mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 13:42:12 -03:00
475 lines
18 KiB
Python
475 lines
18 KiB
Python
import json
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import os
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import logging
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import asyncio
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import shutil
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import time
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import re
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from typing import List, Dict, Optional, Set
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from ..utils.models import LoraMetadata
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from ..config import config
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from .model_scanner import ModelScanner
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from .model_hash_index import ModelHashIndex # Changed from LoraHashIndex to ModelHashIndex
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from .settings_manager import settings
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from ..utils.constants import NSFW_LEVELS
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from ..utils.utils import fuzzy_match
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from .service_registry import ServiceRegistry
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import sys
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logger = logging.getLogger(__name__)
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class LoraScanner(ModelScanner):
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"""Service for scanning and managing LoRA files"""
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_instance = None
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_lock = asyncio.Lock()
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def __new__(cls):
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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return cls._instance
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def __init__(self):
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# Ensure initialization happens only once
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if not hasattr(self, '_initialized'):
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# Define supported file extensions
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file_extensions = {'.safetensors'}
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# Initialize parent class with ModelHashIndex
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super().__init__(
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model_type="lora",
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model_class=LoraMetadata,
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file_extensions=file_extensions,
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hash_index=ModelHashIndex() # Changed from LoraHashIndex to ModelHashIndex
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)
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self._initialized = True
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@classmethod
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async def get_instance(cls):
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"""Get singleton instance with async support"""
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async with cls._lock:
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if cls._instance is None:
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cls._instance = cls()
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return cls._instance
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def get_model_roots(self) -> List[str]:
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"""Get lora root directories"""
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return config.loras_roots
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async def scan_all_models(self) -> List[Dict]:
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"""Scan all LoRA directories and return metadata"""
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all_loras = []
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# Create scan tasks for each directory
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scan_tasks = []
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for lora_root in self.get_model_roots():
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task = asyncio.create_task(self._scan_directory(lora_root))
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scan_tasks.append(task)
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# Wait for all tasks to complete
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for task in scan_tasks:
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try:
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loras = await task
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all_loras.extend(loras)
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except Exception as e:
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logger.error(f"Error scanning directory: {e}")
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return all_loras
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async def _scan_directory(self, root_path: str) -> List[Dict]:
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"""Scan a single directory for LoRA files"""
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loras = []
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original_root = root_path # Save original root path
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async def scan_recursive(path: str, visited_paths: set):
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"""Recursively scan directory, avoiding circular symlinks"""
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try:
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real_path = os.path.realpath(path)
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if real_path in visited_paths:
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logger.debug(f"Skipping already visited path: {path}")
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return
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visited_paths.add(real_path)
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with os.scandir(path) as it:
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entries = list(it)
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for entry in entries:
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try:
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if entry.is_file(follow_symlinks=True) and any(entry.name.endswith(ext) for ext in self.file_extensions):
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# Use original path instead of real path
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file_path = entry.path.replace(os.sep, "/")
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await self._process_single_file(file_path, original_root, loras)
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await asyncio.sleep(0)
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elif entry.is_dir(follow_symlinks=True):
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# For directories, continue scanning with original path
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await scan_recursive(entry.path, visited_paths)
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except Exception as e:
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logger.error(f"Error processing entry {entry.path}: {e}")
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except Exception as e:
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logger.error(f"Error scanning {path}: {e}")
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await scan_recursive(root_path, set())
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return loras
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async def _process_single_file(self, file_path: str, root_path: str, loras: list):
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"""Process a single file and add to results list"""
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try:
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result = await self._process_model_file(file_path, root_path)
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if result:
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loras.append(result)
