LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion Transformers via Explicit Correspondence

## Abstract

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. Here is the project website .

## 1. Introduction

Drag-based editing in diffusion models remains fundamentally challenging. To preserve object identity during editing, prior methods often perform implicit point matching via attention. A common strategy, introduced by MasaCtrl (Cao et al., 2023), shares key and value tokens during attention. However, this strategy allocates more attention weights to spatially nearby regions instead of semantically related ones (Wang et al., 2025a; Feng et al., 2025), which leads to unstable and degrading edits. Rather than tackling this fundamental cause, as a compromise, many methods rely on test-time optimization (TTO) or weakened inversion strength. These compromises mask the mismatch and incur costs, including unreliable inpainting, suppressed text guidance, and distorted edits.

Instead of the compromise, we take a principled alternative: replace implicit attention-based matching with an explicit correspondence map and inject it directly into the generation process. With this reliable map, editing under full-strength inversion becomes stable without TTO, enabling faithful inpainting and text-guided generation. Beyond addressing the fundamental issue, the choice of network architecture remains crucial for editing. The recent transition from U-Nets (Rombach et al., 2022) to Multi-Modal Diffusion Transformers (MM-DiT) (Esser et al., 2024) provides an ideal foundation for this shift, because MM-DiTs offer tighter vision–text fusion, which improves inversion robustness and raises the ceiling for attention control. As shown by ColorCtrl (Yin et al., 2025), this architecture supports stronger semantic consistency and controllability, allowing attention control to be applied across all single-stream attention (SS-Attn) layers without manual selection of specific layer indexes like that in U-Nets. We exploit these advantages by building our method on MM-DiTs.

Unlike in U-Nets, identity preservation in MM-DiTs is non-trivial. Simply sharing key and value tokens, as in DiTCtrl (Cai et al., 2025), does not reproduce the identity-preserving behavior achieved by MasaCtrl with U-Nets (Cao et al., 2023). Recently, CharaConsist (Wang et al., 2025a) showed that re-encoding and injecting semantically aligned tokens can preserve identity in MM-DiTs. However, its point matching relies on the average of attention similarity, which is fragile under full-strength inversion and often yields unsuitable edits. In contrast, drag instructions naturally define a field that maps handle points to target points, forming a deterministic correspondence map. We turn this explicit map into attention controls. This explicit correspondence–driven preservation resolves the root issue, stabilizes edits under full-strength inversion without TTO. As a result, it enhances inpainting and text guidance ability, delivering higher fidelity and controllability than prior methods.

In this work, we present LazyDrag, a training-free method that uses an explicit correspondence map to drive attention controls in MM-DiTs. By resolving the core instability of implicit attention mappings, LazyDrag stabilizes edits under full-strength inversion without TTO, unlocking the full generation ability. Concretely, (i) the drag instructions are converted into an explicit correspondence map, and (ii) identity and background are preserved using attention controls with the map. Together, these components deliver edits under full-strength inversion without TTO, retaining inpainting capability and enabling text-guided edits under ambiguous instructions.
As shown in Fig. [1](https://arxiv.org/html/2509.12203v2#S0.F1), this allows our method to execute complex edits where prior works fail: it can open the mouth of the dog and inpaint its interior, or even generate a “tennis ball” via text guidance, which is impossible for methods constrained by low inversion strength (see Fig. [2](https://arxiv.org/html/2509.12203v2#S2.F2)). Furthermore, it exhibits a deep understanding of scene context. For example, when dragging a hand using drag instructions alone, the ambiguity of the task, whether the hand should be placed behind a back or into a pocket, can be resolved through text guidance, allowing users to make precise and meaningful edits.
Extensive experiments demonstrate that LazyDrag achieves state-of-the-art (SOTA) performance while requiring no test-time optimization. To the best of our knowledge, LazyDrag is the first drag-based editing method built with MM-DiTs and the first to adopt full-strength inversion across all sampling steps, which enables natural inpainting and precise text-guided control. Our contributions are threefold:

- •
We propose LazyDrag, the first to achieve full-strength inversion in drag-based editing with MM-DiTs. It is accomplished by an explicit correspondence-driven attention controls that eliminates the need for TTO and resolves the core instability of previous works.
- •
We resolve the ambiguity of drag instructions by coupling the explicit correspondence map with text guidance, enabling natural inpainting and semantically consistent edits.
- •
Extensive experiments demonstrate that LazyDrag significantly outperforms all existing methods on Drag-Bench in both quantitative metrics and human preference.

## 2. Related Work

Figure: Figure 2. Effect of inversion strength. Examples of LazyDrag under different inversion strengths. The additional prompt is “a red apple in the mouth”.
Refer to caption: x2.png

##### Text-to-image and video generation.

GAN-based models (Reed et al., 2016; Yu et al., 2023; Wang et al., 2023) have been largely replaced by diffusion models with U-Net backbones (Ho et al., 2020; Rombach et al., 2022) due to better fidelity and stability. However, U-Nets scale poorly, prompting a shift toward Diffusion Transformers (DiT) (Peebles and Xie, 2023). Among them, MM-DiT (Esser et al., 2024) has become the backbone of choice in recent state-of-the-art systems (Esser et al., 2024; AI, 2024; Labs, 2024; Yang et al., 2024; Kong et al., 2024; Liu et al., 2025b), including FLUX (Labs, 2025). We are the first to introduce a drag-based editing method within MM-DiTs.

##### Text-based editing.

Training-free text-guided editing methods use pre-trained diffusion models without fine-tuning, offering strong flexibility. Prompt-to-Prompt (Hertz et al., 2023) edits attention maps for localized control, with extensions to images and videos (Wang et al., 2025b; Liu et al., 2024b; Cao et al., 2023; Rout et al., 2025; Xu et al., 2025; Ju et al., 2024). Recent work explores attention control in MM-DiTs: DiTCtrl (Cai et al., 2025) for long video generation, ColorCtrl (Yin et al., 2025) for light-consistent color edits, and CharaConsist (Wang et al., 2025a) for preserving character identity. Modern approaches such as Step1X-Edit (Liu et al., 2025a) and GPT-4o (OpenAI, 2025) have gained popularity due to their efficiency. However, all rely solely on text, which limits spatial precision. We instead introduce a more intuitive and controllable drag-based method.

##### Drag-based editing.

Drag-based editing enables users to specify explicit spatial transformations by defining source and target points. Existing methods can be divided into two categories: those requiring test-time optimization (TTO), and those that do not. Most prior works fall into the former, beginning with DragGAN (Pan et al., 2023), and expanding to diffusion-based approaches (Shi et al., 2024; Mou et al., 2024a, b; Liu et al., 2024a; Hou et al., 2024; Shin et al., 2024; Zhou et al., 2025; Ling et al., 2024; Zhang et al., 2025). RegionDrag (Lu et al., 2024) extends the interface to support region-level editing. Some methods (Jiang et al., 2025; Choi et al., 2025) incorporate textual prompts to improve semantic understanding, but still suffers from complex instructions.
FastDrag (Zhao et al., 2024) is one of only two notable TTO-free methods, achieving faster inference but still falling short of the quality delivered by TTO-based methods. Inpaint4Drag (Lu and Han, 2025) is the other TTO-free method that build on an inpainting model rather than generative model with inversion. However, directly pasting a warped image to fill the edited region introduces strong unnatural warping artifacts. Also, its strong sensitivity to the input mask leads to frequent boundary artifacts and blurring, even with assistance from modern mask generators (e.g., SAM (Kirillov et al., 2023)). Therefore, we adopt a widely used generative model approach with inversion, rather than an inpainting formulation. Additionally, all prior approaches with inversion rely on low inversion strength, which degrades inpainting quality and limits semantic generation. In contrast, we introduce the first drag-based method for MM-DiTs that leverages full-strength inversion and text-guided attention mechanisms, achieving SOTA performance without any per-image tuning.

