VACE is an all-in-one model designed for advanced video generation and editing, supporting tasks such as move-anything, swap-anything, and reference-anything. It unifies R2V, V2V, and MV2V pipelines into a seamless workflow, allowing users to compose complex edits efficiently. Built on PyTorch and Hugging Face, VACE provides a robust foundation for both research and practical applications in video synthesis.
VACE uniquely combines extensive video editing capabilities with modular model design, supporting seamless integration across preprocessing, inference, and output generation. Its structured approach to task switching enables deep workflow flexibility, while active community contributions ensure ongoing enhancement and real-world applicability.
Core functionality spans R2V, V2V, and MV2V, enabling complex video transformations with simple prompts. Preprocessing and inference workflows are modular and customizable via configurable preprocessors and configurable pipelines. Wan2.1-based and Wan2.1-VACE-14B model variants offer scalable inference across multiple GPUs. Supports detailed output tracking including intermediate videos and annotated masks from preprocessing. Comprehensive documentation and code examples accompany releases, enabling rapid adaptation by developers.
Actively maintained with frequent updates, including benchmark releases and configuration improvements. The repository hosts working exemplars, thorough documentation, and ongoing community engagement, indicating stable and evolving reliability.
VACE empowers creators and researchers with a powerful, integrated solution for video editing and generation, addressing the need for flexible, high-performance tools in multimedia applications. Its support across complex pipelines, scalable deployment, and active development make it a valuable asset for both experimental research and production use cases.