LazyDrag: Enabling Stable Drag-Based Editing on Multi-Modal Diffusion Transformers via Explicit Correspondence
Authors: Zixin Yin
Xili Dai
Duomin Wang
Xianfang Zeng
Lionel Ni
Gang YU
Heung-Yeung Shum
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion
DiT
Image Editing
Primary area: generative models
TL;DR: LazyDrag is the first drag-based image editing method for MM-DiTs.  It generates an explicit correspondence map to boost the attention control which obviates the necessity for test-time optimization and unlocks the generative capability.

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, 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 hands into pockets. Moreover, LazyDrag supports multi-round edits with simultaneous move and scale operations. Evaluated on DragBench, our method outperforms baselines in drag accuracy and perceptual quality, as validated by mean distances, VIEScore and user studies. LazyDrag not only sets new state-of-the-art performance, but also paves a new way to editing paradigms. Here is the project website.

Published as a conference paper at ICLR 2026
LAZYDRAG: ENABLING STABLE DRAG-BASED EDIT- ING ON MULTI-MODAL DIFFUSION TRANSFORMERS VIA EXPLICIT CORRESPONDENCE
Zixin Yin
Xili Dai
Duomin Wang²
Xianfang Zeng²
Lionel M. Ni Gang Yu²
Heung-Yeung Shum
TTee Hong Kong University of Science and Technology SStepFun
3 The Hong Kong University of Science and Technology (Guangzhou)
ABSTRACT
The reliance on implicit point matching via attention has become a core bottleneck in drag-based editing, resulting in fundamental compromise on weakened inver- sion strength and costly test-time optimization (TTO). This compromise severely limits the generative capabilities, suppressing high-fidelity inpainting and text- guided creation. In this paper, we introduce LazzyDrag, the first drag-based image editing method for Multi-Modal Diffusion Transformers, which directly elimi- nates the reliance on implicit point matching. In concrete terms, our method gen- erates explicit correspondence map from user drag inputs reliable reference to boost the attention control. This reliable reference opens the potential for a sta- ble full-strength inversion process, which is the first in the drag-based -ased editing task. It obviates the necessity for TTO and unlocks the generative capability of models. Therefore, LazyDrag naturally unifies precise geometric control with text guid- ance, enabling complex edits that were previously out of reach: opening the mouth of dog and inpainting its interior, generating new objects like "tennis ball". or for ambiguous drags, making context-aware changes like moving hands into pock- ets. Moreover, LazyDrag supports multi-round edits with simultaneous move and scale operations. Evaluated on DragBench, our method outperforms baselines in drag accuracy and perceptual quality, as validated by mean distances, VIEScore and user studies. LazyDrag not only sets new state-of-the-ar performance, but also paves a new way to editing paradigms. Here is the project website
INTRODUCTION
Drag-based editing in diffusion models remains fundamentally challenging. To preserve object iden- tity 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 seman- tically related ones (Wang et al., 22025b] 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 principled alternative: replace implicit iicention-basee match- ing 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 net- work 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 foun- dation for this shift, because MM-DiTs offer tighter vision-ter fusion, which improves inversion robustness and raises the ceiling for attention control. As shown by ColorCtrl ( (Yin et al.) 2025b). this architecture supports stronger semantic consistency and controllability, allowing attention control to

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User Tnput
Ours (Training-Preo)
Ours (Training-Free)
GoodDrag (TTO)
GoodDrag DragText (TTO)
N/A
tennis ball in mouth"
N/A
"tannis ball mouth"
"hand the back"
band th trouser pocket"
NAA
hand the trouser pocket
User Input (Step 1)
Result1
User Input (Step 2)
Resalt2
User Input (Step
Result3
mDPe 10 5. spaud
sand castle the left"
N/A
N/A
Comparison between our method and two baselines. The leftmost image shows the input image with multiple drag instructions, each indicated by different color. The text below each result indicates the additional prompt used for generation. "N/A" means no additional prompt. TTO denotes test-time optimization, where the method requires fine-tuning per image and multi-step latent optimization per drag instruction. Notably, our method successfully opens the mouth of the dog and inpaints its interior. Furthermore, with prompt guidance, we can generate diverse results even under ambiguous drag imputs without fine-tuning. (b) Bottom: Multi-round editing results using our approach. Our method supports not only sequential drag operations but also simultaneous actions like movement and scaling, maintaining visual coherence throughout.
Figure 1: (a) Top:
be applied across 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 non-trivial. Simply sharing key and value tokens, 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.) 2025b) showed that re-encoding and injecting semantically aligned tokens can preserve identity in MM-DiTs. How- ever, 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 field that maps handle points to target points, forming a deterministic correspondence map. We turn this explicit map into attention controls. This explicit correspondence-diig drivee preservation resolves the root issue, stabilizes edits under full-strength inversion without TTO. As result, it enhances in- painting and text guidance ability, delivering higher fidelity and controllability than prior methods.