## 3. Method

Our goal is to achieve identity-preserving edits with precise drag control, text guidance, and natural inpainting.
To this end, we introduce LazyDrag, a training-free method built with MM-DiTs under full-strength inversion property. Our approach replaces the fragile, implicit point matching of prior work with a robust, explicit correspondence map derived from user input during attention control, stabilizing the inversion process without test-time optimization. We first review foundational concepts in Sec. [3.1](https://arxiv.org/html/2509.12203v2#S3.SS1). Then detail our two-stage approach: first, how to generate the explicit correspondence map from drag instructions (Sec. [3.2](https://arxiv.org/html/2509.12203v2#S3.SS2)), and second, how this map drives a novel two-part attention control for identity and background preservation (Sec. [3.3](https://arxiv.org/html/2509.12203v2#S3.SS3)). Fig. [3](https://arxiv.org/html/2509.12203v2#S3.F3) shows the pipeline.

Figure: Figure 3. Pipeline of LazyDrag. (a) An input image is inverted to a latent code $\bm{z}_{T}$. Our correspondence map generation then yields an updated latent$\hat{\bm{z}}_{T}$, point matching map, and weights $\alpha$. Tokens cached during inversion are used to guide the sampling process for identity and background preservation. (b) In attention input control, a dual strategy is employed. For background regions (gray color), $\mathbf{Q}$, $\mathbf{K}$, and $\mathbf{V}$ tokens are replaced with their cached originals. For destination (red and blue colors) and transition regions (yellow color), the $\mathbf{K}$ and $\mathbf{V}$ tokens are concatenated with re-encoded ($\mathbf{K}$ only) source tokens retrieved via the map (c) Attention output refinement performs value blending of attention output. $\otimes$ and $\oplus$ denotes element-wise product and addition.
Refer to caption: x3.png

### 3.1. Preliminaries

LazyDrag builds upon insights from training-free drag-based editing methods in U-Nets (Sec. [3.1.1](https://arxiv.org/html/2509.12203v2#S3.SS1.SSS1)) and identity preservation in MM-DiTs (Sec. [3.1.2](https://arxiv.org/html/2509.12203v2#S3.SS1.SSS2)), addressing core limitations of both (Sec. [3.1.3](https://arxiv.org/html/2509.12203v2#S3.SS1.SSS3)).

#### 3.1.1. Training-Free Drag Editing in U-Nets: FastDrag.

FastDrag (Zhao et al., 2024) is the first training-free method for drag-based editing, with U-Net models. It has two parts: (1) it computes a displacement field from drag instructions to create an initial latent $\hat{\bm{z}}_{T}$, filling exposed regions via interpolation, and (2) it applies a MasaCtrl-like (Cao et al., 2023) key and value token replacement during self-attention to preserve object identity. However, beyond the implicit locality bias of self-attention, a central trade-off arises:
we want handle points to reach their targets while surrounding regions inpaint naturally. Yet after latent initialization, the cue specific to handles is lost, and all moved points are treated uniformly. Forcing exact positional accuracy yields warp artifacts, whereas enforcing naturalness reduces positioning accuracy. Thus, editing accuracy and visual fidelity are in inherent tension.
Moreover, its fusion of multiple instructions is brittle: when drags are antagonistic (for example, opening a mouth by moving the upper lip upward and the lower lip downward), averaging the displacements cancels motion near the seam and the mouth fails to open. Moreover, the interpolation used to fill newly exposed regions further replicates nearby textures, producing repeated artifacts in large uncovered areas, as in Fig. [11](https://arxiv.org/html/2509.12203v2#A2.F11).

#### 3.1.2. Identity Preservation in MM-DiTs: CharaConsist.

In parallel, CharaConsist (Wang et al., 2025a) introduces identity preservation in MM-DiTs, though it is not an editing method. To enforce identity preservation, it controls attention by concatenating corresponding source tokens into the key (re-encoded) and value tokens and by blending attention outputs. However, its point matching mechanism is critically flawed: it relies on attention similarity to identify matching points between images, a process that is computationally expensive (requiring additional denoising steps) and inherently unstable. Under full-strength inversion, even minor mismatches in the correspondence map can lead to significant visual artifacts, as proved in Tab. [3](https://arxiv.org/html/2509.12203v2#S4.T3).

#### 3.1.3. LazyDrag: Bridging the Gap.

LazyDrag bridges the gap between these two methods. We extend the training-free approach of FastDrag from U-Nets to MM-DiTs, while incorporating the fine-grained attention control method of CharaConsist. We resolve the core weaknesses of both approaches by introducing a unified solution: an explicit correspondence map derived from drag instructions. This map provides stable, precise attention controls throughout the generation process, enabling high-quality, accurate edits while avoiding the pitfalls of attention similarity-based matching and the trade-offs inherent in FastDrag.

### 3.2. Generating the Explicit Correspondence Map

We first compute an explicit correspondence map from the user drag instructions and the inverted source latent noise $\bm{z}_{T}$. The map comprises a matching point function $\mathcal{M}$ and a weight function $\mathcal{A}$, which provides explicit guidance. Guided by this map, we generate the initial latent noise $\hat{\bm{z}}_{T}$.

##### Displacement field calculation via winner-takes-all (WTA).

Let $\Omega$ denote the latent grid, and let $\mathcal{P}=\{\bm{p}_{j}\}_{j=1}^{m}\subset\Omega$ be the editable regions (the bright area in Fig. [3](https://arxiv.org/html/2509.12203v2#S3.F3)), sampled as feature points.
Let the drag instructions be $\mathcal{D}=\{(\bm{s}_{i},\bm{e}_{i})\}_{i=1}^{k}$, where $\bm{s}_{i}$ and $\bm{e}_{i}$ are the handle and target points of the $i$-th instruction.
We illustrate two modes for computing the displacement field.
In drag mode, we adopt the elasticity-based per-instruction displacement $\bm{v}_{j}^{i}$ for each $\bm{p}_{j}$ under the $i$-th instruction as in Zhao et al. (2024); in move mode, we use standard translation and scaling.
To avoid failures of averaging under opposing drags, we use a robust winner-takes-all (Aurenhammer, 1991) fusion: each $\bm{p}_{j}$ is uniquely assigned to its nearest handle, inducing a Voronoi partition (Aurenhammer, 1991).
The final displacement $\bm{v}_{j}$ and weight $\alpha_{j}$ are determined solely by the winning instruction.

$$ (1) $\begin{gathered}\alpha_{j}^{i}=\begin{cases}\lVert\bm{p}_{j}-\bm{s}_{i}\rVert_{2}^{-1},&\bm{p}_{j}\neq\bm{s}_{i},\\ \infty,&\text{otherwise},\end{cases}\\ \bm{v}_{j}=\bm{v}_{j}^{i^{\star}},\quad\alpha_{j}=\alpha_{j}^{i^{\star}},\quad\text{where}\quad i^{\star}=\operatorname*{arg\,max}_{i}\alpha_{j}^{i}.\end{gathered}$ $$

Here, $\lVert\cdot\rVert_{2}$ denotes the Euclidean $L_{2}$-norm distance. Thus, $\mathcal{V}=\{\bm{v}_{j}\}_{j=1}^{m}$ is defined as the displacement field. This approach preserves the full magnitude of opposing drags, enabling complex edits like opening a mouth, which is impossible with simple averaging. Details are in Appendix [A.2](https://arxiv.org/html/2509.12203v2#A1.SS2).

