collaborators

7 papers

cs.CV2026

Z-Erase: Enabling Concept Erasure in Single-Stream Diffusion Transformers

Nanxiang Jiang, Zhaoxin Fan, Baisen Wang +8

Concept erasure serves as a vital safety mechanism for removing unwanted concepts from text-to-image (T2I) models. While extensively studied in U-Net and dual-stream architectures…

cs.CV2026

EraseAnything++: Enabling Concept Erasure in Rectified Flow Transformers Leveraging Multi-Object Optimization

Zhaoxin Fan, Nanxiang Jiang, Daiheng Gao +2

Removing undesired concepts from large-scale text-to-image (T2I) and text-to-video (T2V) diffusion models while preserving overall generative quality remains a major challenge, par…

cs.CV2025

Erased, But Not Forgotten: Erased Rectified Flow Transformers Still Remain Unsafe Under Concept Attack

Nanxiang Jiang, Zhaoxin Fan, Enhan Kang +6

Recent advances in text-to-image (T2I) diffusion models have enabled impressive generative capabilities, but they also raise significant safety concerns due to the potential to pro…

cs.LG2025

Revoking Amnesia: RL-based Trajectory Optimization to Resurrect Erased Concepts in Diffusion Models

Daiheng Gao, Nanxiang Jiang, Andi Zhang +5

Concept erasure techniques have been widely deployed in T2I diffusion models to prevent inappropriate content generation for safety and copyright considerations. However, as models…

cs.CV2025

Beyond Sliders: Mastering the Art of Diffusion-based Image Manipulation

Yufei Tang, Daiheng Gao, Pingyu Wu +3

In the realm of image generation, the quest for realism and customization has never been more pressing. While existing methods like concept sliders have made strides, they often fa…

cs.CV2025

Undress to Redress: A Training-Free Framework for Virtual Try-On

Zhiying Li, Junhao Wu, Yeying Jin +9

Virtual try-on (VTON) is a crucial task for enhancing user experience in online shopping by generating realistic garment previews on personal photos. Although existing methods have…

cs.CL2025

MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG

Pingyu Wu, Daiheng Gao, Jing Tang +4

Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by using external knowledge, but it struggles with precise entity information retrieval. In this paper, w…