LightningDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos
Authors: Yujun Shi
Jun Hao Liew
Hanshu Yan
Vincent Y. F. Tan
Jiashi Feng
Venue: ICML 2025 poster (2025)
Keywords: drag-based image editing
diffusion model
Primary area: applications->computer_vision
TL;DR: A fast and high-quality drag-based image editing approach on general images.

Abstract: Accuracy and speed are critical in image editing tasks. Pan et al. introduced a drag-based framework using Generative Adversarial Networks, and subsequent studies have leveraged large-scale diffusion models. However, these methods often require over a minute per edit and exhibit low success rates. We present LightningDrag, which achieves high-quality drag-based editing in about one second on general images. By redefining drag-based editing as a conditional generation task, we eliminate the need for time-consuming latent optimization or gradient-based guidance. Our model is trained on large-scale paired video frames, capturing diverse motion (object translations, pose shifts, zooming, etc.) to significantly improve accuracy and consistency. Despite being trained only on videos, our model generalizes to local deformations beyond the training data (e.g., lengthening hair, twisting rainbows). Extensive evaluations confirm the superiority of our approach, and we will release both code and model.

LightningDrag:
Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos
Yujun Shi Jun Hao Liew 2
Hanshu Yan
Vincent Y. F. Tan'
Jiashi Feng?
Abstract
editing is DRAGGAN (Pan et al., 2023), which enables interactive point-based image manipulation on generative adversarial networks (GANs). Using their method, users initiate the editing process by selecting pairs of handle and target points on an image. Subsequently, the model exe- cutes semantically coherent edits by relocating the contents of the handle points to their corresponding targets. More- over, users have the option to delineate editable regions using masks, preserving the integrity of the rest of the im- age. Building upon the foundation laid by Pan et al. (2023), subsequent works (Shi et al., 2023; Mou et al., 2023; Nie et al., 2023; Ling et al., 2023) have endeavored to extend this editing framework large-scale pre-trained diffusion models (Rombach et al., 2022), aiming to further enhance its generality.
Accuracy and speed are critical in image edit- ing tasks. Pan et al. introduced a drag-based framework using Generative Adversarial Net- works, and subsequent studies have leveraged large-scale diffusion models. However, these methods often require over a minute per edit and exhibit low success rates. We present LIGHT- NINGDRAG, which achieves high-quality drag- based editing in about one second on general images. By redefining drag-based editing as conditional generation task, eliminate the need for consucnnn latent optimization or gradient-basee guidance, achieving high-quality editing in <ls. Our model is trained on large- scale paired video frames, capturing diverse mo- tion (object translations, pose shifts, zooming. etc.) significantly improve accuracy and consis- tency. Despite being trained only on videos, our model generalizes to local deformations beyond the training data (e.g., lengthening hair, twist- ing rainbows). Extensive evaluations confirm the superiority of our approach. The code and model are available at http:///ithub..om/maiic rescarch/LighnnnnDDe
However, common drawback among many methods within this framework is their lack of efficiency. Prior to editing a real image input by the user, DRAGGAN (Pan et al., 2023) (Roich
requires applying lengthy pivotal-tuning-inverr et al., 2022), a process that can consume up to to 2 minutes. As for diffusion-based approaches such as DRAGDIFFU- SION (Shi et al., 2023) and DRAGONDIFFUSION (Mou et al., 2023), they typically entail time-consuming operations such as latent-optimization or gradient- -based guidance during editing. This inefficiency poses a significant barrier to prac- tical deployment in real-world scenarios. What undermines the users' experiences even more the low success rate of these methods. Since they are mostly zero-shot methods that lack explicit supervision perform drag-based editing, they frequently struggle with either accurately moving se- mantic content from handle to target points or preserving the appearance and identity of the source image.
1. Introduction
Image editing using generative models (Roich et al., 2022: Endo, 2022; Hertz et al., 2022; Mokady et al., 2023; Kawar et al., 2023; Parmar et al., 2023) has received considerable at- tention in recent years. However, many existing approaches lack the ability to conduct fine-grained spatial control. One landmark work attempting to achieve precise spatial image
In this study, we introduce LIGHTTNINGDRAG. a novel approach that achieves state-of-the-art art drag-based editing while drastically reducing latency to less than 1 second, thereby making drag-based editing highly practical for de- ployment. To attain such rapid drag-based editing, we re- define the task as specific form of conditional generation, where the source image and the user's drag instruction serve as conditions. Drawing inspiration from previous literature (Zhang, 2023; Xu et al., 2023; Hu et al., 2023; Chen et al., 2024; Alzayer et al., 2024), we leverage the reference-only
National University of Sin- gapore *ByteDance Inc.. Correspondence to: Yu- jun Shi <shi.yujun .du.u...duu>. <junhao. hienhao.llewwde @ weweebyyceeccom com?> Jiashi Feng <jsh-
'Equal contribution
Jun Hao Liew
feng @rog@bytocn @ytg@e@yetdnncomm.>.
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 20255 by the author(s).

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LightningDrag: Lightning Fast and Accurate braag Image Editing Emerging from Videos
architecture to process source images for identity preserva- tion. Additionally, to incorporate the user's drag instruction into the generation process, we encode the handle and target points into corresponding embeddings via a Point Embed- ding Network. These embeddings are then injected into self-attention modules of the backbone diffusion model to guide the generation process. This approach eliminates the need for repeatedly computing gradients on diffuusion latents during inference, had been done in previous methods, thereby significantly reducing latency to that of generating an image with diffusion models. As conditional generation pipeline, our approach can be further accelerated by integrat- ing off-the-shelf acceleration modules for diffusion models (e.g., LCM-Lora (Luo et al., 2023b), PeRFlow (Yan et al., 2024)). a capability not possible with previous gradient- based methods.
alization capability in depth in Sec. 6.
Our contributions are summarized as follows: 1) We pro- pose LIGHTNINGDRAG, a fast and accurate drag-based im- age editing framework trained entirely on videos, achieving sub-second latency without test-time optimization; 2) We develop test-time techniques, including a noise prior strat- egy and point-following classifier frree guidance, to further improve editing quality and control: 3) Our model exhibits strong out-of-domain generalization, successfully handling deformation instructions not seen during training. We pro- vide both empirical evidence and conceptual insights to explain this behavior.
2. Related Works
Generative image editing. In light of the initial successes achieved by generative adversarial networks (GANs) in im- age generation (Goodfellow et al., 2014; Karras et al., 2019; 2020), a plethora of image editing techniques have emerged based on the GAN framework (Endo, 2022; Pan et al., 2023;
To train our proposed model, we leverage video frames as our supervision signals. This choice is motivated by the fact that video motions inherently encapsulate transforma- tions relevant to drag- drag-based editing (Fig. 1), such as object translations, changing poses and orientations, zooming in and out, etc. Our training data constructed from paired video frames. Firstly, we sample pixels that exhibit sig- nificant optical flow magnitude on the first frame the handle points. Next, we employ CoTracker2 (Karaev et al., 2023) to identify the handle points' corresponding target points in the second frame. This procedure allows us to construct training pairs for our model on a large scale. By learning from such large-scale video frames, our approach significantly outperforms previous methods in terms of both accuracy and consistency.
