HierEdit：面向高效高分辨率编辑的区域感知分层扩散模型
HierEdit: Region-Aware Hierarchical Diffusion for Efficient High-Resolution Editing
把高分辨率编辑拆成「低分辨率代理定位 + 局部窗口 MMDiT 精修」两级流程，只对改动区域做全分辨率去噪、未改动区域作为条件复用，可直接迁移到 4K 修复/重绘等需要避免全图注意力开销的编辑任务。
原文依据 A hierarchical local-window diffusion model (Local-Window MMDiT) that refines only edited regions within the original high-resolution image
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论高分辨率图像编辑对专业与创意应用至关重要，然而现有多模态扩散编辑器计算效率低下，且被限制在相对较低的分辨率上。当前方法要么冗余地处理整幅图像画布，要么依赖大规模高分辨率数据集，导致训练与推理成本高昂。我们提出 HierEdit，一个区域感知的分层扩散框架，旨在实现高效且可扩展的高分辨率图像编辑。该方法首先借助现成的编辑模型在低分辨率代理图像上执行编辑，以生成参考结果并定位被修改的区域。随后，一个分层局部窗口扩散模型（Local-Window MMDiT）仅对原始高分辨率图像中被编辑的区域进行精修，同时将未改动区域作为条件输入复用。低分辨率代理还进一步提供结构引导与中间去噪监督（推理加速），从而在无需全分辨率注意力计算的情况下保证全局语义一致与稳定生成。这种有针对性的分层设计使得无需任何专门的高分辨率训练数据，即可对高达 4K 分辨率的图像进行快速、高保真的编辑。大量实验表明，HierEdit 在常规分辨率数据集上取得了有竞争力的视觉质量，同时显著加速推理，并可无缝扩展到超高清 4K 编辑。
英文原文摘要 1533 字符
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 (**Local-Window MMDiT**) that refines only edited regions within the original high-resolution image, while reusing the unaltered regions as conditioning inputs. The low-resolution proxy further provides structural guidance and intermediate denoising supervision (**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 requiring 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.