Experiments in using BigGAN to generate anime faces and whole anime images; semi-successful.
Following my StyleGAN anime face experiments, I explore BigGAN, another recent GAN with SOTA results on one of the most complex image domains tackled by GANs so far (ImageNet). BigGAN’s capabilities come at a steep compute cost, however.
Using the unofficial BigGAN-PyTorch reimplementation, I experimented in 2019 with 128px ImageNet transfer learning (successful) with ~6 GPU-days, and from-scratch 256px anime portraits of 10001,024ya characters on an 8StyleGAN for many purposes, BigGAN-like approaches may be necessary to scale to whole anime images.
For followup experiments, Shawn Presser, I and others (collectively, “Tensorfork”) have used Tensorflow Research Cloud TPU credits & the compare_gan BigGAN reimplementation. Running this at scale on the full Danbooru2019 dataset in May 2020, we have reached the best anime GAN results to date (later exceeded by This Anime Does Not Exist).