In normal diffusion you train a model to take lots of tiny steps, all the same small size. e.g. "You're gonna take 20 steps, at times [1.0, 0.95, 0.90, 0.85...]" and each time the model takes that small fixed-size step to make the image look better.

Here they train a model to say "I'm gonna ask you to take a step from time B to A - might be a small step, might be a big step - but whatever size it is, make the image that much better." You you might ask the model to improve the image from t=1.0 to t=0.25 and be almost done. It gets a side variable telling it how much improvement to make in each step.

I'm not sure this right, but that's what I got out of it by skimming the blog & paper.