Denoising Diffusion Probabilistic Models (DDPM) Paper : Denoising Diffusion Probabilistic Models Authors : Jonathan Ho, Ajay Jain, Pieter Abbeel Abstract : 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. Inference DDPM models can use discrete noise schedulers such as: scheduling ddpm scheduling ddim scheduling pndm for inference. Note that while the ddpm scheduler yields the highest quality, it also takes the longest. For a good trade off between quality and inference speed you might want to consider the ddim or pndm sched…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy