# Denoising Diffusion Probabilistic Models (DDPM) ## Overview DDPM was proposed in [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) 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 [???](https://huggingface.co/???). The original code can be found [here](https://github.com/hojonathanho/diffusion).