tencentarc / vqfr

Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder

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Examples

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Run time and cost

This model costs approximately $0.023 to run on Replicate, or 43 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 103 seconds. The predict time for this model varies significantly based on the inputs.

Readme

VQFR (ECCV 2022 Oral)

This paper aims at investigating the potential and limitation of Vector-Quantized (VQ) dictionary for blind face restoration.
We propose a new framework VQFR – incoporating the Vector-Quantized Dictionary and the Parallel Decoder. Compare with previous arts, VQFR produces more realistic facial details and keep the comparable fidelity.

VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder

Yuchao Gu, Xintao Wang, Liangbin Xie, Chao Dong, Gen Li, Ying Shan, Ming-Ming Cheng
Nankai University; Tencent ARC Lab; Tencent Online Video; Shanghai AI Laboratory;
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences

License

VQFR is released under Apache License Version 2.0.

Acknowledgement

Thanks to the following open-source projects:

Taming-transformers

GFPGAN

DistSup

Citation

@inproceedings{gu2022vqfr,
  title={VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder},
  author={Gu, Yuchao and Wang, Xintao and Xie, Liangbin and Dong, Chao and Li, Gen and Shan, Ying and Cheng, Ming-Ming},
  year={2022},
  booktitle={ECCV}
}

Contact

If you have any question, please email yuchaogu9710@gmail.com.