StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN

This is the PyTorch implementation of StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN.

Abstract:
Recently, StyleGAN has enabled various image manipulation and editing tasks thanks to the high-quality generation and the disentangled latent space. However, additional architectures or task-specific training paradigms are usually required for different tasks. In this work, we take a deeper look at the spatial properties of StyleGAN. We show that with a pretrained StyleGAN along with some operations, without any additional architecture, we can perform comparably to the state-of-the-art methods on various tasks, including image blending, panorama generation, generation from a single image, controllable and local multimodal image to image translation, and attributes transfer.

How to use

Everything to get started is in the colab notebook.

Toonification

For toonification, you can train a new model yourself by running

python train.py

For disney toonification, we use the disney dataset here. Feel free to experiment with different datasets.

GAN inversion

To perform GAN inversion with gaussian regularization in W+ space,

python projector.py xxx.jpg

the code will be saved in ./inversion_codes/xxx.pt which you can load by

source = load_source(['xxx'], generator, device)
source_im, _ = generator(source)

Citation

If you use this code or ideas from our paper, please cite our paper:

@article{chong2021stylegan,
  title={StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN},
  author={Chong, Min Jin and Lee, Hsin-Ying and Forsyth, David},
  journal={arXiv preprint arXiv:2111.01619},
  year={2021}
}

Acknowledgments