mlsanityData to 7 Oct 2026

Detecting, Explaining, and Mitigating Memorization in Diffusion Models

arXiv 2407.21720 ↗First posted 31 Jul 2024cs.CVcited by 94

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Recent breakthroughs in diffusion models have exhibited exceptional image-generation capabilities. However, studies show that some outputs are merely replications of training data. Such replications present potential legal challenges for model owners, especially when the generated content contains proprietary information. In this work, we introduce a straightforward yet effective method for detecting memorized prompts by inspecting the magnitude of text-conditional predictions. Our proposed method seamlessly integrates without disrupting sampling algorithms, and delivers high accuracy even at the first generation step, with a single generation per prompt. Building on our detection strategy, we unveil an explainable approach that shows the contribution of individual words or tokens to memorization. This offers an interactive medium for users to adjust their prompts. Moreover, we propose two strategies i.e., to mitigate memorization by leveraging the magnitude of text-conditional predictions, either through minimization during inference or filtering during training. These proposed strategies effectively counteract memorization while maintaining high-generation quality. Code is available at https://github.com/YuxinWenRick/diffusion_memorization.

Key facts. Cited by 94 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 24 of its 30 references that are in the map. First posted on arXiv 31 Jul 2024.

Builds on

24 of 30 references.

Closest ancestors · 6

Same direction, newest first

  1. 2023Understanding and Mitigating Copying in Diffusion Models31 May
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  3. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  4. 2020Denoising Diffusion Implicit Models6 Oct
  5. 2020Denoising Diffusion Probabilistic Models19 Jun
  6. 2020Improved Techniques for Training Score-Based Generative Models16 Jun

Other references · 18

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  4. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  5. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  6. 2020Extracting Training Data from Large Language Modelscited by 2,144
  7. 2022Reproducible scaling laws for contrastive language-image learningcited by 1,163
  8. 2021Membership Inference Attacks From First Principlescited by 839
  9. 2023Extracting Training Data from Diffusion Modelscited by 723
  10. 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 418
  11. 2023Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discoverycited by 329
  12. 2023Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robustcited by 179
6 more
  1. 2022A Self-Supervised Descriptor for Image Copy Detectioncited by 175
  2. 2023Are Diffusion Models Vulnerable to Membership Inference Attacks?cited by 135
  3. 2023Ambient Diffusion: Learning Clean Distributions from Corrupted Datacited by 108
  4. 2023Membership Inference Attacks against Diffusion Modelscited by 84
  5. 2023A Reproducible Extraction of Training Images from Diffusion Modelscited by 60
  6. 2022Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queriescited by 36

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 19; 2025: 41; 2026: 34.

94 papers.

