Consistency models have exhibited remarkable capabilities in facilitating efficient image/video generation, enabling synthesis with minimal sampling steps. It has proven to be advantageous in mitigating the computational burdens associated with diffusion models. Nevertheless, the application of consistency models in music generation remains largely unexplored. To address this gap, we present Music Consistency Models (\texttt{MusicCM}), which leverages the concept of consistency models to efficiently synthesize mel-spectrogram for music clips, maintaining high quality while minimizing the number of sampling steps. Building upon existing text-to-music diffusion models, the \texttt{MusicCM} model incorporates consistency distillation and adversarial discriminator training. Moreover, we find it beneficial to generate extended coherent music by incorporating multiple diffusion processes with shared constraints. Experimental results reveal the effectiveness of our model in terms of computational efficiency, fidelity, and naturalness. Notable, \texttt{MusicCM} achieves seamless music synthesis with a mere four sampling steps, e.g., only one second per minute of the music clip, showcasing the potential for real-time application.

翻译：一致性模型在高效生成图像/视频方面展现出卓越能力，能够通过极少的采样步骤实现合成。该模型已被证实有助于减轻扩散模型带来的计算负担。然而，一致性模型在音乐生成领域的应用仍鲜有探索。为填补这一空白，我们提出了音乐一致性模型（\texttt{MusicCM}），该模型利用一致性模型的思想高效合成音乐片段的梅尔谱图，在保持高质量的同时将采样步骤降至最低。基于现有的文本到音乐扩散模型，\texttt{MusicCM}模型融合了一致性蒸馏与对抗判别器训练。此外，我们发现通过引入共享约束的多重扩散过程生成连贯的长篇音乐具有显著优势。实验结果表明，该模型在计算效率、保真度和自然性方面均表现优异。值得注意的是，\texttt{MusicCM}仅需四个采样步骤即可实现无缝音乐合成（例如每分钟音乐片段仅需一秒生成时间），展现了其实时应用的潜力。