RMFlow: Refined Mean Flow by a Noise-Injection Step for Multimodal Generation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912866664185856 |
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| author | Huang, Yuhao Wang, Shih-Hsin Bertozzi, Andrea L. Wang, Bao |
| author_facet | Huang, Yuhao Wang, Shih-Hsin Bertozzi, Andrea L. Wang, Bao |
| contents | Mean flow (MeanFlow) enables efficient, high-fidelity image generation, yet its single-function evaluation (1-NFE) generation often cannot yield compelling results. We address this issue by introducing RMFlow, an efficient multimodal generative model that integrates a coarse 1-NFE MeanFlow transport with a subsequent tailored noise-injection refinement step. RMFlow approximates the average velocity of the flow path using a neural network trained with a new loss function that balances minimizing the Wasserstein distance between probability paths and maximizing sample likelihood. RMFlow achieves near state-of-the-art results on text-to-image, context-to-molecule, and time-series generation using only 1-NFE, at a computational cost comparable to the baseline MeanFlows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00849 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RMFlow: Refined Mean Flow by a Noise-Injection Step for Multimodal Generation Huang, Yuhao Wang, Shih-Hsin Bertozzi, Andrea L. Wang, Bao Machine Learning Artificial Intelligence Numerical Analysis 68Txx Mean flow (MeanFlow) enables efficient, high-fidelity image generation, yet its single-function evaluation (1-NFE) generation often cannot yield compelling results. We address this issue by introducing RMFlow, an efficient multimodal generative model that integrates a coarse 1-NFE MeanFlow transport with a subsequent tailored noise-injection refinement step. RMFlow approximates the average velocity of the flow path using a neural network trained with a new loss function that balances minimizing the Wasserstein distance between probability paths and maximizing sample likelihood. RMFlow achieves near state-of-the-art results on text-to-image, context-to-molecule, and time-series generation using only 1-NFE, at a computational cost comparable to the baseline MeanFlows. |
| title | RMFlow: Refined Mean Flow by a Noise-Injection Step for Multimodal Generation |
| topic | Machine Learning Artificial Intelligence Numerical Analysis 68Txx |
| url | https://arxiv.org/abs/2602.00849 |