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README.md
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## About
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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**.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.
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## 🧪 Usage
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Our training and inference scripts are now available on [GitHub](https://github.com/CSU-JPG/FlowInOne)!
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## About
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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**.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.
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</div>
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## 🧪 Usage
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Our training and inference scripts are now available on [GitHub](https://github.com/CSU-JPG/FlowInOne)!
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