Efficient Flow Matching using Latent Variables

Understand

Flow matching models have shown great potential in image generation tasks among probabilistic generative models.

• However, most flow matching models in the literature do not explicitly utilize the underlying clustering structure in the target data when learning the flow from a simple source distribution like the standard Gaussian.
• This leads to inefficient learning, especially for many high-dimensional real-world datasets, which often reside in a low-dimensional manifold.
• To this end, we present $\texttt{Latent-CFM}$, which provides efficient training strategies by conditioning on the features extracted from data using pretrained deep latent variable models.