mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-24 06:32:12 -03:00
feat: implement batch import recipe functionality (frontend + backend fixes)
Backend fixes: - Add missing API route for /api/lm/recipes/batch-import/progress (GET) - Add missing API route for /api/lm/recipes/batch-import/directory (POST) - Add missing API route for /api/lm/recipes/browse-directory (POST) - Register WebSocket endpoint for batch import progress - Fix skip_no_metadata default value (True -> False) to allow no-LoRA imports - Add items array to BatchImportProgress.to_dict() for detailed results Frontend implementation: - Create BatchImportManager.js with complete batch import workflow - Add directory browser UI for selecting folders - Add batch import modal with URL list and directory input modes - Implement real-time progress tracking (WebSocket + HTTP polling) - Add results summary with success/failed/skipped statistics - Add expandable details view showing individual item status - Auto-refresh recipe list after import completion UI improvements: - Add spinner animation for importing status - Simplify results summary UI to match progress stats styling - Fix current item text alignment - Fix dark theme styling for directory browser button - Fix batch import button styling consistency Translations: - Add batch import related i18n keys to all locale files - Run sync_translation_keys.py to sync all translations Fixes: - Batch import now allows images without LoRAs (matches single import behavior) - Progress endpoint now returns complete items array with status details - Results view correctly displays skipped items with error messages
This commit is contained in:
Will Miao
@@ -4,32 +4,40 @@ from datetime import datetime
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import os
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from .model_utils import determine_base_model
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@dataclass
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class BaseModelMetadata:
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"""Base class for all model metadata structures"""
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file_name: str # The filename without extension
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model_name: str # The model's name defined by the creator
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file_path: str # Full path to the model file
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size: int # File size in bytes
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modified: float # Timestamp when the model was added to the management system
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sha256: str # SHA256 hash of the file
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base_model: str # Base model type (SD1.5/SD2.1/SDXL/etc.)
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preview_url: str # Preview image URL
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preview_nsfw_level: int = 0 # NSFW level of the preview image
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notes: str = "" # Additional notes
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from_civitai: bool = True # Whether from Civitai
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civitai: Dict[str, Any] = field(default_factory=dict) # Civitai API data if available
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tags: List[str] = None # Model tags
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file_name: str # The filename without extension
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model_name: str # The model's name defined by the creator
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file_path: str # Full path to the model file
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size: int # File size in bytes
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modified: float # Timestamp when the model was added to the management system
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sha256: str # SHA256 hash of the file
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base_model: str # Base model type (SD1.5/SD2.1/SDXL/etc.)
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preview_url: str # Preview image URL
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preview_nsfw_level: int = 0 # NSFW level of the preview image
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notes: str = "" # Additional notes
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from_civitai: bool = True # Whether from Civitai
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civitai: Dict[str, Any] = field(
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default_factory=dict
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) # Civitai API data if available
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tags: List[str] = None # Model tags
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modelDescription: str = "" # Full model description
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civitai_deleted: bool = False # Whether deleted from Civitai
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favorite: bool = False # Whether the model is a favorite
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exclude: bool = False # Whether to exclude this model from the cache
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db_checked: bool = False # Whether checked in archive DB
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skip_metadata_refresh: bool = False # Whether to skip this model during bulk metadata refresh
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favorite: bool = False # Whether the model is a favorite
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exclude: bool = False # Whether to exclude this model from the cache
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db_checked: bool = False # Whether checked in archive DB
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skip_metadata_refresh: bool = (
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False # Whether to skip this model during bulk metadata refresh
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)
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metadata_source: Optional[str] = None # Last provider that supplied metadata
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last_checked_at: float = 0 # Last checked timestamp
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hash_status: str = "completed" # Hash calculation status: pending | calculating | completed | failed
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_unknown_fields: Dict[str, Any] = field(default_factory=dict, repr=False, compare=False) # Store unknown fields
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_unknown_fields: Dict[str, Any] = field(
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default_factory=dict, repr=False, compare=False
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) # Store unknown fields
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def __post_init__(self):
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# Initialize empty lists to avoid mutable default parameter issue
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@@ -40,211 +48,238 @@ class BaseModelMetadata:
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self.tags = []
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@classmethod
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def from_dict(cls, data: Dict) -> 'BaseModelMetadata':
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def from_dict(cls, data: Dict) -> "BaseModelMetadata":
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"""Create instance from dictionary"""
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data_copy = data.copy()
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# Use cached fields if available, otherwise compute them
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if not hasattr(cls, '_known_fields_cache'):
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if not hasattr(cls, "_known_fields_cache"):
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known_fields = set()
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for c in cls.mro():
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if hasattr(c, '__annotations__'):
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if hasattr(c, "__annotations__"):
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known_fields.update(c.__annotations__.keys())
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cls._known_fields_cache = known_fields
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known_fields = cls._known_fields_cache
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# Extract fields that match our class attributes
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fields_to_use = {k: v for k, v in data_copy.items() if k in known_fields}
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# Store unknown fields separately
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unknown_fields = {k: v for k, v in data_copy.items() if k not in known_fields and not k.startswith('_')}
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unknown_fields = {
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k: v
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for k, v in data_copy.items()
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if k not in known_fields and not k.startswith("_")
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}
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# Create instance with known fields
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instance = cls(**fields_to_use)
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# Add unknown fields as a separate attribute
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instance._unknown_fields = unknown_fields
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return instance
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def to_dict(self) -> Dict:
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"""Convert to dictionary for JSON serialization"""
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result = asdict(self)
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# Remove private fields
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result = {k: v for k, v in result.items() if not k.startswith('_')}
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result = {k: v for k, v in result.items() if not k.startswith("_")}
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# Add back unknown fields if they exist
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if hasattr(self, '_unknown_fields'):
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if hasattr(self, "_unknown_fields"):
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result.update(self._unknown_fields)
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return result
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def update_civitai_info(self, civitai_data: Dict) -> None:
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"""Update Civitai information"""
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self.civitai = civitai_data
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def update_file_info(self, file_path: str) -> None:
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"""Update metadata with actual file information"""
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def update_file_info(self, file_path: str, update_timestamps: bool = False) -> None:
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"""
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Update metadata with actual file information.
