- Added `populate_lora_from_civitai` and `populate_checkpoint_from_civitai` methods to enhance the extraction of model information from Civitai API responses.
- These methods populate LoRA and checkpoint entries with relevant data such as model name, version, thumbnail URL, base model, download URL, and file details.
- Improved error handling and logging for scenarios where models are not found or data retrieval fails.
- Refactored existing code to utilize the new methods, streamlining the process of fetching and updating LoRA and checkpoint metadata.
- Introduced MetaFormatParser class to parse metadata from images with Lora_N Model hash format.
- Implemented methods to validate metadata structure, extract prompts, negative prompts, and LoRA information.
- Enhanced error handling and logging for metadata parsing failures.
- Updated RecipeParserFactory to include MetaFormatParser for relevant user comments.
- Introduced ComfyMetadataParser class to parse metadata from Civitai ComfyUI JSON format.
- Implemented methods to validate metadata structure, extract LoRA and checkpoint information, and retrieve additional model details from Civitai.
- Enhanced error handling and logging for metadata parsing failures.
- Updated RecipeParserFactory to prioritize ComfyMetadataParser for valid JSON inputs.
- Updated the early access condition checks in RecipeFormatParser, StandardMetadataParser, and A1111MetadataParser to use `get` method for improved readability and safety.
- Ensured consistent handling of early access status across different parser classes.
- Added error handling for early access restrictions in the API routes, returning appropriate status codes and messages.
- Enhanced the Civitai client to log unauthorized access attempts and provide user-friendly error messages.
- Updated the download manager to check for early access requirements and log warnings accordingly.
- Introduced UI elements to indicate early access status for LoRAs, including badges and warning messages in the import manager.
- Improved toast notifications to inform users about early access download failures and provide relevant information.
- Introduced a new API endpoint to save recipes directly from the LoRAs widget.
- Implemented logic to handle recipe data, including image processing and metadata extraction.
- Enhanced error handling for missing fields and image retrieval.
- Updated the ExifUtils to extract generation parameters from images for recipe creation.
- Added a direct save option in the widget, improving user experience.
- Implemented image optimization in RecipeRoutes, resizing and converting uploaded images to WebP format while preserving metadata.
- Updated ExifUtils to support EXIF data handling for WebP images, ensuring compatibility with various image formats.
- Added a new method for optimizing images, allowing for better performance and quality in image uploads.
- Changed the extraction of model ID to use 'id' instead of 'modelVersionId'.
- Updated the retrieval of model name and version to align with the new Civitai response structure, ensuring accurate metadata parsing for LoRA entries.
- Improved error handling and logging for better traceability during metadata fetching.
- Updated ExifUtils to handle both JPEG/TIFF and non-JPEG/TIFF images for extracting UserComment from EXIF data, improving compatibility with various image formats.
- Introduced A1111MetadataParser to support parsing of images with A1111 metadata format, extracting prompts, negative prompts, and LoRA information.
- Enhanced error handling and logging for metadata parsing processes, ensuring better traceability and debugging capabilities.
- Introduced a new RecipeParserFactory to streamline the parsing of recipe metadata from user comments, supporting multiple formats.
- Removed legacy metadata extraction logic from RecipeRoutes, delegating responsibilities to the new parser classes.
- Enhanced error handling for cases where no valid parser is found, ensuring graceful responses.
- Updated the RecipeScanner to improve the handling of LoRA metadata and reduce logging verbosity for better performance.
