Béz ierFlow parameterizes stochastic interpolant schedulers as Béz ier functions to learn optimal sampling trajectories, achieving 2-3x better few-step performance than prior timestep optimization methods.
On ImageNet, generated sam- ples are drawn to match the class distribution of the reference set
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B\'ezierFlow: Learning B\'ezier Stochastic Interpolant Schedulers for Few-Step Generation
Béz ierFlow parameterizes stochastic interpolant schedulers as Béz ier functions to learn optimal sampling trajectories, achieving 2-3x better few-step performance than prior timestep optimization methods.