[MathYouF]

After an excellent all day workshop at CVPR this year explaining diffusion in more detail, it seemed pretty clear that any noise function could be used. I'm not sure if it should have been obvious, but I felt this paper coming from a mile away after seeing that.

I wonder to what degree certain parts of diffusion dictate using certain noise functions, and how much this paper truly challenges how we understand them. Cool to see it was researched.

Next idea: it seems like a lot of steps could be skipped by using things like momentum during the inference time. I'm sure OpenAI has already implemented several clever tricks like that in production for DallE.

I'm working on (various, non-diffusion) methods for 2D drawing to 3D output right now.