RegionE: Adaptive Region-Aware Generation for Efficient Image Editing
Authors: Pengtao Chen
Xianfang Zeng
Maosen Zhao
Mingzhu Shen
Peng Ye
Bangyin Xiang
Zhibo Wang
Wei Cheng
Gang YU
Tao Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Image Editing
Efficient
Diffusion Transformer
Acceleration
Primary area: generative models
TL;DR: Announcing RegionE, a training-free plug-in that losslessly accelerates SOTA instruction-based image editing models (e.g., Qwen-Image-Edit) by 2–3×. It leverages spatial and timestep redundancy, and installs via pip in just four lines of code.

Abstract: Recently, instruction-based image editing (IIE) has received widespread attention. In practice, IIE often modifies only specific regions of an image, while the remaining areas largely remain unchanged. Although these two types of regions differ significantly in generation difficulty and computational redundancy, existing IIE models do not account for this distinction, instead applying a uniform generation process across the entire image. This motivates us to propose \textbf{RegionE}, an adaptive, region-aware generation framework that accelerates IIE tasks without additional training. Specifically, the RegionE framework consists of three main components: 1) Adaptive Region Partition. We observed that the trajectory of unedited regions is straight, allowing for multi-step denoised predictions to be inferred in a single step. 
Therefore, in the early denoising stages, we partition the image into edited and unedited regions based on the difference between the final estimated result and the reference image. 2) Region-Aware Generation.  After distinguishing the regions, we replace multi-step denoising with one-step prediction for unedited areas. 
For edited regions, the trajectory is curved, requiring local iterative denoising. To improve the efficiency and quality of local iterative generation, we propose the Region-Instruction KV Cache, which reduces computational cost while incorporating global information. 
3) Adaptive Velocity Decay Cache. 
Observing that adjacent timesteps in edited regions exhibit strong velocity similarity, we further propose an adaptive velocity decay cache to accelerate the local denoising process.
We applied RegionE to state-of-the-art IIE base models, including Step1X-Edit, FLUX.1 Kontext, and Qwen-Image-Edit. RegionE achieved acceleration factors of 2.57×, 2.41×, and 2.06×, respectively, with minimal quality loss (PSNR: 30.520–32.133). Evaluations by GPT-4o also confirmed that semantic and perceptual fidelity were well preserved.

Published as a conference paper at ICLR 2026
REGIONE: ADAPTIVE REGION- GENERA-
TION FOR EFFICIENT IMAGE EDITING
Pengtao Chen'
Xianfang Zeng" 21
Maosen Zhao'
Mingzhu Shen
Peng Ye
Bangyin Xiang'
Zhibo Wang
Wei Cheng"
Gang Yu²
Tao Chen
1 Fudan University StepFun Imperial College London
Communication University of China
5 Shannhai Innovation Institute
Code: https://ggiie com cctn///etttnn-Chh/Reeeeeee
ABSTRACT
Recently, instruction baseed image editing (IIE) has received widespread attention. In practice, IIE often modifies only specific regions of an image, while the re- maining areas largely remain unchanged. Although these two types of regions differ significantly in generation difficulty and computational redundancy, existing IIE models do not account for this distinction, instead applying a uniform genera- tion process across the entire image. This motivates us propose RegionE, an adaptive, region-aware generation framework that accelerates IIE tasks without additional training. Specifically, the RegionE framework consists of three main components: 1) Adaptive Region Partition. We observed that the trajectory of unedited regions is straight, allowing for multi-step denoised predictions be inferred in single step. Therefore, in the early denoising stages, we partition the image into edited and unedited regions based on the difference between the final estimated result and the reference image. 2) Region-Aware Generation. After distinguishing the regions, we replace multi-step denoising with one-step prediction for unedited areas. For edited regions, the trajectory is curved, requiring local iterative denoising. To improve the efficiency and quality of local iterative generation, we propose the Region- Instrucinttt KV Cache, which reduces computa- tional cost while incorporating global information. 3) Adaptive Velocity Decay Cache. Observing that adjacent timesteps in edited regions exhibit strong velocity similarity, we further propose an adaptive velocity decay cache to accelerate the local denoising process. We applied RegionE to ofthe-tf IIE base models, in- cluding StepIX-Edi FLUX.1 Kontext, and Dowen-Image acceleration factors of 2.57x, 2.41x, and 2.06x, respectively, with minimal quality loss (PSNR: 30.520-32. 133). Evaluations by GPT-4o also confirmed that semantic and perceptual fidelity were well preserved.
RegionE achieved
INTRODUCTION
In recent years, diffusion models (Rombach et al. 2022 have achieved rapid progress in generative tasks, particularly in visual generation, where state-of-the-arr models can synthesize highly realistic images. Within this context, the task of editing existing images according to user requirements has gradually emerged as an important direction (Kawar et al. (023). Recently, diffusion- based foundation models, such as FLUX.1 Kontext (Labs al. 2025). ). Owen-Image and I X-Edit (Liu et al.. 20255 Yin et al. 2025), have been developed. These models can perform precise image editing using only textual instructions, offering a novel solution for instruction-based image editing and providing more powerful tools for image post-processing (Choi et al. 2024).
(Wu et al.) 2025).
Although diffusion-based HE models can achieve impressive editing results, their high inference lateney limits their use in real-time applications. Previous research efficient diffusion inference has primarily focused on image generation. For instance, some studies reduce model parameters through pruning (Castells al. 2024), others decrease model bit-width via quantization (Zhao al. 2025a). and some accelerate attention (Zhang et al. 2025) Chen et al. 2025). ) and some employ distillation
"Corresponding author. *Project leader. Work done when interned at StepFun.

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Published as a conference paper at ICLR 2026
Noise
Data
Noise
Data
Shop Rutiiaale
Irigection Tarkge
Tearastion Editing
Straight
Luedited Hapion
Trujectory of the Unedited Region Straight
Trajectory of the Edited Region Curved
Figure I: Trajectories of different regions in the IIE task. In unedited regions, the trajectory is nearly linear, allowing early-stage velocity to provide reliable estimate of the multi-step denoised images, including the final result. In contrast, edited regions exhibit curved trajectories, making the final image harder to predict. Despite this, the velocity between consecutive timesteps remains consistent.
to reduce model size (Kim et al. 2023) and the number of timesteps (Sauer et al. 2024). In the
two-stage inversion-band editing paradigm (Pan al., 2023 Wang al. (025). redundancy in the inversion and denoising stages has been analyzed, leading to methods like EEdit (Yan et al.) (2025)
that accelerate both stages simultaneously. However, for the emerging denoising-only paradigm of IIE, the redundancy and feasibility of efficient inference remain largely unexplored.
Our study reveals that current IIE models exhibit two significant types of redundancy: 1) Spatial Generation Redundancy. Unlike image generation tasks, which require reconstructing the entire image. IIE models often need to modify only local regions specified by the instructions, while the remaining areas remain essentially unchanged. For example, shown in Figure [I the model edits only the region around the hat. Nevertheless, IIE models apply the same computational effort to both edited and unedited areas, resulting in significant redundancy in the latter. 2) Redundancy across diffusion timesteps. First, at neighboring timesteps, the key and value within the attention layers at the same network depth highly similar. Second, in the middle stages of denoising, the velocity output by the diffusion transformer (DiT) at adjacent timesteps is also highly similar.
To mitigate spatial and temporal redundancy IIE models, this paper introduces RegionE, training- free, adaptive, and region-awan generative framework that accelcrates the current IIE models. Firstly, we observed that the trajectories of edited regions are often more curved, making difficult to accurately predict the final edited results at early timesteps, shown in Figure I In contrast, unedited regions follow nearly linear trajectories, allowing more reliable predictions from the same early steps. Based this observation, RegionE introduces Adaptive Region Partition (ARP), which performs a one-step estimation for the final image the early stage and compares its similarity with the reference (instruction) image. Regions with high similarity (minimal change after editing) classified as unedited, whereas regions with low similarity are classified as edited. Then, we perform region-aware generation the separated parts. Specifically, We replace multi-step denoising with one-step estimation for the unedited areas and apply region-iterative denoising for edited areas. During edited region generation, RegionE discards unedited region tokens and instruction image tokens, and effectively reinjects global context into local generation through our proposed Region- Instruction KV Cache Vachhe, which leverages the similarity of key and value across timesteps. This process primarily addresses redundancy in spatial. Finally, regarding temporal redundancy, we find that the velocity outputs DiT at adjacent timesteps are highly consistent in direction but decay in magnitude over time, with the decay dependent on the timestep. To exploit this property, RegionE introduces an Adaptive Velocity Decay Cache (AVDCache), which accurately models this pattern and further accelerates the region generation process. Experimental results demonstrate that RegionE achieves speedups approximately 2.57x, 2.41x, and 2.06x on StepIX-Edit, FLUX.1 Kontext, and Qwen-Image Edit, respectively, while maintaining PSNR values of 30.520, 32.133. and 31.115 before and after acceleration. Evaluations using GPT-4o further indicate that the perceptual differences negligible, confirming that RegionE effectively eliminates redundancy in IIE tasks without compromising image quality.
The contributions of paper follows:
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We observe that in IIE tasks, unedited regions exhibit nearly linear generation trajectories, allowing etrly-stage velocities to provide reliable estimates for multi-step denoised images, including the final image. In contrast, edited regions follow more curved trajectories, making the final image harder to predict. Nevertheless, the velocity remains consistent across consecutive timesteps.
We propose RegionE, a training- free, efficient IIE method with adaptive, region-awar generation. reduces spatial redundancy by performing early adaptive predictions for edited and unedited regions and generating each region locally in subsequent stages, while mitigating temporal redundancy via velocity-decay cache across timesteps.
RegionE achieves 2.57x, 2.41x, and 2.06x end-to-end speedups on StepIX-Eai Editt, FLUX.1 Kontext, and Qwen-Image-Bee while maintaining PSNR (30.520, 32. 123, 31.115) and SSIM (0.939, 0.917, 0.937). Evaluations with GPT-4o further confirm that no quality degradation occurs.
RELATED WORK
Efficient Diffusion Model. Although few efficient methods have been developed specifically for IIE models, a variety of acceleration techniques have been proposed for diffusion models more generally. From the perspective of parameter redundancy, researchers have introduced pruning methods such as Diff-Pruning Fang et al. (2023) and LD-Pruner (Castells et al. 2024), quantization methods such as PTQ4DM (Shang et a (023) and SVDQuant (Li et (024a). distillation methods such as BK-SDM (Kim et al. (002)) and CLEAR (Liu et al. 2024). and early-stopping strategies such as ES-DDPM (Lyu et al. 2022). From the perspective of temporal redundancy, methods like DeepCache (Ma et al. (024)) DiT (Chen et al.), FORA (Selvaraju et al.) (024). MD-DiT (Shen al. 2024 and TeaCache (Liu et al., (2025a) reuse intermediate features across timesteps, while approaches such as LCM (Luo et al., 2023) and ADD (Sauer et al. 2024) reduce the number of timesteps through model distillation. From the perspective of spatial redundancy, RAS (Liu et al., 2025c) observes that at each diffusion timestep, the model may focus only on semantically coherent regions; therefore, only those regions need to be updated, thereby accelerating image generation. Similarly. ToCa (Zou et al. (2024)) and DuCa (Zou et al. 2024b) note that during denoising, different tokens exhibit varying sensitivities, and dynamically updating only a subset of tokens at each timestep can further accelerate image generation (Kong et al. (2025). In contrast to the methods above, RegionE leverages the trajectory characteristics unique to MEE tasks, while simultaneously addressing both spatial and temporal redundancies in fiffusion-based image editing to achieve accelerated generation.
