feat: add preset selection to Gradio UI (session based) (#1570)
* add preset selection uses meta parsing to set presets in user session (UI elements only) * add LoRA handling * use default config as fallback value * add preset refresh on "Refresh All Files" click * add special handling for default_styles and default_aspect_ratio * sort styles after preset change * code cleanup * download missing models from preset * set default refiner to "None" in preset realistic * use state_is_generating for preset selection change * DRY output parameter handling * feat: add argument --disable-preset-selection useful for cloud provisioning to prevent model switches and keep models loaded * feat: keep prompt when not set in preset, use more robust syntax * fix: add default return values when preset download is disabled https://github.com/mashb1t/Fooocus/issues/20 * feat: add translation for preset label * refactor: unify preset loading methods in config * refactor: code cleanup
This commit is contained in:
Manuel Schmid
GitHub
parent
8baafcd79c
commit
4a44be36fd
@ -4,7 +4,10 @@ import os
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from tempfile import gettempdir
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args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")
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args_parser.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.")
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args_parser.parser.add_argument("--disable-preset-selection", action='store_true',
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help="Disables preset selection in Gradio.")
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args_parser.parser.add_argument("--language", type=str, default='default',
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help="Translate UI using json files in [language] folder. "
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@ -38,6 +38,7 @@
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"* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)": "* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)",
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"Setting": "Setting",
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"Style": "Style",
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"Preset": "Preset",
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"Performance": "Performance",
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"Speed": "Speed",
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"Quality": "Quality",
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28
launch.py
28
launch.py
@ -93,7 +93,7 @@ if config.temp_path_cleanup_on_launch:
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print(f"[Cleanup] Failed to delete content of temp dir.")
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def download_models():
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def download_models(default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads):
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for file_name, url in vae_approx_filenames:
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load_file_from_url(url=url, model_dir=config.path_vae_approx, file_name=file_name)
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@ -105,30 +105,32 @@ def download_models():
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if args.disable_preset_download:
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print('Skipped model download.')
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return
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return default_model, checkpoint_downloads
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if not args.always_download_new_model:
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if not os.path.exists(os.path.join(config.paths_checkpoints[0], config.default_base_model_name)):
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for alternative_model_name in config.previous_default_models:
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if not os.path.exists(os.path.join(config.paths_checkpoints[0], default_model)):
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for alternative_model_name in previous_default_models:
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if os.path.exists(os.path.join(config.paths_checkpoints[0], alternative_model_name)):
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print(f'You do not have [{config.default_base_model_name}] but you have [{alternative_model_name}].')
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print(f'You do not have [{default_model}] but you have [{alternative_model_name}].')
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print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, '
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f'but you are not using latest models.')
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f'but you are not using the latest models.')
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print('Use --always-download-new-model to avoid fallback and always get new models.')
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config.checkpoint_downloads = {}
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config.default_base_model_name = alternative_model_name
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checkpoint_downloads = {}
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default_model = alternative_model_name
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break
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for file_name, url in config.checkpoint_downloads.items():
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for file_name, url in checkpoint_downloads.items():
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load_file_from_url(url=url, model_dir=config.paths_checkpoints[0], file_name=file_name)
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for file_name, url in config.embeddings_downloads.items():
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for file_name, url in embeddings_downloads.items():
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load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name)
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for file_name, url in config.lora_downloads.items():
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for file_name, url in lora_downloads.items():
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load_file_from_url(url=url, model_dir=config.paths_loras[0], file_name=file_name)
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return
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return default_model, checkpoint_downloads
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download_models()
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config.default_base_model_name, config.checkpoint_downloads = download_models(
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config.default_base_model_name, config.previous_default_models, config.checkpoint_downloads,
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config.embeddings_downloads, config.lora_downloads)
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from webui import *
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@ -97,21 +97,44 @@ def try_load_deprecated_user_path_config():
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try_load_deprecated_user_path_config()
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def get_presets():
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preset_folder = 'presets'
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presets = ['initial']
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if not os.path.exists(preset_folder):
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print('No presets found.')
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return presets
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return presets + [f[:f.index('.json')] for f in os.listdir(preset_folder) if f.endswith('.json')]
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def try_get_preset_content(preset):
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if isinstance(preset, str):
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preset_path = os.path.abspath(f'./presets/{preset}.json')
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try:
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if os.path.exists(preset_path):
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with open(preset_path, "r", encoding="utf-8") as json_file:
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json_content = json.load(json_file)
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print(f'Loaded preset: {preset_path}')
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return json_content
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else:
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raise FileNotFoundError
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except Exception as e:
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print(f'Load preset [{preset_path}] failed')
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print(e)
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return {}
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try:
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with open(os.path.abspath(f'./presets/default.json'), "r", encoding="utf-8") as json_file:
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config_dict.update(json.load(json_file))
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except Exception as e:
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print(f'Load default preset failed.')
