Hugging Face Trending Papers July 9, 2026

AutoAnchor: Stable Diffusion Unlearning Using Cross-Attention as a Manifold Surrogate

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Diffusion unlearning is essential for mitigating the generation of harmful or copyrighted content in text-to-image models. Current diffusion unlearning techniques determine the model update direction by either using alternatives of the target concept as an anchor or using empty prompts.

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arXiv Machine Learning

Jul 10

AutoAnchor: Stable Diffusion Unlearning Using Cross-Attention as a Manifold Surrogate

arXiv:2607. 08337v1 Announce Type: new Abstract: Diffusion unlearning is essential for mitigating the generation of harmful or copyrighted content in text-to-image models.

By Siyuan Wen, Jiahao Zeng, Ningning Ding

diffusion benchmarks

arXiv AI

Jul 8

TILDE: TILt-based Distributional Erasure for Concept Unlearning

arXiv:2607. 06432v1 Announce Type: cross Abstract: Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes, trademark constraints, and safety regulations, deployed systems must be able to suppress unwanted concepts after training.

By Naveen George, Naoki Murata, Yuhta Takida, Konda Reddy Mopuri, Yuki Mitsufuji

diffusion safety

arXiv AI

Jul 3

Locality-Aware Continual Unlearning for Diffusion Models

arXiv:2512. 02657v2 Announce Type: replace-cross Abstract: Real-world deployment of text-to-image diffusion models requires continual concept removal as new privacy, copyright, or safety obligations arise over time.

By Naveen George, Naoki Murata, Yuhta Takida, Konda Reddy Mopuri, Yuki Mitsufuji

diffusion efficiency safety

arXiv AI

3d ago

Concept Unlearning via Cross-Attention Activation Projection for Diffusion Models

arXiv:2605.25765v2 Announce Type: replace-cross Abstract: Existing closed-form methods for concept unlearning in text-to-image diffusion models typically derive editing directions from fixed text emb...

By Saemi Moon, Suhyeon Jun, Seoyeon Lee, Dongwoo Kim

rag diffusion fine-tuning safety

arXiv Machine Learning

Jul 28

To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion

arXiv:2607. 23492v1 Announce Type: cross Abstract: Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets such as NSFW content or copyrighted styles, while preserving model utility on benign concepts.

By Shaswati Saha, Rajasekhar Anguluri, Manas Gaur

llms diffusion multimodal

arXiv AI

Jun 2

You Don't Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models

arXiv:2603. 00133v2 Announce Type: replace-cross Abstract: Generative models have been shown to "memorize" certain training data, leading to verbatim or near-verbatim generating images, which may cause privacy concerns or copyright infringement.

By Kairan Zhao, Eleni Triantafillou, Peter Triantafillou

diffusion benchmarks safety

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Related stories

AutoAnchor: Stable Diffusion Unlearning Using Cross-Attention as a Manifold Surrogate

TILDE: TILt-based Distributional Erasure for Concept Unlearning

Locality-Aware Continual Unlearning for Diffusion Models

Concept Unlearning via Cross-Attention Activation Projection for Diffusion Models

To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion

You Don't Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models