arXiv AI June 2, 2026 By Kairan Zhao, Eleni Triantafillou, Peter Triantafillou

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision

2d ago

Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning

arXiv:2610.01723v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable progress in image synthesis, yet can exhibit memorization by closely reproducing individual tra...

By Hyungjun Joo, Sehwan Kim, Hyeonggeun Han, Sangwoo Hong, Jungwoo Lee

diffusion safety

arXiv AI

Sep 2

Denoising Diffusion Generative Models Secretly Calculate Attentions

arXiv:2609.00885v1 Announce Type: new Abstract: Denoising diffusion models are the dominant architecture for image generation, whereas most natural language generation and modeling are primarily hand...

By Farzan Haddadi, Leila Monfared, Ebrahim Rezaii, Mohammadreza Malek-Mohammadi, Pejman Zakalvand, Narges Mokhtari

llms diffusion

arXiv Computation and Language

Aug 28

Visual Information-Guided Parallel Decoding for Diffusion Multimodal Large Language Models

Visual Information-Guided Parallel Decoding for Diffusion Multimodal Large Language Models introduces the VIG‑Sampler, a method that prioritizes tokens for decoding based on their attention to image tokens and penalizes redundancy in image‑attention distributions. The approach aims to improve the quality of multimodal generation by selecting more informative tokens during diffusion decoding. Experiments on seven captioning and VQA benchmarks with three open‑source dMLLMs show that VIG‑Sampler outperforms the Info‑Gain Sampler by an average of 19.3 CIDEr points and achieves better COCO Caption results using only half as many decoding steps.

By Insu Lee, Wooje Park, Wonseok Shin, Jinwoo Son, Byonghyo Shim

llms diffusion multimodal benchmarks

arXiv Computation and Language

4d ago

POET: Preference Optimization for Enhanced Text-to-Image Generation

arXiv:2510.12041v3 Announce Type: replace Abstract: Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecifie...

By Ruibo Chen, Jiacheng Pan, Heng Huang, Zhenheng Yang

llms diffusion reinforcement-learning fine-tuning multimodal benchmarks 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

Sep 1

TPSO: Training-Free Diverse Image Generation via Semantic Prompt Embedding Optimization

arXiv:2511.19811v2 Announce Type: replace-cross Abstract: Image diversity remains a fundamental challenge for text-to-image diffusion models. Low-diversity generation often leads to repetitive output...

By Debin Meng, Chen Jin, Zheng Gao, Yanran Li, Ioannis Patras, Georgios Tzimiropoulos

rag diffusion