FastVideo: A Unified Framework for Accelerated Video Generation
FastVideo is an open-source, modular, and extensible framework designed to accelerate video generation using diffusion models. It provides a comprehensive end-to-end pipeline that covers the entire workflow for video generation tasks, starting from data preprocessing and extending to model training, finetuning, distillation, and efficient inference. This framework is particularly tailored for state-of-the-art open video Diffusion Transformers (DiTs), making it a powerful tool for researchers and developers working on high-performance video AI models.
Core Features and Capabilities
End-to-End Post-Training Support
FastVideo excels in post-training optimizations, enabling users to enhance model performance without retraining from scratch. One standout feature is sparse distillation, which has been applied to models like Wan2.1 and Wan2.2, achieving remarkable speedups of over 50x in the denoising process. This technique reduces computational overhead while maintaining high-quality outputs.
The framework also includes a robust data preprocessing pipeline specifically optimized for video data, ensuring efficient handling of large-scale datasets. For training and finetuning, FastVideo supports both full finetuning and Low-Rank Adaptation (LoRA) methods, compatible with leading open video DiTs. Scalability is a key focus, with built-in support for Fully Sharded Data Parallel (FSDP2), sequence parallelism, and selective activation checkpointing. This allows for near-linear scaling across up to 64 GPUs, making it suitable for large-scale distributed training environments.
State-of-the-Art Inference Optimizations
Inference is where FastVideo truly shines, incorporating cutting-edge performance optimizations to generate videos faster and more efficiently:
- Video Sparse Attention (VSA): A trainable sparse attention mechanism that significantly reduces memory and compute requirements for video processing (detailed in arXiv:2505.13389).
- Sliding Tile Attention (STA): Enables efficient handling of long video sequences by processing them in overlapping tiles, improving throughput (arXiv:2502.04507).
- TeaCache: A caching strategy for repeated computations in diffusion processes, further boosting speed.
- Sage Attention: An advanced attention variant for sparse data patterns in videos.
These optimizations are integrated seamlessly, allowing users to mix and match them based on their hardware and use case.
Hardware and OS Versatility
FastVideo is designed for broad accessibility, supporting a range of high-end GPUs including NVIDIA H100, A100, and consumer-grade RTX 4090. It is cross-platform compatible, running on Linux, Windows, and macOS, which lowers the barrier to entry for diverse users.
Getting Started and Ecosystem Integration
Installation is straightforward using pip or conda, with a clean environment setup recommended. For example:
conda create -n fastvideo python=3.12
conda activate fastvideo
pip install fastvideoDetailed installation guides, including VSA kernel setup, are available in the documentation. Users can quickly generate videos with minimal code, as shown in the quick start example using the VideoGenerator class from pretrained models on Hugging Face.
FastVideo integrates with popular ecosystems like Hugging Face Diffusers and provides recipes for distillation and finetuning. It also hosts models such as FastWan2.1-T2V-1.3B and supports synthetic datasets for training. An online demo is available at fastwan.fastvideo.org, and community support channels include Slack and WeChat.
Recent Developments and Resources
The project has seen rapid evolution:
- November 2024: Preview release of CausalWan2.2 I2V A14B models with inference code.
- August 2024: FastWan models and sparse distillation techniques released.
- June 2024: Finetuning and inference for VSA.
- April 2024: Initial FastVideo V1 launch.
- February 2024: Sliding Tile Attention inference code.
Documentation covers design overview, contribution guidelines, and specific sections for distillation and inference. The project acknowledges influences from Wan-Video, Diffusers, and others, and is supported by institutions like MBZUAI and Anyscale.
Impact and Community Adoption
FastVideo has inspired several downstream projects, including SGLang's diffusion inference, DanceGRPO for visual generation, and integrations in models like Hunyuan Video 1.5 and Kandinsky-5.0. Its citation encourages academic and practical use, highlighting its role in advancing efficient video generation.
In summary, FastVideo democratizes accelerated video AI by combining ease of use with high-performance optimizations, making it an essential tool for the next generation of generative media applications.
