arXiv AI By Yang Xiao, Kaiyuan Deng, Gen Li, Zinan Ling, Zhiwen Fan, Xiaolong Ma, Bo Hui

Compositional Concepts Erasing: Disentangling Object-Style via Domain Manifold Unlearning

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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
arXiv Computer Vision
5d ago

Differential Vector Erasure: Unified Training-Free Concept Erasure for Flow Matching Models

The paper introduces Differential Vector Erasure (DVE), a training‑free method for removing unwanted concepts from flow matching image generation models. DVE exploits the directional structure of the velocity field that encodes semantic concepts, constructing a differential vector field to isolate and suppress target concepts during inference. Experiments on the FLUX model show that DVE outperforms existing baselines in tasks such as NSFW suppression, style removal, and object erasure while maintaining image quality and diversity.

By Zhiqi Zhang, Xinhao Zhong, Yi Sun, Shuoyang Sun, Jiahui Wang, Bin Chen, Shu-Tao Xia, Xuan Wang