# Metadata Source URL:: https://arxiv.org/abs/2006.11239 Topics:: #ai --- # 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. Our implementation is available at https://github.com/hojonathanho/diffusion ## Highlights > [!quote]+ Updated on 270922_174831 > > 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