HierEdit: 基于区域的分层扩散用于高效的高分辨率编辑
HierEdit: Region-Aware Hierarchical Diffusion for Efficient High-Resolution Editing
AI总结：
本文提出HierEdit，一种区域感知的分层扩散框架，用于高效可扩展的高分辨率图像编辑。通过低分辨率代理生成参考并定位修改区域，结合分层局部窗口扩散模型和推理加速机制，实现无需高分辨率训练数据的快速高保真编辑。
摘要：
高分辨率图像编辑对于专业和创意应用至关重要，但现有的多模态扩散编辑器在计算效率上仍然不足，并且受限于相对较低的分辨率。当前方法要么冗余处理整个图像画布，要么依赖大规模高分辨率数据集，导致显著的训练和推理成本。我们引入HierEdit，一种区域感知的分层扩散框架，专门用于高效且可扩展的高分辨率图像编辑。我们的方法首先使用现成的编辑模型在低分辨率代理上进行编辑，生成参考并定位修改区域。一个分层局部窗口扩散模型（Local-Window MMDiT）仅在原始高分辨率图像中细化编辑区域，同时重用未修改的区域作为条件输入。低分辨率代理进一步提供结构指导和中间去噪监督（Inference Acceleration），确保一致的全局语义和稳定的生成，而无需完整的高分辨率注意力计算。这种针对性和分层的设计使图像编辑能够快速、高质量地达到4K分辨率，而无需任何专门的高分辨率训练数据。广泛的实验表明，HierEdit在商用分辨率数据集上实现了竞争性的视觉质量，同时显著加速了推理过程，并无缝扩展到超高清的4K编辑。请查看我们的项目页面：https://peteryyzhang.github.io/HierEdit-page/
英文摘要：
High-resolution image editing is essential for professional and creative applications, yet existing multimodal diffusion-based editors remain computationally inefficient and constrained to relatively low resolutions. Current approaches redundantly process the entire image canvas or rely on large-scale high-resolution datasets, resulting in substantial training and inference costs. We introduce HierEdit, a region-aware hierarchical diffusion framework designed for efficient and scalable high-resolution image editing. Our method first performs edits on a low-resolution proxy using an off-the-shelf editing model to generate a reference and to localize the modified regions. A hierarchical local-window diffusion model (\textbf{Local-Window MMDiT}) that refines only edited regions within the original high-res image, while reusing the unaltered regions as conditioning inputs. The low-resolution proxy further provides structural guidance and intermediate denoising supervision (\textbf{Inference Acceleration}) , ensuring consistent global semantics and stable generation without the need for full-resolution attention computation. This targeted and hierarchical design enables fast, high-fidelity editing of images up to 4K resolution without any specialized high-resolution training data. Extensive experiments demonstrate that HierEdit achieves competitive visual quality on commodity-resolution datasets while significantly accelerating inference and extending seamlessly to ultra-high-resolution 4K editing. Please check our {\href{https://peteryyzhang.github.io/HierEdit-page/}{\textbf{Project Page}}}.
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。