##### Initial latent construction and map formalization (Latent Init).

With the displacement field $\mathcal{V}$ established, we construct the initial latent $\hat{\bm{z}}_{T}$. This process defines our explicit deterministic correspondence map $(\mathcal{M},\mathcal{A})$ and partitions the latent grid into distinct regions for targeted control.
First, we define the set of discrete destination coordinates $\mathcal{P}^{\star}=\{\Pi(\bm{p}_{j}+\bm{v}_{j})\mid\bm{p}_{j}\in\mathcal{P}\}$, where $\Pi(\cdot)$ projects to the grid. By resolving collisions where multiple source points map to a single destination $\bm{x}\in\mathcal{P}^{\star}$ (using winner-takes-all), we get the winner index $j^{\star}(\bm{x})=\operatorname*{arg\,max}_{\ j:\ \Pi(\bm{p}_{j}+\bm{v}_{j})=\bm{x}}\ \alpha_{j}$ and formalize our correspondence map: Matching point map, $\mathcal{M}(\bm{x})=\bm{p}_{j^{\star}(\bm{x})}$. Matching weight map, $\mathcal{A}(\bm{x})=\operatorname{min}(1,\alpha_{j^{\star}(\bm{x})})$.
Next, we partition the latent space $\Omega$ into four disjoint sets based on the geometry of the warp. These sets correspond directly to the colored regions in Fig. [3](https://arxiv.org/html/2509.12203v2#S3.F3) (a): *Background* $\mathcal{R}^{\mathrm{bg}}$ (gray) that must remain unchanged, *Destinations* $\mathcal{R}^{\mathrm{dst}}$ (red and blue, a.k.a., $\mathcal{P}^{\star}$ ) where moved content is rendered with identity preserved, *Inpainting* $\mathcal{R}^{\mathrm{inp}}$ (yellow) initialized from noise, and *Transition* $\mathcal{R}^{\mathrm{trans}}$ (green) that blends boundaries smoothly.
With these regions clearly defined, the updated latent $\hat{\bm{z}}_{T}$ is constructed by applying a specific rule to each region:

$$ (2) $\hat{\bm{z}}_{T}(\bm{x})=\begin{cases}\bm{z}_{T}(\mathcal{M}(\bm{x})),&\text{if }\bm{x}\in\mathcal{R}^{\text{dst}},\\ \bm{\epsilon}(\bm{x}),&\text{if }\bm{x}\in\mathcal{R}^{\text{inp}},\\ \bm{z}_{T}(\bm{x}),&\text{if }\bm{x}\in\mathcal{R}^{\mathrm{bg}}\cup\mathcal{R}^{\mathrm{trans}},\end{cases}$ $$

where $\bm{\epsilon}\sim\mathcal{N}(0,\mathbf{I})$. Crucially, replacing the BNNI interpolation used in FastDrag with Gaussian noise in $\mathcal{R}^{\text{inp}}$ is essential. Unlike the uniform noise compared in Zhao et al. (2024), this approach aligns with the diffusion prior, prevents repetitive artifacts as shown in Fig. [11](https://arxiv.org/html/2509.12203v2#A2.F11), and enables the ability of high-fidelity, text-guided inpainting discussed in the introduction.

### 3.3. Correspondence-Driven Preservation

Having established the explicit correspondence map, we now detail a two-part mechanism operating at the input (Sec. [3.3.1](https://arxiv.org/html/2509.12203v2#S3.SS3.SSS1)) and output (Sec. [3.3.2](https://arxiv.org/html/2509.12203v2#S3.SS3.SSS2)) of the attention calculation in single-stream attention layers only (Yin et al., 2025; Deng et al., 2025). Using this map, the mechanism provides fine-grained control that preserves identity and background, ensuring robust full-strength inversion.

#### 3.3.1. Attention Input Control via Token Replacement and Concatenation

To preserve the background and identity, the first part modifies the attention inputs of different regions. Let $(\mathbf{Q}_{\bm{x}},\mathbf{K}_{\bm{x}},\mathbf{V}_{\bm{x}})$ denote the current attention tokens at position $\bm{x}$ in a given layer and step, and $(\overline{\mathbf{Q}}_{\bm{x}},\overline{\mathbf{K}}_{\bm{x}},\overline{\mathbf{V}}_{\bm{x}})$ the tokens cached without positional encoding during the previous inversion process. Let $\mathrm{RoPE}_{\bm{x}}(\cdot)$ re-encode tokens with the rotary embedding at position $\bm{x}$ (Su et al., 2024).

##### Background preservation via replacement (BG Pres.).

For the background region $\mathcal{R}^{\text{bg}}$, the purpose of absolute untouched is achieved by hard-replacing the attention tokens with their cached originals at every step and every single-stream layer, similar to ColorCtrl (Yin et al., 2025):

$$ (3) $(\mathbf{Q}_{\bm{x}},\mathbf{K}_{\bm{x}},\mathbf{V}_{\bm{x}})\leftarrow(\mathrm{RoPE}_{\bm{x}}(\overline{\mathbf{Q}}_{\bm{x}}),\mathrm{RoPE}_{\bm{x}}(\overline{\mathbf{K}}_{\bm{x}}),\overline{\mathbf{V}}_{\bm{x}}),\quad\forall\bm{x}\in\mathcal{R}^{\text{bg}}.$ $$

##### Identity preservation via concatenation (ID Pres.).

For the destination and transition regions ($\mathcal{R}^{\text{dst}}\cup\mathcal{R}^{\text{trans}}$), where identity must be preserved while allowing for coherent adaptation, we use token concatenation. Define a unified source point map, $\tilde{\mathcal{M}}(\bm{x})$, which selects correspondence sources:

$$ (4) $\tilde{\mathcal{M}}(\bm{x})=\begin{cases}\mathcal{M}(\bm{x}),&\text{if }\bm{x}\in\mathcal{R}^{\text{dst}},\\ \bm{x},&\text{if }\bm{x}\in\mathcal{R}^{\text{trans}}.\end{cases}$ $$

For any position $\bm{x}\in\mathcal{R}^{\text{dst}}\cup\mathcal{R}^{\text{trans}}$, we form an augmented key $\mathbf{K}^{\prime}_{\bm{x}}$ and value $\mathbf{V}^{\prime}_{\bm{x}}$ by concatenating the cached tokens from its designated source $\tilde{\mathcal{M}}(\bm{x})$:

$$ (5) $\displaystyle\mathbf{K}^{\prime}_{\bm{x}}$ $\displaystyle=\operatorname{concat}\big(\mathbf{K}_{\bm{x}},\,\mathrm{RoPE}_{\bm{x}}(\overline{\mathbf{K}}_{\tilde{\mathcal{M}}(\bm{x})})\big),$ (6) $\displaystyle\mathbf{V}^{\prime}_{\bm{x}}$ $\displaystyle=\operatorname{concat}\big(\mathbf{V}_{\bm{x}},\,\overline{\mathbf{V}}_{\tilde{\mathcal{M}}(\bm{x})}\big).$ $$

This provides a strong, correspondence-driven signal to the attention calculation, robustly preserving identity while allowing for smooth blending at the boundaries.

#### 3.3.2. Attention Output Refinement via Gated Merging (Attn Refine)

The second part refines the attention output so that it cooperates with the above token concatenation (following Wang et al. (2025a)), improving visual quality and emphasizing the importance of handle points over others.
Let $\mathbf{y}_{\bm{x}}$ be the attention output at $\bm{x}$ and $\overline{\mathbf{y}}_{\bm{x}}$ be the cached output. For $\bm{x}\in\mathcal{R}^{\mathrm{dst}}$,

$$ (7) $\mathbf{y}_{\bm{x}}\leftarrow\bigl(1-\gamma_{\bm{x},t}\bigr)\,\mathbf{y}_{\bm{x}}+\gamma_{\bm{x},t}\,\overline{\mathbf{y}}_{\mathcal{M}(\bm{x})},$ $$

where the blending factor $\gamma_{\bm{x},t}$ is gated by our pre-computed matching weight from the map $\mathcal{A}$:

$$ (8) $\gamma_{\bm{x},t}=h_{t}\cdot\mathcal{A}(\bm{x}),$ $$

where $t$ indexes the timestep and $h_{t}\in[0,1]$ is a factor that decays over time. This correspondence-driven gated merge eliminating the extra denoising steps required by CharaConsist, and addressing the instability of attention-similarity matching and scaling under full-strength inversion. By making the weight strongest at the handle points (where $\mathcal{A}(\bm{x})$ is maximal), it ensures precise control where it matters most, removing the need for multi-step latent optimization in previous methods (Zhang et al., 2025; Shi et al., 2024), while allowing for natural relaxation in surrounding regions.

