import os import json import math import numbers import args_manager import tempfile import modules.flags import modules.sdxl_styles from modules.model_loader import load_file_from_url from modules.util import get_files_from_folder, makedirs_with_log from modules.flags import OutputFormat, Performance, MetadataScheme def get_config_path(key, default_value): env = os.getenv(key) if env is not None and isinstance(env, str): print(f"Environment: {key} = {env}") return env else: return os.path.abspath(default_value) config_path = get_config_path('config_path', "./config.txt") config_example_path = get_config_path('config_example_path', "config_modification_tutorial.txt") config_dict = {} always_save_keys = [] visited_keys = [] try: with open(os.path.abspath(f'./presets/default.json'), "r", encoding="utf-8") as json_file: config_dict.update(json.load(json_file)) except Exception as e: print(f'Load default preset failed.') print(e) try: if os.path.exists(config_path): with open(config_path, "r", encoding="utf-8") as json_file: config_dict.update(json.load(json_file)) always_save_keys = list(config_dict.keys()) except Exception as e: print(f'Failed to load config file "{config_path}" . The reason is: {str(e)}') print('Please make sure that:') print(f'1. The file "{config_path}" is a valid text file, and you have access to read it.') print('2. Use "\\\\" instead of "\\" when describing paths.') print('3. There is no "," before the last "}".') print('4. All key/value formats are correct.') def try_load_deprecated_user_path_config(): global config_dict if not os.path.exists('user_path_config.txt'): return try: deprecated_config_dict = json.load(open('user_path_config.txt', "r", encoding="utf-8")) def replace_config(old_key, new_key): if old_key in deprecated_config_dict: config_dict[new_key] = deprecated_config_dict[old_key] del deprecated_config_dict[old_key] replace_config('modelfile_path', 'path_checkpoints') replace_config('lorafile_path', 'path_loras') replace_config('embeddings_path', 'path_embeddings') replace_config('vae_approx_path', 'path_vae_approx') replace_config('upscale_models_path', 'path_upscale_models') replace_config('inpaint_models_path', 'path_inpaint') replace_config('controlnet_models_path', 'path_controlnet') replace_config('clip_vision_models_path', 'path_clip_vision') replace_config('fooocus_expansion_path', 'path_fooocus_expansion') replace_config('temp_outputs_path', 'path_outputs') if deprecated_config_dict.get("default_model", None) == 'juggernautXL_version6Rundiffusion.safetensors': os.replace('user_path_config.txt', 'user_path_config-deprecated.txt') print('Config updated successfully in silence. ' 'A backup of previous config is written to "user_path_config-deprecated.txt".') return if input("Newer models and configs are available. " "Download and update files? [Y/n]:") in ['n', 'N', 'No', 'no', 'NO']: config_dict.update(deprecated_config_dict) print('Loading using deprecated old models and deprecated old configs.') return else: os.replace('user_path_config.txt', 'user_path_config-deprecated.txt') print('Config updated successfully by user. ' 'A backup of previous config is written to "user_path_config-deprecated.txt".') return except Exception as e: print('Processing deprecated config failed') print(e) return try_load_deprecated_user_path_config() def get_presets(): preset_folder = 'presets' presets = ['initial'] if not os.path.exists(preset_folder): print('No presets found.') return presets return presets + [f[:f.index('.json')] for f in os.listdir(preset_folder) if f.endswith('.json')] def try_get_preset_content(preset): if isinstance(preset, str): preset_path = os.path.abspath(f'./presets/{preset}.json') try: if os.path.exists(preset_path): with open(preset_path, "r", encoding="utf-8") as json_file: json_content = json.load(json_file) print(f'Loaded preset: {preset_path}') return json_content else: raise FileNotFoundError except Exception as e: print(f'Load preset [{preset_path}] failed') print(e) return {} available_presets = get_presets() preset = args_manager.args.preset config_dict.update(try_get_preset_content(preset)) def get_path_output() -> str: """ Checking output path argument and overriding default path. """ global config_dict