In this work, we present Lazy Drag, 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, 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 capa- bility and enabling text-guided edits under ambiguous instructions. As shown in Fig. I this allows our method to execute complex edits where prior works fail: it can open the mouth of the dog and inpaint its interior, ods constrained by low inversion strength (see Fig. 2). Furthermore, it exhibits a deep understanding of scene context. For example, when dragging hand using drag instructions alone, the ambiguity
generate "tennis ball" via text guidance, which is impossible for meth-
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User Input
Strength 0.6
Strength 0.65
Strength 0.7
Strength 0.8
Strength
Figure 2: Effect of inversion strength, Examples of LazyDrag under different inversion strengths. The additional prompt is a red apple in the mouth".
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-ard (SOTA) performance while requiring no test- time optimization. To the best of our knowledge, LazyDrag the first drag-basae 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 achieve full- tull-strength inversion in drag- brage editing with MM-DiTs. It is accomplished by an explicit porrespondence-driver 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.
We resolve the ambiguity of drag instructions by coupling the explicit correspondence map with text guidance. This correspondence-drivv method preserves identity and background, while enabling natural inpainting and semantically consistent modifications.
Extensive experiments demonstrate that LazyDrag significantly outperforms all existing methods on BenghBench in both quantitative metrics and human preference.
RELATED WORK
Text-to-image and video generation. GAN-based models (Reed al. 2016 Yu al. 2023 Wang et al. 2023) have been largely replaced by diffusion models with U-Net backbones (Ho et al., Rombach et al. 2022) due to better fidelity and stability. However, U-Nets scale poorly, prompting shift toward Diffusion Transformers (DiT) (Peebles & Xie 2023). Among them, MM-
2020
DiT
Esser et al. 2024) has become the backbone of choice in recent sfate-of-the-an systems (Esser
al.
2024 AI 2024
Labs 2024] Yang et al. 2024 Kong et al.) 2024 Liu et al., (2025a), including We are the first to introduce a drag-based editing method within MM-DiTs.
FLUX
Labs
2025).
Text-based editing. Training-fres text-guided editing methods use pre-trained diffusion models without fine-tuning, offering strong flexibility. Prompt-to-Prompt (Hertz al., 2023) edits attention maps for localized control, with extensions to images and videos (Wang et al. 2025a Liu et al.,
2024b Cao al. 2023 Rout al. 2025 Xu et 2025 Ju al. 2024| Yin et al.) (025a). Recent work explores attention control in MM-DiTs: DiTCtrl (Cai et al. 2025) for long video generation, ColorCtrl (Yin al. (0256) for light-consistent color edits, and CharaConsist (Wang al. (025b)
for preserving character identity. Modern approaches such as Step1 X-Edit (Liu et al. 2025b) and GPT-4o (OpenAT] 2025) have gained popularity due to their efficiency. However, all rely solely on text, which limits spatial precision. We instead introduce 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 for-
mer, beginning with DragGAN (Pan et al. 2023), and expanding to diffusion-bassee
approaches (Shi
et al. 2024b Mou et al. 2024a Liu et al. 2024a Hou et al. 2024 Shin et al.) 2024 Zhou et al.
2025
Ling al. 2024
Zhang al. 2025 Shi et all 2024a). RegionDrag (Lu al. 2024 extends

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Published as a conference paper at ICLR 2026
Inversion
Voromoi boundaries
MM--DD 1Block
SSS. SS.Arr
&
POOO
User Input
Correspondence
Generation
HMM-g m Sinee
Output Control
Input Control
Mh DIT Block
in mouth
Output Control
Atta Output
Re-encade
Q.
Output Image
Sampllig
Zy
Replace
(a) Overall Pipeliie
(b) Input Control
Re-encode
Figure 3: Pipeline of LazyDrag. (a) An input image is inverted to latent code Our corre- spondence map generation then yields an updated latentêr, point matching map, and weights a. 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), Q, K, and V tokens are replaced with their cached originals. For destination (red and blue colors) and transition regions (yellow color), the and V tokens are concatenated with re-encoded (K only) source tokens retrieved via the map (c) Attention output refinement performs value blending of attention output. & and @ denotes glement-wiss product and addition.
the interface support region-level editing. Some methods (Jiang et al. 2025 Choi et al. 2025 in- corporate textual prompts to improve semantic understanding, but still suffers from complex instruc- tions. FastDrag (Zhao al., 2024) is of only two notable TTO-free methods, achieving faster inference but still falling short of the quality delivered by TTO-based methods. Inpaint4Drag (Lu & 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 frequent boundary artifacts and blurring, even with assistance from modern mask generators (e.g.. SAM (Kir- illov al. 2023 Therefore, we adopt 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.
