[dougabug]

If you removed all of the noise in a corrupted image in one step, you would have a denoising autoencoder, which has been around since the mid-aughts or perhaps earlier. Denoising diffusion models remove noise a little bit at a time. Think about an image which only has a slight amount of noise added to it. It’s generally easier to train a model to remove a tiny amount of noise than a large amount of noise. At the same time, we likely introduced a small amount of change to the actual contents of the image.

Typically, in generating the training data for diffusion models, we add noise incrementally to an image until it’s essentially all noise. Going backwards from almost all noise to the original images directly in one step is a pretty dubious proposition.