SoulX-FlashHead: Oracle-guided Generation of Infinite Real-time Streaming Talking Heads

[Tan Yu*](https://jiayoujiayoujiayoua.github.io/), [Qian Qiao*](https://qianqiaoai.github.io/)✉, [Le Shen*](https://openreview.net/profile?id=%7ELe_Shen3), [Ke Zhou](https://github.com/jokerz0624), [Jincheng Hu](#), [Dian Sheng](#), [Bo Hu](#), [Haoming Qin](#), [Jun Gao](#), [Changhai Zhou](#), [Shunshun Yin](#), [Siyuan Liu](#) ✉ *Equal Contribution ✉Corresponding Author  

⚡ Highlights

• Model_Lite Released get 96 FPS, or 3-concurrent real-time(25+ FPS) streaming on single RTX4090.
• Model_Pro Released can generate high-quality videos with 10.8 FPS on single RTX4090, or real-time(25+ FPS) on two RTX5090.
• Model_Pretrained is coming soon, providing high-performance weights and experimental foundations for community research.

🔥 News

• 2026.03.09 - Online demo on HuggingFace is available now. You can try it out directly.
• 2026.03.04 - Gradio app is available now. Both common and streaming mode are supported.
• 2026.03.02 - The ComfyUI node is now available. Thanks for the comfyui support of HM-RunningHub.
• 2026.02.12 - The online demo is now available via the Soul App. Download it today to try it out.
• 2026.02.12 - We have released the inference code, and the model weights.
• 2026.02.12 - We released Project page on SoulX-FlashHead.
• 2026.02.07 - We released Dataset.
• 2026.02.07 - We released SoulX-FlashHead Technical Report on Arxiv and GitHub repository.

📑 Todo List

• [x] Technical report
• [x] Project Page
• [x] Inference code
• [x] Streaming online demo on HuggingFace
• [x] Distilled Checkpoint of Pro-Model & Lite-Model release
• [ ] Pretrained Checkpoint release

🌰 Examples

More examples are available in the project.

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  <td width="30%"><video src="https://private-user-images.githubusercontent.com/176391424/548713661-8cdfd881-7782-403c-9dc0-e93930750dfe.mp4?jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NzA4ODYxMzEsIm5iZiI6MTc3MDg4NTgzMSwicGF0aCI6Ii8xNzYzOTE0MjQvNTQ4NzEzNjYxLThjZGZkODgxLTc3ODItNDAzYy05ZGMwLWU5MzkzMDc1MGRmZS5tcDQ_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjYwMjEyJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI2MDIxMlQwODQzNTFaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT03ZjFkZWZiMGIxNzkyYjZjNWM1YzU5OWRlN2Q4Mzk2ZGQwZGE4OTc1NDMyYjg2YTU5OTE2MDVhYzE3ZGMyMmE3JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.blrq-obdV5NBPojgWEdOaXCVriCAIZjsKQ_x_DDPQ5k" style="width:100%; aspect-ratio:512/512; object-fit:cover;" controls loop></video></td>
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📖 Quickstart

🔧 Installation

1. Create a Conda environment

conda create -n flashhead python=3.10
conda activate flashhead

2. Install PyTorch on CUDA

pip install torch==2.7.1 torchvision==0.22.1 --index-url https://download.pytorch.org/whl/cu128

3. Install other dependencies

pip install -r requirements.txt

4. FlashAttention installation:

pip install ninja
pip install flash_attn==2.8.0.post2 --no-build-isolation

-- If it takes a long time, we recommend the way below.

1. download wheel file from here
2. pip install xxx.whl

5. SageAttention installation (Optional)

pip install sageattention==2.2.0 --no-build-isolation

6. FFmpeg installation

# Ubuntu / Debian
apt-get install ffmpeg
# CentOS / RHEL
yum install ffmpeg ffmpeg-devel

or

# Conda (no root required) 
conda install -c conda-forge ffmpeg==7

🤗 Model download

# If you are in china mainland, run this first: export HF_ENDPOINT=https://hf-mirror.com
pip install "huggingface_hub[cli]"
huggingface-cli download Soul-AILab/SoulX-FlashHead-1_3B --local-dir ./models/SoulX-FlashHead-1_3B
huggingface-cli download facebook/wav2vec2-base-960h --local-dir ./models/wav2vec2-base-960h

🚀 Inference

# Infer with [Pro-Model] on single GPU
bash inference_script_single_gpu_pro.sh


# Infer with [Pro-Model] on multy GPUs
bash inference_script_multi_gpu_pro.sh
# Real-time inference speed of Pro-Model can only be supported on two RTX-5090 with SageAttention.


# Infer with [Lite-Model] on single GPU
bash inference_script_single_gpu_lite.sh
# Real-time inference speed can be supported on single RTX-4090 (up to 3 concurrent).

⚡️ Gradio Demo

# Gradio support needs gradio==5.50.0, and Chrome recommonded.

# common gradio demo
python gradio_app.py

# streaming gradio demo (Only support single GPU)
python gradio_app_streaming.py

🤗 Streaming online demo

Click here to experience the real-time streaming demo on HuggingFace Spaces.

👋 Online Experience

For a real-time interactive experience, scan the QR code to enter the event link. [2026.2.12~2026.3.11]
<a id="online-experience-qr"></a>

Real-time Online Experience(SoulApp 实时在线体验)

📧 Contact Us

If you are interested in leaving a message to our work, feel free to email [email protected] or [email protected] or [email protected] or [email protected] or [email protected]

We have opened a WeChat group. Additionally, we represent SoulApp and warmly welcome everyone to download the app and join our Soul group for further technical discussions and updates!

Join WeChat Group(加入微信技术群) Download SoulApp & Join Group(下载SoulApp加入群组)

📚 Citation

If you find our work useful in your research, please consider citing:

@article{yu2026soulx,
  title={SoulX-FlashHead: Oracle-guided Generation of Infinite Real-time Streaming Talking Heads},
  author={Yu, Tan and Qiao, Qian and Shen, Le and Zhou, Ke and Hu, Jincheng and Sheng, Dian and Hu, Bo and Qin, Haoming and Gao, Jun and Zhou, Changhai and others},
  journal={arXiv preprint arXiv:2602.07449},
  year={2026}
}

🙇 Acknowledgement

• Wan: the base model we built upon.
• LTX-Video: the VAE of our Lite-Model.
• Self forcing: the codebase we built upon.
• DMD and Self forcing++: the key distillation technique used by our method.
• SoulX-FlashTalk is another model developed by our team, featuring 14B parameters and real-time capabilities.

  [!TIP]
  If you find our work useful, please also consider starring the original repositories of these foundational methods.

💡 Star History