Image Editing. Image editing is an essential task in the field of generative modeling. In the early U-Net (Ronneberger et al.) 2015) era, ControlNet (Zhang et al., 2023b) introduced a robust editing solution through repeat- structure design, and InstructPix2PPix (Brooks et al. 2023) introduced an editing method that involves channel expansion. As research advanced, inversion praeesoo--based methods (Pan) et al., 2023 Wang et al. (025) gradually became the dominant approach. These methods apply noise to the original image the latent space and then recover the edited result through a denoising process. However, this paradigm involves both inversion and denoising stages, which increases complexity. At the same time, IIE models began to emerge. Approaches such as InstructEdit (Wang et al. 2023),. MagicBrush (Zhang al. 2023a), and BrushEdit (Li et al.; 2024b) employed modular pipelines, in which large language models generate prompts, spatial cues, or synthetic instruction-iman -imai pairs to guide diffusion-basse editing. Most of these approaches, however, are task-specific and lack generality. More recently, a new class of IIE has been developed to improve general-purpose editing. These models rely solely on textual instructions, without requiring masks or task-specific designs, and still achieve effective editing performance. Concretely, they leverage MLLMs or advanced text encoders to provide richer semantic control signals, and feed both the target image and noise into a DiT (Peebles& & Xie, 2023) architecture to enhance image alignment. In this work, we propose an adaptive, region-aware acceleration method for these emerging IIE models. Although prior work has explored local editing, these studies primarily aim to enhance editing capability rather than improve efficiency. Moreover, methods such as (Simsar et al. 2024) and (Guo & Lin (023) follow the InstructPix2Pix paradigm, while (Mo et al. 2ouarCo
Couairon et al. 2022 Avrahami et al. 2022
Yang et al. 2024) operate within inversion- investigate the problem under the emerging MLLM-assisted IIE paradigm and, for the first time, identify an carly-stage region-partiiions partitioning strategy in modern flow-mater matching models that enables an effective acceleration mechanism.
or mask-dependee
editing frameworks. In contrast,
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(e) Denoising Process
(d) L1 Sim. Between Ps and Ph.,, (e) Cos Sim. Between P. und
Now
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Ty
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Fudunoo
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(b) Instruction-DiT
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Figure 2: Comparison between traditional DiT and DiT in IIE (a, b). Symbolic visualization of the denoising process (c). LI and cosine similarities of velocities between adjacent timesteps during denoising (d, e). Cosine similarity between velocities after 121 in edited and unedited regions with 22 (f). Cross-step key similarity (g) and cross-step similarity of instruction-relate keys (h).
PRELIMINARY
Flow Matching & Rectified Flow. Flow matching (Lipman et al. 2022) has become a widely adopted training technique in advanced diffusion models. It facilitates the transfer from a source distribution πi to target distribution no by learning time-dependent velocity field v(æ; t). This velocity field is used to construct the flow through the ordinary differential equation (ODE):
d v (x)) t), Φ1 (a) T1-
(1)
dt
Rectified Flow (Liu et al. follows noise distribution тı and X, follows the target image distribution To. the equation is
2022) simplifies this process through a linear assumption. Given that X,
x (1 X, + tX,, E 1].
Differentiating both ends with respect timestep f yields: dXx X1 Xo- The velocity of the rectified flow v (X,,,t, t), always points in the direction of X; Xo. Therefore, the training loss is minimized by redueing the deviation between the velocity and X Xo:
L Ee [[[(X] Xo) v(X,, t)]
(3)
The inference process involves starting from X, and iteratively solving for X, in reverse, using the learned velocity v X,,, t). practice, we typically use discrete Euler sampler, which discretizes the timestep & (i N tr t, 0) and approximates:
X t = X, At,;- v(X,, (2), M -4-11
(4)
After 7 iterations, the final target image X, is obtained. This paper, therefore, targets the IIE task and optimizes the inference process of T' iterations in Equation
Instruction- Based Editing Model. Recent HEE models, such as SteplX-Edi (Liu et al. 2025b),
FLUX.1 Kontext (Las al. 2025) and Qwen-Image (Wu al. 2025). follow the same paradigm, as shown in Figure 2p. In these models, the velocity field is estimated using Instruction-DiT, the variants DiT (Peebles & Xie 2023). The input to Instruction DiT consists of three types of tokens: text (prompt) tokens X" noise tokens X,,. and instruction tokens X The noise token corresponds to the generation of the target image, while the text token carries the instruction information. The instruction token is specific to the editing task, representing the part of the image to be edited. Notably, the counts of noise and instruction tokens are roughly comparable and substantially higher than that of text tokens. Temporally, the text and instruction tokens serve static control signals throughout the denoising process, whereas the noise token evolves dynamically at each timestep. Since Instruction- DiT is designed to predict only the noise component, the model's output corresponds exclusively to the portion represented by the noise token. To simplify the expression, the Instruction -iT mentioned below will be referred to simply as DIT.
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Subiiggeio Suage (875)
Reglon- haar Gracration Singe (R4668)
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Overview of the RegionE. RegionE consists of three stages: STS, RAGS, and SMS. In the STS, no acceleration is applied due to unstable DiT outputs, and all KV values are cached at the final step. In the RAGS, an Adaptive Region Partition distinguishes between edited and unedited regions: unedited regions are denoised in one step, while edited regions are generated iteratively. This iterative generation process leverages RIK for injecting global information and AVDCache for acceleration. Certain forced-update steps aggregate the full image refresh VIKhe with complete DiT computation. Finally, in the SMS, several full denoising steps are performed to eliminate artifacts along the boundaries between edited and unedited regions.
Figure 3:
4
METHODOL.OGY
This section introduces RegionE, a method that accelerates the IIE model without additional training. The workflow is shown Figure 3 RegionE consists of three stages: the Stabilization Stage (STS), the Region-Aware Generation Stage (RAGS), and the Smooth Stage (SMS).
Stabilization Stage. In the early steps of denoising, the input X,, to DiT is close to Gaussian noise (i.e., the signal-to-noise ratio is low). This leads to oscillations in DiT's velocity estimation (see Figure 2 and [P). Since the estimates at this stage are inherently unstable, it is not suitable for acceleration. Therefore, we keep the original sampling process unchanged. Additionally, at the last step of this stage, we save the Key and Value in each attention layer of DiT, denoted as K and V
Oegion-Award Generation Stage. This stage is the core component of RegionE and consists of three parts: adaptive region partition, region-aware generation, and adaptive velocity decay cache. The first two parts primarily address spatial redundancy in IIE, while the third further reduces temporal redundancy across timesteps.
Adaptive Region Partition. After the stabilization stage, the output of DiT becomes stable. As previously observed, the generation trajectories in the edited regions are curved, whereas those in the unedited regions are straight, as shown in Figure I and 2f. Therefore, for the unedited regions X we can accurately estimate X at any timestep (f using one-step estimation:
X = X - ( 44) Atr,y
(5)
When f, 0, this corresponds estimating the final unedited regions X which is nearly identical to the true X². However, using Equation for the edited region does not accurately estimate X Based on this difference between the edited and unedited regions, we propose adaptive region partition (ARP), as illustrated in the lower-left corner of Figure Given the velocity tt, at the beginning of the region-aware generation stage and the noisy image X₁, the final edited result X, can be estimated in one step using Equation This estimate is reliable in unedited regions but less accurate in edited ones. Since the unedited region undergoes minimal change before and after editing,
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can compute the cosine similarity between the estimated image X, and the instruction image X along the token dimension. Regions with sufficiently high similarity (> threshold 17), that is, small changes before and after editing, are considered unedited regions, while the remainder is treated as the edited region. To account for potential segmentation noise, morphological opening and closing operations applied to make the two regions more continuous and accurate.
Region-Aware Generation. After identifying the edited and unedited regions, we apply Equation 5 the unedited region to directly estimate the denoised image X at the next timestep l in one step. thereby saving computation for the unedited region. For the edited region, our implementation is as follows: first, the input to DiT is changed from [ ,X;,> x to [XP, ,,,,), so that DiT only estimates the velocity of the edited region - However, since DiT contains attention layers that involve global token interactions, completely discarding the X' and X inputs can gradually inject bias into the estimation of vh during global attention. To compensate for this loss of information, we propose a Ingtron-nnttucction KV Cache (RIKVCache). Specifically, the input to DiT remains [X X.). but within the attention layers of DIT, it is modified follows:
softmar Qp,, QE) Kp, Kg, KG KYTT) [Vp, VE, VG V/).
(6)
vd
The lower corner labels P. E, U, and I represent prompt token, edited region token, unedited region token, and instruction token, respectively. The superscript C' in the upper-right corner indicates that the value is taken from the cache of the previous complete computation. And the middle-lower part of Figure 3 visualizes this process. The feasibility of this approach is supported by the high similarity of the KV pairs between consecutive steps, as shown in Figure 2 and 2h.
Adaptive Velocity Decay Cache. As illustrated in the right part of Figure I although the trajectory of the edited region is curved, the velocities between consecutive timesteps Focusing on the intermediate denoising phase, we observe from Figure 2 that the velocity directions between adjacent steps are almost identical (cosine similarity approaches 1). At the same time, the magnitudes exhibit gradual decay that varies across timesteps (Figure(() Based on this observation, we propose an adaptive velocity decay cache (AVDCache). Specifically, the AVDCache introduces a decay factor:
actually similar.
(7) Here, (1 Att, represents the sample-aware component under discrete Euler solver, while Y represents the timestep-awer component. The solver entirely determines the former, while the latter obtained by fitting on a randomly sampled dataset. Since the decay factor in Eq. charactreess the intrinsic differences between diffusion model timesteps, we introduce the AVDCache criterion:
|| 1l..11/1lo,, .1l = (1 - Ale;1 Tt.
Criterion
II - Atuu).))) -h.
(8)
Here, and le denote the start and end timesteps of the cache, respectively, while the criterion measures the cumulative error of this process. The decision of whether to apply the cache is made using a threshold ö. The complete process is as follows:
D X²) E.C o II. II (1-AAtmm11) Atim=r,
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The right-lower part of Figure 3 visualizes this process. In fact, AVDCache is an improved version of the existing residual cache methods, with further details and analysis provided in the supplementary.
After the above process, we obtain the generated results for both the edited and unedited regions. We then re-gather these results according to their spatial positions to reconstruct the complete image tokens. It is worth noting that the similarity of the KV Cache decreases as the timestep increases. To address this issue, we periodically enforce full-image gathering at certain timesteps within the region-aware generation stage, performing complete DiT computation to update the RIK
Smooth Stage. Small gaps may appear at the boundaries between edited and unedited regions after stitching. Although these gaps often imperceptible in most cases, to ensure the generality of our method, we perform several steps of unaccelerated denoising on the merged full image to smooth these discontinuities. Empirically, two denoising steps are sufficient to eliminate the gaps effectively.

