We use https://github.com/jolibrain/joliGAN which is a lib for image2image with additional "semantic" constraints. I.e. when there's a need to conserve labels, physics, anything between the two domains. This lib aggregates and improves on existing works.

If you are looking for more traditional noise -> xxx GANs, go for https://github.com/autonomousvision/projected_gan/. Another recent work is https://github.com/nupurkmr9/vision-aided-gan.

The key element in GAN convergence is the discriminator. Joligan above defaults to multiple discriminators by combining and improving on the works above, ensuring fast early convergence and stability while the semantic constraints narrow the path to relevant modes.

We've found that tranformers as generators have interesting properties on some tasks and converge well with a ViT-based projected discriminator.