Denoising Diffusion Probabilistic Models (original) (raw)

Algorithms and Results

We show that diffusion probabilistic models resemble denoising score matching with Langevin dynamics sampling, yet provide log likelihoods and rate-distortion curves in one evaluation of the variational bound.

Our training and sampling algorithms for diffusion probabilistic models. Note the resemblance to denoising score matching and Langevin dynamics. Unconditional CIFAR10 samples. Inception Score=9.46, FID=3.17. CIFAR10 sample quality and lossless compression metrics (left), unconditional test set rate-distortion curve for lossy compression (right).