Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps

TL;DR

This paper identifies limitations in current text-to-image models related to syntactic and attribute binding issues and proposes a test-time optimization method to improve semantic alignment by leveraging text attention maps.

Contribution

It introduces a novel approach that transfers syntactic relations from text attention maps to the cross-attention module via test-time optimization, enhancing image-text alignment.

Findings

Improved semantic alignment in generated images.

Better attribute and object binding accuracy.

Enhanced model robustness across diverse prompts.

Abstract

In text-to-image diffusion models, the cross-attention map of each text token indicates the specific image regions attended. Comparing these maps of syntactically related tokens provides insights into how well the generated image reflects the text prompt. For example, in the prompt, "a black car and a white clock", the cross-attention maps for "black" and "car" should focus on overlapping regions to depict a black car, while "car" and "clock" should not. Incorrect overlapping in the maps generally produces generation flaws such as missing objects and incorrect attribute binding. Our study makes the key observations investigating this issue in the existing text-to-image models:(1) the similarity in text embeddings between different tokens -- used as conditioning inputs -- can cause their cross-attention maps to focus on the same image regions; and (2) text embeddings often fail to faithfully capture syntactic relations already within text attention maps. As a result, such syntactic relationships can be overlooked in cross-attention module, leading to inaccurate image generation. To address this, we propose a method that directly transfers syntactic relations from the text attention maps to the cross-attention module via a test-time optimization. Our approach leverages this inherent yet unexploited information within text attention maps to enhance image-text semantic alignment across diverse prompts, without relying on external guidance.