mlsanityData to 13 Oct 2026

Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion

arXiv 2502.21278 ↗First posted 28 Feb 2025cs.LG stat.MLcited by 17

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

There is strong empirical evidence that the state-of-the-art diffusion modeling paradigm leads to models that memorize the training set, especially when the training set is small. Prior methods to mitigate the memorization problem often lead to a decrease in image quality. Is it possible to obtain strong and creative generative models, i.e., models that achieve high generation quality and low memorization? Despite the current pessimistic landscape of results, we make significant progress in pushing the trade-off between fidelity and memorization. We first provide theoretical evidence that memorization in diffusion models is only necessary for denoising problems at low noise scales (usually used in generating high-frequency details). Using this theoretical insight, we propose a simple, principled method to train the diffusion models using noisy data at large noise scales. We show that our method significantly reduces memorization without decreasing the image quality, for both text-conditional and unconditional models and for a variety of data availability settings.

Key facts. Cited by 17 papers in the map (data through 13 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 61 of its 71 references that are in the map. First posted on arXiv 28 Feb 2025.

Builds on

61 of 71 references.

Closest ancestors · 29

Same direction, newest first

  1. 2025On the Interpolation Effect of Score Smoothing in Diffusion Models26 Feb
  2. 2025Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization5 Feb
  3. 2024Classifier-Free Guidance inside the Attraction Basin May Cause Memorization23 Nov
  4. 2024Detecting, Explaining, and Mitigating Memorization in Diffusion Models31 Jul
  5. 2024Iterative Ensemble Training with Anti-Gradient Control for Mitigating Memorization in Diffusion Models22 Jul
  6. 2024Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models4 Jun
  7. 2024Learning general Gaussian mixtures with efficient score matching29 Apr
  8. 2024Learning Mixtures of Gaussians Using Diffusion Models29 Apr
  9. 2024Towards Memorization-Free Diffusion Models1 Apr
  10. 2024Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data20 Mar
  11. 2024Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention17 Mar
  12. 2024Critical windows: non-asymptotic theory for feature emergence in diffusion models3 Mar
17 more
  1. 2024Dynamical Regimes of Diffusion Models28 Feb
  2. 2024Wasserstein proximal operators describe score-based generative models and resolve memorization9 Feb
  3. 2023Closed-Form Diffusion Models19 Oct
  4. 2023On Memorization in Diffusion Models4 Oct
  5. 2023Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localization7 Aug
  6. 2023Learning Mixtures of Gaussians Using the DDPM Objective3 Jul
  7. 2023Understanding and Mitigating Copying in Diffusion Models31 May
  8. 2023GSURE-Based Diffusion Model Training with Corrupted Data22 May
  9. 2023Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent17 Feb
  10. 2022SinDDM: A Single Image Denoising Diffusion Model29 Nov
  11. 2022SinDiffusion: Learning a Diffusion Model from a Single Natural Image22 Nov
  12. 2022Soft Diffusion: Score Matching for General Corruptions12 Sep
  13. 2022Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise19 Aug
  14. 2022Convergence of denoising diffusion models under the manifold hypothesis10 Aug
  15. 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
  16. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  17. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 32

