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

• URL: http://arxiv.org/abs/2608.09057v2
• Date: Thu, 13 Aug 2026 16:50:15 GMT
• Title: Model the Edit, Not the Image: Visual Autoregressive Editing from a Source-Centric Perspective
• Abstract summary: Next-scale visual autoregressive models ( VARs) have emerged as a powerful generative paradigm, producing high-quality images through efficient coarse-to-fine prediction.<n>We propose textbfEditMod, which compares source- and target-conditioned predictions under a shared autoregressive context.<n>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.
• Score: 23.408232115772833
• License: http://creativecommons.org/licenses/by/4.0/
• 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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