Pico-Banana-400K：面向文本引导图像编辑的大规模数据集
Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing
AI总结：
针对文本引导图像编辑领域缺乏大规模高质量真实图像数据集的问题，提出包含40万样本的Pico-Banana-400K数据集，通过系统化质量管控保障编辑多样性与指令忠实度，还设有多轮编辑、偏好对齐等三个专门子集，为下一代编辑模型的训练与基准测试提供支撑。
摘要：
多模态模型的最新进展展现了卓越的文本引导图像编辑能力，GPT-4o、Nano-Banana等系统树立了新的基准。然而，由于缺乏基于真实图像构建的大规模、高质量、可公开获取的数据集，研究界的进展仍受到限制。我们推出Pico-Banana-400K，这是一个包含40万张图像的综合数据集，用于基于指令的图像编辑任务。该数据集利用Nano-Banana从OpenImages数据集的真实照片中生成多样化的编辑对。与以往合成数据集的区别在于，我们采用了系统化的质量与多样性管控方法：使用细粒度图像编辑分类体系确保编辑类型的全面覆盖，同时通过基于多模态大语言模型（MLLM）的质量评分与精心筛选，保证内容的精准保留与指令忠实度。除单轮编辑外，Pico-Banana-400K还支持复杂编辑场景的研究，包含三个专门子集：（1）7.2万样本的多轮编辑子集，用于研究连续修改过程中的序列编辑、推理与规划；（2）5.6万样本的偏好子集，用于对齐研究与奖励模型训练；（3）长短编辑指令对，用于开发指令改写与摘要能力。通过提供这一大规模、高质量且任务丰富的资源，Pico-Banana-400K为下一代文本引导图像编辑模型的训练与基准测试奠定了坚实基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
