{"ID":2851861,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.19808","arxiv_id":"2510.19808","title":"Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing","abstract":"Recent advances in multimodal models have demonstrated remarkable text-guided image editing capabilities, with systems like GPT-4o and Nano-Banana setting new benchmarks. However, the research community's progress remains constrained by the absence of large-scale, high-quality, and openly accessible datasets built from real images. We introduce Pico-Banana-400K, a comprehensive 400K-image dataset for instruction-based image editing. Our dataset is constructed 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 maintaining precise content preservation and instruction faithfulness through MLLM-based quality scoring and careful curation. Beyond single turn editing, Pico-Banana-400K enables research into complex editing scenarios. The dataset includes three specialized subsets: (1) a 72K-example multi-turn collection for studying sequential editing, reasoning, and planning across consecutive 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 providing this large-scale, high-quality, and task-rich resource, Pico-Banana-400K establishes a robust foundation for training and benchmarking the next generation of text-guided image editing models.","short_abstract":"Recent advances in multimodal models have demonstrated remarkable text-guided image editing capabilities, with systems like GPT-4o and Nano-Banana setting new benchmarks. However, the research community's progress remains constrained by the absence of large-scale, high-quality, and openly accessible datasets built from...","url_abs":"https://arxiv.org/abs/2510.19808","url_pdf":"https://arxiv.org/pdf/2510.19808v1","authors":"[\"Yusu Qian\",\"Eli Bocek-Rivele\",\"Liangchen Song\",\"Jialing Tong\",\"Yinfei Yang\",\"Jiasen Lu\",\"Wenze Hu\",\"Zhe Gan\"]","published":"2025-10-22T17:43:15Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CL\",\"cs.LG\"]","methods":"[\"Large Language Model\"]","has_code":false}