Inference-Time Scaling for Visual AutoRegressive modeling by Searching Representative Samples

TL;DR

This paper introduces VAR-Scaling, a novel inference-time scaling framework for visual autoregressive models that uses kernel density estimation and hybrid sampling to improve output quality and diversity.

Contribution

It pioneers inference-time scaling for VQ-based visual autoregressive models by mapping discrete spaces to quasi-continuous ones and employing a density-adaptive hybrid sampling strategy.

Findings

Significant quality improvements in class-conditional and text-to-image tasks.

Effective navigation of sampling distributions via KDE-based mapping.

Enhanced output diversity and fidelity through hybrid sampling.

Abstract

While inference-time scaling has significantly enhanced generative quality in large language and diffusion models, its application to vector-quantized (VQ) visual autoregressive modeling (VAR) remains unexplored. We introduce VAR-Scaling, the first general framework for inference-time scaling in VAR, addressing the critical challenge of discrete latent spaces that prohibit continuous path search. We find that VAR scales exhibit two distinct pattern types: general patterns and specific patterns, where later-stage specific patterns conditionally optimize early-stage general patterns. To overcome the discrete latent space barrier in VQ models, we map sampling spaces to quasi-continuous feature spaces via kernel density estimation (KDE), where high-density samples approximate stable, high-quality solutions. This transformation enables effective navigation of sampling distributions. We propose a density-adaptive hybrid sampling strategy: Top-k sampling focuses on high-density regions to preserve quality near distribution modes, while Random-k sampling explores low-density areas to maintain diversity and prevent premature convergence. Consequently, VAR-Scaling optimizes sample fidelity at critical scales to enhance output quality. Experiments in class-conditional and text-to-image evaluations demonstrate significant improvements in inference process. The code is available at https://github.com/WD7ang/VAR-Scaling.