Denoising Diffusion Probabilistic Models
At a glance
Generated images by learning to turn random noise into a picture, step by step.
In one paragraph
Diffusion models generate images by learning to reverse a process that gradually adds noise to real pictures. Starting from random noise, the model repeatedly removes it to reveal an image. The paper showed that this approach could produce high-quality samples on image benchmarks.
