- Rename nodes to 'Checkpoint Loader (LoraManager)' and 'Unet Loader (LoraManager)'\n- Use os.sep for relative path formatting in model COMBO inputs\n- Update path matching to be robust across OS separators\n- Update docstrings and comments
Refactor _prepare_checkpoint_paths() to return a tuple instead of having
side effects on instance variables. This prevents extra unet paths from
being incorrectly classified as checkpoints when processing extra paths.
- Changed return type from List[str] to Tuple[List[str], List[str], List[str]]
(all_paths, checkpoint_roots, unet_roots)
- Updated _init_checkpoint_paths() and _apply_library_paths() callers
- Fixed extra paths processing to properly isolate main and extra roots
- Updated test_checkpoint_path_overlap.py tests for new API
This ensures models in extra unet paths are correctly identified as
diffusion_model type and don't appear in checkpoints list.
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
- Update get_lora_info() to check both loras_roots and extra_loras_roots
- Add fallback logic to return trigger words even if path not in recognized roots
- Ensure Trigger Word Toggle node displays trigger words for LoRAs from extra folder paths
Fixes issue where LoRAs added from extra folder paths would not show their trigger words in connected Trigger Word Toggle nodes.
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
- Handle non-string hash values by converting to string before lower()
- Add try-except for strength conversion to handle invalid values like empty strings
- Fixes hypothesis test failures when random data generates unexpected types
- Add get_lora_info_absolute() function to return absolute file paths
- Replace LoraLoader().load_lora() with comfy.utils.load_torch_file() +
comfy.sd.load_lora_for_models() to enable loading LoRAs from any path
- This allows LoRA Manager to load LoRAs from non-standard paths (multi-library support)
- Fixes#805
- Add is_dirty flag to track if statistics have changed
- Only write stats file when data actually changes
- Add enable_usage_statistics setting in ComfyUI settings
- Skip backend requests when usage statistics is disabled
- Fix standalone mode compatibility for MetadataRegistry
Fixes#826
Fix KeyError when 'hashes', 'name', or 'model' fields are missing from
Civitai API responses. Use .get() with defaults instead of direct dict
access in:
- LoraMetadata.from_civitai_info()
- CheckpointMetadata.from_civitai_info()
- EmbeddingMetadata.from_civitai_info()
- RecipeScanner._get_hash_from_civitai()
- DownloadManager._process_download()
Fixes#820
- Extract progress file loading to async methods to run in executor
- Refactor start_download to reduce lock time by pre-loading data before entering lock
- Improve check_pending_models efficiency with single-pass model collection and async loading
- Add type hints to get_status method
- Add tests for download task callback execution and error handling
- Use modelVersionId as fallback for all loras in fingerprint calculation (not just deleted)
- Add URL-based duplicate detection using source_path field
- Combine both fingerprint and URL-based duplicate detection in API response
- Fix _download_remote_media return type and unbound variable issue
- Add /api/example-images/check-pending endpoint to quickly check models needing downloads
- Improve DownloadManager.start_download() to return immediately without blocking
- Add _handle_download_task_done callback for proper error handling and progress saving
- Add check_pending_models() method for lightweight pre-download validation
- Update frontend ExampleImagesManager to use new check-pending endpoint
- Add comprehensive tests for new functionality
- Change `model_type` field to `sub_type` for checkpoint models to improve naming consistency
- Add `sub_type="embedding"` for embedding models to properly categorize model subtypes
- Maintain backward compatibility with existing metadata structure
- Add optional `media_type_hint` parameter to `_get_file_extension_from_content_or_headers` method
- When `media_type_hint` is "video" and no extension can be determined from content/headers/URL, default to `.mp4`
- Pass image metadata type as hint in both `process_example_images` and `process_example_images_batch` methods
- Add unit tests to verify media type hint behavior and priority
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
- Centralize cache path resolution in new py/utils/cache_paths.py module
- Migrate legacy cache files to organized structure: {settings_dir}/cache/{model|recipe|fts|symlink}/
- Automatically clean up legacy files after successful migration with integrity verification
- Update Config symlink cache to use new path and migrate from old location
- Simplify service classes (PersistentModelCache, PersistentRecipeCache, RecipeFTSIndex, TagFTSIndex) to use centralized migration logic
- Add comprehensive test coverage for cache paths and automatic cleanup
- Normalize string quotes to double quotes across all constants for consistency
- Add trailing commas in dictionaries and lists to improve diff readability
- Expand DIFFUSION_MODEL_BASE_MODELS with additional Wan Video and Qwen models
- Fix comment spacing in NSFW_LEVELS dictionary
- Maintain all existing functionality while improving code style
When force=true is passed via API, models in failed_models set are
re-downloaded instead of being skipped. On successful download, model is
removed from failed_models set.
