arXiv:2601.21542v3 Announce Type: replace-cross Abstract: Flow Matching (FM) models have emerged as a leading paradigm for high-fidelity synthesis. However, their reliance on iterative Ordinary Differential Equation (ODE) solving creates a significant latency bottleneck. Existing solutions face a dichotomy: training-free solvers suffer from significant performance degradation at low Neural Function Evaluations (NFEs), while training-based one- or few-steps generation methods incur prohibitive training costs and lack plug-and-play versatility. To bridge this gap, we propose the Bi-Anchor Interp
The proliferation of generative AI models necessitates more efficient inference methods, and this research addresses a core bottleneck in Flow Matching models.
Accelerating generative modeling directly impacts the cost, speed, and accessibility of high-fidelity AI content generation, making advanced AI tools more practical for broader applications.
The proposed Bi-Anchor Interpolation Solver offers a pathway to faster and more cost-effective generative AI inference without sacrificing quality or requiring extensive retraining, improving the operational efficiency of these models.
- · AI developers
- · Cloud computing providers
- · Generative AI startups
- · Content creators using AI
- · Inefficient generative model operators
- · Companies reliant on slow iterative inference
Reduced computational costs and latency for deploying high-fidelity generative AI models.
Increased adoption and expansion of generative AI into new applications and industries due to improved efficiency.
Further democratisation of sophisticated generative AI capabilities, fostering a surge in AI-powered innovation across various sectors.
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Read at arXiv cs.AI