{"id":24316201,"url":"https://github.com/nocotan/pytorch-lightning-gans","last_synced_at":"2026-01-17T22:17:38.223Z","repository":{"id":48325735,"uuid":"202523756","full_name":"nocotan/pytorch-lightning-gans","owner":"nocotan","description":"Collection of PyTorch Lightning implementations of Generative Adversarial Network varieties presented in research 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GANs\n\n[![DOI](https://zenodo.org/badge/202523756.svg)](https://zenodo.org/badge/latestdoi/202523756)\n[![GitHub license](https://img.shields.io/github/license/nocotan/pytorch-lightning-gans)](https://github.com/nocotan/pytorch-lightning-gans/blob/master/LICENSE)\n![GitHub Repo stars](https://img.shields.io/github/stars/nocotan/pytorch-lightning-gans?style=social)\n![GitHub code size in bytes](https://img.shields.io/github/languages/code-size/nocotan/pytorch-lightning-gans)\n![GitHub issues](https://img.shields.io/github/issues/nocotan/pytorch-lightning-gans)\n\nCollection of PyTorch Lightning implementations of Generative Adversarial Network varieties presented in research papers.\n\n## Installation\n\n```bash\n$ pip install -r requirements.txt\n```\n\n## Example\nThe minimum code for training GAN is as follows:\n\n```python\nfrom pytorch_lightning.trainer import Trainer\nfrom models import GAN\n\n\nmodel = GAN()\ntrainer = Trainer()\ntrainer.fit(model)\n```\n\nor you can run the following command:\n\n```bash\n$ python models/gan.py --gpus=2\n```\n\n## Implementations\n* ACGAN: Auxiliary Classifier GAN (Odena et al.)\n* BEGAN: Boundary equilibrium generative adversarial networks (Berthelot et al.)\n* DCGAN: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (Radford et al.)\n* GAN: Generative Adversarial Networks (Goodfellow et al.)\n* LSGAN: Least squares generative adversarial networks (Mao et al.)\n* WGAN: Wasserstein GAN (Arjovsky et al.)\n* WGAN-GP: Improved Training of Wasserstein GANs (Gulrajani et al.)\n\n## Acknowledgements\nThis repository is highly inspired by [PyTorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN) repository.\n\n## References\n* Goodfellow, Ian, et al. \"Generative adversarial nets.\" Advances in neural information processing systems. 2014.\n* Radford, Alec, Luke Metz, and Soumith Chintala. \"Unsupervised representation learning with deep convolutional generative adversarial networks.\" arXiv preprint arXiv:1511.06434 (2015).\n* Odena, Augustus, Christopher Olah, and Jonathon Shlens. \"Conditional image synthesis with auxiliary classifier gans.\" International conference on machine learning. PMLR, 2017.\n* Berthelot, David, Thomas Schumm, and Luke Metz. \"Began: Boundary equilibrium generative adversarial networks.\" arXiv preprint arXiv:1703.10717 (2017).\n* Mao, Xudong, et al. \"Least squares generative adversarial networks.\" Proceedings of the IEEE international conference on computer vision. 2017.\n* Arjovsky, Martin, Soumith Chintala, and Léon Bottou. \"Wasserstein generative adversarial networks.\" Proceedings of the 34th International Conference on Machine Learning-Volume 70. 2017.\n* Gulrajani, Ishaan, et al. \"Improved training of wasserstein gans.\" Advances in neural information processing systems. 2017.\n\n## Citation\n\n```bibtex\n@software{https://doi.org/10.5281/zenodo.4404867,\n  doi = {10.5281/ZENODO.4404867},\n  url = {https://zenodo.org/record/4404867},\n  author = {Masanari Kimura},\n  title = {pytorch-lightning-gans},\n  publisher = {Zenodo},\n  year = {2020},\n  copyright = {Open Access}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnocotan%2Fpytorch-lightning-gans","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnocotan%2Fpytorch-lightning-gans","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnocotan%2Fpytorch-lightning-gans/lists"}