- Added validation documentation for AI model tiers.
- Introduced `background.py` for background removal logic, including inference functions for alpha and foreground extraction.
- Created `onnx_birefnet.py` to handle BiRefNet model sessions and alpha inference.
- Developed tests for background models, ensuring proper dispatch and session reuse.
- Added tests for BiRefNet ONNX model to validate alpha output and error handling for invalid values.
- Implemented model export scripts for BiRefNet and HAT Sharper, ensuring proper weight compression and licensing.
- Created `deform_sampling.py` for deformable convolution operations used in BiRefNet.
- Added utility functions for compressing ONNX weights without altering graph arithmetic.
- Introduced a reference plane node for depth field adjustments in geometry processing.
- Added a proposal for depth surface strip cleanup, detailing the rationale and changes to the depth plane and cutout nodes.
- Created specifications for the new cleanup requirements, including scenarios for strip removal and boundary edge detection.
- Developed tasks for implementing shared strip detection logic and integrating new interfaces for depth nodes.
- Established a new CLI structure for organizing node groups and model commands, replacing old entry points with a more intuitive command structure.
- Introduced a prototype for panorama surface generation, including requirements for distance reconstruction and geometry node surfaces.
- Added comprehensive tests for depth strip cleanup functionality, ensuring correct behavior for various mesh topologies and edge cases.
- Implement tests for image layer functionality including alpha curve and depth layer behavior.
- Create tests for image plane evaluation and modifier input handling.
- Add tests to verify the saved asset inventory and its persistence.
- Introduce tests for the documentation command to ensure correct project version deployment.
- Validate the extension manifest structure and required permissions.
- Develop tests for the packaging process, ensuring correct platform handling and dependency management.
- Enhance image depth plane group functionality with new split threshold input and surface processing.
