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English(EN) AsyncPatch Diffusion: spatially-flexible image generation

AsyncPatch Diffusion 实现空间灵活的图像生成

研究人员开发了 AsyncPatch Diffusion,一种新颖的图像生成框架,它允许图像的不同空间区域在不同的时间表上进行去噪。与标准的扩散模型相比,这种方法能够实现更灵活和空间自适应的生成。该方法在 ImageNet 和 LSUN 等基准测试中取得了具有竞争力的生成质量,并且在不进行特定任务微调的情况下,对于图像修复任务特别有效。 AI

影响 引入了一种新的扩散模型技术,提高了图像修复和空间自适应生成的能力。

排序理由 该集群包含一篇详细介绍新图像生成方法的学术论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

AsyncPatch Diffusion 实现空间灵活的图像生成

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该集群包含一篇详细介绍新图像生成方法的学术论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Samuele Papa, Valentin De Bortoli, Guillaume Couairon, Daniel S\'ykora, Romuald Elie, Klaus Greff ·

    AsyncPatch Diffusion:空间灵活的图像生成

    arXiv:2606.07079v1 Announce Type: new Abstract: Standard diffusion models corrupt an entire sample with a single shared noise level, forcing all spatial regions to follow the same denoising trajectory. We introduce AsyncPatch Diffusion, a joint-diffusion framework that assigns di…

  2. arXiv cs.CV TIER_1 English(EN) · Klaus Greff ·

    AsyncPatch Diffusion:空间灵活的图像生成

    Standard diffusion models corrupt an entire sample with a single shared noise level, forcing all spatial regions to follow the same denoising trajectory. We introduce AsyncPatch Diffusion, a joint-diffusion framework that assigns distinct noise levels to different input dimension…