Comment by godelski

You have a few minor errors and I hope I can help out.

  > Diffusion: generate a lot of noise then try to clean it up

You could say this about Flows too. The history of them is shared with diffusion and goes back to the Whitening Transform. Flows work by a coordinate transform so we have an isomorphism where diffusion works through, for easier understanding, a hierarchical mixture of gaussians. Which is a lossy process (more confusing when we get into latent diffusion models, which are the primary type used). The goal of a Normalizing Flow is to turn your sampling distribution, which you don't have an explicit representation of, into a probability distribution (typically Normal Noise/Gaussian). So in effect, there are a lot of similarities here. I'd highly suggest learning about Flows if you want to better understand Diffusion Models.

  > The diffusion approach that is the baseline for sota is Flow Matching from Meta

To be clear, Flow Matching is a Normalizing Flow. Specifically, it is a Continuous and Conditional Normalizing Flow. If you want to get into the nitty gritty, Ricky has a really good tutorial on the stuff[0]

[0] https://arxiv.org/abs/2412.06264