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English(EN) ControlRef: Efficient Layout-Guided Multi-Instance Generation via Anchored 4D-RoPE

ControlRef 框架提升多实例图像生成效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出 ControlRef，一个旨在提高多模态扩散 Transformer (MM-DiTs) 中布局引导多实例图像生成效率和精度的框架。该系统通过采用统一实例-布局控制 (UILC) 注意力掩码和新颖的锚定 4D-RoPE 位置编码，解决了先前方法如高计算成本和空间-频率折衷等局限性。实验表明，ControlRef 在保持最先进的视觉保真度和定位精度的同时，显著降低了推理延迟和内存开销。 AI

影响 引入了一种更高效的可控图像合成方法，有望加速生成式 AI 应用的工作流程。

排序理由 该集群包含一篇详细介绍新图像生成方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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

ControlRef 框架提升多实例图像生成效率

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该集群包含一篇详细介绍新图像生成方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yunkai Yang, Yudong Zhang, Xinying Chen, Haoyuan Liang, Yizhuo Niu, Jinshuai Cheng, Kunquan Zhang, Liziyue Fang, Weitao Wan, Runmin Dong ·

    ControlRef: 通过锚定4D-RoPE实现高效的布局引导多实例生成

    arXiv:2608.06878v1 Announce Type: new Abstract: Layout-guided multi-instance generation is essential for controllable image synthesis in Multi-Modal Diffusion Transformers (MM-DiTs). However, integrating this capability into unified architectures remains challenging. Prior framew…

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