We present HumanEdit, a high-quality, human-rewarded dataset specifically designed for instruction-guided image editing, enabling precise and diverse image manipulations through open-form language instructions. Previous large-scale editing datasets often incorporate minimal human feedback, leading to challenges in aligning datasets with human preferences. HumanEdit bridges this gap by employing human annotators to construct data pairs and administrators to provide feedback. With meticulously curation, HumanEdit comprises 5,751 images and requires more than 2,500 hours of human effort across four stages, ensuring both accuracy and reliability for a wide range of image editing tasks. The dataset includes six distinct types of editing instructions: Action, Add, Counting, Relation, Remove, and Replace, encompassing a broad spectrum of real-world scenarios. All images in the dataset are accompanied by masks, and for a subset of the data, we ensure that the instructions are sufficiently detailed to support mask-free editing. Furthermore, HumanEdit offers comprehensive diversity and high-resolution $1024 \times 1024$ content sourced from various domains, setting a new versatile benchmark for instructional image editing datasets. With the aim of advancing future research and establishing evaluation benchmarks in the field of image editing, we release HumanEdit at \url{https://huggingface.co/datasets/BryanW/HumanEdit}.

翻译：我们提出了HumanEdit，这是一个专为指令引导图像编辑设计的高质量人工奖励数据集，能够通过开放形式的语言指令实现精确且多样化的图像操作。先前的大规模编辑数据集往往仅包含极少的人工反馈，导致数据集与人类偏好对齐方面存在挑战。HumanEdit通过雇佣人工标注员构建数据对，并由管理员提供反馈，从而弥合了这一差距。经过精心筛选，HumanEdit包含5,751张图像，在四个阶段中累计投入超过2,500小时的人工努力，确保了其在广泛图像编辑任务中的准确性和可靠性。该数据集涵盖六种不同类型的编辑指令：动作、添加、计数、关系、移除和替换，覆盖了广泛的现实场景。数据集中的所有图像均附带掩码，并且对于部分数据，我们确保指令足够详细以支持无掩码编辑。此外，HumanEdit提供了全面的多样性以及来自多个领域的高分辨率$1024 \times 1024$内容，为指令式图像编辑数据集树立了一个新的多功能基准。为了推动图像编辑领域的未来研究并建立评估基准，我们在\url{https://huggingface.co/datasets/BryanW/HumanEdit}发布了HumanEdit。