Abdal et al., 2021; Leimkühler & Drettakis, 2021; Patash- nik et al., 2021; Shen et al., 2020; Shen & Zhou, 2021; Tewari al., 2020; Härkönen et al., 2020; Zhu et al., 2016; 2023). However, owing to the limited model capacity of GANs and the inherent challenges in inverting real images into GAN latents (Abdal et al., 2019; Creswell & Bharath, 2018; Lipton & Tripathi, 2017; Roich et al., 2022), the ap- plicability of these methods is inevitably restricted. Recent advancements in large-scale text-to-image diffusion models (Rombach al., 2022; Saharia et al., 2022) have spurred surge of diffusion- image editing methods (Hertz et al., 2022: Cao et al., 2023; Mao et al., 2023; Kawar et al., 2023; Parmar et al., 2023; Liew et al., 2022; Mou et al.,
Through comprehensive evaluation across images of diverse categories and styles, we showcase the substantial advan- tages of our approach in terms of both speed and quality. Our approach adeptly delivers editing results in accordance to the user's instructions with an imperceptible latency of less than 1 second. Furthermore, we delve into two key techniques, namely source noise prior and point-following classifier-free guidance, that enhance the accuracy and con- sistency of our pipeline during inference. Lastly, we explore two test-time strategies that users can employ to further refine drag-based editing results- pointt augmentation and sequential dragging.
2023; Tumanyan et al., 2023; Brooks et al., 2023; Meng
et al., 2021; Bar-Tal et al., 2022; Epstein et al., 2023). While many of these methods aim to manipulate images using tex- tual prompts, conveying editing instructions through text presents its own set of challenges. Specifically, the prompt- based paradigms are often limited to alterations in high-level semantics or styles, lacking the precise spatial control.
Point-based image editing. Point-based image editing is a challenging task aiming to manipulate images in pixel- level precision. Traditional literature (Beier & Neely, 2023; Igarashi al., 2005; Schaefer al., 2006) have relied on non- con-paraeetric techniques. However, recent advancements driven by deep learning- based generative models, such as GANs, have propelled this field forward, with several no- table contributions (Pan et al., 2023; Endo, 2022; Wang et al., 2022; Zhu et al., 2016). One notable work among these is Pan et al. (2023), which achieves impressive in- teractive baset-based editing by optimizing GAN latent codes. Nonetheless, the applicability of this framework is limited by the inherent capacity constraints of GANs.
One potential concern regarding our proposed approach is that certain editing instructions - particularly those in- volving local or nonrigid deformations (e.g., lengthening hair, twisting rainbows) - are not explicitly represented in natural video motions. Intriguingly, however, our model generalizes remarkably well to such out-of-domain edits. We hypothesize this stems from the compositional richness of video-based motion cues, combined with our explicit point-following control mechanism. We analyze this gener-
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LightningDrag: Lightning Fast and Accurate Drag- based Image Editing Emerging from Videos
Source frame
Target frume
Source frame
Target frame
Source frame
Targel frame
(a) Pose change
(b) Object movement
(c) Scale change
Figure 1. Samples of supervision pairs from videos. Video motion contains various transformation cues such as pose change, object movement and scale change, which are useful for the model learn how objects change and deform while avoiding appearance change.
In a bid to enhance its versatility, subsequent efforts have endeavored to extend the framework to large-scale diffu- sion models (Shi al., 2023; Mou et al., 2023; Luo al., 2023a; Geng & Owens, 2024; Cui et al., 2024; Liu al., 2024). However, most of these works still rely on compu- tationally intensive operations such as latent optimization or gradient-based based guidance, necessitating repeated gradient computations on diffusion latents and rendering them im- practical for real-world deployment. Different from these works, Nie al. (2023) introduce a paradigm that obvi- ates the need for gradient computation diffusion latents. However, this paradigm still requires repeated diffusion- denoising operations, resulting in latencies comparable to gradient-based methods such as Shi et al. (2023). Recent works (Li et al., 2024; Chen et al., 2024; Shin et al., 2024) redefine drag-based editing as a generation task, reducing the editing latency to levels comparable image genera- tion with diffusion models. However, these methods remain domain- -emaiin-peeiife:: Li et al. (2024) handles part-level move- ments in articulated objects, Chen et al. (2024) focuses on single-human clothing, and Shin et al. (2024) targets fa- cial images. While some architectural components of our method are inspired by these prior efforts, we are the first to leverage general video data to construct large-scale su- pervision for drag-based editing. This enables our model to handle wide range of motion patterns and deformations without relying on curated or domain-specific datasets. As result, our approach uniquely generalizes to general images, making it suitable for practical deployment across a broad range of scenarios.
guidance and is trained on a limited dataset comprising only around 100 supervision pairs. contrast to these works, we train a conditional generation pipeline on large-scale videos to perform fast and accurate drag-based editing.
3. Preliminaries
3.1. Latent Diffusion Models
Diffusion models (Sohl-Dickstein et al., 2015; Ho et al., 2020) demonstrate promising performance in visual syn- thesis. Rombach et al. (2022) proposed the latent diffu- sion model (LDM), which first maps given image zo into lower-dimensional space via variational auto-encoder (VAE) (Kingma & Welling, 2013) to produce Then, a diffusion model with parameters @ is used to ap- proximate the distribution of ((20) as the marginal pa (2)) of the joint distribution between 2n and collection latent random variables 21:T = (21. 2T). Specifically,
8 (sro).
pz(z) = / (Fo:r) dæy.
where po (2r) is a standard normal distribution and the tran- this Markov chain are all Gaus- sian conditioned on zς. In our context, 2g corresponds the VAE latent of image samples given by users, 2, is the latent after t steps of the diffusion process. Specifically,
sition kernels po(=1-1/2))
2t = Varz + v1 á
(2)
where ~ NO I), and is the cumulative product of the noise coefficient at each step.
Learning image editing from videos Previous methods leveraging videos to aid in learning image editing typically sample two frames from video to form supervision pair. For example, Chen et al. (2023) utilize image pairs col- lected from videos for the same object to learn the appear- ance variations, thus improving their subject-composition pipeline. Alzayer et al. (2024) use video frames to supervise their proposed coarse-to-fine warping-basse image editing pipeline. Luo al. (2023a) train diffusion models on video data to improve dtra-based editing performance. However, their approach still relies on time-consuming gradient-based
Based on the framework of LDM, several power- ful pretrained diffusion models have been released publicly, including the Stable Diffusion (SD) model (htns://huggingffcccoosssta .tt.aaalace.//saaallttaaa.)) In this work, pro- posed pipeline is developed based on SD model.
4. Methodology
In this section, we formally present our LIGG approach. To start, we elaborate on the details of how we
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LightningDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos
construct supervision pairs for our model from videos in Sec. 4.1. Next, we describe the architecture design of our model in Sec. 4.2. Furthermore, we introduce some tech- niques we use during test-time to improve the editing results in Sec. 4.3. Finally, we introduce some strategies that users can employ to fix failure cases in Sec. 4.4.
Reference Emage
Appearance Encoder
Appearance
Encoder
IP Ad-aptter
Inpainting
backbone
Point Embeddiig
Network
P-Addppter
4.1. Paired supervision from video data
noise
One of the challenges we encounter is in collecting large- scale paired data for training the model, as obtaining user- annotated input-output pair on a large scale nearly infeasi- ble. In this work, we redirect our focus towards leveraging video data. Our key insight lies in the inherent motion cap- tured within video, which naturally encompasses various transformations relevant to drag-based editing, including zooming in and out, changes in pose and orientation, etc. These dynamics offer valuable cues for the model to learn how objects undergo changes and deform.
