The paper introduces SEAL, a plug‑and‑play module that enhances single‑image sticker personalization in diffusion models by adding a semantic‑guided spatial attention loss, a split‑merge token strategy, and structure‑aware layer restriction. SEAL integrates without altering the U‑Net backbone and addresses overfitting issues such as visual entanglement and structural rigidity. Alongside SEAL, the authors release StickerBench, a large sticker dataset with six attribute tags to enable systematic evaluation of identity preservation and contextual controllability.
By Changhyun Roh, Yonghyun Jeong, Jonghyun Lee, Chanho Eom, Jihyong Oh
arXiv:2608. 14172v1 Announce Type: cross Abstract: Text-to-image diffusion models have two major drawbacks that severely limit their practical utility: (1) standard models lack an intrinsic mechanism for continuous, concept-specific guidance (e.
By Nikolai R\"ohrich, Isabell Hans, Felix Krause, Bj\"orn Ommer
arXiv:2606. 03792v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) successfully enables personalization in text-to-image generation by adapting pre-trained diffusion models to specific visual concepts and styles.
By Georgios Tsoumplekas, Stella Bounareli, Vasileios Argyriou
arXiv:2608. 03791v1 Announce Type: new Abstract: Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora.
By Chunlin Liu, Junnian Chen, Haitong Jiang, Jianyu Zhao, Yingsen Pang, Jingchen Li, Jiabiao He, Youming Lu, Jinhe Bi, Yuntao Du
arXiv:2608.29897v1 Announce Type: new
Abstract: Long-horizon agents need a context manager to compress growing interaction histories into a bounded working context, via passive strategies or active s...
By Jiaqi Su, Cong Pang, Jiawei Hong, Tiankuo Yao, Zixuan Chen, Xin Lou, Lewei Lu
Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora. Removing such knowledge is essential for building trustworthy AI systems.
arXiv:2607. 03358v1 Announce Type: cross Abstract: We study how visual information is routed in vision-language models (VLMs).
By Israfel Salazar, Stella Frank, Dan Oneata, Desmond Elliott, Constanza Fierro
arXiv:2607.04801v2 Announce Type: replace
Abstract: Personalizing text-to-image diffusion models to render several specific subjects in a coherent image remains challenging: the model must preserve e...
By Marian Lupascu, Sebastian Ripa, Mihai Trascau, Mariana-Iuliana Georgescu, Ionut Mironica
arXiv:2605.19750v2 Announce Type: replace
Abstract: Visual autoregressive (VAR) models have recently emerged as an efficient paradigm for text-to-image generation, yet their personalization capabilit...
By Junhao Li, Xinhao Zhong, Yi sun, Yuxia Qiao, Bin Chen, Shu-Tao Xia, Yaowei Wang
arXiv:2606. 20077v1 Announce Type: cross Abstract: Visual tokens enter Large Language Models (LLMs) as raw, foreign signals.
By Wish Suharitdamrong, Tony Alex, Muhammad Awais, Sara Atito
Personalizing text-to-image diffusion models to render several specific subjects in a coherent image remains challenging: the model must preserve each subject's identity while keeping the scene spatially and visually coherent. Methods that fuse independently trained concept adapters in a shared weight space (via federated averaging, gradient fusion, or orthogonality constraints) suffer from identity confusion and style bleeding and require joint retraining.
HyperErase introduces a hypernetwork-based framework for concept erasure in text-to-image models, replacing static adapters with prompt-conditioned parameter synthesis. The method maps textual descriptions to LoRA updates, eliminating per-prompt gradient optimization and manual merging. A decoupled rectification strategy further stabilizes and refines the synthesized adapters, yielding improved erasure effectiveness, image quality, and semantic alignment across diverse concepts.
By Yi Sun, Xinhao Zhong, Zhiqi Zhang, Yimin Zhou, Junhao Li, Yuxia Qiao