Title: Flow2GAN: Hybrid Flow Matching and GAN with Multi-Resolution Network for One-/Two-step High-Fidelity Audio Generation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Flow2GAN, audio generation, Flow Matching, GAN, multi-resolution

Abstract: Existing dominant methods for audio generation include Generative Adversarial Networks (GANs) and diffusion-based methods like Flow Matching. GANs suffer from slow convergence and potential mode collapse during training, while diffusion methods require multi-step inference that introduces considerable computational overhead. In this work, we introduce Flow2GAN, a two-stage framework that combines Flow Matching training for learning generative capabilities with GAN fine-tuning for efficient one-/two-step inference. Specifically, given audio's unique properties, we first improve Flow Matching for audio modeling through: 1) reformulating the objective as endpoint estimation, avoiding velocity estimation difficulties when involving empty regions; 2) applying spectral energy-based loss scaling to emphasize perceptually salient quieter regions. Building on these Flow Matching adaptations, we demonstrate that a further stage of lightweight GAN fine-tuning enables us to obtain one-step and two-step generators that produce high-quality audio. In addition, we develop a multi-branch network architecture that processes Fourier coefficients at different time-frequency resolutions, which improves the modeling capabilities compared to prior single-resolution designs. Experimental results indicate that our Flow2GAN delivers high-fidelity audio generation from Mel-spectrograms or discrete audio tokens, outperforming existing state-of-the-art GAN-based and Flow Matching-based methods in both quality and efficiency. Online demo samples are available at \url{https://flow2gan.github.io}.