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arXiv · 2608.09057

Model the Edit, Not the Image: Visual Autoregressive Editing from a Source-Centric Perspective

Hongyi Fang·Chuwen Xie·Benjia Zhou·Yu-Xuan Qiu·Chenggong Hu·Zhibin Wang·Chao Chen·Jianbin Qin·Rui Mao
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Abstract

Next-scale visual autoregressive models (VARs) have emerged as a powerful generative paradigm, producing high-quality images through efficient coarse-to-fine prediction. However, their potential for text-guided image editing remains largely underexplored. Existing training-free VAR editing approaches often formulate editing as target-conditioned regeneration guided or constrained by the source image, and may rely on inversion, test-time optimization, attention control, or user-provided masks. This generation-centric formulation does not fully exploit the multiscale source representations provided by VARs and may introduce additional computation or intervention. We instead take a source-centric perspective on VAR editing, in which the encoded source image tokens serve as the primary visual state and the editing process focuses on condition-induced changes. Based on this perspective, we propose \textbf{EditMod}, which compares source- and target-conditioned predictions under a shared autoregressive context, treats their difference as a scale-wise editing direction, and applies it as a residual update to source tokens at selected scales. Experiments show that EditMod achieves leading source-image fidelity while maintaining strong text alignment, and completes end-to-end editing of a 1K image in only 1.57 seconds on a single A100 GPU without per-image preparation.

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Hongyi Fang, Chuwen Xie, Benjia Zhou, Yu-Xuan Qiu, Chenggong Hu, Zhibin Wang, Chao Chen, Jianbin Qin, Rui Mao. 2026-08-10. Model the Edit, Not the Image: Visual Autoregressive Editing from a Source-Centric Perspective. https://arxiv.org/abs/2608.09057

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