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feat: add preset selection to Gradio UI (session based) (#1570)

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* 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
2024-03-15 22:04:27 +01:00
committed by GitHub
parent 8baafcd79c
commit 4a44be36fd
7 changed files with 133 additions and 70 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/config.py
+65 -40
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@@ -97,21 +97,44 @@ def try_load_deprecated_user_path_config():
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 {}
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)
available_presets = get_presets()
preset = args_manager.args.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:
config_dict.update(json.load(json_file))
print(f'Loaded preset: {preset_path}')
else:
raise FileNotFoundError
except Exception as e:
print(f'Load preset [{preset_path}] failed')
print(e)
config_dict.update(try_get_preset_content(preset))
def get_path_output() -> str:
"""
@@ -241,7 +264,7 @@ temp_path_cleanup_on_launch = get_config_item_or_set_default(
default_value=True,
validator=lambda x: isinstance(x, bool)
)
default_base_model_name = get_config_item_or_set_default(
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)
@@ -251,7 +274,7 @@ previous_default_models = get_config_item_or_set_default(
default_value=[],
validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x)
)
default_refiner_model_name = get_config_item_or_set_default(
default_refiner_model_name = default_refiner = get_config_item_or_set_default(
key='default_refiner',
default_value='None',
validator=lambda x: isinstance(x, str)
@@ -451,29 +474,30 @@ example_inpaint_prompts = [[x] for x in example_inpaint_prompts]
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))]
possible_preset_keys = [
"default_model",
"default_refiner",
"default_refiner_switch",
"default_loras_min_weight",
"default_loras_max_weight",
"default_loras",
"default_max_lora_number",
"default_cfg_scale",
"default_sample_sharpness",
"default_sampler",
"default_scheduler",
"default_performance",
"default_prompt",
"default_prompt_negative",
"default_styles",
"default_aspect_ratio",
"default_save_metadata_to_images",
"checkpoint_downloads",
"embeddings_downloads",
"lora_downloads",
]
# 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": "<processed>",
"default_cfg_scale": "guidance_scale",
"default_sample_sharpness": "sharpness",
"default_sampler": "sampler",
"default_scheduler": "scheduler",
"default_overwrite_step": "steps",
"default_performance": "performance",
"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
@@ -530,10 +554,11 @@ def get_model_filenames(folder_paths, extensions=None, name_filter=None):
def update_files():
global model_filenames, lora_filenames, wildcard_filenames
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