G-STAR框架实现64倍压缩，加速图像生成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了G-STAR，一个用于压缩自回归图像生成模型的新型框架。该方法利用乘积量化实现显著的空间压缩（高达64倍），同时保持高质量的表示。G-STAR包含一个半自回归解码器，以有效地建模组内依赖关系，在多个文本到图像基准测试中取得了有竞争力的性能，并比之前的离散自回归模型快20倍。 AI

影响以显著降低的延迟和减少的计算需求实现高质量的自回归图像生成。

排序理由详细介绍图像生成压缩新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.CV TIER_1 English(EN) · Xu Huang, Ye Huang, Xiaotong Li, Zhengrong Yue, Jialv Zou, Minhao Jing, Haodong Li, Wenhao Yan, Yaxin Luo, Duomin Wang, Daquan Zhou · 2026-11-11 05:00

G-STAR：用于深度压缩自回归生成的组规模令牌容量

arXiv:2611.13004v1 Announce Type: new Abstract: Discrete autoregressive models offer a scalable framework for image generation. However, they face a fundamental trade-off: long token sequences preserve representation quality but slow generation, while aggressive spatial compressi…