Abstract
The reliance on implicit point matching via attention has become a core bottleneck in drag-based editing, resulting in a fundamental compromise on weakened inversion strength and costly test-time optimization (TTO). This compromise severely limits the generative capabilities of diffusion models, suppressing high-fidelity inpainting and text-guided creation. In this paper, we introduce LazyDrag, the first drag-based image editing method for Multi-Modal Diffusion Transformers, which directly eliminates the reliance on implicit point matching. In concrete terms, our method generates an explicit correspondence map from user drag inputs as a reliable reference to boost the attention control. This reliable reference opens the potential for a stable full-strength inversion process, which is the first in the drag-based editing task. It obviates the necessity for TTO and unlocks the generative capability of models. Therefore, LazyDrag naturally unifies precise geometric control with text guidance, enabling complex edits that were previously out of reach: opening the mouth of a dog and inpainting its interior, generating new objects like a ``tennis ball'', or for ambiguous drags, making context-aware changes like moving a hand into a pocket. Additionally, LazyDrag supports multi-round workflows with simultaneous move and scale operations. Evaluated on the DragBench, our method outperforms baselines in drag accuracy and perceptual quality, as validated by VIEScore and human evaluation. LazyDrag not only establishes new state-of-the-art performance, but also paves a new way to editing paradigms.
Abstract (translated)
依赖于注意力机制的隐式点匹配已成为基于拖拽编辑的核心瓶颈，导致在反转强度减弱和测试时间优化（TTO）成本高昂之间做出了根本性的妥协。这种妥协严重限制了扩散模型的生成能力，抑制了高保真度的修复和文本引导创作。在这篇论文中，我们引入了LazyDrag，这是首个用于多模态扩散变压器的基于拖拽图像编辑方法，它直接消除了对隐式点匹配的依赖。具体来说，我们的方法从用户拖拽输入生成一个显式的对应关系图作为可靠的参考以增强注意力控制。这个可靠参考开启了稳定全强度反转过程的可能性，这在基于拖拽的编辑任务中尚属首次。它消除了TTO的需求，并释放了模型的生成能力。因此，LazyDrag自然地统一了精确几何控制与文本引导，使得之前难以实现的复杂编辑成为可能：例如为一只狗打开嘴巴并修复其内部细节、生成新的物体如“网球”或对于模糊拖拽进行上下文感知变化，比如将手移入口袋中。此外，LazyDrag支持多轮工作流程，并且可以同时执行移动和缩放操作。在DragBench上评估时，我们的方法在拖拽准确性和感知质量方面超越了基准模型，这通过VIEScore和人工评价得到了验证。LazyDrag不仅建立了新的最先进的性能水平，而且还开辟了一条通往新编辑范式的道路。
URL
https://arxiv.org/abs/2509.12203
