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Collection of generative models in Pytorch version.
| Date | Stars |
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| 2026-07-24 | 2628 |
| 2026-07-25 | 2628 |
| 2026-07-28 | 2628 |
| 2026-07-30 | 2628 |
| 2026-08-06 | 2628 |
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# pytorch-generative-model-collections Original : [[Tensorflow version]](https://github.com/hwalsuklee/tensorflow-generative-model-collections) Pytorch implementation of various GANs. This repository was re-implemented with reference to [tensorflow-generative-model-collections](https://github.com/hwalsuklee/tensorflow-generative-model-collections) by [Hwalsuk Lee](https://github.com/hwalsuklee) I tried to implement this repository as much as possible with [tensorflow-generative-model-collections](https://github.com/hwalsuklee/tensorflow-generative-model-collections), But some models are a little different. This repository is included code for CPU mode Pytorch, but i did not test. I tested only in GPU mode Pytorch. ## Dataset - MNIST - Fashion-MNIST - CIFAR10 - SVHN - STL10 - LSUN-bed #### I only tested the code on MNIST and Fashion-MNIST. ## Generative Adversarial Networks (GANs) ### Lists (Table is borrowed from [tensorflow-generative-model-collections](https://github.com/hwalsuklee/tensorflow-generative-model-collections)) *Name* | *Paper Link* | *Value Function* :---: | :---: | :--- | **GAN** | [Arxiv](https://arxiv.org/abs/1406.2661) | <img src = 'assets/equations/GAN.png' height = '70px'> **LSGAN**| [Arxiv](https://arxiv.org/abs/1611.04076) | <img src = 'assets/equations/LSGAN.png' height = '70px'> **WGAN**| [Arxiv](https://arxiv.org/abs/1701.07875) | <img src = 'assets/equations/WGAN.png' height = '105px'> **WGAN_GP**| [Arxiv](https://arxiv.org/abs/1704.00028) | <img src = 'assets/equations/WGAN_GP.png' height = '70px'> **DRAGAN**| [Arxiv](https://arxiv.org/abs/1705.07215) | <img src = 'assets/equations/DRAGAN.png' height = '70px'> **CGAN**| [Arxiv](https://arxiv.org/abs/1411.1784) | <img src = 'assets/equations/CGAN.png' height = '70px'> **infoGAN**| [Arxiv](https://arxiv.org/abs/1606.03657) | <img src = 'assets/equations/infoGAN.png' height = '70px'> **ACGAN**| [Arxiv](https://arxiv.org/abs/1610.09585) | <img src = 'assets/equations/ACGAN.png' height = '70px'> **EBGAN**| [Arxiv](https://arxiv.org/abs/1609.03126) | <img src = 'assets/equations/EBGAN.png' height = '70px'> **BEGAN**| [Arxiv](https://arxiv.org/abs/1703.10717) | <img src = 'assets/equations/BEGAN.png' height = '105px'> #### Variants of GAN structure (Figures are borrowed from [tensorflow-generative-model-collections](https://github.com/hwalsuklee/tensorflow-generative-model-collections)) <img src = 'assets/etc/GAN_structure.png' height = '600px'> ### Results for mnist Network architecture of generator and discriminator is the exaclty sames as in [infoGAN paper](https://arxiv.org/abs/1606.03657). For fair comparison of core ideas in all gan variants, all implementations for network architecture are kept same except EBGAN and BEGAN. Small modification is made for EBGAN/BEGAN, since those adopt auto-encoder strucutre for discriminator. But I tried to keep the capacity of discirminator. The following results can be reproduced with command: ``` python main.py --dataset mnist --gan_type <TYPE> --epoch 50 --batch_size 64 ``` #### Fixed generation All results are generated from the fixed noise vector. *Name* | *Epoch 1* | *Epoch 25* | *Epoch 50* | *GIF* :---: | :---: | :---: | :---: | :---: | GAN | <img src = 'assets/mnist_results/GAN_epoch001.png' height = '200px'> | <img src = 'assets/mnist_results/GAN_epoch025.png' height = '200px'> | <img src = 'assets/mnist_results/GAN_epoch050.png' height = '200px'> | <img src = 'assets/mnist_results/GAN_generate_animation.gif' height = '200px'> LSGAN | <img src = 'assets/mnist_results/LSGAN_epoch001.png' height = '200px'> | <img src = 'assets/mnist_results/LSGAN_epoch025.png' height = '200px'> | <img src = 'assets/mnist_results/LSGAN_epoch050.png' height = '200px'> | <img src = 'assets/mnist_results/LSGAN_generate_animation.gif' height = '200px'> WGAN | <img src = 'assets/mnist_results/WGAN_epoch001.png' height = '200px'> | <img src = 'assets/mnist_results/WGAN_epoch025.png' height = '200px'> | <img src =
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Tongzhou Wang · MIT
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:41066be907c04bc6, topic:pytorch
matched fp:41066be907c04bc6, topic:gan