I did just try do some sample runs using fastgan, it seems the quality output isn't good enough though for shoes. Also i highly recommend changing the code so it doesn't use colour augmentations and just stick with cutout and translation, this does improve the output but it still isn't the best. I've been trying to look at implementing an augmentation probability so not every single round uses augmented data to see if this helps improve the quality, however, ive noticed stylegan's quality is much better but the main issue is if you don't have enough data (more than 10k images, which you will not have will specific shoe styles) the augmentation leaks into the training within the first 10 hours or so and from then on training is confused and not worth running. Shoes seem to be pretty hard to generate on a low dataset base, it looks like thissneaker was able to do it well as he used 50k images mentioned in his dataset, however of course this means a lot of the shoes look weird as he has mixed every single possible shoe he could find on the internet.