Recent advances in multimodal models have demonstrated remarkable text-guided image editing capabilities, withsystems like GPT-4o and Nano-Banana setting new benchmarks. However, the research community’s progress remainsconstrained by the absence of large-scale, high-quality, and openly accessible datasets built from real images. Weintroduce Pico-Banana-400K, a comprehensive 400K-image dataset for instruction-based image editing. Our dataset isconstructed by leveraging Nano-Banana to generate diverse edit pairs from real photographs in the OpenImages collection.What distinguishes Pico-Banana-400K from previous synthetic datasets is our systematic approach to quality and diversity.We employ a fine-grained image editing taxonomy to ensure comprehensive coverage of edit types while maintainingprecise content preservation and instruction faithfulness through MLLM-based quality scoring and careful curation. Beyondsingle turn editing, Pico-Banana-400K enables research into complex editing scenarios. The dataset includes threespecialized subsets: (1) a 72K-example multi-turn collection for studying sequential editing, reasoning, and planning acrossconsecutive modifications; (2) a 56K-example preference subset for alignment research and reward model training; and (3)paired long-short editing instructions for developing instruction rewriting and summarization capabilities. By providingthis large-scale, high-quality, and task-rich resource, Pico-Banana-400K establishes a robust foundation for training andbenchmarking the next generation of text-guided image editing models.