- Introduced ModelSpec dataclass to define model attributes.
- Added multiple models including DA3 Small, Base, Giant 1.1, Nested Giant Large 1.1, and MoGe-2 variants.
- Implemented functions to retrieve model details and metadata.
- Included BEN2 model specifications for background removal.
- Introduced `onnx_moge2.py` for MoGe-2 ONNX inference with NumPy post-processing.
- Added `onnx_ben2.py` for BEN2 ONNX inference with Pillow and NumPy preprocessing.
- Created `onnx_da3.py` for Depth Anything 3 inference with ONNX Runtime and NumPy.
- Implemented shared ONNX Runtime session helpers in `onnx_runtime.py`.
- Developed unit tests for server functionalities in `test_server.py`, including model caching and job management.
- Enhanced job server to handle frame extraction without requiring models.
- Implement JobClient for handling HTTP requests to the JobServer.
- Create ModalJobOperatorBase for managing Blender job lifecycle.
- Introduce JobContext and JobServer for job management and state handling.
- Add ServiceConnection and ServiceController for managing service processes.
- Implement job submission, cancellation, and resource management endpoints in the server.
- Ensure proper error handling and job state updates throughout the service.
- Updated version in pyproject.toml and blender_manifest.toml to 0.11.2.
- Enhanced uv executable path documentation in client.py.
- Added inference_mode parameter to reconstruct_request function in client.py.
- Implemented uv download functionality in install_env.py with improved error handling and checksum verification.
- Modified install_uv function to download and extract uv binary instead of using bundled version.
- Updated job progress handling in operators.py to track progress stages.
- Improved download progress reporting in worker.py and adjusted related tests.
- Refactored tests to accommodate changes in status reporting and model downloading.
- Removed vendor.lock.toml as part of the cleanup process.
- Added `ben2_onnx.py` for BEN2 ONNX inference with preprocessing.
- Introduced `da3_onnx.py` for Depth Anything 3 inference, including camera projection calculations.
- Created `moge_onnx.py` for MoGe-2 ONNX inference and post-processing.
- Implemented shared ONNX Runtime session helpers in `onnx_runtime.py`.
- Added model synchronization script `sync_models.py` for downloading and verifying ONNX models.
- Developed unit tests for BEN2, Depth Anything 3, and MoGe ONNX models to ensure functionality.
- Updated `vendor.lock.toml` to remove unused artifacts.
- Created development.md for development setup and licensing information.
- Added features.md detailing functionalities and usage instructions for developers and users.
- Introduced index.md to serve as a navigation hub for the documentation.
- Developed outputs.md to explain output formats and their specifications.
- Established service.md to describe the AI Service architecture and environment setup.
- Created surface.md to outline the process of converting Image Empty to perspective meshes.