Han 2025) is the other TTO-free method that build
METHOD
Our goal is to achieve irentity-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 un- der 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 con- trol, stabilizing the inversion process without test-time optimization. We first review foundational concepts in Sec, 3.1 Then detail our two-stage approach: first, how to generate the explicit corre- spondence map from drag instructions (Sec. 3.2). and second, how this map drives a novel two-part attention control for identity and background preservation (Sec. 3.3). Fig. shows the pipeline.
3.1 PRELIMINARIES
LazyDrag builds upon insights from training-free free drag- based editing methods in U-Nets (Sec. 3. and identity preservation in MM-DiT's (Sec. 3.2.2, addressing limitations of both (Sec. 3.1.3

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3.1.1 TRAINING-FREE DRAG EDITING IN U-NETS: FASTDRAG.
FastDrag (Zhao al. 2024 the first training-free method for drag-based editing, with U-Net models. It has two parts: (1) it computes displacement field from drag instructions to create an ini- tial latent εr, filling exposed regions via interpolation, and (2) it applies MasaCtrl-like (Cao 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 instruc- tions is brittle: when drags antagonistic (for example, opening mouth by moving the upper lip upward and the lower lip downward), averaging the displacements cancels motion near the seam and the mouth fails open. Moreover, the interpolation used fill newly exposed regions further replicates nearby textures, producing repeated artifacts in large uncovered areas, as shown in Fig.
3.1.2 IDENTITY PRESERVATION IN MM-DITs: CHARACONSIST.
In parallel. CharaConsist (Wang et al. 2025b 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- (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 (requir- ing additional denoising steps) and inherently unstable. Under full-strength inversion, even minor mismatches in the correspondence map can lead to significant visual artifacts, proved in Tab.
3.1.3 LAZYDRAG: BRIDGING THE GAP.
Naively extending FastDrag from U-Nets to MM-DiTs and combining it with the attention control methods of CharaConsist exposes and amplifies their respective weaknesses, yielding unusable re- sults, as shown in Fig. and Tab. LazyDrag resolves these weaknesses with a unified solution: an explicit correspondence map derived from drag instructions. This map provides stable, precise atten- tion control throughout the generation process, enabling high-quality, accurate edits while avoiding the pitfalls of attention-similariyy matching and the trade-off's 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 zr. The map comprises a matching point function M and weight function A. which provides explicit guidance. Guided by this map, we generate the initial latent noise ž7.
Displacement field calculation via tinner-takes-all (WTA). Let Q denote the latent grid, and let P {p,) C Ω be the editable regions (the bright area in Fig. 3). sampled as feature points. where s; and e; are the handle and target points of
Let the drag instructions be D {(s,, the i-th instruction. We illustrate two modes for computing the displacement field. In drag mode, we adopt the elasticity-based per-instruction displacement vy for each p, under the i-th instruction as in ZZhao 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 199T) fusion: each Pj is uniquely assigned to its nearest handle, inducing Voronoi partition (Aurenhammer 199T). The final displacement vy and weight ay are determined solely by the winning instruction.
e,)]
flp 00,
s: s
Pj + 8i, otherwise,
(1)
f of a af" where i" arg maxa
Here, |l:112 denotes the Euclidean norm distance. Thus, V fu, 1 is defined as the displace- ment 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
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Initial latent construction and map formalization (Latent Init). With the displacement field V established, we construct the initial latent εr. This process defines our explicit deterministic correspondence map (M, A) and partitions the latent grid into distinct regions for targeted control. First, we define the set of discrete destination coordinates P = (TT + -1)) Pj E P), where IT(.) projects the grid. By resolving collisions where multiple source points map to single destination E p** (using winner-takes-all) we get the winner index j* = arg máx j: 11(p,t,)== aj and formalize our correspondence map: Matching point map, .M = (w)) Matching weight map. A(x) = min(1, (z)) Next, we partition the latent space Ω into four disjoint sets based on the geometry of the warp. These sets correspond directly to the colored regions in Fig. (a): Background R. (gray) that must remain unchanged, Destinations Raltt (red and blue, a. a.k.a., P* where moved content is rendered with identity preserved, Inpainting Rimp (yellow) initialized from noise, and Transition Rtrans (green) that blends boundaries smoothly. With these regions clearly defined, the updated latent ên is constructed by applying a specific replacement rule to each region:
(M())),
if Rdst
(T(x)=
c(x);
if R'P, if Rhe U R
2x (x),
where E (O, I). Crucially, replacing the BNNI interpolation used in FastDrag with Gaussian noise in RiPP essential. Unlike the uniform noise compared Zhao al. (2024). this approach aligns with the diffusion prior, prevents repetitive artifacts as shown in Fig. 11 and enables the ability of high-fidelity 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 1S3.. and output (Sec. (.3.2) of the attention calculation in single-strean attention
at the input (Sec. 3.3.1 layers only (Yin et al. 2025b Deng et 2025). Using this map, the mechanism provides fine- grained control that preserves identity and background, ensuring robust full-strength inversion.
