DragFlow: Unleashing DiT Priors with Region-Based Supervision for Drag Editing
Authors: Zihan Zhou
Shilin Lu
Shuli Leng
Shaocong Zhang
Zhuming Lian
Xinlei Yu
Adams Wai-Kin Kong
Venue: ICLR 2026 Poster (2026)
Keywords: Image Editing
Drag Editing
Diffusion Models
Primary area: generative models

Abstract: Drag-based image editing has long suffered from distortions in the target region, largely because the priors of earlier base models, Stable Diffusion, are insufficient to project optimized latents back onto the natural image manifold. With the shift from UNet-based DDPMs to more scalable DiT with flow matching (e.g., SD3.5, FLUX), generative priors have become significantly stronger, enabling advances across diverse editing tasks. However, drag-based editing has yet to benefit from these stronger priors. This work introduces DragFlow, the first framework to effectively harness FLUX’s rich prior via region-based supervision, enabling full use of its finer-grained, spatially precise features for drag-based editing and achieving substantial improvements over existing baselines. We first show that directly applying point-based drag editing to DiTs performs poorly: unlike the highly compressed features of UNets, DiT features are insufficiently structured to provide reliable guidance for point-wise motion supervision. To overcome this limitation, DragFlow introduces a region-based editing paradigm, where affine transformations enable richer and more consistent feature supervision. Additionally, we integrate pretrained open-domain personalization adapters (e.g., IP-Adapter) to enhance subject consistency, while preserving background fidelity through gradient mask-based hard constraints. Multimodal large language models (MLLMs) are further employed to resolve task ambiguities. For evaluation, we curate a novel Region-based Dragging benchmark (ReD Bench) featuring region-level dragging instructions. Extensive experiments on DragBench-DR and ReD Bench show that DragFlow surpasses both point-based and region-based baselines, setting a new state-of-the-art in drag-based image editing. Code and dataset are available at https://github.com/Edennnnnnnnnn/DragFlow.

Published as a conference paper at ICLR 2026
DRAGFLOW: UNLEASHING DIT PRIORS WITH REGION BASED SUPERVISION FOR DRAG EDITING
Zihan Zhou'.*, Shilin Lu Shuli Leng', Shaocong Zhang',
Zhuming Lian', Xinlei Yu?, Adams Wai-Kin Kong'
Nanyang Technological University, National University of Singapore {zihan010, shilin002, nic25.1s344 zhan07ii, zhuming001 /ee wtw..//..e.......u..u... xinlei. yueu. nus edu adamskongentu.com edu.sg
Point Input
DragLoRA
GoodDrag
Region Input
RegionDrag
OURS
ELEVATÉ VISION
EEETTFIIE
PLSPALE MSIIEE
ELEVATE YOUR VISION
CLEVATC ELE YOUR VISION
ELEVATE
CLESTISISILE
VISION
Point-based Methods
Region- Region-based Methods
Figure 1: Comparison of drag-editing results between baselines and our method, DragFlow.
DragFlow successfully unleashes FLUX's stronger generative prior, removing the distortions that previous methods produced challenging scenarios.
ABSTRACT
Drag-based image editing has long suffered from distortions in the target region, largely because the priors of earlier base models, Stable Diffusion, are insufficient project optimized latents back onto the natural image manifold. With the shift from UNet-based DDPMs to more scalable DiT with flow matching (e.g.. SD3.5, FLUX), generative priors have become significantly stronger, enabling advances across diverse editing tasks. However, drag- drag-based editing has yet to benefit from these stronger priors. This work introduces DragFlow, the first framework effec- tively harness FLUX's rich prior via region-based supervision, enabling full use its finer-grained, spatially precise features for drag-based editing and achieving substantial improvements over existing baselines. We first show that directly ap- plying point- based drag editing to DiT's performs poorly: unlike the highly com- pressed features of UNets, DiT features insufficiently structured provide reliable guidance for point-wise motion supervision. To overcome this limitation, DragFlow introduces region-based editing paradigm, where affine transforma- tions enable richer and more consistent feature supervision. Additionally, we in- tegrate pretrained open-domain personalization adapters (c.g.. IP-Adapter) en- hance subject consistency, while preserving background fidelity through gradient mask-based hard constraints. Multimodal large language models (MLLMs) are further employed to resolve task ambiguities. For evaluation, we curate a novel Ragion-based Dragging benchmark (ReD Bench) featuring region-level dragging
"Equal Contribution.

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Published as a conference paper at ICLR 2026
instructions. Extensive experiments on DragBench-DR and ReD Bench show that DragFlow surpasses both point-based and region-based baselines, setting a new state-of-the-art in drag-based image editing. Code and dataset are available https//gghhbbccco///mmmmmmmm/nnp
INTRODUCTION
Text-driven image editing (Labs et al., 2025) has made impressive progress, but natural language often underspecifies geometry and locality, leading to unintended changes. Drag-based image edit- ing (Pan et al., 2023; Jiang et al., 2025; Xia al., 2025) bridges this gap by enabling users to specify finer-gre grer-grained, spatially localized motions through interactive drag instructions, yielding more con- trollable edits. Despite their success, however, these methods often introduce unnatural deformations and distortions, especially in images with intricate details or complex structures.
We attribute this limitation to the insufficient generative prior of Stable Diffusion (SD) (Rombach et al., 2022a), the predominant base model, which struggles to constrain optimized latents back onto the natural image manifold. Past findings align with this view: applying nearly identical loss func- tions for drag editing yields far fewer unnatural distortions when using GAN-based priors compared to SD (Shi et al., 2024b). In parallel, recent advances in generative modeling have shifted from U- Net-based DDPMs to more scalable Diffusion Transformers (DiTs) (Peebles & Xie, 2023) trained with flow matching (Lipman et al., 2022) (e.g., SD 3.5 (Esser et al., 2024a), FLUX.I-dev (Black Forest Labs, 2024)), yielding substantially stronger priors that have propelled progress across vari- ous editing tasks (I.u et al., 2025; Yan et al., 2025; Wei et al., 2025; Deng et al., 2024; Wang et al., 2024; Rout et al., 2024). Yet, drag-based editing has not capitalized on these enhanced priors.
In this work, we pioneer the exploration of leveraging a stronger generative prior for drag editing. We first observe that directly applying previous drag editing methods to DiTs yields suboptimal re- sults. Through detailed analysis of features extracted from U-Nets and DiTs, we identify two core obstacles. First, point- based objectives used by prior drag methods mismatch DiT representations. U-Net bottlenecks produce spatially compact, highly compressed features that aggregate high-level semantics over broad receptive fields; supervising a single feature-map location therefore carries strong semantic evidence. DiTs, in contrast, yield finer-grained, spatially precise features with nar- rower receptive fields. Directly applying point-wise motion or tracking losses to DiTs provides weak semantic supervision and degrades editing efffectiveness. Second, modern DiT models like FLUX are classifier weee-freeuuii frree-guiannc (CFG)-distillee) which exacerbates inversion drift. As a result, standard key-value (KV) injection is insufficient to preserve subject identity consistency during drag edits.
To harness the potent priors of DiT-based models for drag-basee editing, we introduce DragFlow, a novel ragion-based editing framework. DragFlow departs from point supervision and rethinks inver- sion and background handling to align with DiT feature geometry and the realities of CFG-distilled models. DragFlow advances the state of the art through three key innovations: region-level mo- tion supervision, which delivers richer and more consistent feature guidance via affine transfor- mations; (ii) a replacement of traditional background consistency losses with hard constraints that inver-
preserve the background while updating only the editable region; and (iii) sion, which injects subject representations from pretrained open- open- domain personalization adapter (e.g., IP-Adapter (Ye et al., 2023)) into the base model's prior, achieving markedly superior sub- ject fidelity under edits. Together, these components make drag-based editing practical with DiT backbones: they harness the stronger generative prior without sacrificing controllability, reducing deformation artifacts and improving faithfulness on complex, detail-rich images.
For evaluation, we introduce the Region-based Dragging Benchmark (ReD Bench). Each sample in ReD Bench is equipped with point-to-region alignment, explicit task tags spanning relocation, deformation, and rotation, and contextual descriptions that clarify user intent. We validate DragFlow extensively on both DragBench- (Lu et al., 2024a) and ReD Bench. Results demonstrate that DragFlow consistently outperforms -fate-of-the-art (SOTA) baselines.
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2
RELATED WORK
Recent advances diffusion models have led to a surge of interactive image-editing techniques, enabling users intuitively reposition or deform specific regions of an image through drag-based interactions. Existing methods for drag-based editing can be broadly grouped into three categories.
methods (Xia al., 2025; Ling et al., 2024; Liu al., 2024; Zhang al., 2024c; Jiang et al., 2025; Shi et al., 2024b; Karras et al., 2022; Mou et al., 2023; 2024; Lin et al., 2025: Hou et al., 2024; Cui et al., 2024; Luo al., 2024; Choi et al., 2024). which is the most prevalent category, iteratively refine inverted noisy latents during inference. These techniques are predominantly point-based: they accept point-wise drag instructions as input and employ motion supervision and point tracking, both of which operate the point level. However, they often yield unnatural deformations or distortions in the edited images, primarily because the optimized latents deviate from the natural image manifold learned by the base model, residing in outrof-distribution regions. Thus, many studies have focused on more judicious optimization strategies to ensure that the resulting latents can be more readily mapped back to plausible natural images. (ii) Finetuning- based methods (Shin et al., 2024; Shi et al., 2024a), which adapt a base text-to-image (T2I) dif- fusion model using curated video datasets. Yet, the inherent mismatch between video data and the precise instructions required for drag editing, coupled with the scarcity of high-quality, large-scale training data, limits their generalization. These methods usually fail achieve complete drag effeets and are prone to distortions. (iii) Methods that avoid both finetuning and optimization (e.g., Re- gionDrag (Lu et al., 2024a) and FastDrag (Zhao et al., 2024d)), instead directly copying and pasting noisy latent patches to target locations computed via predefined mapping functions during inference. While this approach significantly enhances efficiency, heavily relies on handcrafted priors for the mapping functions, often resulting in edited images that lack faithfulness and realism.
(i) Optimization-
Our method falls within the optimization- based paradigm but innovates by replacing point-based motion supervision with region-level supervision, thereby enabling drag capabilities in DiTs. Like RegionDrag, our approach accepts region-based inputs; however, whereas RegionDrag requires users manually predefine the target region mask (a challenging task in non-rigid scenarios), only necessitate specifying a target point serving as the region's 's center. Moreover, RegionDrag per- forms point-wise copy-pasting within noisy latents, which, due to the handcrafted nature of its map- pings, often fails to preserve internal region structures. In contrast, we treat the region as a cohesive unit, extracting holistic regional features to as supervision signals during latent optimization, thereby ensuring the integrity of internal structures.
