Black Hole-Driven Identity Absorbing in Diffusion Models

Abstract

Recent advances in diffusion models have positioned them as powerful generative frameworks for high-resolution image synthesis across diverse domains. The emerging "h-space" within these models, defined by bottleneck activations in the denoiser, offers promising pathways for semantic image editing similar to GAN latent spaces. However, as demand grows for content erasure and concept removal, privacy concerns highlight the need for identity disentanglement in the latent space of diffusion models. The high-dimensional latent space poses challenges for identity removal, as traversing with random or orthogonal directions often leads to semantically unvalidated regions, resulting in unrealistic outputs. To address these issues, we propose Black Hole-Driven Identity Absorption (BIA), a novel approach for identity erasure within the latent space of diffusion models. BIA uses a "black hole" metaphor, where the latent region representing a specified identity acts as an attractor, drawing in nearby latent points of surrounding identities to "wrap" the black hole. Instead of relying on random traversals for optimization, BIA employs an identity absorption mechanism by attracting and wrapping nearby validated latent points associated with other identities to achieve a vanishing effect for specified identity. Our method effectively prevents the generation of a specified identity while preserving other attributes, as validated by improved scores on identity similarity (SID), FID metrics, qualitative evaluations, and user studies as compared to SOTA.