Guiding Visual Autoregressive Models through Spectrum Weakening

Classifier-free guidance (CFG) has become a widely adopted and practical approach for enhancing generation quality and improving condition alignment. Recent studies have explored guidance mechanisms for unconditional generation, yet these approaches remain fundamentally tied to assumptions specific to diffusion models. In this work, we propose a spectrum weakening framework for visual autoregressive (AR) models. The method is training-free and condition-free by constructing a controllable weak model in the spectral domain without architectural modifications. We theoretically show that invertible spectral transformations preserve information, while selectively retaining only a subset of the spectrum introduces controlled information reduction. Based on this insight, we perform spectrum selection along the channel dimension of internal representations, which avoids the structural constraints imposed by diffusion models. We further introduce a spectrum-renormalization technique that maintains numerical stability during the weakening process. Comprehensive empirical and ablation studies confirm the effectiveness of our approach, including raster-scan, random-order, and scale-wise AR, spanning discrete and continuous modeling and covering class and text-condition scenarios, demonstrating high-quality unconditional generation and strong prompt alignment maintenance for conditional generation. Code will be made available.