3 METHODOLOGY
Sources
UNet Feature Maps
DIT Feature Maps
In this section, we begin by clu- cidating the inherent limitations of previous point-based drag editing frameworks when adapted to DiTs (Sec. 3.1). Building on this analy- sis, we introduce region-level affine supervision strategy (Sec. 3.2). To further enhance fidelity, we incor- porate hard-constrained background preservation (Sec. 3.3) and adapter- enhanced subject consistency mecha- nisms (Sec. 3.4), addressing inversion drifts in DiT'.
Up-samp.
Mid
Down-samp.
Double
Double 18th block
block
block
block
17th block
Comparison of feature maps extracted from UNet and DiT at the same denoising step. UNet produces spatially compact, highly compressed features that capture high-level semantic information, whereas DiT generates finer- mmer-grained, spatially precise representations.
Figure 2:
3.1 WHY BOINT-BASED DRAG FAILS ON DIT
To harness the robust prior FLUX for drag-based based image editing, we initially applied established point-based drag editing frameworks. including image inversion, motion supervision, point tracking, and key-value injection, directly to FLUX. Surprisingly. as shown in Fig. 6, this straightforward adaptation offers only limited improvements compared to its counterpart in SD.
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Adegher
VAE Encoder
YAE
Ro
Sofhe
Dregging Procedure
Mediee Zepuft:
raage
[TM6]
dadble Outtate::
MLLM
(GLASS) apaniiin "ypes
Aradinn
Presden Accworr
ROTATION Ratute
RELOCATION Raice the lowne back
LECORMATION
Subfeske
Figure 3: Overview of the DragFlow framework. The original image is inverted into a noisy latent space and iteratively optimized under the proposed region-leve affine supervision. Subject con- sistency is reinforced through key-value (KV) injection and our adapter-enhances inversion, while background fidelity is maintained via gradient mask-based hard constraints. In addition, multi- modal large language model (MLLM) employed to better interpret and clarify user intents.
We attribute this performance gap to fundamental differences in the feature granularity extracted by the DiT and UNet, which significantly impact the effectiveness of point-wise motion supervision and tracking methods. As illustrated in Fig. the UNet architecture, due its bottleneck design, produces spatially compact and highly compressed features that encapsulate high-level semantic representations. In contrast, DiT generates finer-grained, spatially precise features. Consequently, in UNet, each point on the feature map aggregates semantic information from a broad receptive field in the input image, whereas in DiT, each point corresponds a narrower region.
This difference directly impacts point-based methods, which rely computing losses for motion supervision and point tracking using individual points on the feature map. In UNet, the broader receptive field of each feature point provides rich semantic context, enabling effective motion super- vision and tracking. However, in DiT, the finer-grained features, with their narrower receptive fields, capture less semantic information per point, undermining the efficacy of these point-based methods when applied directly to DiT.
3.2 REGIIO--LEVEL AFFINE SUPERVISION
To leverage the powerful prior of FLUX for drag-based image editing, we introduce DragFlow, a region-based framework.
User Input Specification. In our framework, the user designates source region masks {M, L. each paired with a corresponding target point 4, (x,,, Wz). The target point serves the centroid of the target region. The expected target region mask can be obtained via an affine transformation (see Appendix C.1 for details). As illustrated Fig. 3, we harness the capabilities of multimodal large language model (MLLM), GPT-5 (OpenAI, 2025), to infer users' underlying intentions and thereby facilitate drag- wrage editing. The model receives input the original image together with user- provided drag instructions (i.e., source region masks and target points). We then prompt the MLLM with carefully designed in-context examples, which guide it to produce two outputs: (i) class label indicating the type of editing operation, and (ii) textual description articulating the inferred editing intent (see Appendix D.3 for details on the prompting strategy). The class label determines which affine transformation is applied, while the textual description serves natural- language prompt for the generative model during the drag-editing process.
i==
Iterative Latent Optimization. Given input image æ; is first encoded by the VAE to produce
the latent z, which is then inverted to obtain the noisy latent z, where f E [0, T). We optimize z over k iterations where [0, K], denoted as 2 such that subsequent denoising of 2; produces
(k)
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an output image that fulfills the user-specified drag operations. This optimization is guided by the following loss function:
N
21 (0) 7t | (R) & F (=(")) sg M F
Diry
where
74 7=1. (1)
Here, x00) is the initial unoptimized latent, while 2, represents the latent after optimization iterations. The function F(.) extracts features from DiT, with the specific feature layers detailed in Appendix C.5. The operator sg[-] denotes stop-gradient. The weighting coefficient % balances multiple drag operations within the same image, determined adaptively according to the relative size of the corresponding manipulated regions (see Appendix D. for the formulation in details). Finally,
(R)
M, (0) A M; is the user-provided mask for the source region, and M( specifies the corresponding target region, where we enforce similarity the features of the source region.
Affine Transformation for Mask Propagation. The target mask M M( is derived from the source mask M, via affine transformation:
(t. bi),
(Relocation & Deformation)
M = O : (K)
K k D.aati ai).
(Rotation)
where Ω applies the affine transformation (affine computation detailed in Appendix C) M. with parameters governed by (k) Different drag types influence the affine matrix parameters distinctly: for relocation and deformation, these are determined by the vector from the target point to the centroid b, of the source region, where b; = (1/IM, (0) Ee qi for rotation, they are governed by the angle Lb formed by t;, b; and the user-specified anchor point a, The linear schedule k/ K moves the mask smoothly from the source configuration toward the target over K iterations.
Why Region-level Supervision. This formulation causes the target mask M, (k) to translate (or rotate) progressively from the source centroid toward the target point as k increases. Intuitively, it transports the object's features from the source region to the destination step by step. Although this echoes the high-level idea of point-based dragging, there are two crucial differences:
Feature granularity. Point-based methods compare features only a handle point, either against its previous location or against the original source point. In contrast, we match features between entire source and target regions. Region-level supervision provides richer semantic context and mitigates myopic gradients, which leads to more effective latent updates on FLUX.
Tracking requirement. Point-based approaches require handle point tracking to keep the extracted features aligned with the moving content. Without tracking, naively advancing the handle along straight line is brittle: even a slight deviation of the optimized content from that line causes sub- sequent local features to mismatch the intended structure, causing error accumulation and eventual drag failure. Our region-level supervision avoids this failure mode. Because we compare features regions rather than at single point, do not need to pinpoint feature extraction location the object at each step. This makes the procedure substantially more stable and robust, eliminating the need for explicit tracking; it suffices to shift the source region mask along the path from the source centroid to the target point.
3.3 BACKGROUND PRESERVATION
Prior work typically enforces fidelity in non-editable regions via an auxiliary consistency loss:
LBG || ( (k) agi=("i)) @ (1 (11-BBl,
where B denotes the mask that specifies the editable region. In practice, this term competes with the feature-matching objective, making performance highly sensitive its weight. The issue is exacer- bated in FLUX, CFG-distilled model that exhibits larger image-inversion drift than non-distilled counterparts. Compounding this, the consistency loss is evaluated against the inverted latent 2(0),
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which is treated ground truth; when the inversion is biased, this target is unreliable, and the loss misguides optimization rather than helping it.
Instead of balancing competing losses, we hard constrain the background and update only the ed-
itable region:
@Lrra az1 (k)
(k+1) B
(k)
+ (1-B) o zt 2
where B denotes the mask that specifies the editable region (see Appendix D.2 for extraction details), and 2:th is obtained from pure reconstruction path. Implementationally, this requires an additional reconstruction branch that starts from the inverted latent 21; the overhead is modest and, critically, yields substantially better background preservation in challenging FLUX settings.
3.4 SUBJECT CONSISTENCY ENHANCEMENT
Table 1: Inversion Performance (3,000 images).
While our proposed region-level affine supervision enables drag-based editing using the prior of FLUX, it still suffers from subject inconsis- tency between the source and edited images. A natural remedy the KV injection, which widely used when SD serves as the base model. In FLUX, however, KV injection underperforms, as shown in Fig. 4 (left). We attribute this gap to FLUX being a CFG-distilled model, which exhibits more pronounced inversion drift com- pared to non-distilled counterparts, evidenced in Tab. 1.
Method
LPIPS 4
SSIM f
PSNR
Polvvr (Iu et al., 2022) Iny. (SD)
0.167
0.799
26.31
Fireflow Inv. w/o udapter (FLUX)
0,283
0.703
20.43
Fireflow Inv. w/ udapter (FLUX)
0,173
0,784
25.87
To address this, we introduce adapter-enhanced inversion for DiT-based models. Specifically, pre- trained open-domain personalization adapters (e.g., IP-Adapter (Ye et al., 2023), PuLID (Guo al., 2024), and InstantCharacter (Tao et al., 2025)) are trained to extract subject's representation from reference image, enabling its seamless integration into a T2I base model for generation across varied contexts. Leveraging this insight, we propose employing such adapters as auxiliary subject representation extractors. Without any additional fine-tuning, we inject the adapter's subject repre- sentation into the model prior, which substantially improves inversion quality (Tab. 1) and yields visibly better subject consistency under edits (Fig. (right)).
EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
We implement DragFlow using 1-UX.1- dev (Black Forest Labs, 2024) as the base model. We adopt FireFlow (Deng et al., 2024) as the inversion algorithm for FLUX, employ- ing 25 diffusion steps, with 6 steps skipped and drag editing commencing at the 19th step. We perform optimization at the 7th denoising step over 70 iterations, using a learning rate of 1000 for the first 50 iterations and 1200 for the fi- nal 20. The adapter employed is InstantCharac- ter (Tao et al., 2025). Additional implementa- tion details are provided in Appendix C.5.
4.2 EXPERIMENTAL SETUP
Benchmark. To facilitate systematic evalua- tion of region-based image drag-editing meth- ods, we introduce a new Region-basse Drag- ging Bench (ReD Bench). Existing datasets are often limited in scope. For example, Drag- Bench (Shi et al., 2024b) primarily provides
Sources
w/ KV
w/ KV IP-Adap
Figure 4: Visualization of the effect of adapter- enhanced inversion on subject consistency, com- pared with KV injection alone.