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print(e)
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available_presets = get_presets()
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preset = args_manager.args.preset
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if isinstance(preset, str):
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preset_path = os.path.abspath(f'./presets/{preset}.json')
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try:
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if os.path.exists(preset_path):
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with open(preset_path, "r", encoding="utf-8") as json_file:
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config_dict.update(json.load(json_file))
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print(f'Loaded preset: {preset_path}')
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else:
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raise FileNotFoundError
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except Exception as e:
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print(f'Load preset [{preset_path}] failed')
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print(e)
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config_dict.update(try_get_preset_content(preset))
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def get_path_output() -> str:
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"""
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@ -241,7 +264,7 @@ temp_path_cleanup_on_launch = get_config_item_or_set_default(
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default_value=True,
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validator=lambda x: isinstance(x, bool)
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)
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default_base_model_name = get_config_item_or_set_default(
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default_base_model_name = default_model = get_config_item_or_set_default(
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key='default_model',
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default_value='model.safetensors',
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validator=lambda x: isinstance(x, str)
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@ -251,7 +274,7 @@ previous_default_models = get_config_item_or_set_default(
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default_value=[],
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validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x)
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)
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default_refiner_model_name = get_config_item_or_set_default(
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default_refiner_model_name = default_refiner = get_config_item_or_set_default(
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key='default_refiner',
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default_value='None',
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validator=lambda x: isinstance(x, str)
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@ -451,29 +474,30 @@ example_inpaint_prompts = [[x] for x in example_inpaint_prompts]
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config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [['None', 1.0] for _ in range(default_max_lora_number - len(default_loras))]
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possible_preset_keys = [
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"default_model",
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"default_refiner",
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"default_refiner_switch",
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"default_loras_min_weight",
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"default_loras_max_weight",
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"default_loras",
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"default_max_lora_number",
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"default_cfg_scale",
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"default_sample_sharpness",
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"default_sampler",
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"default_scheduler",
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"default_performance",
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"default_prompt",
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"default_prompt_negative",
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"default_styles",
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"default_aspect_ratio",
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"default_save_metadata_to_images",
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"checkpoint_downloads",
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"embeddings_downloads",
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"lora_downloads",
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]
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# mapping config to meta parameter
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possible_preset_keys = {
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"default_model": "base_model",
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"default_refiner": "refiner_model",
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"default_refiner_switch": "refiner_switch",
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"previous_default_models": "previous_default_models",
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"default_loras_min_weight": "default_loras_min_weight",
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"default_loras_max_weight": "default_loras_max_weight",
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"default_loras": "<processed>",
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"default_cfg_scale": "guidance_scale",
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"default_sample_sharpness": "sharpness",
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"default_sampler": "sampler",
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"default_scheduler": "scheduler",
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"default_overwrite_step": "steps",
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"default_performance": "performance",
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"default_prompt": "prompt",
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"default_prompt_negative": "negative_prompt",
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"default_styles": "styles",
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"default_aspect_ratio": "resolution",
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"default_save_metadata_to_images": "default_save_metadata_to_images",
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"checkpoint_downloads": "checkpoint_downloads",
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"embeddings_downloads": "embeddings_downloads",
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"lora_downloads": "lora_downloads"
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}
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REWRITE_PRESET = False
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@ -530,10 +554,11 @@ def get_model_filenames(folder_paths, extensions=None, name_filter=None):
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def update_files():
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global model_filenames, lora_filenames, wildcard_filenames
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global model_filenames, lora_filenames, wildcard_filenames, available_presets
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model_filenames = get_model_filenames(paths_checkpoints)
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lora_filenames = get_model_filenames(paths_loras)
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wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])
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available_presets = get_presets()
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return
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@ -210,9 +210,8 @@ def parse_meta_from_preset(preset_content):
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height = height[:height.index(" ")]
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preset_prepared[meta_key] = (width, height)
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else:
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preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[
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settings_key] is not None else getattr(modules.config, settings_key)
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preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[settings_key] is not None else getattr(modules.config, settings_key)
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if settings_key == "default_styles" or settings_key == "default_aspect_ratio":
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preset_prepared[meta_key] = str(preset_prepared[meta_key])
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@ -570,4 +569,4 @@ def get_exif(metadata: str | None, metadata_scheme: str):
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exif[0x0131] = 'Fooocus v' + fooocus_version.version
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# 0x927C = MakerNote
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exif[0x927C] = metadata_scheme