## 4. Experiments

### 4.1. Setup

##### Baselines.

We compare against eight prior arts: DragDiffusion (Shi et al., 2024), DragNoise (Liu et al., 2024a), FreeDrag (Ling et al., 2024), DiffEditor (Mou et al., 2024a), GoodDrag (Zhang et al., 2025), DragText (Choi et al., 2025)(^1^11Since DragText is a plug-and-play method, we evaluate it in conjunction with best-performing GoodDrag.), FastDrag (Zhao et al., 2024), and Inpaint4Drag (Lu and Han, 2025). Others are excluded due to incompatible settings or unusable official implementations.

##### Implementation details.

Unless otherwise noted, all baselines are run with their official implementations and default hyperparameters. For Inpaint4Drag (Lu and Han, 2025), we adopt the refined masks and point pairs provided by the authors at inference, and replace distilled models with original models. Our method is built on FLUX.1 Krea-dev (Labs, 2025), adopting the inversion method of UniEdit-Flow (Jiao et al., 2025) while replacing the editing strategy with our approach. For a fair comparison, the number of denoising steps is fixed to 50 for all methods. Following CharaConsist, we activate ID Pres. and Attn Refine (Sec. [3.3](https://arxiv.org/html/2509.12203v2#S3.SS3)) for the first 40 denoising steps, referring to the last activate timestep as the activation timestep. Additional details are in Appendix [A.1](https://arxiv.org/html/2509.12203v2#A1.SS1).

##### Benchmark and evaluation protocol.

We evaluate on DragBench (Shi et al., 2024), which contains 205 images with 349 handle and target point pairs.
Our primary accuracy metric is MD (mean distance) (Pan et al., 2023).
Although IF (image fidelity) (Kawar et al., 2023), typically computed with LPIPS (Zhang et al., 2018), is widely used, we *do not* report IF.
Previous work (Choi et al., 2025; Lu et al., 2024) shows that successful drag edits necessarily change the image, often increasing LPIPS, whereas an unchanged image trivially attains the best score.
Hence, IF can be misleading for drag editing. To obtain a complementary, perceptually grounded view, we adopt the VIEScore (Ku et al., 2024) metrics from GEdit-Bench (Liu et al., 2025a): SC (Semantic Consistency): whether the intended edit has been achieved. PQ (Perceptual Quality): the naturalness of the result and absence of artifacts. O (Overall): the overall performance defined in Liu et al. (2025a).
In our setting, the “intended edit” is specified by the dragging instruction rather than a natural-language instruction, but the scoring criteria remain unchanged. Each score ranges from 0 to 10 (higher is better) and is produced by the state-of-the-art MLLM evaluator, GPT-4o(^2^22API access as of August 2025) (Hurst et al., 2024). To mitigate stochasticity in evaluation, we run every evaluation metrics three times and report both the mean and standard deviation. We additionally report a binary TTO-Req (Test-Time Optimization Required) flag indicating whether a method requires per-edit test-time optimization (e.g., LoRA fine-tuning or multi-step latent optimization) during inference. More evaluation details are in Appendix [A.3](https://arxiv.org/html/2509.12203v2#A1.SS3).

**Table 1. Quantitative results compared with baselines on Drag-Bench.**
| Method | TTO-Req | $\text{MD}~\downarrow$ | $\text{SC}~\uparrow$ | $\text{PQ}~\uparrow$ | $\text{O}~\uparrow$ |
| --- | --- | --- | --- | --- | --- |
| DragNoise (Liu et al., 2024a) | ✓ | 37.87 $\pm$ 0.23 | 7.793 $\pm$ 0.04 | 8.058 $\pm$ 0.01 | 7.704 $\pm$ 0.01 |
| DragDiffusion (Shi et al., 2024) | ✓ | 34.84 $\pm$ 0.30 | 7.905 $\pm$ 0.01 | 8.325 $\pm$ 0.02 | 7.798 $\pm$ 0.03 |
| FreeDrag (Ling et al., 2024) | ✓ | 34.09 $\pm$ 0.60 | 7.928 $\pm$ 0.02 | 8.281 $\pm$ 0.03 | 7.816 $\pm$ 0.02 |
| DiffEditor (Mou et al., 2024a) | ✓ | 26.95 $\pm$ 0.24 | 7.603 $\pm$ 0.01 | 8.266 $\pm$ 0.01 | 7.715 $\pm$ 0.01 |
| GoodDrag (Zhang et al., 2025) | ✓ | 22.17 $\pm$ 0.16 | 7.834 $\pm$ 0.03 | 8.318 $\pm$ 0.01 | 7.795 $\pm$ 0.01 |
| DragText (Choi et al., 2025) | ✓ | 21.51 $\pm$ 0.21 | 7.992 $\pm$ 0.02 | 8.227 $\pm$ 0.03 | 7.886 $\pm$ 0.01 |
| FastDrag (Zhao et al., 2024) | ✗ | 31.84 $\pm$ 0.96 | 7.935 $\pm$ 0.09 | 8.278 $\pm$ 0.01 | 7.904 $\pm$ 0.06 |
| Inpaint4Drag (Lu and Han, 2025) | ✗ | 23.68 $\pm$ 0.05 | 7.802 $\pm$ 0.06 | 7.961 $\pm$ 0.04 | 7.615 $\pm$ 0.06 |
| Ours | ✗ | 21.49 $\pm$ 0.04 | 8.205 $\pm$ 0.03 | 8.395 $\pm$ 0.03 | 8.210 $\pm$ 0.03 |

Figure: Figure 4. Qualitative results compared with baselines on Drag-Bench. Best viewed with zoom-in.
Refer to caption: x4.png

### 4.2. Quantitative Evaluation

Tab. [1](https://arxiv.org/html/2509.12203v2#S4.T1) presents the benchmark results on DragBench. Despite not requiring LoRA fine-tuning or multi-step latent optimization for each image and drag operation, our method consistently outperforms existing approaches in all metrics, especially in terms of drag accuracy and the perceptual quality of the generated images. Notably, our approach achieves SOTA performance out-of-the-box, without the need for test-time optimization, making it both efficient and effective. Specifically, Inpaint4Drag (Lu and Han, 2025) often produces boundary artifacts and color shifts between edited and unedited regions. Consequently, the LLM evaluator assigns lower scores under its over-editing rule. This indicates that, even with additional optimization of masks and point pairs, mask sensitivity of inpainting models degrades results. By contrast, our full-strength inversion method with attention controls attains strong performance while being more robust to the choice of masks and point pairs.

### 4.3. Qualitative Evaluation

Fig. [4](https://arxiv.org/html/2509.12203v2#S4.F4) qualitatively demonstrates the superiority of our method over existing baselines. In the first example, only our method correctly lift the arm with background maintained, while others introduce artifacts, such as distorted hands (e.g., DragText (Choi et al., 2025)) or unintended background changes (e.g., DragNoise (Liu et al., 2024a)). In the second example, most baselines fail to preserve the front structure of the vehicle, whereas our approach maintains it faithfully while applying the desired transformation. Inpaint4Drag (Lu and Han, 2025) generates artifacts in the background. The third case shows that only our method successfully modifies the sofa geometry while preserving the integrity of pillows. In the fourth example, our approach correctly interprets hand proximity as intent to insert it into the pocket, while other baselines introducing artifacts. Finally, in the fifth example, only our approach and DragText successfully rotates the head of the tiger to the right without compromising overall image quality. These results are consistent with our quantitative evaluations and highlight the robustness and generality of our method, even without per image tuning or per instruction multi-step latent optimization. More results are shown in Appendix [B.1](https://arxiv.org/html/2509.12203v2#A2.SS1).

