Denoising Diffusion Probabilistic Models (DDPM)

Overview

DDPM was proposed in Denoising Diffusion Probabilistic Models by Jonathan Ho, Ajay Jain, Pieter Abbeel.

The abstract from the paper is the following:

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. Our implementation is available at this https URL

Tips:

This model was contributed by ???. The original code can be found here.