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  1. 2025Why Diffusion Models Don't Memorize: The Role of Implicit Dynamical Regularization in Trainingcited by 78
  2. 2024Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributionscited by 53
  3. 2024A Geometric Framework for Understanding Memorization in Generative Modelscited by 40
  4. 2025On the Edge of Memorization in Diffusion Modelscited by 22
  5. 2024Classifier-Free Guidance inside the Attraction Basin May Cause Memorizationcited by 21
  6. 2024Understanding and Mitigating Memorization in Generative Models via Sharpness of Probability Landscapescited by 20
  7. 2025Diffusion Models and the Manifold Hypothesis: Log-Domain Smoothing is Geometry Adaptivecited by 20
  8. 2024Exploring Local Memorization in Diffusion Models via Bright Ending Attentioncited by 19
  9. 2025Data Unlearning in Diffusion Modelscited by 18
  10. 2025A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspectivecited by 18
  11. 2025Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusioncited by 17
  12. 2025Privacy Attacks on Image AutoRegressive Modelscited by 15
38 more
  1. 2025LatexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blendingcited by 10
  2. 2025Generalization of Diffusion Models Arises with a Balanced Representation Spacecited by 10
  3. 2025Demystifying Diffusion Policies: Action Memorization and Simple Lookup Table Alternativescited by 9
  4. 2026Image Generation with a Sphere Encodercited by 8
  5. 2024What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Modelscited by 7
  6. 2024SIDE: Surrogate Conditional Data Extraction from Diffusion Modelscited by 7
  7. 2025Adjusting Initial Noise to Mitigate Memorization in Text-to-Image Diffusion Modelscited by 7
  8. 2024Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Surveycited by 6
  9. 2025How Diffusion Models Memorizecited by 6
  10. 2024Investigating Memorization in Video Diffusion Modelscited by 5
  11. 2024CopyrightShield: Enhancing Diffusion Model Security against Copyright Infringement Attackscited by 5
  12. 2025Enhancing Privacy-Utility Trade-offs to Mitigate Memorization in Diffusion Modelscited by 5
  13. 2026Local Coverage Governs Memorization in Diffusion Modelscited by 5
  14. 2024Image Copy Detection for Diffusion Modelscited by 4
  15. 2025CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Modelscited by 4
  16. 2025EAR: Erasing Concepts from Unified Autoregressive Modelscited by 4
  17. 2024CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusioncited by 3
  18. 2024Dual-Model Defense: Safeguarding Diffusion Models from Membership Inference Attacks through Disjoint Data Splittingcited by 3
  19. 2025Generative AI Training and Copyright Lawcited by 3
  20. 2025SolidMark: Evaluating Image Memorization in Generative Modelscited by 3
  21. 2025Quantifying Cross-Modality Memorization in Vision-Language Modelscited by 3
  22. 2025Concept-TRAK: Understanding how diffusion models learn concepts through concept-level attributioncited by 3
  23. 2025Responsible Diffusion: A Comprehensive Survey on Safety, Ethics, and Trust in Diffusion Modelscited by 3
  24. 2026Two Calm Ends and the Wild Middle: A Geometric Picture of Memorization in Diffusion Modelscited by 3
  25. 2024Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion Modelscited by 2
  26. 2025The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray Generationcited by 2
  27. 2025RAPID: Retrieval Augmented Training of Differentially Private Diffusion Modelscited by 2
  28. 2025Demystifying Foreground-Background Memorization in Diffusion Modelscited by 2
  29. 2025Mitigating data replication in text-to-audio generative diffusion models through anti-memorization guidancecited by 2
  30. 2025A Novel Metric for Detecting Memorization in Generative Models for Brain MRI Synthesiscited by 2
  31. 2025Review of Hallucination Understanding in Large Language and Vision Modelscited by 2
  32. 2025Not Every Time and Frequency Need to Be Forgotten in Diffusion Unlearningcited by 2
  33. 2026Detecting and Mitigating Memorization in Diffusion Models through Anisotropy of the Log-Probabilitycited by 2
  34. 2026Diffusion Models Memorize in Training -- and Generalize in Inferencecited by 2
  35. 2024Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Modelscited by 1
  36. 2024Real-World Benchmarks Make Membership Inference Attacks Fail on Diffusion Modelscited by 1
  37. 2024Shallow Diffuse: Robust and Invisible Watermarking through Low-Dimensional Subspaces in Diffusion Modelscited by 1
  38. 2024LoyalDiffusion: A Diffusion Model Guarding Against Data Replicationcited by 1

50 most cited of 94 All 94 (JSON)

Similar, not cited

Close in wording, no citation either way

  1. 2024Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention
  2. 2024Memorized Images in Diffusion Models share a Subspace that can be Located and Deleted
  3. 2025Finding DoRI: Discovery of Retained Images in Diffusion Models
  4. 2023Understanding (Un)Intended Memorization in Text-to-Image Generative Models
  5. 2024MemBench: Memorized Image Trigger Prompt Dataset for Diffusion Models
  6. 2025Provable Separations between Memorization and Generalization in Diffusion Models
  7. 2023On Memorization in Diffusion Models
  8. 2026Characterizing Memorization in Diffusion Language Models: Generalized Extraction and Sampling Effects