A novel dataset, SEED-Data-Edit, has emerged as a valuable resource for instruction-guided image editing, catering to the burgeoning demand for seamless integration of natural language instructions into image manipulation processes.

Comprising three distinct data types, this hybrid dataset offers a multifaceted approach to image editing. The automated pipeline-generated data, created through meticulous processes, encompasses both object removal and addition, as well as stylistic alterations, ensuring a diverse range of editing pairs for model training.

Real-world scenario data extracted from online platforms captures genuine editing requests made by amateur photographers, met with expert-level interventions using tools like Photoshop. This real-world input infuses practicality into the dataset, reflecting the complexities and nuances of actual image editing tasks.

The multi-turn editing data, annotated by human experts, simulates iterative editing processes commonly encountered in professional settings. These sequences of edits provide invaluable insights into the decision-making processes underlying complex image manipulations.

With a colossal collection of 3.7 million image editing pairs and over 21,000 multi-turn editing sequences, SEED-Data-Edit stands as a robust foundation for training language-guided image editing models. By amalgamating diverse data sources, it seeks to overcome the challenges associated with instruction-guided image editing, offering enhanced controllability and flexibility to users navigating the realm of image manipulation through natural language instructions.