Masked input mask
Output
Prozen
Handle Target points points
Trainable
Point Embedding
We begin by curating videos with static camera movement, simulating drag- based editing where only local regions are manipulated while others remain static. Subsequently, we randomly sample two frames from these videos to serve as source lare and target images lgt, respectively. We resam- ple another pair the optical flow between the two images too small. Next, we sample N handle points Phal on Are with probability proportional the optical flow strength, ensuring the selection of points with significant movement. We then employ CoTracker2 (Karaev et al., 2023), state-of- the-art point tracking algorithm to extract the corresponding target points Pigt in the target image lug. Finally, we adopt a similar approach as in Dai et al. (2023) to extract binary mask M highlighting the motion areas, indicating regions to be edited. Collectively, the tuple (Jares Jigt, Pucl, Pogt, M) Exam-
Figure 2. The pipeline of LIGHTNINGDRAG. Our LIGHTNING- DRAG consists of three components, including (1) inpaint- ing diffusion backbone to enforce unmasked regions remain un- touched; (2) Appearance Encoder for preserving the identity of the reference image; and (3) a Point Embedding Network to encode the (handle, target) points pairs.
each component in more details.
1.2.1. INPAINTING BACKBONE.
We utilize the Stable Diffusion Inpainting U-Net as our backbone, which takes concatenation of the following as input: noise latents 2,, binary mask M, and masked latents M @ 20 It is worth noting that the inpainting backbone typically takes in text prompt to indicate the inpainted content. However, in trag-based editing application, a text prompt is not only redundant as the image content already provided by the source image, but also difficult for users to provide. Instead, we extract the image feature of the source image using IP-Adapter (Ye et al., 2023) and use an empty text prompt, freeing the users from this requirement.
form the sample to train our LIGHTNINGDRAC ples showcasing the versatility of video data for training drag-based editing can be found in Fig. 1.
4.2. Architecture Design
We formulate the drag-based image editing task as ditional generation problem, where the generated image needs to fulfill the following criteria: (1) unmasked area remains untouched; (2) image identity (e.g.. human face, tex- ture, etc.) should be preserved after dragging; (3) the areas indicated by handle points should move to the target coor- dinates. To achieve this, our LIGHTNINGDRAG comprises three components: (1) an image inpainting backbone to force unmasked region remains identical; (2) appearance encoder preserves identity of Jur.. (3) point embedding net- work encodes the (handle, target) points pairs, accompanied by a point-following attention mechanism, which explicitly enables the model to follow the point instructions. The over- all framework is depicted in Fig. 2. We next elaborate on
4.2.2. APPEARANCE ENCODER.
To maintain the identity of the reference image. we draw inspiration from recent works on ID-consistent generation, such as Xu al. (2023); Hu et al. (2023); Chen et al. (2024). Specifically, we employ the reference-only architecture (Zhang, 2023) to process the source image. Unlike CLIP image encoder (Radford et al., 2021) which can only guaran- tee the overall colors and semantics, the reference-only ap- proach has demonstrated efficacy in preserving fine- -reiggr details of the reference image. Inherited from the weights of a pre-trained text-to-image U-Net diffusion model, our A Appearance Encoder takes the reference latents =0 as in-

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LightningDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos
put. It extracts the reference feature maps from the self- attention layers, which are subsequently used to guide the self-attention process in the denoising backbone. The self- attention in the backbone is thus defined as follows:
Mixed noise latents
Nossed source latents
User imput
Pure noise
Copy and paste
Q KK, Kret]
[V. Vietl, (3) where Krof and Viof denote the keys and values extracted from the reference features, [] denotes the concatenation operator, and SM is the softmax function. Following prior works (Xu et al., 2023), we clean reference latents inputs to the Appearance Encoder (as opposed to noised latents used in original reference-only model (Zhang, 2023)). As a result, unlike backbone UNet that requires multiple denoising steps, the Apppearance Encoder only needs to extract features once throughout the entire editing process, which improves the model inference efficiency.
Attu Q K, V, Kit,, Viet)= SM
Vd
Figure 3. Different strategies for constructing the noise prior. We find that the "noise source latents" strategy produces the best results. Image credit (source image): Pexels
sults, as depicted in Fig. 3. This instability may stem from the discrepancy between the initial noise during training and testing of diffusion models, as discussed in prior works (Lin et al., 2024a;b). In contrast to text-to-image generation, where obtaining a suitable initial noise prior is challenging, our task allows for more accurate initialization of the noise prior by adding noise to the VAE latent of the source im- age. This technique enables us to narrow the gap between training and testing, resulting more stable outcomes.
4.2.3. POINT EMBEDDING ATTENTION.
Given the user-specified handle and target points, we first convert them into handle and target point map that is of the same resolution of the input image. Specifically, we randomly assign each pair of handle and target points with an integer number & E .1. maximum allowed points. Then, put the integer i to the pixel location the point map given coordinates specified by handle and target points. The rest of the pixel locations on handle and target point maps are with value O.
We ablate three strategies construct the noise prior:
Noised source latents directly adds noise on the source image latent with Eqn. (2) to the terminal diffusion time-step (L = 999).
where N is the
Mixed noise latents re-initializes the mask region of the noised source latents with pure Gaussian noise for potentially greater editing flexibility.
Once obtaining the handle and target point maps, we encode them into embedding via a point embedding network, which composed of 12 layers of convolution and SiLU activation. This network outputs embedding four different resolu- tions, corresponding to the four different resolutions of SD UNet activation maps. To enable the model to follow point instructions effectively, we draw inspiration from Chen et al. (2024) and introduce a point-following mechanism into Eqn. 3, resulting in the following formulation:
Copy and paste noise latents borrows the "copy and paste" approach of Nie al. (2023) along with the handle and target points to form the initial noise prior.
We report results using two metries: Mcan Distance (MD.), which measures how accurately the handle points are moved to the target positions, and Image Fidelity (IFf), calculated as 1-LPIPS, which reflects how well the edited image pre- serves the original appearance. As shown in Tab. 1, the "copy and paste" and "noise source latent" strategies yield the best overall performance, with the latter slightly out- performing others in IF. However, since "copy and paste" involves much more complicated implementation, we select "Noise Source Latents" as our default strategy in the spirit of simplicity. Moreover, the numerical results presented here also align well with the visual illustration in Fig. 3.
Atta(Q, Q2A K, V, Krof, Vrof: Endl, Engt)
= =SM( (Q+ Eigg)[K + Euge, Kyot + Emuill) [V, Veer (4) where Endi and Eigs are embeddings of handle and tar- get point maps, respectively. In this way, we explicitly strengthen the similarity between the target points of the generated images and the handle points the user input image, facilitating learning of drag-based editing.
Vd
Pure
Mixed Noise Copy and Noise Source
Noise
Latents
Paste
Latents
MD(J) 19.76
19.01
18.53
18.95
4.3. Test-time Techniques to Improve Editing Results
IF(t) 0.887
0.886
0.887
0.890
1. NOISE PRIOR
Table I. Quantitative ablation on different noise prior strategies during inference.
