SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing
<|reference_start|>SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing: In this technical report, we introduce SEED-Data-Edit: a unique hybrid dataset for instruction-guided image editing, which aims to facilitate image manipulation using open-form language. SEED-Data-Edit is composed of three distinct types of data: (1) High-quality editing data produced by an automated pipeline, ensuring a substantial volume of diverse image editing pairs. (2) Real-world scenario data collected from the internet, which captures the intricacies of user intentions for promoting the practical application of image editing in the real world. (3) High-precision multi-turn editing data annotated by humans, which involves multiple rounds of edits for simulating iterative editing processes. The combination of these diverse data sources makes SEED-Data-Edit a comprehensive and versatile dataset for training language-guided image editing model. We fine-tune a pretrained Multimodal Large Language Model (MLLM) that unifies comprehension and generation with SEED-Data-Edit. The instruction tuned model demonstrates promising results, indicating the potential and effectiveness of SEED-Data-Edit in advancing the field of instructional image editing. The datasets are released in https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit.<|reference_end|>
@article{ge2024seed-data-edit,
    title={SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional
  Image Editing},
    author={Yuying Ge, Sijie Zhao, Chen Li, Yixiao Ge, Ying Shan},
    journal={arXiv preprint arXiv:2405.04007},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04007},
    primaryClass={cs.CV}
}