sczhou / codeformer

Robust face restoration algorithm for old photos / AI-generated faces

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Examples

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Run time and cost

This model costs approximately $0.0021 to run on Replicate, or 476 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 L40S GPU hardware. Predictions typically complete within 3 seconds.

Readme

Towards Robust Blind Face Restoration with Codebook Lookup Transformer (NeurIPS 2022) Paper | Project Page | Video

visitors

This web demo is for research purposes! If you want to use our CodeFormer for permanent free, you can run the [Github Code] locally or try out [Colab Demo] instead.

  ☕️ CodeFormer is a robust face restoration algorithm for old photos or AI-generated faces. 🚀 Try CodeFormer for improved stable-diffusion generation!

If CodeFormer is helpful, please help to ⭐ the [Github Repo]. Thanks!

GitHub Stars

📋 License This project is licensed under S-Lab License 1.0. Redistribution and use for non-commercial purposes should follow this license. Note that Replicate API of CodeFormer cannot be used commercially.

📝 Citation If our work is useful for your research, please consider citing:

@inproceedings{zhou2022codeformer,
    author = {Zhou, Shangchen and Chan, Kelvin C.K. and Li, Chongyi and Loy, Chen Change},
    title = {Towards Robust Blind Face Restoration with Codebook Lookup TransFormer},
    booktitle = {NeurIPS},
    year = {2022}
}