We have observed that directly using randomly initialized noise latents for generation sometimes yields unstable re-
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LightningDrag: Lightning Fast and Accurate based Image Editing Emerging from Videos
4.3.2. POINT-FOLLOWING CLASSIFIER-REES GUIDANCE
To further improve the model's capability follow the point instruction during inference, we implement the follow- ing point-following classifier-free guidance (PF-CFG) to strengthen the effects of given (handle, target) points pairs:
Original image
User input
w/o CFG
Constant
Ee(2z,Cappr: Cpoints) = E0(2,, Cappe: 0)
+ (e( Ze: Cappe, Cpoints) - Ea (Ze, Cappr 0)),
(5) where (+) the time-depeme deme-deenddent CFG scale, Cappe denotes the source image condition encoded by appearance encoder, and Cpoints denotes the condition of handle and target points. To be more specific, when computing co (2(2,, Cappr: 0), we use Eqn. (3) in all self-attention layers of the main backbone UNet. When computing ca 60(2), Cappr, Cpoints), we employ Eqn. (4).
Square
Linear
Inverse square
Figure 4. Effects of different CFG scale schedules. Our model struggles to conduct successful drag when CFG is not used, Constant CFG often leads to over-saturation problem. Overall, fast decaying strategy (Inverse square) attains best results.
off between control and realism. Among these schedulers, we select inverse square as our default CFG scale sched- uler to attain the best overall appearance preservation while maintaining good point-following.
Most previous works involving diffusion models apply fixed CFG scale across different denoising time-steps. How- ever, recent literature (Kynkäänniemi al., 2024; Wang et al., 2024) demonstrate the benefits of using time- dependent CFG scale during denoising. In this work, we similarly find that a dynamic dimeddependent CFG scale can help strike an approrpriate balance between the accuracy of point-following and image quality of the results.
w/o CFG
Constant
Square Linear Inv. Square
MD(J)
22.27
17.76
17.73
18.59
18.95
IF(t)
0.9071
0.8486
0.8743
0.8829
0.8896
Denoting Wmax the maximum value of CFG, explore the following CFG scale schedules:
Table 2. Quantitative ablation of different CFG scale schedules.
No CFG: wt)
Constant: w(t) = Wmax-
4.4. "Drag engineering" to improve the editing
Square: wt) = Wmax x (1-(1-11 (1-t/1000)3 - (1
t/1000)?.
Inspired by the use of prompt engincering technique in Large Language Models (LLM) to obatin ideal answers, find that some failure cases produced by our LIGHTNING- DRAG can also be mitigated by engineering the input drag instruction. Here, we introduce two strategies, namely Point augmentation and Sequential dragging, for users to consider when facing imperfect results with our model.
Linear: w(t) Wmax 1/1000 (1 1/1000).
Inverse square: w=t))= (Wmax - 1) x (t/1000)² + 1.
We compare these schedules in Fig. 4. As can be observed, without using our CFG, the model struggles to conduct suc- cessful drag-based editing. On the other hand, using CFG with a constant scale can successfully drag the handle points the target, but the results may suffer from saturaatuatiion By using schedules that decay the CFG scale from wmax to 1.0 during the denoising process such Square, Linear, and Inverse square, we achieve accurate drag-based editing while markedly improve the image quality. Among these decaying schedules, we find fast decaying strategy such as Inverse square achieves the best image quality, while slow decaying strategy such as Linear and Square still suffer from slight quality degradation (e.g., over-saturation) on generated images.
4.4.1. POINT AUGMENTATION
When the region specified by handle points fail to move to the target locations, augmenting the drag instruction with additional pairs of handle and target points has proven ef- fective in improving results. Examples showcasing this augmentation are depicted in Fig. 5. is evident that by incorporating more pairs of handle and target points, users' editing intentions can be more explicitly conveyed, resulting in better outcomes.
To complement the qualitative observations above, we present a quantitative comparison of the different CFG schedules in Tab. 2. The results confirm our findings: con- stant CFG improves point accuracy (MD) but hurts image fidelity (IF), while dynamic schedules offer a better trade-
4.4.2. SEQUENTIAL DRAGGING
In cases where drag editing results are sub-optimal after one round of editing, users may opt to break down the drag instruction into multiple rounds and sequentially move
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LightningDrag: Lightning Fast and Accurate Drag-basce baag-based Image Editing Emerging from Videos
Method
IF (1)
MD (4)
mi
DragGAN (Pan et al., 2023)
0.696
57.155
DragDiffusion (Shi et al., 2023)
0.881
33.162
DiffEditor (Mou et al., 2024)
0.856
28.579
Readout Guidance (Iuo et al., 2023a)
0.790
52.224
SDEDrag (Nie et al., 2023)
0.921
45.779
LightingDrag (ours)
0.885
18.62
LightiingDrag LCMM-LOR (ours)
0.890
18.95
Table 3. Quantitative comparison on DragBench. IF and MD denote Image Fidelity (I-LPIPS) and Mean Distance, respectively.
User
Retalts
User Input
Results
use guidance scale Wmax of 3.0 and adopt an inverse square decay (Sec. 4.3.2) that gradually reduces the guidance scale to 1.0 over time to prevent over-saturation issue.
Figure Point Augmentation. Augmenting with additional pairs of handle and target points can better convey the user's editing intention, which often leads better performance.
5.2. Evaluation on DragBench
semantic contents from handle points to final targets. Ex- amples illustrating how such sequential dragging can rectify certain failure cases are presented in Fig. 6. This strategy is facilitated by our model's exceptional ability to maintain the appearance and identity of the source image during editing. Without this capability, cumulative appearance shifts might occur, leading undesired results. Additionally, given our model's negligible latency, employing sequential dragging does not significantly undermine user experience.
We provide a quantitative assessment of our method on DragBench (Shi al., 2023), comprising 205 samples with pre-defined drag points and masks. As is standard (Shi et al., 2023; Ling et al., 2023; Cui al., 2024; Liu al., 2024), we use the Image Fidelity (IF) and Mean Distance (MD) metrics for our analysis. IF is calculated as 1-LPIPS (Zhang et al., 2018), while MD assesses the accuracy with which handle points are moved to their designated targets. An ideal drag-based editing method would achieve a low MD, indicating effective drag editing, coupled with a high IF, signifying robust appearance preservation.
5. Experiments
5.1. Implementation details
Tab. 3 demonstrates the superiority of our LIGIITNING-
Network. The base inpainting U-Net inherits the pre- trained weights from Stable Diffusion V1.5 inpainting model, whereas the Appearance Encoder initialized from the pre-trained weights of Stable Diffusion V1.5. The Point Embedding Network is randomly initialized, except for the last convolution layer which is zero-initialized (Zhang et al., 2023) to ensure the model starts training as if no modifica- tion has been made.
DRAG in term of point following, as evidenced by its lowest MD. We also notice that our LIGHTNINGDR AG outperforms others in term of IF, except SDEDrag. However, further in- spection reveals that SDEDrag often results the undesired identity mapping (Fig. 7 row 1. and 4), leading to its high IF. Additional qualitative results supporting this observation are presented in Fig. 8 in the appendix.
5.3. Time efficiency
Training, We sample 220k training samples from our in- ternal video dataset to train our model. We set the learning rate to 5e with a batch size of 256. We freeze both the inpainting U-Net and Ad-perreer, training both Appearance Encoder and Point Embedding Network. During training, randomly sample [1, 20) points pairs. We randomly crop square patch covering the sampled points and resize to 512 x 512.