path_output = get_dir_or_set_default('path_outputs', '../outputs/', make_directory=True) if args_manager.args.output_path: print(f'Overriding config value path_outputs with {args_manager.args.output_path}') config_dict['path_outputs'] = path_output = args_manager.args.output_path return path_output def get_dir_or_set_default(key, default_value, as_array=False, make_directory=False): global config_dict, visited_keys, always_save_keys if key not in visited_keys: visited_keys.append(key) if key not in always_save_keys: always_save_keys.append(key) v = os.getenv(key) if v is not None: print(f"Environment: {key} = {v}") config_dict[key] = v else: v = config_dict.get(key, None) if isinstance(v, str): if make_directory: makedirs_with_log(v) if os.path.exists(v) and os.path.isdir(v): return v if not as_array else [v] elif isinstance(v, list): if make_directory: for d in v: makedirs_with_log(d) if all([os.path.exists(d) and os.path.isdir(d) for d in v]): return v if v is not None: print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.') if isinstance(default_value, list): dp = [] for path in default_value: abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), path)) dp.append(abs_path) os.makedirs(abs_path, exist_ok=True) else: dp = os.path.abspath(os.path.join(os.path.dirname(__file__), default_value)) os.makedirs(dp, exist_ok=True) if as_array: dp = [dp] config_dict[key] = dp return dp paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/checkpoints/'], True) paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True) path_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/') path_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/') path_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/') path_inpaint = get_dir_or_set_default('path_inpaint', '../models/inpaint/') path_controlnet = get_dir_or_set_default('path_controlnet', '../models/controlnet/') path_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vision/') path_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion') path_wildcards = get_dir_or_set_default('path_wildcards', '../wildcards/') path_outputs = get_path_output() def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False): global config_dict, visited_keys if key not in visited_keys: visited_keys.append(key) v = os.getenv(key) if v is not None: print(f"Environment: {key} = {v}") config_dict[key] = v if key not in config_dict: config_dict[key] = default_value return default_value v = config_dict.get(key, None) if not disable_empty_as_none: if v is None or v == '': v = 'None' if validator(v): return v else: if v is not None: print(f'Failed to load config key: {json.dumps({key:v})} is invalid; will use {json.dumps({key:default_value})} instead.') config_dict[key] = default_value return default_value def init_temp_path(path: str | None, default_path: str) -> str: if args_manager.args.temp_path: path = args_manager.args.temp_path if path != '' and path != default_path: try: if not os.path.isabs(path): path = os.path.abspath(path) os.makedirs(path, exist_ok=True) print(f'Using temp path {path}') return path except Exception as e: print(f'Could not create temp path {path}. Reason: {e}') print(f'Using default temp path {default_path} instead.') os.makedirs(default_path, exist_ok=True) return default_path default_temp_path = os.path.join(tempfile.gettempdir(), 'fooocus') temp_path = init_temp_path(get_config_item_or_set_default( key='temp_path', default_value=default_temp_path, validator=lambda x: isinstance(x, str), ), default_temp_path) temp_path_cleanup_on_launch = get_config_item_or_set_default( key='temp_path_cleanup_on_launch', default_value=True, validator=lambda x: isinstance(x, bool) ) default_base_model_name = default_model = get_config_item_or_set_default( key='default_model', default_value='model.safetensors', validator=lambda x: isinstance(x, str) ) previous_default_models = get_config_item_or_set_default( key='previous_default_models', default_value=[], validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x) ) default_refiner_model_name = default_refiner = get_config_item_or_set_default( key='default_refiner', default_value='None', validator=lambda x: isinstance(x, str) ) default_refiner_switch = get_config_item_or_set_default( key='default_refiner_switch', default_value=0.8, validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1 ) default_loras_min_weight = get_config_item_or_set_default( key='default_loras_min_weight', default_value=-2, validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10 ) default_loras_max_weight = get_config_item_or_set_default( key='default_loras_max_weight', default_value=2, validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10 ) default_loras = get_config_item_or_set_default( key='default_loras', default_value=[ [ True, "None", 1.0 ], [ True, "None", 1.0 ], [ True, "None", 1.0 ], [ True, "None", 1.0 ], [ True, "None", 1.0 ] ], validator=lambda x: isinstance(x, list) and all( len(y) == 3 and isinstance(y[0], bool) and isinstance(y[1], str) and isinstance(y[2], numbers.Number) or len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number) for y in x) ) default_loras = [(y[0], y[1], y[2]) if len(y) == 3 else (True, y[0], y[1]) for y in default_loras] default_max_lora_number = get_config_item_or_set_default( key='default_max_lora_number', default_value=len(default_loras) if isinstance(default_loras, list) and len(default_loras) > 0 else 5, validator=lambda x: isinstance(x, int) and x >= 1 ) default_cfg_scale = get_config_item_or_set_default( key='default_cfg_scale', default_value=7.0, validator=lambda x: isinstance(x, numbers.Number) ) default_sample_sharpness = get_config_item_or_set_default( key='default_sample_sharpness', default_value=2.0, validator=lambda x: isinstance(x, numbers.Number) ) default_sampler = get_config_item_or_set_default( key='default_sampler', default_value='dpmpp_2m_sde_gpu', validator=lambda x: x in modules.flags.sampler_list ) default_scheduler = get_config_item_or_set_default( key='default_scheduler', default_value='karras', validator=lambda x: x in modules.flags.scheduler_list ) default_styles = get_config_item_or_set_default( key='default_styles', default_value=[ "Fooocus V2", "Fooocus Enhance", "Fooocus Sharp" ], validator=lambda x: isinstance(x, list) and all(y in modules.sdxl_styles.legal_style_names for y in x) ) default_prompt_negative = get_config_item_or_set_default( key='default_prompt_negative', default_value='', validator=lambda x: isinstance(x, str), disable_empty_as_none=True ) default_prompt = get_config_item_or_set_default( key='default_prompt', default_value='', validator=lambda x: isinstance(x, str), disable_empty_as_none=True ) default_performance = get_config_item_or_set_default( key='default_performance', default_value=Performance.SPEED.value, validator=lambda x: x in Performance.list() ) default_advanced_checkbox = get_config_item_or_set_default( key='default_advanced_checkbox', default_value=False, validator=lambda x: isinstance(x, bool) ) default_max_image_number = get_config_item_or_set_default( key='default_max_image_number', default_value=32, validator=lambda x: isinstance(x, int) and x >= 1 ) default_output_format = get_config_item_or_set_default( key='default_output_format', default_value='png', validator=lambda x: x in OutputFormat.list() ) default_image_number = get_config_item_or_set_default( key='default_image_number', default_value=2, validator=lambda x: isinstance(x, int) and 1 <= x <= default_max_image_number ) checkpoint_downloads = get_config_item_or_set_default( key='checkpoint_downloads', default_value={}, validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()) ) lora_downloads = get_config_item_or_set_default( key='lora_downloads', default_value={}, validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()) ) embeddings_downloads = get_config_item_or_set_default( key='embeddings_downloads', default_value={}, validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()) ) available_aspect_ratios = get_config_item_or_set_default( key='available_aspect_ratios', default_value=[ '704*1408', '704*1344', '768*1344', '768*1280', '832*1216', '832*1152', '896*1152', '896*1088', '960*1088', '960*1024', '1024*1024', '1024*960', '1088*960', '1088*896', '1152*896', '1152*832', '1216*832', '1280*768', '1344*768', '1344*704', '1408*704', '1472*704', '1536*640', '1600*640', '1664*576', '1728*576' ], validator=lambda x: isinstance(x, list) and all('*' in v for v in x) and len(x) > 1 ) default_aspect_ratio = get_config_item_or_set_default( key='default_aspect_ratio', default_value='1152*896' if '1152*896' in available_aspect_ratios else available_aspect_ratios[0], validator=lambda x: x in available_aspect_ratios ) default_inpaint_engine_version = get_config_item_or_set_default( key='default_inpaint_engine_version', default_value='v2.6', validator=lambda x: x in modules.flags.inpaint_engine_versions ) default_cfg_tsnr = get_config_item_or_set_default( key='default_cfg_tsnr', default_value=7.0, validator=lambda x: isinstance(x, numbers.Number) ) default_overwrite_step = get_config_item_or_set_default( key='default_overwrite_step', default_value=-1, validator=lambda x: isinstance(x, int) ) default_overwrite_switch = get_config_item_or_set_default( key='default_overwrite_switch', default_value=-1, validator=lambda x: isinstance(x, int) ) example_inpaint_prompts = get_config_item_or_set_default( key='example_inpaint_prompts', default_value=[ 'highly detailed face', 'detailed girl face', 'detailed man face', 'detailed hand', 'beautiful eyes' ], validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x) ) default_save_metadata_to_images = get_config_item_or_set_default( key='default_save_metadata_to_images', default_value=False, validator=lambda x: isinstance(x, bool) ) default_metadata_scheme = get_config_item_or_set_default( key='default_metadata_scheme', default_value=MetadataScheme.FOOOCUS.value, validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x] ) metadata_created_by = get_config_item_or_set_default( key='metadata_created_by', default_value='', validator=lambda x: isinstance(x, str) ) example_inpaint_prompts = [[x] for x in example_inpaint_prompts] config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [[True, 'None', 1.0] for _ in range(default_max_lora_number - len(default_loras))] # mapping config to meta parameter possible_preset_keys = { "default_model": "base_model", "default_refiner": "refiner_model", "default_refiner_switch": "refiner_switch", "previous_default_models": "previous_default_models", "default_loras_min_weight": "default_loras_min_weight", "default_loras_max_weight": "default_loras_max_weight", "default_loras": "", "default_cfg_scale": "guidance_scale", "default_sample_sharpness": "sharpness", "default_sampler": "sampler", "default_scheduler": "scheduler", "default_overwrite_step": "steps", "default_performance": "performance", "default_image_number": "image_number", "default_prompt": "prompt", "default_prompt_negative": "negative_prompt", "default_styles": "styles", "default_aspect_ratio": "resolution", "default_save_metadata_to_images": "default_save_metadata_to_images", "checkpoint_downloads": "checkpoint_downloads", "embeddings_downloads": "embeddings_downloads", "lora_downloads": "lora_downloads" } REWRITE_PRESET = False if REWRITE_PRESET and isinstance(args_manager.args.preset, str): save_path = 'presets/' + args_manager.args.preset + '.json' with open(save_path, "w", encoding="utf-8") as json_file: json.dump({k: config_dict[k] for k in possible_preset_keys}, json_file, indent=4) print(f'Preset saved to {save_path}. Exiting ...') exit(0) def add_ratio(x): a, b = x.replace('*', ' ').split(' ')[:2] a, b = int(a), int(b) g = math.gcd(a, b) return f'{a}×{b} \U00002223 {a // g}:{b // g}' default_aspect_ratio = add_ratio(default_aspect_ratio) available_aspect_ratios = [add_ratio(x) for x in available_aspect_ratios] # Only write config in the first launch. if not os.path.exists(config_path): with open(config_path, "w", encoding="utf-8") as json_file: json.dump({k: config_dict[k] for k in always_save_keys}, json_file, indent=4) # Always write tutorials. with open(config_example_path, "w", encoding="utf-8") as json_file: cpa = config_path.replace("\\", "\\\\") json_file.write(f'You can modify your "{cpa}" using the below keys, formats, and examples.\n' f'Do not modify this file. Modifications in this file will not take effect.\n' f'This file is a tutorial and example. Please edit "{cpa}" to really change any settings.