If you don't have TFRC access, this is out of the question as a hobbyist. It's >32 GPU-months. Sorry. However, you can still transfer learn this particular mode

If you don't have TFRC access, this is out of the question as a hobbyist. It's >32 GPU-months. Sorry. However, you can still transfer learn this particular model with <1 GPU-month, if you have some anime or artwork dataset you want (eg a specific character). The good news is that there's been huge progress in GAN efficiency over the past few years, and we are rapidly approaching the point where ImageNet-scale datasets can be reasonably trained with 1 GPU-month or less. Worth noting in that vein are VQ-GAN https://compvis.github.io/taming-transformers/ https://compvis.github.io/taming-transformers/ and not-so-BigGAN https://arxiv.org/abs/2009.04433 https://arxiv.org/abs/2009.04433 . There's also a resurgence of VAE work which is relatively efficient: VD-VAE https://arxiv.org/abs/2011.10650#openai https://arxiv.org/abs/2011.10650#openai and NVAE https://arxiv.org/abs/2007.03898#nvidia https://arxiv.org/abs/2007.03898#nvidia ; DALL-E https://openai.com/blog/dall-e/ https://openai.com/blog/dall-e/ also appears to be surprisingly lightweight on the VAE side. So, if you feel left out, don't worry. In the near future, between the RTX GPUs and optimization and new VAE/GANs, I expect things like Danbooru2020 https://www.gwern.net/Danbooru2020 https://www.gwern.net/Danbooru2020 to be totally doable on hobbyist hardware.