MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition

Abstract

In controllable image generation, synthesizing coherent and consistent images from multiple reference inputs, i.e., **Multi-Image Composition** (MICo), remains a challenging problem, partly hindered by the lack of high-quality training data.To bridge this gap, we conduct a systematic study of MICo, categorizing it into 7 representative tasks and curate a large-scale collection of high-quality source images and construct diverse MICo prompts.Leveraging powerful proprietary models, we synthesize a rich amount of balanced composite images, followed by human-in-the-loop filtering and refinement, resulting in **MICo-150K**, a comprehensive dataset for MICo with identity consistency.We further build a Decomposition-and-Recomposition (**De&Re**) subset, where 11K real-world complex images are decomposed into components and recomposed, enabling both real and synthetic compositions.To enable comprehensive evaluation, we construct **MICo-Bench** with 100 cases per task and 300 challenging De&Re cases, and further introduce a new metric, **Weighted-Ref-VIEScore**, specifically tailored for MICo evaluation.Finally, we fine-tune multiple models on**MICo-150K** and evaluate them on **MICo-Bench**. The results show that MICo-150K effectively equips models without MICo capability and further enhances those with existing skills.Notably, **Qwen-MICo**, fine-tuned from Qwen-Image-Edit, matches **Qwen-Image-2509** in 3-image composition while supporting arbitrary multi-image inputs beyond the latter's limitation.Our dataset and benchmark will be valuable resources for advancing MICo research.