- Introduced a new `Downloader` class to centralize HTTP/HTTPS download management.
- Replaced direct `aiohttp` session handling with the unified downloader in `MetadataArchiveManager`, `DownloadManager`, and `ExampleImagesProcessor`.
- Added support for resumable downloads, progress tracking, and error handling in the new downloader.
- Updated methods to utilize the downloader's capabilities for downloading files and images, improving code maintainability and readability.
- Added ModelMetadataProvider and CivitaiModelMetadataProvider for handling model metadata.
- Introduced SQLiteModelMetadataProvider for SQLite database integration.
- Created metadata_service.py to initialize and configure metadata providers.
- Updated CivitaiClient to register as a metadata provider.
- Refactored download_manager to use the new download_file method.
- Added SQL schema for models, model_versions, and model_files.
- Updated requirements.txt to include aiosqlite.
- Introduced MODEL_TYPES and MODEL_CONFIG for centralized model type management.
- Created a unified API client for checkpoints and loras to streamline operations.
- Updated all API calls in checkpointApi.js and loraApi.js to use the new client.
- Simplified context menus and model card operations to leverage the unified API client.
- Enhanced state management to accommodate new model types and their configurations.
- Added virtual scrolling functions for recipes and improved loading states.
- Refactored modal utilities to handle model exclusion and deletion generically.
- Improved error handling and user feedback across various operations.
- 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.
- Implemented the WanVideo Lora Select node in Python with input handling for low memory loading and LORA syntax processing.
- Updated the JavaScript side to register the new node and manage its widget interactions.
- Enhanced constants files to include the new node type and its corresponding ID.
- Modified existing Lora Loader and Stacker references to accommodate the new node in various workflows and UI components.
- Added example workflow JSON for the new node to demonstrate its usage.
