让图像生成更准确：通过语义锚定引导信号，解决渐进式生成中的信息错位问题。
Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance
- 用注意力动态加权，将生成引导信号锚定在重要语义片段上。
- 在图文生成和类别条件生成中均提升图像清晰度与语义一致性。
- 适合关注生成质量、追求视觉连贯性的研究者和开发者。
基于逐尺度预测的自回归（AR）模型已成为强大的图像生成工具，但其面临一个关键缺陷：随着分辨率逐步提升，不同图像块间的时间步信息不一致，导致引导信号分散，偏离图像显著区域，使生成过程产生模糊、失真的特征。本文提出信息锚定引导（IGG）框架，通过基于注意力的动态加权机制，将引导信号锚定在具有语义重要性的标记上，确保引导与语义内容紧密对齐。在类别条件生成和文本到图像生成任务中，IGG均生成了更清晰、更连贯且语义准确的图像，有效提升了基于AR方法的生成质量。代码已开源于 https://github.com/dnngky/infoground-guidance。
原文摘要 · Abstract (English)
Autoregressive (AR) models based on next-scale prediction have emerged as a powerful tool for image generation, but they face a critical weakness: information inconsistencies between patches across timesteps introduced by progressive resolution scaling. These inconsistencies scatter guidance signals, causing them to drift away from salient regions within the image and leaving behind ambiguous, unfaithful features during sampling. We tackle this challenge with Information-Grounding Guidance (IGG), a novel framework that anchors guidance to semantically important tokens via an attention-based dynamic weighting formulation, consequently ensuring that guidance and semantic contents remain tightly aligned. Across both class-conditioned and text-to-image generation tasks, IGG delivers sharper, more coherent, and semantically grounded images, demonstrating its efficacy for correcting AR-based methods. Our code is available at https://github.com/dnngky/infoground-guidance.
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