See It Before You Edit: A Large-Scale Dataset for Operation-Aware Image Editing Anticipation

With the development of multi-modal large language models, many fields have begun to introduce related agents, among which photographer agents are one example. Existing agents are typically trained on holistic parameter data, outputting Lightroom editing parameters for images, yet lacking the ability to anticipate the results of their operations. This paper introduces SIBYE, a tripartite dataset comprising about 190,000 image pairs and operation parameters, corresponding to cropping, global editing, and masked local editing in photographic post-processing. The cropping subset provides about 47,000 compositional variations, generated using normalized cropping ratios along the top, bottom, left, and right edges. The editing subset includes about 100,000 global adjustments based on 10 basic operations, 24 HSL color grading operations, and 6 color calibration operations in Lightroom. The masking subset contains about 43,000 linear and radial gradient masks, with 10 basic operations applied per mask type to achieve local editing. Each data entry is a quadruple (original image, result image, operation type, operation parameters), enabling the model to learn the forward mapping from operations to visual outcomes and the reverse inference from results to operations. We design tasks of varying difficulty, including single-operation classification, operation parameter prediction, multi-operation parameter prediction, among others—and establish benchmark experiments to validate the dataset’s effectiveness and challenges. This dataset will provide critical support for research areas such as interpretable image editing, operation-aware generative models, and intelligent photography assistants. The dataset can be downloaded at https://doi.org/10.5281/zenodo.19369384.