A python library for generative learning methods with PyTorch.
This repo has PyTorch implementations of training a Gan.
- Trainers for Unconditional Gans
- sgan
- lsgan
- hingegan
- wgan
- wgan-gp
- qpgan
- rasgan
- ralsgan
- rahingegan
- Trainers for Conditional Gans
- sgan
- lsgan
- Trainers for CycleGan
- lsgan
- Trainers for Pix2Pix
- sgan
- lsgan
- ralsgan
- rahingegan
- Trainers for Pix2PixHD
- ralsgan
- Trainers for Progressive Gans
- rasgan
- ralsgan
- rahingegan
- wgan-gp
- Gans
- Deconv DCGAN
- PixelShuffle DCGAN
- ResizeConv DCGAN
- Conditional Gans
- Deconv DCGAN (with label)
- Img2Img (Pix2Pix, Pix2PixHD, CycleGan)
- U-Net (Pix2Pix)
- ResNetGan (Pix2Pix)
- PatchGAN (70x70)
- PatchGAN (286x286)
- Progressive Gan
- Progressive Architecture
Gans, Conditional Gans, Img2Img mostly has options to chose normalization type, such as batchnorm, instancenorm, spectralnorm.
The code for this training had some errors in the code, the new training video will be updated soon.
Pix2Pix Trained on edge2shoe dataset. Because of the U-Net architecture, it trains faster than Pix2PixHD, and seems to converge faster, but in my opinion Pix2PixHD seems to have better generalized results, and less artifacts.
Pix2PixHD Trained on edge2shoe dataset as you see, around 0:20 the dimension changes, which is where the Local Network gets added to the network and training begins. Around 0:41, the entire network will start finetuning. (Since Pix2PixHD's biggest advantage is that it trains on very high resolutions, I'll later upload Pix2PixHD trained on CityScapes dataset.)
- Add Spectral Normalization
- Implement Progressive Gan
- Implement Pix2PixHD
- Add trained results
- Implement WaveGan (raw audio generation)
- make the losses into a function and put them in a single file (code cleaning)
- Implement BicycleGan (multimodal Pix2Pix)
- Improve README
- Implement StyleGAN
- Implement SPADE