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Args:
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file_path: Path to the model file
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update_timestamps: If True, update size and modified from filesystem.
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If False (default), only update file_path and file_name.
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Set to True only when file has been moved/relocated.
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"""
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if os.path.exists(file_path):
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self.size = os.path.getsize(file_path)
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self.modified = os.path.getmtime(file_path)
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self.file_path = file_path.replace(os.sep, '/')
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# Update file_name when file_path changes
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if update_timestamps:
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# Only update size and modified when file has been relocated
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self.size = os.path.getsize(file_path)
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self.modified = os.path.getmtime(file_path)
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# Always update paths when this method is called
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self.file_path = file_path.replace(os.sep, "/")
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self.file_name = os.path.splitext(os.path.basename(file_path))[0]
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@staticmethod
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def generate_unique_filename(target_dir: str, base_name: str, extension: str, hash_provider: callable = None) -> str:
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def generate_unique_filename(
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target_dir: str, base_name: str, extension: str, hash_provider: callable = None
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) -> str:
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"""Generate a unique filename to avoid conflicts
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Args:
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target_dir: Target directory path
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base_name: Base filename without extension
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extension: File extension including the dot
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hash_provider: A callable that returns the SHA256 hash when needed
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Returns:
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str: Unique filename that doesn't conflict with existing files
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"""
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original_filename = f"{base_name}{extension}"
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target_path = os.path.join(target_dir, original_filename)
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# If no conflict, return original filename
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if not os.path.exists(target_path):
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return original_filename
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# Only compute hash when needed
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if hash_provider:
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sha256_hash = hash_provider()
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else:
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sha256_hash = "0000"
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# Generate short hash (first 4 characters of SHA256)
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short_hash = sha256_hash[:4] if sha256_hash else "0000"
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# Try with short hash suffix
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unique_filename = f"{base_name}-{short_hash}{extension}"
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unique_path = os.path.join(target_dir, unique_filename)
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# If still conflicts, add incremental number
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counter = 1
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while os.path.exists(unique_path):
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unique_filename = f"{base_name}-{short_hash}-{counter}{extension}"
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unique_path = os.path.join(target_dir, unique_filename)
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counter += 1
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return unique_filename
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@dataclass
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class LoraMetadata(BaseModelMetadata):
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"""Represents the metadata structure for a Lora model"""
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usage_tips: str = "{}" # Usage tips for the model, json string
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usage_tips: str = "{}" # Usage tips for the model, json string
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@classmethod
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def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'LoraMetadata':
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def from_civitai_info(
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cls, version_info: Dict, file_info: Dict, save_path: str
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) -> "LoraMetadata":
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"""Create LoraMetadata instance from Civitai version info"""
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file_name = file_info.get('name', '')
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base_model = determine_base_model(version_info.get('baseModel', ''))
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file_name = file_info.get("name", "")
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base_model = determine_base_model(version_info.get("baseModel", ""))
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# Extract tags and description if available
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tags = []
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description = ""
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model_data = version_info.get('model') or {}
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if 'tags' in model_data:
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tags = model_data['tags']
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if 'description' in model_data:
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description = model_data['description']
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model_data = version_info.get("model") or {}
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if "tags" in model_data:
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tags = model_data["tags"]
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if "description" in model_data:
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description = model_data["description"]
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return cls(
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file_name=os.path.splitext(file_name)[0],
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model_name=model_data.get('name', os.path.splitext(file_name)[0]),
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file_path=save_path.replace(os.sep, '/'),
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size=file_info.get('sizeKB', 0) * 1024,
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model_name=model_data.get("name", os.path.splitext(file_name)[0]),
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file_path=save_path.replace(os.sep, "/"),
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size=file_info.get("sizeKB", 0) * 1024,
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modified=datetime.now().timestamp(),
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sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
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sha256=(file_info.get("hashes") or {}).get("SHA256", "").lower(),
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base_model=base_model,
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preview_url='', # Will be updated after preview download
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preview_nsfw_level=0, # Will be updated after preview download
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preview_url="", # Will be updated after preview download
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preview_nsfw_level=0, # Will be updated after preview download
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from_civitai=True,
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civitai=version_info,
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tags=tags,
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modelDescription=description
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modelDescription=description,
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)
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@dataclass
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class CheckpointMetadata(BaseModelMetadata):
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"""Represents the metadata structure for a Checkpoint model"""
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sub_type: str = "checkpoint" # Model sub-type (checkpoint, diffusion_model, etc.)
