The recent surge in popularity of diffusion models for image generation has
brought new attention to the potential of these models in other areas of media
synthesis. One area that has yet to be fully explored is the application of
diffusion models to music generation. Music generation requires to handle
multiple aspects, including the temporal dimension, long-term structure,
multiple layers of overlapping sounds, and nuances that only trained listeners
can detect. In our work, we investigate the potential of diffusion models for
text-conditional music generation. We develop a cascading latent diffusion
approach that can generate multiple minutes of high-quality stereo music at
48kHz from textual descriptions. For each model, we make an effort to maintain
reasonable inference speed, targeting real-time on a single consumer GPU. In
addition to trained models, we provide a collection of open-source libraries
with the hope of facilitating future work in the field.
We open-source the following: Music samples for this paper:
this https URL all music samples for all models:
this https URL and codes:
this https URL

本研究探索了扩散模型在生成音乐方面的潜力，提出了一种级联的潜在扩散方法来根据文本描述生成高质量的立体声音乐，同时开源相关代码和音频样本以促进领域内未来研究。