Title: End-to-End One Step Flow Matching via Flow Fitting
Primary area: generative models
Keywords: Flow matching, Single step generative models

Abstract: Diffusion and flow-matching models have demonstrated impressive performance in generating diverse, high-fidelity images by learning transformations from noise to data. However, their reliance on multi-step sampling requires repeated neural network evaluations, leading to high computational cost. We propose FlowFit, a family of generative models that enables high-quality sample generation through both single-phase training and single-step inference. FlowFit learns to approximate the continuous flow trajectory between latent noise (x_0) and data (x_1) by fitting a basis of functions parameterized over time (t \in [0, 1]) during training. At inference time, sampling is performed by simply evaluating the flow only at the terminal time (t = 1), avoiding iterative denoising or numerical integration. Empirically, FlowFit outperforms prior diffusion-based single-phase training methods achieving superior sample quality.