Due to the elimination of test-time latent optimization gradient-based guidance, our LIGHTNINGDRAG is ex- tremely fast. We compare the time efficiency of LIGHT- NINGDRAG against the state-of-the- methods for drag- based editing on general images. For fair comparisons, we extracted subset square images from DragBench (Shi et al., 2023), resulting in 67 images, and perform inference at a resolution of 512 x 512. We report the time cost on an NVIDIA A100 GPU in Tab. 4. We notice that the execution time of SDEDrag has a high variance. This is because its inference speed depends on the distance between the handle and target points. In contrast, our LIGHTNINGDRAG runs at a constant speed regardless of the dragging distance. Sec- ondly, even without LCM-LoRA, our approach is already
Inference. We use DDIM (Song al., 2021) sampling with 25 steps for inference by default. We found that our model is also compatible with recent diffusion acceleration tech- niques such as LCM-LLRA (Luo et al., 2023b) and PeRFlow (Yan et al., 2024) without additional training. When using LCM-LoRA or PeRFlow, we use steps for sampling. We

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LightningDrag:
Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos
Single dragging
Sequential dragging
Drag
Drag
Drag
Figure 6. Sequential dragging, In cases when single dragging operation cannot attain the desired outcome, simple workaround is to break operation down into sequence shorter dragging trajectories. Image credit (source images): Pexels.
Model
Time (s)
stretching hair, bending structures), even though such trans- formations are not explicitly observed in training vidcos.
DragDiffusion (Shi et al., 2023)
55.05+3.70
DiffEditor (Mou et al., 2024)
9.47 9.47 20.20
We attribute this to two factors:
Readout Guidance (Luo et al., 2023a)
98. 98.13
SDEDrag (Nie et 2023)
61.78 18.84
(1) Compositional Generalization from Natural Video Cues. Although our model is trained solely videos with- out explicit dragging instructions, video motion contains rich non-rigid dynamics. For example, Fig. I(c) shows syrup deforming as accumulates - providing cues for transformations like stretching or warping. This suggests that the model learns general motion prior from such ex- amples, allowing extrapolate unseen edits. We view this as form of compositional generalization: the model internalizes structural transformations and recombines them during inference.
Lightning (ours)
2.06 2.06
LightningDrag + LCM-LoRA (ours)
0.92_0.02
Table Time efficiency. The reported time cost is obtained by running inference 512x512 images sampled from DragBench (Shi et al., 2023) on single NVIDIA A100 GPU.
an order of magnitude faster than most baselines, making suitable for practical applications. Lastly, when com- bined with recent diffusion acceleration methods such LCM-LoRA, our LightningDrag can be further accelerated, requiring only < Is for each dragging operation.
(2) Explicit Control via PF-CEG. Our Point-Following Classifier-Free Guidance (PF-CFG) critical in guiding this generalization during inference. By enforcing align- ment between semantic regions around handle and target points, PF-CFG improves the model's control and robust- ness. As evidenced in both Fig. 4 and our ablation study, this component enhances the reliability of drag instructions even under challenging or ambiguous cases.
5.4. Qualitative results
Comparisons with Prior Methods. We compare our LIGHTNINGIRR AG with prior methods in Fig. 7. We ob- serve that DiffEditor and Readout Guidance often strug- gle to preserve reference identity (e.g., 2nd and 3rd row), while DragDiffusion and SDEDrag sometimes fail drag the desired locations. In contrast,
Together, these insights suggest that our approach not only achieves speed and accuracy but also establishes promising path toward more generalized and controllable image editing using natural video supervision.
the cesions-of-interes our LIGHTNINGDRAG effectively handles various dragging needs, such as pose change, object scaling, translation, local deformation, while preserving the source image appearance.
Multi-round Dragging, Our LIGHTNINGDRAG supports multi-round dragging, allowing users to iteratively refine edits based on previous outputs. Examples of multi-round dragging shown in Fig. 9 in Appendix.
7. Conclusion
We introduced LIGITTNINDDRAG, a practical approach for high-quality drag-based image editing in 1s. By lever- aging large-scale natural videos rich source of motion cues, model learns how objects change and deform in diverse scenarios. Extensive experiments confirm Lightning- Drag surpasses prior methods in both speed and quality. We hope this work will inspire further research on controllable and precise image editing.
6. Diseussion: Out-of-Domain Generalization
key strength of LIGHTNINGDRAC lies in its surprising ability to generalize to oft-of--omain editing instructions, particularly those involving non-rigid deformations (e.g.,
8

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LightningDrag: Lightning Fast and Accurate Drag-basce base- Image Editing Emerging from Videos
LightningDrag (ours)
User input
DragDiffusion
DiffEditor
SDEDrag
Readout Guidance
Figure 7. Qualitative comparison on DragBench. Our LightningDrag can handle various dragging instructions, such as pose change, scaling, translation etc. while preserving the object identity.

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LightningDrag:
Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos
Impact Statement
Chen, X., Huang, L.,, Liu, Y., Shen, Y., Zhao, D., and Zhao,
H. Anydoor: Zero-shot object-level image customization. arXiv preprint (7Xiv:2307.0948/ 2023.
This paper introduces a fast and high-quality drag-based im- age editing approach, making advanced editing tools more accessible wider audience. Although this democratiza- tion of image manipulation has many positive applications, it also raises concerns about the potential misuse of gen- erated content, such as creating deceptive or fake imagery. However, such risks are inherent in most research in visual content synthesis, including image and video generation.
Creswell, A. and Bharath, A. A. Inverting the generator of generative adversarial network. IEEE transactions on neural networks and learning systems, 30(7):1967-1974, 2018
Cui, Y., Zhao, X., Zhang, G., Cao, S., Ma, K., and Wang, 1. Stabledrag: Stable dragging for point-based image editing. arXiv preprint arXiv:2403.044
2024.
Acknowledgment
Dai, Z., Zhang, Z., Yao, Y., Qiu, B., Zhu, S., Qin, L., and Wang, W. Animateanything: Fine-grained open domain image animation with motion guidance. arXiv e-prints, pp. arXiv-2311, 2023.
The authors would like to thank Zhongcong Xu, Zhijic Lin, Jianfeng Zhang, Zilong Huang, and the anonymous reviewers for their helpful discussion and feedback.
Endo, Y. User-controllable latent transformer for stylegan image layout editing. arXiv preprint arXiv:2208. 12408, 2022.
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LightningDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos
A. Video Demo Please see our project website for a video demonstration: http://fighhiiigdddagggibbbbbbdd
B. Additional Qualitative Results
Figure Qualiative results of LIGHTNINGDRAG, Image credit (source images): Pexels.
User input
User Input Figure Multi-round dragging, Image credit (source images): Pexels.
