RefTree: Scaling Reference-to-Image Generation via Hierarchical Image Decomposition

Although open-weight image generation models increasingly support reference images as inputs, they lag substantially behind proprietary counterparts, particularly as references grow in number and form complex hierarchical dependencies. To address this bottleneck, we introduce RefTree, a scalable and extensible data engine for multi-reference image generation. RefTree recursively decomposes target images into trees of visual concepts, which guide collaborative agents to extract the concepts as reference images, verify their consistency, and compose reference-grounded prompts via bottom-up merging. Operating entirely without closed-source models, the engine yields RefTree-480K, a dataset of 480K verified samples with 3.72M reference images and up to 20 references per sample, spanning characters, objects, scenes, and abstract controls such as poses, attributes, and styles. In addition, we present RefTree-Bench, which evaluates multi-reference generation using tree-derived, fine-grained visual rubrics. Models fine-tuned on RefTree-480K substantially improve reference consistency and prompt following, achieving state-of-the-art open-weight performance across four public benchmarks and RefTree-Bench and even matching Nano Banana Pro on two public benchmarks. Code, datasets, and models will be released.