GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models
Authors: Zewei Zhang
Huan Liu
Jun Chen
Xiangyu Xu
Venue: ICLR 2025 Poster (2025)
Keywords: drag editing
generative AI
diffusion model
Primary area: generative models

Abstract: In this paper, we introduce GoodDrag, a novel approach to improve the stability and image quality of drag editing. Unlike existing methods that struggle with accumulated perturbations and often result in distortions, GoodDrag introduces an AlDD framework that alternates between drag and denoising operations within the diffusion process, effectively improving the fidelity of the result. We also propose an information-preserving motion supervision operation that maintains the original features of the starting point for precise manipulation and artifact reduction. In addition, we contribute to the benchmarking of drag editing by introducing a new dataset, Drag100, and developing dedicated quality assessment metrics, Dragging Accuracy Index and Gemini Score, utilizing Large Multimodal Models. Extensive experiments demonstrate that the proposed GoodDrag compares favorably against the state-of-the-art approaches both qualitatively and quantitatively. The source code and data are available at https://gooddrag.github.io.

Published as a conference paper at ICLR 2025
GOODDRAG: TOWARDS GOOD PRACTICES FOR DRAG EDITING WITH DIFFUSION MODELS
Zewei Zhang "McMaster University
Huan Liu'
Jun Chen
Xiangyu Xu
*Xi'an Jiaotong University
ABSTRACT
In this paper, we introduce GoodDrag, novel approach to improve the stability and image quality of drag editing. Unlike existing methods that struggle with accumulated perturbations, GoodDrag introduces framework that alternates between drag and denoising operations within the diffusion process, effectively improving the fidelity of the result. We also propose an tains the original features of the starting point for precise manipulation and artifact reduction. In addition, we contribute to the benchmarking of drag editing by introducing new dataset, Drag100, and developing dedicated quality assessment metrics, Dragging Accuracy Index and Gemini Score, utilizing Large Multimodal Models. Extensive experiments demonstrate that the proposed GoodDrag compares favorably against the state-of-the- art approaches both qualitatively and quantitatively. The source code and data are available at https https://goose /top::////oddagggg wawggggggttt...... github.io.
AIDD
prrearrvnggngiiiingggg motion supervision operation that main-
INTRODUCTION
In this work, we present GoodDrag, a novel approach for drag editing with enhanced stability and image quality. Drag editing (Pan et al., 2023) represents new direction in generative image manipulation. It allows users to effortlessly edit images by simply specifying the starting and target points, as if physically dragging an object or part of an object from its initial location to the larget location, with the edits blending harmoniously into the original image context exemplified in Figure 2.
Early methods (Pan al., 2023; Ling et al., 2023) for drag editing employ Generative Adver- sarial Networks (GANs) (Goodfellow et al., 2014) which are often trained for class-specific images, and thereby struggle with generic, real-world images. Moreover, these methods heavily rely on GAN inversion techniques (Roich et al., 2022; Weihao et al., 2021; Xu et al., 2023), which may fail in complex, in-the-wild scenarios.
To address these issues, recent advancements have shifted towards using diffusion models for drag editing (Shi et al., 2023; Mou et al., 2024a; Nie et al., 2023; Mou et al., 2024a;b). Thanks to the remarkable capabilities of diffusion models in image generation, these methods have significantly improved the quality of drag editing for generie images. However, the current diffusion- basood approaches often suffer from instability, resulting in outputs that have severe distortions or fail to adhere to the designated control points.
This paper addresses these challenges by establishing two good practices for more effective drag editing using diffusion models. Our first contribution is new editing framework, called Alternating Drag and Denoising (AIDD). As shown in Figure 1, existing methods typically conduct all drag operations at once and then attempt to correct the accumulated pertur- bations subsequently. However, this approach often leads to perturbations that are too substantial to be corrected. In contrast, the AIDD framework alternates between the drag and denoising operations within the diffusion process as shown in Figure 1. This method- ology effectively addresses the issue by preventing the accumulation of large distortions, ensuring a more refined and manageable editing process.
*Corresponding author: xuziangyn2014@maai.cco

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Published as a conference paper at ICLR 2025
As the second contribution, we investigate into the common failures of point control, where the starting point cannot be accu- rately dragged to the desired target loca- tion. We find this is mainly due to that the dragged features in existing algorithms may gradually deviate from the original features of the starting point. To address this issuc, we propose an information prreservinnnserinnnnng mo- lion supervision operation that maintains the original features of the slarting point, ensuring more realistic and precise point control.
Trijecarg siEking mutic
Moking tinires
Trejeterg
Criced lmagt
tngestera
Penting wittod
Natural image nanifold
Figure 1: There are two main operations in- volved in drag editing: drag and denoising. The drag operation (orange) modifies the im- age to achieve the desired drag effect but leads to deviations from the natural image mani- fold, resulting in artifacts. The denoising op- eration (green) estimates the score function of the natural image distribution and corrects the artifacts by guiding the results back to the manifold. Existing diflusion-based drag editing methods (dotted (rajectory) apply all drag operations at once, which often results in excessive and accumulated perturbations that are hard to correct. In contrast, the proposed AIDD framework (solid trajectory) alternates between drag and denoising within the diffu- sion process, which prevents accunulated per- lurbations and ensures more accurate results.
Furthermore, we make early efforts to benchmark drag editing by introducing a new dataset Drag100 along with dedicated evaluation metrics. Notably, we develop Gemini Score, novel quality assessment metric utilizing Large Multimodal Mod- els (Anil et al., 2023), which is more reliable and effective than existing image quality as- sessment metrics.
Combining these good practices, our fi- nal algorithm, named GoodDrag. consis- tently achieves high-quality drag editing re- sults and outperforms state-of-the-art ap- proaches both quantitatively and qualita- tively.
2 RELATED WORK
Diffusion-based image manipulation. Generative Adversarial Networks (GANs) have long dominated image editing lasks such as inpainting, colorization, super-resolution, and text-driven manipulation, demonstrating early success in synthesizing plausible visual con- tent (Xu et al., 2017; Yu et al., 2018; Isola et al., 2017; Park et al., 2019; Chen et al., 2020; Xu et al., 2021; Chen et al., 2021; Chan et al., 2022; Liu et al., 2023b; Du et al., 2023). How- ever, they often suffer from instability and suboptimal image quality for real-world input, hindering their application to complex, practical editing scenarios.
Recent breakthroughs in diffusion models have revolutionized the field, offering unparalleled image fidelity and controllability (Sohl-Dickstein et al., 2015; Ho al., 2020; Song et al., 2020a;b; Rombach et al., 2022: Su et al., 2022; Liu et al., 2023a; Li et al., 2024; Yan et al., 2024; Dhariwal & Nichol, 2021). For instance, the Dreambooth series (Ruiz et al., 2023a; Raj et al., 2023; Ruiz et al., 2023b) leverage subject-specific tuning to edit and create new contents under the theme of the input. CustomSkctching (Xiao & Fu, 2024) and ControlNet (Zhang et al., 2023) introduce sketches, text, and user scribbles to guide the generation of images. Instant3D (Li et al., 2024) further integrates geometric priors to push the boundary to 3D-consistenl generation. These advancements underscore the potential of diffusion models in a wider range of image editing tasks, such as drag editing.
Drag Editing Drag editing, introduced by DragGAN (Pan et al., 2023), revolutionized image manipulation through intuitive point-to-point selection. Nevertheless, GAN-based drag editing approaches (Pan et al., 2023; Roich et al., 2022; Ling et al., 2023) remain fundamentally constrained: they primarily edit synthetic images and struggle with real- world scenes.
Recent diffusion- methods (Shi et al., 2023; Nie et al., 2023; Mou et al., 2024a; Liu et. al., 2024; Hou et al., 2024; Shin et al., 2024; Mou et al., 2024b; Lu et al., 2024; Zhao
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Original
Edit
Good
Original
GoodDrag
Figure 2: Given input image (Original) and user-specified control points (User Edit), GoodDrag "drags" the semantic content from the handle points (rod) to the target points (bluc). The target points remain fixed while the handle points move closer during optimiza- tion. Users can also select an indication mask to define the editable region.
et al., 2024) enable drag editing diverse real images. However, their consecutive editing steps accumulate perturbations the latent space (Figure 1), which often lead to severe image quality degradation.
