research-article

Mix-DDPM: Enhancing Diffusion Models through Fitting Mixture Noise with Global Stochastic Offset

Authors:

Xianming LiuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 9

Article No.: 283, Pages 1 - 24

https://doi.org/10.1145/3672080

Published: 23 September 2024 Publication History

Abstract

Denoising diffusion probabilistic models (DDPM) have shown impressive performance in various domains as a class of deep generative models. In this article, we introduce the mixture noise-based DDPM (Mix-DDPM), which considers the Markov diffusion posterior as a Gaussian mixture model. Specifically, Mix-DDPM randomly selects a Gaussian component and then adds the chosen Gaussian noise, which can be demonstrated as a more efficient way to perturb the signals into a simple known distribution. We further define the reverse probabilistic model as a parameterized Gaussian mixture kernel. Due to the intractability in calculating the KL divergence between Gaussian mixture models, we derive a variational bound to maximize the likelihood, offering a concise formulation for optimizing the denoising model and valuable insights for designing the sampling strategies. Our theoretical derivation highlights that Mix-DDPM need only shift image which requires the inclusion of a global stochastic offset in both the diffusion and reverse processes, which can be efficiently implemented with just several lines of code. The global stochastic offset effectively fits a Gaussian mixture distribution enhancing the degrees of freedom of the entire diffusion model. Furthermore, we present three streamlined sampling strategies that interface with diverse fast dedicated solvers for diffusion ordinary differential equations, boosting the efficacy of image representation in the sampling phase and alleviating the issue of slow generation speed, thereby enhancing both efficiency and accuracy. Extensive experiments on benchmark datasets demonstrate the effectiveness of Mix-DDPM and its superiority over the original DDPM.

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Index Terms

Mix-DDPM: Enhancing Diffusion Models through Fitting Mixture Noise with Global Stochastic Offset
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 9

September 2024

780 pages

EISSN:1551-6865

DOI:10.1145/3613681

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 23 September 2024

Online AM: 07 June 2024

Accepted: 04 June 2024

Revised: 22 April 2024

Received: 02 February 2024

Published in TOMM Volume 20, Issue 9

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Gao HSu YWang FLi H(2024)Heterogeneous Fusion and Integrity Learning Network for RGB-D Salient Object DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365647620:7(1-24)Online publication date: 15-May-2024
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