Logit Refiner: Improving Visual Autoregressive Models via Intra-Scale Dependency Modeling

• URL: http://arxiv.org/abs/2609.11804v1
• Date: Thu, 10 Sep 2026 16:57:05 GMT
• Title: Logit Refiner: Improving Visual Autoregressive Models via Intra-Scale Dependency Modeling
• Abstract summary: Visual Autoregressive Models generate images through next-scale prediction.<n>Mean-field approximation discards spatial dependencies among same-scale tokens.<n>Logit Refiner restores intra-scale dependencies by sequentially sampling tokens conditioned on frozen backbone features.
• Score: 21.906953897227442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Autoregressive Models (VAR) generate images through next-scale prediction, producing all tokens within each scale in parallel. We show that this parallel decoding constitutes a mean-field-style approximation that discards spatial dependencies among same-scale tokens, causing locally incoherent samples regardless of backbone capacity -- a limitation of the decoding rule. Addressing this limitation, we introduce the Logit Refiner, a lightweight autoregressive module that restores intra-scale dependencies by sequentially sampling tokens conditioned on frozen backbone features. Adding only ~10% parameters and less than 5% of the base model's training compute, it plugs into any pretrained VAR checkpoint without retraining. Controlled ablations isolate joint intra-scale sampling -- rather than additional capacity or training -- as the critical ingredient. Across backbones from 310M to 2B parameters on class-conditional ImageNet 256x256, the refiner consistently improves generation quality, enabling a 1.1B-parameter model to surpass one twice its size. The approach further generalizes to text-to-image generation, confirming that the mean-field bottleneck persists across VAR variants and is effectively alleviated by our method. Project page: https://compvis.github.io/logit-refiner/

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