ReHiFace-S 🤖🤖🤖

🚀 Introduction

ReHiFace-S, short for “Real Time High-Fidelity Faceswap”, is a real-time high-fidelity faceswap algorithm created by Silicon-based Intelligence. By open-sourcing the capabilities of digital human generation, developers can easily generate large-scale digital humans who they want, enabling real-time faceswap capability.

💪 Project features

• Real-time on NVIDIA GTX 1080Ti
• Zero-shot inference
• High Fidelity faceswap
• Support ONNX and live camera mode
• Support super resulution and color transfer
• Better Xseg model for face segment

🔥 Examples

We show some faceswap examples.

🔧 Getting Started

Clone the code and prepare the environment

• Python >= 3.9 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.13
• CUDA 11.7
• Linux Ubuntu20.04
  

conda create --name faceswap python=3.9
conda activate faceswap
pip install -r requirements.txt

😊 Pretrained models

Download all pretrained weights from Google Drive or Baidu Yun. We have packed all weights in one directory 😊. Download and place them in ./pretrain_models folder ensuring the directory structure is as follows:

pretrain_models
├── 9O_865k.onnx
├── CurricularFace.tjm
├── gfpganv14_fp32_bs1_scale.onnx
├── pfpld_robust_sim_bs1_8003.onnx
├── scrfd_500m_bnkps_shape640x640.onnx
├── xseg_230611_16_17.onnx

💻 How to Test

CUDA_VISIBLE_DEICES='0' python inference.py

Or, you can change the input by specifying the --src_img_path and --video_path arguments:

CUDA_VISIBLE_DEICES='0' python inference.py --src_img_path --video_path

Live Cam faceswap

You should at least run by NVIDIA GTX 1080Ti.

Notice: The time taken to render to a video and warm up the models are not included.

Not support Super Resolution.

CUDA_VISIBLE_DEICES='0' python inference_cam.py

Notice: Support change source face during live with 'data/image_feature_dict.pkl' !

🤗 Gradio interface

We also provide a Gradio interface for a better experience, just run by:

python app.py

✨ Acknowledgments

• Thanks to Hififace for base faceswap framework.
  
• Thanks to CurricularFace for pretrained face feature model.
  
• Thanks to Xseg for base face segment framework.
• Thanks to GFPGAN for face super resolution.
• Thanks to LivePortrait and duix.ai for README template.

🌟 Citation

If you find ReHiFace-S useful for your research, welcome to 🌟 this repo.