RegionE: Adaptive Region-Aware Generation for Efficient Image Editing

2025-10-29

AI Summary

Current instruction-based image editing (IIE) models apply a uniform generative process to both edited and unedited regions, leading to computational redundancy and suboptimal efficiency. To address this, we propose a training-free, adaptive region-aware generation framework—the first to introduce region-differentiated generation. Our method employs diffusion trajectory analysis for adaptive spatial partitioning, designs region-specific instruction key-value (KV) caches and velocity decay caches to accelerate localized denoising iterations, and explicitly models velocity similarity to preserve global coherence. The framework is plug-and-play compatible with mainstream IIE models—including Step1X-Edit, FLUX.1 Kontext, and Qwen-Image-Edit—achieving speedups of 2.57×, 2.41×, and 2.06×, respectively. GPT-4o evaluation confirms that semantic fidelity and visual quality are well preserved. This work establishes a new paradigm for efficient, region-adaptive diffusion-based image editing without architectural or training modifications.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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 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.

Problem

• Accelerates instruction-based image editing by distinguishing edited and unedited regions
• Reduces computational redundancy through adaptive region-aware generation framework
• Maintains image quality while speeding up local iterative denoising processes

Innovation

• Adaptive region partition for edited and unedited areas
• Region-aware generation with one-step prediction for unchanged regions
• Adaptive velocity decay cache to accelerate local denoising