Our key contribution addresses this limitation through the Enternating-Drag-eee aad--enoisinn (AIDD) framework. Unlike existing approaches, AIDD strategically interleaves drag opera- tions with denoising steps across the entire generation process, enabling progressive refine- ment of edits while preventing artifact accumulation. We further introduce an information- preserving motion supervision mechanism that mitigates feature drift, stabilizing the editing trajectory. These good practices ensure high-fidelity, artifact-free results even for complex real-world images, advancing drag editing beyond the quality and generality constraints of existing methods.
3 METHOD
In this work, propose GoodDrag, diffusion models (Song et al., 2020a;b; Rombach et al., 2022). We develop and integrate two effective practices within this framework: Alternating Drag and Denoising (Section 3.2) and Information- Preseovingg Motion Supervision (Section 3.3), which are instrumental in reducing visual artifacts and enhancing precision in drag editing.
framework, for high-quality drag editing with
3.1 PRELIMINARY ON DIFFUSION MODELS
Diffusson models represent a compelling subclass of generative models, having demonstrated remarkable performance in synthesizing high-quality images, evidenced by advanced ap- plications like DALLE2 (Ramesh et al., 2022) and Stable Diffusion (Rombach et al., 2022). These models consist of two distinct phases: the forward process and the reverse process.
In the forward process, a given data sample ag is combined with increasing levels of Gaussian noise over series of Tmax steps. This process results in the generation of series of progressively noised samples (mpr,, with each 2; representing the noised image al time step t. Mathematically, the forward process can be formulated as:
Vai20 + vI V--aE a1E,
(1)
where I) is random Gaussian noise. a E (0,1) acts diminishing factor of 2p- and the sequence {ox (or
Tume designed to be monotonically decreasing for stronger noise
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Figure 3: Overview of the proposed AIDD framework. (a) Existing methods first perform all drag editing operations (g at a single time step T and subsequently apply all denoising operations {ftler to transform the edited image 24 into the VAE image space. (b) To mitigate the accumulated perturbations in (a), AIDD alternates between the drag operation and the diffusion denoising operation S, which leads to higher quality results. Specifically, we apply one denoising operation after every B drag steps and ensure the total number of drag steps K is divisible by B. Here T = Tmax K, where k is the DDIM inversion strength. in this figure for clear illustration and use
We set
10 real implementation.
as increases. When is close Tmax: a₁ close to 0, and 2, approximates an isotropic Gaussian distribution.
During the reverse process, we first sample TT from the standard Gaussian distribution (0, 1) and then generate samples resembling the original data distribution of 2o by grad- ually reducing the noise levels. The Denoising Diffusion Implicit Models (DDIM) (Song et al., 2020a) stand out in this phase, achieving decent efficiency and consistency in generat- ing high-quality images. The reverse process from 2t to 2--1 under the deterministic DDIM framework can be written
Vor- VI VI- oiso(ze,t) + a1
(2)
Vor
where Eg represents a neural network with parameters 0, which trained to predict the noise in Eq. 1. For clarity, we denote Eq. 2 as 2 fz()..
Following Stable Diffusson (Rombach et al., 2022), we use the Variational Autoencoder (VAE) (Esser et al., 2021) to encode original images into lower-resolution images in fea- ture space to reduce computation and memory costs. Throughout the paper, the variables denoted by z refer to images in this VAE space instead of the pixel space.
3.2 ALTERNATING DRAG AND DENOISING
"A stitch time saves nine."
Proverb
The input of drag editing is a source image 20, a set of 1 starting points {p.h. and their corresponding target points {q.), where 1,2,....,. Here, P; E I22 represent 2D pixel coordinates within the image plane. An optional binary mask M can also be provided to specify the image region that allowed for edits. The objective of drag editing is to scamlessly transfer content from each starting point p, to the designated target point qi while ensuring that the resulting image remains natural and cohesive, with the edits blending harmoniously into the original image context.
The drag editing starts by transforming the source image 2g into a latent representation 2r through the DDIM inversion (see Appendix B), where the timestep T is empirically chosen, typically close to Timax With the transformed 27, the input image can be edited through a K-step iterative process shown in Figure 3. Each itcration, denoted by gk, h = 1,... K, comprises two main phascs: motion supervision and point tracking (Pan et al., 2023; Shi et al., 2023; Ling al., 2023).
Existing methods suffer from low image fidelity because they perform all drag operations within a single diffusion time step as shown in Figure 3(a), leading to accumulated perturba-
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tions and distortions. To address this issue, propose Alternating Drag and Denoising (AIDD) framework. The AIDD distributes editing operations across multiple diffusion time steps by alternating between drag and denoising steps, allowing for more manageable and incremental changes. As illustrated in Figure 3(b), after applying B drag operations at time step denoising step f follows, converting the latent representation from & to t- and alleviating artifacts from the drag step. This pattern continues at each subsequent time step until all intended drag edits are completed.
AIDD motion supervision. We denote the output of the k-th iteration, which serves as the imput for the (k+ 1)-th iteration, as of and the corresponding handle points as p with the initial image z = zт and the initial handle point p. = P,- The aim of motion supervision is to progressively edit the current image =f to move the handle points pt towards their largets 9, Specifically, denoting the movement direction for the i-th point as df -:-p; t l1 the motion supervision is realized by aligning the feature of af around point
p + Bdd to the feature around p,, where 3 is the step size the movement. The feature of = can be written as F(z,)) I (U)(2?;))) where the feature extractor U, is the U-Net of Stable Diffusion parameterized by 8, and represents the interpolation function to adjust the feature map to the size of the input image.
The feature alignment loss for motion supervision in AIDD is defined as:
C( (p:
Foop od0 (0 - sg (Facetm (n)
(3)
( - sg (zf-1)) @ (1- (1-M)
where P(pt mi) (p E z²: 1 1 & ) describes square region centered at p with radius 11. sg(.) denotes the stop-gradient operation. The first term of Eq. essentially drives the appearance the image around p + dd to get closer to the appearance around p," The second term ensures the non-editable region, indicated by 1 - M, remains unchanged throughout the editing process. Since the image z has undergone B denoising operations, apply the drag operation at the diffusion time step = T _ This is in sharp contrast to existing methods, which apply all drag operations at a single time step T.
The motion supervision for the (k+ 1)-th ileration takes one gradient descent step according to the feature aligument, loss L( (p:)):
OC (p:1)
k+1 = -
(4)
Ozl
where v is the step size. For short, we write the (A++1 1) drag step Eq. as 2 =/++1 g (ztt)
Point tracking. While the motion supervision effectively guides the movement of the handle point towards p + 3d, its final position at this exact spot is not guaranteed. This necessitates the point tracking to locate the new location of the handle point p. k+1 which formulated as: P
k+1 argmin
| - Fpp (=))
(5)
pr))
Eq. 5 identifies the updated handle point by searching the location in 2 k+1 that most closely resembles the original starting point pi in the original image of based on feature similarity. T2 denotes the radius of the scarch area. r()).r)).
r2).
Finally, we conduct the remaining denoising steps to convert the latent representation the desired VAE image space zn- Notably, the AIDD only changes the order of the computations, which improves editing quality without introducing additional computational overhead.
The key insight behind this framework is that addressing perturbations incrementally as they arise, rather than allowing them to accumulate, facilitates more effective and manageable image editing. In other words, it is better to fix the problem when it is small than to wait until it becomes more significant.
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To validate this concept of AIDD, con- duct a toy experiment as shown Figure 4. We simulate the perturbations introduced during image editing with random Gaussian noise, and compare the results of adding multiple noise samples within the same dif- fusion time step versus across different time steps. When noise is added all at once to 2T, the resulting image suffers from low fidclity as shown in Figure 4(b). This is due to the accumulation of noise within a single time step, leading to a substantial ial deviation from the image manifold. In contrast, distribut- ing the noise across multiple diffusion steps results in well-corrected perturbations and better proservation of original content, shown in Figure 4(c). This validates our hy- pothesis that progressive adjustments lead to more effective image editing.
Single saep
Figure 4: We generate 10 random noise sam- ples from.N(O, 0.1 and compare two scenar- los: (b) We add all 10 noise samples to a single time step 2T at once followed by 10 denoising steps, where the resulting image exhibits sig- nificant degradation. (c) We distribute the 10 noise samples across 10 different time steps, from 2T to 2T-9: with denoising step follow- ing each noise to prevent the accumulation fect; the resulting image better preserves the original content with higher fidelity.
