⚡ 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

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

🌰 Examples

More examples are available in the project.

📖 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

Model Component	Description	Link
SoulX-FlashHead-1_3B	Our 1.3B model	🤗 Huggingface
wav2vec2-base-960h	wav2vec2-base-960h	🤗 Huggingface

# 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]

📧 Contact Us

If you are interested in leaving a message to our work, feel free to email yutan@soulapp.cn or qiaoqian@soulapp.cn or le.shen@mail.dhu.edu.cn or zhouke@soulapp.cn or liusiyuan@soulapp.cn

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!

📚 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.

💡 Star History