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except Exception as e:
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logger.error(f"Error processing {file_path}: {e}")
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async def get_paginated_data(self, page: int, page_size: int, sort_by: str = 'name',
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folder: str = None, search: str = None, fuzzy_search: bool = False,
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base_models: list = None, tags: list = None,
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search_options: dict = None, hash_filters: dict = None,
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favorites_only: bool = False, first_letter: str = None) -> Dict:
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"""Get paginated and filtered lora data
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Args:
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page: Current page number (1-based)
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page_size: Number of items per page
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sort_by: Sort method ('name' or 'date')
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folder: Filter by folder path
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search: Search term
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fuzzy_search: Use fuzzy matching for search
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base_models: List of base models to filter by
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tags: List of tags to filter by
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search_options: Dictionary with search options (filename, modelname, tags, recursive)
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hash_filters: Dictionary with hash filtering options (single_hash or multiple_hashes)
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favorites_only: Filter for favorite models only
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first_letter: Filter by first letter of model name
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"""
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cache = await self.get_cached_data()
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# Get default search options if not provided
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if search_options is None:
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search_options = {
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'filename': True,
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'modelname': True,
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'tags': False,
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'recursive': False,
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}
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# Get the base data set
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filtered_data = cache.sorted_by_date if sort_by == 'date' else cache.sorted_by_name
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# Apply hash filtering if provided (highest priority)
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if hash_filters:
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single_hash = hash_filters.get('single_hash')
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multiple_hashes = hash_filters.get('multiple_hashes')
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if single_hash:
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# Filter by single hash
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single_hash = single_hash.lower() # Ensure lowercase for matching
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filtered_data = [
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lora for lora in filtered_data
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if lora.get('sha256', '').lower() == single_hash
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]
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elif multiple_hashes:
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# Filter by multiple hashes
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hash_set = set(hash.lower() for hash in multiple_hashes) # Convert to set for faster lookup
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filtered_data = [
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lora for lora in filtered_data
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if lora.get('sha256', '').lower() in hash_set
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]
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# Jump to pagination
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total_items = len(filtered_data)
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start_idx = (page - 1) * page_size
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end_idx = min(start_idx + page_size, total_items)
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result = {
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'items': filtered_data[start_idx:end_idx],
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'total': total_items,
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'page': page,
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'page_size': page_size,
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'total_pages': (total_items + page_size - 1) // page_size
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}
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return result
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# Apply SFW filtering if enabled
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if settings.get('show_only_sfw', False):
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filtered_data = [
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lora for lora in filtered_data
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if not lora.get('preview_nsfw_level') or lora.get('preview_nsfw_level') < NSFW_LEVELS['R']
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]
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# Apply favorites filtering if enabled
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if favorites_only:
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filtered_data = [
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lora for lora in filtered_data
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if lora.get('favorite', False) is True
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]
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# Apply first letter filtering
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if first_letter:
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filtered_data = self._filter_by_first_letter(filtered_data, first_letter)
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# Apply folder filtering
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if folder is not None:
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if search_options.get('recursive', False):
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# Recursive folder filtering - include all subfolders
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filtered_data = [
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lora for lora in filtered_data
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if lora['folder'].startswith(folder)
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]
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else:
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# Exact folder filtering
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filtered_data = [
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lora for lora in filtered_data
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if lora['folder'] == folder
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]
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# Apply base model filtering
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if base_models and len(base_models) > 0:
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filtered_data = [
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lora for lora in filtered_data
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if lora.get('base_model') in base_models
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]
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# Apply tag filtering
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if tags and len(tags) > 0:
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filtered_data = [
