Implicit Dynamical Flow Fusion (IDFF) for Generative Modeling

Mohammad Reza Rezaei, Rahul Krishnan, Milos Radomir Popovic, Milad Lankarany

Keywords: Generative modeling, Conditional Flow Matching (CFM), Image generation, Time-series generation, Flow-based models

TL;DR: IDFF is a new conditional flow generative model that decreases the number of function necessary to generate high-dimensional data by a factor of 10, without sacrificing sample quality.

Abstract: 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, where we achieve 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.

Primary Area: generative models

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Submission Number: 11611