# Classifier-Free Guidance inside the Attraction Basin May Cause Memorization

arXiv: https://arxiv.org/abs/2411.16738 · HTML: https://mlsanity.com/paper/2411.16738/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 23 Nov 2024.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Muse: Text-To-Image Generation via Masked Generative Transformers](https://mlsanity.com/paper/2301.00704/) (2023)
- [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://mlsanity.com/paper/2212.03860/) (2022)
- [Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models](https://mlsanity.com/paper/2404.07724/) (2024)
- [Understanding and Mitigating Copying in Diffusion Models](https://mlsanity.com/paper/2305.20086/) (2023)
- [A Self-Supervised Descriptor for Image Copy Detection](https://mlsanity.com/paper/2202.10261/) (2022)
- [Detecting, Explaining, and Mitigating Memorization in Diffusion Models](https://mlsanity.com/paper/2407.21720/) (2024)
- [Analysis of Classifier-Free Guidance Weight Schedulers](https://mlsanity.com/paper/2404.13040/) (2024)
- [A Reproducible Extraction of Training Images from Diffusion Models](https://mlsanity.com/paper/2305.08694/) (2023)
- [Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention](https://mlsanity.com/paper/2403.11052/) (2024)
- [Understanding the Impact of Negative Prompts: When and How Do They Take Effect?](https://mlsanity.com/paper/2406.02965/) (2024)
- [Towards Memorization-Free Diffusion Models](https://mlsanity.com/paper/2404.00922/) (2024)
- [In search of dispersed memories: Generative diffusion models are associative memory networks](https://mlsanity.com/paper/2309.17290/) (2023)
- [Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data](https://mlsanity.com/paper/2404.10177/) (2024)
- [Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models](https://mlsanity.com/paper/2309.16750/) (2023)
- [Memorized Images in Diffusion Models share a Subspace that can be Located and Deleted](https://mlsanity.com/paper/2406.18566/) (2024)

## Built on by (most cited first, 21 of 21)

- [Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion](https://mlsanity.com/paper/2502.21278/) (2025)
- [Demystifying Diffusion Policies: Action Memorization and Simple Lookup Table Alternatives](https://mlsanity.com/paper/2505.05787/) (2025)
- [Adjusting Initial Noise to Mitigate Memorization in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2510.08625/) (2025)
- [How Diffusion Models Memorize](https://mlsanity.com/paper/2509.25705/) (2025)
- [Not Every Time and Frequency Need to Be Forgotten in Diffusion Unlearning](https://mlsanity.com/paper/2510.17917/) (2025)
- [Detecting and Mitigating Memorization in Diffusion Models through Anisotropy of the Log-Probability](https://mlsanity.com/paper/2601.20642/) (2026)
- [Diffusion Models Memorize in Training -- and Generalize in Inference](https://mlsanity.com/paper/2603.13419/) (2026)
- [Manifold Generalization Provably Proceeds Memorization in Diffusion Models](https://mlsanity.com/paper/2603.23792/) (2026)
- [Memorization in 3D Shape Generation: An Empirical Study](https://mlsanity.com/paper/2512.23628/) (2025)
- [Steering Away from Memorization: Reachability-Constrained Reinforcement Learning for Text-to-Image Diffusion](https://mlsanity.com/paper/2603.00140/) (2026)
- [CAPTAIN: Semantic Feature Injection for Memorization Mitigation in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2512.10655/) (2025)
- [You Don't Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2603.00133/) (2026)
- [Filtering Memorization from Parameter-Space in Diffusion Models](https://mlsanity.com/paper/2605.10439/) (2026)
- [Machine Unlearning for Masked Diffusion Language Models](https://mlsanity.com/paper/2605.18253/) (2026)
- [Broken Memories: Detecting and Mitigating Memorization in Diffusion Models with Degraded Generations](https://mlsanity.com/paper/2605.22050/) (2026)
- [Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences](https://mlsanity.com/paper/2605.26756/) (2026)
- [Points as Tori: Fast Pointwise Signed Distance for Point Clouds](https://mlsanity.com/paper/2607.16946/) (2026)
- [Signed Rectified Flow: Negativity-Controlled Generation](https://mlsanity.com/paper/2607.18516/) (2026)
- [When Guidance Goes Off-Scale: Recalibrating Diffusion Transformers under Analog Compute-in-Memory Nonidealities](https://mlsanity.com/paper/2608.19644/) (2026)
- [From Modes to Memories: Characterizing the Scale-Space Dynamics of Diffusion Models](https://mlsanity.com/paper/2609.39648/) (2026)
- [Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning](https://mlsanity.com/paper/2610.01723/) (2026)

## Abstract (arXiv)

Diffusion models are prone to exactly reproduce images from the training data. This exact reproduction of the training data is concerning as it can lead to copyright infringement and/or leakage of privacy-sensitive information. In this paper, we present a novel perspective on the memorization phenomenon and propose a simple yet effective approach to mitigate it. We argue that memorization occurs because of an attraction basin in the denoising process which steers the diffusion trajectory towards a memorized image. However, this can be mitigated by guiding the diffusion trajectory away from the attraction basin by not applying classifier-free guidance until an ideal transition point occurs from which classifier-free guidance is applied. This leads to the generation of non-memorized images that are high in image quality and well-aligned with the conditioning mechanism. To further improve on this, we present a new guidance technique, opposite guidance, that escapes the attraction basin sooner in the denoising process. We demonstrate the existence of attraction basins in various scenarios in which memorization occurs, and we show that our proposed approach successfully mitigates memorization.