Group Relative Attention Guidance for Image Editing
Authors: name: Xuanpu Zhang; name: Xuesong Niu; name: Ruidong Chen; name: Dan Song; name: Jianhao Zeng; name: Penghui Du; name: Haoxiang Cao; name: Kai Wu; name: An-an Liu
Published: 2025-10-28 17:22:44

Abstract: Recently, image editing based on Diffusion-in-Transformer models has
undergone rapid development. However, existing editing methods often lack
effective control over the degree of editing, limiting their ability to achieve
more customized results. To address this limitation, we investigate the
MM-Attention mechanism within the DiT model and observe that the Query and Key
tokens share a bias vector that is only layer-dependent. We interpret this bias
as representing the model's inherent editing behavior, while the delta between
each token and its corresponding bias encodes the content-specific editing
signals. Based on this insight, we propose Group Relative Attention Guidance, a
simple yet effective method that reweights the delta values of different tokens
to modulate the focus of the model on the input image relative to the editing
instruction, enabling continuous and fine-grained control over editing
intensity without any tuning. Extensive experiments conducted on existing image
editing frameworks demonstrate that GRAG can be integrated with as few as four
lines of code, consistently enhancing editing quality. Moreover, compared to
the commonly used Classifier-Free Guidance, GRAG achieves smoother and more
precise control over the degree of editing. Our code will be released at
https://github.com/little-misfit/GRAG-Image-Editing.