arXiv:2607. 11442v1 Announce Type: new Abstract: Flow matching trains a neural network to regress the conditional velocity along a linear interpolant between noise and data, and the number of network evaluations~(NFE) sets the cost of sampling.
By Vitalii Bondar
The paper introduces Difficulty-Calibrated Flow Matching, a method that adapts the noise-to-data interpolation schedule in Conditional Flow Matching based on a pilot run’s loss profile. By setting the schedule to the quantile function of this difficulty profile, the training trajectory spends more time where the velocity is hardest to learn. Experiments on CIFAR-10, MNIST, and Fashion‑MNIST show that this calibrated path achieves the best FID on CIFAR‑10 and outperforms all fixed schedules in large‑batch, few‑update settings, where compute is most limited.
By Airin Akter Tania, Md Raihan Khan
I‑SplineFlow introduces a new way to learn monotone spline stochastic interpolant schedulers for few‑step generation with pretrained diffusion and flow models. By parameterizing the scheduler with integrated monotone splines (I‑splines), the method decouples polynomial degree from the number of mixture weights, enabling compact support, better‑conditioned Jacobians, and strictly monotone signal‑to‑noise ratios without ordering constraints. Experiments on EDM, ReFlow, and Simple ReFlow show that I‑SplineFlow consistently improves few‑step FID over Bézier scheduling, especially at low NFEs, while training in only minutes.
By Md Sakib Hossain Shovon, Md Rifat Ur Rahman, Md Abtahi Majeed Chowdhury, Yunhong Min, Jaesik Choi, Minhyuk Sung
arXiv:2610.01408v1 Announce Type: new
Abstract: Trajectory crossing remains a critical bottleneck in Flow Matching (FM), and previous works typically view these crossings from a theoretical optimizat...
By Ziqi Jiang, Zhenqi He, Long Chen
arXiv:2604. 18194v2 Announce Type: replace Abstract: Single-step generators promise high-fidelity synthesis at a fraction of the inference and training cost of ordinary differential equation (ODE)-based flow models, a central concern when compute is limited.
By Arkadii Kazanskii, Tatiana Petrova, Andrey Ustyuzhanin, Konstantin Bagrianskii, Aleksandr Puzikov, Radu State
arXiv:2607. 06114v1 Announce Type: cross Abstract: Diffusion and flow matching models generate high-quality samples, but their ODE samplers often need tens to hundreds of neural function evaluations (NFEs).
By Xin Peng, Ang Gao