Denoising Diffusion Probabilistic Models

We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9. 46 and a state-of-the-art FID score of 3. 17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.

Authors

• Jonathan Ho
• Ajay Jain
• Pieter Abbeel UC Berkeley

Keywords

No keywords are indexed for this paper.