ContraD VS stylegan2-ada-pytorch

Well, I've been trying to train a 1024 GAN from scratch on stylegan2-ada-pytorch with a small dataset 300 samples of not so diversity in images of painting faces. Fact is that on first try FID went as low as 71 and started deteriorating. Now I x-flip augmented the dataset (700 images) and at 900kimg FID went 64 but I doubt it will get lower. I lowered the learning rate to 0.0001 as they say it might help... Recently found this way of dataset augmentation... probably will use this https://github.com/jh-jeong/ContraD

> there is really nothing that "normal" AI requires that is bound to CUDA. pyTorch and Tensorflow are backend agnostic (ideally...).

There are a lot of optimizations that CUDA has that are nowhere near supported in other software or even hardware. Custom cuda kernels also aren't as rare as one might think, they will often just be hidden unless you're looking at libraries. Our more well known example is going to be StyleGAN[0] but it isn't uncommon to see elsewhere, even in research code. Swin even has a cuda kernel[1]. Or find torch here[1] (which github reports that 4% of the code is cuda (and 42% C++ and 2% C)). These things are everywhere. I don't think pytorch and tensorflow could ever be agnostic, there will always be a difference just because you have to spend resources differently (developing kernels is time resource). We can draw evidence by looking at Intel MKL, which is still better than open source libraries and has been so for a long time.

I really do want AMD to compete in this space. I'd even love a third player like Intel. We really do need competition here, but it would be naive to think that there's going to be a quick catchup here. AMD has a lot of work to do and posting a few bounties and starting a company (idk, called "micro grad"?) isn't going to solve the problem anytime soon.

And fwiw, I'm willing to bet that most AI companies would rather run in house servers than from cloud service providers. The truth is that right now just publishing is extremely correlated to compute infrastructure (doesn't need to be but with all the noise we've just said "fuck the poor" because rejecting is easy) and anyone building products has costly infrastructure.

[0] https://github.com/NVlabs/stylegan2-ada-pytorch/blob/d72cc7d...

[1] https://github.com/microsoft/Swin-Transformer/blob/2cb103f2d...

[2] https://github.com/pytorch/pytorch/tree/main/aten/src

I am talking about the original Nvidia implementation here: https://github.com/NVlabs/stylegan2-ada-pytorch

I think you should be able to find a latent vector that returns a cat that is part of the original training data (or at least very close to it). Most of the outputs will not be real cats at all though. However, it's pretty simple to try and find the latent vector that reproduces a given image, e.g. https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/pr...

One irritating flaw with FID is that scores are massively biased by the number of samples, that is, the fewer samples you use, the larger the score. So to make comparisons fair it's absolutely crucial to use the same number of samples. From what I've seen on standard benchmarks it's pretty common now to compute Inception features for every single data point, but only for 50k samples from generative models (for reference off the top of my head StyleGAN2-ADA does this, see Appendix A).

You can follow the development of stylegan from NVIDIA: https://github.com/NVlabs/stylegan2-ada-pytorch They have formed datasets containing human faces, maybe you can use human faces with expressions as classes and train conditional GAN with your own classes.

Given the lack of data StyleGan 2 by Nvidia, which was specifically created to handle small datasets could be an option - https://github.com/NVlabs/stylegan2-ada-pytorch

First, you have to collect a few thousand images of the same thing (maybe more or less depending on how complex your thing is or how good the results should be). Then, you train a generative adversarial neural network on those images to generate new images. https://github.com/NVlabs/stylegan2-ada-pytorch works quite well. https://github.com/NVlabs/stylegan3 is supposedly even better, but I did not try it yet.

What NVIDIA does when computing Perceptual Path Length is to center crop the faces before computing the metric. Here you can find the code to get an idea https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/metrics/perceptual_path_length.py

I\m trying to train StyleGAN2 ADA Pytorch https://github.com/NVlabs/stylegan2-ada-pytorch on my own dataset.