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.
Cite
@article{arxiv.2508.21090,
title = {Q-Align: Alleviating Attention Leakage in Zero-Shot Appearance Transfer via Query-Query Alignment},
author = {Namu Kim and Wonbin Kweon and Minsoo Kim and Hwanjo Yu},
journal= {arXiv preprint arXiv:2508.21090},
year = {2025}
}