |
| import args_manager | |
| from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays | |
| 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, ordinal_suffix, get_enabled_loras | |
| from modules.upscaler import perform_upscale | |
| from modules.flags import Performance | |
| from modules.meta_parser import get_metadata_parser, MetadataScheme | |
| 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, do_not_show_finished_images=False): | |
| if not isinstance(imgs, list): | |
| imgs = [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 handler(async_task): | |
| execution_start_time = time.perf_counter() | |
| async_task.processing = True | |
| args = async_task.args | |
| args.reverse() | |
| prompt = args.pop() | |
| negative_prompt = args.pop() | |
| style_selections = args.pop() | |
| performance_selection = Performance(args.pop()) | |
| aspect_ratios_selection = args.pop() | |
| image_number = args.pop() | |
| output_format = args.pop() | |
| image_seed = args.pop() | |
| read_wildcards_in_order = args.pop() | |
| sharpness = args.pop() | |
| guidance_scale = args.pop() | |
| base_model_name = args.pop() | |
| refiner_model_name = args.pop() | |
| refiner_switch = args.pop() | |
| loras = get_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop())] for _ in range(modules.config.default_max_lora_number)]) | |
| input_image_checkbox = args.pop() | |
| current_tab = args.pop() | |
| uov_method = args.pop() | |
| uov_input_image = args.pop() | |
| outpaint_selections = args.pop() | |
| inpaint_input_image = args.pop() | |
| inpaint_additional_prompt = args.pop() | |
| inpaint_mask_image_upload = args.pop() | |
| disable_preview = args.pop() | |
| disable_intermediate_results = args.pop() | |
| disable_seed_increment = args.pop() | |
| adm_scaler_positive = args.pop() | |
| adm_scaler_negative = args.pop() | |
| adm_scaler_end = args.pop() | |
| adaptive_cfg = args.pop() | |
| sampler_name = args.pop() | |
| scheduler_name = args.pop() | |
| overwrite_step = args.pop() | |
| overwrite_switch = args.pop() | |
| overwrite_width = args.pop() | |
| overwrite_height = args.pop() | |
| overwrite_vary_strength = args.pop() | |
| overwrite_upscale_strength = args.pop() | |
| mixing_image_prompt_and_vary_upscale = args.pop() | |
| mixing_image_prompt_and_inpaint = args.pop() | |
| debugging_cn_preprocessor = args.pop() | |
| skipping_cn_preprocessor = args.pop() | |
| canny_low_threshold = args.pop() | |
| canny_high_threshold = args.pop() | |
| refiner_swap_method = args.pop() | |
| controlnet_softness = args.pop() | |
| freeu_enabled = args.pop() | |
| freeu_b1 = args.pop() | |
| freeu_b2 = args.pop() | |
| freeu_s1 = args.pop() | |
| freeu_s2 = args.pop() | |
| debugging_inpaint_preprocessor = args.pop() | |
| inpaint_disable_initial_latent = args.pop() | |
| inpaint_engine = args.pop() | |
| inpaint_strength = args.pop() | |
| inpaint_respective_field = args.pop() | |
| inpaint_mask_upload_checkbox = args.pop() | |
| invert_mask_checkbox = args.pop() | |
| inpaint_erode_or_dilate = args.pop() | |
| save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False | |
| metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS | |
| cn_tasks = {x: [] for x in flags.ip_list} | |
| for _ in range(flags.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: | |
| cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight]) | |
| outpaint_selections = [o.lower() for o in outpaint_selections] | |
| base_model_additional_loras = [] | |
| raw_style_selections = copy.deepcopy(style_selections) | |
| uov_method = uov_method.lower() | |
| if fooocus_expansion in style_selections: | |
| use_expansion = True | |
| style_selections.remove(fooocus_expansion) | |
| else: | |
| use_expansion = False | |
| use_style = len(style_selections) > 0 | |
| if base_model_name == refiner_model_name: | |
| print(f'Refiner disabled because base model and refiner are same.') | |
| refiner_model_name = 'None' | |
| steps = performance_selection.steps() | |
| if performance_selection == Performance.EXTREME_SPEED: | |