  1. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,857
  2. 2022Flow Matching for Generative Modelingcited by 5,542
  3. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,141
  4. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,077
  5. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,607
  6. 2023Stochastic Interpolants: A Unifying Framework for Flows and Diffusionscited by 917
  7. 2023Extracting Training Data from Diffusion Modelscited by 729
  8. 2020What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimationcited by 495
  9. 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 456
  10. 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 421
  11. 2017Information-theoretic analysis of generalization capability of learning algorithmscited by 302
  12. 2023Holistic Evaluation of Text-To-Image Modelscited by 160
20 more
  1. 2020Reasoning About Generalization via Conditional Mutual Informationcited by 147
  2. 2023Ambient Diffusion: Learning Clean Distributions from Corrupted Datacited by 111
  3. 2020When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?cited by 104
  4. 2024An analytic theory of creativity in convolutional diffusion modelscited by 103
  5. 2015How much does your data exploration overfit? Controlling bias via information usagecited by 95
  6. 2022RoentGen: Vision-Language Foundation Model for Chest X-ray Generationcited by 89
  7. 2022Position: Considerations for Differentially Private Learning with Large-Scale Public Pretrainingcited by 88
  8. 2022Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domainscited by 85
  9. 2024Learning Diffusion Priors from Observations by Expectation Maximizationcited by 61
  10. 2024A Geometric Framework for Understanding Memorization in Generative Modelscited by 40
  11. 2024An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observationscited by 20
  12. 2023Polynomial Time and Private Learning of Unbounded Gaussian Mixture Modelscited by 19
  13. 2024How much is a noisy image worth? Data Scaling Laws for Ambient Diffusioncited by 18
  14. 2023Information Theoretic Lower Bounds for Information Theoretic Upper Boundscited by 17
  15. 2022Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regressioncited by 14
  16. 2024Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorizationcited by 14
  17. 2024Evaluating and Mitigating IP Infringement in Visual Generative AIcited by 13
  18. 2024Integrating Amortized Inference with Diffusion Models for Learning Clean Distribution from Corrupted Imagescited by 12
  19. 2022Strong Memory Lower Bounds for Learning Natural Modelscited by 10
  20. 2024CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusioncited by 3

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 6; 2026: 11.

17 papers.

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  1. 2025Why Diffusion Models Don't Memorize: The Role of Implicit Dynamical Regularization in Trainingcited by 79
  2. 2025Locality in Image Diffusion Models Emerges from Data Statisticscited by 25
  3. 2025Ambient Diffusion Omni: Training Good Models with Bad Datacited by 18
  4. 2025Selective Underfitting in Diffusion Modelscited by 17
  5. 2026Diffusion Model's Generalization Can Be Characterized by Inductive Biases toward a Data-Dependent Ridge Manifoldcited by 5
  6. 2026Ambient Dataloops: Generative Models for Dataset Refinementcited by 2
  7. 2026Diffusion Models Memorize in Training -- and Generalize in Inferencecited by 2
  8. 2026Manifold Generalization Provably Proceeds Memorization in Diffusion Modelscited by 2
  9. 2026Concurrence of Symmetry Breaking and Nonlocality Phase Transitions in Diffusion Modelscited by 2
  10. 2025A Closer Look on Memorization in Tabular Diffusion Model: A Data-Centric Perspectivecited by 1
  11. 2025GuessBench: Sensemaking Multimodal Creativity in the Wildcited by 1
  12. 2026Riemannian AmbientFlow: Towards Simultaneous Manifold Learning and Generative Modeling from Corrupted Datacited by 1
5 more
  1. 2026Is Memorization Helpful or Harmful? Prior Information Sets the Thresholdcited by 1
  2. 2026Initialization is Half the Battle: Generating Diverse Images from a Guidance Potential Posteriorcited by 1
  3. 2026Reducing information dependency does not cause training data privacy. Adversarially non-robust features docited by 0
  4. 2026Breakdown of Local Denoising as Semantic Speciationcited by 0
  5. 2026From Modes to Memories: Characterizing the Scale-Space Dynamics of Diffusion Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025How Diffusion Models Memorize
  2. 2026Memorization Control in Diffusion Models from Denoising-centric Perspective
  3. 2025On the Edge of Memorization in Diffusion Models
  4. 2025Generalization of Diffusion Models Arises with a Balanced Representation Space
  5. 2025Provable Separations between Memorization and Generalization in Diffusion Models
  6. 2024Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapes
  7. 2026Diffusion Models Preferentially Memorize Prototypical Examples or: Why Does My Diffusion Model Love Slop?
  8. 2026Understanding diffusion models requires rethinking (again) generalization