arXiv:2510.25590v1 Announce Type: new 
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.

تقدم دراسة جديدة مفهوم RegionE، وهو نهج مبتكر لتحرير الصور يركز على التوليد الواعي للمناطق. تعترف هذه الطريقة بالتعقيدات المختلفة لمناطق الصورة، مما يسمح بتحرير أكثر كفاءة من خلال استهداف مناطق معينة مع ترك مناطق أخرى دون تغيير. هذه الخطوة مهمة لأنها تعزز فعالية تحرير الصور المعتمد على التعليمات، مما يجعلها أسرع وأكثر كفاءة في استخدام الموارد، مما قد يحدث ثورة في كيفية معالجة الصور الرقمية في مجالات متعددة.

Un nuevo estudio presenta RegionE, un enfoque innovador para la edición de imágenes que se centra en la generación adaptativa consciente de las regiones. Este método reconoce las diferentes complejidades de las distintas áreas de una imagen, lo que permite una edición más eficiente al dirigirse a regiones específicas mientras deja otras intactas. Este avance es significativo ya que mejora la efectividad de la edición de imágenes basada en instrucciones, haciéndola más rápida y eficiente en recursos, lo que podría revolucionar la forma en que se manipulan las imágenes digitales en varios campos.

Une nouvelle étude présente RegionE, une approche innovante de l'édition d'images qui se concentre sur la génération adaptative consciente des régions. Cette méthode reconnaît les complexités variées des différentes zones d'une image, permettant un montage plus efficace en ciblant des régions spécifiques tout en laissant d'autres intactes. Cette avancée est significative car elle améliore l'efficacité de l'édition d'images basée sur des instructions, la rendant plus rapide et plus économe en ressources, ce qui pourrait révolutionner la manière dont les images numériques sont manipulées dans divers domaines.

A new study introduces RegionE, an innovative approach to image editing that focuses on adaptive region-aware generation. This method recognizes the varying complexities of different image areas, allowing for more efficient editing by targeting specific regions while leaving others untouched. This advancement is significant as it enhances the effectiveness of instruction-based image editing, making it faster and more resource-efficient, which could revolutionize how digital images are manipulated in various fields.

RegionE: Adaptive Region-Aware Generation for Efficient Image Editing

arXiv:2204.13620v5 Announce Type: replace-cross 
Abstract: Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We begin by surveying the development of GANs and popular GAN variants for image-related applications, and then analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Secondly, we compare the performances of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR.

استعراض حديث حول الشبكات التنافسية التوليدية (GAN) لتحسين الصورة الفردية (SISR) يسلط الضوء على التقدم في معالجة الصور، مع التركيز على تطبيقات GAN المختلفة وأدائها المقارن على مجموعات البيانات العامة. تؤكد الدراسة على نقص الأدبيات الشاملة التي تلخص هذه التطورات، والتي تعتبر حاسمة لتحسين الصور ذات الدقة المنخفضة.

Una reciente encuesta sobre Redes Generativas Antagónicas (GAN) para la Super-Resolución de Imagen Única (SISR) destaca los avances en el procesamiento de imágenes, centrándose en diversas implementaciones de GAN y su rendimiento comparativo en conjuntos de datos públicos. El estudio enfatiza la falta de literatura completa que resuma estos desarrollos, que son cruciales para mejorar imágenes de baja resolución.

Une récente enquête sur les réseaux antagonistes génératifs (GAN) pour la super-résolution d'image unique (SISR) met en lumière les avancées dans le traitement d'image, en se concentrant sur diverses implémentations de GAN et leur performance comparative sur des ensembles de données publiques. L'étude souligne le manque de littérature complète résumant ces développements, qui sont cruciaux pour améliorer les images de basse résolution.

A recent survey on Generative Adversarial Networks (GANs) for Single Image Super-Resolution (SISR) highlights the advancements in image processing, focusing on various GAN implementations and their comparative performance on public datasets. The study emphasizes the lack of comprehensive literature summarizing these developments, which are crucial for enhancing low-resolution images.

Generative Adversarial Networks for Image Super-Resolution: A Survey

One More Thing in AI – Your Shortcut to AI Mastery

RegionE: Adaptive Region-Aware Generation for Efficient Image Editing

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