Result
Result
User Input
Result
13


Reviews:
[{"review_id": "Uugd6vt8oU", "reviewer": "Reviewer_4Wjk", "summary": "This paper proposes LightningDrag, a feed-forward diffusion model designed to achieve drag-based image editing. LightningDrag employs a point embedding network to encode drag instructions from users and preserves the image identity using an ID adapter and appearance encoder. Experiments demonstrate that LightningDrag achieves superior drag-based editing results without the need for slow optimization.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the insightful feedback and appreciations in our work. Please find our response to your questions below.\n\n**Q1**: Limited novelties in architectural design.\n\n**A1**: As discussed in the related work section, although we take inspiration from some previous literature for architecture design, we are targeting a more general setting: drag-based editing on **general images**, rather than specific domains like faces or objects.\n\nOne of the key novelties of our work is the insight that natural videos can serve as a viable and scalable training signal for general drag-based image editing. Moreover, as discussed in the introduction (e.g., Page 1 L80 to L86) and supported by empirical results (e.g., Table 1 and Figure 7), our approach generalizes well to out-of-domain manipulations that are not explicitly observed during training (e.g., hair lengthening, furniture deformation). This demonstrates a new perspective on leveraging video-based supervision for image editing with conditional diffusion models (See **A2** of Response to Reviewer oUVx for more discussion).\n\nIn addition, our training data construction pipeline can inspire following work on how to preprocess video frames to train image editing models. Also, our framework introduces several novel test-time techniques, including point-following classifier-free guidance (PF-CFG), point augmentation, and sequential dragging, which improve controllability and performance. Finally, we will release our code and models to support open research.\n\nWe will revise the paper to better highlight these contributions and clarify the novelty of our approach.\n\n\n\n**Q2**: Encoding dragging conditions as full-resolution features might be redundant.\n\n**A2**: For the dragging conditions, we would like to clarify that the point-embedding modules are relatively lightweight, accounting for only about 2% of the total model parameters (40M out of 2B). As such, encoding the point conditions as full-resolution feature maps does not introduce significant computational or memory overhead. More importantly, this design choice provides a clear spatial inductive bias, allowing the model to more accurately interpret and follow user-specified dragging instructions. We found this spatial encoding to be beneficial for learning precise and controllable drag-based editing behaviors. We will include this discussion in the updated version to better explain the motivation behind our design.\n\n\n\n**Q3**: More analysis are needed for \"Drag Engineering\" operations. What is the success rate without them?\n\n**A3**: All evaluation results --- both qualitative (Figures 7 and 8) and quantitative (Table 1) --- are obtained **without** using additional techniques such as point augmentation or sequential dragging. This demonstrates that our approach is robust and effective in many common cases without requiring extra operations. The additional techniques are designed to handle more challenging scenarios. Based on our experience with the DragBench dataset, our model produces satisfactory results on 162 out of 205 test samples without any additional operations. With point augmentation and sequential dragging, we are able to improve editing results on 21 more samples. We will clarify this in the revised version for better transparency.\n\n\n\n**Q4**: Need more clarification on results of SDEDrag.\n\n**A4**: A robust drag-based editing approach must achieve strong performance on **both** Image Fidelity (IF) and Mean Distance (MD). IF evaluates how well the overall appearance is preserved after editing, while MD measures how accurately the handle points are moved to their target positions. Although SDEDrag achieves a slightly better IF score, its MD (45.779) is significantly worse than ours (18.95), suggesting that it often fails to complete the intended dragging operation and instead produces results that remain close to the original image. This is also evident in the qualitative comparisons in Figure 7, where SDEDrag introduces minimal changes without effectively moving the handle points. We will include additional qualitative examples in the supplementary material to further support this point.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "Accelerate drag-based image editing.", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "Strengths:\n\n1. The idea of injecting drag instructions as conditions into an inpainting diffusion model is well-founded. This approach eliminates the need for heavy latent optimization, resulting in significantly faster editing speeds.\n\n2. The authors explore various noise initialization strategies and implement point-following classifier-free guidance to enhance performance.\n\n3. LightningDrag achieves much faster inference speeds compared to existing methods.\n\n4. The presentation is clear and easy to follow.\n\nWeaknesses:\n\n1. The overall framework involves fine-tuning an inpainting diffusion model with additional conditions. Both the ID-preserving attention and point-following attention techniques are borrowed from previous work. Additionally, LightningDrag encodes dragging conditions as full-resolution image features, which is redundant since most values are set to zero. Although the authors implement several optimizations to accelerate the diffusion process, some module designs remain sub-optimal.\n\n2. The authors mention additional operations, such as point augmentation, but more analysis and experiments are needed. For example, what is the success ratio without these augmentations?\n\n3. SDEDrag outperforms LightningDrag on DragBench. While the authors claim that SDEDrag often results in undesired identity mapping, it would be beneficial to conduct a user study to evaluate this claim more thoroughly.", "other_comments_or_suggestions": "NA", "questions_for_authors": "NA", "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": {"ethical_review_concerns": "NA"}}, {"review_id": "YguMh42ds0", "reviewer": "Reviewer_RdPN", "summary": "This paper presents Lightningdrag, a diffusion model trained on video data, enabling accurate and consistent drag-based edits within seconds, leveraging source noise prior and point-following classifier-free guidance for improved accuracy and consistency.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the insightful feedback and appreciations in our work. Please find our response to your questions below.\n\n**Q1**: Lacking in quantitative ablation studies.\n\n**A1**: Thanks for the advice. We conduct the detailed quantitative ablation study as you suggested. Results are given below.\n\nTo start with, we conduct an ablation study to understand the effect of point-following classifier-free-guidance (PF-CFG).\n\n||No CFG|Constant CFG|Square CFG|Linear CFG|Inv. Square CFG|\n|----|-----|-----|-----|-----|-----|\n|MD(&darr;)|22.27|17.76|17.73|18.59|18.95|\n|IF(&uarr;)|0.9071|0.8486|0.8743|0.8829|0.8896|\n\nIn the above table, the first column is the results without PF-CFG, while the rest are results with PF-CFG under different CFG scale schedules. Comparing the first column with the second columns, we show that adding PF-CFG will significantly enhance point-following (better MD) while compromise the overall appearance preservation (worse IF). As we explore different CFG scale schedules (from square to inv. square, the CFG value decreases increasingly faster throughout denoising process), we show that decaying CFG values during denoising process can effectively reduce the degradation on IF. Among these schedulers, we select inv. square as our default CFG scale scheduler to attain the best overall appearance preservation while maintain good point-following. Our numerical results here align well with the visual illustration in Figure 4.\n\n\nFurther more, we conduct ablation study on the choice of noise prior.\n||Pure Noise|Mixed Noise Latents|Copy and Paste|Noise Source Latents|\n|----|-----|-----|-----|-----|\n|MD(&darr;)|19.76|19.01|18.53|18.95|\n|IF(&uarr;)|0.8878|0.8861|0.8876|0.8896|\n\nFrom the results, one can observe that \"Noise Source Latents\" and \"Copy and Paste\" can outperform the other two strategies. Comparing to \"copy and paste\", \"noise source latents\" have slightly better IF and slightly worse MD. Therefore, it is hard to discern which one is better among these two. However, since \"copy and paste\" involves much more complicated implementation, we select \"Noise Source Latents\" as our default strategy in the spirit of simplicity. Moreover, the numerical results presented here also align well with the visual illustration in Figure 3.\n\n**Q2**: The reason behind the improved inference speed is not sufficiently explained.