https://github.com/mchong6/SOAT

Official PyTorch repo for StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN.
https://github.com/mchong6/SOAT

Last synced: 8 days ago
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Official PyTorch repo for StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN.

• Host: GitHub
• URL: https://github.com/mchong6/SOAT
• Owner: mchong6
• License: mit
• Created: 2021-10-28T07:00:01.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2021-11-20T15:38:35.000Z (almost 3 years ago)
• Last Synced: 2024-10-29T18:21:12.110Z (14 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 82.3 MB
• Stars: 377
• Watchers: 11
• Forks: 56
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN

![](teaser.jpg)

This is the PyTorch implementation of [StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN](https://arxiv.org/abs/2111.01619). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mchong6/SOAT/blob/main/infinity.ipynb)

**Web Demo**

Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo for Panorama Generation for Landscapes: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/SOAT)

>**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](https://colab.research.google.com/github/mchong6/SOAT/blob/main/infinity.ipynb).

## Toonification

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

```bash

python train.py

```

For disney toonification, we use the disney dataset [here](https://github.com/justinpinkney/toonify). Feel free to experiment with different datasets.

## GAN inversion

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

```bash

python projector.py xxx.jpg

```

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

```python

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

This code borrows from [StyleGAN2 by rosalinity](https://github.com/rosinality/stylegan2-pytorch)