ComfyUI SeC Nodes provide custom functionality for video object segmentation using the SeC (Segment Concept) model, which surpasses the performance of traditional models like SAM 2.1. Leveraging a Large Vision-Language Model, it enables advanced semantic understanding for tracking objects across complex scenes.
- State-of-the-art video segmentation capabilities that adapt to scene complexity and occlusions.
- Supports various input formats, allowing for flexible segmentation methods tailored to specific use cases.
- Includes features for efficient memory management and improved performance, such as offloading video processing to CPU.
Context
The ComfyUI SeC Nodes are specialized extensions designed to enhance video object segmentation within the ComfyUI framework. They utilize the SeC-4B model developed by OpenIXCLab, which focuses on high-level conceptual understanding of objects, enabling improved tracking capabilities in challenging scenarios.
Key Features & Benefits
The tool offers several practical features that significantly enhance the video segmentation process. The SeC Model Loader allows users to easily select and load models, while the SeC Video Segmentation node facilitates robust object tracking using various input methods such as bounding boxes, points, and masks. Additionally, the Coordinate Plotter helps visualize input coordinates, aiding in debugging and fine-tuning segmentation tasks.
Advanced Functionalities
One of the standout capabilities of the SeC Nodes is the integration of a Large Vision-Language Model (LVLM) which provides semantic understanding beyond mere visual similarity. This allows for smarter tracking that can dynamically adjust based on the complexity of the scene and the presence of occlusions. The system also includes a keyframe bank that maintains diverse object views, enhancing its robustness in tracking.
Practical Benefits
By utilizing the ComfyUI SeC Nodes, users can expect a more streamlined workflow with improved control over video segmentation tasks. The ability to adapt to varying scene complexities and the reduction in VRAM usage through efficient processing techniques lead to higher quality outputs and increased efficiency in handling video data.
Credits/Acknowledgments
The SeC Nodes implement the SeC-4B model created by OpenIXCLab, with its repository available at OpenIXCLab/SeC-4B. The project is licensed under the Apache 2.0 License, and the original research paper can be found at arXiv:2507.15852.