2 docs tagged with "diffusion"

A trained diffusion model is only half the system. Diffusion Models trains a noise predictor — but how you turn that predictor into actual samples, how many steps you take, and what you condition on are all choices made after training, and they are where most of the practical control lives.

Generating an image from nothing in one shot is hard. Diffusion sidesteps the difficulty entirely: destroy an image with noise across many small steps, then train a network to undo just one of those small steps at a time — a thousand easy problems standing in for one hard one.