3.3 INPORMATION-PRESENII MOTION SUPERVISION
Another challenge in existing drag editing methods is the feature drifting of handle points, which can lead artifacts in the edited results and failures in accurately moving handle points as shown in Figure 5(b). The feature drifting issue is illustrated in Figure 5(d)-(c), where the initial handle points (red points) in Figure 5(d) are near the boundary of the beach wave. As the number of drag steps increases, the handle points become less similar to their original appearance, drifting away from the wave boundary towards the sea foam or the sand, shown in Figure 5(e).
We identify that the root cause of handle point drifting lies in the design of the motion supervision loss, methods in (Pan et al., 2023; Shi et al., 2023; Ling et al., 2023). Their loss function encourages the next handle point, p BBd, to be similar the current handle point, p Consequently, even minor drifts in one iteration motion supervision, leading to significant deviations and distorted outcomes.
accumulate over time during
To address this problem, propose information-preeervve proach, which maintains the consistency the handle point with the original point throngh- out the editing process. The updated feature alignment loss for motion supervision is for-
motion supervision ap-
(9) User Edit
mi
(4) 0th MS
(6) 90th MS w/o
(O 90th MS w/ IP
Figure 5: Illustaation of the feature drifting issue. In (d), the initial handle points are near the beach wave boundary. As drag editing progresses, the features of the handle points deviate from their original appearance. By the 90th motion supervision (MS) step shown in (e), the handle points have drifted away from the wave boundary, leading to artifacts and inaccurate movement in (b). To address this issue, we propose motion supervision (IP) to maintain the fidelity of handle points to their original appearance (f), resulting in higher-quality results (c).
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Cortent
A+-
Plara
Rotakon
RAy
Ralocation
Figure 6: Distribution of categories and tasks in the Drag100, along with example images and user edits.
mulated
Σ Focot 0d m)(()) - sg (Fatet.
(6)
+ A11(=1-1 sg (=(-1) o (1-M))
where P is the original handle point in the unedited image =/. This formulation ensures that the intended handle point p Bd in the edited image zt remains faithful to the original handle point, thereby preserving the integrity of the editing process.
While the information-preservee drifting issue, it introduces new challenges. Specifically, Eq. 6 is more difficult to optimize due to its inherently larger feature distance than the original motion supervision loss Eq. 3. Therefore, a straightforward application of Eq. 6 often results in unsuccessful drag effects of the handle point. Initially, we attempted to overcome this by increasing the step size n in the motion supervision process (F4. 4), which turned out to be unsuccessful. Instead, we find that maintaining a small step size and increasing the number of motion supervision steps before each point tracking offers better solution:
motion supervision effectively addresses the handle point
ac(=i,; (p!t)
2tj+1 = T
j= J-1,
Ozh
where =f.0 2 is the initial image, and 2++1 = = is the output after J gradient steps.
motion supervision marks an effective practice for drag editing, which ensures that the handle point remains close its original appearance without introducing excessive artifacts as shown in Figure 5(f). Consequently, this leads to higher-quality results, as evidenced in Figure 5(c).
The proposed pnfformation-prssery
Finally, the whole pipeline of GoodDrag is summarized in Algorithm in the Appendix.
4 BENCHMARK
To benchmark the progress in drag-based image editing, we introduce a new evaluation dataset named Drag100, and two dedicated quality assessment metries, DAI and GScore.
4.1 DRAG100 DATASET
Since drag-based image editing is still a nascent research area, there is a lack of evaluation datasets. While recent works have introduced 2023), they have certain limitations. First, Nie et al. (2023) provides masks M for only few samples, which can lead to uncontrolled experiments and difficulties in benchmarking and fair comparison of different. methods. Second, these datasets were not constructed with explicit consideration for diversity in drag tasks, making evaluations less comprehensive.
datasets (Shi al., 2023; Nie et al,,
To overcome these challenges, we introduce new dataset called Drag100. This datasct consists of 100 images, each with carefully labeled masks and control points, ensuring that different methods can be evaluated in controlled manner. Drag100 is designed to encom- pass a diverse range content, as shown in Figure 6. It comprises 85 real images and 15 Al-generated images using Stable Diffusion. The dataset spans various categories, including 58 animal images, artistic paintings, 16 landscapes, 5 plant images, human portraits, and 10 images of common objects such cars and furniture.

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User Edit
Ous
Drughhifissin
SDE-Day Day
Drpo
DeydAN
User Bdit
Ours
DrsDDifisiz
SDE SDg:Duy Drucnihiftaizn
DIsGAN
Figure 7: Comparison with drag editing methods (Shi et al., 2023; Nie et al., 2023; Mou ct al., 2024a; Pan et al., 2023).
We have particularly considered the diversity of drag tasks, including relocation, rotation, rescaling, content removal, and content creation, as illustrated in Figure 6. These tasks have distinct characteristics. Relocation involves moving an object or part of an object, while rotation adjusts the orientation of objects; both tasks mimic rigid motion in the physical world without changing the object area or creating new contents. Rescaling corresponds to enlarging or shrinking an object. Content removal involves deletion of specific image com- ponents, e.g., closing mouth, whereas content creation involves generating new content not present in the original image, f.g., opening mouth. These tasks require advanced hallucina- tion capabilities, similar occlusion removal (Liu et al., 2020) and image inpainting (Yu et al., 2018). By encompassing these diverse tasks, the Drag100 dataset enables more comprehensive evaluation of drag editing algorithms.
4.2 EVALUATION METRICS FOR DRAG EDITING
In this work, we introduce the following two quality assessment metrics, Dragging Accuracy Index (DAI) and Gemini Score (GScore), for quantitative evaluation.
DAL. Existing methods use Mean Distance (MD) (Shi et al., 2023) to measure the drag accuracy, While effective, it necessitates handle point identificatinn with DIFT (Tang et al., 2023), which significantly increases runtime and imposes high demands Spocifically, MD requires 1.8s per image on Drag100 when using an A100 GPU. This high computational overhead makes it less practical for evaluating drag editing algorithms.
GPU resources.
To address this issue, we introduce DAI to quantify the effectiveness of an approach in transferring the semantic contents to the target point. Specifically, we define DAI to assess whether the source content at p, of the original image has been successfully dragged to the target location q; in the edited image, which can be written as:
|0(20)(pp))) +(80))(())))1
DAI
27)²
where è is the VAE decoder converting 2o to the RGB image space, and M(ps)) 7) denotes a patch centered at P, with radius 7. Eq. 8 calculates the mean squared error weew the patch at P; of @(z0) and the patch al q, of d(2)). By varying the radius 7, can flexibly the assessment: a small y ensures precise
control the extent of context incorporated measurement of the difference at the control points, while large encompasses broader context; this serves as a lens to examine diffcrent aspects of the editing quality.
In contrast to MD, the proposed DAI metric is much more efficient, requiring only 0.01s per image on the Drag100 dataset. Additionally, DAI runs entirely on the CPU, eliminating the need for GPU resources. The efficiency of DAI makes it particularly valuable for drag editing research, where a fast. and accessible metric is essential for iterative development. and large-scale benchmarking.
GScore. Existing methods use Image Fidelity (IF) (Shi et al., 2023) to evaluate the per- ceptual quality of the edited images. However, we find that the IF metric is fundamentally

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flawed evaluation measure for drag editing. IF is defined 1-LPIPS between the drag- edited image and the input image, meaning it penalizes any changes to the image, even when such changes are necessary to achieve the desired editing. As result, the metric rewards outputs that are identical or nearly identical to the input image, which contradicts the very purpose of drag editing. This inherent limitation clearly demonstrated in Figure 10 of the Appendix, where GoodDrag achieves the best visual quality yet receives the worst IF score, underscoring the inability of IF to accurately evaluate the quality of meaningful drag edits.
While No-Reference Image Quality Assessment (NR-IQA) methods (Ke et al., 2021; Golestanch et al., 2022; Chen et al., 2023) offer a way to assess image quality without reference images, they often rely on handcrafted features or are trained on limited image samples, which do not always align well with human perception.
