The Checkpoint/Unet Loader (LoraManager) nodes now support ComfyUI's
built-in control_after_generate mechanism on the ckpt_name/unet_name combos,
letting users pick a random model on every queue with the selected model
written back into the widget (visible, and lockable via the 'fixed' mode).
A base_model input narrows the random pool: a front-end extension fetches
the name/base_model mapping from the new /api/lm/checkpoints/loader-pool
endpoint and filters the combo options, wired through the node callback,
the refreshComboInNodes extension hook, and a graph.onConfigure hook
installed from onAdded (onNodeCreated fires before the node is attached to
a graph, so the graph reference is unavailable there).
- Fix config.py: save and restore main paths when processing extra folder paths to prevent
_prepare_checkpoint_paths from overwriting checkpoints_roots and unet_roots
- Fix lora_manager.py: apply library settings during initialization to load extra folder paths
in ComfyUI plugin mode
- Fix checkpoint_routes.py: merge checkpoints/unet roots with extra paths in API endpoints
- Add logging for extra folder paths
Fixes issue where extra folder paths were not recognized for checkpoints and unet models.
- Add checkpoint hash parameter parsing to backend routes
- Implement checkpoint hash filtering in frontend API client
- Add click navigation from recipe modal to checkpoints page
- Update checkpoint items to use pointer cursor for better UX
Checkpoint items in recipe modal are now clickable and will navigate to the checkpoints page with appropriate hash filtering applied. This improves user workflow when wanting to view checkpoint details from recipes.
- Pass ModelUpdateService to CheckpointService, EmbeddingService, and LoraService constructors
- Add has_update query parameter filter to model listing handler
- Update BaseModelService to accept optional update_service parameter
These changes enable model update functionality across different model types and provide filtering capability for models with available updates.
- Updated all relevant routes in `stats_routes.py` and `update_routes.py` to include the new '/api/lm/' prefix for consistency.
- Modified API endpoint configurations in `apiConfig.js` to reflect the new structure, ensuring all CRUD and bulk operations are correctly routed.
- Adjusted fetch calls in various components and managers to utilize the updated API paths, including recipe, model, and example image operations.
- Ensured all instances of the old API paths were replaced with the new '/api/lm/' prefix across the codebase for uniformity and to prevent broken links.
- 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.
Will Miao