Not All Tokens are Guided Equal: Improving Guidance in Visual Autoregressive Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915527295762432 |
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| author | Nguyen, Ky Dan Tran, Hoang Lam Dinh, Anh-Dung Liu, Daochang Cai, Weidong Wang, Xiuying Xu, Chang |
| author_facet | Nguyen, Ky Dan Tran, Hoang Lam Dinh, Anh-Dung Liu, Daochang Cai, Weidong Wang, Xiuying Xu, Chang |
| contents | Autoregressive (AR) models based on next-scale prediction are rapidly emerging 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 conditioning information and leaving behind ambiguous, unfaithful features. We tackle this challenge with Information-Grounding Guidance (IGG), a novel mechanism that anchors guidance to semantically important regions through attention. By adaptively reinforcing informative patches during sampling, IGG ensures that guidance and content remain tightly aligned. Across both class-conditioned and text-to-image generation tasks, IGG delivers sharper, more coherent, and semantically grounded images, setting a new benchmark for AR-based methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23876 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Not All Tokens are Guided Equal: Improving Guidance in Visual Autoregressive Models Nguyen, Ky Dan Tran, Hoang Lam Dinh, Anh-Dung Liu, Daochang Cai, Weidong Wang, Xiuying Xu, Chang Computer Vision and Pattern Recognition Artificial Intelligence Autoregressive (AR) models based on next-scale prediction are rapidly emerging 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 conditioning information and leaving behind ambiguous, unfaithful features. We tackle this challenge with Information-Grounding Guidance (IGG), a novel mechanism that anchors guidance to semantically important regions through attention. By adaptively reinforcing informative patches during sampling, IGG ensures that guidance and content remain tightly aligned. Across both class-conditioned and text-to-image generation tasks, IGG delivers sharper, more coherent, and semantically grounded images, setting a new benchmark for AR-based methods. |
| title | Not All Tokens are Guided Equal: Improving Guidance in Visual Autoregressive Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.23876 |