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ComfyUI-HunyuanPortrait

ComfyUI-HunyuanPortrait is now available in ComfyUI, HunyuanPortrait is a diffusion-based condition control method that employs implicit representations for highly controllable and lifelike portrait animation.

Installation

  1. Make sure you have ComfyUI installed

  2. Clone this repository into your ComfyUI's custom_nodes directory:

cd ComfyUI/custom_nodes
git clone https://github.com/Yuan-ManX/ComfyUI-HunyuanPortrait.git
  1. Install dependencies:
cd ComfyUI-HunyuanPortrait
pip install torch torchvision torchaudio
pip install -r requirements.txt

Model

Download pretrained checkpoint

All the weights should be placed under the ComfyUI/models/HunyuanPortrait/pretrained_weights direcotry. You can download weights manually as follows:

All models are stored in pretrained_weights by default:

pip install "huggingface_hub[cli]"
cd pretrained_weights
huggingface-cli download --resume-download stabilityai/stable-video-diffusion-img2vid-xt --local-dir . --include "*.json"
wget -c https://huggingface.co/LeonJoe13/Sonic/resolve/main/yoloface_v5m.pt
wget -c https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/vae/diffusion_pytorch_model.fp16.safetensors -P vae
wget -c https://huggingface.co/FoivosPar/Arc2Face/resolve/da2f1e9aa3954dad093213acfc9ae75a68da6ffd/arcface.onnx
huggingface-cli download --resume-download tencent/HunyuanPortrait --local-dir hyportrait

And the file structure is as follows:

.
├── arcface.onnx
├── hyportrait
│   ├── dino.pth
│   ├── expression.pth
│   ├── headpose.pth
│   ├── image_proj.pth
│   ├── motion_proj.pth
│   ├── pose_guider.pth
│   └── unet.pth
├── scheduler
│   └── scheduler_config.json
├── unet
│   └── config.json
├── vae
│   ├── config.json
│   └── diffusion_pytorch_model.fp16.safetensors
└── yoloface_v5m.pt

Requirements

  • An NVIDIA 3090 GPU with CUDA support is required.

    • The model is tested on a single 24G GPU.
  • Tested operating system: Linux

About

ComfyUI-HunyuanPortrait is now available in ComfyUI, HunyuanPortrait is a diffusion-based condition control method that employs implicit representations for highly controllable and lifelike portrait animation.

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