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Table 2: Comparison of composition performance across two benchmarks. Optimal results are bolded, where the second-best own underlines. Abbreviation: OPT optimization-based FT sattunnn--bsedd NFT neither fine-tuning nor optimization-
Image Fidelity
Mean Distance
Benclunark
Method
Category #Params (M)
1F,g
.2:
IF 12,2
MD:
MD; J
RegionDrag (Lu et 202-la)
NFT
0
1,000
0.957
0.957
33.69
6.38
FasiDrag (Zhan et al., 20244)
NFT
0.928
0.950
0.941
23.37
5.00
InstamtDrag (Shin al, 2024)
FT
914
0.930
0.949
0.946
24.38
4.54
DragLLoRA (Xia el 2025)
OPT
3.19
0.927
0.950
0.938
26.04
4.86
FreeDrag [Iing al., 2024)
OPT
0.07
0.941
0.947
0.956
30.31
6.08
ReD Bench
DragNoise (Liu ct al., 2024)
OPT
0,33
0.942
0.932
0.975
45.46
8.85
GoodDrag (Zhang
2024c)
OPT
0.956
20.38
CLIPDrag (Jiang et ol., 2025)
OPT
0.07
0.952
0.942
0.965
33.84
6.98
DragDiffusion (Shi
2024b)
OPT
0.944
0.948
DragFlow (Ours)
OPT
0.992
0.958
0.934
19.46
4.48
Region (La et 20244))
NFT
1,000
0.942
0,960
32.32
FastDrag (Zhao et al., 2024d)
NFT
0.938
0.947
0,952
35.96
6.60
InstanDrrag (Shin et 2024)
FT
914
0.944
0,945
0.966
36.26
6.99
DragLoRA (Xia
2025)
OPT
3.19
0.942
0.941
0.952
42.03
6.77
FreeDrag (Ling et al., 2024)
OPT
0.07
0.955
0,946
0.967
34.77
6.81
DragBench-DIR
DragNoise (Liu al., 2024)
OPT
0.33
0.956
0.943
0.977
39.31
7.69
GoodDrag (Zhang et al., 2024c)
OPT
0.07
0.948
0.946
0.956
37.87
6.91
CLIPDrag (Jiang al., 2025)
OPT
0.07
0.962
0.945
0.972
38.06
7.45
DragDiffusion (Shi ol., 2024b)
OPT
0.07
0.954
0.944
0.958
39 41
7.05
DragFlow (Ours)
OPT
0.969
0.948
31.59
woint-to-poing guidance for dragging operations, which fails to capture the complexity of region- level manipulation. Extensions of DragBench with coarse region annotations also fall short, as they lack explicit point-to-region alignment and operation-specific instructions, such as task tags and anchor points.
In ReD, each sample not only includes point-level operations, but also offers the translated region- to-region correspondences, together with explicit task labels covering the three most common cat- egories of dragging (i.e., relocation, deformation, and rotation). Moreover, ReD is enriched with detailed contextual descriptions and intent prompts. This richer supervision allows ReD to serve as a more reliable testbed for assessing our approach and comparing it with range of SOTA baselines.
Evaluation Metrics. Following prior work (Liu et al., 2024: Zhang et al., 2024c; Shi et al., 2024b), we evaluate drag-based editing using a combination of Mean Distance (MD) and Image Fidelity (IF). The standard MD metric (Shi et al., 2024b) quantifies the spatial correspondence of dragged content. We employ a masked variant, denoted as MD,, which computes correspondences only within the edited region, providing a more focused evaluation of alignment quality. In addition, we also adopt the variant proposed by Lu et al. (2024a), denoted as MD2. IF assesses visual consistency between the original and edited images via LPIPS (Zhang et al., 2018). 8). We employ three variants for fine- grained analysis: IF IFby: LPIPS computed on non-edited regions, capturing how well background content is preserved. IF e2: LPIPS between the original source region and the edited target region, indicating how faithfully source content is transferred. 22. LPIPS between the original and edited source regions, measuring how effectively the source is cleared after transfer.
4.3 COMPARISON WITH BASELINES
Quantitative Analysis. As shown in Tab. 2, our method achieves the lowest MD across both benchmarks, demonstrating the strongest spatial correspondence between user instructions and the resulting drag operations. The superior performance on IF s2s highlights the method's ability to de- liver precise and reliable content manipulation with high structural consistency and completeness. Although our approach ranks second on 1F the margin is marginal and largely attributable to the inherent inversion limitations of the CFG-distilled model. Nevertheless, our gradient mask-bases hard constraints ensure robust background integrity, allowing our method to outperform most base- lines despite these limitations.
Qualitative Analysis. Fig. 5 presents side-by-side comparisons with representative baselines. Our method consistently produces edits that accurately follow the specified dragging operations while preserving global scene coherence. In contrast, RegionDrag and InstantDrag often introduce struc-
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Instruction
Inputs
CLIPDrog
C
DrogLoRA
FastDrag
Point based
FreeDrag
GoodDrog
Instantbrag
Reglonnbrgg
Region based
Regg
OURS
Figure Qualitative comparison of our method with multiple baselines in challenging scenarios.
tural distortions, while FreeDrag and FastDrag struggle with complex transformations such as rota- tions. CLIPDrag and DragLoRA frequently misinterpret relocation deformation, leading umin- tended artifacts. Across all these scenarios, our approach demonstrates superior structural preserva- tion, faithful intent alignment, and robust performance over diverse range of editing tasks.
4.4 ABLATION STUDY
To assess the contribution of each component, we perform ablation studies (results shown in Fig. and Tab. 3) by progressively incorporating modules into our framework. Specifically, examine the impact of adopting (a) region- based manipulation, introducing (b) mask-led background preser- vation, and applying (c) adapter-enhance inversion. In (a), transitioning from points to region- based control leads to consistent gains across all evaluation metrics. Compared with the baseline, regional manipulation reduces MD, by 19.95 and increases IF by 0.027, highlighting its ability to provide semantically richer and more coherent feature guidance. These results support our hypoth- esis that the regional affine strategy constitutes more principled paradigm for modern generative
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Point-based FLUX Dragger
Region-Level Affine Supervision
Background Preservation
Adapter Enhanced Inversion
Region Input
Drag oo
DragFlow
DragfRow
DragRlow
Figure 6: Qualitative ablation study comparing different variants of our framework.
Table 3: Ablation study on ReD Bench examining the impact of some key components.
Image Fidelity
Mean Distance
Configs
FFg f
IF
IF
MD;
MD2 1
Baseline (Point-basee based FLUX)
0.765
0,932
0.962
51.21
+ Region-Level Affine Supervision
0.757
0,946
0.936
31.26
5.88
+ Background Preservation
0.925
0.948
0.943
29.67
5.39
+ Addaper-Enhaneee Inversion
0.991
0.959
0.938
20.15
4.48
priors, effectively alleviating the intrinsic limitations of sparse point-level supervision. For (b), our gradient mask design substantially improves background consistency, with IF, rising from 0.757 to 0.925, underscoring its effectiveness preserving global backdrop integrity. Finally, the adapter- enhanced inversion in (c) significantly strengthens subject fidelity, as reflected by increase in 1221 from 0.948 to 0.959, thereby confirming its capability to maintain foreground consistency un- der drag-editing Taken together, these results clearly demonstrate that each component is effective on its own while synergizing to validate the overall design of our method.
5 CONCLUSION
We propose DragFlow, the first drag-based image editing framework tailored for DiTs. By rethinking supervision, inversion, and background handling in light of the unique representational and training properties DiT's, DragFlow unlocks their powerful generative priors for controllable, fine-grained drag editing. Our three contributions, namely region-level motion supervision, background hard constraints, and adapter-enhanced inversion, collectively address the weaknesses of prior drag ap- proaches, mitigating deformation artifacts while preserving subject identity and image realism. To support rigorous evaluation, we introduce ReD Bench, a benchmark designed around region-aware annotations and explicit task categories. Across both ReD Bench and DragBench- DDR, DragFlow consistently surpasses existing saate-of-the-ae methods, demonstrating stronger faithfulness, better controllability, and higher-quality outputs.
Limitations and Future Work. Since the FLUX model we employ for drag-based editing is CFG-distilled variant, its inversion drift notably larger than that non-distilled counterparts. Although mitigate this issue through adapter-enhanced inversion, images with highly intricate structures still exhibit detail loss in the reconstruction. Consequently, the drag-editing results inherit these artifacts, leading degraded visual quality. Future research could benefit from techniques

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advanced adapter architectures that further strengthen inversion fidelity, thereby reducing such artifacts and enhancing overall editing performance.
ACKNOWLEDGMENTS
This research is supported by the National Research Foundation, Prime Minister's Office, Singapore, and the Ministry of Digital Development and Information, under its Online Trust and Safety (OTS) Any opinions, findings and conclusions
Research Programme (MDDI-OTS-OO)) tions expressed this material are those of the author(s) and do not reflect the views of National Research Foundation, Prime Minister's ' Office, Singapore, the Ministry of Digital Development and Information, or the Centre for Advanced Technologies Online Safety.
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Appendix
Table of Contents
A Additional Related Work
17
B Preliminary Information
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B.I Diffusion Methods
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Point-based Image Drag-editing
C Implementation Details
19
C.I. Progressive Transformation in Subtasks
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C.2 Relocation Tasks
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C,3 Deformation Tasks
20
C,4 Rotation Tasks
21
Details about Experimental Settings
22
C.6 Quantitative Analysis for DiT Supervision Granularity
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24
D Adaptive Input Processing
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D.1 Region Weight Regularization for Multi CultiiOppeattions
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D.2 Adaptive Gradient Mask Generation
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D.3
Leveraging MLLM for Prompt and Tag Generation
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D.4
Effectiveness MLLM-driven Intent Parsing.
26
E
Criterion Details
27
F.1 Computation of Image Fidelity (IF)
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E.2
Computation of Mean Distance (MD)
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F Additional Baseline Information
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Benchmark Details
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G.1 Formation of the ReD Benchmark
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G.2 Adoption of the DragBench- Benchmark
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G.3
Demonstration of Data Samples
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H Extra Qualitative Results
31
H.1
Additional Qualitative Samples
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H.2
Challenging Scenarios
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31
H.3 Failure Scenarios
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Runtime and Memory Comparison
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J Framework and Module Generalizability
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J.1 Generalizability of Dragging Framework
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J.2
Generalizability of ID-preservation Design
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L.M Usage Statement
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A ADDITIONAL RELATED WORK
Generative Model-based Image Editing. Recent and significant advancements in generative models (Chang al., 2023; Ding al., 2022: Nichol et al., 2021; Ramesh et al., 2022; Rombach et al., 2022a; Saharia et al., 2022; Yu et al., 2022; Peebles & Xie, 2023; Esser et al., 2024a) have enhanced many applications (Avrahami et al., 2023; Ruiz et al., 2023; Hertz et al., 2022; Kim al.,
2022; Tumanyan et al., 2023; Zhou et al., 2025; 2023; 2024a;b;c; Zhao et al., 2024a; 2025b;a; 2024b;
2026; Wang et al., 2025; Wang & Chen, 2025a;b; Lu al., 2024b;e; et al., 2025; Ren al., 2025; Gao et al.. 2024; 2025; Zhu et al., 2024; 2025; Yu et al.. 2025a:b). In this study, we focus on real image editing. which allows users to freely modify actual photographs, producing highly realistic results. Typically, the inputs for image editing include an image and various conditions that help users accurately describe their desired changes. These conditions can encompass text prompts using natural language to specify the edits (Brooks et al., 2023; Zhang et al., 2024b; Fu et al., 2023; Zhang et al., 2024a), region masks to designate areas for modification (Zhao et al., 2024c; Zhuang et al., 2023; Wang et al., 2023), additional images to provide desired styles or objects (Chen et al., 2024; Lu et al., 2023; Yang et al., 2023), and drag points (Lu & Han, 2025; Nie et al., 2024) that enable users to interactively move specific points in the image to target positions.
