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<p align="center"> |
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<img width="60%" src="https://raw.githubusercontent.com/POSTECH-CVLab/PyTorch-StudioGAN/master/docs/figures/studiogan_logo.jpg" /> |
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</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. |
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This hub provides all the checkpoints we used to create the GAN benchmarks below. |
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Please visit our github repository ([PyTorch-StudioGAN](https://github.com/POSTECH-CVLab/PyTorch-StudioGAN)) for more details. |
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<p align="center"> |
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<img width="95%" src="https://raw.githubusercontent.com/POSTECH-CVLab/PyTorch-StudioGAN/master/docs/figures/StudioGAN_Benchmark.png"/> |
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</p> |
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## License |
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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). |
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## Citation |
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StudioGAN is established for the following research projects. Please cite our work if you use StudioGAN. |
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```bib |
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@article{kang2022StudioGAN, |
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title = {{StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis}}, |
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author = {MinGuk Kang and Joonghyuk Shin and Jaesik Park}, |
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journal = {2206.09479 (arXiv)}, |
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year = {2022} |
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} |
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``` |
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```bib |
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@inproceedings{kang2021ReACGAN, |
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title = {{Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training}}, |
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author = {Minguk Kang, Woohyeon Shim, Minsu Cho, and Jaesik Park}, |
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journal = {Conference on Neural Information Processing Systems (NeurIPS)}, |
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year = {2021} |
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} |
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``` |
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```bib |
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@inproceedings{kang2020ContraGAN, |
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title = {{ContraGAN: Contrastive Learning for Conditional Image Generation}}, |
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author = {Minguk Kang and Jaesik Park}, |
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journal = {Conference on Neural Information Processing Systems (NeurIPS)}, |
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year = {2020} |
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} |
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``` |