3.3. ATTENTION INPUT CONTROL VIA TOKEN REPLACEMENT AND CONCATENATION
To preserve the background and identity, the first part modifies the attention inputs of different gions. Let (Qu: K Va) denote the current attention tokens at position æ in given layer and step, and (Q Kz, Va) ) the tokens cached without positional encoding during the previous inversion process. Let (-)Ex(() re-encode tokens with the rotary embedding at position (Su et al. 2024).
Background preservation via replacement (BG Pres.). For the background region Rhe the pur- pose 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. 2025b):
(Q.,, K ; V=)= (RoPE, (Qw), RoPE (Kx),, Vn), Vo: Rhe
Identity preservation via concatenation (ID Pres.). For the destination and transition regions where identity must be preserved while allowing for coherent adaptation, we use token concatenation. Define a unified source point map, M(x). which selects correspondence sources:
(M(x), if Rdu M(x) 11x,
if f Rtrans
For any position dil U Rirmm, we form augmented key K2 and value V by concatenating the cached tokens from its designated source M(x):
K' concat (Kr, RoPE,, (K ))
(5)
V concat (Va, V V(()))
(6)
This provides a strong, correspondence-drivv signal to the attention calculation, robustly preserving identity while allowing for smooth blending at the boundaries.

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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. (025b)), improving visual quality and emphasizing the importance of handle points over others. Let y be the attention output at x and Væ be the cached output. For æ E R Rdst
Yæ f (1 - Ta,1) Yæ + 7a V M(=)
where the blending factor T is gated by our pre-computed matching weight from the map A::
T h4 A(x),
(8)
where indexes the timestep and h4 [0, 1] is factor that decays over time. This correspondence-
driven gated merge eliminating the extra denoising steps required by CharaConsist, and addressing matching and scaling under full-strength inversion. By making the weight strongest at the handle points (where A(x: is maximal), it ensures precise control where matters most, removing the need for multi-step latent optimization in previous methods (Zhang
the instability of attention-simiin
et al. 2025 Shi et al. 2024b), while allowing for natural relaxation in surrounding regions.
EXPERIMENTS
4.1 SETUP
Baselines.
We compare against eight baselines: DragDiffusion (Shi et al. 2024b), DragNoise (Liu
et al.
2024a), FreeDrag (Ling et al. 2024), DifiEdiior (Mou et al. (2024a), GoodDrag (Zhang et al.
2025
DragText (Choi et al. 2025 FastDrag (Zhao et al. 2024). and Inpaint4Drag (Lu & Han
2025
Notably, all baselines are NeNeebadedd, whereas ours is the first MM-DiT- method.
Implementation details. Unless otherwise noted, all basclines are run with their official imple- mentations and default hyperparameters. For Inpaint4Drag (Lu & Han 2025). we adopt the refined masks and point pairs provided by the authors inference, and replace distilled models with original models. Our method is built on FLUX.1 Krea-dev (Labs) (025), adopting the inversion method of UniEdit-Flow (Jiao et al., 2025) while replacing the editing strategy with our approach. Following CharaConsist (Wang et al.) 2025b), we activate ID Pres. and Attn Refine (Sec. (33) for the first 40 denoising steps, referring to the last activate timestep as the activation timestep. For a fair compar- ison, the number of denoising steps is fixed to 50 for all methods. More details are in Appendix A.
Benchmark and evaluation protocol. We evaluate on DragBench (Sh et al. 2024b which con- tains 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 at 2224)) 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 complementary, perceptually grounded view, adopt the VIEScore (Ku let al. 2024) metrics from GEdit-Bench (Liu et al., 2025b): SC (Semantic Consistency): whether the intended edit has been achieved. PQ (Perceptual Quality): the naturalness of the result and ab- sence of artifacts. O (Overall): the overall performance defined in Liu et al.] (2025h). In our setting, the "intended edit" is specified by the dragging instruction rather than a natural-language instruc- tion, but the scoring criteria remain unchanged. Each score ranges from 0 to 10 (higher is better) and is produced by the state-of of-the-art art MLLM evaluator, GPT-40 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 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
'Since DragText is a plug-and-play method, we evaluate it in conjunction with hest-performing GoodDrag. "PP access of August 2025
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Table Quantitative results compared with baselines on Drag- BengBBench.
Method
TTO-Req
MD
SC
PQ
0 f
DragNoise (Luu
2024a
37.87
7.793
8.058
7.704 4:
DragDiffusion (Shi al. 2024h
34.84 4
7.905
0.0
8.325
7.798 0.3
FreeDrag [iigg ct a..
