| <p align="center"> | |
| <img width="60%" src="https://raw.githubusercontent.com/POSTECH-CVLab/PyTorch-StudioGAN/master/docs/figures/studiogan_logo.jpg" /> | |
| </p>**StudioGAN** is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation. StudioGAN aims to offer an identical playground for modern GANs so that machine learning researchers can readily compare and analyze a new idea. | |
| This hub provides all the checkpoints we used to create the GAN benchmarks below. | |
| Please visit our github repository ([PyTorch-StudioGAN](https://github.com/POSTECH-CVLab/PyTorch-StudioGAN)) for more details. | |
| <p align="center"> | |
| <img width="95%" src="https://raw.githubusercontent.com/POSTECH-CVLab/PyTorch-StudioGAN/master/docs/figures/StudioGAN_Benchmark.png"/> | |
| </p> | |
| ## License | |
| PyTorch-StudioGAN is an open-source library under the MIT license (MIT). However, portions of the library are avaiiable under distinct license terms: StyleGAN2, StyleGAN2-ADA, and StyleGAN3 are licensed under [NVIDIA source code license](https://github.com/POSTECH-CVLab/PyTorch-StudioGAN/blob/master/LICENSE-NVIDIA), and PyTorch-FID is licensed under [Apache License](https://github.com/POSTECH-CVLab/PyTorch-StudioGAN/blob/master/src/metrics/fid.py). | |
| ## Citation | |
| StudioGAN is established for the following research projects. Please cite our work if you use StudioGAN. | |
| ```bib | |
| @article{kang2022StudioGAN, | |
| title = {{StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis}}, | |
| author = {MinGuk Kang and Joonghyuk Shin and Jaesik Park}, | |
| journal = {2206.09479 (arXiv)}, | |
| year = {2022} | |
| } | |
| ``` | |
| ```bib | |
| @inproceedings{kang2021ReACGAN, | |
| title = {{Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training}}, | |
| author = {Minguk Kang, Woohyeon Shim, Minsu Cho, and Jaesik Park}, | |
| journal = {Conference on Neural Information Processing Systems (NeurIPS)}, | |
| year = {2021} | |
| } | |
| ``` | |
| ```bib | |
| @inproceedings{kang2020ContraGAN, | |
| title = {{ContraGAN: Contrastive Learning for Conditional Image Generation}}, | |
| author = {Minguk Kang and Jaesik Park}, | |
| journal = {Conference on Neural Information Processing Systems (NeurIPS)}, | |
| year = {2020} | |
| } | |
| ``` |