ComfyUI FLOAT

This project provides a ComfyUI wrapper of FLOAT for Generative Motion Latent Flow Matching for Audio-driven Talking Portrait

For a more advanced and maintained version, check out: ComfyUI-FLOAT_Optimized

0506.4.mp4

⭐ Support

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🚀 Installation

git clone https://github.com/yuvraj108c/ComfyUI-FLOAT.git
cd ./ComfyUI-FLOAT
pip install -r requirements.txt

☀️ Usage

• Load example workflow
• Upload driving image and audio, click queue
• Models autodownload to /ComfyUI/models/float
• The models are organized as follows:

  |-- float.pth                                       # main model
  |-- wav2vec2-base-960h/                             # audio encoder
  |   |-- config.json
  |   |-- model.safetensors
  |   |-- preprocessor_config.json
  |-- wav2vec-english-speech-emotion-recognition/     # emotion encoder
      |-- config.json
      |-- preprocessor_config.json
      |-- pytorch_model.bin
  

🛠️ Parameters

• ref_image: Reference image with a face (must have batch size 1)
• ref_audio: Reference audio (For long audios (e.g 3+ minutes), ensure that you have enough ram/vram)
• a_cfg_scale: Audio classifier-free guidance scale (default:2)
• r_cfg_scale: Reference classifier-free guidance scale (default:1)
• emotion: none, angry, disgust, fear, happy, neutral, sad, surprise (default:none)
• e_cfg_scale: Intensity of emotion (default:1). For more emotion intensive video, try large value from 5 to 10
• crop: Enable only if the reference image does not have a centered face
• fps: Frame rate of the output video (default:25)

Citation

@article{ki2024float,
  title={FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait},
  author={Ki, Taekyung and Min, Dongchan and Chae, Gyeongsu},
  journal={arXiv preprint arXiv:2412.01064},
  year={2024}
}

Acknowledgments

Thanks to simplepod.ai for providing GPU servers

License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)