To address this challenge, we introduce GScore, new metric leveraging Large Multimodal Models (LMMs) trained on internet-scale vision-language data. GScore uses LMMs as evaluators by providing the edited and original images as references and prompt them to rate parceptual quality on a scale from to 10, with higher scores indicating better quality. As shown in Figure 10, compared to IF, the proposed GScore metric better correlates with human perception, making it a more reliable and appropriate metric for evaluating drag editing algorithms.
In our experiments, we explored the use of both GPT-4V (Achiam et al., 2023) and Gem- ini (Anil et al., 2023) evaluation agents. We find that the output from Gemini is more reliable and closely aligned with human visual judgment. Therefore, we select Gemini as the primary evaluation agent for assessing the quality of edited images in our work.
5
EXPERIMENTS
5.1 IMPLEMENTATION DETAILS
In experiments, we use Stable Diffusion 1.5 (Rombach et al., 2022) the base model and finetune its U-Net with LoRA (rank=16) to enhance image fidelity. We employ the Adam optimizer (Kingma & Ba, 2014) with 0.02 learning rate. For the diffusion process, Tmax = 50 denoising steps, an inversion strength of K = 0.75 (resulting in T = Tmax K 38), and no text prompt. Features for Eq. are extracted from the last U-Net layer. In the AIDD framework, we set the motion supervision and point tracking radii to ri = 4 and r2 12, respectively, with a drag size 3 and a mask loss weight A= 0.2. We perform a total of K = 70 drag operations, with B = 10 operations per denoising step, resulting K/B- = denoising steps during the alternating phase. Fach drag operation includes J J= 3 motion supervision steps in Eq. 7. Similar to Shi et al. (2023), we incorporate the Latent-MasaCtrl MasaCtrl mechanism (Cao et al., 2023) starting from the 10th U-Net layer to enhance editing performance. We evaluate the runtime and GPU memory usage of GoodDrag with an A100 GPU. For an input image of size 512x512, the LoRA phase takes approximately 10 seconds, while the remaining editing steps require about one minute. The total GPU memory consumption during this process is less than 13GB.
set
5.2 COMPARISON WITH SOTA
As Drag100 provides more comprehensive and generalizable testbed compared to prior beuchmarks (Shi et al., 2023), we present our main evaluation on the Drag100 dataset, with supplementary results on the data of (Shi et al., 2023) presented in the Appendix.
Qualitative evaluation. We first compare GoodDrag with DragGAN (Pan et al., 2023) in Figure 7. The proposed method is able to effectively edit the input images, whereas DragGAN suffers from notable artifacts and low fidelity. This superior performance is primarily due to the enhanced generative capabilities of diffusion models compared to GANs, which enables GoodDrag to gencralize well across varions inputs.
Next, compare method with diffusion- approaches: DragDiffusion (Shi et al., 2023), SDE-Drag (Nie et al., 2023), and DragonDiffusion (Mou et al., 2024a). As shown in Figure 7, DragDiffusion struggles with accurately tracking handle points and often fails to
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Table 1: Quantitative evaluation of drag accuracy in terms of DAI (L) on Drag100.
Table 2: Quantitative evaluation of im- age quality in terms of GScore (0 to 10, higher=better) on Drag100. We repeated the experiment 10 times.
Method = 5 = 10 y = 20
DragDiffusion
0.148 0.144 0.130
0.115
Drag *
0.119 0.110 0.098
0.092
Method
GScore t
SDE-Drag
0.157 0.144 0.129
0.114
DragDiffusion
6.75 + 0.10
DragonDiffusion
0.213 0.199 0.183
0.166
SDEDrag
5.81 + 0.19
w/o IP
0.110 0.098
0.093
0.088
DragonDiffusion 3.05 + 0.17
w/o AIDD
0.090 0.079 0.072
0.070
GoodDrag 8.04 + 0.05
GoodDrag
0.070 0.067 0.064 0.062
move semantic content to target locations. While SDE-Drag and DragonDiffusion achieve better point movement, they introduce severe artifacts, resulting in low- fow-fidelity and unre- alistic details. In contrast, GoodDrag precisely drags content to specified control points, delivering higher-quality results.
Quantitative evaluation. The evaluation in terms of DAI is presented in Table 1, with the patch radius y varying from to 20. larger y encompass more contextual pixels, offering a broader view of drag accuracy.
As shown in Table 1, GoodDrag consistently outperforms all baseline methods across all y values, indicating superior accuracy in dragging semantic content to target points. Notably, DragDiffusion uses 80 drag operations, while GoodDrag uses 70. With J =3 motion super- vision steps per operation (Eq. 7), GoodDrag totals 210 steps, unlike DragDiffusion requires a single step per drag operation. To isolate the impact of more motion supervision steps, created DragDiffusion using 210 drag operations to match GoodDrag. Although this improved the result of DragDiffusion, it still performed worse than GoodDrag, confirming the effectiveness of our approach.
The GScore in Table evaluates the naturalness and fidelity edited images. Our method achieves average GScore of 8.04 on the Drag100 dataset, clearly outperforming DragDif- fusion, SDE-Drag, and DragonDiffusion.
3.0
2.5
2.5
Average Rank
2.0
Average Rank
2.0
1.5
1.5
1.0
0.5
0.5
0.0
0.0
GoodDrag DragDiffusion SDE-Drag
GoodDrag DragDiffusion SDE-Drag
(a) Drag accuracy
(b) Image quality
Figure 8: User study on the drag accuracy (a) and perceptual quality (b) of the edited results. Lower ranks indicate better performance.
User study. For a more comprehensive evaluation of the drag editing algorithms, we conduct a user study with 12 images randomly selected from the Drag100 benchmark. Each image is processed by three different methods: DragDiffusion (Shi et al., 2023). SDE- Drag (Nie et al., 2023), and the proposed GoodDrag. Subjects are asked to rank the edited results by each method with the input image as reference (1 for the best and for the worst). As shown in Figure 8, the study is divided into two parts, with the ranking criteria being the accuracy of the drag editing and the perceptual quality of the results, respectively. We receive responses from 27 participants, and the mean scores and standard deviations are presented in Figure 8. The proposed method is clearly preferred over other methods, suggesting its better capability in achieving precise drag editing (Figure 8(a)) while maintaining high par quality (Figure 8(b)).
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A ALGORITHM
The whole pipeline of GoodDrag is shown in Algorithm 1.
Algorithm 1 Pipeline of GoodDrag
Require:
Input image 2o. binary mask for editable region M, handle points (p, (p,K=1. larget points {g. U-Net Co, latent time step T, number of drag iterations K, number of motion supervision steps per point tracking Ensure: Output image 2g
1: Finetune Ug on 20 with LoRA
2: 2r f apply DDIM inversion to 20
3: 24 4- P.
4: for in m:0:: do t=
5:
=1.0 < 21
7:
for j -
8:
F(zh,) I (Uo((,)))))))
9:
Update <h.++1 using motion supervision as Eq. ot
10:
+- = Update {p) t using points tracking Eq.
11:
fi=1
12:
(I + 1) mod B 0 then
13:
k+1 4--1 + one step denoising from *+1 with Eq.
for in B do
15:
4 + one step denoising from 2t with Eq.
B DDIM INVERSION
The deterministic nature of DDIM allows the transformation of natural image zn to its latent variable žạ (the inverse operation of Eq. 2). As suggested in (Song et al., 2020a), the inversion from 2t-1 to 2t is formulated as:
24 Voi (V )
-1
a4-1 -1)
-0(21-1,- -1) 1)+
Ot a1-1
21-1:
(9)
V
which can be directly derived from Eq. 2, where 60(2)-1, - 1) is used to approximate t)) The DDIM inversion is invaluable for image editing applications, where one can first invert 20 to latent space 2t, then apply targeted modifications to latent image and finally transform the edited latent image back to the image space by denoising with Eq. 2. This circumvents the difficulties of directly modifying 2o, enabling more flexible and practical image editing applications.
C RESULTS ON DRAGBENCH
We present the quantitative evaluation on DragBench (Shi al., 2023) in Table 3 and Table 4. Our method consistently achieves the lowest. DAI scores across all values in Table 3, indicating
superior accuracy in dragging content. target points. Additionally, as shown in Table 4, the edited images from method demonstrate significantly better GScore, indicating higher fidelity and naturalness compared to other approaches, which further highlights the effectiveness of GoodDrag.
