EraseSAE: Surgical Concept Erasure in Text-to-Video Diffusion Models via Sparse Autoencoders
This work proposes EraseSAE, a novel framework that leverages sparse autoencoders to achieve surgical concept erasure in DiT-based T2V diffusion models via a principled decompose-attribute-erase pipeline, and introduces the Partitioned Convolutional Sparse Autoencoder.
Xing-Hao Wang, Dong Li, Wei Yu et al.
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