GEdit-Bench is a real-world image-editing evaluation benchmark released by the StepFun / Step1X-Edit team to assess image-editing models on authentic user instructions. The Hugging Face dataset contains ~1.21k examples (single split: train) of image + editing instruction pairs and metadata. The schema includes fields such as task_type (11 edit categories), key, instruction, instruction_language (en/zh), input_image / input_image_raw, and Intersection_exist. The benchmark was designed for automatic/LLM-based evaluation — the Step1X-Edit paper and project report model scores computed by GPT-4.1 (and comparisons to other graders such as Qwen2.5-VL). Dataset is hosted on Hugging Face (MIT license) and was introduced alongside the Step1X-Edit paper (arXiv:2504.17761).
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