AutoAnchor: Stable Diffusion Unlearning Using Cross-Attention as a Manifold Surrogate

AutoAnchor is a new technique for removing unwanted capabilities from Stable Diffusion models by leveraging cross-attention mechanisms as a surrogate for the model's learned representations, enabling safer and more controllable generative AI systems. This research addresses the growing need for AI model governance and content safety in production environments where removing harmful behaviors or copyrighted training data is essential.

AutoAnchor is a new technique for removing unwanted capabilities from Stable Diffusion models by leveraging cross-attention mechanisms as a surrogate for the model's learned representations, enabling safer and more controllable generative AI systems. This research addresses the growing need for AI model governance and content safety in production environments where removing harmful behaviors or copyrighted training data is essential.

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