用流形约束提升单步语音生成质量，兼顾速度与保真度。
FdAudio: MeanFlow-Anchored Fréchet-Distance Post-Training for One-Step Text-to-Audio Generation
- 基于多表示弗雷歇距离损失，直接优化生成分布
- 相比基线降低11.4%弗雷歇距离，FAD提升28.8%
- 引入均值流锚定机制，解决后训练退化问题
尽管近期少步采样文本到音频生成模型如MeanAudio通过建模平均速度显著加速生成，其单步生成质量仍明显落后于多步模型。本文提出FdAudio，通过在预训练嵌入空间中利用多表示弗雷歇距离（FD）损失直接优化最终单步分布来缩小这一差距。为防止使用FD损失进行朴素后训练导致的多步退化问题，我们引入均值流一致性目标作为结构锚点。实验表明，FdAudio在少步系统中实现了领先的单步文本到音频生成质量，相较于基线MeanAudio框架，弗雷歇距离降低11.4%，FAD得分提升28.8%。特别地，通过提出均值流锚点，成功解决了FD后训练的多步退化问题，使25步采样路径能保持高保真音频合成，性能达到或超越强大多步模型，同时计算延迟仅为后者的极小部分。
原文摘要 · Abstract (English)
While recent few-step sampling text-to-audio generation models like MeanAudio substantially accelerate generation by modeling average velocities, their strict one-step generation quality still lags significantly behind multi-step counterparts. We propose FdAudio to bridge this gap. Unlike MeanAudio, which relies solely on regression against target velocity fields, our post-training approach optimizes the final one-step distribution directly across pre-trained embedding spaces via a multi-representation Fréchet-distance (FD) loss. Crucially, to prevent the multi-step degradation that naive post-training with FD-loss causes, we introduce a MeanFlow consistency objective as a structural anchor. Results demonstrate that FdAudio establishes state-of-the-art one-step T2A generation quality among few-step systems, yielding an 11.4% reduction in FD score and a 28.8% improvement in FAD score relative to the baseline MeanAudio framework. Notably, we solve FD post-training's naive multi-step degradation issue by proposing the MeanFlow anchor, enabling a 25-step sampling path to maintain high-fidelity audio synthesis that matches or surpasses strong multi-step models at a fraction of their computational latency.
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