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5 EXPERIMENT
5.1 EXPERIMENTAL SETTINGS
Pretrained Model & Dataset. We evaluate RegionE on three open- source state-of-the-ar art IIE models:
(Liu et al. 2025b), FLUX.1 Kontext (Labs ct al. (025), and Qwen-Image-Bal (Wu) al. 2025). StepIX-Fddi adopts a CFG (classifier-fres guidance) (Ho & Salimans/ 2022) scale of 6, FLUX.T Kontext uses a scale of 2.5, and SwwenImmae-FFae applies a scale of 4. All models are evaluated with 28 sampling steps. For evaluation, we follow the dataset protocols described in the respective technical reports. Specifically, we use 606 image prompt pairs covering 11 tasks from GEdit-Bench English (Liu et al.) 2025b) for SteplX-Edit and Owen-Image--ode and 1026 image prompt pairs spanning five tasks from KontextBench (Labs et al. 2025 for FLUX.1 Kontext.
Evaluation Metrics We design a comprehensive evaluation framework to assess both the quality and efficiency of IEE models. For quality assessment, we adopt two complementary approaches. First, we evaluate reconstruction quality by measuring deviations before and after acceleration, using PSNR (Zhao et al.; ). SSIM (Wang & Bovik 2002), and LPIPS (Zhang et al. 2018) metrics. Second, we conduct an editing evaluation using vision-language models (VLMs), specifically GPT- 4o, assess image quality, semantic alignment, and overall performance (Ku et al. 2024), as shown in Table T Evaluation dimensions are denoted by the suffixes SC, PQ, and O, consistent with (Liu al., 2025b) and (Wu et al., 2025). For efficiency evaluation, we report actual runtime latency as well as the relative speedup compared to the vanilla pretrained models.
Baseline. Currently, there are no acceleration methods designed explicitly for IIEmodels. Therefore, we adapt several effective acceleration techniques initially developed for diffusion models as base- lines, since they are also applicable to diffusion- redundancy, Steoskip performs larger jumps in the sampling steps, FORA (Selvaraju et al.) (022) employs block-level cache, and A-DiT (Chen et al.) and TeaCache (Liu et al. 2025a) use residual cache. From the perspective of spatial redundancy, RAS (Liu et al. (025cc and ToCa (Zou et al. 2024a) perform colundannc-reductiin denoising at the token level.
IIE tasks. From the perspective of timestep
Implementation Details. For all three models, RegionE uses six steps in the stabilization stage, enforces an update at step 16 in the region-aware generation stage, and adopts two steps in the smooth stage. For Step1X-Edit, FLUX.11 Kontext, and SwwenImmae--Bae ARP are 0.88, 0.93, and 0.80, respectively, while the decision thresholds of AVDCache are 0.02, 0.04, and 0.03, respectively. Latency measured on single NVIDIA H800 GPU.
the segmentation thresholds 1 of
5.2 EXPERIMENTAL RESULTS ANALYSIS
We evaluate RegionE against several state-of-the-art acceleration methods on three prominent IIE models: StepIX-Edit, FLUX.1 I Kontext, and Qwen-Image-Edit. Our evaluation encompasses quanti- tative metrics, efficiency measurements, and visualization, demonstrating that RegionE achieves a superior balance between acceleration and quality preservation. The quantitative results are shown in T Since both GEdit-Bench and KontextBench involve multiple editing tasks, the table reports results averaged over tasks, while the per-task quantitative results are provided in the supplementary.
Deviation Analysis Compared to Pre-trained Models. The Against Vanilla evaluation reveals RegionE's exceptional fidelity to original model outputs across all evaluation metrics, significantly outperforming competing accelcration methods. RegionE achieves the highest PSNR values: 30.520 (SteplX-Edit), 32. 133 (FLUX.1 Kontext), and 31.115 Lewen-Imaee-Edil representing substantial improvements of 2-4 over the next-best methods, indicating minimal pixel-level deviation from the original outputs. The SSIM scores of 0.939, 0.917, and 0.937 demonstrate superior preservation of structural coherence across different model architectures. In contrast, the LPIPS scores of 0.054, 0.057, and 0.046 represent 25-50% improvements over competing methods. This consistent performance across three diverse model architectures validates RegionE's architectural agnosticism. RegionE consistently maintains stable, high-quality results.
GPT-4o Editing Quality Assessment & User Study. The GPT-4o evaluation provides additional quality validation through automated semantic and perceptual analysis across three dimensions, con- sistently demonstrating RegionE's s superior performance. For semantic consistency (G-SC), RegionE achieves scores of 7,552, 7.278, and 8.242, matching or exceeding original models while maintaining
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Table 1: Comparison editing quality and efficiency between RegionE and the baseline. All the evaluations are carried out on a single NVIDIA H800 GPU. denotes the strategy for reducing spatial redundancy, while
T denotes the strategy for reducing temporal redundancy.
Aguinst Vanilla SSIM
GPT-4o Score
Efficiency
Model
Type
PSNRT
LPIPS
G-SC 1
G-PQ
G-0
Latency (6)
Speedup
Step1X-Edl -Edit
2025b
7.479
6.906
27.945
1,000
Stepskip
26.719
0.898
0.096
7.491
7.343
6.880
12.299
2.272
FORA Selvaraiu al.
2024
22.126
0.835
0.178
6.078
7.588
5.863
14,330
1,950
A-DIT
Chen al
T
24.659
0.874
0.122
7,432
7.233
6,795
12.728
2.196
TeaCache (Lau et
2025a
28.262
0.924
0.072
7.455
7.361
6.866
11,212
2,493
RAS Liu al.
20256
26.819
0.892
0,100
7,339
7.072
6.615
15.239
1.834
ToCa
Zou et al.
2024a
24.699
0.844
0.152
7.185
6.705
6.350
22. 22.149
1.262
Ours (KeglonE)
&
30.520
0.939
0.054
7,552
7.405
6.948
10.865
2,572
FLUX.1
(Labs
2025
7.197
14.682
Stepskip
26.199
0.838
0.123
7.126
6.938
6.463
8.512
1,725
FORA Selvaraiu al.
2024
24.685
0.809
0.146
7,085
6.897
6.383
7.497
1,958
A-DIT
Chen et all
20.227
0.723
0.225
7,055
6.918
6.411
6.751
2,175
TeuCache (Láu ml.
2025u
T
28.307
0.869
0.097
7,233
6.846
6.455
6.203
2.367
RAS
Liu al
20256
26.217
0.829
0.132
7,216
6.785
6,460
8.219
1,786
ToCa
Zou et al. 2024a
S
23.906
0.767
0.192
6.985
6.589
6.237
11.299
1.299
Ours (RegionE)
T &
32.133
0.917
0,017
7,278
6.953
6.538
6.096
2.409
Owen-Image-Edit
2025
8.242
32.125
Stepskip
28.439
0.892
0.077
8.090
7.875
7.572
17,555
1,830
FORAA Selvaraiu
2024
26.508
0.863
0.098
8.032
7.760
7.501
17.815
1,803
A-DIT
Chen et al.
T
25 25.020
0.821
0.116
7.964
7.718
7:417
17.470
1,839
TeaCache (1
2025n
28.314
0.900
0.075
8.084
7.841
7.563
16.445
1,954
RAS
Liu al 20250
27.251
0.879
0,090
8.152
7.680
7.515
22.327
1.439
ToCa
Zou et al.
2024a
00M
0OM
0OM
0OM
OOM
0OM
COM
0OM
-Ours (Regionk)
&
31.115
0.937
0,046
8.242
7.968
7.731
15.604
2.059
substantial acceleration, with showing perfect preservation (8.242) despite 2.059x speedup. The perceptual quality (G-PQ) scores of 7.405, 6.953, and 7.968 consistently outperform competing acceleration methods by 0.1 to 0.3 points, demonstrating the practical preservation of visual coherence through region- aware processing. Overall quality (G-O) scores of 6.948, 6.538, and 7.731 provide holistic as- sessment validation, with the alignment between GPT-4o assessments and quantitative metrics (PSNR, SSIM, LPIPS) strengthening confidence in RegionE's comprehensive quality preservation across multiple evaluation dimensions and provid- ing additional evidence of the hybrid temporal-spatial optimization approach's 's effectiveness. We also conducted user study, and the results shown in Figure The findings indicate that participants had difficulty discerning whether the edited images were accelerated using RegionE, further validating the high-fidelity capabilities of RegionE.
RLUK1 Kontext RegionÉ
FLUXC Kontect
SteptX-Edr RegionE
SteptX- Edit
Qucn Image Edt RegionE
Image
Figure 4: User study results for the RegionE.
Efficiency Analysis. RegionE demonstrates substantial efficiency gains while maintaining superior quality, achieving an optimal balance between acceleration and performance preservation with impressive results across all evaluated models. The method achieves speedups of 2.572x, 2.409x, and 2.059x across StepIX-Edit, FLUX.1 Kontext, and Qwen-Image- significant absolute latency reductions: from 27.945s to 10.865s, from 14.682s to 6.096s, and from 325s to 15.604s respectively. RegionE occupies the optimal position on the efficiency-quality curve, maintaining the highest quality metrics while achieving competitive superior acceleration compared to methods that sacrifice substantial quality for higher speedups.
Image-Edit Edit respectively. translating to
Visualization. Figure presents partial visualizations of different acceleration methods on SteplX- Edit. Among the baselines, RegionE produces edited outputs closest to the vanilla setting at higher speedups. preserving both details and contours. The last column shows ARP predictions of spatial regions in RegionE, where unedited regions are masked. These masked regions closely match human perception. Additional visualizations for other tasks and models provided in the supplementary.
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Published as a conference paper at ICLR 2026
Table Ablation study on cache design and stage design in RegionE.
Against Vanilla
GPT-4o Score
Efficiency
Variant
PSNR
SSIM
LPIPS
G-SC
G-PQ
G-0
Latency
Speedup
RegionE:
30.520
0.939
0.054
7.552
7.405
6,948
10.865
2.572
w/o RIK VCache
22.868
0.822
0.207
5.997
5.389
5.191
10.223
2.734
Cache Design
AVDCache
31.139
0.946
0.046
7.570
7.482
7.023
16.122
1.733
STS w/o SMS
21.441
0.814
0.161
7.045
6.758
6.325
7.149
3.909
Stage Design
28.857
0,904
0.085
7.456
7.207
6,773
9.766
2.862
w/o Forced Step
28.452
0.915
0.080
7.536
7.305
6,925
10.204
2.739
Ours (RegionE)
Edited Region
Input
Vanilla
Stepskip
FORA
A-DiT TeaCache
RAS
ToCa
Material Alter
Change the bear's material to glass.
Attwind
caplerezze
preze
calenere
Text Change
Change the text 'hotwind' 'cool breeze'
Subject Add
Add potted green plant the right of the sofa.
Subject Replace
Replace the children with fire truck.
