- Implemented `DownloadModel` and `DownloadRequiredModels` operators for downloading models.
- Created `ClearModels` operator to clear model resources.
- Added `ConvertToSurface` operator for converting image empties into textured surfaces.
- Introduced `GenerateDa3Surface` and `GenerateMoge2Surface` operators for generating surfaces using DA3 and MoGe-2 models respectively.
- Developed utility functions for handling image mesh data, assigning attributes, and managing geometry nodes.
- Established runtime configuration and post-installation model downloading in `runtime.py`.
- Introduced new guides for Depth Plane, Surface, and animation workflows.
- Added installation instructions and FAQs to assist users in setup and troubleshooting.
- Created a quick-start guide for first-time users to convert images to Depth Planes.
- Documented the process for removing backgrounds from images.
- Established a models and storage section to clarify data management.
- Included a stylesheets file for improved documentation styling.
- Updated mkdocs configuration for better navigation and theme settings.
- Implemented surface generation functionality in `surface.py`, including geometry estimation and texture writing.
- Introduced media input handling in `input.py`, supporting image and video frame extraction.
- Added model management for geometry adapters in `models/__init__.py`.
- Created BEN2 and DA3 model inference logic in `ben2.py` and `da3.py`, respectively.
- Developed ONNX model inference utilities for BEN2 and DA3 in `onnx_ben2.py` and `onnx_da3.py`.
- Added support for MoGe-2 model inference in `moge2.py` with necessary ONNX utilities.
- Implemented output handling for depth and surface data in `depth.py` and `surface.py`.
- Created debug output utilities for DA3 and MoGe-2 predictions in `debug_da3.py` and `debug_moge2.py`.
- Enhanced ONNX runtime session management in `onnx_runtime.py`.
- Updated `__init__.py` files to include new modules and functions for better organization.
- 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.