B PRELIMINARY INFORMATION
This section provides essential background knowledge on diffusion models and point-based image drag-editing techniques. We begin by discussing the evolution of diffusion methods, tracing the transition from early frameworks of Stable Diffusion (SD) Series with Denoising Diffussio Implicit Models (DDIM) (Song et al., 2022) to more advanced rectified flow models and ODE solutions. These advancements enhance the generative process, facilitating more efficient and deterministic sampling. In the latter part, we explore point-based drag-editing methods, highlighting how they allow users to manipulate key points for image modification directly. This approach contrasts with region- region-based methods by leveraging specific user-gh user-given control points for supervision and tracking.
B.1 DIFFUSION METHODS
Recent advances in text-to-image generative models have driven rapid evolution in architectural paradigms, providing a strong impetus for advancing existing image editing methods. Innovations in newer architectures and learning objectives enrich prior knowledge and exhibit significantly im- proved capacity in data comprehension and semantic alignment. Early UNet-based diffusion frame- works, such as SD 1.5 and SD 2 from Rombach et al. (2022b), have been increasingly supplanted by DiT-based rectified flow designs with more robust pre-trained priors (e.g.. FLUUX.1 (Black Forest Labs, 2024) and SD 3 (Esser et al., 2024b)).
Stable Diffusion with DDIM Inversion. Early trag-based image editing techniques primarily use SD as the foundational framework. They leverage SD's UNet architecture for noise prediction and rely on DDIM inversion to derive noisy latents 2; from the clean latent encoding 20 of an input image æ. DDIM operates through two core processes: a forward inversion process that iteratively adds noise to transform clean latents into noisy ones, and a backward denoising process that removes noise to generate edited outputs.
To formalize these processes, we first define ã, = =I (1-B,,) B.), where B, is the noise schedule at step 8, and / [0, 7' indexes the diffusion steps, with the fully noisy state. Starting from the clean latent zn (i.e., the latent output of the VAE encoder), the forward inversion process computes 2, from 1 by first using the UNet noise predictor c.(..)) )) to estimate the noise in z;-1, which reconstructs an approximation of the original clean latent en. This estimated clean latent is then perturbed to produce the next noisy latent 24:
representing the clean state and / = T
2 = aa 20 + v1 - at E0(2 1--t--1... 1).
(5)
where the term Vā.- E0 retains a scaled version of the estimated clean signal, and vI - dg adds controlled noise with scaling factors aligning with the pre-defined schedule ā,.
co(-)
Oppositelly, the backward denoising process iteratively refines 2, to 2;-1, gradually reducing noise. From the current noisy latent 2,, the model first estimates the clean latent ćn by inverting the noise
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addition
Sudition-subtractttng subtracting the predicted noise Eg E2(, t) (scaled by the schedule v1 āg)::
vI Eo (z(,tt
20
(6)
Vat
Using this estimated clean latent 20, the denoised latent 2 is computed by re-adding a controlled amount of noise E0. with the noise level reduced compared 2,, such as
2 0,-1 20 + V ã.-1 co t).
This re-noising step ensures gradual transition toward the clean state: as decreases, ūt-1 increases to approaching 1, so the weight on the estimated clean structure 20 grows, while the weight on the noise term shrinks. This stepwise denoising critical for editing, as the iterative formulation enables conditional guidance (e.g.. text prompts or drag constraints) to be seamlessly injected at each stage, thereby ensuring precise and progressively refined control over the final output.
Rectified Flow with ODE Solver. In contrast, rectified flow models, adopted in DragFlow framework leveraging the DiT prior, reformulate the generative process through approximate straight-line trajectories between data and noise distributions, enabling more efficient and deter- ministic sampling with fewer steps. For simplicity, we normalize the time parameter to E [0. 1].
The forward process linearly interpolates the latent
2: = (1 - z) + te,
(8)
where 2n is the clean VAE-encoded latent (same as the UNet's), and E ~ (0, is standard Gaussian noise. The associated velocity field constant, defined as dz: = c zy- In this way, the model can model parameterized velocity Uz matching, by minimizing
di 20 via objectives like conditional flow
= Ex,x, [100(2,, t) (E -0))
(9)
For the backward denoising process, we solve the ordinary differential equation (ODE) dz/dt = va(z,t) backward in time from & = 1 to f = 0, starting from ø In discrete steps (c.g., via Fireflow Solver (Deng et al., 2024)), this approximates:
zt- 2, (-At) vo t),
(10)
where At > is the step size. Conversely, to obtain the noisy latent during inversion (from clean 2o at = 0 to 21 at t = 1), we integrate the ODE forward:
Z++ 21 + Al- vo (Z),
(11)
This straight- path formulation in rectified flow supports fewer function evaluations compared to the curved trajectories with more complicated noise schedules DDIM, enabling our region-level affine supervision in DragFlow to leverage more robust priors for drag-based editing.
B.2 POINT-BASED IMAGE DRAGINDITING
Point-based drag editing was first introduced by DragGAN (Pan et al., 2023), as an interactive paradigm for image manipulation, enabling users to directly move key points on an image achieve desired transformations. Unlike text-guided methods, which often struggle with ambiguity in com- plex scenes, point- point-based dragging encodes editing intentions through spatially localized control points. This approach aligns well with users' intuitive interaction patterns, providing straight- forward yet effective means of specifying editing goals.
User Input. Each editing task requires the following basic inputs:
An original image x (converted to the initial latent zn through VAE encoding).
A set of handle points { indicating locations to be manipulated.
Corresponding target points {t,) = representing desired positions after dragging.
Mask B to protect constrain regions that should remain unchanged.
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Core Components. The workflow typically consists of two interconnected modules:
1. Motion Supervision (MS). MS is designed to ensure the model preserves image features while enforcing alignment between source and target points. MS computes losses based on:
Alignment Loss: Measures feature differences between patches around original han- dle points and patches around their current predicted locations.
Lalign Σ Σ
Σ Fg(2)) sg(F, sg(Fp(z))111
(12)
PER(H..) .r) gE
where F(-) extracts local features, (2(.)) denotes patch of radius and sg(.) is the stop-gradient operator. and p define the source patch and the manipulated patches, respectively.
Smoothness Loss: Encourages gradual changes in the feature space.
smooth
Σ
Σ
[[g(())) - sg (F (2
(13)
gES(K .r)
Mask Loss: Penalizes unintended modifications outside user-defined regions.
maakk |1(2 sg(zz)) (1 B)Il,,
(14)
The overall motion supervision loss is:
LMs = 3Lalign + (1 - Bmoooh + ALmask:
(15)
which is backpropagated to iteratively update the latent code:
*+1 = Ir 0LMS
(16)
dz
2. Point Tracking (PT). PT updates handle point locations across diffusion steps to ensure they follow intended trajectories. For each handle point h. the new location is determined via nearest-neighbor feature matching:
k+1 urgmii (2 (+)+ Fng (=)))
(17)
Workflow Summary. The point-based editing process proceeds iteratively:
1. Apply several MS steps to align features toward target positions while preserving image consistency during the updating processes.
2. Perform PT to update handle points based on feature tracking.
3. Repeat the MS and PT cycle until the handle points converge to their targets.
To sum up, classic baset drag editing leverages feature-level supervision and PT enable localized manipulations, ensuring that the edited image remains coherent with the original while reflecting user-specified modifications.
However, this pipeline inherently suffers from several limitations: nearest-neighhor search and PT introduce high uncertainty during optimization; the explicit influence of each control point con- fined to narrow feature neighborhood, which restricts the scope of effective guidance. By contrast, region-basee based dragging naturally extends these ideas by operating over semantically coherent masks, thereby offering more stable, interpretable, and semantically meaningful editing outcomes.
C IMPLEMENTATION DETAILS
This section highlights the core motion strategy of region-based drag operations through progressive affine transformations. We define multiple types of drag operations, each designed to enhance user control and precision in image editing. Subsequent subsections detail the implementations of these operations, all leveraging progressively interpolated affine transformations for seamless transitions. Additionally, the final subsection offers supplementary settings related to the experimental section.
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C.I PROGRESSIVE TRANSFORMATION IN SUBTASKS
Operation in Subtasks. As introduced in the main text, we define three types of drag operations: relocation, local deformation, and rotation. Recall these definitions, the source masks M. denote the initial regions, with centroids b, guiding relocation and local deformation toward the target region indicated by the centroid ty. For rotation, the anchor a; specifies the pivot. Each transformation is realized through affine updates, with parameters 4 interpolated over K iterations to gradually propagate the source mask toward its target configuration. In the following subsections, detail how these subtask settings facilitate the affine transformation in region-based image dragging.
Progressive Motion Schedule. In practice, for each region-specifie drag operation, only com- pute the full motion schedule 5: {!K)) during the initial update iteration:
<!!)) [4:-00 b,),
[(4-bb),
(Relocation & Deformation)
(18)
(Lb,art, az), (Rotation),
where b; tis and a, denote the begin, target, and anchor points, respectively, Rather than recom- puting the dragged and left distances at every iteration, subsequent steps obtain their progressive schedules directly via a linear interpolation:
(*h) = A 5((,)
(19)
K
This design ensures that the motion evolves smoothly from the initial to the target state. The resulting progressive motion schedule is then injected into the affine transformation operator O(., (/)).. to enforce consistent supervision over the sequence of incremental updates.
C.2 RELOCATION TASKS
Relocation involves shifting an entire region new position while preserving its original geometry and scale. Recall our definition in Subsec. 3.2 This operation is parameterized by displacement vector derived from the difference between the target point t; and the source centroid b,, scaled k linearly as 5 k
(t: b²).
To apply this, for example, we can consider point = (u, v) within the source region mask M. The transformed point p' (u', x') be computed via homogeneous coordinates:
d,
u
V'
d
(20)
0
where (du, d,) is the displacement vector from (R).
Breaking down the matrix:
The top-left 2 x submatrix is the identity, ensuring no rotation or scaling;
The relocation components du and dy the first two rows of the third column directly add to the coordinates: ut + du, of v dui
The bottom row maintains homogeneity.
This matrix is applied across the whole operation patch, resulting in an efficient shift of the entire region dragging steps.
C.3 DEFORMATION TASKS
Deformation enables localized adjustments by selectively displacing subregions, effectively altering the overall shape without global rigidity. In our setup, this is treated similarly to relocation, utilizing the same affine transformation method described in the relocation operation. The key difference lies in its application: targets only the edge areas of the object to be edited based on scene require- ments, achieving effects such elongation shortening.
This selective application distinguishes deformation from full-region relocation, allowing for intu- itive shape modifications visualized in our method's overview.
