SpotEdit: Selective Region Editing in Diffusion Transformers

Examples of edited images by SpotEdit. The blue area reveals the regenerated region

SpotEdit: Selective Region Editing in Diffusion Transformers

Zhibin Qin¹, Zhenxiong Tan¹, Zeqing Wang¹, Songhua Liu², Xinchao Wang^1
1 National University of Singapore
² Shanghai Jiao Tong University

📚 Overview

SpotEdit is a training-free, region-aware framework for instruction-based image editing with Diffusion Transformers (DiTs).
While most image editing tasks only modify small local regions, existing diffusion-based editors regenerate the entire image at every denoising step, leading to redundant computation and potential degradation in preserved areas. SpotEdit follows a simple principle: edit only what needs to be edited.

SpotEdit dynamically identifies non-edited regions during the diffusion process and skips unnecessary computation for these regions, while maintaining contextual coherence for edited regions through adaptive feature fusion.

The overview of SpotEdit pipeline

🛠️ Setup

conda create -n spotedit python=3.10
conda activate spotedit
pip install -r requirements.txt

Usage example

1. For Flux-Kontext basemodel: example\flux.ipynb
2. For Qwen-Image-Edit basemodel: example\qwen.ipynb

Guidelines for Spotedit

1. Experiments and test examples are typically conducted at a resolution of 1024×1024. We recommend setting both input and output image sizes to 1024×1024 when running SpotEdit.

limitation

1. SpotEdit is not intended for global edits that affect most or all regions of the image, such as full-scene style transfer or global color changes. In these cases, SpotEdit cannot reliably identify non-edited regions, and thus falls back to computation that is effectively equivalent to the original full-image diffusion process.

Generated samples

more results of SpotEdit

Ciatation

@artical{qin2025spotedit,
  title= {SpotEdit: Selective Region Editing in Diffusion Transformers},
  author= {Qin, Zhibin and Tan, Zhenxiong and Wang, Zeqing and Liu, Songhua and Wang, Xinchao},
  journal={arXiv preprint arXiv:2512.22323},
  year={2025}
}