gigagan-pytorch VS lightweight-gan

My hope these days is that newer (not actually new but you get the point) techniques like StyleGAN and GigaGAN may give the open source generative ai community a fresh boost going forward. We'll see how well those projects can be optimized for consumer-grade hardware.

https://github.com/lucidrains/gigagan-pytorch seems there are some implements lol

Found this? https://github.com/lucidrains/gigagan-pytorch

GAN details: I trained using the code from https://github.com/lucidrains/lightweight-gan, image size is 256, attn-res-layers is [32,64], disc_output_size is 5 and I trained with AMP.

Long short story, i trained AI called 'Lightweight' GAN with some image from the game (Steam version). The result isn't satisfying (either too similar with original image or distorted), but i decided to share it since it took 3-4 days.

Code for training your own [original] [simple] [light]

Trained lightweight-gan on public domain nebulae images. The blogpost covers how it was assembled end-to-end, from data scraping, to model training, and finally deploying it as a web app using free cloud services only

https://github.com/lucidrains/stylegan2-pytorch is the simplest implementation I know of, and https://github.com/lucidrains/lightweight-gan is similar but designed for relatively lower performance machines.

I also regret using https://github.com/lucidrains/lightweight-gan over the standard StyleGAN2. The training time wasn't an issue for 128x128 images and I should have gone for the higher quality model.

It's hard to tell if the medicore results are due to the small dataset (~305 semi-aligned images, yes there's that many of these) or my own inexperience in GAN generation (i used https://github.com/lucidrains/lightweight-gan and the only changes i made from the defaults were to decrease the batch size and add color augmentation)