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SINGLE-00
SINGLE-01
SINGLE-02
SINGLE-03
SINGLE-04
SINGLE-CO -5
Source Image
DOUBLE-00
DOUBLE-01
DOUBLE-02
DOUBLE:03
SINGLE-12
SINGLE-14
SINGLE-15
SINGLE-16
SINGLE-17
DOUBLE-04
DOUBLE-05
DOUBLE-06
DOUBLE-07
SINGLE-18
SINGLE-19
SINGLE-20
SINGLE-21
SINGLE-22
SINGLE-23
DOUBLE-12
DOUBLE-13
DOUBLE-14
DOUBLE-15
SINGLE-30
SINGLE-31
SINGLE-32
SINGLE-33
SINGLE 34
SINGLE-33 35
DOUBLE-16
DOUBLE-17
DOUBLE-18
STNGLE-36
SINGLE-37
Figure 7: Visualization of DiT latent features (Sample out of 2) based PCA using the top principal components.
C.4 ROTATION TASKS
Rotation pivots region around fixed anchor, reorienting it by progressively interpolated angle. The rotation angle is determined geometrically from the triangle formed by b, (i.e., the source re- gion centroid), a; (i.e., the user-specified anchor), and (i.e., the target region centroid). Over K iterations, the interpolated parameters are defined as
!!a! ( Lb,at,, a;),
which guarantees smooth progression from the original orientation toward the desired angular displacement.
For point (w,z) =) in ((A)), rotated around anchor (wer ze) by an angle & derived from (k) the updated coordinates r' (w', x') are obtained as
we]
sin Φ
0
-W,-
z
0
sin ø 0
cos ø
0
0
1
-
(21)
0
1
This composite transformation can be interpreted step by step:
The rightmost matrix translates the point that the anchor a; coincides with the origin, removing bias from the global coordinate system.
The central rotation matrix applies the angular displacement o, producing the intermediate rotated coordinates (w² z²) where ro"
sin Φ and z" sin ø Φ
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SINGLE
SINGLE-01
SINSLE-02
SINGLE-01
SINBLE-04
SINSLE
Source Image
DOUBLE-00
DOUBLE-O1
DOUSLE-02 02
DOUBLE-03
SINGLE-12
SINGLE-13
SINGLE-14
SINGLE-15
SINGLE-16
SINGLE-17
DOUBLE-04
DOUBLE-05
BOUBLE-07
SINGLE-18
SINGLE-19
SINGLE-21
DOUBLE-08
DOUBLE-09
DOUBLE-10
DOUBLE-11
SINGLE-24
SINGLE-25
SINGLE-26 6
SINGLE-27
SINGLE-28
SINGLE-29
DOUBLE-12
DOUBLE-13
DOUBLE-14
1OUBLE-15
SINSLE- 30
SINGLE-31
SINGLE-32
SINGLE-38
SINGLE-34
SINGLE
DOUBLE-16
DOUBLE-17
DOUSLE-18
SINGLE-36
SINGLE-37
Figure 8: Visualization of DiT latent features (Sample 2 out of 2) based on PCA using the top principal components.
The leftmost matrix translates the rotated point back into the original coordinate frame, re-centering the result around a,
This decomposition not only clarifies the geometric intuition behind rotation but also integrates seamlessly with our iterative framework: at each step k, the patch masks M are updated under this rotation transformation, ensuring gradual controlled reorientation. Compared with relocation and deformation, rotation requires an explicit anchor to define the pivot, highlighting its distinct interaction design while still being governed by the same affine transformation principles.
C.5 DETAILS ABOUT EXPERIMENTAL. SETTINGS
Layers for Feature Manipulation. We empirically identify the 17th and 18th double-stream blocks of the DiT backbone the most representative features facilitate drag optimization. To substantiate this choice, we conduct a visualization based analysis of the main DiT modules (refer Fig. and Fig. 8 for details). Our selection guided by the principle that effective feature blocks should retain high representational fidelity to the input. While certain blocks may encode large amounts of information, much of can be dominated by noise. By examining PCA visualizations, qualitatively assess whether the leading components align with the appearance of the original image, thereby confirming that the selected layers capture the essential visual characteristics required for precise and robust editing.
As illustrated in the figures, the latent representations from "DOUBLE-17" and "DOUBLE-18" retain rich and flexible features, which be effectively manipulated during editing. In contrast, certain blocks (e.g.. "SINGLE-20" and "SINGLE-30") exhibit overly "clean" latents, making difficult to perform meaningful edits. Other blocks (e.g.. "DOUBLE-04" and "SINGLE--66)
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MIN
631
Figure 9: Comparison of dragging effectiveness feature manipulation layers in the FLUX model. The analysis is evaluated on ReD Bench, demonstrating that double-stream layers (prefixed with "D", in blue) consistently outperform single-stream layers (prefixed with "S", in purple).
tain too few semantically informative features, where optimization tends to struggle in preserving source identity and achieving precise dragging control. Taken together, these comparisons suggest that default setting, by using "DOUBLE-17" and "DOUBLE-18", offers favorable balance: it preserves stable feature representations with sufficient semantic and spatial information, while also maintaining rich identity-related features to support effective editing. This justifies its adoption as the backbone choice in DragFlow.
Layer-based Performance Comparison. To further validate the effectiveness of selected fea- ture manipulation layers, we conducted an extensive ablation study focused on layer-wist wise feature selection. Specifically, under the same configuration, we compute the loss each time using the fea- tures from particular layer. This procedure was repeated across all 57 layers from both the double- stream and single-stream branches of the FLUX model, enabling a systematic comparison editing behaviors across various layers.
The results are summarized in Fig. 9, We evaluate three metrics of dragging effectiveness: MD IF 12... and IF 2: As shown, operations performed the double-stream layers (i.e., layers prefixed with "D"" consistently outperform those on the single-stream layers (i.e., layers prefixed with "S") under the same dragging configuration. Notably, the 17th and 18th double-stream blocks (i.e., D17 and D18), which we ultimately adopt in our framework, exhibit the optimal performance robustly across all these metrics. This indicates that our chosen layers yield the highest-efiectiveness solu- tions in terms of spatial displacement precision, preservation of post-dragging local features, and effective suppression of original-location features.
Affine Transformation Steps. During the dragging process, the motion from the source region to the target region is primarily executed over the first 50 steps (A = 0 to 49), where each iteration progressively applies affine transformations to the latent representations, thereby facilitating the drag procedure through gradient updates. After this stage, DRAGFLOW performs an additional 20 optimization steps (k 50 to 69) by repeating the final affine transformation, which further refines the feature expression of the dragged object, helps it better adapt to the new semantic context, and enhances the consistency between the pre- and post-dragging regions.
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Deformation Hon
Rohatiio
Relocation
STOP
Task Preview
k-9
1=19
k=
k=39
k:49
K+50 K-69
Figure 10: Region operation masks M, (k) created by progressive affine transformations at each step k across multiple subtasks. Each dragging process consists of 50 steps 20 additional steps (k = 50 to 69) that repeat the final motion iteration to further refine the feature quality of the post-dragging region.
49), followed by
As illustrated in Fig. 10, the patch operation mask !( obtained through progressive transfor- mations, evolves smoothly with increasing k, thereby driving the gradient optimization process to ensure effective dragging.
C.6 QUANTITATIVE ANALYSIS FOR DIT SUPERVISION GRANULARITY
Table 4: Quantitative comparison on DragBench-DR between Point-based and Region-based strate- gies for the DiT architecture. Both methods utilize the same backbone and auxiliary modules.
Image Fidelity
Mean Distance
Attempts
IF t
IF f
IF
MD;
MD2 +
Point-based variant
0.961
0.943
0.958
37.02
6.94
Region-based variant (DragFlow)
0.969
0.948
0.941
31.59
5.93
To further validate the analysis in Sec. 3.1, that DiTs' fine-grained features render sparse point supervision ineffective, we conducted controlled quantitative comparison.
By evaluating distinct supervision paradigms: baseline Point-based variant (i.e., applying standard point to the DiT features) and our proposed Region-based variant (DragFlow). To ensure rigorous evaluation of the supervision mechanisms in isolation, both variants were equipped with identical auxiliary modules, specifically Background Preservation and Adapter-Enhanced Inversion.
The comparative results are summarized Tab. 4. These quantitative findings empirically corrobo- rate our assertion that sparse point tracking is insufficient for the high-frequent feature landscape of DiT's whereas region-level affine supervision effectively bridges this gap to fully unleash the model's generative potential.
D ADAPTIVE INPUT PROCESSING
To ensure robust and user-aligned editing, DragFlow incorporates an adaptive input processing pipeline that systematically addresses three key challenges. First, when multiple operations are ap- plied to single image, we introduce an adaptive weighting scheme to balance region-level opti- mization and prevent dominance by large areas (see Appendix D. Second, to protect uneditable content during optimization, we design adaptive gradient mask generation strategy that provides strict spatial constraints while maintaining flexibility for complex transformations (refer to Ap- pendix D.2). Finally, further reduce effort and improve precision, we leverage multimodal large language model (MLLM) to automatically generate candidate prompts and semantic tags, which subsequently refined through minimal human intervention (detailed in Appendix D.3).
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Together, these components form cohesive imput processing framework that enhances both the accuracy and practicality of DragFlow in real-world interactive editing scenarios.
D.1 REGION WEIGHT REGULARIZATION FOR MULTI-OPERATION
When multiple drag operations are specified for single image (i.e., N > 1), the loss function in Eq. incorporates weighting coefficients (nx / balance the influence of each region, preventing larger regions from dominating the optimization. These weights are computed adaptively based on the relative sizes of the manipulated regions, ensuring equitable gradient contributions.
Formally, for each operation i, let S; denote the relative size of the source mask M, (0) defined as:
Σ M (0)
S.
(22)
M
where Σ M, (0) is the number of non-zero (unmasked) pixels, and |M, is the total number of elements in the mask. Under this setup. raw weight wi can then be calculated as:
WA clamp 1.0
0.5
1.0, 5.0) 5.0).
(23)
S. 0.1
where clamp(( a, b) restricts the value to the interval (a, b). This formulation assigns higher weights to smaller regions to amplify their impact. And the total raw weight sum is W Σ wy The normalized weights are:
if 0,
7
(24)
otherwise.
W
N = 1, we set " = directly. This adaptive scheme ensures Σ = promoting balanced optimization across diverse region scales.
D.2 ADAPTIVE GRADIENT MASK GENERATION
To sufficiently protect uneditable regions in our procedure, we generate an adaptive bounding mask B that only uncovers the edited areas. This mask B is derived from the user-provided source masks (M Ji=1 and their target points J providing static envelope for feature preservation dur- ing optimization. It differs from the iterative patch masks M. (8) by serving as a holistic boundary for the entire dragging process.
