Cross-attention maps serve as a tractable surrogate for manifold proximity, enabling automatic synthesis of anchors that suppress normal-space drift in diffusion unlearning.
The illusion of unlearning: The unstable nature of machine unlearning in text-to-image diffusion models
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TILDE derives a minimum-deviation, energy-tilted target distribution for concept unlearning in diffusion models and realizes it via residual ∇-GFlowNet training.
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AutoAnchor: Stable Diffusion Unlearning Using Cross-Attention as a Manifold Surrogate
Cross-attention maps serve as a tractable surrogate for manifold proximity, enabling automatic synthesis of anchors that suppress normal-space drift in diffusion unlearning.
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TILDE: TILt-based Distributional Erasure for Concept Unlearning
TILDE derives a minimum-deviation, energy-tilted target distribution for concept unlearning in diffusion models and realizes it via residual ∇-GFlowNet training.