LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion
  Transformers via Explicit Correspondence
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.
68c8cea2733e345e52ac1e79
Zixin Yin
68c8cea2733e345e52ac1e7a
Xili Dai
68c8cea2733e345e52ac1e7b
Duomin Wang
claimed_verified
2025-09-16T09:42:04.461Z
64ae9b88a22a179fc4d07992
/avatars/c9065f04a1188ea3129e56a90328ffd3.svg
wang
user
dorni
68c8cea2733e345e52ac1e7c
Xianfang Zeng
68c8cea2733e345e52ac1e7d
Lionel M. Ni
68c8cea2733e345e52ac1e7e
Gang Yu
68c8cea2733e345e52ac1e7f
Heung-Yeung Shum
LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion
  Transformers via Explicit Correspondence
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.
LazyDrag, a drag-based image editing method for Multi-Modal Diffusion Transformers, eliminates implicit point matching, enabling precise geometric control and text guidance without test-time optimization.
attention drag-based editing Multi-Modal Diffusion Transformers explicit correspondence map full-strength inversion test-time optimization high-fidelity inpainting text-guided creation DragBench VIEScore