\n' + 'Remember to split the paths with "\\\\" rather than "\\", ' 'and there is no "," before the last "}". \n\n\n') json.dump({k: config_dict[k] for k in visited_keys}, json_file, indent=4) model_filenames = [] lora_filenames = [] wildcard_filenames = [] sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors' sdxl_lightning_lora = 'sdxl_lightning_4step_lora.safetensors' loras_metadata_remove = [sdxl_lcm_lora, sdxl_lightning_lora] def get_model_filenames(folder_paths, extensions=None, name_filter=None): if extensions is None: extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch'] files = [] for folder in folder_paths: files += get_files_from_folder(folder, extensions, name_filter) return files def update_files(): global model_filenames, lora_filenames, wildcard_filenames, available_presets model_filenames = get_model_filenames(paths_checkpoints) lora_filenames = get_model_filenames(paths_loras) wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt']) available_presets = get_presets() return def downloading_inpaint_models(v): assert v in modules.flags.inpaint_engine_versions load_file_from_url( url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/fooocus_inpaint_head.pth', model_dir=path_inpaint, file_name='fooocus_inpaint_head.pth' ) head_file = os.path.join(path_inpaint, 'fooocus_inpaint_head.pth') patch_file = None if v == 'v1': load_file_from_url( url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint.fooocus.patch', model_dir=path_inpaint, file_name='inpaint.fooocus.patch' ) patch_file = os.path.join(path_inpaint, 'inpaint.fooocus.patch') if v == 'v2.5': load_file_from_url( url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v25.fooocus.patch', model_dir=path_inpaint, file_name='inpaint_v25.fooocus.patch' ) patch_file = os.path.join(path_inpaint, 'inpaint_v25.fooocus.patch') if v == 'v2.6': load_file_from_url( url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v26.fooocus.patch', model_dir=path_inpaint, file_name='inpaint_v26.fooocus.patch' ) patch_file = os.path.join(path_inpaint, 'inpaint_v26.fooocus.patch') return head_file, patch_file def downloading_sdxl_lcm_lora(): load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors', model_dir=paths_loras[0], file_name=sdxl_lcm_lora ) return sdxl_lcm_lora def downloading_sdxl_lightning_lora(): load_file_from_url( url='https://huggingface.co/ByteDance/SDXL-Lightning/resolve/main/sdxl_lightning_4step_lora.safetensors', model_dir=paths_loras[0], file_name=sdxl_lightning_lora ) return sdxl_lightning_lora def downloading_controlnet_canny(): load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/control-lora-canny-rank128.safetensors', model_dir=path_controlnet, file_name='control-lora-canny-rank128.safetensors' ) return os.path.join(path_controlnet, 'control-lora-canny-rank128.safetensors') def downloading_controlnet_cpds(): load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_xl_cpds_128.safetensors', model_dir=path_controlnet, file_name='fooocus_xl_cpds_128.safetensors' ) return os.path.join(path_controlnet, 'fooocus_xl_cpds_128.safetensors') def downloading_ip_adapters(v): assert v in ['ip', 'face'] results = [] load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/clip_vision_vit_h.safetensors', model_dir=path_clip_vision, file_name='clip_vision_vit_h.safetensors' ) results += [os.path.join(path_clip_vision, 'clip_vision_vit_h.safetensors')] load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_ip_negative.safetensors', model_dir=path_controlnet, file_name='fooocus_ip_negative.safetensors' ) results += [os.path.join(path_controlnet, 'fooocus_ip_negative.safetensors')] if v == 'ip': load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/ip-adapter-plus_sdxl_vit-h.bin', model_dir=path_controlnet, file_name='ip-adapter-plus_sdxl_vit-h.bin' ) results += [os.path.join(path_controlnet, 'ip-adapter-plus_sdxl_vit-h.bin')] if v == 'face': load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/ip-adapter-plus-face_sdxl_vit-h.bin', model_dir=path_controlnet, file_name='ip-adapter-plus-face_sdxl_vit-h.bin' ) results += [os.path.join(path_controlnet, 'ip-adapter-plus-face_sdxl_vit-h.bin')] return results def downloading_upscale_model(): load_file_from_url( url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_upscaler_s409985e5.bin', model_dir=path_upscale_models, file_name='fooocus_upscaler_s409985e5.bin' ) return os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin') update_files()