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---
license: mit
library_name: diffusers
pipeline_tag: text-to-video
---
# AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset
This repository contains the pre-trained weights of [AccVideo](https://arxiv.org/abs/2503.19462). AccVideo is a novel efficient distillation method to accelerate video diffusion models with synthetic datset. Our method is 8.5x faster than HunyuanVideo.
[](https://arxiv.org/abs/2503.19462)[](https://aejion.github.io/accvideo/)
## 🔥🔥🔥 News
* Mar, 2025: We release the inference code and model weights of AccVideo.
## 📑 Open-source Plan
- [x] Inference
- [x] Checkpoints
- [ ] Multi-GPU Inference
- [ ] Synthetic Video Dataset, SynVid
- [ ] Training
## 🔧 Installation
The code is tested on Python 3.10.0, CUDA 11.8 and A100.
```
conda create -n accvideo python==3.10.0
conda activate accvideo
pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
pip install flash-attn==2.7.3 --no-build-isolation
pip install "huggingface_hub[cli]"
```
## 🤗 Checkpoints
To download the checkpoints, use the following command:
```bash
# Download the model weight
huggingface-cli download aejion/AccVideo --local-dir ./ckpts
```
## 🚀 Inference
We recommend using a GPU with 80GB of memory. To run the inference, use the following command:
```bash
export MODEL_BASE=./ckpts
python sample_t2v.py \
--height 544 \
--width 960 \
--num_frames 93 \
--num_inference_steps 50 \
--guidance_scale 1 \
--embedded_cfg_scale 6 \
--flow_shift 7 \
--flow-reverse \
--prompt_file ./assets/prompt.txt \
--seed 1024 \
--output_path ./results/accvideo-544p \
--model_path ./ckpts \
--dit-weight ./ckpts/accvideo-t2v-5-steps/diffusion_pytorch_model.pt
```
The following table shows the comparisons on inference time using a single A100 GPU:
| Model | Setting(height/width/frame) | Inference Time(s) |
|:------------:|:---------------------------:|:-----------------:|
| HunyuanVideo | 720px1280px129f | 3234 |
| Ours | 720px1280px129f | 380(8.5x faster) |
| HunyuanVideo | 544px960px93f | 704 |
| Ours | 544px960px93f | 91(7.7x faster) |
## 🔗 BibTeX
If you find [AccVideo](https://arxiv.org/abs/2503.19462) useful for your research and applications, please cite using this BibTeX:
```BibTeX
@article{zhang2025accvideo,
title={AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset},
author={Zhang, Haiyu and Chen, Xinyuan and Wang, Yaohui and Liu, Xihui and Wang, Yunhong and Qiao, Yu},
journal={arXiv preprint arXiv:2503.19462},
year={2025}
}
```
## Acknowledgements
The code is built upon [FastVideo](https://github.com/hao-ai-lab/FastVideo) and [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), we thank all the contributors for open-sourcing. |