Figure 5: Examples of edited images by RegionE and baseline Step E-itt--Eddiillpppe
5.3 ABLATION STUDY
We conduct ablation studies to investigate the contributions of different components in RegionE, primarily on the StepIX-Edit-vil The quantitative results are summarized in Table 2
Cache Design. We propose two key components: RIKVCache and AVDCache. Removing RIKV- Cache, i.e., performing local attention within the edited region without injecting instruction infor- mation context from the unedited region, results in a 2.734x speed-up. However, this comes at significant cost to editing quality, with PSNR dropping from 30.520 to 22.868 and G-O decreasing from 6.948 5.191. This demonstrates that global context supervision is crucial even during region generation. In contrast, removing AVDCache results in a slight improvement in editing quality (G-O increases from 6.948 7.023), but without eliminating redundancy across timesteps, the acceleration is limited to 1.733. This indicates that AVDCache significantly improves inference efficiency with minimal degradation in quality.
Stage Design. We introduce two auxiliary stages: Stabilization Stage (STS) and Smooth Stage (SMS), as well forced step in the region-aware generation stage (RAGS). Removing STS causes substantial drops in editing quality (PSNR: 30.520 - 21 21.441, I.IPS: 0.054 - 0.161, G-O: 6.948 - 6.325). discussed Section 4 STS addresses the instability in speed estimation, and skipping results in degraded performance. Removing SMS leads to smaller declines in both pixel-level (PSNR: 30.520 - 28.857, SSIM: 0.939 - 0.904) and perceptual metrics (G-O: 6.948 - 6.773), reflecting its role in bridging the gap between edited and unedited regions. Finally, when the forced step in RAGS was removed, since its role was to mitigate the decay of KV similarity over time, its removal led to 2-point drop in PSNR, further validating its necessity.
Sensitivity of Parameters and 17. The parameter controls the proportion cached timesteps. The parameter 7) is used to distinguish between edited and unedited regions. We evaluate 25 different

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Published as a conference paper at ICLR 2026
Figure 6: Sensitivity analysis of hyperparameters 8 and performed on Step1 X-Fdit-vlpl.
FLUXI Kontext
RegionE
StepiX-Edit
RegionE
Quen Quen-Image Edit
RegionE
Speedup
GPU Memory
Seate
Huin
Huser
11:011
Kanila 13.440 (-.77 Mentry morkhed)
Yorill cr.55 10.2% Moncry cnathead)
Vor 65336 1- 12% Memory overtes))
Figure 7: Speedup and GPU memory usage at different levels of spatiotemporal redundancy.
combinations of and vi the Step1X-Edit and the quantitative results of editing quality shown in Figure 6. The results indicate that: (1) as 5 increases, more timesteps are skipped and editing quality deteriorates; and (2) as 7) increases, larger portion of the image is considered edited, resulting in slower generation due to the increased area requiring local synthesis, but improved editing quality,
Speedup and GPU Memory Across Redundancy Levels. Figure summarizes the speedup and memory consumption of RegionE under varying redundancy levels. The horizontal axis represents the number of cached timesteps, and the vertical axis denotes the proportion of unedited regions, evaluated on 1024x (024 images. As the edited region shrinks and more timesteps are skipped, RegionF. yields higher speedups, reaching up to 3. 3.15x, 3.39x, and 2.55x on FLUX.1 Kontext, Step1 Edit, and Qwen-Image, respectively. Memory usage remains largely unaffected across redundancy levels, with RegionE incurring only 6%-10% additional overhead compared to the vanilla setting.
CONCLUSION
Inspired by temporal and spatial redundancy IIE, we propose RegionE, an adaptive, region-aware generation framework that accelerates the IIE process. Specifically, we perform early prediction on spatial regions using ARP and combine it with RIKVCache for region-wise wise editing to reduce spatial redundancy. We also use AVDCache to minimize temporal redundancy. Experiments show that RegionE achieves 2.57x, 2.41x, and 2.06x end-to-end speedups on StepIX-EA and FLUX.1 respectively, while maintaining minimal bias (PSNR 30.52-32.13
Kontext, and Dwen-Image- and negligible quality loss (GPT-4o evaluation results remain comparable). These results demonstrate the effectiveness of RegionE in reducing redundancy in IIF.
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ACKNOWLEDGMENTS
This work is supported by Shanghai Natural Science Foundation (No. 23ZR14009000, 402900), Shanghai Science and Technology Commission Explorer Program Project (24TS1401300).
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RegionE: Adaptive Region-Aware Generation for Efficient Image Editing
Supplementary Material
We organize the supplementary material follows:
Section
Pseudocode of RegionE
Section
Analysis of Adaptive Velocity Decay Cache
Section
One-Step Prediction from Different Denoising Timesteps
Section
Discussion on Using AVDCache During the Stabilization Stage
Section
Discussion on High-Resolution Image Editing
Section
Discussion Editing Boundaries
Section G
Discussion on Multi-Region Editing
Section
Discussion on Global Editing
Section 0
Discussion on Bad Cases
Section J
Experimental Setup of the User Study
Section
Per-Task Visualization Results in the Benchmark
Section
Per-Task Quantitative Results in the Benchmark
Section
M
Statement on LLM Usage
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Published as a conference paper at ICLR 2026
A PSEUDOCODE OF REGIONE
Algorithm RegionE: Adaptive Region-Awaan Generation for Efficient Image Editing
Input: Diffusion transformer ((-), sampling step T, insturction image X text tokens X random noise Xr. total steps stabilization stage t"t total steps in smooth stage tam threshold of adaptive region partition 7,. threshold of adaptive velocity decay cache 5, sorted foreed steps list If list.
1: // Initialization
2: RIKVCache CCv = None, RIKVCache flag f = (False, False); AVDCache CA=None;
3: Accumulative Error e 0; f (0 (²);; tist....nne -1,, t 1);
4: // Stabilization Stage
5: for +- T to T ta do
6: if = T tst then
7:
[[0] = True b first dimension represents storing, second dimension represents relrieving
8:
end if
Ut. Cr. ( Xt,, 10: X X1, (t, 4--1) Vt.
9:
11: end for
12: // Region-Aware Generation Stage
13: & Adaptive Region Partition
14: Xo Xty_eee -1++++1 tr-
15: Einder: Usnder Erosion_& Errssionkk-Dilatecos x') >n)
16: Region-Aware Generation
17: for + 0 to len(t, list) 2 do
18:
prev [ist((] next t Jist
19:
X
y0ce =x, igren Einder]: X
w0er X -Xprre [Uindez]
20:
= XU byrv
v Utrreet1 (tprev (next++)) ) bo one-step estimation for unedited region
21:
f(0) False, f[1] True D iteritive denoising for edited region
22:
for j < prev to next + 1 do
23:
Adaptive Velocity Decay Cache Calculate according to Eq e ô then
24:
25:
26:
E U Cxv = ([Xx, X x1],CKv,
CA U
28:
X E = X E (1j - 1 1)-1) oi
29:
else
30:
CA deeay factor according to Eq
31:
end if
32:
end for
33:
X. ( = gathhr(( Inezs:
XE Enezets
f( True, f[1] False
35:
Un CKV ((XX* 1 X C'V,
36: XInest Xiname1 (Inest - bneat+1) Veneatt1
37: end for
38: Smooth Stage
39: f[o] False, f[]] False
40: for + + tam 110 do
41: Uc., C.V T CKV,
42: X,,_, = Xt, (ta 4+11) Ut.
end for
Output: Target image after editing Xo
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Published as a conference paper at ICLR 2026
B ANALYSIS OF ADAPTIVE VELOCITY DECAY CACHE
Block
Black
DET Black
DIT Block
DNT Blnck
DET Black
DCT Black
DVT Block
X
Black
Block
x
X----1
Full Computation
Cached Step
Cached Step
Fall Computation
Figure 8: Pipeline Based on Residual Cache
In current research on diffusion model caching, many studies focus residual- based caches CChnn et al.] Liu et al. 2025a Zhou et al. 22025] Bu et al. (025), which store the shown in Figure 8 on the sampling formula in Equation and the definition of caching, can derive the following expression:
Based
Xt_ = -X, (t: - 14-1)- vt.
A Vt. X1,
(10)
Vt-- = X + A
It can be solved as:
Vi,-1 [] - (t, ts-1)) V1.
(11)
Similarly, for the timestep 2; we have:
0t [1 (fi-1 1i-2)l 01--1"
(12)
Therefore, if we perform N steps of residual caching, illustrated in Figure 8
we can obtain:
U
II
(fi-++1 t=)m) Us.
m=1
At At-m+11-mm] Vt. -t.
(13)
Determined Solver
This further indicates that the current residual cache and the velocity cache equivalent. Since At;-m+l,i-m is a minimal value approaching zero, the coefficient before Ut, is less than one. Therefore, be seen that the current residual cache is essentially a decayed form of the velocity cache. Furthermore, we observe that the solver determines the decay coefficient in Equation [3] However, as shown in Figure 21. the decay of velocity exhibits a Lumetep-dependder account for this, we introduce an external timestep correction coefficient Vt, Notably, the AVDCache proposed in this paper reduces Equation 13 when the correction coefficient "/, equals
behavior. To
C ONE-STEP PREDICTION FROM DIFFERENT DENOISING TIMESTEPS
In the manuscript, we demonstrate that the generative trajectories differ significantly between edited and unedited regions (see Figures [ [.n Specifically, trajectories in unedited regions are nearly linear, allowing early-stage velocity to provide reliable one-step estimates the multi-step denoised images, including the final output. In contrast, edited regions exhibit noticeably curved trajectories, making the final result substantially harder to predict from early timesteps. To further provide intuitive and comprehensive evidence for this observation, the final edited image using velocities from different denoising timesteps (Figures 9 and [0]. The results clearly show that unedited regions converge relatively early, whereas edited regions do
visualize full-image one-step predictions of
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not. Consistent with our trajectory analysis, the near-linear dynamies in unedited regions enable accurate early prediction, whereas the complex, curved trajectories in edited regions require iterative refinement. This empirical evidence further supports the need to treat the two regions differently during denoising.
Prompt: Add a hat to the cat.
Reference Image
Sep
Slpp
Figure 9: One-step predictions of the final edited image using velocity from different denoising timesteps.
D DISCUSSION ON USING AVDCACHE DURING THE STABILIZATION STAGE
In RegionE, AVDCache is used exclusively in the Region-Aware Generation Stage This design choice is motivated by the following considerations:
The first stage of RegionE is the Stabilization Stage. We do not apply caching in this stage for four reasons. (a) As discussed in Section 4 of the manuscript, the input in this stage has low
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Prompt: Change the background to high mountains
Reference Image
Figure 10: One-step predictions of the final edited image using velocity from different denoising timesteps.
signal-to-noise ratio, and the DiT predictions are inherently unstable, making it unsuitable for acceleration techniques such as caching. (b) Velocity similarity between consecutive steps is very low at the beginning, and since this stage is responsible for shaping the coarse structure of the image, introducing caching would harm generation quality. (c) This stage concludes with the separation of edited and unedited regions; thus, avoiding any loss during this stage is crucial.
