- Extract checkpoint entry from multiple metadata locations using helper method
- Sanitize checkpoint metadata by removing transient/local-only fields
- Remove checkpoint duplication from generation parameters to store only at top level
- Update frontend to properly populate checkpoint metadata during import
- Add tests for new checkpoint handling functionality
This ensures consistent checkpoint metadata structure and prevents data duplication across different storage locations.
Add _normalize_preview_url method to ensure preview URLs are properly formatted for browser access. The method handles absolute paths by converting them to static URLs via config.get_preview_static_url, while preserving API paths and other valid URLs. This ensures consistent preview image display across different URL formats.
Update _enrich_lora_entry to apply URL normalization to preview URLs obtained from both hash-based lookups and version entries. Add comprehensive test coverage for absolute path normalization scenarios.
Add _get_lora_from_version_index method to fetch cached LoRA entries by modelVersionId when hash is unavailable. This improves LoRA enrichment by using version index as fallback when hash is missing, ensuring proper library status, file paths, and preview URLs are set even without hash values.
Update test suite to include version_index in stub cache and add test coverage for version-based lookup functionality.
Add support for importing recipes from remote sources by:
- Adding import_remote_recipe endpoint to RecipeHandlerSet
- Injecting downloader_factory and civitai_client_getter dependencies
- Implementing image download and resource parsing logic
- Supporting Civitai resource payloads with checkpoints and LoRAs
- Adding required imports for regex and temporary file handling
This enables users to import recipes directly from external sources like Civitai without manual file downloads.
- Add auto_organize_exclusions to settings handler proxy keys
- Refactor model file service to handle exclusions relative to model roots
- Improve auto-organize progress reporting for empty operations
- Fix exclusion pattern matching to consider relative paths within model roots
- Ensure proper validation when no model roots are configured
- Add comprehensive cleanup reporting for empty auto-organize operations
- Track when Civitai API returns "Model not found" for default provider
- Use dedicated flag instead of error string comparison for deletion detection
- Ensure archive-sourced models don't get marked as deleted
- Add test coverage for archive source deletion flag behavior
- Fix deletion flag logic to properly handle provider fallback scenarios
- 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 ModelUpdateService dependency to ModelLifecycleService to enable synchronization during model deletion. The service is now passed through BaseModelRoutes initialization and used in delete_model to trigger updates when a model is removed. This ensures external systems stay in sync with local model state changes.
Key changes:
- Inject update_service into ModelLifecycleService constructor
- Extract model ID from metadata during deletion
- Call update service sync method after successful deletion
- Add proper type hints and TYPE_CHECKING imports
- 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 new "updateFlags" section to settings navigation and implement update flag strategy configuration. The strategy allows users to choose when update badges appear:
- Match updates by base model (only show when new release shares same base model)
- Flag any available update (show whenever newer version exists)
Includes translations for English, German, Spanish, and French locales.
Add support for configurable update flag strategy with new "same_base" mode that considers base model versions when determining update availability. The strategy is controlled by the "update_flag_strategy" setting.
When strategy is set to "same_base":
- Uses get_records_bulk instead of has_updates_bulk
- Compares model versions against highest local versions per base model
- Provides more granular update detection based on base model relationships
Fallback to existing bulk or individual update checks when:
- Strategy is not "same_base"
- Bulk operations fail
- Records are unavailable
This enables more precise update flagging for models sharing common bases.
- Update frontend tag filter to cycle through include/exclude/clear states
- Add backend support for tag_include and tag_exclude query parameters
- Maintain backward compatibility with legacy tag parameter
- Store tag states as dictionary with 'include'/'exclude' values
- Update test matrix documentation to reflect new tag behavior
The changes enable more granular tag filtering where users can now explicitly include or exclude specific tags, rather than just adding tags to a simple inclusion list. This provides better control over search results and improves the filtering user experience.
Add support for filtering models by license requirements:
- credit_required: filter models that require credits or allow free use
- allow_selling_generated_content: filter models based on commercial usage rights
These filters use license_flags bitmask to determine model permissions and enable users to find models that match their specific usage requirements and budget constraints.
