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English(EN) IS-Diff: Improving Diffusion-Based Inpainting with Better Initial Seed

IS-Diff 通过更好的初始种子改进扩散图像修复

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的免训练方法 IS-Diff，用于增强基于扩散的图像修复。该方法解决了标准扩散模型中随机初始化种子可能导致蒙版区域语义不匹配的问题。IS-Diff 利用从蒙版区域采样的初始种子来更好地模仿蒙版数据分布，从而促进更和谐的修复结果。还包括一个动态选择性细化机制，用于在扩散过程中调整初始化先验，提高一致性和连贯性。 AI

影响 通过提高蒙版区域的语义一致性来增强图像修复技术。

排序理由 介绍一种新的图像修复方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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IS-Diff 通过更好的初始种子改进扩散图像修复

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介绍一种新的图像修复方法的论文。[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) · Yongzhe Lyu, Yu Wu, Yutian Lin, Bo Du ·

    IS-Diff：通过改进初始种子来提升基于扩散的图像修复效果

    arXiv:2509.11638v2 Announce Type: replace Abstract: Diffusion models have shown promising results in free-form inpainting. Recent studies based on refined diffusion samplers or novel architectural designs led to realistic results and high data consistency. However, random initial…

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