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The Amazing Stability of Flow Matching

arXiv cs.CV / 4/20/2026

💬 OpinionIdeas & Deep AnalysisModels & Research
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Key Points

  • The paper studies how architecture choices and dataset size affect the sample quality and diversity produced by flow-matching generative models.
  • Experiments on the CelebA-HQ dataset show that flow matching stays stable even after pruning 50% of the training data, preserving both quality and diversity.
  • The latent representations are only slightly affected by pruning, meaning models trained on full versus pruned data produce visually similar outputs for the same seed.
  • Similar stability is observed under changes to model architecture and training configuration, indicating robustness of the learned latent mapping to various perturbations.

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.

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