ConceptAttention: Diffusion Transformers Learn Highly Interpretable
  Features
Authors: name: Alec Helbling; name: Tuna Han Salih Meral; name: Ben Hoover; name: Pinar Yanardag; name: Duen Horng Chau
Published: 2025-02-06 18:59:00

Abstract: Do the rich representations of multi-modal diffusion transformers (DiTs)
exhibit unique properties that enhance their interpretability? We introduce
ConceptAttention, a novel method that leverages the expressive power of DiT
attention layers to generate high-quality saliency maps that precisely locate
textual concepts within images. Without requiring additional training,
ConceptAttention repurposes the parameters of DiT attention layers to produce
highly contextualized concept embeddings, contributing the major discovery that
performing linear projections in the output space of DiT attention layers
yields significantly sharper saliency maps compared to commonly used
cross-attention mechanisms. Remarkably, ConceptAttention even achieves
state-of-the-art performance on zero-shot image segmentation benchmarks,
outperforming 11 other zero-shot interpretability methods on the
ImageNet-Segmentation dataset and on a single-class subset of PascalVOC. Our
work contributes the first evidence that the representations of multi-modal DiT
models like Flux are highly transferable to vision tasks like segmentation,
even outperforming multi-modal foundation models like CLIP.