stylegan2ada VS maua-stylegan2

This is the easiest to train StyleGAN that I have found. StyleGAN3 and the official Pytorch StyleGAN variants from Nvidia just are horribly difficult to train. Training your own GAN model is a pretty good way to learn about them, and this is a pretty easy starting point if you are already a developer and understand your way around a command line. You can generate a dataset using Stable Diffusion of about 5000 images and train a GAN model from scratch on a single RTX 3090 in about 16 hours: https://github.com/eps696/stylegan2ada

Vadim Epstein had multi-latent blending working at least in December last year (although his repo was published a little later).

You don't even need to really do model surgery. All the convolutions will accept arbitrary dimensions. You can just use network bending padding operations to get any output size you like Vadim Epstein's repo does something slightly different which let's you even use different latents per section: https://github.com/eps696/stylegan2ada Or mine which has the simpler, single latent version https://github.com/JCBrouwer/maua-stylegan2 Or for training, then all you have to do is change the size of your constant layer Or just graft on some more upsamples Either way though, there's not too much point to training at weird rectangular resolutions. You'll get pretty much identical results by just forcefully resizing to a square and then stretching the generated versions back out to square Unless you've got a ridiculous amount of VRAM, larger models don't really make too much sense either. Especially because it'll be hard to find 10k images at such a big resolution

Originally I wanted to run https://github.com/JCBrouwer/maua-stylegan2. I was trying to run the convert_weight.py but it resulted in shape mismatch errors in torch torch.Size([1, 512, 4, 4]) vs torch.Size([1]), so I tried the version here https://github.com/rosinality/stylegan2-pytorch/blob/master/convert_weight.py and the result was the same.

You don't even need to really do model surgery. All the convolutions will accept arbitrary dimensions. You can just use network bending padding operations to get any output size you like Vadim Epstein's repo does something slightly different which let's you even use different latents per section: https://github.com/eps696/stylegan2ada Or mine which has the simpler, single latent version https://github.com/JCBrouwer/maua-stylegan2 Or for training, then all you have to do is change the size of your constant layer Or just graft on some more upsamples Either way though, there's not too much point to training at weird rectangular resolutions. You'll get pretty much identical results by just forcefully resizing to a square and then stretching the generated versions back out to square Unless you've got a ridiculous amount of VRAM, larger models don't really make too much sense either. Especially because it'll be hard to find 10k images at such a big resolution