| print('Enter LCM mode.') | |
| progressbar(async_task, 1, 'Downloading LCM components ...') | |
| loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)] | |
| if refiner_model_name != 'None': | |
| print(f'Refiner disabled in LCM mode.') | |
| refiner_model_name = 'None' | |
| sampler_name = 'lcm' | |
| scheduler_name = 'lcm' | |
| sharpness = 0.0 | |
| guidance_scale = 1.0 | |
| adaptive_cfg = 1.0 | |
| refiner_switch = 1.0 | |
| adm_scaler_positive = 1.0 | |
| adm_scaler_negative = 1.0 | |
| adm_scaler_end = 0.0 | |
| elif performance_selection == Performance.LIGHTNING: | |
| print('Enter Lightning mode.') | |
| progressbar(async_task, 1, 'Downloading Lightning components ...') | |
| loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)] | |
| if refiner_model_name != 'None': | |
| print(f'Refiner disabled in Lightning mode.') | |
| refiner_model_name = 'None' | |
| sampler_name = 'euler' | |
| scheduler_name = 'sgm_uniform' | |
| sharpness = 0.0 | |
| guidance_scale = 1.0 | |
| adaptive_cfg = 1.0 | |
| refiner_switch = 1.0 | |
| adm_scaler_positive = 1.0 | |
| adm_scaler_negative = 1.0 | |
| adm_scaler_end = 0.0 | |
| print(f'[Parameters] Adaptive CFG = {adaptive_cfg}') | |
| print(f'[Parameters] Sharpness = {sharpness}') | |
| print(f'[Parameters] ControlNet Softness = {controlnet_softness}') | |
| print(f'[Parameters] ADM Scale = ' | |
| f'{adm_scaler_positive} : ' | |
| f'{adm_scaler_negative} : ' | |
| f'{adm_scaler_end}') | |
| patch_settings[pid] = PatchSettings( | |
| sharpness, | |
| adm_scaler_end, | |
| adm_scaler_positive, | |
| adm_scaler_negative, | |
| controlnet_softness, | |
| adaptive_cfg | |
| ) | |
| cfg_scale = float(guidance_scale) | |
| print(f'[Parameters] CFG = {cfg_scale}') | |
| initial_latent = None | |
| denoising_strength = 1.0 | |
| tiled = False | |
| width, height = aspect_ratios_selection.replace('×', ' ').split(' ')[:2] | |
| width, height = int(width), int(height) | |
| skip_prompt_processing = False | |
| inpaint_worker.current_task = None | |
| inpaint_parameterized = 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 | |
| seed = int(image_seed) | |
| print(f'[Parameters] Seed = {seed}') | |
| goals = [] | |
| tasks = [] | |
| if input_image_checkbox: | |
| if (current_tab == 'uov' or ( | |
| current_tab == 'ip' and mixing_image_prompt_and_vary_upscale)) \ | |
| and uov_method != flags.disabled and uov_input_image is not None: | |
| 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 | |
| else: | |
| steps = performance_selection.steps_uov() | |
| progressbar(async_task, 1, 'Downloading upscale models ...') | |
| modules.config.downloading_upscale_model() | |
| if (current_tab == 'inpaint' or ( | |
| current_tab == 'ip' and mixing_image_prompt_and_inpaint)) \ | |
| and isinstance(inpaint_input_image, dict): | |
| inpaint_image = inpaint_input_image['image'] | |
| inpaint_mask = inpaint_input_image['mask'][:, :, 0] | |
| if inpaint_mask_upload_checkbox: | |
| if isinstance(inpaint_mask_image_upload, np.ndarray): | |
| if inpaint_mask_image_upload.ndim == 3: | |
| H, W, C = inpaint_image.shape | |
| inpaint_mask_image_upload = resample_image(inpaint_mask_image_upload, width=W, height=H) | |
| inpaint_mask_image_upload = np.mean(inpaint_mask_image_upload, axis=2) | |
| inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255 | |
| inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload) | |
| if int(inpaint_erode_or_dilate) != 0: | |
| inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate) | |
| if 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(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( | |
| inpaint_engine) | |
| base_model_additional_loras += [(inpaint_patch_model_path, 1.0)] | |
| print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}') | |
| if refiner_model_name == 'None': | |
| use_synthetic_refiner = True | |
| refiner_switch = 0.8 | |
| else: | |
| inpaint_head_model_path, inpaint_patch_model_path = None, None | |
| print(f'[Inpaint] Parameterized inpaint is disabled.') | |
