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https://github.com/willmiao/ComfyUI-Lora-Manager.git
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cc20d3b992
- Backend auto-tag extraction service: detect HIGH/LOW (Wan-only), I2V/T2V/TI2V, Lightning/Turbo from filename, base_model, and CivitAI version name - HIGH/LOW badge in card footer (inline before version name), color-coded: blue for HIGH, teal for LOW; abbreviated to H/L in medium/compact density - Auto-tag filter panel (I2V, T2V, TI2V, Lightning, Turbo) with tri-state include/exclude filtering - Full filter pipeline: FilterCriteria → ModelFilterSet → baseModelApi params - AUTO_TAG_GROUPS exported for frontend use - 19 unit tests for auto-tag extraction edge cases
60 lines
2.8 KiB
Python
60 lines
2.8 KiB
Python
import os
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import logging
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from typing import Dict
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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) -> Dict:
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"""Format Embedding data for API response"""
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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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return {
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"model_name": embedding_data["model_name"],
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"file_name": embedding_data["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": embedding_data["folder"],
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"sha256": embedding_data.get("sha256", ""),
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"file_path": embedding_data["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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}
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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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