- Introduced `test_edge_depth.py` to validate the functionality of edge depth extension, ensuring core alpha preservation, proper depth handling, and edge padding behavior.
- Created `test_empty_image_edits.py` to verify the behavior of image edits in Blender, focusing on shared edits, preservation of image properties, and handling of failed operations.
- Developed `test_material_view_adaptation.py` to test material adaptation preferences, ensuring color space and alpha settings are correctly applied and preserved across various scenarios.
- Introduced tests for color image handling, ensuring source preservation and premultiplied alpha usage.
- Implemented tests for brush stroke operations, validating segment boundaries and brush anti-aliasing.
- Added tests for frame anti-aliasing, confirming correct rendering behavior with various alpha values and depth settings.
- Created tests for frame projection, ensuring accurate overlap calculations and half-plane clipping.
- Developed tests for image premultiplied retention, verifying behavior during loading and packing processes.
- Added undo functionality tests for mask operations, ensuring single transaction behavior during edits.
- Renamed "Base Plane Depth" to "Reference Depth" across validate_cutout.py for consistency.
- Introduced a new specification for Cutout mesh sampling to ensure source pixel spacing is preserved and limits are applied correctly.
- Unified naming conventions for image editing results to maintain consistency across Remove Background, Upscale, and Crop operations.
- Updated the handling of Refine Selection to ensure it retains the original bounds and does not apply unnecessary cropping.
- Enhanced material generation logic to connect image color directly to IOR Level, improving the rendering of dark areas.
- Established clear requirements for geometry depth controls and image result materials to ensure consistent behavior across different components.
- Removed outdated logic and ensured all changes are documented and tested for regression.
- Introduced a new design for selection rasterization that decouples the selection geometry from the image content, ensuring that the SelectionMask is independent of the source image's alpha and color.
- Updated the `rasterize_selection_path` function to only accept selection paths and image dimensions, removing parameters related to source alpha and alpha thresholds.
- Implemented a new approach for handling selection paths using non-zero winding fill, ensuring overlapping areas are preserved.
- Enhanced performance by utilizing a scanline algorithm for rasterization, significantly reducing processing time for high-resolution images.
- Updated internal documentation and added comprehensive tests to validate the new behavior and performance improvements.
- Ensured backward compatibility by maintaining existing interfaces where necessary while introducing breaking changes in the selection processing logic.
- Introduced a new memory management policy for ONNX Runtime to release temporary GPU memory after inference completion.
- Established a shared inference entry point for all ONNX models, differentiating between long-lived sessions and temporary memory arenas.
- Updated CUDA sessions to utilize a shrinkable memory arena, reclaiming GPU memory post-inference.
- Ensured consistent memory release semantics across BEN2, DA3, MoGe-2, and Upscale models while reusing existing sessions.
- Added lifecycle tests to validate temporary memory release and session reuse.
- Modified model adapters to integrate the new memory management features, ensuring backward compatibility with existing inference behavior.
- Enhanced unit tests to cover new memory release scenarios and verify correct behavior across different execution providers.
- Implemented viewport.py for handling 3D view projections, gestures, and overlay drawing.
- Created refine_image_selection.py to manage the refinement of gesture masks and output image bounds.
- Added selection module with refine_selection.py to process and crop images based on alpha channel visibility.
- Introduced __init__.py for selection module to expose refine_selection function.
- Implement sync_models.py for preparing and uploading ONNX artifacts.
- Create build_node.py for building and validating Blender scripts.
- Add docs.py for building and previewing documentation with Mike or MkDocs.
- Introduce model_exports.py for exporting PyTorch models to ONNX format.
- Develop pack.py for packaging the project with dependencies for different platforms.
- Add test_sync_models.py to validate sync_models functionality.
- Add test_tool_commands.py to ensure repository tools have correct command scripts.
- Initialize tools package with __init__.py.
