FineEdit: Fine-Grained Image Edit with Bounding Box Guidance

Diffusion-based image editing models have achieved signifi-cant progress in real world applications. However, conventional modelstypically rely on natural language prompts, which often lack the pre-cision required to localize target objects. Consequently, these modelsstruggle to maintain background consistency due to their global im-age regeneration paradigm. Recognizing that visual cues provide an in-tuitive means for users to highlight specific areas of interest, we uti-lize bounding boxes as guidance to explicitly define the editing tar-get. This approach ensures that the diffusion model can accurately lo-calize the target while preserving background consistency. To achievethis, we propose FineEdit, a multi-level bounding box injection methodthat enables the model to utilize spatial conditions more effectively.To support this high precision guidance, we present FineEdit-1.2M,a large scale, fine-grained dataset comprising 1.2 million image edit-ing pairs with precise bounding box annotations. Furthermore, we con-struct a comprehensive benchmark, termed FineEdit-Bench, which in-cludes 1,000 images across 10 subjects to effectively evaluate region basedediting capabilities. Evaluations on FineEdit-Bench demonstrate thatour model significantly outperforms state-of-the-art open-source models(e.g., Qwen-Image-Edit and LongCat-Image-Edit) in instruction compli-ance and background preservation. Further assessments on open bench-marks (GEdit and ImgEdit Bench) confirm its superior generalizationand robustness. The dataset and code will be publicly available throughthe project page at https://laulampaul.github.io/FineEdit.