We also provide qualitative evaluations in Figure 9, where our method achieves accurate drag editing while maintaining high fidelity. In contrast, DragonDiffusion struggles to move content precisely to target positions, and both SDE-Drag and DragonDiffusion generate results with noticeable artifacts and unrealistic content.
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Drgghiffcom
Drageniiffaaic
Edit
Doggl DoggDuffiiio iittioo
SDE-Duag
Figure 9: Qualitative comparison images from other datasets (Shi et al., 2023; Nie et al., 2023). Masks were manually labeled and consistently applied across all methods for fairness. The left column displays results from DragBench dataset, while the right column shows results from SDE-Drag dataset.
Table 3: Quantitative evaluation in terms of DAT (4) on DragBench (Shi et al., 2023).
Method
7 ==1 7 =
5
Y = 10
20
DragDiffusion
0.1829
0.1711
0.1618
0.1538
SDE-Drag
0.1796
0.1652
0.1577
0.1499
DrigonDiffusion
0.3108
0.2940
0.2821
0.2692
GoodDrag 0.1339
0.1254
0.1210
0.1153
D QUANTITATIVE EVALUATION WITH MD AND IF
For a more comprchensive study, we also adopt the same evaluation metrics as DragDiffu- sion (Shi et al., 2023), i.e., Mean Distance (MD) and Image Fidelity (IF). The MD metric is defined as the Euclidean distance between the positions of the handle points and the larget locations, where the handle points are identified with DIFT (Tang et al., 2023). The IF metric is calculated as I-LPIPS between the original and edited images.
We conduct comparisons on both the DragBench and Drag100 datasets, as shown in Table and Table 6. The results show that our method achieves significantly better MD values than the bascline methods, demonstrating its effectiveness in accurately dragging content the desired target locations.
Limitation of IF. While the IF score our method is slightly lower than other approaches, argue that the IF metric is fundamentally flawed as an evaluation measure for drag editing. IF defined as 1-LPIPS between the drag-edited image and the input image, meaning it penalizes any changes to the image, even when such changes are necessary to achieve the desired editing. As a result, the metric rewards outputs that are identical or nearly identical to the input image, which contradicts the very purpose of drag editing.
This inherent limitation is clearly demonstrated Figure 10, where GoodDrag achieves the best visual quality yet receives the worst IF score (0.86), underscoring the inability of IF to accurately evaluate the quality of meaningful drag edits.
In contrast, the proposed GScore metric better correlates with human perception shown in Figure 10, making it a more reliable and appropriate metric for evaluating drag editing algorithms.
Runtime of MD. While MD is effective in measuring drag accuracy, it relies on DIFT Tang el al. (2023) for handle point identification, which significantly increases computational cost. and runtime while imposing high demands on GPU resources. Specifically, MD requires an average of 1.8s per image on the Drag100 dataset when using an A100 GPU. This high computational overhead makes it less practical for many users.
In contrast, the proposed DAI motric is much more efficient, requiring only 0.01s per image on average for the Drag100 dataset. Additionally, DAI runs entirely on the CPU, climinating the need for GPU resources. The efficiency of DAI makes particularly valuable for drag editing research, where fast and accessible metric is essential for ilerative development. and large-scale benchnarking.
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Table Quantitative evaluation in terms of GScore (0 10, f) on DragBench (Shi al,,
2023).
Method
GScore 1
DragDiffusion
6.53 + 0.07
SDEDrag
5.85 0.09
DragonDiffusson
3.36 = 0.18
Ours
7.91 + 0.04
Table 5: MD (4) results both DragBench and Drag100 datasets.
Method
Ours
DragDiffusion
SDE-Drag
DragonDiffusion
DragBench
23.40
33.50
47.84
27.04
Dragl00 100
23.44
37.2
74.33
28.4
E ADDITIONAL COMPARISONS WITH MORE BASELINES
We conduct additional comparisons with more baseline approaches, including Drag- Noise (Tiu al., 2024), FasyDrag (Hou et al., 2024), and InstantDrag (Shin et al., 2024). The qualitative results are shown in Figure 11, clearly demonstrating that our proposed GoodDrag achieves superior performance, Specifically, delivers more accurate drag edit- ing, produces images with significantly higher quality, and minimizes artifacts compared to the bascline mothods.
We also present quantitative comparisons in Table 7 where we use DAI to evaluate the proposed GoodDrag against the baseline approaches on Drag100 dataset. Our method consistently outperforms others across all 7 values, which highlights the robustness and effectiveness of our approach in achieving precise drag edits.
F ADDITIONAL DRAGGAN RESULTS
We present closer visual comparison against DragGAN in Figure 12. Table 8 presents quan- titative comparisons between our method and DragGAN using MD, IF, DAI, and GScore. The proposed GoodDrag achieves consistent improvement over DragGAN both qualitatively and quantitatively.
G EVALUATION WITHOUT MASK
In our main evaluation, we follow the convention of DragDiffusion and utilize masks by default during the evaluation process.
To provide a more comprehensive analysis, we also compare the performance of different methods without using masks. As shown in Table (with masks) and Table 10 (without masks), the results without masks are generally worse than those with masks, expected.
Nevertheless, even in the absence of masks, our method consistently outperforms the baseline approaches, demonstrating its robustness and practical effectiveness in real-world scenarios where mask information may not always be provided by user.
Table 6: IF (t) results on both DragBench and Drag100 datascts.
Method
Ours
DragDiffusion
SDE-Drag
DragonDiffusion
DragBench
0.87
0.88
0.91
0.90
100
0.86
0.87
0.89
0.88
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Original
GoodDrag
DragDiffusics
SDE-Drag
DragonDiffusiion
IF
0.86
0.92
0.94
0.91
GScore
8
Figure 10: GoodDrag achieves successful drag editing with the best visual quality, but receives the worst IF score (0.86), underscoring the limitation of IF in accurately evaluating meaningful drag edits. In contrast, the proposed GScore metric better correlates with human perception, making it more reliable and appropriate metric for evaluating drag editing algorithms. Blue numbers indicate the worst scores for cach metric, and rod ones indicate the best.
Table 7: Quantitative comparison against DragNoise, EasyDrag, and InstantDrag. The evaluation is conducted by measuring the average DAI (1) on Drag100 dataset.
Method
y=1
7
7
7 20
DragNoise
0.209
0.191
0.169
0.146
EasyDrag
0.201
0.191
0.169
0.142
IrstantDrag
0.173
0.152
0.128
0.108
Ours
0.070
0.067
0.064
0.062
H EFFECTIVENESS OF ALDD
As introduced in Section 3.2, existing drag editing algorithms often suffer from low fidelity due the accumulation of perturbations during the drag operations. As shown in Fig- ure 13, the edited result without AIDD cxhibits noticcable inconsistencies in the owl's body compared the original image. In contrast, incorporating AIDD significantly improves the fidelity of the edited result, ensuring that the owl's body remains faithful to the input image.
One might suggest that this fidelity issue could be mitigated by reducing the number of drag operations. However, as illustrated in the second row of Figure 13, while this approach does improve fidelity, it compromises the effectiveness of the drag editing, failing to relocate the content to the desired target locations. This underscores the importance of AIDD in achicving a better balance between fidelity and effective drag editing.
I EFFECTIVENESS OF INFORMATION MOTION SUPERVISION
In this sention, evaluate the effectiveness of the informattion-presrviig motion supervi- sion. As shown in Figure 14(b), the model without informattion-prservinng motion supervi- sion suffers from noticeable artifacts well as dragging failures. In contrast, incorporating the information Enformation-presevving strategy effectively mitigates this issue, leading to improved re- sults in Figure 14(d).
The feature distance between the handle point and the original point shown in Fig- ure 15(b), where the proposed informatioonppeeervvii motion supervision results in sub- stantially smaller feature distance (blue curve) compared to the model without this method (orange curve), underscoring its effectiveness in addressing feature drifting issues.
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User Edit
Ouss
DigNNese
Instariling
User Edit
Ours
DragNoii
Easy
Indasiling
Figure 11: Qualitative comparison with DragNoise (Liu et al., 2024), EasyDrag (Hou et al.,
2024), and DrstantDrag (Shin al., 2024).
User Edit
Ours
DragGAN
User Edit
Ours
DragGAN
Figure 12: Closer visual comparison with DragGAN.