### 4.4. User Study

**Table 2. User study on Drag-Bench.**
| Method | Preference (%) |
| --- | --- |
| DragNoise (Liu et al., 2024a) | 5.00 $\pm$ 7 |
| DragDiffusion (Shi et al., 2024) | 8.75 $\pm$ 8 |
| FreeDrag (Ling et al., 2024) | 8.75 $\pm$ 8 |
| DiffEditor (Mou et al., 2024a) | 2.50 $\pm$ 4 |
| GoodDrag (Zhang et al., 2025) | 3.75 $\pm$ 4 |
| DragText (Choi et al., 2025) | 3.13 $\pm$ 4 |
| FastDrag (Zhao et al., 2024) | 3.13 $\pm$ 6 |
| Inpaint4Drag (Lu and Han, 2025) | 3.13 $\pm$ 4 |
| Ours | 61.88 $\pm$ 19 |

Figure: Figure 5. Comparison between drag and move mode on Drag-Bench.
Refer to caption: x5.png

A total of 20 expert participants evaluated comparisons between methods on 32 cases randomly sampled from DragBench. For each comparison, method order positions were randomized and method identities were anonymized. Participants selected the preferred result according to predefined criteria (edit success, naturalness, and background preservation).
Overall, LazyDrag was preferred in 61.88% of comparisons, outperforming all baselines (Tab. [2](https://arxiv.org/html/2509.12203v2#S4.T2)). More details are in Appendix [A.4](https://arxiv.org/html/2509.12203v2#A1.SS4).

Figure: Figure 6. Qualitative cumulative ablation on Drag-Bench. Rows remove one component relative to the row above. When WTA and Latent Init are removed we use latent init in FastDrag. When ID Pres. and Attn Refine are removed we switch to CharaConsist attention-similarity control.
Refer to caption: x6.png

### 4.5. Comparison Between Drag and Move Modes

We evaluate LazyDrag with both drag and move modes on Drag-Bench, with qualitative results shown in Fig. [5](https://arxiv.org/html/2509.12203v2#S4.F5). The move mode tends to better preserve identity, as seen in the last two cases, rather than performing edits involving rotation or extension, as in the second and third examples. In contrast, the drag mode enables natural geometric transformations, including 3D rotations and extensions, albeit with a slight degradation in detail texture preservation. Both of two modes can generate reasonable results. These findings highlight the flexibility of our explicit correspondence map when paired with our correspondence-driven preservation strategy. Future work may explore more matching strategies, such as 2D rotation, to further enhance diversity and controllability.

### 4.6. Ablation Study

##### Effect of each component.

We conduct an ablation study in which components are progressively removed from the full method.
To keep functionality comparable when a component is absent, we adopt controlled replacements: (i) Without WTA and Latent Init (Sec. [3.2](https://arxiv.org/html/2509.12203v2#S3.SS2)) we revert to latent warpage optimization of FastDrag (Zhao et al., 2024) as the latent initialization. (ii) Without ID Pres. and Attn Refine (Sec. [3.3](https://arxiv.org/html/2509.12203v2#S3.SS3)) we switch to the attention-similarity matching and scaling introduced in CharaConsist (Wang et al., 2025a).
Fig. [6](https://arxiv.org/html/2509.12203v2#S4.F6) and Tab. [3](https://arxiv.org/html/2509.12203v2#S4.T3) report benchmark results on Drag-Bench. Removing WTA and Latent Init increases MD and slightly reduces PQ and O, indicating that our initialization with the winner-takes-all fusion strategy and random initialization for inpainting regions suppresses repetitive artifacts and improves inpainting quality as proven in the figure. Further disabling background preservation causes additional drops in SC and O due to color shifting and artifacts in the background. Finally, replacing our correspondence-driven preservation with attention-similarity control leads to a sharp degradation, highlighting the sensitivity of full-strength inversion to mismatched attention alignment. The full method achieves the best performance.

##### Effect of activation timesteps.

We conduct an ablation study on the effect of activation timesteps by varying the activation timestep to 20, 40, and 50, as shown in Fig. [7](https://arxiv.org/html/2509.12203v2#S4.F7) and Tab. [4](https://arxiv.org/html/2509.12203v2#S4.T4). From the results, we observe that increasing the number of the activation timestep leads to more accurate destination points for dragging, though it may introduce more warping artifacts. Conversely, reducing the activation timestep results in more natural outputs, but may cause slight variations in identity or motion. More results are in Appendix [B.4](https://arxiv.org/html/2509.12203v2#A2.SS4). For benchmark evaluations, we use 40 as a balanced value.

Figure: Figure 7. Qualitative ablation of activation timesteps on Drag-Bench. From left to right, the activation timestep is increased.
Refer to caption: x7.png

**Table 3. Quantitative cumulative ablation on Drag-Bench under the same setting as Fig. [6](https://arxiv.org/html/2509.12203v2#S4.F6)**
| Method | $\text{MD}~\downarrow$ | $\text{SC}~\uparrow$ | $\text{PQ}~\uparrow$ | $\text{O}~\uparrow$ |
| --- | --- | --- | --- | --- |
| Ours | 21.49 $\pm$ 0.04 | 8.205 $\pm$ 0.03 | 8.395 $\pm$ 0.03 | 8.210 $\pm$ 0.03 |
| - WTA - Latent Init | 23.69 $\pm$ 0.16 | 8.129 $\pm$ 0.03 | 8.060 $\pm$ 0.05 | 7.938 $\pm$ 0.01 |
| - BG Pres. | 24.73 $\pm$ 0.08 | 7.998 $\pm$ 0.04 | 8.043 $\pm$ 0.01 | 7.863 $\pm$ 0.03 |
| - ID Pres. - Attn Refine | 56.49 $\pm$ 0.49 | 5.307 $\pm$ 0.08 | 7.944 $\pm$ 0.02 | 5.953 $\pm$ 0.06 |

**Table 4. Quantitative ablation of activation timesteps on Drag-Bench.**
| Method | $\text{MD}~\downarrow$ | $\text{SC}~\uparrow$ | $\text{PQ}~\uparrow$ | $\text{O}~\uparrow$ |
| --- | --- | --- | --- | --- |
| Ours (40 as activation timestep) | 21.49 $\pm$ 0.04 | 8.205 $\pm$ 0.03 | 8.395 $\pm$ 0.03 | 8.210 $\pm$ 0.03 |
| + 20 as activation timestep | 34.23 $\pm$ 0.29 | 7.036 $\pm$ 0.03 | 8.788 $\pm$ 0.01 | 7.605 $\pm$ 0.02 |
| + 50 as activation timestep | 21.81 $\pm$ 0.26 | 8.298 $\pm$ 0.03 | 8.072 $\pm$ 0.01 | 8.087 $\pm$ 0.03 |

## 5. Conclusion

We presented LazyDrag, the first training-free method for drag-based editing with MM-DiTs under full-strength inversion. We begin by identifying the fundamental cause of instability in drag-based editing: the unreliability of implicit attention-based point matching. This diagnosis explains why prior methods adopted compromises such as test-time optimization or weakened inversion strength, which suppress text guidance, harm inpainting, and limit generative ability.
Our approach directly solves this core issue by replacing fragile implicit point matching with an explicit correspondence map that drives attention controls during generation. This correspondence-driven preservation enables robust edits under full-strength inversion without TTO. As a result, LazyDrag preserves identity and background, supports faithful inpainting, and leverages text guidance to resolve ambiguity in drag instructions.
Extensive experiments show that LazyDrag achieves state-of-the-art performance, unifying precise control with text guidance to execute complex semantic edits. By revealing that the perceived stability–quality compromise is an artifact of flawed point matching, LazyDrag establishes a more powerful and principled foundation for future research and marks a concrete step toward intuitive, AI-native creative workflows and more sophisticated generative control.

