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English(EN) InstancePin: Instance-Addressable Layout-to-Image Diffusion via Coordinate Pinning

InstancePin 扩散模型增强了实例级图像生成控制

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 InstancePin，一个新颖的扩散模型框架，旨在提高从布局输入生成图像时的实例级控制能力。与之前的类别对齐模型不同，InstancePin 将每个对象实例显式地锚定在坐标标记上，从而能够对单个对象进行更精细的控制，尤其是在城市环境等密集场景中。该框架使用实例感知适配器和坐标固定注意力来保持全局语义一致性，同时优化局部实例细节，实验表明其保真度有所提高，实例纠缠有所减少。 AI

影响 提高了生成模型中的细粒度控制能力，可能增强复杂场景合成的真实感。

排序理由 该集群包含一篇详细介绍扩散模型新方法的论文。[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 →

InstancePin 扩散模型增强了实例级图像生成控制

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该集群包含一篇详细介绍扩散模型新方法的论文。[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) · Chaoyue Wu, Yunfei Zhang, Si Wu ·

    InstancePin: 通过坐标固定实现实例可寻址的布局到图像扩散

    arXiv:2608.00588v1 Announce Type: new Abstract: Layout-to-image diffusion models have achieved impressive semantic controllability by conditioning generation on category-level segmentation maps. However, such category-aligned control is not necessarily instance-addressable: multi…

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