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arXiv Computer Vision By Chen Dai, Ganyu Zou, Nathan Self, Kevin Piper, Ramachandra Rao Seethiraju, Karthik Shyamsunder, Chang-Tien Lu, Naren Ramakrishnan

Compositional Concept Erasure in Text-to-Image Diffusion Models via Hierarchically Grounded Semantic Surgery

Read the original on arXiv Computer Vision →

The paper introduces Hierarchically Grounded Semantic Surgery (HGSS), a training‑free method for removing specific concepts from text‑to‑image diffusion models without altering model weights. HGSS uses hierarchical span grounding to accurately identify target spans and dynamic attribute binding to refine text conditioning during early denoising, thereby preventing routing failures on compositional prompts. Experiments on SEE and UnlearnCanvas show that HGSS significantly reduces hierarchical evasion and attribute leakage, outperforming existing erasure techniques.

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 Computer Vision.

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