RegionE: Adaptive Region-Aware Generation for Efficient Image Editing
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 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. Evaluations by GPT-4o
confirmed that semantic and perceptual fidelity were well preserved.
6902c36272739622ee92a6a0
Pengtao Chen
claimed_verified
2025-10-30T14:32:08.272Z
64c9bac33cfe45b07179568d
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Pengtao Chen
PengtaoChen
user
PengtaoChen
6902c36272739622ee92a6a1
Xianfang Zeng
6902c36272739622ee92a6a2
Maosen Zhao
6902c36272739622ee92a6a3
Mingzhu Shen
6902c36272739622ee92a6a4
Peng Ye
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Bangyin Xiang
6902c36272739622ee92a6a6
Zhibo Wang
6902c36272739622ee92a6a7
Wei Cheng
claimed_verified
2025-10-30T14:32:00.572Z
64b914c8ace99c0723ad83a9
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Wei Cheng
wchengad
user
wchengad
6902c36272739622ee92a6a8
Gang Yu
claimed_verified
2025-10-30T14:31:56.518Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63417332c5565a4b8d43a0d8/MmnYG7Wu2Z_lqCBvTfJmy.png
Gang Yu
skicy
user
skicy
6902c36272739622ee92a6a9
Tao Chen
RegionE: Adaptive Region-Aware Generation for Efficient Image Editing
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 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. Evaluations by GPT-4o
confirmed that semantic and perceptual fidelity were well preserved.
RegionE is a region-aware framework that accelerates instruction-based image editing by differentiating between edited and unedited regions, optimizing the denoising process for each.
instruction-based image editing adaptive region partition denoised predictions region-aware generation Region-Instruction KV Cache adaptive velocity decay cache