stylegan2-flax-tpu VS JAXFLUIDS

I’ll take this opportunity to mention our research scaling StyleGAN 2 to larger datasets (using LAION) on food images, leveraging free TPU compute through the TRC program.

We trained for 36 days on a v4-8 on 558k images.

https://nyx-ai.github.io/stylegan2-flax-tpu/

We were hopeful GANs would beat diffusion models when trained on specific domains. But we’ve now switched to Stable Diffusion and Dreambooth training which has proven more much efficient for this purpose.

I still have hopes for GANs! I miss their insane inference speed.

https://nyx-ai.github.io/stylegan2-flax-tpu

https://nyx-ai.github.io/stylegan2-flax-tpu

This is an incremental update on the work we shared 3 months ago: https://news.ycombinator.com/item?id=32167704

We keep scaling up StyleGAN2 training using more data, larger models, and more compute. In this release we open-source a 5-class model able to generate images of burgers, cheesecakes, cocktails, cookies, and sushis at resolution 512x512.

This research is part of the technology underlying our AI-generated photography platform Nyx.gallery that we shared here 2 weeks ago: https://news.ycombinator.com/item?id=33179730

It is also part of an academic research into data augmentation using synthetic methods in partnership with the Food & You project (https://www.foodandyou.org/).

This is a continuation of our work on "This Food Does Not Exist" (Reddit discussion, Github, checkpoints).

Models for the 256px food images were previously released here:

https://github.com/nyx-ai/stylegan2-flax-tpu

This is the continuation of our work on "This Food Does Not Exist", using old-school StyleGAN2 with more data and larger models to generate bigger images (now 512x512 instead of 256x256).

I believe you had seen it before on HN: https://nyx-ai.github.io/stylegan2-flax-tpu/

📘 Repo: https://github.com/nyx-ai/stylegan2-flax-tpu

You are completely correct; right now it is just mechanics that we have built out. But, there isn't any theoretical reason you couldn't use this framework for other types of simulation. In particular, the Monte Carlo runner is super flexible. Since we are based on JAX you can utilize a ton of the tooling that others have built in the physics space like https://github.com/tumaer/JAXFLUIDS or https://github.com/DifferentiableUniverseInitiative/jax_cosm... . The goal right now though is pretty firmly focused on controls engineers and their needs, but we envision this becoming broadly used.