OmniAvatar: Efficient Audio-Driven Avatar Video Generation with Adaptive Body Animation

Qijun Gan · Ruizi Yang · Jianke Zhu · Shaofei Xue · Steven Hoi

Zhejiang University, Alibaba Group

🔥 Latest News!!

• July 2-nd, 2025: We released the model weights for Wan 1.3B!
• June 24-th, 2025: We released the inference code and model weights!

Quickstart

🛠️Installation

Clone the repo:

git clone https://github.com/Omni-Avatar/OmniAvatar
cd OmniAvatar

Install dependencies:

pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu124
pip install -r requirements.txt
# Optional to install flash_attn to accelerate attention computation
pip install flash_attn

🧱Model Download

Download models using huggingface-cli:

mkdir pretrained_models
pip install "huggingface_hub[cli]"
huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir ./pretrained_models/Wan2.1-T2V-14B
huggingface-cli download facebook/wav2vec2-base-960h --local-dir ./pretrained_models/wav2vec2-base-960h
huggingface-cli download OmniAvatar/OmniAvatar-14B --local-dir ./pretrained_models/OmniAvatar-14B

File structure (Samples for 14B)

OmniAvatar
├── pretrained_models
│   ├── Wan2.1-T2V-14B
│   │   ├── ...
│   ├── OmniAvatar-14B
│   │   ├── config.json
│   │   └── pytorch_model.pt
│   └── wav2vec2-base-960h
│       ├── ...

🔑 Inference

# 480p only for now
# 14B
torchrun --standalone --nproc_per_node=1 scripts/inference.py --config configs/inference.yaml --input_file examples/infer_samples.txt

# 1.3B
torchrun --standalone --nproc_per_node=1 scripts/inference.py --config configs/inference_1.3B.yaml --input_file examples/infer_samples.txt

💡Tips

• You can control the character's behavior through the prompt in examples/infer_samples.txt, and its format is [prompt]@@[img_path]@@[audio_path]. The recommended range for prompt and audio cfg is [4-6]. You can increase the audio cfg to achieve more consistent lip-sync.

• Control prompts guidance and audio guidance respectively, and use audio_scale=3 to control audio guidance separately. At this time, guidance_scale only controls prompts.

• To speed up, the recommanded num_steps range is [20-50], more steps bring higher quality. To use multi-gpu inference, just set sp_size=$GPU_NUM. To use TeaCache, you can set tea_cache_l1_thresh=0.14 , and the recommanded range is [0.05-0.15].

• To reduce GPU memory storage, you can set use_fsdp=True and num_persistent_param_in_dit. An example command is as follows:

torchrun --standalone --nproc_per_node=8 scripts/inference.py --config configs/inference.yaml --input_file examples/infer_samples.txt --hp=sp_size=8,max_tokens=30000,guidance_scale=4.5,overlap_frame=13,num_steps=25,use_fsdp=True,tea_cache_l1_thresh=0.14,num_persistent_param_in_dit=7000000000

We present a detailed table here. The model is tested on A800.

We train train 14B under 30000 tokens for 480p videos. We found that using more tokens when inference can also have good results. You can try 60000, 80000. Overlap overlap_frame can be set as 1 or 13. 13 could have more coherent generation, but error propagation is more severe.

• ❕Prompts are also very important. It is recommended to [Description of first frame]- [Description of human behavior]-[Description of background (optional)]

🧩 Community Works

We ❤️ contributions from the open-source community! If your work has improved OmniAvatar, please inform us. Or you can directly e-mail ganqijun@zju.edu.cn. We are happy to reference your project for everyone's convenience. 🥸Have Fun!

🔗Citation

If you find this repository useful, please consider giving a star ⭐ and citation

@misc{gan2025omniavatar,
      title={OmniAvatar: Efficient Audio-Driven Avatar Video Generation with Adaptive Body Animation}, 
      author={Qijun Gan and Ruizi Yang and Jianke Zhu and Shaofei Xue and Steven Hoi},
      year={2025},
      eprint={2506.18866},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.18866}, 
}

Acknowledgments

Thanks to Wan2.1, FantasyTalking and DiffSynth-Studio for open-sourcing their models and code, which provided valuable references and support for this project. Their contributions to the open-source community are truly appreciated.