\n\n**A2**: Thank you for pointing this out. The significant speedup arises from our reformulation of drag-based image editing as a conditional generation task, as mentioned in the abstract and introduction. Unlike prior methods such as DragDiffusion or DiffEditor, which rely on iterative latent optimization or gradient-based guidance (sometimes requiring up to 80 iterations of forward and backward passes through the diffusion UNet), our method avoids this costly process. Instead, we achieve drag-based editing via a single feed-forward pass on our conditional-diffusion-model, making the latency comparable to standard diffusion image generation (around 1~2 second). We will clarify and include this detailed explanation in the revised version of the paper.", "claims_and_evidence": "1. Lightningdrag formulates drag-based image editing as a diffusion-based approach trained on video data, incorporating user-specified points via point embedding attention, which is novel.\n\n2. The paper proposes novel strategies, including test-time refinements using noised source latents and inverse square classifier-free guidance, which significantly improve the quality of editing results.\n\n3. As demonstrated in the supplementary videos and Table 2, Lightningdrag achieves notable improvements in time efficiency, completing edits within approximately one second, a substantial advantage over previous methods.", "methods_and_evaluation_criteria": "1. The proposed methods and evaluation criteria make sense for the targeted application. Table 2 and supplementary videos effectively demonstrate the advantages of the method in terms of efficiency and editing quality, clearly outperforming previous approaches.\n\n2. Figures and qualitative results illustrate the effectiveness of source noise prior, inverse square classifier-free guidance, and sequential dragging. However, the paper lacks quantitative ablation studies for each individual technical contribution, limiting detailed insights into their respective impacts.", "theoretical_claims": "No Theoretical Claims", "experimental_designs_or_analyses": "Overall, the experiment is extensive and thorough.Authors provide the detailed experimental settings and extra quantitate results in the supplementary.", "relation_to_broader_scientific_literature": "Already discussed in the paper.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "**Strengths:**  \n- The presented approach achieves clear improvements in editing quality and efficiency, outperforming prior latent-diffusion and GAN-based methods.\n\n**Weaknesses:**  \n1. Although the methods introduce beneficial components such as source noise prior and point-following classifier-free guidance, the paper lacks quantitative ablation studies, limiting the understanding of each component’s individual contribution.\n2. The reason behind the significantly improved inference speed (as shown in Table 2) is not sufficiently explained, leaving the underlying factors contributing to high efficiency unclear.", "other_comments_or_suggestions": "No", "questions_for_authors": "See weakness", "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": "PU175dzpkI", "reviewer": "Reviewer_oUVx", "summary": "The paper presents a new approach, LightningDrag, for drag-style editing problem. LightningDrag features in significantly faster inference speed and good generalization. LightningDrag is trained by watching paired video frames. Finally, the authors showcase the performance of LightningDrag by comparing with other drag-style editing baselines.\n\n## update after rebuttal\nThe rebuttal resolved my concerns. Please include discussions accordingly to the revised paper. I've increased my rating to weak accept.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the insightful feedback and appreciations in our work. Please find our response to your questions below.\n\n**Q1**: Need discussion on differences with Magic Fixup\n\n**A1**: We acknowledge that our approach shares with Magic Fixup the general idea of leveraging video data; however, we respectfully highlight several key differences:\n\n1) **Editing Instruction**:\nMagic Fixup relies on user-provided \"coarse edits\", typically involving rough object translation or duplication. In contrast, our method employs drag-based editing, allowing users to specify precise points and exact movement trajectories. Thus, the editing intentions we target fundamentally differ from Magic Fixup. This further leads to difference in architetural design choice, where our architecture directly inject the encoded point embedding into self-attention modules for precise spatial control, while Magic Fixup place editing instruction at the input of the model.\n\n2) **Complex Editing Capability**:\nMagic Fixup excels at edits such as object translation/movement, but might struggle with intricate transformations involving rotations with complex local details. For example, in Figure 7 (row 3), the editing intention of significantly rotating a face from side-view to frontal-view would be challenging to deliver using coarse edits. On the other hand, our drag-based approach clearly conveys and effectively executes such complex editing intentions.\n\n3) **Data Construction Pipeline**:\nOur data preparation process differs significantly from Magic Fixup, involving precise point-tracking using state-of-the-art methods (e.g., CoTracker-2) and stringent filtering for static-camera videos. This pipeline provides accurate, scalable data specifically suited for general drag-based editing.\n\n4) **Novel Test-Time Techniques**:\nWe further introduce novel test-time strategies, including Point-Following Classifier-Free Guidance (PF-CFG), dynamic scale scheduling for PF-CFG, point augmentation, and sequential dragging. These innovations enhance precision and robustness, which are absent from Magic Fixup.\n\n5) **Simplified Real-World User Interface**:\nMagic Fixup’s user interface inherently requires complex modules (e.g., segmentation models, rotation tools, zooming functions), making the interaction complicated. Conversely, our approach simplifies user interaction to just point-clicking and mask-painting, enabling intuitive editing and better user experience.\n\nWe will incorporate these clarifications into our revised manuscript.\n\n**Q2**: Need more explanation for out-of-domain generalization edits.\n\n**A2**: We would like to clarify our claim with both empirical evidence and further explanation.\n\nEmpirically, we demonstrate generalization to out-of-domain editing with deformation through a range of qualitative examples in Figures 3 to 7. In addition, our quantitative results are based on DragBench, which includes many samples involving local deformations (e.g., lengthening an object or deforming parts of a rigid structure). The strong performance on this benchmark supports our claim.\n\nConceptually, we view this generalization as a form of compositional generalization. While our training videos do not contain explicit editing instructions such as stretching hair or lifting mountains, they do include non-rigid deformations --- such as in Figure 1\\(c\\), where syrup accumulates and changes shape over time. By learning to model such non-rigid transformations, the model is able to generalize to similar deformation behaviors even in unseen or rigid-object scenarios.\n\nFurthermore, our proposed Point-Following Classifier-Free Guidance (PF-CFG) plays a critical role in enabling this behavior. As shown in Figure 4, PF-CFG helps ensure that the semantic content around handle points is faithfully dragged toward the target, improving control and robustness. The benefit of PF-CFG is further confirmed in our ablation study (see **A1** of \"Response to Reviewer RdPN\").\n\nWe will incorporate these points into the revised manuscript to better support and clarify our claim.\n\n**Q3**: Missing comparisons of CLIPDrag, DragonDiffusion, MagicFixup.\n\n**A3**: Since our work is concurrent to CLIPDrag, we did not include it in this version of submission. We will update their results in the updated version.\n\nAs for the DragonDiffusion, we have compared with its improved version, which is DiffEditor. In addition, we have discussed DragonDiffusion in our introduction and related work. We will include the results of DragonDiffusion for sake of completeness in our updated version.\n\nAs for MagicFixup, since it follows a very different editing instruction, it might be challenging to fairly conduct large-scale comparison with our approach. We will include qualitative comparisons with MagicFixup in our updated version and update the detailed discussion among these two approaches.\n\n**Q4**: Lacking in quantitative ablation studies.