# Q-Align: Alleviating Attention Leakage in Zero-Shot Appearance Transfer via Query-Query Alignment

## TL;DR

Q-Align introduces a novel Query-Query alignment method to reduce attention leakage in zero-shot appearance transfer, significantly improving semantic alignment and appearance fidelity in large-scale image generation models.

## Contribution

The paper proposes Q-Align, a new approach that uses Query-Query alignment, key-value rearrangement, and attention refinement to enhance zero-shot appearance transfer.

## Key findings

- Q-Align outperforms state-of-the-art methods in appearance fidelity.
- It effectively mitigates attention leakage in large-scale image generation.
- Q-Align maintains competitive structure preservation.

## Abstract

We observe that zero-shot appearance transfer with large-scale image generation models faces a significant challenge: Attention Leakage. This challenge arises when the semantic mapping between two images is captured by the Query-Key alignment. To tackle this issue, we introduce Q-Align, utilizing Query-Query alignment to mitigate attention leakage and improve the semantic alignment in zero-shot appearance transfer. Q-Align incorporates three core contributions: (1) Query-Query alignment, facilitating the sophisticated spatial semantic mapping between two images; (2) Key-Value rearrangement, enhancing feature correspondence through realignment; and (3) Attention refinement using rearranged keys and values to maintain semantic consistency. We validate the effectiveness of Q-Align through extensive experiments and analysis, and Q-Align outperforms state-of-the-art methods in appearance fidelity while maintaining competitive structure preservation.