Related papers: Bilateral Denoising Diffusion Models

Bilateral Denoising Diffusion Models

URL: http://arxiv.org/abs/2108.11514v1
Date: Thu, 26 Aug 2021 13:23:41 GMT
Title: Bilateral Denoising Diffusion Models
Authors: Max W. Y. Lam, Jun Wang, Rongjie Huang, Dan Su, Dong Yu
Abstract summary: Denoising diffusion probabilistic models (DDPMs) have emerged as competitive generative models. We propose novel bilateral denoising diffusion models (BDDMs) which take significantly fewer steps to generate high-quality samples.
Score: 34.507876199641665
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Denoising diffusion probabilistic models (DDPMs) have emerged as competitive generative models yet brought challenges to efficient sampling. In this paper, we propose novel bilateral denoising diffusion models (BDDMs), which take significantly fewer steps to generate high-quality samples. From a bilateral modeling objective, BDDMs parameterize the forward and reverse processes with a score network and a scheduling network, respectively. We show that a new lower bound tighter than the standard evidence lower bound can be derived as a surrogate objective for training the two networks. In particular, BDDMs are efficient, simple-to-train, and capable of further improving any pre-trained DDPM by optimizing the inference noise schedules. Our experiments demonstrated that BDDMs can generate high-fidelity samples with as few as 3 sampling steps and produce comparable or even higher quality samples than DDPMs using 1000 steps with only 16 sampling steps (a 62x speedup).

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