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## Appendix A Implementation Details

### A.1. Inference Settings

For all baselines, we use their official code with default hyperparameters for inference. The number of denoising steps is set to 50, and classifier-free guidance (CFG) (Ho and Salimans, 2021) is set to 1. All images on Drag-Bench are generated at their original resolution, while other images are generated at $1024\times 1024$. All generations are performed on a single NVIDIA H800 GPU.

EasyDrag (Hou et al., 2024) and CLIPDrag (Jiang et al., 2025) are excluded from comparison because their released implementations either fail to execute reliably or do not reproduce the results reported in the papers.

For Inpaint4Drag, we remove the LCM (Luo et al., 2023) LoRA and fix the number of denoising steps to 50. We also replace the distilled VAE (Kingma and Welling, 2013) with the original VAE to improve reconstruction and generation quality. These settings are chosen to obtain the strongest editing performance rather than to optimize for speed.

For our inversion process, we adopt the official inversion method of UniEdit-Flow (Jiao et al., 2025) but replace the editing component with our proposed strategy. We apply our correspondence-driven preservation (Sec. [3.3](https://arxiv.org/html/2509.12203v2#S3.SS3)) only to the single-stream attention layers in FLUX.1 Krea-dev (Labs, 2025). Since additional manipulation in dual-stream attention layers does not lead to noticeable improvements (Deng et al., 2025; Yin et al., 2025; Wang et al., 2025b), we adopt a more efficient and concise design by limiting modifications to single-stream layers only.

### A.2. Implementation Details of Displacement Field Calculation

##### Per-instruction displacement.

Following the principles of elasticity (Naylor, 1969; Zhao et al., 2024), the influence of an external force decays inversely with distance from the force origin, and the direction of the induced displacement aligns with the direction of the applied force. We represent each drag instruction $\bm{d}_{i}$ as a vector from source $\bm{s}_{i}$ to target $\bm{e}_{i}$. For $\bm{p}_{j}\in\mathcal{P}$, we write

$$ (9) $\bm{v}_{j}^{i}=\lambda_{j}^{i}\,\bm{d}_{i},$ $$

where $\lambda_{j}^{i}$ is a stretch factor. Using a reference circle $O$ that circumscribes the bounding rectangle of $\mathcal{P}$, extend the ray $\bm{s}_{i}\!\to\!\bm{p}_{j}$ to intersect $O$ at $\bm{q}_{j}^{i}$. Enforcing parallelism between $\bm{v}_{j}^{i}$ and $\bm{d}_{i}$ yields

$$ (10) $\lambda_{j}^{i}=\frac{\lVert\bm{v}_{j}^{i}\rVert_{2}}{\lVert\bm{d}_{i}\rVert_{2}}=\frac{\lVert\bm{p}_{j}-\bm{p}_{j}^{i}\rVert_{2}}{\lVert\bm{s}_{i}-\bm{e}_{i}\rVert_{2}}=\frac{\lVert\bm{p}_{j}-\bm{q}_{j}^{i}\rVert_{2}}{\lVert\bm{s}_{i}-\bm{q}_{j}^{i}\rVert_{2}}.$ $$

##### Winner-takes-all blending.

Weighted averaging multiple instruction can fail when different drags point in opposite directions. We therefore assign each $\bm{p}_{j}$ to its nearest handle $\bm{s}_{i}$ (a Voronoi partition (Aurenhammer, 1991)) as illustrated in Fig. [3](https://arxiv.org/html/2509.12203v2#S3.F3) (a), where the red and blue regions correspond to two drag instructions, with weights

$$ (11) $\alpha_{j}^{i}=\begin{cases}\lVert\bm{p}_{j}-\bm{s}_{i}\rVert_{2}^{-1},&\bm{p}_{j}\neq\bm{s}_{i},\\ \infty,&\text{otherwise}.\end{cases}$ $$

The final displacement is determined by the winning instruction $i^{\star}=\arg\max_{i}\alpha_{j}^{i}$:

$$ (12) $\bm{v}_{j}=\bm{v}_{j}^{i^{\star}}=\lambda_{j}^{i^{\star}}\,\bm{d}_{i^{\star}}.$ $$

This yields sharper spatial separation and avoids interference between opposing drags.

##### Unified move/scale model.

For axis-aligned resizing, we introduce a scaling vector $\bm{r}\in\mathbb{R}^{2}$ to form a unified model:

$$ (13) $\bm{v}_{j}=\lambda_{j}^{i^{\star}}\,\bm{d}_{i^{\star}}+(\bm{r}-\mathbf{1})\otimes(\bm{p}_{j}-s_{i^{\star}}),$ $$

where $\otimes$ denotes element-wise product. For a move-and-scale operation, we set $\lambda_{j}^{i^{\star}}=\alpha_{j}^{i^{\star}}=1$.

### A.3. Evaluation Details

For the VIEScore evaluation, we follow GEdit-Bench (Liu et al., 2025a), using the same prompts for PQ and O. For SC, we adopt the instruction shown in Fig. [14](https://arxiv.org/html/2509.12203v2#A2.F14), together with the source image, drag-instruction image, and the edited image. Score collection and calculation are carried out using the official GEdit-Bench codebase.

### A.4. User Study Details

To evaluate the effectiveness of our method, we randomly selected 32 results for nine comparison methods on Drag-Bench (Shi et al., 2024) and shuffled their indices to ensure a fair comparison. We invited 20 participants, each with relevant skills, to perform the tasks following the instructions provided through the user interface, as shown in Fig. [13](https://arxiv.org/html/2509.12203v2#A2.F13).

## Appendix B More Results and Analysis

Figure: Figure 8. Examples of Drag-Bench cases with various additional text prompts.
Refer to caption: x8.png

Figure: Figure 9. Effect of activation timestep sensitivity on Drag-Bench. From left to right, the activation timestep is progressively increased.
Refer to caption: x9.png

**Table 5. Quantitative ablation of WTA and Latent Init with U-Nets on Drag-Bench.**
| Method | $\text{MD}~\downarrow$ | $\text{SC}~\uparrow$ | $\text{PQ}~\uparrow$ | $\text{O}~\uparrow$ |
| --- | --- | --- | --- | --- |
| FastDrag (Zhao et al., 2024) | 31.84 $\pm$ 0.96 | 7.935 $\pm$ 0.09 | 8.278 $\pm$ 0.01 | 7.904 $\pm$ 0.06 |
| + WTA | 28.55 $\pm$ 0.07 | 8.049 $\pm$ 0.06 | 8.339$\pm$ 0.01 | 8.012 $\pm$ 0.03 |
| + Latent Init | 28.97 $\pm$ 0.17 | 8.081 $\pm$ 0.03 | 8.341 $\pm$ 0.01 | 8.050 $\pm$ 0.02 |

Figure: Figure 10. Failure cases on Drag-Bench.
Refer to caption: x10.png

Figure: Figure 11. Qualitative ablation of WTA and Latent Init with U-Nets on Drag-Bench.
Refer to caption: x11.png

### B.1. More Results on DragBench

Fig. [12](https://arxiv.org/html/2509.12203v2#A2.F12) presents additional qualitative results on Drag-Bench. As shown, our method produces more natural and accurate outputs while better preserving background consistency compared to other baselines. These results further demonstrate the robustness and effectiveness of LazyDrag.

