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Found 4 papers, 0 papers with code
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models
This paper introduces Bespoke Non-Stationary (BNS) Solvers, a solver distillation approach to improve sample efficiency of Diffusion and Flow models.
Guided Flows for Generative Modeling and Decision Making
Classifier-free guidance is a key component for enhancing the performance of conditional generative models across diverse tasks.
Bespoke Solvers for Generative Flow Models
For example, a Bespoke solver for a CIFAR10 model produces samples with Fr\'echet Inception Distance (FID) of 2. 73 with 10 NFE, and gets to 1% of the Ground Truth (GT) FID (2. 59) for this model with only 20 NFE.
On Kinetic Optimal Probability Paths for Generative Models
We investigate Kinetic Optimal (KO) Gaussian paths and offer the following observations: (i) We show the KE takes a simplified form on the space of Gaussian paths, where the data is incorporated only through a single, one dimensional scalar function, called the \emph{data separation function}.
