Adversarial Error Correction for Visual Autoregressive Generation

• URL: http://arxiv.org/abs/2605.24843v1
• Date: Sun, 24 May 2026 03:22:22 GMT
• Title: Adversarial Error Correction for Visual Autoregressive Generation
• Abstract summary: We propose a plug-and-play framework that enhances pre-trained Visual Autoregressive (or VAR) models.<n>Instead of a standard passive generation, AID-more introduces a proactive error-correction mechanism inspired by the adversarial feedback in GANs.<n>Results demonstrate that AID-more delivers sharper textural details and fewer structural distortions with negligible overhead.
• Score: 32.13829630849993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Autoregressive (VAR) models have emerged as a powerful paradigm for image synthesis by performing hierarchical next-scale prediction. However, VAR models are inherently prone to cascading error propagation, where subtle coarse-scale mispredictions are amplified across the hierarchy, ultimately distorting the final synthesis. To mitigate this, we propose AID-VAR, a plug-and-play framework that enhances pre-trained VARs through Adversarially Injected Diagnosis. Instead of a standard passive generation, AID-VAR introduces a proactive error-correction mechanism inspired by the adversarial feedback in GANs. We deploy a discriminator to diagnose fidelity gaps at each scale transition, coupled with a lightweight guidance injector. This module operates as a non-invasive adapter that refines the feature manifold of a frozen VAR backbone, effectively steering the generation toward the distribution of real images without destabilizing the pre-trained latent space. Furthermore, to rigorously evaluate this cross-scale progression, we introduce the Inter-Scale Consistency Score (ISCS), a novel metric that quantifies the fidelity and structural alignment between consecutive resolution scales. Experimental results across various backbones demonstrate that AID-VAR delivers sharper textural details and fewer structural distortions with negligible overhead. For instance, AID-VAR-d20 achieves a 16% improvement in FID with only a 3% increase in parameters. These results establish AID-VAR as a highly efficient and scalable pathway for upgrading large-scale VAR generators, enhancing global coherence and local detail without altering training data, base architectures, or sampling schedules. Code is available at https://github.com/bijiw515/AID-VAR.

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