Comment by aDyslecticCrow
3 days ago
It has other advantages and properties to diffusion models. I doubt it will generate "art" anytime soon better than diffusion... But it's zero-shot and relative* shallow structure could make it amazing at edge compute or image/data analysis (as another comment discusses) at limited datasets or compute.
Even one of the examples is a very effective re-colorized that beat other approaches I've seen with less risk of modifying the subject. It's clever, and simple.
it's compared more with GAN in the article than Diffusion, and that excites me. GAN are badly behaved, but are really powerful reinforcement learners. If this method can compensate for the greatest bane of GAN (mode collapse), it can be very useful.
Exactly what i think!
- The DDN single-shot generator architecture is more efficient than diffusion.
- DDN is fully end-to-end differentiable, allowing for more efficient optimization when integrated with discriminative models or reinforcement learning.
- Moreover, DDN inherently avoids mode collapse.
These points are all mentioned in the blog: https://github.com/Discrete-Distribution-Networks/Discrete-D...