| if inpaint_additional_prompt != '': | |
| if prompt == '': | |
| prompt = inpaint_additional_prompt | |
| else: | |
| prompt = inpaint_additional_prompt + '\n' + prompt | |
| goals.append('inpaint') | |
| if current_tab == 'ip' or \ | |
| mixing_image_prompt_and_vary_upscale or \ | |
| mixing_image_prompt_and_inpaint: | |
| goals.append('cn') | |
| progressbar(async_task, 1, 'Downloading control models ...') | |
| if len(cn_tasks[flags.cn_canny]) > 0: | |
| controlnet_canny_path = modules.config.downloading_controlnet_canny() | |
| if len(cn_tasks[flags.cn_cpds]) > 0: | |
| controlnet_cpds_path = modules.config.downloading_controlnet_cpds() | |
| if len(cn_tasks[flags.cn_ip]) > 0: | |
| clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip') | |
| if len(cn_tasks[flags.cn_ip_face]) > 0: | |
| clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters( | |
| 'face') | |
| progressbar(async_task, 1, 'Loading control models ...') | |
| # Load or unload CNs | |
| 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) | |
| if overwrite_step > 0: | |
| steps = overwrite_step | |
| switch = int(round(steps * refiner_switch)) | |
| if overwrite_switch > 0: | |
| switch = overwrite_switch | |
| if overwrite_width > 0: | |
| width = overwrite_width | |
| if overwrite_height > 0: | |
| height = overwrite_height | |
| print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}') | |
| print(f'[Parameters] Steps = {steps} - {switch}') | |
| progressbar(async_task, 1, 'Initializing ...') | |
| if not skip_prompt_processing: | |
| 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 [] | |
| progressbar(async_task, 3, 'Loading models ...') | |
| pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name, | |
| loras=loras, base_model_additional_loras=base_model_additional_loras, | |
| use_synthetic_refiner=use_synthetic_refiner) | |
| progressbar(async_task, 3, 'Processing prompts ...') | |
| tasks = [] | |
| for i in range(image_number): | |
| if disable_seed_increment: | |
| task_seed = seed % (constants.MAX_SEED + 1) | |
| else: | |
| task_seed = (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, read_wildcards_in_order) | |
| task_prompt = apply_arrays(task_prompt, i) | |
| task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order) | |
| task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_positive_prompts] | |
| task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_negative_prompts] | |
| positive_basic_workloads = [] | |
| negative_basic_workloads = [] | |
| if use_style: | |
| for s in style_selections: | |
| p, n = apply_style(s, positive=task_prompt) | |
| positive_basic_workloads = positive_basic_workloads + p | |
| negative_basic_workloads = negative_basic_workloads + n | |
| 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), | |
| )) | |
| if use_expansion: | |
| for i, t in enumerate(tasks): | |
| progressbar(async_task, 5, 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. | |
| for i, t in enumerate(tasks): | |
| progressbar(async_task, 7, f'Encoding positive #{i + 1} ...') | |
| t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k']) | |
| for i, t in enumerate(tasks): | |
| if abs(float(cfg_scale) - 1.0) < 1e-4: | |
| t['uc'] = pipeline.clone_cond(t['c']) | |
| else: | |
| progressbar(async_task, 10, f'Encoding negative #{i + 1} ...') | |
| t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k']) | |
| if len(goals) > 0: | |
| progressbar(async_task, 13, 'Image processing ...') | |
| if 'vary' in goals: | |
| if 'subtle' in uov_method: | |
| denoising_strength = 0.5 | |
| if 'strong' in uov_method: | |
| denoising_strength = 0.85 | |
| if overwrite_vary_strength > 0: | |
| denoising_strength = 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) | |
| progressbar(async_task, 13, 'VAE encoding ...') | |
| candidate_vae, _ = pipeline.get_candidate_vae( | |
| steps=steps, | |
| switch=switch, | |
| denoise=denoising_strength, | |
| refiner_swap_method=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((height, width))}.') | |
| if 'upscale' in goals: | |