(d) Prior studies
have also emphasized avoiding efficient methods at early timesteps, such as SVG (Xi et al. 2025).
ViDiT-Q (Zhao et 2025b), and others.
For completeness, we also applied AVDCache to the Stabilization Stage. and the quantitative results shown in Table We observe that applying AVDCache in the STS yields higher speedups but also leads to noticeable degradation in editing quality. Specifically, PSNR largely drops by 1.91, SSIM decreases by 0.013, LPIPS worsens by 0. 0.16, and G-O declines by 0.066. To achieve better balance between generation quality and efficiency, we therefore choose not to apply AVDCache in the STS.
E DISCUSSION ON HIGH-RESOLUTION IMAGE EDITING
In the main experiments, we focus on Ik-resolution reference images since StepIX-Edit, FLUX.1 Kontext, and Qwen-Image-Fdir are all native Ik-resolution editing models. Here, we provide
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Table Quantitative impact of applying AVDCache in the Stabilization Stage (STS).
Against Vanilla
GPT-4o Score
Efficiency
Model
PSNR
SSIM 1
LPIPS
G-SC
G-PQ 1
G-01 1
Latency (s) I Speedup
StepIX-Edit
7.479
7.466
6.906
27.945
1.000
+ Ours wo STS Cache
30.520
0.939
0.054
7.552
7,405
6.948
10.865
2.572
+ Ours STS Cache
28.610
0.926
0.070
7.455
7.395
6.882
8.583
3.256
preliminary evaluation of RegionE on high high-resolution image editing, as shown in Figure 11 High- resolution images contain more tokens after tokenization, resulting in greater spatial redundancy, which allows RegionE to achieve higher acceleration. The results in Figure [1 demonstrate both high fidelity and increased speedup, further validating this observation. Since there is currently no suitable benchmark for high-resolution image editing, we do not report quantitative results on dataset.
Reference Image
Step1X-Edit
Step1X-Edit RegionE
Resplution: 1874x1248
80. 555
30.32s X2.66]
Prompt: Remove the moon.
KALK prrple
&
Resolution: 1328x1760
80.24s
29.89 L X2.99]
Prompt: Remove the word 'am'.
Figure 11: Visualization results in high-resolution image editing scenarios.
F DISCUSSION ON EDITING BOUNDARIES
Image editing with RegionE does not introduce boundary artifacts. This can be attributed to the following reasons: a) During local generation in the edited region, RegionE uses RIKVCache, which allows the Attention computation to access global key-value information. As result, the edited region maintains awareness of the entire image, and only extremely minor boundary artifacts may occur this stage. b) The final stage in RegionE is the Smooth Stage, which effectively eliminates any subtle boundary artifacts between edited and unedited regions. Two randomly selected visualization examples are shown in Figure 12] where the boundaries between edited and unedited regions are imperceptible.
G DISCUSSION ON MULTI-REGION EDITING
In Figure 5
we show generation results when the edited region is contiguous. In practice, whether the edited region is contiguous or dispersed does not affect RegionE's performance. This is because RegionE leverages RIKVCache, where the local query (Q) accesses global key-value (KV) informa- tion during Attention computation. Consequently, even dispersed edited regions attend to the same
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Reference
Step1X
Hask
Prampt: turrrr the pearnis
Prompt: remrov the freight train
Figure 12: Visualization of image editing boundaries.
Reference Image
Step 1X-Edit
Step1X-Edit RegionE
Edited Mask
Prompt: Alter color the mirror frame to orange.
Prompt: Retouch this photo, making the hair platinum blonde, improving the hairstyle at the ends, and making the fireworks more brilliant and colorful.
Figure 13: Visualization in multi-region image editing scenarios.
global context, avoiding significant computational bias. Figure 13 visualizes several examples with dispersed edited regions, demonstrating that RegionE achieves accelerated editing while maintaining high fidelity.
H DISCUSSION ON GLOBAL EDITING
In practical applications, fully global editing scenarios also occur. In such cases, the spatial redun- dancy in the editing task is low, and RegionE primarily exploits redundancy across timesteps to accelerate the editing process. I shoss an example of this type of task, demonstrating that RegionE can still achieve high-fidelity generation.
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RegionE
Prompt: Acjust the Image style to an paincing with bold br wrssstskkeess..
Prompt: Add vintage film grain and facidd effects.
Figure 14: Visualization results global image editing scenarios.
I DISCUSSION ON BAD CASES
At higher speedup, RegionE may produce some rare bad cases. Upon reviewing the entire dataset, we found that these few instances typically involve minor generation deviations that do not affect instruction adherence. As shown in Figure T5] in the first example, the color of the top corner slightly deviates, and in the second example, the shape of the ceramic shows a small discrepancy. However, these deviations not compromise the overall adherence to the editing instructions.
Reference Image
1K-Eltt
Step 1K IK-Edit RegionE
Edited Mask
Prompt: alter the color of doughnut stiver
Prompt: Change
Figure 15: Visualization of failure cases.
J EXPERIMENTAL SETUP OF THE USER STUDY
In this section, we provide a detailed description of the user study setup. For evaluating RegionE on X-Fdt and Qwen-Image-Fddit we selected a total 11 tasks from GEdit-Bench, randomly sampling 5 image-instruction pairs per task, resulting in 55 samples. For FLUX.1 Kontext, selected tasks from Kontext Bench, randomly sampling imagc-instruction pairs per task, also totaling 55 samples.
After constructing the evaluation sets, we generated edited images using the base models both with and without RegionE, and saved the corresponding outputs. We then collected votes from 10 participants, who were asked to choose the image with higher quality and better instruction adherence. The order of the images randomized, and participants unaware of which method was used
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for each image. If the two images were similar, participants could select neutral option. Finally, the scores for the two methods were aggregated. The layout of the questionnaire is shown in Figure 16
User Study on Image Editing
We are ovaluating innge-odiing models, You will be presented original image, the editing instruction, the outpui from Model A, the ouiput from Model B. and evaluation options (Model
items, each containing five components::
Please compare the two edited images in temss of output quality and adherence to the instruction, and select the model that performs better overall. If their results comparable, select "Neutral." Thank for your participation coopenticn. (Model A Model randoiized item ensure objoctivityy))
Q1.
Instruction
Original Image
Change this bag to red.
Output from Model A
Output from Model
Preference.
Model
Model B
Neutral
Figure 16: Thumbnails from the user study questionnaire.
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K PER-TASK VISUALIZATION RESULTS IN THE BENCHMARK
Due to space limitations, we put the visualization results of some tasks in the manuscript. Here, we provide visual comparison of additional tasks and models. Figure 17 and Figure [8 show the visualization results of 11 tasks on Step Figure 20 and Figure 21 show the visualization results of 11 tasks on lwenn-Iaae--Fii Edit. Figure 19 show the visualization results of tasks on FLUX.1 Kontext.
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Imput
Vanilla
SStepskip
+FORA
+A--DIT
*TeaCache
#RAS
#ToCa
+Owrs
(Reglan))
Edited
Region
Background change
Please change the background wall to green forest with high.
Change the backeround an indoor setting.
Color alter
Change the bed sheet color sky blue.
Alter the color of doughnut silver.
Material alter
Change the bear's 's material to glass.
Change the plane's material to feathers.
Motion change
Make the expression sorrowful.
Change the expression to cryine face
Ps human
Without altering beautifying anything else, just shape my eyebrows to suit
Remove his abs and add more fat to his body.
Figure 17: Examples of edited images by RegionE and baseline on Step1X SopplX-Edit-lppp
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*TeaCache
Iaput
Vanilla
+Stepakip
+FDRA
+2 -DI
+RAS
*ToCa
+Our
(RegionE)
Edited
Region
Style Change
Generate cyberpunk-styye photo.
Convert this image into am anime style
Subject Add
Add potted green plant the right of the sofa.
Add flying baseball coming towards the player.
Subject Remove
Remove the umbrella.
Remove the freight train.
Subject Replace
Replace the two children with fire truck.
Replace the cat on the laptop with a robot.
hatwing
callavee
Text Change
Change the text 'hotwind' 'cool breeze'.
Replace the text 'PROJECT with 'PROMPT'
Tone Transfer
Change the time nighttime.
Change the weather to snow.
Figure 18: Examples of edited images by RegionE and baseline on SteplX-Edite Ipl.
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topu
Vanilla
+Stepskip
+FORA
DA-DiT
#TeaCache
+RAS
+ToCa
+Ours
(RegionE)
Edited
Region
Character Reference
The man now sitting throne.
A
Place this logo on the homescreen of an iphone as an app icon.
Instruction Editing-Global
Its now spring scason with flowers blooming on the sidewalk.
Make this real photo. real-poto.
Instruction Editing-Local
Remove the text.
The man now slanding the skis and riding away.
Style Reference
Make a photo of a cat smelling flower in the style of this image.
Using this style make art of castle made ice cream on top of cone.
Text Editing
Change the text from "Black Forest Labs" to "Make Pixels Beautiful".
Replace all the text with "Black Forest Labs"
Figure 19; Examples of edited images by RegionE and baseline on FLUX.1 Kontext.
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Inpar
Vanilla
SSepskipp
+FORA
+ PA-DIT
+TeaCache
+RAS
+Ours
(RegionE)
Edited
Region
Background change
Change the background starry sky.
Change the backeround to green grassland.
Turn the color of dog pink.
Color alter
Change the color of suit cases silver. Replace the bench's material with marble.
Material alter
Replace the doctor's _estor'sscaaa___o with a Merino wooLsweater,
Motion change
Change the action of cat to sleeping.
Make the action of the man to cheering
Ps human
Make his beard longer.
Make him Look sad
Style Change
Adjust the image style to a bubble-like acsthctic.
Edit this image into a bright and sunny style use an avatar.
Figure 20: Examples of edited images by RegionE and baseline Qwen-Image-Fdiit
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Toput
Vanilla
+Stepskip
+FORA
PA-DiT
+TeaCache
+RAS
+Ours
(RegionE)
Edited
Region
Subject Add
Add chair in the background.
Add poolside lounge chair.
Please remove the woman outside the car while keeping everything else intact, then re-output the image for me.
Subject Remove
Remove the black railing.
Subject Replace
Give me long hair shoulder-length or waist-length.
Replace the laptop in front of the girl with book.
Text Change
Little Yue, can you replace the character with "at" inside?
Change the text 'SCHOOL' 'COLLEGE'
Tone Transfer
Change the nighttime scene in the image to daytime.
Change the weather to foggy.
Figure 21: Examples of edited images by RegionE and baseline on Qwen- Sieen-Image-ddit.
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L PER-TASK QUANTITATIVE RESULTS IN THE BENCHMARK
In this section, we present the performance of RegionE and the baseline methods on each task in the benchmark. Table Table [4 show the performance on the tasks: motion-change, ps-human, color-alter, material-altes subject-add, subject-remove, style-change, tone-transfer, subject-replace, text-change, and background-change Table 15 T9 show the performance on the five tasks: Character Reference, Style Reference, Text Editing, Instruction Editing -ooiig, and Instruction Editing-Local.
Table Comparison of RegionE and other baselines on the motion[-change task of GEdit-Bench, evaluated in terms of quality and efficiency.
