The original GAN:

A clean, simple and readable implementation of the original GAN in PyTorch. I've tried to replicate the original paper as closely as possible, so if you read the paper the implementation should be pretty much identical. You can find a video where I explain the code here: https://www.youtube.com/watch?v=iyUv7QyMAIw&list=PL3uMf2H28qwdtxJPWwOrYd6n_ztY_5jAf

ProGAN

ProGAN is one of the revolutionary papers that was the first to generate high-quality images I implemented ProGAN from scratch in PyTorch to generate fashion trying to make it compact but a goal is also to keep it readable and understandable. Specifically the key points implemented are:

1. Progressive growing (of model and layers)
2. Minibatch std on Discriminator
3. Normalization with PixelNorm
4. Equalized Learning Rate.

Dataset I used in the code: https://www.kaggle.com/datasets/tauilabdelilah/women-clothes

StyleGAN

StyleGAN from the paper: https://arxiv.org/abs/1812.04948 is one of the best GANs today.

I implemented StyleGAN from scratch in PyTorch to generate fashion trying to make it compact but a goal is also to keep it readable and understandable. Specifically the key points implemented are:

1. Noice apping network
2. AdaIN (Adaptive Insctance Normalisation)
3. Progressice growing

Dataset I used in the code: https://www.kaggle.com/datasets/tauilabdelilah/women-clothes