Adversarial Self Flow Matching: Few-steps Image Generation with Straight Flows
Keywords: generative models, flow matching, adversarial training
TL;DR: We propose Adversarial Self Flow Matching, which can straighten flows and align the generated data distribution with the real data distribution.
Abstract: Flow Matching provides a method to train Ordinary Differential Equation (ODE)-based generative models and facilitates various probability path designs between initial and target distributions. Among these designs, straight flows are particularly interesting for reducing sampling steps. While some works have successfully straightened flows and achieved image generation in a few steps, they often suffer from cumulative errors or provide only piecewise or minibatch-level straightness. We propose Adversarial Self Flow Matching (ASFM), which can straighten flows and align the generated data distribution with the real data distribution. ASFM consists of two complementary components. Online Self Training straightens flows by constructing a conditional vector field using paired data, enabling one-step image generation during training. Adversarial Training aligns the one-step generated data with real data, thereby reducing cumulative errors when straightening flows. Experiments demonstrate that ASFM can build straight flows across the entire time span between two complete distributions and achieve highly competitive results across multiple datasets among Flow Matching-based methods. For instance, ASFM achieves 8.15 and 14.9 FID scores with NFE=6 on CelebA-HQ (256) and AFHQ-Cat (256), respectively.
Primary Area: generative models
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Submission Number: 7055
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