Files
ComfyUI-Lora-Manager/py/services/embedding_service.py
Will Miao 5e91073476 refactor: unify model_type semantics by introducing sub_type field
This commit resolves the semantic confusion around the model_type field by
clearly distinguishing between:
- scanner_type: architecture-level (lora/checkpoint/embedding)
- sub_type: business-level subtype (lora/locon/dora/checkpoint/diffusion_model/embedding)

Backend Changes:
- Rename model_type to sub_type in CheckpointMetadata and EmbeddingMetadata
- Add resolve_sub_type() and normalize_sub_type() in model_query.py
- Update checkpoint_scanner to use _resolve_sub_type()
- Update service format_response to include both sub_type and model_type
- Add VALID_*_SUB_TYPES constants with backward compatible aliases

Frontend Changes:
- Add MODEL_SUBTYPE_DISPLAY_NAMES constants
- Keep MODEL_TYPE_DISPLAY_NAMES as backward compatible alias

Testing:
- Add 43 new tests covering sub_type resolution and API response

Documentation:
- Add refactoring todo document to docs/technical/

BREAKING CHANGE: None - full backward compatibility maintained
2026-01-30 06:56:10 +08:00

57 lines
2.6 KiB
Python

import os
import logging
from typing import Dict
from .base_model_service import BaseModelService
from ..utils.models import EmbeddingMetadata
from ..config import config
logger = logging.getLogger(__name__)
class EmbeddingService(BaseModelService):
"""Embedding-specific service implementation"""
def __init__(self, scanner, update_service=None):
"""Initialize Embedding service
Args:
scanner: Embedding scanner instance
update_service: Optional service for remote update tracking.
"""
super().__init__("embedding", scanner, EmbeddingMetadata, update_service=update_service)
async def format_response(self, embedding_data: Dict) -> Dict:
"""Format Embedding data for API response"""
# Get sub_type from cache entry (new field) or fallback to model_type (old field)
sub_type = embedding_data.get("sub_type") or embedding_data.get("model_type", "embedding")
return {
"model_name": embedding_data["model_name"],
"file_name": embedding_data["file_name"],
"preview_url": config.get_preview_static_url(embedding_data.get("preview_url", "")),
"preview_nsfw_level": embedding_data.get("preview_nsfw_level", 0),
"base_model": embedding_data.get("base_model", ""),
"folder": embedding_data["folder"],
"sha256": embedding_data.get("sha256", ""),
"file_path": embedding_data["file_path"].replace(os.sep, "/"),
"file_size": embedding_data.get("size", 0),
"modified": embedding_data.get("modified", ""),
"tags": embedding_data.get("tags", []),
"from_civitai": embedding_data.get("from_civitai", True),
# "usage_count": embedding_data.get("usage_count", 0), # TODO: Enable when embedding usage tracking is implemented
"notes": embedding_data.get("notes", ""),
"sub_type": sub_type, # New canonical field
"model_type": sub_type, # Backward compatibility
"favorite": embedding_data.get("favorite", False),
"update_available": bool(embedding_data.get("update_available", False)),
"civitai": self.filter_civitai_data(embedding_data.get("civitai", {}), minimal=True)
}
def find_duplicate_hashes(self) -> Dict:
"""Find Embeddings with duplicate SHA256 hashes"""
return self.scanner._hash_index.get_duplicate_hashes()
def find_duplicate_filenames(self) -> Dict:
"""Find Embeddings with conflicting filenames"""
return self.scanner._hash_index.get_duplicate_filenames()