Compare to Mask Loss. Compared to the conventional mask loss strategy, gradient masking offers more precise mechanism for constraining gradient flow, thereby preventing optimization from introducing unintended perturbations into uneditable regions. key limitation of mask loss is that it can only partially restrict future updates, while inadvertently retaining undesired changes that have already been introduced. In contrast, gradient masking directly blocks gradient propagation to these regions, ensuring that they remain unaffected throughout the optimization process.
Automatic Mask Creation. For each source mask M.") first apply the appropriate affine transformation (as defined in Eq. 3.2) with full interpolation (i.c., k=K) to obtain the final target mask M (K) = ((M) (0) e (K)), where e! (K) t; b; for relocation/deformation or (Lb,a, a.)
for rotation.
Next, for each i, we compute the union mask U, M U ((K) To ensure comprehensive coverage. we enclose U, with minimal rotated bounding rectangle. The points U, are collected as P. = {p (p(p) 1}. The rectangle parameters are then derived
(Ct, (wi, h2), ox) minAreaRect((P.).
(25)
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where is the center, (w, hy) are the dimensions, and @; is the rotation angle. The vertices of this rectangle are obtained via
Vi = boxPoints((c;, (w,, (0),))))))))
(26)
Since multiple operations may exist, each yielding an independent rectangle, we merge them into a single adaptive mask B by filling all convex polygons from image-shape initiated canvas 0:
B U
fillConvexPoly(0.) V:).
(27)
Finally, B is binarized to [0, 1]. ensuring no excessive expansion while covering all transformed regions for effective integration into the latent optimization.
D.3 LEVERAGING MILM FOR PROMPT AND TAG GENERATION
In practical usage, the DragFlow framework provides a user-friendly interactive interface, enhanced by multimodal large language model (MILM) to support intuitive and precise image editing. The system emphasizes high automation to reduce user effort, while a two-step confirmation workflow ensures that user intentions are accurately communicated and ambiguities are minimized.
The interaction begins with the user providing an input image and roughly indicating the target region via simple scribble, along with click specifying the desired target position. The system converts the scribbled region into an initial operational mask representing the affected area. Leverag- ing an automatic mask generation algorithm (Appendix D.2), users do not need to redraw the target region for task specification. The original image and operational mask are then processed by the MILM,, which infers the user's editing intent and proposes a set of candidate prompts. Specifically, the MLLM generates ten potential prompts paired with corresponding task classes, from which users select the one that best reflects their intended operation.
After confirming the operational intent and task class, the interface produces preview of the ex- pected outcome based on the current operation parameters. For rotation operations, users specify an additional anchor point as the rotation center, which can be interactively adjusted while observing real-time updates in the preview. This iterative adjustment allows for fine-grained control, ensur- ing the final result closely aligns with user expectations. By combining automated inference with user-in-thee -ote-intte-loopp refinement, this design streamlines the editing process while faithfully translating high-level user intentions into precise image manipulations.
Here we present the prompt used for the prompt and tag generation procedure:
Refer to the original image, and the "dragged" image with the blue starting re- gion, estimated green larget region, and the arrow direction. You need to describe the content and the object for editing of the picture in English, in terms of "back- ground details" and "editing changes". Then you should guess the editing intents from the user by selecting one label for each answer, where the label classes have (relocation, deformation, rotation).
Your tasks:
(a) You should first provide a detailed description about the original image (e.g.. include, but are not limited objects, spatial relationship, color, style, structure).
Then try to describe the motion/editing in short words.
(b) You should provide the ten most possible guesses about the static condition of the after-dragged image, and at most 60 words for each. See if you can provide more details to facilitate the editing.
D.4 EFFECTIVENESS OF MLLM- -DRIVEN INTENT PARSING
To quantitatively assess the contribution and robustness of the MLLM-driver intent parsing module, we conducted an extensive ablation study on the Drag DR dataset. This analysis compares the performance of DragFlow under three distinct experimental scenarios:
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Table 5: To validate the effectiveness of prompts automatically generated by MLLM, we conducted an additional test. First, we examined whether using accurate and matching prompts is effective for drag operations. We considered three scenarios: Null Prompt. Incorrect Prompr (i.e., intentionally misinterpreted prompts), and Matched Prompt generated by MLLMs based on image and operation inputs. For the last scenario, we examine the effectiveness of two MLLMs (GPT-5 vs. QWen-VL). This experiment was conducted and scored on DragBench-DR.
Image Fidelity
Mean Distance
DragFlow w/
IF bg
IF
IF |
MD1 +
MD2 J
Null Prompt
0.966
0.944
0.948
33.81
6.81
Incorrect Prompt
0.955
0.936
0.955
36.87
7.73
Matched Prompt (by QWem-VL)
0.968
0.947
0.945
32.42
6.25
Matched Prompt (by GPT-5)
0.969
0.948
0.941
31.59
5.93
(1) Null Prompt: The optimization is driven solely by image and mask inputs, removing the text guidance entirely;
(2) Incorrect Prompt:
The model is provided with intentionally misinterpreted prompts (c.g., describing rotation task as deformation) to simulate worst-case MLLM failure;
(3) Matched Prompt: The standard configuration using prompts generated by MLLMs (comparing both GPT-5 and QWen-VL) based on the image and operation inputs.
The averaged quantitative results are presented in Tab. 5. As evidenced by the data, the framework remains robust even without textual guidance (i.e., Null Prompt), demonstrating the efficacy of the core region-level affine supervision.
In contrast, including Matched Prompls provides valuable performance boost, whereas Incorrect Prompts degrade results due to conflicting guidance. However, thanks to the "Double Confirmation" workflow (dctailed in Appendix D.3) we implemented, helps prevent such misinterpretations in practice: rather than autonomous execution, the system requires users to select the optimal intent from generated candidates before optimization, ensuring the process driven strictly by accurate, user-validated instructions.
E CRITERION DETAILS
To systematically evaluate the effectiveness of dragging operations, we introduce a set of quantita- tive criteria that assess both visual fidelity and spatial accuracy. These criteria fall into two comple- mentary families: Image Fidelity (IF), which measures the extent to which semantic content, source regions, and background integrity preserved or altered intended (detailed in Appendix E,1); and Mean Distance (MD), which provides geometric and feature-based assessments of the dragging process (see Appendix E.2). Together, these metrics capture different yet complementary aspects of editing quality, enabling a fair and comprehensive evaluation of diverse dragging approaches.
E. COMPUTATION IMAGE FIDELITY (IF)
IF Evalutaion. The identity fidelity from the source the target (i.e., 1F22)) evaluates how well original source features are preserved in the target regions after moving, promoting semantic consistency in dragged content. A higher IF signifies superior fidelity, indicating minimal percep- tual loss during the feature transfer. For each region i, we first affin-transform the masked original image align with the target configuration using the full interpolation parameter of k K:
Taff = M
x. 5
(28)
followed by masking both sides with the target region mask M. (K) Then the score is averaged as:
N
IF 22 = 1
Σ
LPIPS
(M(K)) o oan, ((K) o oor).
(29)
N i=1
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Reg
(M O Xan
Reg
IF :2 = 11- LPIPS(
AORNOONN 1968
BORSI Reg
Reg
[MP O
C MM
O
Initial Region Mask [M
IF.a. = LPIPS(
VS.
Reg. (1-8) O
Reg. - ( (1-8) O
IF.g = 1 LPIPS(
VS.
Target Region Mask [MPS]]
Source Image
Affined Source Image [arr] (w.r... target centrold)
Dragged Image
Gradient Mask [B] (Block Uneditable Region (1-81)
Figure 11: For 12s and 1F,2s, the criterion computation considers only the feature discrepancies within the labeled blocks. In FF the purple region on the left image denotes (M, (K) Daft), corresponding to the source region of (M.") C æz), while the red patch on the right image represents the post-drag target (M c a²). By contrast, the criterion IF,2, compares the same purple original region M, (0) across two images: the source æ (left) and the dragged result æ' (right). Lastly, F9,9 evaluates all uneditable areas, as indicated by the black areas on the gradient mask (1 B).
IF 1252s Evalutaion. To assess the extent which original features are effectively removed from the source regions after editing, we define the identity fidelity from source region to source region (i.e.. 1.)..). which measures the perceptual dissimilarity between the source region features in the original image æ and the edited image x'_ A lower IFs2s indicates better performance, as it reflects greater divergence and implies successful "moving-out" of the selected features. Formally, for each source region i, compute the LPIPS distance on region-masked image tensors and get the final mean score over all task regions via
N LPIPS M (1) G
1F22. =
1
Σ
M. o oxa).
(30)
N
where @ indicates the element-wiss multiplication, and M, indicates the original region mask (i.e., the operation region given by the user);
IF Evalutaion. To ensure background preservation outside edited regions, introduce the background identity fidelity IFag. quantifying feature consistency in protected areas defined by the complement of the adaptive gradient mask B. A higher IFo, denotes better integrity, with minimal changes to non-targeted zones. Using the protection mask (1 B), we yield the score:
FFog = LPIPS ((1 B) @ xr, (1 B) @ x').
(31)
To aid interpretation, Fig. 11 presents a visual example demonstrating how the three proposed crite- ria applied in practice.
E.2 COMPUTATION OF MEAN DISTANCE (MD)
MD, Implementation. We implement MD, following the existing criteria established by Xia al. (2025). MD, enables precise feature matching within the uneditable region, allowing to
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validate the effectiveness of the dragging procedure by measuring the distance between the centroid of the source feature region and most similar corresponding feature.
MD2 Implementation. Building upon the original MD2 design proposed in Lu et al. (2024a), introduce an enhanced version that provides more precise and informative feedback for region-based drag operations. Unlike the original method, which computes feature matching distances based on manually annotated sample points, our approach automatically evaluates feature differences around the centroid scope of the pre- and post-drag regions. By leveraging this centroid centroid-based formula- tion, we eliminate the inaccuracies and subjective biases inherent in manual annotations and also ensure more consistent metric for assessing the effectiveness of various dragging strategies. This improvement allows for more faithful reflection of the actual feature transformations induced by the dragging process and facilitates fairer comparisons across different methods.
F ADDITIONAL BASELINE INFORMATION
Here we provide the official project pages for the baseline methods used our comparisons. All implementations follow the default configurations and instructions provided by their authors:
C
DragDiflusion: http://ggttbbbbonnn//uugSSSiiipp
DragLoRA: http://atthbbcomm//yxxeeXXXXagp
DragNoise: DraNNoise:ttpps:///gne http://giinbbbcom//hoooeg////ggg
FastDrag: FastDrag http://gtthabccco///x/ajj////aar
FreeDrag: http://sthhbbbcom/////yygg//iing
GoodDrag: Guw.Drraggttps/ggggiibbbbbbb http://giihabbooozzzwww//////gng
InstantDrag: http://gghhabcoomsSSSsuwvvaaaaaa
RegionDrag: hutp://gittobbooom//ssaaaAAAA//p
And some encapsulated modules applied in our framework experiments:
InstanCCharacter:
G BENCHMARK DETAILS
FORMATION OF THE ReD BENCHMARK
To evaluate model performance the regional drag- image editing task, we introduce
benchmark, the Regional-bere based Dragging (ReD) Bench, consisting of 120 images annotated with precise drag instructions at both point and region levels. Each manipulation in the dataset associ- ated with an intention label, selected from relocation, deformation, or rotation.
