低分辨率编辑足以实现高分辨率编辑
Low-Resolution Editing is All You Need for High-Resolution Editing
AI总结：
本文提出一种测试时优化框架，通过分块优化、细节迁移和同步策略，实现高分辨率图像编辑。
摘要：
高分辨率内容创作正迅速成为视觉和图形社区的核心挑战。图像是视觉表达最基本的形式，符合用户意图的内容生成需要有效、可控的高分辨率图像编辑机制。然而，现有方法仍局限于低分辨率设置，通常仅支持最高1K分辨率。本文提出高分辨率图像编辑任务，并引入一种测试时优化框架来解决该问题。我们的方法对高分辨率源图像进行分块优化，随后采用细粒度细节迁移模块和一种新颖的同步策略来保持块间一致性。大量实验表明，我们的方法能够产生高质量编辑结果，促进高分辨率内容创作。
英文摘要：
High-resolution content creation is rapidly emerging as a central challenge in both the vision and graphics communities. Images serve as the most fundamental modality for visual expression, and content generation that aligns with the user intent requires effective, controllable high-resolution image manipulation mechanisms. However, existing approaches remain limited to low-resolution settings, typically supporting only up to 1K resolution. In this work, we introduce the task of high-resolution image editing and propose a test-time optimization framework to address it. Our method performs patch-wise optimization on high-resolution source images, followed by a fine-grained detail transfer module and a novel synchronization strategy to maintain consistency across patches. Extensive experiments show that our method produces high-quality edits, facilitating high-resolution content creation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