Against Vanilla SSIM
GPT-4o Score
Efficiency
Model
PSNR"
LPIPS
G-SC
G-PQ 1
6-0
Latency (8)
Speedup
XtepIX-Edit (Li et al.
20256
4.350
7.950
4.444
27. 27.950
1.000
Stepskip
25.887
0.902
0.093
4.350
8.100
4.562
12.306
2.271
+ -FORA Selvaraju et
2024
20.935
0.818
0.189
2.175
7.575
2.385
14.339
1.949
---DIT
24.549
0.876
7.975
12.730
2.196
TeaCCahee
Liu el
2025a
26.926
0.925
0.068
4,475
8.050
4.524
11.218
2.492
25.888
ToCa Zou A1R05
24.428
0.843
0.165
3.775
6.975
3.578
22.225
1.258
Ours (Kegionl.)
29.633
0.937
0.053
4.625
7.775
4.763
10.739
2,603
Qwen-Image- SStesskip
27.791
0.905
0.066
4.725
8.625
5.029
17.566
1,830
+ +FORA Selvaraiu et 2024
26.744
0.889
0.079
8.325
4.995
17.827
1.803
*A-DIT ef
25.756
0.848
0.095
4.675
8.575
4.921
17.481
1.839
TeaCachee [1.10
2025a
26.776
0.911
0.070
5.025
8,500
5.251
16.389
1.961
RAS
26.585
0.882
8.625
22.300
1.441
+ ToCa Zou cl 20240
0OM
OOM
0OM
OOM
OOM
OOM
OOM
OOM
(Kegion):)
Table 5: Comparison of RegionE and other baselines the ps-human task of GEdit-Bench, evaluated in terms of quality and efficiency.
Against Vanilla SSIM
GPT--o Score
Efficiency
Model
PSNR
LPIPS
G-SC
G-PQ
G-0
Latency (6)
Speedup
StepIX-Edlt Lio
2025b
4.614
8.086
4.649
Stepskip
29.220
0.916
0.069
4.600
8.086
4.728
12.296
2.271
-FORA Selvaraju et
2024
23.596
0.863
0.142
3.414
8,529
3.920
14.323
1,950
A-DIT
hen
26.348
0.884
0.099
4,800
8.086
4.893
12.728
2,194
Cnche L
2025a
31.428
0.942
0.047
5.114
7.929
5.191
11.208
2.492
20269
29.077
0.921
0.072
7.886
4,486
15.237
+ ToCa Zou et 20348
26.716
0.878
0.125
4.786
7.914
4.838
22.073
1,265
(Kegionl.)
Qwen-Image-Faaee Edit (Wa
2025
5.814
8,500
5.972
32.100
1,000
-Stepskip
32,080
0.936
0.040
5.757
8.414
5.904
17.553
1.829
-FORA Selvaraju
30.120
2--DTT ( Chen ci al
28.323
0.887
0.062
5.743
8.500
5.911
17.462
1,838
TecCCahhe he (Liret
2025a
32.347
5.714
8,400
16.360
1,962
RAS Liu cuu 60.69
29.857
0.917
0.061
5.843
8.271
5.884
22,340
1.437
ToCC (2m et al 20248
0OM
OOM
0OM
00M
OOM
OOM
OOM
OOM
Ours (KegionF.)
33,550
0.963
0.029
6.086
8.486
6.227
15.473
2.075
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Table 6: Comparison of RegionE and other baselines on the color-alter task of GEdit-Bence evaluated terms of quality and efficiency.
Against Vanilla
GPT-4o Score
Efficiency
Model
PSNR
SSIM 1 LPIPS
G-SC
G-PQ
G-0
Latency
Speedup
StepIX-Edit (Lu al.
20256
8.750
6.875
7.395
28.019
1,000
Stepskip
27.291
0.919
0.080
8.325
6.975
7.349
12.330
2.273
-FORA Selvaraju
2024
21.871
0.838
0.132
8.800
7.525
7.889
14.356
1.952
33DDT
nen el
24,942
0.901
0.107
8.075
6.600
6.968
12.770
2.194
TeaCCahie
28.084
0.938
7.345
11.242
2.492
RAS [Lu et ml 20Zho
28.800
0.909
0.069
8.700
6.925
7.432
15.274
1.834
ToCa Zou et 207246
25.917
0.864
6.725
7.232
21.996
Ours (KegionE)
32.739
0.956
0.032
8.850
7,250
7.347
11.188
2.504
Owen-Image-Bon Edit (Wu et al
2025
inf
1,000
0.000
9.250
7.525
8.170
32,082
1,000
-Stepskip
29.795
0.896
0.064
9.050
7.450
8.084
17.527
1.830
+ +FORA Selvaraju et
2024
28.035
0.879
0.078
8.875
7.350
7.872
17.795
1,803
-3-DIT
ar
25.892
0.835
0.094
9.025
7.375
8.021
17.479
1.835
TeaCaahie (Lau
2025a
30.757
0.922
0.057
8.775
7.250
7.840
16.566
1,937
RAS Liu eral. 20250
29.132
0.909
0.060
9.150
7,050
7,860
22.356
1.455
TooCa
0OM
0OM
00M
OOM
0OM
OOM
0OM
0OM
Ours (RegionE)
33.144
0.951
0.032
9.225
7.475
8.172
15.527
2,066
Table 7: Comparison of RegionE and other baselines on the material-alter task of Benct, evaluated in terms of quality and efficiency.
Against Vanilla SSIM
GPT-4o Score
Efficiency
Model
PSNR
LPIPS
G-SC
G-PQ 1
G-0
Latency (8)
Speedup
StepIX-Edit (Liu et 20256
8.300
6.575
7.226
27.880
1.000
Stepskip
24.377
0.858
0.117
8.050
5.900
6.676
12.260
2.274
+ -FORA Selvaraju et
2024
20.406
0.763
0.224
7.175
6.875
6.579
14.286
1.952
21.995
2.198
TeaCache [Lii cf
2025n
25.630
0.875
0.099
8.175
6.000
6.796
11.163
2.498
at
20250
24.302
0.844
8.275
5,700
6.633
15.202
1.834
ToCa Zou et 202443
22.503
0.793
0.186
7.850
5,450
6.352
22.306
1,250
Ours (KegionE.)
27.248
0.897
0.080
8.475
6,200
6.997
11.251
2.478
Qwen-Imaged
et 2025
32.156
Stepskip
26,300
0.870
0.093
8.650
6.875
7.557
17.578
1,829
-FORA Selvaraju et
2024
24.699
0.841
0.116
8.525
6.675
7.389
17.839
1.803
-A-DTT
23.827
0.799
0.133
8.425
6.475
7.205
17.472
1,840
-TeCCache [Lm cl
20755n
26.788
0.876
0.092
8.725
6.775
7.564
16.485
1,951
RAS [iu
26.927
0.862
0.098
8,400
6.625
7.192
22.357
1.438
+ ToCa cun 20248
0OM
OOM
0OM
OOM
0OM
OOM
0OM
OOM
Ours (KegionE)
30.024
0.917
2.052
Table 8: Comparison of RegionE and other baselines the subject-add task of evaluated in terms of quality and efficiency.
Against Vanilla
GPT-4o Score
Efficiency
Model
PSNR
SSIM t LPIPS
G-SC
G-PQ1
G-0
Latency (s)
Speedup
StepIX-Edit Liu
2025b
8,283
7.950
7.905
27.912
1.000
Stepskip
25.692
0.892
0.085
8,583
8.083
8.142
12.290
2.271
FORA Selvarnjo
3-DiT
Chen al
24.203
0.868
0.099
8.017
8.050
7.655
12.727
2.193
Cache [Liu
2025a
26.413
0.914
0.073
8.600
8.067
8.177
11.204
2.491
RAS
Liu al. 20253
25.008
0.880
0.101
8.100
7.517
7.532
15.232
1.832
ToCa
Zou et 0. 20246
23.524
0.820
0.159
7.650
6.950
6.939
22.062
1,265
(KegionE))
0.923
7.950
10.528
2.651
Qwen-Image-Bil (Wu et
2025
9.117
8.017
8,381
32.081
1.000
-Stepskip
27.666
0.890
0.092
8.767
7.950
8.146
17.532
1.830
-FORA Sclvamiju
2024
26.871
0.879
0.093
9.017
7,933
8.313
17.810
1,801
-A-DiT Chhnrerat
25.559
0.849
0.108
8.617
7.817
7.967
17.452
1,838
-TeaCache
2025a
7.933
RAS Liu et al.
20250
27.398
0.891
0.081
9.100
7.933
8.267
22.278
1.40
*oCa Zou cl 20246
0OM
0OM
00M
0OM
0OM
OOM
0OM
0OM
-Ouss (KegionE)
30.763
0.938
0.050
8.983
8.233
8.441
15.295
2.097
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Table 9: Comparison of RegionE and other baselines on the subject-remove task of GEdit-Benchs evaluated terms of quality and efficiency.
Against Vanilla
GPT-4o Score
Efficiency
Model
PSNR
SSIM
LPIPS
G-sC
G-PQ
G-0
Latency
Speedup
StepIX-Edit Edit (Lu al.
20256
7.351
7.947
6.973
27.954
1,000
Stepskip
33.649
0.954
0.038
7.579
7.684
6.969
12.300
2.273
-ORA Selvaraju
2024
30.330
0.943
0.062
5.474
7.895
5.285
14.330
1.951
33DDT
nen al
31.847
0.948
0.047
7,930
7,684
7.319
12.724
2.197
TeaCCahie
2025a
36.735
0.973
11.213
2.493
RAS [Lu et ml 20Zho
32,966
0.936
0.052
7.211
7,860
6.861
15.236
1,835
ToCa Zou et 207446
29.806
0.894
7.175
7,088
6.481
22.378
Ours (KegionE)
35.772
0.963
0.028
7.719
7.737
7.182
10.453
2.674
Owen-Image-Bon Edit (Wu et al
2025
8.965
8.246
8.477
32,170
1,000
-Stepskip
32.187
0.913
0.048
9.035
8.298
17.572
1.811
+ +FORA Selvaraju et
2024
29.288
0.865
0.072
9.175
7.930
8,475
17.820
1,805
-3-DIT
ar
27.056
0.826
0.090
8.895
7.947
8.348
17.486
1.840
TeaCaahie (Lau
2025a
31.687
0.899
0.051
8.895
8.228
8.441
16.434
1,958
RAS Liu eral. 20250
28.440
0.876
0.080
8.842
8.035
8.371
22.331
1.441
TooCa
0OM
0OM
00M
OOM
OOM
OOM
0OM
0OM
Ours (RegionE)
32.122
0.925
0.052
9.333
8.351
8.787
15.349
2.096
Table 10: Comparison RegionE and other baselines on the style-change task of Benct, evaluated in terms of quality and efficiency.