Figure: Figure 12. Additional qualitative results compared with baselines on Drag-Bench.
Refer to caption: x12.png

Figure: Figure 13. User interface for user study.
Refer to caption: imgs/user_study.jpg

Figure: Figure 14. Instruction of SC evaluation.
Refer to caption: x13.png

### B.2. Effect of Text Guidance

Fig.[8](https://arxiv.org/html/2509.12203v2#A2.F8) shows examples from Drag-Bench with different text guidance prompts. The results demonstrate that LazyDrag effectively resolves ambiguities caused by drag instructions alone when additional guided prompts are provided. Unlike prior methods such as DragText(Choi et al., 2025) and CLIPDrag (Jiang et al., 2025), our approach enables more complex and precise text guidance.

### B.3. Effect with U-Nets

While our full method is designed for MM-DiTs, key components such as WTA and Latent Init (Sec. [3.2](https://arxiv.org/html/2509.12203v2#S3.SS2)) are also compatible with U-Nets. To demonstrate this, we conduct an ablation study on the U-Net-based FastDrag (Zhao et al., 2024). First, we replace the original average blending of multiple drag instructions with our WTA blending. Second, we substitute the original BNNI interpolation with standard normal noise added to the image latent, scaled to the inversion strength.
As shown in the top row of Fig. [11](https://arxiv.org/html/2509.12203v2#A2.F11), our blending method improves target localization under complex, multi-instruction scenarios. This is reflected in improved MD and SC scores in Tab. [5](https://arxiv.org/html/2509.12203v2#A2.T5), computed on Drag-Bench (which includes 97 multi-drag cases). In the bottom row of Fig. [11](https://arxiv.org/html/2509.12203v2#A2.F11), our random initialization reduces repetitive pattern artifacts, aligning with the quantitative gains in PQ and O.

### B.4. Limitations

Fig. [9](https://arxiv.org/html/2509.12203v2#A2.F9) illustrates failure cases on Drag-Bench when the final activation timestep is set too high for handling multiple dragging instructions. While the results show accurate target positions for the dragged points, they exhibit unnatural artifacts, especially when target points overlap. By slightly reducing the final activation timesteps, the results appear more natural while still preserving reasonable target positions. Additionally, due to the VAE compression in diffusion models and the latent patching strategy(Esser et al., 2024), the model struggles with very small drag distances. As shown in Fig. [10](https://arxiv.org/html/2509.12203v2#A2.F10), the model can execute fine-grained edits such as closing the eyes, but slight positional shifts may still occur.

Moreover, the quality of both the edit and generation heavily depends on the underlying base model. As foundation models continue to improve, we anticipate that the performance and applicability of our method will evolve accordingly.

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: - Can you provide numbers for runtime and memory usage per edit (including inversion) compared to speed-oriented methods such as FastDrag-style or inpainting-based approaches on same hardware?

- Are there architectural assumptions that make LazyDrag significantly easier to implement on MM-DiTs than on U-Net backbones, or is it a design choice?

- How robust is the method to some poorly placed drag points, for instance when both handle and target lie slightly off the intended object or cross semantic boundaries?
soundness: 3
strengths: - Clear diagnosis of why attention-similarity–based matching fails under strong inversion, which directly motivates the shift to explicit correspondence maps and gives the method a coherent conceptual foundation.

- Elegant use of a single correspondence map to control both latent initialization and attention, enabling principled handling of background, inpainting, and transition regions and reducing artifacts from naïve displacement blending.

- Strong emprical results on a standard drag-editing benchmark, with consistent gains in spatial accuracy, semantic consistency, and perceptual quality over several recent drag-based baselines, and without test-time optimization.

- Qualitative and user-study evidence that the method handles complex and text-guided edits (e.g. ambiguous drags, newly revealed content) in a way that is preferred by human raters.
summary: This paper presents LazyDrag, a training-free drag-based image editing method tailored to multi-modal diffusion transformers. The core idea is to replace implicit attention-based point matching with an explicit correspondence map computed from user drag instructions. This map drives both latent initialization in uncovered regions and attention control via token replacement and gated blending, which allows edits under strong inversion while preserving image identity. Experiments on a standard drag-editing benchmark and several recent baselines show improved spatial accuracy, semantic consistency, and perceptual quality without test-time optimization. A user study further indicates that human raters prefer LazyDrag in most cases, suggesting practical relevance for interactive editing.
weaknesses: - Comparative results are confounded by differences in backbone and inversion quality, because LazyDrag runs on a strong MM-DiT while several baselines still use UNet models. Implementing LazyDrag on a shared U-Net backbone or porting at least one baseline to the same MM-DiT would clarify how much of the gain comes from the editing strategy itself.

- Efficiency claims remain qualitative, even though interactive editing hinges on latency and resource usage; reporting wall-clock runtime and memory consumption against speed-oriented methods would make the practical advantages more credible.

- Generalization beyond a single MM-DiT backbone and benchmark is not demonstrated.

- The text-guided resolution of ambiguous drags is mainly shown through qualitative examples. Maybe a small controlled benchmark or focused user study on this aspect would better quantify its advantage over purely drag-based methods.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 2
questions: 1.	This paper treats background and identity differently. How are background and objects detected and distinguished in practice? Are there any additional steps, or do you rely on other methods?
2.	The shift to DiT-based image models is natural, since newer models are generally more powerful than earlier ones. This could bring future benefits to the drag editing community.
soundness: 3
strengths: 1.	Modern backbone: Extends drag editing from U-Net diffusion (e.g., Stable Diffusion) to contemporary DiT architectures, which is timely and relevant.
2.	No test time optimization: The method avoids test-time optimization, which can reduce engineering complexity and potentially runtime.
3.	Instruction clarity: The correspondence map makes drag constraints explicit and easier to interpret.
4.	Compelling results: Both qualitative figures and reported metrics indicate performance gains over prior work.
summary: The paper introduces LazyDrag, a drag-based image editing method for DiT-family models (MM-DiTs). It performs full-strength inversion and avoids test-time optimization (TTO). The core idea is to learn an explicit correspondence map guided by text, combined with background and identity preservation to deliver high-quality, controllable drags. The paper provides broad comparisons with prior drag-editing approaches and reports strong qualitative and quantitative results.
weaknesses: 1.	Runtime and efficiency claims need evidence
No TTO does not automatically imply faster end-to-end editing if additional modules (inversion, correspondence estimation, preservation) are heavy. 
Could the authors report end-to-end wall-clock time, GPU memory for LazyDrag vs. other methods? Include all components and required steps related to that method, both TTO free and non-free methods.

2.	The paper lists full-strength inversion as a main contribution. Why is full-strength inversion necessary for drag editing? In Fig. 2, full-strength inversion appears to improve LazyDrag’s results, but is this inherently required by the drag-editing setup? Also, higher inversion strength likely increases computation time.

3.	Please add experiments demonstrating that the performance gains come from the LazyDrag design rather than from the MM-DiT backbone. Prior methods use U-Net models (e.g., Stable Diffusion), while this paper uses FLUX.1 Krea-dev. How do you show that improvements are not dominated by the model shift? 

4.	The experiments report Semantic Consistency and Perceptual Quality. Could you add results to justify that these metrics are suitable for drag editing, for example, by also reporting other metrics used in related work and comparing against them?

5.	The stated contributions and the method description feel slightly misaligned. The methods section is divided into two parts, with the second devoted to preservation, but “preservation” is not clearly reflected among the main contributions. Meanwhile, the introduction emphasizes no test-time optimization and full-strength inversion. Clarifying and aligning the concepts and contributions across the introduction and methods would improve the paper.

