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StarGAN v2 - Official PyTorch Implementation (CVPR 2020)
| Date | Stars |
|---|---|
| 2026-07-24 | 3613 |
| 2026-07-25 | 3613 |
| 2026-07-28 | 3613 |
| 2026-07-30 | 3613 |
| 2026-08-06 | 3613 |
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## StarGAN v2 - Official PyTorch Implementation
<p align="left"><img width="95%" src="assets/teaser.jpg" /></p>
> **StarGAN v2: Diverse Image Synthesis for Multiple Domains**<br>
> [Yunjey Choi](https://github.com/yunjey)\*, [Youngjung Uh](https://github.com/youngjung)\*, [Jaejun Yoo](http://jaejunyoo.blogspot.com/search/label/kr)\*, [Jung-Woo Ha](https://www.facebook.com/jungwoo.ha.921)<br>
> In CVPR 2020. (* indicates equal contribution)<br>
> Paper: https://arxiv.org/abs/1912.01865<br>
> Video: https://youtu.be/0EVh5Ki4dIY<br>
> **Abstract:** *A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. We propose StarGAN v2, a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our superiority in terms of visual quality, diversity, and scalability. To better assess image-to-image translation models, we release AFHQ, high-quality animal faces with large inter- and intra-domain variations. The code, pre-trained models, and dataset are available at clovaai/stargan-v2.*
## Teaser video
Click the figure to watch the teaser video. <br/>
[](https://youtu.be/0EVh5Ki4dIY)
## TensorFlow implementation
The TensorFlow implementation of StarGAN v2 by our team member junho can be found at [clovaai/stargan-v2-tensorflow](https://github.com/clovaai/stargan-v2-tensorflow).
## Software installation
Clone this repository:
```bash
git clone https://github.com/clovaai/stargan-v2.git
cd stargan-v2/
```
Install the dependencies:
```bash
conda create -n stargan-v2 python=3.6.7
conda activate stargan-v2
conda install -y pytorch=1.4.0 torchvision=0.5.0 cudatoolkit=10.0 -c pytorch
conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge
pip install opencv-python==4.1.2.30 ffmpeg-python==0.2.0 scikit-image==0.16.2
pip install pillow==7.0.0 scipy==1.2.1 tqdm==4.43.0 munch==2.5.0
```
## Datasets and pre-trained networks
We provide a script to download datasets used in StarGAN v2 and the corresponding pre-trained networks. The datasets and network checkpoints will be downloaded and stored in the `data` and `expr/checkpoints` directories, respectively.
<b>CelebA-HQ.</b> To download the [CelebA-HQ](https://drive.google.com/drive/folders/0B4qLcYyJmiz0TXY1NG02bzZVRGs) dataset and the pre-trained network, run the following commands:
```bash
bash download.sh celeba-hq-dataset
bash download.sh pretrained-network-celeba-hq
bash download.sh wing
```
<b>AFHQ.</b> To download the [AFHQ](https://github.com/clovaai/stargan-v2/blob/master/README.md#animal-faces-hq-dataset-afhq) dataset and the pre-trained network, run the following commands:
```bash
bash download.sh afhq-dataset
bash download.sh pretrained-network-afhq
```
## Generating interpolation videos
After downloading the pre-trained networks, you can synthesize output images reflecting diverse styles (e.g., hairstyle) of reference images. The following commands will save generated images and interpolation videos to the `expr/results` directory.
<b>CelebA-HQ.</b> To generate images and interpolation videos, run the following command:
```bash
python main.py --mode sample --num_domains 2 --resume_iter 100000 --w_hpf 1 \
--checkpoint_dir expr/checkpoints/celeba_hq \
--result_dir expr/results/celeba_hq \
--src_dir assets/representative/celeba_hq/src \
--ref_dir assets/representative/celeba_hq/ref
```
To transform a custom image, first crop the image manually so that the proportion of face occupied in the whole is similar to that of CelebA-HQ. Then, run the following command for additional fine rotation and cropExcerpt of 10,637 characters
Read on GitHub
Yunjey Choi · NAVER AI Lab · South Korea
31
Kim Minjong · South Korea
4
Recep S
1
Admin of Clova AI Open Source · Clova CIC, NAVER Corp.
1
1
Youngjung · Yonsei University
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:08f280fd3fc0c054, topic:pytorch