\n\n**A4**: Please see **A1** of \"Response to Reviewer RdPN\"", "claims_and_evidence": "I appreciate most of the claims from the authors, but there are some claims that may need further discussions:\n- The training mechanism proposed by the paper seems to show a significant overlap with Magic Fixup [1], which also uses paired video frames for training. However, there is no discussion or comparison with it in the paper. It might be very helpful to discuss or show by experiments the main difference with Magic Fixup. \n- It is mentioned in L080-L086 that LightningDrag generalizes well to out-of-domain editing even if certain transformations have deformations. However, there is no theoretical or empirical evidence for it. It would be helpful if the authors could show some explanation for this part. \n\n\n[1] Alzayer, H., Xia, Z., Zhang, X., Shechtman, E., Huang, J.B., and Gharbi, M. Magic fixup: Streamlining photo editing by watching dynamic videos. arXiv preprint arXiv:2403.13044, 2024.", "methods_and_evaluation_criteria": "The method part generally looks good to me. The authors design the model based on Stable Diffusion (SD)'s inpainting model, conditioned on features from IP-Adapter, appearance & reference image features from the additional appearance encoder, point control from point embedding. It might be a plus to visualize these feature maps for a clear understanding of the model, but it is not necessary. \n\nThe evaluation criteria also look good to me. The authors follow DragBench and use IF and MD as evaluation metrics. The time efficiency is evaluated by runtime under the same experiment settings.", "theoretical_claims": "There is no theoretical claim mentioned in the paper. This paper is application-driven.", "experimental_designs_or_analyses": "Authors compare with most of the previous drag-style editing methods, but there are some missing:\n- [1] Alzayer, H., Xia, Z., Zhang, X., Shechtman, E., Huang, J.B., and Gharbi, M. Magic fixup: Streamlining photo editing by watching dynamic videos. arXiv preprint arXiv:2403.13044, 2024.\n- [2] Mou, Chong, et al. \"Dragondiffusion: Enabling drag-style manipulation on diffusion models.\" ICLR 2024.\n- [3] Jiang, Ziqi, Zhen Wang, and Long Chen. \"CLIPDrag: Combining Text-based and Drag-based Instructions for Image Editing.\" ICLR 2025.\n\nAlso, there's no ablation study in the paper. It might be helpful to include an ablation study to show the effectiveness of each component.", "relation_to_broader_scientific_literature": "N/A", "essential_references_not_discussed": "One of the contributions, learning from paired video frames, is somehow pretty similar to Magic Fixup as mentioned in \"Claims And Evidence\". However, it has not been well discussed in the paper.", "other_strengths_and_weaknesses": "Other strengths:\n- The paper shows a promising result in fast drag-style editing. The quality of the end product is competitive to state-of-the-art methods. \n- LightningDrag shows a good appearance preservation ability compared to other methods, as shown in Figure 7.\n- The test-time strategies provide further refinement for users if needed. \n\nOther weaknesses: N/A.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "Please see my reviews above. My major concern for this paper is its similarity to Magic Fixup. More discussions or evaluations would be beneficial to differentiate two papers.", "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": "kluumsvIf1", "reviewer": "Reviewer_qwJU", "summary": "This paper introduces LIGHTNINGDRAG, a high-speed, high-quality drag-based image editing framework that significantly outperforms existing methods in both efficiency and success rate. Unlike prior approaches that rely on Generative Adversarial Networks or large-scale diffusion models—often requiring over a minute per edit—LIGHTNINGDRAG completes edits in about one second. By redefining drag-based editing as a conditional generation task, the method eliminates the need for computationally expensive latent optimization or gradient-based guidance. Trained on large-scale paired video frames, the model captures diverse motion patterns, such as object translations, pose shifts, and zooming, leading to improved accuracy and consistency. Despite being trained only on videos, LIGHTNINGDRAG generalizes well to various local deformations beyond its training data, such as lengthening hair or twisting rainbows. Extensive evaluations demonstrate the superiority of this approach.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the insightful feedback and appreciations in our work. Please find our response to your questions below.\n\n**Q1**: Architectural design similar to Wear-Any-Way.\n\n**A1**: Thank you for pointing this out. While our architectural design indeed draws inspiration from prior literature, including Wear-Any-Way, we respectfully emphasize several critical differences that clearly distinguish our work:\n\n1) **Broader Scope:** Our approach addresses the general setting of drag-based editing on **arbitrary images**, whereas Wear-Any-Way specifically targets editing human clothing images. The difference in scope significantly impacts the underlying challenges and method pipeline design.\n\n2) **Novelty in Video as Supervision Signal:** A core novelty of our paper is the insight that natural videos serve as a scalable and effective source of supervision for training general-purpose drag-based editing models. In contrast, Wear-Any-Way does not utilize video data and is restricted to image-based supervision within a narrow human-clothing domain. Due to our broader and more challenging scenario, our data pipeline also requires additional sophisticated steps (e.g., filtering camera movements, filtering frames with object key-points go out-of-bound, etc.) to accurately and scalably construct training pairs from diverse video data.\n\n3) **Out-of-domain Generalization Insights:** As discussed in introduction (L80-L86) and illustrated in our empirical evaluation, our model notably generalizes to editing tasks that involve motions unseen in training (e.g., local deformation of rigid structures or objects). This observation provides novel insights into how video-based supervision can effectively enable image editing across broader, unseen domains, which is absent from Wear-Any-Way. For a more detailed discussion on this aspect, please see **A2** of \"Response to Reviewer oUVx\".\n\n4) **Additional Novel Test-time Techniques:** Given the nature of our task and approach, we introduce additional novel test-time techniques not covered by Wear-Any-Way, such as Point-Following Classifier-Free Guidance (PF-CFG), CFG scale scheduler for PF-CFG, point augmentation, and sequential dragging. These innovations significantly improve performance and reliability for general-purpose drag editing.\n\nWe will revise the paper to better highlight these contributions and clarify the novelty of our approach.\n\n\n**Q2**: The qualitative experiments compared to other methods are not very comprehensive.\n\n**A2**: Thank you for the suggestion. We will include additional qualitative comparisons in the updated version to further strengthen our empirical analysis. That said, we would like to respectfully clarify that the seven examples presented were carefully selected to be representative, spanning a diverse range of styles, objects, and editing intentions. Moreover, the quantitative results in Table 1 are based on DragBench, a widely adopted benchmark dataset with diverse samples, which provides statistically meaningful evidence of our method’s effectiveness.\n\n\n\n**Q3**: Example in Fig. 1\\(c\\) seems to be a forward-moving camera. Does the author keep such data during the filtering process?\n\n**A3**: Thank you for the question. We confirm that all videos, including the one in Figure 1\\(c\\), were filtered according to the criteria stated in the paper to ensure static camera movement. The example in Figure 1\\(c\\) may appear to involve a forward-moving camera, but it actually features a static camera observing a scene where syrup is being poured onto a surface. The apparent change in scale results from the growing size of the syrup pile, not from any camera motion. We will clarify this in the revised manuscript to avoid confusion.\n\n\n**Q4**: It is suggested that the author add a section at the end of the introduction to summarize the contributions of this paper.\n\n**A4**: Thanks for the suggestion. We will include a summary of contribution at the end of the introduction in our updated version.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "There is no theoretical claim.", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "This paper improves the efficiency and accuracy of drag-based image editing methods using samples from videos.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "1. The technical contribution is somewhat limited. The overall structure of LightningDrag is essentially the same as Wear-Any-Way[1], especially in the reference encoder and point embedding attention components. \n2. The qualitative experiments compared to other methods are not very comprehensive. There are only seven comparative examples in the paper and supplementary materials.\n3. The author claims in the paper P3L150 that 'We begin by curating videos with static camera movement,' but the example in Fig. 1(c) shows a forward-moving camera. Does the author keep such data during the filtering process?\n\n[1] Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment. In ECCV 2024.", "other_comments_or_suggestions": "1. It is suggested that the author add a section at the end of the introduction to summarize the contributions of this paper.", "questions_for_authors": "The reviewer's primary concern is the novelty of this paper, and the score will be adjusted based on the author's response.", "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:

Meta-review: This work initially received mixed reviews and the major concerns were around novelty, comparison to previous methods and ablation studies. The authors addressed the reviewers' concerns well during the rebuttal and a positive consensus has been achieved among reviewers.

I therefore recommend acceptance of the paper and strongly encourage the authors to integrate the rebuttal into the revision and to rewrite it as necessary to clarify their contribution.
Decision: Accept (poster)