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@classmethod
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def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'CheckpointMetadata':
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def from_civitai_info(
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cls, version_info: Dict, file_info: Dict, save_path: str
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) -> "CheckpointMetadata":
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"""Create CheckpointMetadata instance from Civitai version info"""
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file_name = file_info.get('name', '')
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base_model = determine_base_model(version_info.get('baseModel', ''))
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sub_type = version_info.get('type', 'checkpoint')
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file_name = file_info.get("name", "")
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base_model = determine_base_model(version_info.get("baseModel", ""))
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sub_type = version_info.get("type", "checkpoint")
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# Extract tags and description if available
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tags = []
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description = ""
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model_data = version_info.get('model') or {}
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if 'tags' in model_data:
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tags = model_data['tags']
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if 'description' in model_data:
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description = model_data['description']
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model_data = version_info.get("model") or {}
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if "tags" in model_data:
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tags = model_data["tags"]
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if "description" in model_data:
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description = model_data["description"]
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return cls(
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file_name=os.path.splitext(file_name)[0],
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model_name=model_data.get('name', os.path.splitext(file_name)[0]),
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file_path=save_path.replace(os.sep, '/'),
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size=file_info.get('sizeKB', 0) * 1024,
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model_name=model_data.get("name", os.path.splitext(file_name)[0]),
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file_path=save_path.replace(os.sep, "/"),
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size=file_info.get("sizeKB", 0) * 1024,
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modified=datetime.now().timestamp(),
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sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
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sha256=(file_info.get("hashes") or {}).get("SHA256", "").lower(),
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base_model=base_model,
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preview_url='', # Will be updated after preview download
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preview_url="", # Will be updated after preview download
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preview_nsfw_level=0,
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from_civitai=True,
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civitai=version_info,
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sub_type=sub_type,
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tags=tags,
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modelDescription=description
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modelDescription=description,
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)
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@dataclass
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class EmbeddingMetadata(BaseModelMetadata):
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"""Represents the metadata structure for an Embedding model"""
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sub_type: str = "embedding"
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@classmethod
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def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'EmbeddingMetadata':
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def from_civitai_info(
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cls, version_info: Dict, file_info: Dict, save_path: str
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) -> "EmbeddingMetadata":
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"""Create EmbeddingMetadata instance from Civitai version info"""
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file_name = file_info.get('name', '')
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base_model = determine_base_model(version_info.get('baseModel', ''))
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sub_type = version_info.get('type', 'embedding')
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file_name = file_info.get("name", "")
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base_model = determine_base_model(version_info.get("baseModel", ""))
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sub_type = version_info.get("type", "embedding")
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# Extract tags and description if available
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tags = []
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description = ""
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model_data = version_info.get('model') or {}
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if 'tags' in model_data:
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tags = model_data['tags']
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if 'description' in model_data:
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description = model_data['description']
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model_data = version_info.get("model") or {}
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if "tags" in model_data:
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tags = model_data["tags"]
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if "description" in model_data:
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description = model_data["description"]
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return cls(
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file_name=os.path.splitext(file_name)[0],
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model_name=model_data.get('name', os.path.splitext(file_name)[0]),
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file_path=save_path.replace(os.sep, '/'),
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size=file_info.get('sizeKB', 0) * 1024,
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model_name=model_data.get("name", os.path.splitext(file_name)[0]),
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file_path=save_path.replace(os.sep, "/"),
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size=file_info.get("sizeKB", 0) * 1024,
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modified=datetime.now().timestamp(),
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sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
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sha256=(file_info.get("hashes") or {}).get("SHA256", "").lower(),
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base_model=base_model,
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preview_url='', # Will be updated after preview download
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preview_url="", # Will be updated after preview download
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preview_nsfw_level=0,
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from_civitai=True,
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civitai=version_info,
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sub_type=sub_type,
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tags=tags,
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modelDescription=description
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modelDescription=description,
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)
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