Add RateLimitRetryingProvider and _RateLimitRetryHelper classes to handle rate limiting with exponential backoff retries. Update get_metadata_provider function to automatically wrap providers with rate limit handling. This improves reliability when external APIs return rate limit errors by implementing automatic retries with configurable delays and jitter.
- Simplify error handling logic by checking for "not found" message directly
- Extract model data fetching into separate _fetch_model_data method
- Extract version enrichment into separate _enrich_version_with_model_data method
- Improve logging consistency using %s formatting
- Rename variables for better clarity (result -> version, e -> exc)
- Update DEFAULT_LICENSE_FLAGS from 57 to 127 to enable all commercial modes by default
- Replace CommercialUseLevel enum with bitwise commercial permission handling
- Simplify commercial value normalization and validation using allowed values set
- Adjust bit shifting in license flag construction to accommodate new commercial bits structure
- Remove CommercialUseLevel from exports and update tests accordingly
- Improve handling of empty commercial use values with proper type checking
The changes streamline commercial permission processing and align with CivitAI's default license configuration while maintaining backward compatibility.
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.
Remove legacy HTML scraping implementation of get_model_by_url method
and associated BeautifulSoup dependency. The functionality has been
replaced by API-based implementation in get_model_version method.
This simplifies the codebase and removes the optional bs4 dependency,
making the client more maintainable and reliable.
Convert all tags to lowercase in tag processing logic to prevent case sensitivity issues on Windows filesystems. This ensures consistent tag matching and prevents duplicate tags with different cases from being created.
Changes include:
- TagUpdateService now converts tags to lowercase before comparison
- Utils function converts model tags to lowercase before priority resolution
- Test cases updated to reflect lowercase tag expectations
Refactor `_extract_size_bytes` method to prioritize primary model files when calculating size. The new implementation:
- Extracts size parsing into separate `parse_size` function
- Adds logic to prefer files marked as both "model" type and "primary"
- Falls back to first valid size if no primary model file found
- Adds comprehensive tests for primary preference and fallback behavior
This ensures more accurate size reporting for model files, particularly when multiple file types are present in the response.
Add a new filter option to show only models with available updates across all supported languages. This includes:
- Adding "updates" filter translations in all locale files (de, en, es, fr, he, ja, ko)
- Extending BaseModelApiClient to handle update_available_only query parameter
- Implementing update filter button in PageControls component with event listeners
- Adding corresponding CSS styles for active filter state
The feature allows users to quickly identify and focus on models that have updates available, improving the update management workflow.
Refactor _get_multipart_ext method to use known suffixes list for more reliable file extension detection. The new implementation handles compound file extensions like '.metadata.json.bak' and '.safetensors' by checking against predefined suffixes in order of length. Falls back to existing logic for unknown file types. This improves accuracy when working with model files that have complex naming conventions.
- Normalize folder paths before library bootstrap to ensure consistent structure
- Add _has_configured_paths helper to detect valid folder configurations
- Enhance bootstrap logic to handle edge cases with single libraries and empty paths
- Update library payload construction to use normalized paths
- Add example settings file changes to demonstrate new path structure
The changes ensure more robust library initialization when folder paths are configured at the top level but not properly propagated to individual libraries.
- Add standalone mode detection via LORA_MANAGER_STANDALONE environment variable
- Improve error handling for settings file loading with specific JSON decode errors
- Add startup messages system to communicate configuration warnings and errors to users
- Include settings file path and startup messages in settings API response
- Automatically save settings when bootstrapping from defaults due to missing/invalid settings file
- Add configuration warnings collection for environment variables and other settings issues
The changes improve robustness of settings management and provide better user feedback when configuration issues occur.
Add automatic synchronization of downloaded model versions with the update tracking system. After a successful download, the system now resolves model and version IDs from the download response and updates the update service with the newly downloaded version along with any existing local versions.
This ensures that:
- Update tracking accurately reflects which versions are available locally
- The system properly tracks both newly downloaded and existing versions
- Failed sync operations are gracefully handled with appropriate logging
- Support is included for LoRA, checkpoint, and embedding model types