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lora for lora in filtered_data
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if any(tag in lora.get('tags', []) for tag in tags)
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]
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# Apply search filtering
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if search:
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search_results = []
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search_opts = search_options or {}
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for lora in filtered_data:
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# Search by file name
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if search_opts.get('filename', True):
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if fuzzy_match(lora.get('file_name', ''), search):
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search_results.append(lora)
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continue
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# Search by model name
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if search_opts.get('modelname', True):
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if fuzzy_match(lora.get('model_name', ''), search):
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search_results.append(lora)
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continue
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# Search by tags
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if search_opts.get('tags', False) and 'tags' in lora:
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if any(fuzzy_match(tag, search) for tag in lora['tags']):
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search_results.append(lora)
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continue
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filtered_data = search_results
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# Calculate pagination
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total_items = len(filtered_data)
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start_idx = (page - 1) * page_size
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end_idx = min(start_idx + page_size, total_items)
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result = {
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'items': filtered_data[start_idx:end_idx],
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'total': total_items,
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'page': page,
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'page_size': page_size,
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'total_pages': (total_items + page_size - 1) // page_size
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}
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return result
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def _filter_by_first_letter(self, data, letter):
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"""Filter data by first letter of model name
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Special handling:
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- '#': Numbers (0-9)
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- '@': Special characters (not alphanumeric)
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- '漢': CJK characters
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"""
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filtered_data = []
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for lora in data:
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model_name = lora.get('model_name', '')
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if not model_name:
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continue
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first_char = model_name[0].upper()
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if letter == '#' and first_char.isdigit():
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filtered_data.append(lora)
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elif letter == '@' and not first_char.isalnum():
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# Special characters (not alphanumeric)
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filtered_data.append(lora)
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elif letter == '漢' and self._is_cjk_character(first_char):
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# CJK characters
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filtered_data.append(lora)
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elif letter.upper() == first_char:
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# Regular alphabet matching
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filtered_data.append(lora)
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return filtered_data
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def _is_cjk_character(self, char):
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"""Check if character is a CJK character"""
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# Define Unicode ranges for CJK characters
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cjk_ranges = [
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(0x4E00, 0x9FFF), # CJK Unified Ideographs
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(0x3400, 0x4DBF), # CJK Unified Ideographs Extension A
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(0x20000, 0x2A6DF), # CJK Unified Ideographs Extension B
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(0x2A700, 0x2B73F), # CJK Unified Ideographs Extension C
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(0x2B740, 0x2B81F), # CJK Unified Ideographs Extension D
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(0x2B820, 0x2CEAF), # CJK Unified Ideographs Extension E
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(0x2CEB0, 0x2EBEF), # CJK Unified Ideographs Extension F
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(0x30000, 0x3134F), # CJK Unified Ideographs Extension G
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(0xF900, 0xFAFF), # CJK Compatibility Ideographs
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(0x3300, 0x33FF), # CJK Compatibility
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(0x3200, 0x32FF), # Enclosed CJK Letters and Months
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(0x3100, 0x312F), # Bopomofo
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(0x31A0, 0x31BF), # Bopomofo Extended
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(0x3040, 0x309F), # Hiragana
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(0x30A0, 0x30FF), # Katakana
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(0x31F0, 0x31FF), # Katakana Phonetic Extensions
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(0xAC00, 0xD7AF), # Hangul Syllables
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(0x1100, 0x11FF), # Hangul Jamo
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(0xA960, 0xA97F), # Hangul Jamo Extended-A
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(0xD7B0, 0xD7FF), # Hangul Jamo Extended-B
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]
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code_point = ord(char)
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return any(start <= code_point <= end for start, end in cjk_ranges)
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async def get_letter_counts(self):
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"""Get count of models for each letter of the alphabet"""
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cache = await self.get_cached_data()
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data = cache.sorted_by_name
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# Define letter categories
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letters = {
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'#': 0, # Numbers
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'A': 0, 'B': 0, 'C': 0, 'D': 0, 'E': 0, 'F': 0, 'G': 0, 'H': 0,
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'I': 0, 'J': 0, 'K': 0, 'L': 0, 'M': 0, 'N': 0, 'O': 0, 'P': 0,
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'Q': 0, 'R': 0, 'S': 0, 'T': 0, 'U': 0, 'V': 0, 'W': 0, 'X': 0,
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'Y': 0, 'Z': 0,
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'@': 0, # Special characters