| H, W, C = uov_input_image.shape | |
| progressbar(async_task, 13, f'Upscaling image from {str((H, W))} ...') | |
| 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: | |
| d = [('Upscale (Fast)', 'upscale_fast', '2x')] | |
| uov_input_image_path = log(uov_input_image, d, output_format=output_format) | |
| yield_result(async_task, uov_input_image_path, do_not_show_finished_images=True) | |
| return | |
| tiled = True | |
| denoising_strength = 0.382 | |
| if overwrite_upscale_strength > 0: | |
| denoising_strength = overwrite_upscale_strength | |
| initial_pixels = core.numpy_to_pytorch(uov_input_image) | |
| progressbar(async_task, 13, 'VAE encoding ...') | |
| candidate_vae, _ = pipeline.get_candidate_vae( | |
| steps=steps, | |
| switch=switch, | |
| denoise=denoising_strength, | |
| refiner_swap_method=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((height, width))}.') | |
| if 'inpaint' in goals: | |
| if len(outpaint_selections) > 0: | |
| H, W, C = inpaint_image.shape | |
| if 'top' in 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 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 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 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()) | |
| inpaint_strength = 1.0 | |
| inpaint_respective_field = 1.0 | |
| denoising_strength = inpaint_strength | |
| inpaint_worker.current_task = inpaint_worker.InpaintWorker( | |
| image=inpaint_image, | |
| mask=inpaint_mask, | |
| use_fill=denoising_strength > 0.99, | |
| k=inpaint_respective_field | |
| ) | |
| if debugging_inpaint_preprocessor: | |
| yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), | |
| do_not_show_finished_images=True) | |
| return | |
| progressbar(async_task, 13, '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=steps, | |
| switch=switch, | |
| denoise=denoising_strength, | |
| refiner_swap_method=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: | |
| progressbar(async_task, 13, 'VAE SD15 encoding ...') | |
| latent_swap = core.encode_vae( | |
| vae=candidate_vae_swap, | |
| pixels=inpaint_pixel_fill)['samples'] | |
| progressbar(async_task, 13, '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_height, final_width))}, latent is {str((height, width))}.') | |
| if 'cn' in goals: | |
| for task in 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 skipping_cn_preprocessor: | |
| cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold, canny_high_threshold) | |
| cn_img = HWC3(cn_img) | |
| task[0] = core.numpy_to_pytorch(cn_img) | |
| if debugging_cn_preprocessor: | |
| yield_result(async_task, cn_img, do_not_show_finished_images=True) | |
| return | |
| for task in 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 skipping_cn_preprocessor: | |
| cn_img = preprocessors.cpds(cn_img) | |
| cn_img = HWC3(cn_img) | |
| task[0] = core.numpy_to_pytorch(cn_img) | |
| if debugging_cn_preprocessor: | |
| yield_result(async_task, cn_img, do_not_show_finished_images=True) | |
| return | |
| for task in 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 debugging_cn_preprocessor: | |
| yield_result(async_task, cn_img, do_not_show_finished_images=True) | |
| return | |
| for task in cn_tasks[flags.cn_ip_face]: | |
| cn_img, cn_stop, cn_weight = task | |
| cn_img = HWC3(cn_img) | |
| if not 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 debugging_cn_preprocessor: | |
| yield_result(async_task, cn_img, do_not_show_finished_images=True) | |
| return | |
| all_ip_tasks = cn_tasks[flags.cn_ip] + 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) | |
| if freeu_enabled: | |
| print(f'FreeU is enabled!') | |
| pipeline.final_unet = core.apply_freeu( | |
| pipeline.final_unet, | |
| freeu_b1, | |
| freeu_b2, | |
| freeu_s1, | |
| freeu_s2 | |
| ) | |
| all_steps = steps * image_number | |
| 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() - execution_start_time | |
| print(f'Preparation time: {preparation_time:.2f} seconds') | |
| final_sampler_name = sampler_name | |
| final_scheduler_name = scheduler_name | |