Against Vanilla SSIM
GPT-4o Score
Efficiency
Model
PSNR
LPIPS
G-SC
G-PQ
G-0
Latency (8)
Speedup
StepIX-Edit (Liu et 20256
8.150
6.917
7.359
27. 27.898
1.000
Stepskip
21.064
0.828
0.185
8.183
6.583
7.199
12.277
2.272
+ -FORA Selvaraju et
2024
15.851
0.680
0.372
7.183
7.017
6.883
14.300
1.951
0.233
2.200
TeaCache [Lii cf
2025n
21.695
0.857
0.156
8.000
6.733
7.213
11.187
2.494
at
20250
21.355
0.814
0.193
8.217
6.400
7.108
15.217
1.833
ToCa Zou et 202443
19.819
0.760
0.250
8.283
6.000
6.927
22.327
1,250
Ours (KegionE.)
25.449
0.900
0.102
8.267
6.617
7.251
11.797
2.365
Qwen-Images
et 2025
7,133
32.115
Stepskip
23.954
0.805
0.139
8.067
7,083
7.355
17.560
L,829
-FORA Selvaraju et
2024
21.784
0.745
0.185
8.017
7.100
7.385
17.807
1.804
-A-DTT
20.552
0.662
0.219
8.117
7.033
7.395
17.455
1,840
-TeCCache [Lm cl
20755n
23.137
0.816
0.152
8,300
7.133
7.544
16.414
1.957
RAS [iu
24.073
0.772
0.169
7.983
7.000
7.348
22.275
1.442
+ ToCa cen 20248
0OM
OOM
OOM
OOM
OOM
OOM
0OM
OOM
Ours (KegionE)
27.980
0.897
8.233
7.250
16.822
Table 11: Comparison of RegionE and other baselines the snnettrassfer task of GEdit-Bench evaluated terms of quality and efficiency.
Against Vanilla
GPT-4o Score
Efficiency
Model
PSNR
SSIM t LPIPS
G-PQ1
G-O
Latency (s)
Speedup
StepIX-Edit Liu
2025b
6.950
7.325
6.679
27.874
1.000
Stepskip
24.744
0.899
0.122
7.200
7.325
6.917
12.260
2.274
FORA Selvarnjo
14.293
3-DiT
Chen al
22.104
0.862
0.164
7.000
7.250
6.852
12.678
2.199
Tea Cache [Liu
2025a
27.915
0.933
0.072
6.825
7.400
6.600
11.171
2.495
RAS
Liu al. 20223
26.455
0.895
0.111
7.200
7.000
6.688
15.187
1,835
ToCa Zou et 0. 20246
23.954
0.840
0.159
6.500
6,550
5.991
22.408
1,244
(KegionE))
30.860
0.945
0.064
7.275
6.641
2.425
Qwen-Image-Bil (Wu et
2025
8.475
8.025
8.084
32.160
1.000
-Stepskip
29.715
0.862
0.092
8.150
8.000
7.820
17.562
1.831
-FORA Sclvamiju
2024
27.514
0.839
0.111
8.025
7.875
7.771
17.841
1,803
-A-DiT Chhnrerat
25.471
0.792
0.139
7.950
7.725
7.592
17.462
1,842
-TeaCache
RAS Liu et al.
20250
29.142
0,880
0.089
8,500
8.075
8.142
22,372
1.377
*oCa Zou cl 20246
0OM
0OM
00M
OOM
0OM
OOM
0OM
0OM
-Ouss (KegionE)
34.051
0.948
0.034
8.450
8.275
8.199
15.851
2,029
32

---

Published as a conference paper at ICLR 2026
Table 12: Comparison of RegionE and other baselines on the subject-replace task of GEdit-Bon BBncch evaluated terms of quality and efficiency.
Against Vanilla SSIM
GPT-4o Score
Efficiency
Model
PSNR
LPIPS
G-sC
G-PQ
G-O
Latency
Speedup
StepIX-Edir (Lu al.
20256
8.650
7.233
7.718
27.983
1,000
Stepskip
25.233
0.875
0.111
8,683
6.867
7.548
12.325
2.270
-ORRA Selvaraju
2024
20.594
0.831
0.189
5.833
6.817
5.306
14.359
1.949
33DDT
nen ar.
22.927
0.835
0.141
8.500
6.733
7.345
12.766
2.192
TeaCCahhe
25.856
0.915
7.536
11.245
2.488
RAS [Lu et ml 20Zho
25.072
0.888
0.116
8.250
6.433
6.996
15.268
1,833
ToCa Zou et 207446
23.407
0.840
0.168
8.267
6:211
22,080
1.267
Ours (KegionE)
28.654
0.935
0.064
8.517
7.167
7.585
10.647
2.628
Owen-Image-Bon Edit (Wu et al
2025
inf
1,000
0.000
8.883
7.683
8.136
32,
1,000
-Stepskip
26.344
0.897
0.076
8.783
7.733
8.128
17.575
1.830
+ +FORA Selvaraju et
2024
24.578
0.864
0.104
8.600
7.550
7.930
17.836
1,803
-3-DIT
ar
23.745
0.829
0.120
8,450
7.267
7.687
17.496
1.838
TeaCaahie (Lau
2025a
25.993
0.891
0.084
8.700
7.733
8.120
16.391
1,962
RAS Liu eral. 20250
25.579
0.881
0.095
8.867
7,400
7.996
22.341
1.440
TooCa
0OM
OOM
0OM
OOM
OOM
OOM
OOM
0OM
Ours (RegionE)
29.388
0.938
0.047
8.967
7.767
8.242
15.446
2.082
Table 13: Comparison of RegionE and other baselines on the text-change task of Bendt, evaluated in terms of quality and efficiency.
Against Vanilla SSIM
GPT-4o Score
Efficiency
Model
PSNR
LPIPS
G-SC
G-PQ
G-0
Latency (8)
Speedup
StepIX-Edit (Liu et al. 20256
8.293
8.091
7.900
28.027
1.000
Steeskip
30.069
0.955
0.032
8.222
8.192
7.951
12.331
2.273
+ -FORA Selvaraju et
2024
26.368
0.941
0.049
7.000
7,899
6.926
14.375
1,950
28.615
8.222
12.768
2.195
TeaCache [Lii cf
2025n
31.420
0.967
0.023
8.222
8.192
7.925
11.254
2.491
at
20250
28.434
0.939
0.042
7.929
7,970
7.649
15.270
1.835
ToCa Zou et 202443
26.305
0.902
0.078
7.949
7.707
7.609
21.723
1.290
Ours (KegionE.)
32.404
0. 0.968
0.020
8.212
8.242
8.002
10.237
2.738
Owen-Imaged
et 2025
8.394
32.071
Stepskip
29.577
0.929
0.047
8.828
8.222
8.202
17.519
1.83
-FORA Selvaraju et
2024
27.408
0.909
0.061
8.818
8.303
8.192
17.790
1.803
-A-DTT
25.837
0.881
0.072
8.879
8,333
8.259
17.432
1,840
-TeCCache [Lm cl
20755n
29.126
0.932
0.047
8.778
8.222
8.184
16.539
1,939
RAS [iu
26.732
0.912
0.061
8.010
8.187
22.302
1.438
+ ToCa co
20248
0OM
OOM
0OM
OOM
OOM
OOM
0OM
OOM
Ours (KegionE)
31.357
0.950
8.313
8.260
14.813
2.165
Table 14: Comparison of RegionE and other basclines the background task of GEdit- Bench, evaluated terms of quality and efficiency.
Against Vanilla SSIM t LPIPS
GPT--o Score
Efficiency
Model
PSNR
G-PQ1 f G-0
Latency (s)
Speedup
StepIX-Edit Liu
2025b
8,575
7.175
7.722
27.886
1.000
Stepskip
21.011
0.812
0.218
8.625
6.975
7.635
12.267
2.273
FORA Selvarnjo
15.897
3-DiT
Chen al
18.641
0.781
0.271
8.375
6.625
7.338
12.687
2.198
Tea Cache [Liu
2025a
23.549
0.866
0.151
8.375
6.725
7.379
11.163
2.498
RAS
Liu al. 20223
25.468
0.840
0.169
8.425
6.725
7.372
15.206
1.834
ToCa Zou et 0. 20246
22.925
0.768
0.255
8.200
6.175
6.995
22.631
1.232
(KegionE))
29.076
0.917
7.675
2.463
Qwen-Image-Bil (Wu et
2025
9.125
8.200
8.603
32.221
1.000
-Stepskip
25.088
0.854
0.133
9.175
7.975
8.511
17.603
1,830
-FORA Sclvamiju
2024
22.473
0.802
0.184
8.775
7.875
8.252
17.815
1,809
-A-DiT Chhnrerat
21.263
0.750
0.210
8.825
7.850
8.281
17.552
1,836
-TeaCache
2025a
24.016
1.954
RAS Liu et al.
20250
26.550
0.858
0.128
9.100
7.450
8.153
22,411
1438
*oCa Zou cl 20246
0OM
00M
00M
0OM
0OM
OOM
0OM
0OM
-Ours (KegionE)
30.462
0.939
0.053
9.175
8.050
8.547
16.694
1,930
33

---

Published as a conference paper at ICLR 2026
Table 15: Comparison of RegionE and other baselines on the Character Reference task of Kon- textBench, evaluated in terms of quality and efficiency.
Against Vanilla
GPT-4o Score
Efficiency
Model
PSNR
SSIM 1 LPIPS
G-SC
G-PQ
G-O
Latency (8)
Speedup
FLUX.1 Kontext (Labs et al.
2025
7.549
6.642
6.664
14.677
1.000
SStesskip
18.793
0.730
0.238
7.741
6.803
6.917
8,502
1.726
+ +FORA (Selvaraju et al.
2024
17.898
0.697
0.275
7.617
6.788
6.813
7.494
1.958
-3-DIT ar
15.560
0.604
0.387
7.668
6.451
6.704
6.737
2.178
TeaCCalin Liu 2025a
20.313
0.770
0.197
7.865
6.565
6.842
6.271
2.341
RAS Liu atal. 2025c
21.320
0.752
0.214
7.632
6.352
6.657
8.211
1.788
TooCa
19.596
0.679
0.298
7.570
6.047
6.454
11.279
1,301
Ours (KegionE)
26.980
0.880
0.086
7.637
6.611
6.715
6,406
2.291
Table 16: Comparison of RegionE and other baselines on the Instruction Editing-Global task of KontextBench, evaluated terms of quality and efficiency.
Against Vanilla
GPT-to Score
Efficiency
Model
PSNR
SSIM t LPIPS
G-SC
G-PQ 1
G-0
Latency
Speedup
FLXXX.I Kontext (Labs
2025
7.023
6.798
6.380
14.688
1.000
Stepskip
23.957
0.797
0.132
7.000
6.870
6.435
8,516
1.725
-FORA (Selvaraiu 2024
22.611
0.760
0.159
6.840
7,506
1.957
*A-DiT' (Chen el at
18.687
0.639
0.252
7.073
6.882
6.574
6,754
2.175
Cache
2,350
RRAS [iu et 20250
24.845
0.778
0.157
7.302
6.866
6.668
8,221
1,787
.ooa ca 20248
23.030
0.711
0.218
7.179
6.588
6.483
11.412
1.287
Ours (KegionE)
30.403
0.886
0.071
7.126
6.943
6.572
6.379
2.303
Table 17: Comparison of RegionE and other baselines on the Instruction Editing-Local task of KontextBench, evaluated in terms of quality and efficiency.
