readme: <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}
  }
  ```