AlignVAR: Towards Globally Consistent Visual Autoregression for Image Super-Resolution

• URL: http://arxiv.org/abs/2603.00589v2
• Date: Thu, 05 Mar 2026 05:53:46 GMT
• Title: AlignVAR: Towards Globally Consistent Visual Autoregression for Image Super-Resolution
• Authors: Cencen Liu, Dongyang Zhang, Wen Yin, Jielei Wang, Tianyu Li, Ji Guo, Wenbo Jiang, Guoqing Wang, Guoming Lu,
• Abstract summary: Visual autoregressive models offer stable training, non-iterative inference, and high-fidelity synthesis through next-scale prediction.<n>But their application remains underexplored and faces two critical challenges: locality-biased attention, and residual-only supervision.<n>We propose a globally consistent visual autoregressive framework tailored for image super-resolution.
• Score: 16.90182090355781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual autoregressive (VAR) models have recently emerged as a promising alternative for image generation, offering stable training, non-iterative inference, and high-fidelity synthesis through next-scale prediction. This encourages the exploration of VAR for image super-resolution (ISR), yet its application remains underexplored and faces two critical challenges: locality-biased attention, which fragments spatial structures, and residual-only supervision, which accumulates errors across scales, severely compromises global consistency of reconstructed images. To address these issues, we propose AlignVAR, a globally consistent visual autoregressive framework tailored for ISR, featuring two key components: (1) Spatial Consistency Autoregression (SCA), which applies an adaptive mask to reweight attention toward structurally correlated regions, thereby mitigating excessive locality and enhancing long-range dependencies; and (2) Hierarchical Consistency Constraint (HCC), which augments residual learning with full reconstruction supervision at each scale, exposing accumulated deviations early and stabilizing the coarse-to-fine refinement process. Extensive experiments demonstrate that AlignVAR consistently enhances structural coherence and perceptual fidelity over existing generative methods, while delivering over 10x faster inference with nearly 50% fewer parameters than leading diffusion-based approaches, establishing a new paradigm for efficient ISR.

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