/
34.09
7.928
8.281
7,816 0.02
DiffEditor Mou al.
26.95
0.24
7.603
8.266
7.715 0.1
GoodDrag (Zhang
2025
22.17 0.16
7.834
8.318
7.795
TeagText (Chor cl
/
21.51
0.21
7.992
8.227
7.886
FastDrag 7haa et al,
2124
X
31.84
0.96
7.935
0.09
8,278 +
7.904 0.06
Trpaint4Drag Luu Han
2025
X
23.68
0.05
7.802
7,961
7.615 0.06
Ours
X
21.49
0.04
8.205
0.03
8.395 +: 0.03
8,210 # 0.03
User Inget
Clutne
Ing Ingadee
Fadtinag
BingTiit
Gardling
BiSt diter
Freding
Braghttmats
Braghittee
Figure 4: Qualitative results compared with baselines on Drag-Bench Best viewed with zoom-in.
4.2 QUANTITATIVE EVALUATION
Tab. [ 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 outper- forms existing approaches in all metrics, especially in terms of drag accuracy and the perceptual quality 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, In- paint4Drag (Lu & 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 masks and point pairs.
4.3 QUALITATIVE EVALUATION
Fig. 4 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 intro- duce artifacts, such as distorted hands (e.g., Drag'Text (Choi et al., (025)) or unintended background changes (e.g., DragNoise (Liu et al., (024aa). In the second example, most baselines fail to preserve the front structure of the vehicle, whereas approach maintains it faithfully while applying the desired transformation. Specifically, Inpaint4Drag (Lu & Han 2025) generates artifacts the back- ground. The third shows that only method successfully modifies the sofa geometry while preserving the integrity of pillows. In the fourth example, our approach correctly interprets hand proximity intent to insert into the pocket, while other baselines introducing artifacts. Finally, in
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Table 2: User study on Drag-Bench.
Coe Tege
Method
Preference (%)
DragNoise (Liu
2024a
6.64
Oux Oloottalli Ours Or Draa Modo
DragDiffusion (Smi ct al. 2024h
6,25
FreeDrag (Ling ai 2024
6.64
DiffEditor Moo ct ai
2024a
2.34 =
GoodDrag ZZang et al: 2025
5.86 1
DragText (Chor al. 2025
2.73 =:4
FasIDrag Zhao al..
2.34 1:5
Inpain14Drag (Lu 2025
3.13
Ours
63.67 +
Figure 5: Comparison between drag and move
mode on Drag-Bench
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 quantita- tive 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
4.4 USER STUDY
A total of 32 expert participants evaluated comparisons between methods on 32 cases randomly sam- pled from DragBench. For each comparison, method order positions were randomized and method identities were anonymized. Participants selected the preferred result according to predefined cri- teria (edit success, naturalness, and background preservation). Overall, LazyDrag was preferred in 61.88% of comparisons, outperforming all baselines (Tab. 2 More details are in Appendix
A..4 A.4
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. The move mode tends to better preserve identity, rather than performing edits involving rotation or extension, as the second and third examples. In contrast, the drag mode enables natural geometric transformations, including 3D rotations and extensions, albeit with 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-drin -divve preservation strategy. Future work may explore more matching strategies, such 2D rotation, to further enhance diversity and controllability.
in the last two cases,
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 component is absent, we adopt controlled replacements: (i) Without WTA and Latent Init (Sec. 3.2) 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 we switch to the cncention-similaiig matching and scaling intro- duced in CharaConsist (Wang et al., 20225).. Fig. 6 and Tab. 3 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 wanner-takes-all fusion strategy and random initialization for inpaint- ing regions suppresses repetitive artifacts and improves inpainting quality proven in the figure. Further disabling background preservation causes additional drops in SC and O due to color shift- ing and artifacts the background. Finally, replacing our correspondence-drivee preservation with attention antention-similarity control leads to sharp degradation, highlighting the sensitivity of full-strengee strength inversion 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 and Tab. 4 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 acti-

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Published as a conference paper at ICLR 2026
Souce image
User Inpon
Outs
Talent Int
Input
Artivation Timesteps
WIA
Figure 7: Qualitative ablation of activation timesteps on Drag-Bench. From left to right, the activation timestep is increased.
-B6 Pres
Table 3:
Quantitative cumulative ablation on Drag-Bent Bench under the same setting as Fig. 6
MD
10 Pr66 AtHn Rofan
Our
21.49
8,308
8,995
8,210
WTA Latent Init
23.69 111
8.129 308
8,000 are
7,908
IBG Pres.
26.73 1m
7,958 3M
8,045
7,865 am0
ID Pres Are Refine
56.49 2.
5,307 3m
7,944 3m
5.955 km
Figure 6: Qualitative cumulative ablation on Drag-Bol Trag-Bench. Rows remove one component rela- tive to the row above. When WTA and Latent Init removed use latent init in FastDrag. When ID Pres. and Attn Refine are removed we switch CharaConsist attention-similarity control.
