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# AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset
This repository is the official PyTorch implementation 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/)
[](https://huggingface.co/aejion/AccVideo)
## 🔥🔥🔥 News
* May 26, 2025: We release the inference code and [model weights](https://huggingface.co/aejion/AccVideo-WanX-T2V-14B) of AccVideo based on WanXT2V-14B.
* Mar 31, 2025: [ComfyUI-Kijai (FP8 Inference)](https://huggingface.co/Kijai/HunyuanVideo_comfy/blob/main/accvideo-t2v-5-steps_fp8_e4m3fn.safetensors): ComfyUI-Integration by [Kijai](https://huggingface.co/Kijai)
* Mar 26, 2025: We release the inference code and [model weights](https://huggingface.co/aejion/AccVideo) of AccVideo based on HunyuanT2V.
## 🎥 Demo (Based on HunyuanT2V)
https://github.com/user-attachments/assets/59f3c5db-d585-4773-8d92-366c1eb040f0
## 🎥 Demo (Based on WanXT2V-14B)
https://github.com/user-attachments/assets/ff9724da-b76c-478d-a9bf-0ee7240494b2
## 📑 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 (based on HunyuanT2V), use the following command:
```bash
# Download the model weight
huggingface-cli download aejion/AccVideo --local-dir ./ckpts
```
To download the checkpoints (based on WanX-T2V-14B), use the following command:
```bash
# Download the model weight
huggingface-cli download aejion/AccVideo-WanX-T2V-14B --local-dir ./wanx_t2v_ckpts
```
## 🚀 Inference
We recommend using a GPU with 80GB of memory. We use AccVideo to distill Hunyuan and WanX.
### Inference for HunyuanT2V
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 5 \
--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) |
### Inference for WanXT2V
To run the inference, use the following command:
```bash
python sample_wanx_t2v.py \
--task t2v-14B \
--size 832*480 \
--ckpt_dir ./wanx_t2v_ckpts \
--sample_solver 'unipc' \
--save_dir ./results/accvideo_wanx_14B \
--sample_steps 10
```
The following table shows the comparisons on inference time using a single A100 GPU:
| Model | Setting(height/width/frame) | Inference Time(s) |
|:-----:|:---------------------------:|:-----------------:|
| Wanx | 480px832px81f | 932 |
| Ours | 480px832px81f | 97(9.6x 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.
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