ImgEdit is a large-scale, high-quality image-editing dataset and benchmark introduced to improve open-source image-editing model performance. The dataset contains approximately 1.2 million curated edit pairs covering novel and complex single-turn edits as well as challenging multi-turn editing tasks. Data were produced via a multi-stage pipeline that leverages a vision-language model, object detection, segmentation, task-specific in-painting procedures, and strict post-processing to ensure high quality. The release also includes ImgEdit-Bench, a benchmark suite that evaluates instruction adherence, editing quality, and detail preservation (with basic, challenging single-turn, and multi-turn test suites), and the authors train an editing model (ImgEdit-E1) demonstrating gains over prior open-source editors. Code and data pointers are provided in the project repository.
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