Mingguksky/PyTorch-StudioGAN

**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) for more details.

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, and PyTorch-FID is licensed under Apache License.

Citation

StudioGAN is established for the following research projects. Please cite our work if you use StudioGAN.

@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}
}

@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}
}

@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}
}