Rethinking Structure Preservation in Text-Guided Image Editing with Visual Autoregressive Models

TL;DR

This paper introduces a novel text-guided image editing framework using visual autoregressive models that improves structural consistency and editing fidelity through a coarse-to-fine localization and adaptive feature injection.

Contribution

The work presents a new approach that enhances structure preservation and editing accuracy in VAR-based image editing by analyzing intermediate features and employing reinforcement learning for feature injection.

Findings

Achieves superior structural consistency compared to state-of-the-art methods.

Balances editing fidelity and background preservation effectively.

Demonstrates improved results in both local and global editing scenarios.

Abstract

Visual autoregressive (VAR) models have recently emerged as a promising family of generative models, enabling a wide range of downstream vision tasks such as text-guided image editing. By shifting the editing paradigm from noise manipulation in diffusion-based methods to token-level operations, VAR-based approaches achieve better background preservation and significantly faster inference. However, existing VAR-based editing methods still face two key challenges: accurately localizing editable tokens and maintaining structural consistency in the edited results. In this work, we propose a novel text-guided image editing framework rooted in an analysis of intermediate feature distributions within VAR models. First, we introduce a coarse-to-fine token localization strategy that can refine editable regions, balancing editing fidelity and background preservation. Second, we analyze the intermediate representations of VAR models and identify structure-related features, by which we design a simple yet effective feature injection mechanism to enhance structural consistency between the edited and source images. Third, we develop a reinforcement learning-based adaptive feature injection scheme that automatically learns scale- and layer-specific injection ratios to jointly optimize editing fidelity and structure preservation. Extensive experiments demonstrate that our method achieves superior structural consistency and editing quality compared with state-of-the-art approaches, across both local and global editing scenarios.