Mechanistic Dissection of Cross-Attention Subspaces in Text-to-Image Diffusion Models

Abstract

Text-to-image diffusion models utilize cross-attention to integrate textual information into the visual latent space, yet the transformation from text embeddings to latent features remains largely unexplored. We provide a mechanistic analysis of the output-value (OV) circuits within cross-attention layers through spectral analysis via singular value decomposition. Our analysis reveals that semantic concepts are encoded in low-dimensional subspaces spanned by singular vectors in OV circuits across cross-attention heads. To verify this, we intervene on concept-related components in the diffusion process, demonstrating that intervention on identified spectral components affects conceptual changes. We further validate these findings by examining visual outputs of isolated subspaces and their alignment with text embedding space. Through this mechanistic understanding, we demonstrate that only nullifying these spectral components can achieve targeted concept removal with performance comparable to existing methods while providing interpretability. Our work reveals how cross-attention layers encode semantic concepts in spectral subspaces of OV circuits, providing mechanistic insights and enabling precise concept manipulation without retraining.