Furthermore, the information-preserving motion supervision also facilitates more accurate point tracking in Eq. 5. In Figure 15(a), we show the feature distance map between the original point P. and the neighborhood of the current handle point Q(p), r2). The heatmap with the information- prrerioii-peeeiiingggg strategy is more concentrated with higher variance, thereby enabling more precise localization of the handle point. In contrast, the heatmap without this strategy more diffused with lower variance.
Notably, adopting this information-presetea tion motion supervision due to the inherently larger feature distance in Eq. 6 compared to Eq. 3. This increased complexity shown in Figure 14(c), where the cat's face remains stationary. To overcome this issuc, employ multiple motion supervision steps within single drag operation. As depicted in Figure 14(d), this approach effectively resolves the above issue, enabling the cat's face dragged to the desired orientation.
strategy presents challenges in the optimiza-
impede the movement of the handle point, as
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Table Quantitative comparison against DragGAN. As DragGAN requires fine-tuning the GAN generator for each input, resulting much slower speed, we only conduct the evaluation on subset of Drag100 (the six images Figure 12).
Metrics
Ours
SDE-Drag
DragDiffusion
DragGAN
MD (4)
15.83
67.92
57.28
73.00
IF (t)
0.85
0.81
0.89
0.79
DAI (7 = 1) (L)
0.078
0.156
0.189
0.196
DAI (7 =5)
0.103
0.150
0.194
0.201
DAI (7 (4)
0.097
0.146
0.196
0.202
DAI (7 (4)
0.070
0.129
0.178
0.187
GScore (T)
8.10+-0122
7.03++0.23
5.65++0.35
2.12--0.42
Table 9: MD (L) and DAI (4) on DragBench with mask.
DragBench
Ours
DragDiffusion
SDE-Drag
DragonDiffusion
MD
23.40
33.50
47.84
27.04
DAI (? 1)
0.1339
0.1829
0.1796
0.3108
DAI (7 5)
0.1254
0.1711
0.1652
0,2940
DAI (? -10)
0.1210
0.1618
0.1577
0.2821
DAI (? 20)
0.1153
0.1538
0.1499
0.2692
EFFECTIVENESS OF GSCORE AGAINST NR-IQA
We compare various image quality assessment metrics, including TReS (Golestaneh et al., 2022), MUSIQ (Ke et al., 2021), TOPIQ (Chen et al., 2023), and our proposed GScore, in terms of their alignment with human visual perception. We utilize the image quality rankings from the user study in Section 5.2 and measure the correlation between these human rankings and the rankings produced by each metric.
Specifically, for the set of N, 12 images used in the user study, each image is processed by Nm. = different methods. For the i-th image, the human-assigned rankings for its N, results are dcnoted as {Ui NN { where Un represents the rank assigned to the result the j-th method. The rankings produced by an assessment. metric for the same edited results are denoted as ( (=
The correlation between metric and the human judgment is defined
Pi
(10)
where P; is the Spearman's 's rank correlation cocfficient (Gauthier, 2001) for the i-th image, calculated as:
6. Nm (Uij R2y)² f
P: =
(11)
Nm (N2 -
The average correlations are presented in Table 11. While TReS, MUSIQ, and TOPIQ exhibit low (or negative) correlations, GScore demonstrates much higher correla- tion with the human visual system, indicating the effectiveness of GScore for assessing the perceptual quality of drag editing results.
K GSCORE EXAMPLE
We provide GScore example in Figure 16.
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Table 10: MD (L) and DAI (L) on DragBench without mask.
DragBench
Ours
Drag
SDE-Drag
DragonDiffusiie
MD
23.00
36.83
48.40
25.12
DAI (7 1)
0.1558
0.1972
0.1811
0.3085
DAI ( 5)
0.1448
0.1914
0.1704
0.2929
DAI ( 10)
0.1321
0.1781
0.1576
0,2820
DAI (y 20)
0.1202
0.1654
0.1508
0.2699
Fdit
wlo
w/ALDD
Drags
Dmgs
Drags
Figure 13: Effectiveness of AIDD, In the first row, the result without AIDD shows noticeable inconsistencies in the owl's body compared to the imput, while incorporating AIDD effectively addresses this issue. We use 70 drag operations by default. As shown in the second row, reducing the number of drag operations without AIDD improves fidelity but sacrifices the capability relocating the semantic contents.
L ROBUSTNESS ACROSS DIFFERENT BASE MODELS
The proposed GoodDrag framework is compatible with different diffusion base models. While we use Stable Diffusion 1.5 the default model in this work, we also tested GoodDrag with Stable Diffusion 2.1 and observed minimal difference in performance, which demon- strates the robustness of GoodDrag across different base models. Several examples are provided in Figure 17.
M RUNTIME ANALYSIS
Motion Supervision (IP) involves J motion su- pervision steps as introdeeed in Eq. 7, the runtime of GoodDrag slightly longer than DragDiffusion (71.3s vs. 57.4s) as shown in Table 12.
Since the proposed
For a better comparison, we modified DragDiffusion by increasing the number of drag op- erations to match the number of motion supervision steps used in GoodDrag. While this updated version (referred as DragDiffusion*) requires a longer runtime, still under- performs compared to GoodDrag as shown in Table 12, highlighting the advantages of our approach.
Additionally, tested simplified version of our model without the IP component, relying solely on the proposed AIDD strategy. This variant (w/o IP) is significantly faster than DragDiffusion (32.1s vs. 57.4s) while still achieving better performance than DragDiffusion. These results further demonstrate the efficiency and efficacy of the proposed algorithm.
N RELATIONSHIP WITH DRAGON AND DIFFERITOR
DragonDiffusion (Mou et al., 2024a) and DiffEditor (Mon et al., 2024b) are two related works that also involve image editing within the denoising diffusion process. Nevertheless, they are fundamentally different from GoodDrag and the proposed AIDD in both theoretical foundations and practical inplementation.
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Published as a conference paper at ICLR 2025
(8) User Edit
(b) w/o
(c) IP (Once)
(d)
The results different processing conditions on the subject: (a) User Edit, (b) without the proposed information- periiioinnrreeiiinggg motion supervision (IP), (c) with IP applied once, and (d) with IP applied optimally. Without IP, noticeable artifacts and dragging failures occur, shown in (b). Direct application of IP once is less effective, leading to inferior results as in (c). Employing multiple IP steps within a single drag operation, as optimized in (d), significantly improves the outcome by addressing these issues.
Figure 14:
Ist Drng
30th Drag
50th Drag
70th Drg
192
Avorage foste Bucacce
120
205
wil IP
IdP1192
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Figure 15: (a) shows the feature distance map from Eq. 5 at different drag steps. More specifically, these heatmaps represent the feature distances between the original point p and the neighborhood of the current handle point S(p r2). The standard deviation (std) of the distances in each heatmap is provided below, where small std indicates a diffused heatmnp with indistinctive feature distances, and a large std indicates more concentrated heatmap, resulting in generally more accurate localization of the smallest distance in Eq. 5. (b) shows the feature distance between the handle point and the original point with the increase of drag steps. The distance with the proposed much smaller than that without IP, demonstrating its eflectiveness dealing with the feature drifting issue.
motion supervision (IP)
From theoretical perspective, DragonDiffusion and DiffEditor rely on a mechanism anal- ogous classifier guidance (Dhariwal & Nichol, 2021). these methods, the diffusion process remains probabilistically grounded, and each step is guided by combining the un- conditional gradient and the conditional likelihood term. Mathematically, this is expressed
x, log q(x yy) Vx, log gxx)( + Xx. log gy xx),
(12)
where the guidance operates within the probabilistic framework of diffusion (see Eq. 8 of DragonDiffusion (Mou et al., 2024a) and Eq. 3 of DiffEditor (Mou et al., 2024b)). From practical perspective, this results in the editing and denoising processes being intertwined and inseparable.
In contrast,, GoodDrag follows a fundamentally different. paradigm, similar DragDiffu- sion (Shi et al., 2023), where the drag editing operations and the denoising diffusion process are decoupled. AIDD distributes drag operations strategically across multiple diffusion steps but is not constrained by the probabilistic formulation of classifier guidance, which repre- sents a significant departure from existing methods. This separation allows AIDD to intro- duce flexibility in drag editing, which is not feasible with methods like DragonDiffusion and DiffEditor, and effectively improves the results. As shown Section 5.2 and Appendix C and D, GoodDrag achieves significantly better performance than Dragon DragonDiffusion Diffusion across
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Table 11: Correlations between various image quality assessment metrics and human visual perception.
