mlsanityData to 14 Oct 2026

Provable Efficiency of Guidance in Diffusion Models for General Data Distribution

arXiv 2505.01382 ↗First posted 2 May 2025stat.ML cs.LG math.STcited by 8

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Diffusion models have emerged as a powerful framework for generative modeling, with guidance techniques playing a crucial role in enhancing sample quality. Despite their empirical success, a comprehensive theoretical understanding of the guidance effect remains limited. Existing studies only focus on case studies, where the distribution conditioned on each class is either isotropic Gaussian or supported on a one-dimensional interval with some extra conditions. How to analyze the guidance effect beyond these case studies remains an open question. Towards closing this gap, we make an attempt to analyze diffusion guidance under general data distributions. Rather than demonstrating uniform sample quality improvement, which does not hold in some distributions, we prove that guidance can improve the whole sample quality, in the sense that the average reciprocal of the classifier probability decreases with the existence of guidance. This aligns with the motivation of introducing guidance.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 34 of its 37 references that are in the map. First posted on arXiv 2 May 2025.

Builds on

34 of 37 references.

Closest ancestors · 26

Same direction, newest first

  1. 2025Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures7 Apr
  2. 2025Minimax Optimality of the Probability Flow ODE for Diffusion Models12 Mar
  3. 2024Provable Acceleration for Diffusion Models under Minimal Assumptions30 Oct
  4. 2024Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality24 Oct
  5. 2024Instance-dependent Convergence Theory for Diffusion Models17 Oct
  6. 2024What does guidance do? A fine-grained analysis in a simple setting19 Sep
  7. 2024Classifier-Free Guidance is a Predictor-Corrector16 Aug
  8. 2024A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models5 Aug
  9. 2024Guiding a Diffusion Model with a Bad Version of Itself4 Jun
  10. 2024Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel3 Jun
  11. 2024Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models23 May
  12. 2024Accelerating Convergence of Score-Based Diffusion Models, Provably6 Mar
14 more
  1. 2024Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models3 Mar
  2. 2023Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localization7 Aug
  3. 2023The probability flow ODE is provably fast19 May
  4. 2022Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions3 Nov
  5. 2022Convergence of score-based generative modeling for general data distributions26 Sep
  6. 2022Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions22 Sep
  7. 2022Diffusion Models in Vision: A Survey10 Sep
  8. 2022Classifier-Free Diffusion Guidance26 Jul
  9. 2022Convergence for score-based generative modeling with polynomial complexity13 Jun
  10. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  11. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  12. 2021Maximum Likelihood Training of Score-Based Diffusion Models22 Jan
  13. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  14. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 8

  1. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  3. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  6. 2016Improved Techniques for Training GANscited by 2,970
  7. 2018A Note on the Inception Scorecited by 226
  8. 2020Efficient Learning of Generative Models via Finite-Difference Score Matchingcited by 49

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 4.

8 papers.

  1. 2025Stage-wise Dynamics of Classifier-Free Guidance in Diffusion Modelscited by 16
  2. 2025Optimal Convergence Analysis of DDPM for General Distributionscited by 16
  3. 2025Adaptive Diffusion Guidance via Stochastic Optimal Controlcited by 10
  4. 2025Towards a unified framework for guided diffusion modelscited by 8
  5. 2026Emergence of Distortions in High-Dimensional Guided Diffusion Modelscited by 4
  6. 2026Stay Fair! Ensuring Group Fairness in Diffusion Models Across Guidance Scalescited by 2
  7. 2026On the Robustness of Distribution Support under Diffusion Guidancecited by 1
  8. 2026Adversarial Learning of Classifier-Free Guidance Schedulescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Bridging the Gap: Addressing Discrepancies in Diffusion Model Training for Classifier-Free Guidance
  2. 2025Stochastic Self-Guidance for Training-Free Enhancement of Diffusion Models
  3. 2024Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
  4. 2025Feedback Guidance of Diffusion Models
  5. 2025Improving Classifier-Free Guidance in Masked Diffusion: Low-Dim Theoretical Insights with High-Dim Impact
  6. 2025What Exactly Does Guidance Do in Masked Discrete Diffusion Models
  7. 2024Gradient Guidance for Diffusion Models: An Optimization Perspective
  8. 2026Visualizing Distribution Coverage in Generative Diffusion Models