Table 4: Quantitative ablation of activation timesteps on Drag- BenggBencc.
Methad
SC
Ours (40 ss adtivation timestopp
21.49 wos
8.205
8.355 c:
5.210 a.s
us activation timesipp
7.036
timasip
vation timestep results in more natural outputs, but may cause slight variations in identity motion. More results are given in Appendix B.5 For benchmark evaluations, we use as a balanced value.
Additional results. Appendix B presents additional evaluations on with U-Nets, effects text guidance, runtime analysis, and limitations.
ablation studies
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 based editing: the unreliability of implicit attention-basee point matching. This diagnosis explains why prior methods adopted compromises such as test-time optimization 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 en- ables robust edits under full-strength inversion without TTO. a result, LazyDrag preserves iden- tity and background, supports faithful inpainting, and leverages text guidance to resolve ambiguity in drag instructions. Extensive experiments show that LazyDrag achieves state-of-the-ar perfor- mance, unifying precise control with text guidance to execute complex semantic edits. By revealing that the perceived stability-quality compromise an artifact of flawed point matching, LazyDrag establishes more powerful and principled foundation for future research and marks concrete step toward intuitive, Al-native creative workflows and more sophisticated generative control.
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ETHICS STATEMENT
The development advanced image editing technologies inevitably raises important ethical con- cerns. Although our method enhances editing precision through and drag-based controls, also introduces potential risks, including the creation of misleading or harmful visual content. To address this, we emphasize the importance of using such tools responsibly, with clear attention to transparency and user consent in practical deployments. In addition, the underlying pre-trained models may encode and reproduce societal biases, which could influence the outputs in unintended ways. We view this as open research challenge and encourage future work aimed at bias detection and mitigation. All human evaluation participants were fully informed of the purpose of the study and provided consent before participation.
REPRODUCIBILITY STATEMENT
We have made every effort ensure the reproducibility of LazyDrag. Detailed descriptions of the inference procedure and evaluation settings are provided in Sec. [ Sec. [4.] and Appendix AA] All
source code will be released to the public upon acceptance of this paper, enabling researchers to fully replicate and build upon our results.

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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 & Salimans] (021) is set All images on Drag-Bench are generated at their original resolution, while other images are generated at 1024 x 1024. All generations are performed on single NVIDIA H800 GPU.
EasyDrag (Hou 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 re- sults reported in the papers. DragGAN (Pan et al., 2023) is also excluded due to its inferior per- formance and slower processing speed compared to diffusion-basee methods, as demonstrated in GoodDrag (Zhang et al. 2025).
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 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 inversion process, adopt the official inversion method of UniEdit-Flow (Jiao al. 2025 but replace the editing component with our proposed strategy. We apply our correspondence driven preservation (Sec. 3.. 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 improve-
ments (Deng et 2025; Yin al., 2025b Wang et al. 2025a), we adopt 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 elasticity (Naylor 1969 Zhao et al, 2024), the influence of external force decays inversely with distance from the force origin, and the direction the induced displacement aligns with the direction the applied force. We represent each drag instruction d; vector from source 8; target e;. For Pj E P, we write
X3 di,
where Xÿ is stretch factor. Using a reference circle O that circumscribes the bounding rectangle of P, extend the ray 8: P intersect O at q). Enforcing parallelism between of and d, yields
| |d
Ip, p
P,
o l1
(10)
| edl12
8
9
Winner-takes-all blending. Weighted averaging multiple instruction can fail when different drags point in opposite directions. We therefore assign each P5 to its nearest handle 8, (a Voronoi parti- tion (Aurenhammer 1991h) as illustrated in Fig. (a), where the red and blue regions correspond to two drag instructions, with weights
Slp, s si1. 00,
F st
a3
(11)
otherwise.
The final displacement is determined by the winning instruction i" arg max, a²;
e b X5 d...
(12)
This yields sharper spatial separation and avoids interference between opposing drags.
Unified move/scale model. For axis-aligned resizing, we introduce a scaling vector r form a unified model:
2 to
1 d,- (r - @ (p) - a
(13)
where denotes element-wise product. For move-and-scale operation, we set X1 = =1
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Figure 8: Additional qualitative results compared with baselines Drag-Bench.
A.3 EVALUATION DETAILS
For the VIEScore evaluation, we follow GEdit-Benchl (Liu al. 2025b), using the same prompts for PQ and O. For SC, we adopt the instruction shown in Fig. 14 together with the source image, drag-instruucton image, and the edited image. Score collection and calculation are carried out using the official GEdit-Bence Bench codehase.