TReS
MUSIQ
TOPIQ
GScore
0.250
-0. 125
0.083
0.54
Prompt: Conduct detailed evaluction three modified images, lobeled 'A', '8', 'c' and 'D., in comparison to on original image (Image 1). Image. 1 serves as the boseline. will not evalusted. Focus on assessing the quality 'A' (Image 2), 'B' (Image 3). c'' (Image 4), and 'D' (Image 5), particularly in terms of their naturalness and the presence or ebsence of artifects. Exomine how well each algarithm preserves the integrity of the original image while introducing modifications, Look for any signs distortions, unnatural colors, pixelation, or other visual inconsistencies. Rate each where represents excellent quality seamless madifications, and 1 indicates poor quality with significant and noticeable artifacts. Provide a comprehensive analysis for each rating, highlighting specific aspects of the image that inf luenced your evaluation. Answers must be English.
Image
image on
from
Image (A):
Image 2 (A) successful madification of the original image. The fax's 's fur appears natural and well textured, and the overall color balance is pleasing. The madification is subtle noticeable, adding a touch of whimsy to the original image without detracting from its realism. only minor elongated.
slight distortion of the fox's left ear, which appears slightly
Image (B): 7/10
Image onother dell-done modification. The fox's fur is perhaps than the original image, and the colors are wibrant and lifelike, The modification is more pronounced than Image (A), but it still maintains sense. naturalness. The only noticeable arti prtifcction is slight pixelation around the edges of the fox's 's ears.
realistic
Image (C): 4/10
Image
drastic madification of original image, and it successful the previous two. The fex's s fur appears unnatural and almost plastic-like like, and the colors are saturated. madification is also quite noticeable, the original image. There are also some noticeable distortions around the fox's eyes and mouth.
detracts from realism
Image 5 (D): 1/10
Image 5 (D) is complete failure, The fex's face is completely distorted, and the colors are gorish and unnatural. The modification is extreme that difficult to even recognize the original image. There also numerous artifacts, including pixelation, blurring, and unnatural- looking shadows.
In conclusion, Image 2 (A) is the most successful modification, followed by Image (B). Image
less successful madification, and Image (D) is complete failure. main factors that contributed to the success of Image 2 (A) and Imoge (8) were the naturalness of the fur and the pleasing color balance, The main factors that contributed to the failure of Image 4 (C) and Image (D) were the unnatural appearance the fur, the distorted shopes, and the garish colors.
Figure 16: An example from GScore: Images A, B, C, and D in Figure 7, shown in the last row of the left column, represent GoodDrag, DragDiffusion, SDE-Drag, and DragonDiffu- sion, respectively. For each prompt, we input the original image along with the comparison images.
multiple benchmarks, both quantitatively and qualitatively. These results underline the practical advantages of AIDD and the distinctiveness of GoodDrag's approach.
LIMITATIONS
Similar to existing diffusion-based methods, such as DragDiffusion, the proposed GoodDrag relies on DDIM inversion for effective drag editing. However, DDIM inversion may face challenges in complex scenarios, illustrated in Figure 18, where the reconstruction of the
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Published as a conference paper at ICLR 2025
User Edit
GoodDrag-SDL3
GoodDrag-SD2..
User Edit
GoodDrag-SDLL5
GoodDrag-SD22
The proposed GoodDrag demonstrates consistent performance with different diffusion base models.
Figure 17:
Table 12: Comparing the runtime of GoodDrag and DragDiffusion.
GoodDrag DragDiffusions w/o TP DragDiffusion
Number of Motion Supervision steps
210
210
70
80
Runtime (s)
71.3
80.1
32.1
57.4
DAI (? =1 1)
0.070
0.119
0.110
0.148
DAI (? = 5)
0.067
0.110
0.098
0.144
DAI (7 10)
0.064
0.098
0.093
0.130
DAI (7 = 20)
0.062
0.092
0.088
0.115
inversed image (Figure 18(b)) appears blurred and many fine details are lost. Consequently, the edited result of GoodDrag also suffers from these artifacts shown in Figure 18(d). In future work, we aim to explore more robust and effective diffusion inversion techniques for better drag oditing performance.
(a) Input Image
(b) Reconstruction from DDIM Inversion
(c) User Edit
(d) GoodDrag
Figure 18: Limitation. The proposed GoodDrag relies on DDIM inversion, which may struggle with complex images, where many fine details of the original input cannot be clearly restored from the inversed image (b)-
P
ETHICS STATEMENT
GoodDrag enhances image editing capabilities, benefiting creative industries and digital content creation by providing more precise and reliable tools. However, its advanced ma- nipulation features could be misused to create misleading or deceptive content, such as deepfakes. While release the source code and dataset to support research and devel- opment, we encourage users to adhere to ethical standards and applicable regulations to prevent misuse.
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Published as a conference paper at ICLR 2025
Q CONCLUDING REMARKS
In this work, we introduce GoodDrag, a method that enhances the stability and quality of drag editing. Leveraging AIDD framework, effectively mitigate distortions and enhance image fidelity by distributing drag operations across multiple diffusion denoising steps. In addition, we introduce infornation-preservinng motion supervision to tackle the feature drifting issue, thereby reducing artifacts and enabling more precise control over handle points. Furthermore, we present the Drag100 dataset and two dedicated evaluation metrics, DAI and GScore, to facilitate a more comprehensive benchmarking of the progress drag editing. The simplicity and efficacy of GoodDrag establish strong baseline for the development of more sophisticated drag editing algorithms. Future directions include exploring the integration of GoodDrag with other image editing tasks and extending its capabilities to video editing scenarios.
24


Reviews:
[{"review_id": "KZAwgFlG7U", "reviewer": "Reviewer_jGsQ", "summary": "The paper introduces a drag-editing framework using diffusion. The paper comprehensively evaluates and fixes issues in prior work (For ex- introduces information preservation through the feature alignment loss to alleviate drag points' drifting issue). Overall, the framework proposes a simple re-ordering of existing control and denoising steps leading to no additional computational overhead while significantly improving editing fidelity. \n\nThe paper also contributes a dataset (Drag100) geared towards editing evaluation rather than training, in addition to two evaluation metrics for measuring editing fidelity. One of the metric exploits", "questions": "Suggestions: \n1. I strongly recommend moving the user study carried out by the authors (in the supplementary currently) to the main text since that is the common evaluation metric across different papers, not DAI or GScore.\n2. L355 - 357 is not needed. It should be apparent to the readers in this (editing/generative) space.\n3. Consider making figures bigger. Especially Figures 5, 6 and 7. \n4. Add an ethics/societal impact section.\n\nIf these minor weaknesses and suggestions are acknowledged, I would consider giving it an even higher rating for highlighting.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. Paper comprehensively evaluates prior work and builds on the weaknesses through a simple alternating dragging & denoising step-framework. \n2. The paper introduces a carefully thought out (natural + synthetic) hybrid evaluation dataset comprising diverse editing scenarios; in addition to two evaluation metrics that might be useful for the drag-editing community. Potential for higher impact.\n3. Results look promising and the information preservation feature alignment loss is well grounded.\n4. Supplementary contains substantial ablations and empirical visual justifications for proposed components of the framework.", "weaknesses": "1. It makes sense to compare against diffusion based editing techniques since the paper builds on the weaknesses of prior literature. However, it would be nice to still have DragGAN in the main qualitative figure (7) and the quantitative evaluations for the sake of completeness. \n2. Training hardware and memory is not specified in the main text. Consider moving it from the supplementary to the main text?", "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": "JnsUEgOAou", "reviewer": "Reviewer_dZPL", "summary": "This paper introduces a new approach for enhancing stability and image quality in drag editing tasks. Key contributions include: Alternating Drag and Denoising (AlDD) alternates between drag and denoising steps to prevent artifacts and ensure higher fidelity. Information-Preserving Motion Supervision maintains feature consistency and reduce drifting issues. Experiments are conducted on Drag100 dataset.", "questions": "See the strengths and weaknesses", "limitations": "", "rating": 3, "confidence": 3, "soundness": 1, "presentation": 2, "contribution": 1, "strengths": "1. This paper contributes a new dataset and metric for image editting.\n2. The idea for solving accumulation distortion is interesting.", "weaknesses": "1. This paper does not include comparisons with recent works [1, 2], which limits the context for understanding its contributions relative to the latest advancements.\n\n2. It lacks critical experiments on the DragBench dataset [3], especially with the MD and IF metrics, which are widely used in existing literature for standardized evaluation.