This is the PyTorch implementation of JoJoGAN: One Shot Face Stylization.
Abstract:
While there have been recent advances in few-shot image stylization, these methods fail to capture stylistic details that are obvious to humans. Details such as the shape of the eyes, the boldness of the lines, are especially difficult for a model to learn, especially so under a limited data setting. In this work, we aim to perform one-shot image stylization that gets the details right. Given a reference style image, we approximate paired real data using GAN inversion and finetune a pretrained StyleGAN using that approximate paired data. We then encourage the StyleGAN to generalize so that the learned style can be applied to all other images.
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2022-02-03Updated the paper. Improved stylization quality using discriminator perceptual loss. Added sketch model
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2021-12-26Added wandb logging. Fixed finetuning bug which begins finetuning from previously loaded checkpoint instead of the base face model. Added art model
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2021-12-25Added arcane_multi model which is trained on 4 arcane faces instead of 1 (if anyone has more clean data, let me know!). Better preserves features
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2021-12-23Paper is uploaded to arxiv. -
2021-12-22Integrated into Huggingface Spaces 🤗 using Gradio. Try it out -
2021-12-22Added pydrive authentication to avoid download limits from gdrive! Fixed running on cpu on colab.
Everything to get started is in the colab notebook.
If you use this code or ideas from our paper, please cite our paper:
@article{chong2021jojogan,
title={JoJoGAN: One Shot Face Stylization},
author={Chong, Min Jin and Forsyth, David},
journal={arXiv preprint arXiv:2112.11641},
year={2021}
}
This code borrows from StyleGAN2 by rosalinity, e4e. Some snippets of colab code from StyleGAN-NADA