RMFlow: Refined Mean Flow by a Noise-Injection Step for Multimodal Generation

• URL: http://arxiv.org/abs/2602.00849v1
• Date: Sat, 31 Jan 2026 18:27:05 GMT
• Title: RMFlow: Refined Mean Flow by a Noise-Injection Step for Multimodal Generation
• Authors: Yuhao Huang, Shih-Hsin Wang, Andrea L. Bertozzi, Bao Wang,
• Abstract summary: Mean flow (MeanFlow) enables efficient, high-fidelity image generation, yet its single-function evaluation (1-NFE) generation often cannot yield compelling results.<n>We introduce RMFlow, an efficient multimodal generative model that integrates a coarse 1-NFE MeanFlow transport with a tailored noise-injection refinement step.<n> 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.
• Score: 12.979642182577157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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