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https://github.com/willmiao/ComfyUI-Lora-Manager.git
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16f5222efd
Cache corruption (NULL model_name/file_name from legacy DB rows or partial writes) caused format_response to raise KeyError/AttributeError, failing the entire /loras/list request and showing no models in the UI. Fix across three layers: - format_response (lora/checkpoint/embedding): replace direct dict[] access with .get() fallbacks; return None for entries missing file_path - handlers: filter None entries from list/excluded/fetch/duplicate/conflict endpoints instead of letting them crash or appear as null in responses - model_scanner: always use validate_batch repaired copies (previously discarded when no invalid entries, leaving None values in raw_data) - persistent_model_cache: add or-empty-string guards on read and write for nullable TEXT columns (model_name, file_name, folder, base_model, etc.)
78 lines
3.5 KiB
Python
78 lines
3.5 KiB
Python
import os
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import logging
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from typing import Dict, Optional
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from .base_model_service import BaseModelService
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from .auto_tag_service import extract_auto_tags
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from ..utils.models import EmbeddingMetadata
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from ..config import config
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logger = logging.getLogger(__name__)
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class EmbeddingService(BaseModelService):
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"""Embedding-specific service implementation"""
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def __init__(self, scanner, update_service=None):
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"""Initialize Embedding service
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Args:
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scanner: Embedding scanner instance
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update_service: Optional service for remote update tracking.
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"""
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super().__init__("embedding", scanner, EmbeddingMetadata, update_service=update_service)
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async def format_response(self, embedding_data: Dict) -> Optional[Dict]:
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"""Format Embedding data for API response.
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Returns None when the entry is missing critical fields (corrupted cache
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row), so the handler layer can filter it out. See issue #730.
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"""
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# Guard against corrupted cache entries missing critical fields
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file_path = embedding_data.get("file_path")
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if not file_path or not isinstance(file_path, str):
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logger.warning(
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"Skipping corrupted embedding entry (missing file_path): %s",
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embedding_data.get("file_name", "<unknown>"),
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)
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return None
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# Get sub_type from cache entry (new canonical field)
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sub_type = embedding_data.get("sub_type", "embedding")
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file_name = embedding_data.get("file_name") or ""
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model_name = embedding_data.get("model_name") or file_name
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folder = embedding_data.get("folder") or ""
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return {
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"model_name": model_name,
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"file_name": file_name,
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"preview_url": config.get_preview_static_url(embedding_data.get("preview_url", "")),
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"preview_nsfw_level": embedding_data.get("preview_nsfw_level", 0),
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"base_model": embedding_data.get("base_model", ""),
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"folder": folder,
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"sha256": embedding_data.get("sha256", ""),
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"file_path": file_path.replace(os.sep, "/"),
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"file_size": embedding_data.get("size", 0),
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"modified": embedding_data.get("modified", ""),
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"tags": embedding_data.get("tags", []),
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"from_civitai": embedding_data.get("from_civitai", True),
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# "usage_count": embedding_data.get("usage_count", 0), # TODO: Enable when embedding usage tracking is implemented
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"notes": embedding_data.get("notes", ""),
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"sub_type": sub_type,
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"favorite": embedding_data.get("favorite", False),
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"exclude": bool(embedding_data.get("exclude", False)),
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"update_available": bool(embedding_data.get("update_available", False)),
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"skip_metadata_refresh": bool(embedding_data.get("skip_metadata_refresh", False)),
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"civitai": self.filter_civitai_data(embedding_data.get("civitai", {}), minimal=True),
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"auto_tags": embedding_data.get("auto_tags") or extract_auto_tags(embedding_data),
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"version_count": embedding_data.get("version_count"),
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}
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def find_duplicate_hashes(self) -> Dict:
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"""Find Embeddings with duplicate SHA256 hashes"""
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return self.scanner._hash_index.get_duplicate_hashes()
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def find_duplicate_filenames(self) -> Dict:
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"""Find Embeddings with conflicting filenames"""
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return self.scanner._hash_index.get_duplicate_filenames()
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