Checkpoints are typically large (10GB+). This change delays SHA256
hash calculation until metadata fetch from Civitai is requested,
significantly improving initial scan performance.
- Add hash_status field to BaseModelMetadata
- CheckpointScanner skips hash during initial scan
- On-demand hash calculation during Civitai fetch
- Background bulk hash calculation support
Introduce extra_folder_paths feature to allow users to add additional
model roots that are managed by LoRA Manager but not shared with ComfyUI.
Changes:
- Add extra_folder_paths support in SettingsManager (stored per library)
- Add extra path attributes in Config class (extra_loras_roots, etc.)
- Merge folder_paths with extra_folder_paths when applying library settings
- Update LoraScanner, CheckpointScanner, EmbeddingScanner to include
extra paths in their model roots
- Add comprehensive tests for the new functionality
This enables users to manage models from additional directories without
modifying ComfyUI's model folder configuration.
- Remove backward compatibility code for `model_type` in `ModelScanner._build_cache_entry()`
- Update `CheckpointScanner` to only handle `sub_type` in `adjust_metadata()` and `adjust_cached_entry()`
- Delete deprecated aliases `resolve_civitai_model_type` and `normalize_civitai_model_type` from `model_query.py`
- Update frontend components (`RecipeModal.js`, `ModelCard.js`, etc.) to use `sub_type` instead of `model_type`
- Update API response format to return only `sub_type`, removing `model_type` from service responses
- Revise technical documentation to mark Phase 5 as completed and remove outdated TODO items
All cleanup tasks for the model type refactoring are now complete, ensuring consistent use of `sub_type` across the codebase.
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
- Added BaseModelRoutes class to handle common routes and logic for model types.
- Created CheckpointRoutes class inheriting from BaseModelRoutes for checkpoint-specific routes.
- Implemented CheckpointService class for handling checkpoint-related data and operations.
- Developed LoraService class for managing LoRA-specific functionalities.
- Introduced ModelServiceFactory to manage service and route registrations for different model types.
- Established methods for fetching, filtering, and formatting model data across services.
- Integrated CivitAI metadata handling within model routes and services.
- Added pagination and filtering capabilities for model data retrieval.
