  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.
