The Amazing Stability of Flow Matching

Rania Briq, Michael Kamp, Ohad Fried, Sarel Cohen, Stefan Kesselheim

Keywords: Flow matching, stability of flow matching, generative models, diffusion models

TL;DR: We put flow matching stability (consistency) to stress tests that include distributional shifts and model capacity changes.

Abstract:

The success of deep generative models in generating high-quality and diverse samples is often attributed to particular architectures and large training datasets. In this paper, we investigate the impact of these factors on the quality and diversity of samples generated by \emph{flow-matching} models. Surprisingly, in our experiments on CelebA-HQ dataset, flow matching remains stable even when pruning 50\% of the dataset. That is, the quality and diversity of generated samples are preserved. Moreover, pruning impacts the latent representation only slightly, that is, samples generated by models trained on the full and pruned dataset map to visually similar outputs for a given seed. We observe similar stability when changing the architecture or training configuration, such that the latent representation is maintained under these changes as well.
Our results quantify just how strong this stability can be in practice, and help explain the reliability of flow-matching models under various perturbations.

Submission Number: 17