godisboy / Sn Gan
pyTorch implementation of Spectral Normalization for Generative Adversarial Networks
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SN-GAN
PyTorch implementation of Spectral Normalization for Generative Adversarial Networks
Prerequistes
Usage
- Clone this repo:
git clone https://github.com/godisboy/SN-GAN.git
cd SN-GAN
- Train the standard model for 32*32 image size
python train.py --cuda(optional)
- Train the Res-Generator and Res-Discriminator for 64*64 image size
python train-res.py --cuda --dataPath /Path/to/yourdataset/
- Result of SN-GAN on CIFAR10 dataset
- Generated
- Generated
- Generated samples with Standard Generator and ResDiscriminator
Note:
The ResBlock of Res-Generator is different from what implemented in original paper.
This repo use UpsamplingBilinear instead of Uppooling for Upsampling operation.
To Do
- Conditional version of SNGAN with Conditional BatchNormalization
- ImageNet 1000 classes
- Latent Inference
- ...
Acknowledgments
Based on the implementation DCGAN and official implementation with Chainer sngan_projection
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