FlowInOne: Unifying Multimodal Generation as Image-in, Image-out Flow Matching

Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason orcreate within it. We challenge this paradigm by asking whether all modal-ities, including textual descriptions, spatial layouts, and editing instruc-tions, can be unified into a single visual representation. We presentFlowInOne, a framework that reformulates multimodal generation as apurely visual flow, converting all inputs into visual prompts and enablinga clean image-in, image-out pipeline governed by a single flow matchingmodel. This vision-centric formulation naturally eliminates cross-modalalignment bottlenecks, noise scheduling, and task-specific architecturalbranches, unifying text-to-image generation, layout-guided editing, andvisual instruction following under one coherent paradigm. To supportthis, we introduce VisPrompt-5M, a large-scale dataset of 5 millionvisual prompt pairs spanning diverse tasks including physics-aware forcedynamics and trajectory prediction, alongside VP-Bench, a rigorouslycurated benchmark assessing instruction faithfulness, spatial precision,visual realism, and content consistency. Extensive experiments show thatFlowInOne achieves state-of-the-art performance across unified generationtasks, outperforming open-source models and rivaling competitive com-mercial systems. These results establish FlowInOne as a strong foundationfor fully vision-centric generative modeling, where perception and creationare unified within a single continuous visual space. Code is available atcsu-jpg.github.io/FlowInOne.github.io.