StyleGAN.pytorch VS maua-stylegan2

I've still yet to try this one: https://github.com/huangzh13/StyleGAN.pytorch but at this point it might just be worth trying the Jupiter thingie. I sort of understand what it is.

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