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**FastVideo is a unified post-training and inference framework for accelerated video generation.**
## NEWS
- ```2025/11/19```: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py)
- ```2025/08/04```: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
More
- ```2025/06/14```: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389)
- ```2025/04/24```: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
- ```2025/02/18```: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
## Key Features
FastVideo has the following features:
- End-to-end post-training support for bidirectional and autoregressive models:
- Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs
- Data preprocessing pipeline for video, image, and text data
- Distribution Matching Distillation (DMD2) stepwise distillation.
- Sparse attention with [Video Sparse Attention](https://arxiv.org/pdf/2505.13389)
- [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achineve >50x denoising speedup
- Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing.
- Causal distillation through Self-Forcing
- See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for full list of supported models and recipes.
- State-of-the-art performance optimizations for inference
- Sequence Parallelism for distributed inference
- Multiple state-of-the-art attention backends
- User-friendly CLI and Python API
- See this [page](https://hao-ai-lab.github.io/FastVideo/inference/optimizations/) for full list of supported optimizations.
- Diverse hardware and OS support
- Support H100, A100, 4090
- Support Linux, Windows, MacOS
- See this [page](https://hao-ai-lab.github.io/FastVideo/inference/hardware_support/) for full list of supported hardware and OS.
## Getting Started
We recommend using an environment manager such as `Conda` to create a clean environment:
```bash
# Create and activate a new conda environment
conda create -n fastvideo python=3.12
conda activate fastvideo
# Install FastVideo
pip install fastvideo
```
Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions.
## Sparse Distillation
For our sparse distillation techniques, please see our [distillation docs](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) and check out our [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
See below for recipes and datasets:
| Model | Sparse Distillation | Dataset |
|:-------------------------------------------------------------------------------------------: |:---------------------------------------------------------------------------------------------------------------: |:--------------------------------------------------------------------------------------------------------: |
| [FastWan2.1-T2V-1.3B](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P) | [FastVideo Synthetic Wan2.1 480P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k) |
| [FastWan2.1-T2V-14B-Preview](https://huggingface.co/FastVideo/FastWan2.1-T2V-14B-Diffusers) | Coming soon! | [FastVideo Synthetic Wan2.1 720P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x768x1280_250k) |
| [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free) | [FastVideo Synthetic Wan2.2 720P](https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k) |
## Inference
### Generating Your First Video
Here's a minimal example to generate a video using the default settings. Make sure VSA kernels are [installed](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation/). Create a file called `example.py` with the following code:
```python
import os
from fastvideo import VideoGenerator
def main():
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
# Create a video generator with a pre-trained model
generator = VideoGenerator.from_pretrained(
"FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
num_gpus=1, # Adjust based on your hardware
)
# Define a prompt for your video
prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest."
# Generate the video
video = generator.generate_video(
prompt,
return_frames=True, # Also return frames from this call (defaults to False)
output_path="my_videos/", # Controls where videos are saved
save_video=True
)
if __name__ == '__main__':
main()
```
Run the script with:
```bash
python example.py
```
For a more detailed guide, please see our [inference quick start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/).
### Other docs:
- [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview/)
- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/)
## Distillation and Finetuning
- [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/)
## Awesome work using FastVideo or our research projects
- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based on a fork of FastVideo on Sept. 24, 2025. [](https://github.com/sgl-project/sglang)
- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo. [](https://github.com/XueZeyue/DanceGRPO)
- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo. [](https://github.com/Tencent-Hunyuan/SRPO)
- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo. [](https://github.com/Vchitect/DCM)
- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention. [](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5)
- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch. [](https://github.com/kandinskylab/kandinsky-5)
- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention. [](https://github.com/meituan-longcat/LongCat-Video)
## 🤝 Contributing
We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview/).
See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/899).
## Acknowledgement
We learned and reused code from the following projects:
- [Wan-Video](https://github.com/Wan-Video)
- [ThunderKittens](https://github.com/HazyResearch/ThunderKittens)
- [Triton](https://github.com/triton-lang/triton)
- [DMD2](https://github.com/tianweiy/DMD2)
- [diffusers](https://github.com/huggingface/diffusers)
- [xDiT](https://github.com/xdit-project/xDiT)
- [vLLM](https://github.com/vllm-project/vllm)
- [SGLang](https://github.com/sgl-project/sglang)
We thank [MBZUAI](https://ifm.mbzuai.ac.ae/), [Anyscale](https://www.anyscale.com/), and [GMI Cloud](https://www.gmicloud.ai/) for their support throughout this project.
## Citation
If you find FastVideo useful, please considering citing our work:
```bibtex
@software{fastvideo2024,
title = {FastVideo: A Unified Framework for Accelerated Video Generation},
author = {The FastVideo Team},
url = {https://github.com/hao-ai-lab/FastVideo},
month = apr,
year = {2024},
}
@article{zhang2025vsa,
title={Vsa: Faster video diffusion with trainable sparse attention},
author={Zhang, Peiyuan and Chen, Yongqi and Huang, Haofeng and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
journal={arXiv preprint arXiv:2505.13389},
year={2025}
}
@article{zhang2025fast,
title={Fast video generation with sliding tile attention},
author={Zhang, Peiyuan and Chen, Yongqi and Su, Runlong and Ding, Hangliang and Stoica, Ion and Liu, Zhengzhong and Zhang, Hao},
journal={arXiv preprint arXiv:2502.04507},
year={2025}
}
```