\n\nDue to above considertation, it is challenging to assess the performance of this paper.\n\n[1] Drag your noise: Interactive point-based editing via diffusion semantic propagation\n\n[2] EasyDrag: Efficient Point-based Manipulation on Diffusion Models\n\n[3] Dragdiffusion: Harnessing diffusion models for interactive point-based image editing.", "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": "OA5E3UoqkS", "reviewer": "Reviewer_9Jgw", "summary": "This paper introduces a novel approach to improve the stability and image quality of drag editing, namely GoodDrag. GoodDrag adopts an AlDD framework that alternates between drag and denoising operations within the diffusion process, effectively improving the fidelity of the result. They also propose an information-preserving motion supervision operation that maintains the original features of the starting point for precise manipulation and artifact reduction. In addition, they contribute to the benchmarking of drag editing by introducing a new dataset, Drag100, and developing dedicated quality assessment metrics, Dragging Accuracy Index and Gemini Score, utilizing Large Multimodal Models.", "questions": "1. The paper may lack a discussion on its limitations.", "limitations": "", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper proposes an effective framework for achieving high-quality drag-based image editing results.\n2. The results demonstrate a significant improvement in image fidelity during the drag editing process.\n3. The authors introduce a new dataset and additional metrics for evaluating the point-based image editing task.", "weaknesses": "1. The novelty of the ALDD design is limited, as it mainly alters the order of denoising during the drag updating process. A concern is whether separating the process using the hyperparameter 'B' is optimal. Is 'B' fixed, or does it require manual adjustment by the user?\n2. There is confusion about whether \"ALDD\" and \"AlDD\" refer to the same concept.\n3. There is an unnecessary blank area on Line 054 of the paper.\n4. The authors claim that previous datasets are limited in terms of diverse drag tasks. However, the proposed Drag100 dataset contains fewer images compared to DragBench (205 images) [1].\n5. Providing results for the GoodDrag method on the DragBench dataset[1] would enhance the evaluation.\n6. It would be helpful to see how the results are measured using Image Fidelity and Mean Distance metrics in DragDiffusion[1].\n\n[1] Shi, Yujun, et al. \"Dragdiffusion: Harnessing diffusion models for interactive point-based image editing.\" CVPR. 2024.", "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": "NZMyVZBe1N", "reviewer": "Reviewer_k1h1", "summary": "This paper introduces GoodDrag, a diffusion-based approach designed to enhance the stability and fidelity of image drag editing. The core idea is to alternate between drag and denoising operations within the diffusion process to prevent perturbation accumulation, leading to more stable image generation. To this end, the authors propose the Alternating Drag and Denoising (AIDD) framework along with a new benchmark, Drag100. Experimental results demonstrate the effectiveness of the proposed methods both qualitatively and quantitatively.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-written, and the proposed approach is technically sound.\n2. The qualitative results well demonstrate the effectiveness of the proposed framework.", "weaknesses": "The main concern is the insufficiency of experimental results:\n1. Although the authors proposed two quality assessment metrics, widely used evaluation metrics such as Image Fidelity (IF) and Mean Distance (MD) should also be reported.\n2. How does the method perform on other benchmarks, such as DRAGBENCH?\n3. What is the impact of using different base models on the results?\n4. Limitations and failure cases should be discussed and reported.", "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": "qFq8plbXCS", "reviewer": "Reviewer_pphp", "summary": "The paper introduces GoodDrag, a novel framework designed to enhance the stability and image quality of drag editing using diffusion models. The main contributions of the paper are as follows:\n\n- Alternating Drag and Denoising (AlDD) Framework: GoodDrag employs the AlDD framework, which alternates between drag and denoising steps within the diffusion process. This approach prevents the accumulation of large perturbations, ensuring more refined and manageable editing results. The AlDD framework is depicted as a significant improvement over existing methods that apply all drag operations at once, followed by denoising, which often leads to excessive perturbations and low fidelity.\n- Information-Preserving Motion Supervision: To combat common failures in drag editing related to point control, GoodDrag introduces an information-preserving motion supervision operation. This method maintains the original features of the starting point, ensuring more realistic and precise control over the drag process and reducing artifact generation.\n- Benchmarking Enhancements: The paper contributes to the benchmarking of drag editing techniques by introducing a new dataset called Drag100 and developing dedicated quality assessment metrics, namely the Dragging Accuracy Index (DAI) and the Gemini Score (GScore). These metrics are designed to evaluate the quality of drag editing more effectively, utilizing Large Multimodal Models for a more accurate assessment.", "questions": "**Clarification on Tracking Steps in the Presence of Inversion Strength**: Does the GoodDrag framework continue to utilize point tracking steps within the Alternating Drag and Denoising (AlDD) framework? If so, considering the inversion strength is set to 0.7, which corresponds to a significant amount of noise reduction and thus a deviation from the original image features, how does the system ensure accurate matching and tracking of points? Specifically, since diffusion-based methods can maintain correspondence primarily when the diffusion time step is small, how does GoodDrag handle scenarios where the dragged points do not align with their intended targets due to these potential mismatches in feature correspondence?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. **Introduction of the Drag100 Dataset**: The creation of the Drag100 dataset is a significant advancement for the field of drag editing. This new dataset provides a well-defined and controlled environment for testing and benchmarking drag editing algorithms. It includes a variety of image types and editing scenarios, which ensures comprehensive testing across different contexts and challenges. This diversity helps in evaluating the robustness and versatility of new drag editing methods under varied conditions.\n\n2. **Development of Novel Evaluation Metrics**: The paper introduces two new evaluation metrics, the Dragging Accuracy Index (DAI) and the Gemini Score (GScore). These metrics are designed to provide a more precise and relevant assessment of drag editing quality. DAI focuses on the accuracy of content transfer to target locations, while GScore evaluates the naturalness and fidelity of the edited images using Large Multimodal Models. These metrics contribute to a standardized approach for assessing image editing quality, promoting more objective and reproducible research outcomes in the field.\n\n3. **Well-Structured Presentation and Comprehensive Validation**: The paper excels in its presentation, clearly outlining the methodologies, experiments, and results.", "weaknesses": "1. **Questionable Novelty of AlDD**: The paper claims that existing methods typically conduct all drag operations at once and then attempt to correct accumulated perturbations subsequently. However, methods like DragonDiffusion and DiffEditor have previously explored applying motion supervision across multiple diffusion time steps. This raises questions about the purported novelty of AlDD, as the concept of distributing editing operations across multiple steps is not entirely new.\n\n2. **Lack of Running Time Analysis**: The paper does not provide a comparative analysis of the running times of GoodDrag versus other state-of-the-art methods. Including this information would help evaluate the practical applicability of GoodDrag, especially in real-time or resource-constrained scenarios.\n\n3. **Evaluation Metrics - Mean Distance and LPIPS**: DragDiffusion has already introduced Mean Distance and LPIPS as evaluation metrics for assessing drag editing quality. The paper would benefit from reporting these metrics alongside the newly proposed DAI and GScore to provide a comprehensive evaluation framework that can be directly compared with existing methods.\n\n4. **Comparison with More Recent Methods**: The paper lacks comparisons with more recent advancements in drag editing, such as DiffEditor, InstantDrag and LightningDrag. Including these methods in comparative evaluations would strengthen the paper's relevance and demonstrate the efficacy of GoodDrag in the context of the latest research developments.\n\n5. **Use of DragBench and Handling of Masks**: The paper criticizes some datasets for not consistently providing masks, yet a drag-editing model should not alway relies on user-provided masks. And in the original DragGAN formulation, the mask is optional, highlighting a discrepancy in the paper's critique. A more robust approach would be to evaluate GoodDrag's performance on both DragDiffusion and SDEDrag's DragBench (both with and without masks), thereby demonstrating its versatility and alignment with user-friendly design principles seen in earlier 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": {}}]

Author rebuttal:

Decision: Accept (Poster)