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return exif
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return exif
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@ -1,6 +1,6 @@
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{
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"default_model": "realisticStockPhoto_v20.safetensors",
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"default_refiner": "",
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"default_refiner": "None",
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"default_refiner_switch": 0.5,
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"default_loras": [
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[
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57
webui.py
57
webui.py
@ -15,6 +15,7 @@ import modules.style_sorter as style_sorter
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import modules.meta_parser
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import args_manager
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import copy
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import launch
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from modules.sdxl_styles import legal_style_names
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from modules.private_logger import get_current_html_path
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@ -252,6 +253,11 @@ with shared.gradio_root:
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with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column:
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with gr.Tab(label='Setting'):
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if not args_manager.args.disable_preset_selection:
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preset_selection = gr.Radio(label='Preset',
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choices=modules.config.available_presets,
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value=args_manager.args.preset if args_manager.args.preset else "initial",
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interactive=True)
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performance_selection = gr.Radio(label='Performance',
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choices=flags.Performance.list(),
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value=modules.config.default_performance)
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@ -518,13 +524,50 @@ with shared.gradio_root:
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modules.config.update_files()
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results = [gr.update(choices=modules.config.model_filenames)]
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results += [gr.update(choices=['None'] + modules.config.model_filenames)]
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if not args_manager.args.disable_preset_selection:
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results += [gr.update(choices=modules.config.available_presets)]
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for i in range(modules.config.default_max_lora_number):
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results += [gr.update(interactive=True), gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
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results += [gr.update(interactive=True),
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gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
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return results
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refresh_files.click(refresh_files_clicked, [], [base_model, refiner_model] + lora_ctrls,
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refresh_files_output = [base_model, refiner_model]
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if not args_manager.args.disable_preset_selection:
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refresh_files_output += [preset_selection]
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refresh_files.click(refresh_files_clicked, [], refresh_files_output + lora_ctrls,
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queue=False, show_progress=False)
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state_is_generating = gr.State(False)
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load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections,
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performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection,
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overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive,
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adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, base_model,
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refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random, image_seed,
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generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls
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if not args_manager.args.disable_preset_selection:
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def preset_selection_change(preset, is_generating):
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preset_content = modules.config.try_get_preset_content(preset) if preset != 'initial' else {}
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preset_prepared = modules.meta_parser.parse_meta_from_preset(preset_content)
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default_model = preset_prepared.get('base_model')
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previous_default_models = preset_prepared.get('previous_default_models', [])
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checkpoint_downloads = preset_prepared.get('checkpoint_downloads', {})
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embeddings_downloads = preset_prepared.get('embeddings_downloads', {})
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lora_downloads = preset_prepared.get('lora_downloads', {})
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preset_prepared['base_model'], preset_prepared['lora_downloads'] = launch.download_models(
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default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads)
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if 'prompt' in preset_prepared and preset_prepared.get('prompt') == '':
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del preset_prepared['prompt']
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return modules.meta_parser.load_parameter_button_click(json.dumps(preset_prepared), is_generating)
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preset_selection.change(preset_selection_change, inputs=[preset_selection, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \
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.then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \
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performance_selection.change(lambda x: [gr.update(interactive=not flags.Performance.has_restricted_features(x))] * 11 +
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[gr.update(visible=not flags.Performance.has_restricted_features(x))] * 1 +
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[gr.update(interactive=not flags.Performance.has_restricted_features(x), value=flags.Performance.has_restricted_features(x))] * 1,
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@ -600,8 +643,6 @@ with shared.gradio_root:
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ctrls += ip_ctrls
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state_is_generating = gr.State(False)
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def parse_meta(raw_prompt_txt, is_generating):
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loaded_json = None
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if is_json(raw_prompt_txt):
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@ -617,13 +658,6 @@ with shared.gradio_root:
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prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False)
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load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections,
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performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection,
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overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive,
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adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, base_model,
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refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random, image_seed,
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generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls
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load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False)
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def trigger_metadata_import(filepath, state_is_generating):
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@ -637,7 +671,6 @@ with shared.gradio_root:
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return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)
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metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \
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.then(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False)
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