This provides a manual batch repair mechanism for users when CivitAI
media server is temporarily down and causes empty folders.
Changes:
- Backend: Add force parameter to start_download(), _download_all_example_images(), _process_model()
- Backend: Skip failed_models check when force=true
- Backend: Remove model from failed_models on successful force retry
- Frontend: GlobalContextMenu now calls API with force=true directly
- Tests: Update mock to accept force parameter
CivitAI does not distinguish between checkpoint and diffusion model types -
both are labeled as "checkpoint". For certain base model types like
"ZImageTurbo", all models are actually diffusion models and should be
saved to the unet/diffusion model folder instead of the checkpoint folder.
- Add DIFFUSION_MODEL_BASE_MODELS constant for known diffusion model types
- Add default_unet_root setting with auto-set logic
- Route downloads to unet folder when baseModel matches known diffusion types
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add failed_model_timestamps to track when models fail
- Retry failed models after 24-hour cooldown period
- Skip retry if example folder already has files
- Skip retry if failure was less than 24 hours ago
- Log count of failed models with retry message
- Fix unbound snapshot variable in exception path
- Remove duplicate/unreachable directory check code
- Update string quotes to double quotes (PEP 8)
This fixes the issue where failed models were permanently skipped in
auto-download mode, even when their example folders were empty.
- Initialize logging configuration via `setup_logging()` when not in standalone mode
- Detect standalone mode using environment variables `LORA_MANAGER_STANDALONE` and `HF_HUB_DISABLE_TELEMETRY`
- Remove redundant `STANDALONE_MODE` variable that previously checked `sys.modules`
Add new POST endpoint `/api/lm/example-images/set-nsfw-level` to allow updating NSFW classification for individual example images. The endpoint supports both regular and custom images, validates required parameters, and updates the corresponding model metadata. This enables users to manually adjust NSFW ratings for better content filtering.
Add support for parsing comma-separated and JSON-style commercial use permission values in both Python backend and JavaScript frontend. Implement helper functions to split aggregated values into individual permissions while preserving original values when no aggregation is detected.
Added comprehensive test coverage for the new parsing functionality to ensure correct handling of various input formats including strings, arrays, and iterable objects with aggregated commercial use values.
Add support for storing checkpoint information in image EXIF metadata. The checkpoint data is simplified and includes fields like model ID, version, name, hash, and base model. This allows for better tracking of AI model checkpoints used in image generation workflows.
Change `load_metadata` method to return a tuple containing both the metadata object and a boolean success flag instead of just the metadata object. This provides clearer error handling and allows callers to distinguish between successful loads with null metadata versus actual load failures.
- 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.
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
Add support for {model_name} and {version_name} placeholders in download path templates. These new placeholders allow for more flexible and descriptive file organization by including the actual model name and version name in the download directory structure.
Changes include:
- Updated download_manager.py and utils.py to handle new placeholders
- Added placeholders to constants.js for UI reference
- Updated settings modal template to show available placeholders
- Added comprehensive tests to verify placeholder functionality
This enhancement provides users with more control over how downloaded models are organized on their file system.
