This is a PyTorch imp

Style GAN 2

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Style Pass
2021-05-22 09:30:07

This is a PyTorch implementation of the paper Analyzing and Improving the Image Quality of StyleGAN which introduces Style GAN2. Style GAN2 is an improvement over Style GAN from the paper A Style-Based Generator Architecture for Generative Adversarial Networks. And Style GAN is based on Progressive GAN from the paper Progressive Growing of GANs for Improved Quality, Stability, and Variation. All three papers are from the same authors from NVIDIA AI.

Our implementation is a minimalistic Style GAN2 model training code. Only single GPU training is supported to keep the implementation simple. We managed to shrink it to keep it at less than 500 lines of code, including the training loop.

Generative adversarial networks have two components; the generator and the discriminator. The generator network takes a random latent vector ($z \in \mathcal{Z}$) and tries to generate a realistic image. The discriminator network tries to differentiate the real images from generated images. When we train the two networks together the generator starts generating images indistinguishable from real images.

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