**FastVideo is a unified framework for accelerated video generation.**
It features a clean, consistent API that works across popular video models, making it easier for developers to author new models and incorporate system- or kernel-level optimizations.
With FastVideo's optimizations, you can achieve more than 3x inference improvement compared to other systems.
| Documentation | Quick Start | 🤗 FastHunyuan | 🤗 FastMochi | 🟣💬 Slack |
## Key Features
FastVideo has the following features:
- State-of-the-art performance optimizations for inference
- [Sliding Tile Attention](https://arxiv.org/pdf/2502.04507)
- [TeaCache](https://arxiv.org/pdf/2411.19108)
- [Sage Attention](https://arxiv.org/abs/2410.02367)
- Cutting edge models
- Wan2.1 T2V, I2V
- HunyuanVideo
- FastHunyuan: consistency distilled video diffusion models for 8x inference speedup.
- StepVideo T2V
- Distillation support
- Recipes for video DiT, based on [PCM](https://github.com/G-U-N/Phased-Consistency-Model).
- Support distilling/finetuning/inferencing state-of-the-art open video DiTs: 1. Mochi 2. Hunyuan.
- Scalable training with FSDP, sequence parallelism, and selective activation checkpointing, with near linear scaling to 64 GPUs.
- Memory efficient finetuning with LoRA, precomputed latent, and precomputed text embeddings.
## 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.html) for more detailed installation instructions.
## Inference
### Generating Your First Video
Here's a minimal example to generate a video using the default settings. Create a file called `example.py` with the following code:
```python
from fastvideo import VideoGenerator
def main():
# Create a video generator with a pre-trained model
generator = VideoGenerator.from_pretrained(
"Wan-AI/Wan2.1-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.html).
### Other docs:
- [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview.html)
- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation.html)
## Distillation and Finetuning
- [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/training/distillation.html)
- [Finetuning Guide](https://hao-ai-lab.github.io/FastVideo/training/finetuning.html)
## 📑 Development Plan
- More models support
- [x] Add StepVideo to V1
- Optimization features
- [x] Teacache in V1
- [x] SageAttention in V1
- Code updates
- [x] V1 Configuration API
- [ ] Support Training in V1
## 🤝 Contributing
We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/developer_guide/overview.html)
## Acknowledgement
We learned and reused code from the following projects:
- [PCM](https://github.com/G-U-N/Phased-Consistency-Model)
- [diffusers](https://github.com/huggingface/diffusers)
- [OpenSoraPlan](https://github.com/PKU-YuanGroup/Open-Sora-Plan)
- [xDiT](https://github.com/xdit-project/xDiT)
- [vLLM](https://github.com/vllm-project/vllm)
- [SGLang](https://github.com/sgl-project/sglang)
We thank MBZUAI and [Anyscale](https://www.anyscale.com/) for their support throughout this project.
## Citation
If you use FastVideo for your research, please cite our paper:
```bibtex
@misc{zhang2025fastvideogenerationsliding,
title={Fast Video Generation with Sliding Tile Attention},
author={Peiyuan Zhang and Yongqi Chen and Runlong Su and Hangliang Ding and Ion Stoica and Zhenghong Liu and Hao Zhang},
year={2025},
eprint={2502.04507},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2502.04507},
}
@misc{ding2025efficientvditefficientvideodiffusion,
title={Efficient-vDiT: Efficient Video Diffusion Transformers With Attention Tile},
author={Hangliang Ding and Dacheng Li and Runlong Su and Peiyuan Zhang and Zhijie Deng and Ion Stoica and Hao Zhang},
year={2025},
eprint={2502.06155},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2502.06155},
}
```
