https://genforce.github.io/mganprior/

[CVPR 2020] Image Processing Using Multi-Code GAN Prior
https://genforce.github.io/mganprior/

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[CVPR 2020] Image Processing Using Multi-Code GAN Prior

• Host: GitHub
• URL: https://genforce.github.io/mganprior/
• Owner: genforce
• Created: 2020-03-31T09:08:44.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-07-17T05:28:13.000Z (about 4 years ago)
• Last Synced: 2024-07-05T16:09:28.354Z (3 months ago)
• Language: Python
• Homepage: https://genforce.github.io/mganprior/
• Size: 33.5 MB
• Stars: 288
• Watchers: 14
• Forks: 44
• Open Issues: 12
• Metadata Files:
  • Readme: README.md

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• awesome-generative-modeling - mGAN prior

README

        
# Image Processing Using Multi-Code GAN Prior

![Python 3.7](https://img.shields.io/badge/python-3.7-green.svg?style=plastic)

![pytorch 1.1.0](https://img.shields.io/badge/pytorch-1.1.0-green.svg?style=plastic)

![CUDA 10.1](https://camo.githubusercontent.com/5e1f2e59c9910aa4426791d95a714f1c90679f5a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f637564612d31302e312d677265656e2e7376673f7374796c653d706c6173746963)

![image](./docs/assets/teaser.jpg)

**Figure:**  Multi-code GAN prior facilitates many image processing applications using the reconstruction from fixed GAN models.

In this work, we propose a new inversion approach to applying well-trained GANs as effective prior to a variety of image processing tasks, such as image colorization, super-resolution, image inpainting, and semantic manipulation.

> **Image Processing Using Multi-Code GAN Prior**


> Jinjin Gu, Yujun Shen, Bolei Zhou 


> *Computer Vision and Pattern Recognition (CVPR), 2020*

[[Paper](https://arxiv.org/pdf/1912.07116.pdf)]

[[Project Page](https://genforce.github.io/mganprior/)]

## How To Use

### Install dependencies

Install dependencies using the following code before performing Multi-Code GAN Inversion.

```bash

python -m pip install -r requirements.txt

```

### Download the Pre-train GAN Models

In this work, we use the well-trained GAN models as prior, including [PGGAN](https://github.com/tkarras/progressive_growing_of_gans) and [StyleGAN](https://github.com/NVlabs/stylegan). Pytorch version models are borrowed from [HiGAN](https://github.com/genforce/higan). See [here](./models/README.md) for more details.

As both PGGAN and StyleGAN use aligned face for GAN training, all faces used in this repo are pre-aligned. The alignment method can be found at [stylegan-encoder](https://github.com/Puzer/stylegan-encoder).

### Invert Images with Multi-Code GAN Inversion

With a given GAN model and a target image, you can invert the image to multiple latent codes by running

```bash

python multi_code_inversion.py

       --gan_model pggan_bedroom

       --target_images ./examples/gan_inversion/bedroom

       --outputs ./gan_inversion_bedroom

       --composing_layer 8

       --z_number 20

```

### Colorization

For image colorization task, run

```base

python colorization.py

       --gan_model pggan_bedroom

       --target_images ./examples/colorization/bedroom

       --outputs ./colorization

       --composing_layer 6

       --z_number 20

```

### Inpainting

For image inpainting task (inpainting mask should be known in advance), run

```bash

python inpainting.py

       --gan_model pggan_churchoutdoor

       --target_images ./examples/inpainting/church

       --outputs ./inpainting

       --mask ./examples/masks/mask-1.png

       --composing_layer 4

       --z_number 30

```

### Super-Resolution

For image super-resolution task (SR factor should be known in advance), run

```bash

python super_resolution.py

       --gan_model pggan_celebahq

       --target_images ./examples/superresolution

       --outputs ./SR_face

       --factor 16

       --composing_layer 6

       --z_number 20

```

### Semantic Face Editing

We achieve semantic face editing together with [InterfaceGAN](https://genforce.github.io/interfacegan/). Please refer to [this repo](https://github.com/genforce/interfacegan) to see how to train semantic boundaries in the latent space as well as how to achieve face manipulation by varying the latent code.

In this project, you can simply run

```bash

python face_semantic_editing.py

       --gan_model pggan_celebahq

       --target_images ./examples/face

       --outputs ./face_manipulation

       --attribute_name gender

       --composing_layer 6

       --z_number 30

```

## BibTeX

```bibtex

@inproceedings{gu2020image,

  title     = {Image Processing Using Multi-Code GAN Prior},

  author    = {Gu, Jinjin and Shen, Yujun and Zhou, Bolei},

  booktitle = {CVPR},

  year      = {2020}

}

```