  Music generation has attracted growing interest with the advancement of deep
generative models. However, generating music conditioned on textual
descriptions, known as text-to-music, remains challenging due to the complexity
of musical structures and high sampling rate requirements. Despite the task's
significance, prevailing generative models exhibit limitations in music
quality, computational efficiency, and generalization. This paper introduces
JEN-1, a universal high-fidelity model for text-to-music generation. JEN-1 is a
diffusion model incorporating both autoregressive and non-autoregressive
training. Through in-context learning, JEN-1 performs various generation tasks
including text-guided music generation, music inpainting, and continuation.
Evaluations demonstrate JEN-1's superior performance over state-of-the-art
methods in text-music alignment and music quality while maintaining
computational efficiency. Our demos are available at
http://futureverse.com/research/jen/demos/jen1