Against Vanilla
GPT-4o Score
Efficiency
Model
PSNR
SSIM t LPIPS
G-SC
G-PQ 1
G-0
Latency (s)
Speedup
FLUX.1 Kontext (Labs et
2025
6.779
6.909
5.817
14.677
1.000
Stepskip
31.147
0.913
0.058
6.839
6.887
5.872
8.510
1,725
FORA (Selvariju et al.
2024
29.279
0.895
0.072
6,800
6.901
5.873
7.491
1.959
2-DIT
23.390
0.824
0.130
6.822
6.829
5.846
6,751
2,174
TeaCache LLu
2025a
33.341
0.938
0.040
6.942
6,800
5.896
6.113
2.401
ToCa Zou or 20245
26.996
0.855
0.112
6.851
6.635
5.790
11.231
1.307
Ours (RegionE)
36.334
0.959
0.025
6.889
6.880
5.917
5.799
2.531
Table 18: Comparison of RegionE and other baselines on the Style Reference task of KontextBench. evaluated in terms of quality and efficiency.
Against Vanilla
GPT-4o Score
Emfciency
Model
PSNR1
SSIM 1 LPIPS.
G-SC
G-PQ1
G-0
Latency
Speedup
FLUX.1 Kontext (Labs et al. 2025
6.810
6.556
6.331
14.684
1.000
Stepskip
18.606
0.678
0.290
6.333
6.381
5.947
8,501
1.727
FORA (elvarrju
2024
17.508
0.631
0.329
6.222
6.413
5.667
7.476
1.964
A-DDT (Chen al.
14.639
0.525
0.450
6.397
6.873
6.108
6.731
2.182
TeaCache (Liu
2025a
19.781
0.712
0.264
6.444
6.286
5.832
6,261
2.345
*RAS [Li dra. 20250
19.481
0.638
0.343
6.603
6.381
6.031
8,202
1.790
18.245
0.553
0.439
11440
1.279
+ Ours (KegionE)
24.433
0.811
0.165
6.921
6.571
6.277
6.411
2.291
Table 19: Comparison of RegionE and other baselines the Text Editing task of KontextBench. evaluated terms of quality and efficiency.
Against Vanilla
GPT-4o Score
Efficiency
Model
PSNR
SSIM 1 LPIPS
G-SC
G-PQ 1
G-0
Latency (s)
Speedup
FLUX.11 Kontext (Labs
2025
7.826
7.913
7.295
14.697
1.000
Stepskip
30.950
0.943
0.033
7.717
7.750
7.142
8.535
1.722
FORA Selvariju
1,954
3--iT Chen ot al
23.931
0.839
0.085
7.315
7.554
6.823
6.779
2,168
TeeaCachi [Liu or 2025a
31.283
0.955
0.026
7.620
7.696
7.076
6,290
2.336
RAS etall
27.504
0.913
0.048
7.598
7,402
6.971
8.238
1.784
+ ToCa (ou at 20148
25.342
0.857
0.093
7.326
7.500
6.791
11.206
1.312
(KegionE))
0.962
5.767
2.548
34

---

Published as a conference paper at ICLR 2026
M STATEMENT ON LLM USAGE
We declare that large language models (LLMs) were used to assist in polishing the writing of this paper. All ideas, methods, and experimental results are original contributions of the authors. The authors take full responsibility for the content of this work.
35


Reviews:
[{"review_id": "t0WQ1gEiVo", "reviewer": "Reviewer_nyRe", "summary": "This paper introduces RegionE, a training-free framework that accelerates instruction-based image editing by treating edited and unedited regions differently. It detects unedited areas with near-linear trajectories that can be estimated in one step, while edited regions are refined iteratively. Using adaptive region partitioning, cached attention features, and velocity decay reuse, RegionE achieves 2–2.6× faster inference on Step1X-Edit, FLUX.1 Kontext, and Qwen-Image-Edit with minimal quality loss and no retraining required.", "questions": "1. How sensitive is RegionE to the partition threshold $\\eta$ and decay threshold $\\delta$? How did the authors determine these parameters in practice? Could they be learned adaptively from attention maps or reconstruction loss?\n2. How does ARP handle ambiguous or overlapping boundaries between edited and unedited regions, especially when the change is gradual or when multiple small edits occur in different parts of the image?\n3. Have the authors observed drift or accumulated errors from AVDCache over long denoising sequences, and is the forced full-image update mechanism sufficient to prevent visible artifacts?\n4. How does RegionE perform on global or style editing tasks that modify most of the image content? In such cases, does the region partition still provide computational benefits?\n5. What is the memory and runtime overhead of the caching mechanisms, and how does RegionE scale with image resolution or batch size compared to standard inference?\n\n\n\n(Minor - short discussion)\n\n6. Could the approach extend to video or 3D editing, where temporal/spatial coupling is stronger?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Addresses an important and practical issue: the high inference cost of instruction-based image editing.\n- Proposes a training-free framework (RegionE) combining spatial and temporal acceleration strategies.\n- Demonstrates consistent 2–2.6× speedups across several strong IIE baselines with minimal perceptual degradation.\n- Includes solid ablation studies confirming the contributions of key components like RIKVCache and AVDCache.\n- The framework is model-agnostic and can be applied to different diffusion-based editors without retraining.", "weaknesses": "- The approach is primarily engineering-oriented, integrating existing acceleration techniques (e.g., caching and region partitioning) into a coherent framework focused on practical efficiency.\n- Relies heavily on region partition accuracy; there is no ablation or sensitivity analysis of the Adaptive Region Partition (ARP)\n- The parameter choices for thresholds $\\eta$ and $\\delta$ are fixed heuristically, with no explanation or adaptive tuning strategy.\n- Limited discussion of computational trade-offs, such as memory footprint or the cost of region masking.\n- No discussion of failure cases, such as edits involving multiple or overlapping regions, or complex global changes that may break the region partition.\n- In addition to the previous point, the evaluation focuses on overall metrics, lacking analysis of cases where RegionE may underperform (e.g., subtle texture or lighting edits).\n\n(Minor)\n\n- The related work section misses recent region-aware diffusion editing methods such as LIME [1] and Focus on Your Instruction [2], which could better situate RegionE within related spatial editing research.\n\n[1] Simsar, E., Tonioni, A., Xian, Y., Hofmann, T., & Tombari, F. (2025, February). Lime: localized image editing via attention regularization in diffusion models. In WACV'25.\n\n[2] Guo, Q., & Lin, T. (2024). Focus on your instruction: Fine-grained and multi-instruction image editing by attention modulation. In CVPR'24.", "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": "kRF2UqIs2t", "reviewer": "Reviewer_Npav", "summary": "This paper proposes an acceleration framework for instruction-based image editing tasks. The authors identify two types of redundancy in existing methods: spatial redundancy and temporal redundancy. Spatially, certain image regions remain unchanged before and after editing, and the denoising process for these regions can be significantly accelerated. To address this, the authors design a region partition method to distinguish between fixed regions and editable regions, applying one-step generation to the fixed regions. Temporally, the authors observe strong similarity in velocity between adjacent denoising timesteps, enabling acceleration through a feature cache-based method. Due to the task-specific improvements in the framework's design, it achieves both efficiency and effectiveness enhancements on image editing benchmarks compared with existing acceleration methods.", "questions": "- Please clarify the strengths of the proposed region partition method compared to previous partition methods in inversion-based editing methods. \n- The adaptive velocity decay cache is mainly based on velocity similarities. The results of cache reuse are not explored. For example, at the very early stage of denoising process, the L1 sim / cos sim are low, but the errors introduced by cache reuse may be re-corrected in later denoising process, thus applying cache strategy under low similarities can also be considered.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- It effectively tackles both spatial and temporal redundancies. By partitioning images into edited and unedited regions and leveraging temporal similarities between timesteps, it achieves comprehensive speed improvements. \n- The proposed framework is specifically tailored for DiT-based instruction image editing. The authors provide some deep insights on DiT cache designs, which may encourage future works.", "weaknesses": "- The region partition approach, while highly effective, may not be fundamentally novel. Previous inversion-based image editing methods have been using attention scores to extract masked regions for editing[1][2]. \n- The temporal acceleration method shares conceptual similarities with existing work in general-purpose diffusion acceleration.\n\n[1]Uniform Attention Maps: Boosting Image Fidelity in Reconstruction and Editing. WACV 2025.\n[2]DiffEdit: Diffusion-based semantic image editing with mask guidance. ICLR 2023.", "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": "9Nt1ISp02P", "reviewer": "Reviewer_uJ7h", "summary": "The paper “RegionE: Adaptive Region-Aware Generation for Efficient Image Editing” proposes a training-free acceleration framework for instruction-based image editing (IIE). Unlike previous diffusion-based editing models that apply uniform denoising to all image regions, RegionE distinguishes between edited and unedited areas to eliminate spatial and temporal redundancy.\nThe system consists of three key modules:\n\n* Adaptive Region Partition (ARP) — identifies edited and unedited regions based on cosine similarity between estimated and instruction images.\n\n* Region-Instruction KV Cache (RIKVCache) — reuses cached key–value pairs from previous full computations to maintain global context while locally generating edited regions.\n\n* Adaptive Velocity Decay Cache (AVDCache) — models velocity decay across timesteps to accelerate iterative denoising.", "questions": "* How does the acceleration performance scale with the size or proportion of the edited region? Would larger editing areas significantly reduce the overall efficiency gains?\n\n* To what extent does the proposed method introduce conceptual novelty beyond existing acceleration ideas?\n\n* Have the authors considered conducting a user study or subjective perceptual evaluation to validate the visual quality beyond GPT-4o-based automatic scoring?\n\n* What are the potential failure cases of the proposed adaptive region partition? For example, could boundary misclassification between edited and unedited regions cause visible artifacts?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* The combination of ARP, RIKVCache, and AVDCache addresses both spatial and temporal redundancy coherently and without retraining.\n\n* Evaluations on three major IIE models with consistent metrics and ablations (on cache design and stage structure) convincingly support the claims.\n\n* The paper is technically detailed and easy to follow, with illustrative figures and pseudocode.", "weaknesses": "* Pipeline complexity: The full system involves multiple interdependent stages (STS, RAGS, SMS) and caches, making implementation and integration non-trivial.\n\n* Limited discussion of efficiency factors: It remains unclear whether acceleration benefits scale with the size of the edited region—larger edits may reduce efficiency gains.\n\n* Lack of novelty: Although the combination of adaptive partitioning and caching is well-engineered, each component conceptually extends known acceleration ideas (e.g., residual cache, spatial redundancy) rather than introducing a fundamentally new principle.\n\n* Missing user/perceptual study: Although GPT-4o scoring is used, no human user study or subjective evaluation supports perceptual fidelity claims.\n\n* Potential failure cases not analyzed: No discussion of situations where region partitioning might misclassify boundaries or produce artifacts.\n\n* Incomplete citation coverage: Some prior works on region-aware or local editing (e.g., Blended Diffusion, Object-aware Inversion and Reassembly) are not cited or compared.", "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)