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'漢': 0 # CJK characters
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}
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# Count models for each letter
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for lora in data:
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model_name = lora.get('model_name', '')
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if not model_name:
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continue
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first_char = model_name[0].upper()
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if first_char.isdigit():
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letters['#'] += 1
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elif first_char in letters:
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letters[first_char] += 1
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elif self._is_cjk_character(first_char):
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letters['漢'] += 1
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elif not first_char.isalnum():
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letters['@'] += 1
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return letters
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# Lora-specific hash index functionality
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def has_lora_hash(self, sha256: str) -> bool:
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"""Check if a LoRA with given hash exists"""
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return self.has_hash(sha256)
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def get_lora_path_by_hash(self, sha256: str) -> Optional[str]:
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"""Get file path for a LoRA by its hash"""
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return self.get_path_by_hash(sha256)
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def get_lora_hash_by_path(self, file_path: str) -> Optional[str]:
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"""Get hash for a LoRA by its file path"""
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return self.get_hash_by_path(file_path)
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async def get_top_tags(self, limit: int = 20) -> List[Dict[str, any]]:
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"""Get top tags sorted by count"""
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# Make sure cache is initialized
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await self.get_cached_data()
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# Sort tags by count in descending order
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sorted_tags = sorted(
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[{"tag": tag, "count": count} for tag, count in self._tags_count.items()],
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key=lambda x: x['count'],
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reverse=True
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)
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# Return limited number
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return sorted_tags[:limit]
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async def get_base_models(self, limit: int = 20) -> List[Dict[str, any]]:
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"""Get base models used in loras sorted by frequency"""
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# Make sure cache is initialized
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cache = await self.get_cached_data()
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# Count base model occurrences
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base_model_counts = {}
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for lora in cache.raw_data:
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if 'base_model' in lora and lora['base_model']:
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base_model = lora['base_model']
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base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
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# Sort base models by count
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sorted_models = [{'name': model, 'count': count} for model, count in base_model_counts.items()]
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sorted_models.sort(key=lambda x: x['count'], reverse=True)
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# Return limited number
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return sorted_models[:limit]
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async def diagnose_hash_index(self):
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"""Diagnostic method to verify hash index functionality"""
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print("\n\n*** DIAGNOSING LORA HASH INDEX ***\n\n", file=sys.stderr)
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# First check if the hash index has any entries
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if hasattr(self, '_hash_index'):
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index_entries = len(self._hash_index._hash_to_path)
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print(f"Hash index has {index_entries} entries", file=sys.stderr)
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# Print a few example entries if available
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if index_entries > 0:
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print("\nSample hash index entries:", file=sys.stderr)
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count = 0
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for hash_val, path in self._hash_index._hash_to_path.items():
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if count < 5: # Just show the first 5
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print(f"Hash: {hash_val[:8]}... -> Path: {path}", file=sys.stderr)
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count += 1
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else:
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break
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else:
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print("Hash index not initialized", file=sys.stderr)
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# Try looking up by a known hash for testing
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if not hasattr(self, '_hash_index') or not self._hash_index._hash_to_path:
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print("No hash entries to test lookup with", file=sys.stderr)
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return
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test_hash = next(iter(self._hash_index._hash_to_path.keys()))
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test_path = self._hash_index.get_path(test_hash)
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print(f"\nTest lookup by hash: {test_hash[:8]}... -> {test_path}", file=sys.stderr)
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# Also test reverse lookup
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test_hash_result = self._hash_index.get_hash(test_path)
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print(f"Test reverse lookup: {test_path} -> {test_hash_result[:8]}...\n\n", file=sys.stderr)
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async def get_lora_info_by_name(self, name):
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"""Get LoRA information by name"""
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try:
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# Get cached data
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cache = await self.get_cached_data()
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# Find the LoRA by name
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for lora in cache.raw_data:
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if lora.get("file_name") == name:
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return lora
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return None
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except Exception as e:
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logger.error(f"Error getting LoRA info by name: {e}", exc_info=True)
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return None
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