| if scheduler_name == 'lcm': | |
| final_scheduler_name = 'sgm_uniform' | |
| if pipeline.final_unet is not None: | |
| pipeline.final_unet = core.opModelSamplingDiscrete.patch( | |
| pipeline.final_unet, | |
| sampling='lcm', | |
| zsnr=False)[0] | |
| if pipeline.final_refiner_unet is not None: | |
| pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch( | |
| pipeline.final_refiner_unet, | |
| sampling='lcm', | |
| zsnr=False)[0] | |
| print('Using lcm scheduler.') | |
| async_task.yields.append(['preview', (13, 'Moving model to GPU ...', None)]) | |
| def callback(step, x0, x, total_steps, y): | |
| done_steps = current_task_id * steps + step | |
| async_task.yields.append(['preview', ( | |
| int(15.0 + 85.0 * float(done_steps) / float(all_steps)), | |
| f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling', y)]) | |
| for current_task_id, task in enumerate(tasks): | |
| execution_start_time = time.perf_counter() | |
| try: | |
| if async_task.last_stop is not False: | |
| ldm_patched.modules.model_management.interrupt_current_processing() | |
| positive_cond, negative_cond = task['c'], task['uc'] | |
| 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 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=final_sampler_name, | |
| scheduler_name=final_scheduler_name, | |
| latent=initial_latent, | |
| denoise=denoising_strength, | |
| tiled=tiled, | |
| cfg_scale=cfg_scale, | |
| refiner_swap_method=refiner_swap_method, | |
| disable_preview=disable_preview | |
| ) | |
| del task['c'], task['uc'], 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] | |
| 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(raw_style_selections)), | |
| ('Performance', 'performance', performance_selection.value)] | |
| if performance_selection.steps() != steps: | |
| d.append(('Steps', 'steps', steps)) | |
| d += [('Resolution', 'resolution', str((width, height))), | |
| ('Guidance Scale', 'guidance_scale', guidance_scale), | |
| ('Sharpness', 'sharpness', 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', base_model_name), | |
| ('Refiner Model', 'refiner_model', refiner_model_name), | |
| ('Refiner Switch', 'refiner_switch', refiner_switch)] | |
| if refiner_model_name != 'None': | |
| if overwrite_switch > 0: | |
| d.append(('Overwrite Switch', 'overwrite_switch', overwrite_switch)) | |
| if refiner_swap_method != flags.refiner_swap_method: | |
| d.append(('Refiner Swap Method', 'refiner_swap_method', 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)) | |
| d.append(('Sampler', 'sampler', sampler_name)) | |
| d.append(('Scheduler', 'scheduler', scheduler_name)) | |
| d.append(('Seed', 'seed', str(task['task_seed']))) | |
| if freeu_enabled: | |
| d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, 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 save_metadata_to_images: | |
| metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme) | |
| metadata_parser.set_data(task['log_positive_prompt'], task['positive'], | |
| task['log_negative_prompt'], task['negative'], | |
| steps, base_model_name, refiner_model_name, loras) | |
| d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images)) | |
| d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version)) | |
| img_paths.append(log(x, d, metadata_parser, output_format)) | |
| yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results) | |
| except ldm_patched.modules.model_management.InterruptProcessingException as e: | |
| if async_task.last_stop == 'skip': | |
| print('User skipped') | |
| async_task.last_stop = False | |
| continue | |
| else: | |
| print('User stopped') | |
| break | |
| execution_time = time.perf_counter() - execution_start_time | |
| print(f'Generating and saving time: {execution_time:.2f} seconds') | |
| async_task.processing = False | |
| return | |
| while True: | |
| time.sleep(0.01) | |
| if len(async_tasks) > 0: | |
| task = async_tasks.pop(0) | |
| generate_image_grid = task.args.pop(0) | |
| try: | |
| handler(task) | |
| if 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() | |