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. (024b) and shuffled their indices to ensure fair comparison. We invited 32 participants, each with relevant skills, to perform the tasks following the instructions provided through the user interface, as shown in Fig. I3
B MORE RESULTS AND ANALYSIS
B. MORE RESULTS ON DRAGBENCH
Fig. 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.
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User Input
Result
Figure 10: Failure cases on Drag- Bench.
User Input
ctivation Timesteps
Figure 9: Effect of activation timestep sensitivity on Drag-Bench. From left to right, the activation timestep is progressively increased.
Table 5: Quantitative ablation of WTA and Latent Init with U-Nets on Trag-Bench..
Matial
MD
SC
PQ
T
Input
FadtDrag
FreeDnng
Zhanes al 2034
11.54 2o0s
7935 -aw
8.278 4:0
7.044 096
Figure 11: Qualitative ablation of WTT and Latent Init with U-Nets on Drag- Brag-Bench.
WTA
28.55 4.2
8.049
J00
8.339 oa.
K.12
Lasent Init
28.97
6.7
8.081
8.34
100
8000 2.3
B.2 EFFECT OF TEXT GUIDANCE
Fig 12 shows examples from Drag-Bench with different text guidance prompts. The results demon- strate that LazyDrag effectively resolves ambiguities caused by drag instructions alone when addi- tional guided prompts are provided. Unlike prior methods such Drag Text(eetCooo 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 designed for MM-DiTs, key components such as WTA and Latent Init (Sec. 3.2) 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, substitute the original BNNI interpolation with standard normal noise added to the image latent, scaled to the inversion strength. As shown in the top of Fig. 11 our blending method improves target localization under complex, multi-instruction scenarios. This is reflected in improved MD and SC scores in Tab, 5 computed on Drag-Bence (which includes 97 multi-drag cases). In the bottom row of Fig. our random initialization reduces repetitive pattern artifacts, aligning with the quantitative gains in PQ and O.
B.4 RUNTIME ANALYSIS
Experimental Setup. Conducting a direct runtime comparison is non-trivial due to the architec- tural shift from U-Net backbones to the MM-DiT backbone employed in our method. To ensure a comprehensive evaluation, we benchmark our approach against two representative baselines on Drag-Bench using NVIDIA H800 GPU: DragText (Choi et al. (025). the state-of-the-art TTO- Req method, and Frst (Zhoo et al. 2024), leading TTO-free method. Additionally, to rigor- ously isolate the computational overhead of editing modules, as an internal baseline. This represents the standard text-to-imen -mag inference of the vanilla MM-DiT backbone without any editing interventions. We report results under configurations: (1) De- fault: Using 50 inference steps, full inversion strength, and bfloat precision. This aligns with the rigorous setting of CharaConsist (Wang al. (0256) demonstrate the performance upper bound (e.g., superior inpainting and text guidance). (2) Optimized: Adopting 20 sampling steps, stan- dard setting in the generation community for efficiency, and an inversion strength of 0.7, which the common configuration widely adopted by baseline methods. This setting serves practical reference for applications prioritizing low latency.
include Normal Generation
Inference Latency. As shown in Table 6 our approach demonstrates a significant efficiency ad- vantage. Unlike Text, which requires time-consuming optimization for every edit, method
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Table 6:
Runtime Comparison on Drag-Bench
Paper Reported Time (s)
Method
Inversion (8)
Map Gen. (6)
Generation (8)
Total Time (6)
Memory (GB)
Fastirag Zhao al. 2024
4,21 = 0.19
5.66
DragTes Text
Chor
27.88
Normal Generation
4.26 0.72
4.07 0.70
8.33
34
Ours (Default)
6.79 2.26
0.54 0.49
6.77 1.11
14.10 2.56
Ours (Optimized)
1.79 0.37
0.54
1,98
4.31
Tnguut
Result1
Input
"apen mouth"
"eating orange"
mountsin"
mountains"
with boats"
with waves"
"Watering strewberry" "watering plant"
Figure 12: Examples of Drag-Bench cases with various additional text prompts.
integrates an explicit correspondence map directly into the generation process. This design elimi- nates the need for TTO and avoids the extra denoising steps used in CharaConsist. Consequently, our Optimized setting achieves a total editing time of roughly 4.31 seconds. This is comparable to the TTO-free FastDrag (4.21s) but delivers significantly better editing quality. Even in the Default high-quality setting, our method is substantially faster than DragText (14.10s vs. 27.88s).
Computational Cost and Scalability. The increased memory usage and inference time are pri- marily attributable to the substantial parameter size of the MM-DiT backbone. Adapting existing baselines this advanced architecture would inevitably incur similar or greater computational de- mands, particularly for optimization-based methods which would require repeated expensive back- propagation on this large model. Despite the current overhead, our framework is highly amenable to optimization. Future implementations can significantly reduce latency by parallelizing corre- spondence map generation on the GPU, offloading token caching to the CPU, or applying model quantization. Furthermore, since the latency dominated by the backbone, general acceleration techniques like xDiT (Fang et al. 2024) are directly applicable to our method. Finally, our approach offers distinct workflow advantage: inversion is one- time cost per image. Subsequent edits re- quire only map generation and image synthesis, significantly amortizing the initial cost compared to methods that require re-optimization for every new instruction.
