标题： DragFlow：基于区域监督的拖动编辑中DiT先验的释放 显示英文标题

标题： DragFlow: Unleashing DiT Priors with Region Based Supervision for Drag Editing

摘要： 基于拖动的图像编辑长期以来在目标区域遭受失真，这主要是因为早期基础模型Stable Diffusion的先验知识不足以将优化后的潜在表示投影到自然图像流形上。 随着从基于UNet的DDPMs转向更具可扩展性的DiT与流匹配（例如SD3.5、FLUX），生成先验知识变得显著更强，从而在各种编辑任务中取得了进展。 然而，基于拖动的编辑尚未受益于这些更强的先验知识。 本工作提出了第一个有效利用FLUX丰富先验的基于拖动的编辑框架，称为DragFlow，在基线上取得了显著提升。 我们首先表明，直接将基于点的拖动编辑应用于DiT效果不佳：与UNet的高度压缩特征不同，DiT特征结构不足，无法为逐点运动监督提供可靠指导。 为了克服这一限制，DragFlow引入了一种基于区域的编辑范式，其中仿射变换能够提供更丰富且一致的特征监督。 此外，我们集成了预训练的开放领域个性化适配器（例如IP-Adapter）以增强主体一致性，同时通过基于梯度掩码的硬约束保持背景保真度。 多模态大语言模型（MLLMs）进一步用于解决任务歧义。 在评估方面，我们整理了一个新的基于区域的拖动基准（ReD Bench），包含区域级别的拖动指令。 在DragBench-DR和ReD Bench上的大量实验表明，DragFlow超越了基于点和基于区域的基线，在基于拖动的图像编辑中达到了新的最先进水平。 代码和数据集将在发表后公开。 显示英文摘要

摘要： Drag-based image editing has long suffered from distortions in the target region, largely because the priors of earlier base models, Stable Diffusion, are insufficient to project optimized latents back onto the natural image manifold. With the shift from UNet-based DDPMs to more scalable DiT with flow matching (e.g., SD3.5, FLUX), generative priors have become significantly stronger, enabling advances across diverse editing tasks. However, drag-based editing has yet to benefit from these stronger priors. This work proposes the first framework to effectively harness FLUX's rich prior for drag-based editing, dubbed DragFlow, achieving substantial gains over baselines. We first show that directly applying point-based drag editing to DiTs performs poorly: unlike the highly compressed features of UNets, DiT features are insufficiently structured to provide reliable guidance for point-wise motion supervision. To overcome this limitation, DragFlow introduces a region-based editing paradigm, where affine transformations enable richer and more consistent feature supervision. Additionally, we integrate pretrained open-domain personalization adapters (e.g., IP-Adapter) to enhance subject consistency, while preserving background fidelity through gradient mask-based hard constraints. Multimodal large language models (MLLMs) are further employed to resolve task ambiguities. For evaluation, we curate a novel Region-based Dragging benchmark (ReD Bench) featuring region-level dragging instructions. Extensive experiments on DragBench-DR and ReD Bench show that DragFlow surpasses both point-based and region-based baselines, setting a new state-of-the-art in drag-based image editing. Code and datasets will be publicly available upon publication.