For every image, we provide complementary instruction sets corresponding to point-based and region-basc baseed dragging. The region-based annotations are supplied as multiple PNG masks, with each region uniquely represented by its centroid for cross-reference. The drag annotations include multiple start-to-target point pairs, which can be directly aligned with the region annotations, ensur- ing consistency in task intention. Additionally, we provide background prompts and editing inten- tion prompts for each image to facilitate multimodal tasks, along with masks generated using the DragFlow automatic masker Appendix D.2. These design choices enable a more faithful represen- tation of user intents underlying the provided drag instructions.
G.2 ADOPTION OF THE DragBench-DR BENCHMARK
To further assess the effectiveness of DragFlow on a broader spectrum of images, we adapt and evaluate it DragBench-DR (Lu et al., 2024a). DragBench-DR extends the classic point-based
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SAMPLE R
SAMPLE (
Task Preview
Original Image
Operation
Mask
Figure 12: Real samples from ReD Bench: the first colume shows the dragging preview, where the green region is estimated from the user-specified target centroid; the second colume presents the source images, while the third colume highlights the user-marked operation regions in the form of masks, which may include multiple valid regions; and the last colume depicts the adaptive masks generated (detailed in Appendix D.2). Other matched instructions are provided in Appendix G.3.
dragging benchmark DragBench to region-based operations. Unlike the original benchmark, which relics on sparse point guidance, DragBench- DR formulates edits over regions, thereby providing clearer reflection of user intentions. For evaluation, the accompanying metrics compare the pre- drag source region with the post-drag target region by computing differences over pre-annotated correspondence points. Despite this extension, noted by the authors, DragBench-DR remains consistent with its point-based counterpart, while more effectively capturing region-level semantics in interactive editing tasks.
While DragBench-DR extends the benchmark to region-based operations, its evaluation protocol (MD2) still relies point comparisons: differences computed between pre- and post-drag gions using pre-annotated correspondence points. This design can introduce mismatches, as region- level edits are not faithfully captured by sparse point feature correspondences, leading potentially unfair assessments for region methods. To better align the evaluation with region- editing and integrate existing datasets into our experiment, we update the feature comparison criterion by re- placing point-based annotations with automatic centroid-based formulation (see Appendix E.2).
G.3 DEMONSTRATION OF DATA SAMPLES
We present two real data samples (i.e., SAMPLE (A) and SAMPLE (B)) from the ReD Bench. The corresponding instructions are provided as follows, and the images are shown in Fig. 12.
SAMPLE (A) srrgiiinoopeaations" {
"0"; {
"task": "rotation", "centroids": [[337, 175], [379, 179]],
"anchors": [351, 256]
}, "osaatt_operaionss:: "begin_points": [1326, 1111, 1342, 190]), "target_points": 11400, 1161, 1376, 198]]
11
12
},
13
"backgroun__promptt" "From a rear view, a student in blue denim jacket raises their hand in classroom. Wooden deaks, large windows (letting in light), and distant teacher form the backdrop. The scene captures an engaged learning moment, with realistic, observational style.",
14
"editing_prompt": The student in à blue denim jacket moves his arm rightward, with his hand closer to the right side this image."
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SAMPLE (B)
"regicn_operaateni operations":
"0":
"task"; "deformation", "centroids"; I[251, 521, 1357, 5211, "anchors": null
b
"I":
"task"; "deformation", "centroids"; [[281, 2001, [192, 195]], "anchors"; null
11
12
b,
"2":
"task"; "deformation", "centroids"; 11221, 3351, 1307, 33511, "anchors"; null
16
}, "point_cperations" "begin_points": [[284, 11], [244, 96], [280, 165], [287, 2351, [243, 305], (244, 365]],
20
"target starget_points": [[392, 11], [356, 97], [193, 162], [199, 233], (332, 306], [335, 367]]
22
23
"The image is an aerial view of a coastal scene. There's s beach with light colored sand between dense forest (with green, ye' and orange foliage) and turquoise blue sea. The forest covers mi ddle, and the sea is on the right.", "editing_proomttteenn
Dackgrouud__propge
left side, the beach
along the
24
"The top and bottom sections of the beach are narrowed to the outside, and the middle part is narrowed inside, altering the coastline shape to form bay." "
25
H EXTRA QUALITATIVE RESULTS
H.1 ADDITIONAL. QUALITATIVE SAMPLES
In addition to the qualitative studies reported in the main experiments, we provide further exam- ples in Fig. 15 and Fig. 16. These additional visualizations help to illustrate the advantages of our approach across diverse editing scenarios.
H.2 CHALLENGING SCENARIOS
To further assess the robustness DragFllw, we evaluate its performance under extreme conditions. As illustrated in Fig. 14, our analysis focuses on two distinct dimensions: (a) complex operational requirements and (b) unusual feature structures. Following this, Subsee. H.3, we are also advised to provide some illustrations about the failure cases. We believe these parts may inspire and offer insights for future research about image drag-editing.
(a) Cases with Complex Instructions. Fundamentally, we conceptualize the defined operations as elementary "building blocks." Consequently, sophisticated instructions that involve composite movements (e.g.. do both rotation and relocation) or non-affine warps (c.g., bending) are effectively exccuted by composing these basic units. Among the composite movements, we consider two sce- narios, where multiple distinct operations processed parallel or in sequence. The lower section of Fig. 14 demonstrates how our framework effectively handles these complex instructions and over- comes the challenging scenarios.
(b) Cases with Complex Structures. Regarding the non non-rigid deformations inherent to complex textures such as hair and cloth, our empirical results confirm that the affine assumption remains valid.
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Instruction
Dragged
Instruction
Dragged
Failure Scenarios
Success Scenarios
Figure 13: Extra qualitative results for failure scenarios. Left: The incorrect operation mask and failure outcomes; Right: The suitable operation mask with failure outcomes.
By applying flexible single operations or iteratively composing multiple blocks to approximate the target motion, the framework maintains high editing effectiveness and structural fidelity even on these intricate features. Representative samples validating this capability are presented in the upper part of Fig. 14.
H.3 FAILURE SCENARIOS
Dependency on Input Precision. A primary limitation of the proposed framework is its reliance on the quality of user-provided inputs. Given that both the region-level affine supervision and the adaptive gradient masking mechanisms hinge directly on the initial mask annotation, ambiguous or inaccurate inputs can inevitably introduce generation artifacts.
Empirically, we observe that while the framework exhibits considerable tolerance for slightly over- sized masks, ensuring complete coverage of the targeted semantic object is critical. Specifically, undersized incomplete masks tend to compromise the editing quality. Fig. illustrates this phe- nomenon by comparing outcomes derived from precise versus careless annotations. To relax these strict precision requirements, future iterations could explore integrating auxiliary techniques such as superpixel segmentation implementing error-tolerant peripheral buffer zones to robustly handle manual variances.
RUNTIME AND MEMORY COMPARISON
To provide comprehensive understanding of DragFlow's practical performance, especially as optimization--uis based method, a quantitative analysis of its computational cost is necessary. This dis- cussion is intended help users understand the quality versus speed trade-off's inherent in the ap- proach. Tab. 6 presents detailed comparison of the average inference time and GPU memory con- sumption for DragFlow alongside various baseline methods. The results were recorded as an average over all samples contained in DragBench- DR using one NVIDIA H100 GPU.
In our implementation, we applied qint8 quantization the FLUX backbone to accelerate inference and reduce memory usage during the optimization. Additionally, by enabling CPU Offloading, the framework capable of running on a consumer-gramn gradee 24 GB GPU. Beyond the current level, we believe there is still plenty of room to further reduce memory costs and increase speed through future improvements in both the algorithm and the code.
As the data illustrates, DragFlow's 's resource requirements are higher than the baseline methods. This an anticipated result and direct consequence of the underlying model architecture. DragFlow is the only method in this comparison engineered to operate the FLUX.1 model, parameeer, DiT-based architecture. In contrast, all other baselines are built upon the significantly smaller and less complex Stable Diffusion models.
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Table This table collects and summarizes the inference time and memory consumption depending on different image drag-editing methods. Among them, DragFlow, as the only DiT-based dragger, shows the highest resource demand due to its more complex backbone architecture. The results are recorded as averages DragBench-DR.
Methods
Prep Edit Time (s)
Peak Memory (GB)
RegionDrag
9.2
4.3
FastDrag
5.4
5.2
InstantDrag
1.2
4.1
DragLoRA
58.6
14.6
FreeDraag
113.2
13.2
DragNoise
103.4
12.2
Good
104.6
12.8
CLIPDrag
101.0
19.4
DragDiflusion
82.1
12.1
DragFlow
158.7
23.5
Table 7: The editing performance of our DragFlow framework on different DiT-based generative priors (SD3.5 FLUX.I). The results are averaged DragBench-DR.
Image Fidelity
Mean Distance
Method
IF bg
IF e22
IF 222
MD1 4
MD2
DragFlow (based Stable Diffusion 3.5)
0.962
0.945
0.952
35.21
6.58
DragFlow (based on FLUX.I-dev)
0.969
0.948
0.941
31.59
5,93
J FRAMEWORK AND MODULE GENERALIZABILITY
J.1 GENERALIZABILITY OF DRAGGING FRAMEWORK
To validate the generalization capability of DragFlow beyond the specific architecture of FLUX, conducted additional evaluation applying framework to Stable Diffusion 3.5 (SD3.5), another prominent text-to-image model based on the DiT architecture. Notably, since the default InstaniCharacter adapter is FLUX-specific, we employed comparison. Tab. presents the comparative editing performance averaged over all samples from the DragBench- -hr dataset. As the results indicate, DragFlow maintains robust performance when transferred to the SD3.5 backbone.
fair
Specifically, the SD3.5-based implementation achieves an MD, of 35.21 and IF .22 of 0.945, versus 31.59 and 0.948 of the FLUX edition, respectively. The FLUX-base version demonstrates slight performance advantage, likely due its substantially larger parameter count and enhanced generative prior, while the performance ings provide empirical evidence that the key contributions of DragFllw, including region-level affine supervision, gradient mask constraints, and adapter-enhanced inversion, agnostic the architec- ture used. The framework effectively generalizes across different DiT-based backbones, confirming that its efficacy is not limited to the specific training paradigm of FLUX but rather serves gen- eralized solution for modern DiT generative models.
SD3.5 remains competitive and consistent. These find-
J.2 GENERALIZABILITY OF ID-PRESERVATION DESIGN
To further evaluate the design choices behind our method, we conducted an experiment in which the IP-Adapter replaced with subject-specifie LoRA. In this setup, LoRA was first trained for the target subject and then applied during the drag-editing stage. Performance on DragBench-DD is summarized in Table 8.
