PIE-Bench comprises 700 images featuring 10 distinct editing types. Images are evenly distributed in natural and artificial scenes (e.g., paintings) among four categories: animal, human, indoor, and outdoor. Each image in PIE-Bench includes five annotations: source image prompt, target image prompt, editing instruction, main editing body, and the editing mask. Notably, the editing mask annotation (indicating the anticipated editing region) is crucial in accurate metrics computations as we expect the editing to only occur within a designated area.
ImgEdit is a large-scale, high-quality image-editing dataset comprising 1.2 million carefully curated edit pairs, which contain both novel and complex single-turn edits, as well as challenging multi-turn tasks.
This dataset is a new benchmark, grounded in real-world usages is developed to support more authentic and comprehensive evaluation of image editing models.
ImgEdit is a large-scale, high-quality image-editing dataset comprising 1.2 million carefully curated edit pairs, which contain both novel and complex single-turn edits, as well as challenging multi-turn tasks.
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NHR-Edit is a training dataset for instruction-based image editing. Each sample consists of an input image, a natural language editing instruction, and the corresponding edited image. All samples are generated fully automatically using the NoHumanRequired pipeline, without any human annotation or filtering. This dataset is designed to support training of general-purpose image editing models that can follow diverse, natural editing commands. Each sample also includes additional metadata such as editing type, style, and image resolution, making it suitable for training fine-grained, controllable image editing models.