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.

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