---

confidence: 3
contribution: 2
initial_rating: 6
presentation: 3
questions: * The quantitative evaluation seems impressive, but why does the qualitative evaluation seem worse than the baselines? E.g., In Figure 8 last row, the man's face seems distorted compared with DragNoise.
soundness: 2
strengths: * From the experiments, this method achieves the state-of-the-art performance in DragBench (Table 1), as well as achieving solid empirical results with user preference. In addition, this paper also integrates editing, like scaling and movement. Although these operations are relatively common in latent editing, it's good to show such ability in one pipeline.
* Compared with optimization-based methods like Dragdiffusion and DragGAN, the proposed method provides faster inference speed.
* The overall pipeline is clearly illustrated and easy to understand.
summary: The paper proposes LazyDrag, a training-free drag-based image editing method for MM-DiTs that replaces point matching with an explicit correspondence map derived from user drags. This design enables stable full-strength inversion without test-time optimization. LazyDrag supports multi-round operations (move/scale) and resolves ambiguous drags via prompt guidance, delivering higher drag accuracy and visual quality than prior baselines on DragBench and in user studies. From the paper, this is: (1) the first MM-DiT drag editor under full-strength inversion, (2) explicit correspondence-driven attention controls that remove TTO, and (3) SOTA results with text-guided, natural inpainting.
weaknesses: * The core idea of explicit drag-to-correspondence plus attention control feels incremental relative to prior drag/edit pipelines. Specifically, the TTO-free idea has been used in many previous works (like SDE-Edit, RegionDrag), although the way of replacement may be different. The full-strength editing is also natural in the inversion-based editing. From this perspective, the technical novelty of this paper is not strong. The abstract and relative part needs to claim the core contribution more carefully and clearly. 
* Although the paper shows significant improvements in Table 1, the metrics' increase seems much more marginal in Table 5, meaning that the major improvement factor comes from changing to DiT. Since it makes such a big difference, it is helpful to remind of this detail in Table 1, which shows both cases (UNet and DiT).
* A detailed memory and time cost of the proposed method is missing, especially when the paper emphasizes the efficiency of TTO-free design.

---

confidence: 3
contribution: 2
initial_rating: 4
presentation: 2
questions: Could you elaborate on the failure modes of the "winner-takes-all" strategy for displacement field calculation? For instance, how does it behave with a high density of conflicting drag points in a small region, and what are the computational implications?
soundness: 3
strengths: 1.  **Novel Application and Success on DiT Architectures:** This is arguably the first work to successfully and robustly implement drag-based editing on the emerging MM-DiT architecture. As the field moves from U-Nets to Transformers, this paper provides a timely and valuable contribution, demonstrating a viable path for precise spatial control on these powerful new backbones.

2.  **Principled Solution to a Fundamental Problem:** The authors correctly identify a core bottleneck in previous drag-editing methods—the unreliability of implicit attention-based point matching. Their proposed solution, an explicit correspondence map, is a direct and elegant way to solve this issue. This allows them to eliminate the need for costly test-time optimization (TTO) and, more importantly, enables stable full-strength inversion, which is a significant step forward for the task.

3.  **Impressive Qualitative and Quantitative Results:** The method demonstrates state-of-the-art performance on the DragBench benchmark. The qualitative results are compelling, showcasing complex edits (e.g., opening a mouth and inpainting the interior, generating new objects with text guidance) that prior methods struggle with. The quantitative improvements in mean distance and VIEScore, despite requiring no TTO, strongly support the effectiveness of the proposed approach.
summary: The paper introduces LazyDrag, a training-free, drag-based image editing method designed for Multi-Modal Diffusion Transformers (MM-DiTs). The core problem it addresses is the instability of prior methods, which rely on implicit point matching via attention, forcing them to use test-time optimization (TTO) or weakened inversion strength. LazyDrag's key contribution is to replace this fragile implicit matching with an explicit correspondence map derived directly from user drag instructions. This deterministic guidance enables stable editing under full-strength inversion without TTO, unlocking high-fidelity inpainting and robust text-guided control. The method is shown to outperform existing baselines on the DragBench benchmark in both accuracy and perceptual quality.
weaknesses: 1.  **Confounded Comparison of Foundation Models:** The primary quantitative results claim state-of-the-art performance by comparing the proposed method, built on a powerful MM-DiT backbone (FLUX.1), against baselines developed for older U-Net architectures (e.g., Stable Diffusion v1.5). This comparison is confounded, as it's unclear how much of the performance improvement is due to the novel LazyDrag algorithm versus the superior generative capabilities of the underlying foundation model. A more rigorous evaluation would involve comparing the methods on the same backbone to isolate the algorithmic contribution.

2.  **Insufficient Differentiation from Prior Motion-Guided Methods:** The core idea of using an explicit motion field to guide a training-free editing process has precedents in recent literature (e.g., InstantDrag, LightningDrag). While the paper's specific implementation—transforming this field into a correspondence map to directly control attention is novel, the manuscript would be stronger if it more clearly articulated this distinction. A direct, mechanistic comparison of how LazyDrag *uses* the motion information versus how prior works use it (e.g., for latent warping or loss-based supervision) is needed to better situate the paper's unique contribution.

3.  **Sensitivity to Key Hyperparameters:** The method's effectiveness appears highly sensitive to the `activation timestep` hyperparameter, as revealed in the paper's own ablation study (Sec. 4.6). The results show a stark trade-off: lower values fail to move points accurately, while higher values introduce significant artifacts, especially with complex instructions. This implies that while the method avoids test-time optimization, it may require careful, per-edit manual tuning to achieve satisfactory results, which somewhat tempers the "lazy" and automated nature of the proposed workflow.

---

confidence: 5
contribution: 2
initial_rating: 6
presentation: 2
questions: The paper reports a user study with 20 expert participants evaluating 32 samples each. Could the authors clarify whether this sample size is sufficient to ensure statistical significance and generalization?
soundness: 2
strengths: 1 The method explicitly associates point matching with the user’s drag instructions following latent warpage function, removing the uncertainty of attention-based implicit matching. This design eliminates many stability and consistency issues seen in previous drag-editing approaches.


2 LazyDrag achieves state-of-the-art results on DragBench across all major metrics, demonstrating both higher drag accuracy and better perceptual quality without requiring test-time optimization.
summary: LazyDrag is a training-free drag-based image editing method for Multi-Modal Diffusion Transformers. It replaces unstable attention-based point matching with an explicit correspondence map derived from user drags, which is very similar to Latent Warpage Function of FastDrag, enabling stable full-strength inversion without test-time optimization. This design preserves identity, maintains background consistency, and supports text-guided edits for complex transformations like movement or scaling. Evaluated on DragBench, LazyDrag achieves state-of-the-art accuracy and perceptual quality while remaining efficient and robust, demonstrating that explicit geometric correspondence can unify precise spatial control and semantic guidance in diffusion-based editing.
weaknesses: 1: Computational complexity not included. The paper does not provide quantitative analysis of runtime or memory costs. Since attention modification is applied across multiple layers and steps, its efficiency in large-scale or real-time scenarios is unclear.

2: Limited novelty beyond integration. While the explicit correspondence map is effective, serveral components build upon existing ideas from FastDrag in method section but not discussed in the intro sec. The contribution may be seen as a strong integration rather than a fundamentally new mechanism. 

Anyway, I still view it as a meaningful and well-executed improvement. So my suggetion is the authors could explicitly position their contribution as the first to replace implicit point matching with an explicit correspondence map, and visually emphasize this transition and its advantages in the Introduction (e.g., replace the current Figure 1 with a more conceptual figure illustrating this replacement and maybe with a table which shows the adavantages, and move the current example figure to the next page). Secondly, in the method section, a concise side-by-side illustration figure comparing FastDrag’s and LazyDrag’s formulations would also help readers immediately see where the improvements lie. A good example is the left part of Fig1 in https://arxiv.org/pdf/2503.20783.

3: Experiments are mainly conducted on FLUX-based MM-DiT and a single benchmark.


Decision: Accept (Poster)