Implicit Dynamical Flow Fusion (IDFF) for Generative Modeling
<|reference_start|>Implicit Dynamical Flow Fusion (IDFF) for Generative Modeling: Conditional Flow Matching (CFM) models can generate high-quality samples from a non-informative prior, but they can be slow, often needing hundreds of network evaluations (NFE). To address this, we propose Implicit Dynamical Flow Fusion (IDFF); IDFF learns a new vector field with an additional momentum term that enables taking longer steps during sample generation while maintaining the fidelity of the generated distribution. Consequently, IDFFs reduce the NFEs by a factor of ten (relative to CFMs) without sacrificing sample quality, enabling rapid sampling and efficient handling of image and time-series data generation tasks. We evaluate IDFF on standard benchmarks such as CIFAR-10 and CelebA for image generation. We achieved likelihood and quality performance comparable to CFMs and diffusion-based models with fewer NFEs. IDFF also shows superior performance on time-series datasets modeling, including molecular simulation and sea surface temperature (SST) datasets, highlighting its versatility and effectiveness across different domains.<|reference_end|>
@article{rezaei2024implicit,
    title={Implicit Dynamical Flow Fusion (IDFF) for Generative Modeling},
    author={Mohammad R. Rezaei, Rahul G. Krishnan, Milos R. Popovic, Milad
  Lankarany},
    journal={arXiv preprint arXiv:2409.14599},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14599},
    primaryClass={cs.LG}
}