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

Abstract

Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason or create within it. We challenge this paradigm by asking whether all modalities, including textual descriptions, spatial layouts, and editing instructions, can be unified into a single visual representation. We present FlowInOne, a framework that reformulates multimodal generation as a purely visual flow, converting all inputs into visual prompts and enabling a clean image-in, image-out pipeline governed by a single flow matching model. This vision-centric formulation naturally eliminates cross-modal alignment bottlenecks, noise scheduling, and task-specific architectural branches, unifying text-to-image generation, layout-guided editing, and visual instruction following under one coherent paradigm. To support this, we introduce VisPrompt-5M, a large-scale dataset of 5 million visual prompt pairs spanning diverse tasks including physics-aware force dynamics and trajectory prediction, alongside VP-Bench, a rigorously curated benchmark assessing instruction faithfulness, spatial precision, visual realism, and content consistency. Extensive experiments demonstrate that FlowInOne achieves state-of-the-art performance across all unified generation tasks, surpassing both open-source models and competitive commercial systems, establishing a new foundation for fully vision-centric generative modeling where perception and creation coexist within a single continuous visual space.

Figures (22)

• Figure 1: Comparison of generation paradigms. Left: Traditional T2I only uses the text encoder to condition the Latent Diffusion Model(LDM); Middle: Traditional TI2I requires the joint conditioning of both the text and image encoders; Right: We unify the conditions as visual input and form a simple image-in, image-out framework with a single model.
• Figure 2: VisPrompt-5M is a comprehensive dataset that comprises eight distinct data types, including class-to-image generation, text-to-image generation, text-in-image editing, text bounding box editing, visual marker editing, doodles editing, force understanding, and trajectory understanding. The dataset covers a wide spectrum of image-to-image generation, ranging from basic text-in-image generation to compositional editing, and further to physics-aware instruction following.
• Figure 3: Overview of the FlowInOne architecture, a general and simple framework using flow matching for continuous evolution in only one modality. FlowInOne employs a Dual-Path Spatially-Adaptive Modulation to adapt computation by modality. For input image rendering with only text, the structural branch is bypassed to strictly follow semantic evolution. Conversely, for image editing, a spatially-adaptive gated network and cross attention activates to selectively inject source priors, dynamically balancing original image preservation with instruction-driven reconstruction.
• Figure 4: Visual instruction editing comparison across methods.
• Figure 5: Overall error types in VP-Bench. For brevity in the figures, the labels Fidelity, Spatial, Realism, and Consistency correspond strictly to Instruction Fidelity, Spatial Precision, Visual Realism, and Content Consistency, respectively.