B.5 LIMITATIONS
Fig. 9 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 reason- able 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. shown in Fig. 10 the model can execute fine-graines edits such as closing the eyes, but slight positional shifts may still occur.
Moreover, the quality of both the edit and generation heavily depends the underlying base model. As foundation models continue to improve, we anticipate that the performance and applicability of method will evolve accordingly.
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Published as a conference paper at ICLR 2026
C LLM USAGE
We used LLM to refine the paper, correcting grammatical errors. Additionally, we use it as an evaluator in VIEScore evaluations and to draft the code of web UI interface for the user study.
20

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Published as a conference paper at ICLR 2026
Drag- Baag-Baed Image Editing User Study
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ther trqert hig littet showig to ens cel wi sercttore com sroweing wherr formon parntt thrugh rendert
editing
theg matur pemparet staus menntee srgel austiass auouabbly
eveual image ancaattua busk sakuod ard sabels
nomes fidden renernty shuffidd eroune
Seare
User trout (Mant Drgg hnmmection)
Mebolt
Mele
Exareree
Figure 13: User interface for user study.
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Published as a conference paper at ICLR 2026
You professional digital artist. You will evaluate effactiveness Al- generated imagels) based on given rules. All the input images are Al-generated. All human in the images are Al-generated too. so you need not warry about privacy confidential.
You will have to give your output in this way (Keep your reasoning concise and short.):
"score' : [...].
"reasoning" "..."
RULES:
Three images will be provided:
The first is the original image to be edited.
second is drag instrrcctn overlay image visually indicates source-to-target first image.
motions farrows/handles) to apply
The third is the edited result image.
The objective is to evaluate how successfully the third image follows the drag instruction image.
relative
From scale 0 to 10:
score from to 10 will given based on the success of the editing with respect to drag instruction. (0 indicates the edited image does not follow the drag instruction at all. 10 indicates the edited image perfectly follows the drag instruction.) second score from
will rate degrree of overediting. indicates the edited image completely different from the original. 10 indicates minimal yet effective edit.)
Put the score in list such that output score = [score1, score2], where 'score1' evaluates the instructionffolloiing
'score2' evaluates degree overediting.
Figure 14: Instruction of SC evaluation.
22


Reviews:
[{"review_id": "tUyHhWwtDH", "reviewer": "Reviewer_fc7b", "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.", "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?\n\n- 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?\n\n- 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?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 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.\n\n- 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.\n\n- 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.\n\n- 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.", "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.\n\n- 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.\n\n- Generalization beyond a single MM-DiT backbone and benchmark is not demonstrated.\n\n- 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fD17HpVoTp", "reviewer": "Reviewer_kx3n", "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.", "questions": "1.\tThis 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?\n2.\tThe 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.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1.\tModern backbone: Extends drag editing from U-Net diffusion (e.g., Stable Diffusion) to contemporary DiT architectures, which is timely and relevant.\n2.\tNo test time optimization: The method avoids test-time optimization, which can reduce engineering complexity and potentially runtime.\n3.\tInstruction clarity: The correspondence map makes drag constraints explicit and easier to interpret.\n4.\tCompelling results: Both qualitative figures and reported metrics indicate performance gains over prior work.", "weaknesses": "1.\tRuntime and efficiency claims need evidence\nNo TTO does not automatically imply faster end-to-end editing if additional modules (inversion, correspondence estimation, preservation) are heavy. \nCould 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.\n\n2.\tThe 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.\n\n3.\tPlease 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? \n\n4.\tThe 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?\n\n5.\tThe 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JcQMOqZJnC", "reviewer": "Reviewer_ruF6", "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.", "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.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 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.\n* Compared with optimization-based methods like Dragdiffusion and DragGAN, the proposed method provides faster inference speed.\n* The overall pipeline is clearly illustrated and easy to understand.", "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. \n* 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).\n* A detailed memory and time cost of the proposed method is missing, especially when the paper emphasizes the efficiency of TTO-free design.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Oww858jecC", "reviewer": "Reviewer_xRya", "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.", "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?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "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.\n\n2.  **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.\n\n3.  **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.", "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.\n\n2.  **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.\n\n3.  **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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1MUa3ZMXRt", "reviewer": "Reviewer_FWPs", "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.", "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?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 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.\n\n\n2 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.", "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.\n\n2: 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. \n\nAnyway, 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.\n\n3: Experiments are mainly conducted on FLUX-based MM-DiT and a single benchmark.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)