The LoRA-basee variant exhibits slightly stronger performance, which aligns with expectations since the LoRA is expressly optimized for the specific subject rather than functioning generic,
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Table The editing performance of DragFlow framework on different ID-preservation modules [IP-Adapter vs. LoRA). The results are averaged on DragBench-DR.
Image Fidelity
Mean Distance
Method
IF f
12 f
IF
MD; +
MD2 +
DragFlow (w/ LoRA) 0.970
0,949
0.940
30.98
5.88
DragFlow (w/ IP-Adepter)
0.969
0.948
0.941
31.59
5.93
open- open-domain adapter. However, this improvement comes with the overhead of training a new LoRA for each subject. In contrast, many modern text-to-image models now include high-qualit,, read- ily usable IP-Adapters, making the IP-Adapter approach more practical and broadly applicable in typical workflows.
K LLM USAGE STATEMENT
We used large language models for text polishing and grammar correction during manuscript prepa- ration. No LLMs were involved in the design of the method, experiments, analysis. All content has been carefully verified and validated by the authors.
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Instruction
DragNoise
GoodDrag
FreeDrag
Irstantrrog
RegionDrag
OURS
Complex Structures
Hair
Hai 123
11
Benciig E3
Complex Instructions
Composite
(Sequesta))
Compostt (2b) (Sequatta)
Figure 14: Extra qualitative comparison for challenging scenarios, with complex feature structures (e.g., clothes and hair) or complex operational instructions (e.g., bending and composite opera-
tions). Two cases of composite operations are provided: the parallel and the sequential.
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Instruction
CLIPDreg
Drogbiffusion
Dragl. LRR
DragNoise
FastDreg
FreeDrag
GoodDrag
InstantDrag
RegionDrag
OURS
Figure 15: Extra qualitative comparison (Part out of 2) of DragFlow with multiple baselines.
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Instruction
SToP
CLIPbrag
STOP
DragDiffusion
STOP
DrogLoRA
STOP
DragNeise
STOP
FastDrag
STOP
FreeDrag
STOP
Good
STOP
Instantrrog
STOP
RegienDreg
STOP
OURS
STOP
Figure 16: Extra qualitative comparison (Part out of 2) DragFlow with multiple baselines.
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Reviews:
[{"review_id": "Akjwrn8Edj", "reviewer": "Reviewer_mi22", "summary": "This paper addresses the challenge of applying drag-based image editing, a paradigm popularized by models like DragGAN, to modern generative models based on Diffusion Transformers (DiTs), such as FLUX.", "questions": "1.  Since the input for point-based and region-based editing is different, how can you guarantee the fairness of the comparison?\n\n2.  I am confused about the statement in Lines 79-80. What is inversion drift? And why will CFG-distilled exacerbate it? Then why does inversion drift make KV injection insufficient?\n\n3.  In this paper, only three kinds of operation are defined (relocation, deformation, and rotation).  Can they represent all the drag instructions? What if there is an instruction consists of a rotation and relocation?\n\n4.  Lines 307- Line 309. About the experimental setting, it is quite rare in deep learning with a learning rate of 1000 or 1200. What's the reason?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The experiments are very sufficient, compared with almost all mainstream baselines, and the results are very convincing.\n2. The proposed method sounds novel.  Similar to RegionDrag, this method doesn't need Point Tracking, which is very promising compared with previous drag-edit methods.", "weaknesses": "1. Motivation for some proposed components is not clearly explained. For details, see the questions part.\n\n2.  Lack an introduction to the \"drag edit\" method, like the meaning of motion supervision, tracking. It is unrealistic to assume that all readers are familiar with drag edit.\n\n3. The discussion about ReD Bench is insufficient. Since the authors claim ReD Bench as one of the contributions, I think more details should be provided (such as how the editing instructions and corresponding ground truth are generated, the dataset size, or even some examples).", "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": "PZC9dXBUBe", "reviewer": "Reviewer_EW7a", "summary": "This paper introduces DragFlow, a novel framework for region-based drag editing tailored to Diffusion Transformers, addressing the limitations of point-based methods when applied to models like FLUX. The work first provides a detailed analysis of why point-based drag fails on DiTs and motivates the first region-based approach. DragFlow incorporates region-level affine supervision, gradient mask-based background preservation, and adapter-enhanced inversion to improve subject consistency. It also leverages MLLMs for intent inference and introduces the ReD Bench benchmark. Experiments demonstrate outstanding performance.", "questions": "1. How essential are the MLLM-generated editing prompts? Would the method achieve comparable results without editing prompts?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper thoroughly investigates the failure of point-based drag editing on DiTs, contrasting feature granularity between UNets and DiTs. This analysis provides valuable insights into model architectures and justifies the shift to region-based supervision. The motivation is strong and clear, as it addresses a critical gap in adapting drag editing to modern DiT-based models like FLUX.\n2. DragFlow introduces a well-designed pipeline that replaces point-based supervision with region-level affine transformations, avoiding the need for explicit tracking and enhancing feature guidance. Together with gradient masks and pre-trained adapters to enhance background preservation and subject consistency.\n3. Valuable ReD Bench benchmark with detailed annotations and demonstrates SOTA performance on both ReD Bench and DragBench-DR across multiple  metrics.\n4. Effectively uses multimodal LLMs to interpret user intents and generate editing prompts, significantly reducing user interaction burden.", "weaknesses": "1. The paper lacks detailed statistics on time and memory consumption, which is critical given the optimization-based nature of DragFlow. With multiple iterations, inversion steps, and adapter integrations, the method likely incurs higher computational costs. A comparison of inference time and GPU memory usage would provide practical insights for real-world applications.\n2. While DragFlow is validated on FLUX, its adaptability to other DiT-based models (e.g., SD3) is not explored. The paper does not address potential architectural or training differences that might affect performance, leaving questions about generalization.", "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": "c6QNaZHol6", "reviewer": "Reviewer_yXpA", "summary": "This paper presents DragFlow, a drag-based editing method specifically designed for Diffusion Transformers (DiTs). The authors' core insight is that point-based supervision, standard in prior work, is fundamentally mismatched with the fine-grained feature structure of DiTs. They solve this by introducing a region-level supervision scheme that treats the object as a whole, guiding the generation process with progressive affine transformations. This is combined with a clever adapter-based inversion technique to preserve subject identity and a hard-constrained background mask. The method sets a new state-of-the-art, producing more realistic and structurally coherent results on a new, more challenging benchmark (ReD Bench) also introduced by the authors.", "questions": "- The MLLM front-end for intent parsing is a nice usability feature. How robust is it in practice? What happens if it misinterprets the user's intended transformation (e.g., deformation vs. rotation), and is there a simple way for the user to correct it?\n\n- The framework currently handles relocation, deformation, and rotation via affine transforms. How would it generalize to more complex, non-affine warps like twisting or bending?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  **Clear, Fundamental Insight:** The paper's main strength is its correct diagnosis of *why* prior methods fail on DiTs. The shift from point- to region-level supervision isn't just an incremental tweak; it's a necessary conceptual shift grounded in a solid understanding of the underlying model architectures. This is a valuable contribution.\n\n2.  **Effective and Cohesive System:** The proposed solution is elegant and well-engineered. Region-level supervision provides robust guidance, while the adapter-enhanced inversion is a very practical solution to the known identity drift problem in distilled models like FLUX. The components work together to solve the problem comprehensively.\n\n3.  **Strong Empirical Support:** The experiments are convincing. DragFlow clearly outperforms a wide range of competitors on both quantitative metrics and visual quality, especially in complex non-rigid deformations where other methods falter. The ablation study effectively validates the importance of each design choice, and the new ReD Bench is a welcome contribution for future research in this area.", "weaknesses": "1.  **Dependency on External Adapters:** The method's excellent identity preservation relies on a pre-trained IP-Adapter. This is a pragmatic choice, but it means performance is coupled to the quality of this external module. The paper would be stronger with a brief discussion on this dependency.\n\n2.  **Missing Efficiency Analysis:** The method is iterative and involves multiple components. A quantitative comparison of the runtime against faster, non-optimizing methods (like FastDrag) is missing. This would help users understand the quality vs. speed trade-off. Surpassing non-optimizing methods in speed is not necessary, but a discussion section is necessary for user's information.\\\n\n3. Some prior works (like LightningDrag InstantDrag) predict dense motion field from user's sparse input, can their dense motion be used as region supervision in DragFlow?", "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": "B3pvTVbbVO", "reviewer": "Reviewer_ZsfJ", "summary": "This paper proposes a region-based framework for drag-based image editing using Diffusion Transformers (DiTs) instead of UNet-based diffusion models. The authors argue that point-based supervision fails on DiTs due to overly fine-grained features and address this with region-level learning. They also introduce a new benchmark extending DragBench with region annotations, achieving state-of-the-art results as shown in Table 2, with further component analysis in Table 3.", "questions": "1: The method assumes that all drag operations can be represented by affine transformations such as translation, deformation, or rotation. How well does this assumption hold for complex non-rigid deformations, for example hair, cloth, or water surfaces?\n\n2: Which layer of the DiT the feature extractor F(⋅) is taken for region-level supervision. Is the performance sensitive to this choice, and has any analysis been conducted on different feature layers?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1: The paper provides a meaningful analysis of why DiT-based models fail with point-based drag supervision. This motivation is well grounded and timely, as future generative systems are likely to rely on DiTs rather than UNet backbones.\n\n2: The method achieves clear state-of-the-art performance on both ReD Bench and DragBench-DR, showing consistent improvements over existing drag-based editing approaches.", "weaknesses": "1: The paper does not provide any discussion or quantitative analysis of runtime or inference time. Reporting the average optimization time per image or per drag operation would make the comparison with prior work more complete.\n\n2: The paper mainly presents successful examples but does not include qualitative results in more challenging scenarios such as complex backgrounds or strong occlusions. Showing a few representative failure cases or difficult examples would help readers better understand the method’s limitations and potential failure modes.\n\n3: Section 3.4 mainly integrates existing personalization adapters to improve inversion, which is effective but not novel. More discussion on how the adapter interacts with DiT features would clarify the contribution.\n\nMinor suggestion:\n1 The notion of a “stronger generative prior” is repeatedly emphasized in the abstract and introduction, yet its meaning and mechanism of utilization in DiT remain unclear. The underlying reasoning of why and how this prior facilitates more faithful or controllable edits only becomes explicit in Section 3.1–3.2. It would be helpful to briefly clarify in the abstract how this stronger prior is actually leveraged/achieved by your framework, perhaps in one sentence.\n\n2 The analysis in Section 3.1 relies mainly on qualitative visualization to argue that point-based supervision fails on DiT due to finer feature granularity. While reasonable, the claim lacks quantitative evidence, such as metrics of using DiT to support the argument.\n\n3 It is unclear which dataset was used for the ablation in Table 3.", "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)