Make preview file discovery case-insensitive so files with uppercase
extensions like .WEBP are found on case-sensitive filesystems. Also
explicitly list image/webp in the file picker accept attribute for
broader browser compatibility.
https://claude.ai/code/session_01SgT2pkisi27bEQELX5EeXZ
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
- Backend: Support limit=0 to return all tags in top-tags API
- Frontend: Remove tags limit setting and fetch all tags by default
- UI: Implement virtual scrolling in TagsModal for performance
- Initial display 200 tags, load more on scroll
- Show all results when searching
- Remove lora_pool_tags_limit setting to simplify UX
Fixes#819
- Add cache entry validator service for data integrity checks
- Add cache health monitor service for periodic health checks
- Enhance model cache and scanner with validation support
- Update websocket manager for health status broadcasting
- Add initialization banner service for cache health alerts
- Add comprehensive test coverage for new services
- Update translations across all locales
- Refactor sync translation keys script
- 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- Revise technical documentation to mark Phase 5 as completed and remove outdated TODO items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
- Return cache entry data from model move operations for immediate UI updates
- Add recalculate_type parameter to update_single_model_cache for proper type adjustment
- Propagate cache entry through API layer to frontend MoveManager
- Enable virtual scroller to update moved items with new cache data
- Add optional main_extension parameter to delete_model_artifacts function
- Extract file extension from model filename to handle different file types
- Update model scanner to pass file extension when deleting models
- Add test case for GGUF file deletion to ensure proper cleanup
- Maintain backward compatibility with existing safetensors models
This change allows the model lifecycle service to properly delete GGUF model files along with their associated metadata and preview files, expanding support beyond just safetensors format.
- Add model_types parameter to ModelListingHandler to support filtering by model type
- Implement get_model_types endpoint in ModelQueryHandler to retrieve available model types
- Register new /api/lm/{prefix}/model-types route for model type queries
- Extend BaseModelService to handle model type filtering in queries
- Support both model_type and civitai_model_type query parameters for backward compatibility
This enables users to filter models by specific types, improving model discovery and organization capabilities.
Add license resolution utilities and integrate license information into model metadata processing. The changes include:
- Add `resolve_license_payload` function to extract license data from Civitai model responses
- Integrate license information into model metadata in CivitaiClient and MetadataSyncService
- Add license flags support in model scanning and caching
- Implement CommercialUseLevel enum for standardized license classification
- Update model scanner to handle unknown fields when extracting metadata values
This ensures proper license attribution and compliance when working with Civitai models.
- Add metadata_source field to track origin of model metadata
- Define MODEL_COLUMNS constants for consistent column management
- Refactor SQL queries to use dynamic column selection
- Improve Civitai data detection to include creator_username and trained_words
- Update database operations to handle new metadata field and tag management
Updated the `ModelScanner` class to extract and format the creator username from Civitai data. This enhancement ensures that the creator information is properly included in slim model data.
fix(example_images_download_manager): re-raise specific exception on download error
refactor(usage_stats): define constants locally to avoid conditional imports
test(example_images_download_manager): update exception handling in download tests
test(example_images_file_manager): differentiate between os.startfile and subprocess.Popen in tests
test(example_images_paths): ensure valid example images root with single-library mode
test(usage_stats): use string literals for metadata payload to avoid conditional imports
- Replaced direct usage of Civitai client with a fallback metadata provider across all recipe parsers.
- Updated metadata service to improve initialization and error handling.
- Enhanced download manager to utilize a downloader service for file operations.
- Improved recipe scanner to fetch model information through the new metadata provider.
- Updated utility functions to streamline image downloading and processing.
- Added comprehensive logging and error handling for better debugging and reliability.
- Introduced `get_default_metadata_provider()` for simplified access to the default provider.
- Ensured backward compatibility with existing APIs and workflows.
