mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 13:42:12 -03:00
36e6ac2362
- Introduced a new CheckpointMetadata dataclass to encapsulate metadata for checkpoint models. - Included fields for file details, model specifications, and additional attributes such as resolution and architecture. - Implemented a __post_init__ method to initialize tags as an empty list if not provided, ensuring consistent data handling.
106 lines
4.7 KiB
Python
106 lines
4.7 KiB
Python
from dataclasses import dataclass, asdict
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from typing import Dict, Optional, List
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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 LoraMetadata:
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"""Represents the metadata structure for a Lora model"""
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file_name: str # The filename without extension of the lora
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model_name: str # The lora's name defined by the creator, initially same as file_name
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file_path: str # Full path to the safetensors file
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size: int # File size in bytes
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modified: float # Last modified timestamp
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sha256: str # SHA256 hash of the file
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base_model: str # Base model (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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usage_tips: str = "{}" # Usage tips for the model, json string
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notes: str = "" # Additional notes
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from_civitai: bool = True # Whether the lora is from Civitai
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civitai: Optional[Dict] = None # 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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def __post_init__(self):
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# Initialize empty lists to avoid mutable default parameter issue
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if self.tags is None:
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self.tags = []
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@classmethod
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def from_dict(cls, data: Dict) -> 'LoraMetadata':
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"""Create LoraMetadata instance from dictionary"""
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# Create a copy of the data to avoid modifying the input
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data_copy = data.copy()
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return cls(**data_copy)
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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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"""Create LoraMetadata instance from Civitai version info"""
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file_name = file_info['name']
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base_model = determine_base_model(version_info.get('baseModel', ''))
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return cls(
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file_name=os.path.splitext(file_name)[0],
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model_name=version_info.get('model').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['hashes'].get('SHA256', '').lower(),
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base_model=base_model,
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preview_url=None, # Will be updated after preview download
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preview_nsfw_level=0, # Will be updated after preview download, it is decided by the nsfw level of the preview image
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from_civitai=True,
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civitai=version_info
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)
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def to_dict(self) -> Dict:
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"""Convert to dictionary for JSON serialization"""
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return asdict(self)
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@property
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def modified_datetime(self) -> datetime:
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"""Convert modified timestamp to datetime object"""
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return datetime.fromtimestamp(self.modified)
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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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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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@dataclass
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class CheckpointMetadata:
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"""Represents the metadata structure for a Checkpoint model"""
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file_name: str # The filename without extension
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model_name: str # The checkpoint'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 # Last modified timestamp
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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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model_type: str = "checkpoint" # Model type (checkpoint, inpainting, etc.)
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notes: str = "" # Additional notes
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from_civitai: bool = True # Whether from Civitai
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civitai: Optional[Dict] = None # 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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# Additional checkpoint-specific fields
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resolution: Optional[str] = None # Native resolution (e.g., 512x512, 1024x1024)
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vae_included: bool = False # Whether VAE is included in the checkpoint
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architecture: str = "" # Model architecture (if known)
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def __post_init__(self):
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if self.tags is None:
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self.tags = []
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