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Cited by 1,704Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine LearningImage and Signal Denoising Methods

The DOI matches its Crossref record; 236 of the 236 references it cites were found in a registry or an index; none of the 245 sources its claims cite is checked for support yet.

Diffusion Models: A Comprehensive Survey of Methods and Applications

Open access
Yang, Ling, Zhang, Zhilong, Song, Yang, Hong, Shenda, Xu, Runsheng, Zhao, Yue, Zhang, Wentao, Cui, Bin, Yang, Ming-Hsuan·2024·ACM Computing Surveys
DOI 10.1145/3626235
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Abstract

Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative models for enhanced results. We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language processing, temporal data modeling, to interdisciplinary applications in other scientific disciplines. This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration. Github: https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy

Overview

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Written from the paper’s full text.

TL;DR

This survey organizes diffusion-model research into three core areas—efficient sampling, improved likelihood estimation, and handling data with special structures—and reviews connections to other generative models plus six application domains. It also introduces DDPMs, score-based generative models, and Score SDEs as the main formulations and outlines future research directions.

Key findings

  • The paper proposes a taxonomy of diffusion-model advances: efficient sampling, improved likelihood estimation, and methods for data with special structures such as discrete data, invariant structures, and manifold structures.
  • Efficient sampling methods are split into learning-free and learning-based approaches; customized ODE solvers such as DPM-solver and exponential integrators can produce high-quality samples in just 10 to 20 iterations, far fewer than the hundreds typically required without accelerated sampling.
  • Improved-likelihood methods include noise schedule optimization, reverse variance learning, and exact likelihood computation; Variational Diffusion Models show that noise schedules do not affect the VLB as long as they share the same values at \(R_{\min}\) and \(R_{\max}\), and only affect Monte Carlo estimator variance.
  • Applications are grouped into six categories: computer vision, natural language processing, temporal data modeling, multi-modal learning, robust learning, and interdisciplinary applications.

Methods

  • Design: a comprehensive survey/overview of diffusion models, not an empirical study with new experiments.
  • Data/sample: existing published research literature on diffusion models and related generative models, cited throughout the reference list.
  • Main analysis: conceptual and taxonomic synthesis, including a self-contained introduction to DDPMs, SGMs, and Score SDEs, followed by categorized review of methods, connections, applications, and future directions.

Limitations

  • Stated: the paper identifies open issues including revisiting assumptions such as complete information erasure in finite time, lack of theoretical understanding for why and when diffusion models work, weaker latent representations than VAEs/GANs, and pitfalls such as dataset bias, improper image generation, and privacy issues.
  • Stated: for scene-graph-to-image generation, high-quality paired SG-image datasets are scarce, and leveraging large-scale text-image datasets for augmentation or semantic diffusion priors remains an open problem.
  • Evident: the survey does not report a systematic search protocol, inclusion/exclusion criteria, or quantitative meta-analysis, so its coverage is broad but potentially selective.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1145/3626235

Cites

236 of 236 references verified0 citation errors · 0 not checkable

Statistics

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Verification

Claims the paper makes

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Show all 100 claims

  • It may state two separate claims as one. 66
  • Probably not a substantial claim. 67
119 of 119

  • The paper relies on this without support.

    Over the past two years, the body of research on diffusion models has grown significantly, making it increasingly challenging for new researchers to stay abreast of recent developments.

    “Over the past two years, the body of research on diffusion models has grown significantly, making it increasingly challenging for new researchers to stay abreast of the recent developments in the field.”

  • The paper relies on this without support.

    The sheer volume of work on diffusion models can obscure major trends and hinder further research progress.

    “Additionally, the sheer volume of work can obscure major trends and hinder further research progress.”

  • The authors state this as an assumption.

    Langevin dynamics converges to its equilibrium at every noise level.

    “under the assumption that Langevin dynamics converges to its equilibrium at every noise level”

  • The paper relies on this without support.

    Text-to-image generation models struggle to faithfully reproduce complex sentences with many objects and relationships.

    “Despite text-to-image generation models have made significant progress, they struggle to faithfully reproduce complex sentences with many objects and relationships.”

  • The paper relies on this without support.

    High-quality paired scene-graph-image datasets are scarce.

    “However, highquality paired SG-image datasets are scarce.”

  • The paper relies on this without support.

    Diffusion models have emerged as a powerful framework, notably as the only one that can rival generative adversarial networks in most applications without resorting to adversarial training.

    “Diffusion models have emerged as a powerful framework, notably as the only one that can rival generative adversarial networks (GANs) in most applications without resorting to adversarial training.”

  • The paper relies on this without support.

    Unlike variational autoencoders or generative adversarial networks, diffusion models are less effective for providing good representations of data in their latent space.

    “Unlike variational autoencoders or generative adversarial networks, diffusion models are less effective for providing good representations of data in their latent space.”
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Claims other papers make about this one

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Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Grad-TTS2 name disagrees with its cited reference

    Minor

    The text calls the model Grad-TTS2, but reference [118] is titled Guided-TTS 2.

  • Latent Diffusion Models is cited as DPM-solver

    Minor

    The citation attached to Latent Diffusion Models points to a bibliography entry for DPM-solver, not the paper identified elsewhere in the text as LDM [196].

  • GeoDiff citation year disagrees with the reference

    Minor

    The text dates reference [261] to 2022, while its bibliography entry gives 2021.

  • DDPM reverse SDE has the opposite score-term sign

    Minor

    Equation (18) subtracts the score term in a reverse-time SDE. Substituting the DDPM drift from Equation (16) therefore gives a negative score term, but Equation (21) gives it a positive sign.

  • Annealed Langevin sampling uses incompatible time-step ranges

    Minor

    The stated iteration runs from T down to 1, but the immediately following initialization rule applies from 0 up to T−1. As written, the rule does not initialize the first stated step, t = T, and the stated iteration does not perform the t = 0 step.

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Citations

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Metadata

Authors
Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H.
Affiliations
  • Peking University (CN)
  • OpenAI (United States) (US)
  • University of California, Los Angeles (US)
  • University of Southern California (US)
  • HEC Montréal (CA)
  • University of California, Merced (US)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2023-11-09
Volume
56
Issue
4
Pages
1-39
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3626235
OpenAlex
W4387195417
Cited by
1,704
References
199
Citations by year
2026: 392 · 2025: 683 · 2024: 483 · 2023: 142
FWCI
154.02
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Computing Surveys (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
Keywords
diffusion models, deep generative models, efficient sampling, likelihood estimation, structured data, hybrid generative models, image generation, video generation, temporal data modeling

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