Discussed in another thread https://news.ycombinator.com/item?id=41234415
As someone who has worked on diffusion model, it's a clear reject and not a very interesting architecture. The idea is to train a diffusion model to fit to low dimensional data using two MLPs: one accounts for high-level structure and one accounts for low level details. These kind of "global-local" architecture is very common in computer vision/graphics (with the paper mentioned none of the relevant work), so the novelty is low. The experiments also do not clearly showcase where exactly this "dual" structure brings benefits.
That being said, it's very hard to tell it apart from a normal poorly-written paper from a quick glance. If you tell me it's written by a graduate student, I would probably believe it. It is also interesting in a way that maybe for low-dimensional signals there are some architecture tweaks we can do to modify the existing diffusion model architectures to make things better, so maybe not 100% BS.
That being said, it's very hard to tell it apart from a normal poorly-written paper from a quick glance. If you tell me it's written by a graduate student, I would probably believe it. It is also interesting in a way that maybe for low-dimensional signals there are some architecture tweaks we can do to modify the existing diffusion model architectures to make things better, so maybe not 100% BS.