Dual-Channel Attention Guidance for Training-Free Image Editing Control in Diffusion Transformers

TL;DR

This paper introduces a training-free dual-channel attention guidance method for diffusion transformers, leveraging both Key and Value spaces to improve image editing control and fidelity without additional training.

Contribution

It reveals the bias-delta structure in Key and Value projections and proposes a dual-channel manipulation framework for more precise image editing control.

Findings

Outperforms Key-only guidance across all fidelity metrics.

Significant improvements in localized editing tasks like object deletion and addition.

Demonstrates the effectiveness of dual-channel guidance on the PIE-Bench benchmark.

Abstract

Training-free control over editing intensity is a critical requirement for diffusion-based image editing models built on the Diffusion Transformer (DiT) architecture. Existing attention manipulation methods focus exclusively on the Key space to modulate attention routing, leaving the Value space -- which governs feature aggregation -- entirely unexploited. In this paper, we first reveal that both Key and Value projections in DiT's multi-modal attention layers exhibit a pronounced bias-delta structure, where token embeddings cluster tightly around a layer-specific bias vector. Building on this observation, we propose Dual-Channel Attention Guidance (DCAG), a training-free framework that simultaneously manipulates both the Key channel (controlling where to attend) and the Value channel (controlling what to aggregate). We provide a theoretical analysis showing that the Key channel operates through the nonlinear softmax function, acting as a coarse control knob, while the Value channel operates through linear weighted summation, serving as a fine-grained complement. Together, the two-dimensional parameter space $(\delta_k, \delta_v)$ enables more precise editing-fidelity trade-offs than any single-channel method. Extensive experiments on the PIE-Bench benchmark (700 images, 10 editing categories) demonstrate that DCAG consistently outperforms Key-only guidance across all fidelity metrics, with the most significant improvements observed in localized editing tasks such as object deletion (4.9% LPIPS reduction) and object addition (3.2% LPIPS reduction).