We demonstrate how conditional generation from diffusion models can be used to tackle a variety of realistic tasks in the production of music in 44.1kHz stereo audio with sampling-time guidance. The scenarios we consider include continuation, inpainting and regeneration of musical audio, the creation of smooth transitions between two different music tracks, and the transfer of desired stylistic characteristics to existing audio clips. We achieve this by applying guidance at sampling time in a simple framework that supports both reconstruction and classification losses, or any combination of the two. This approach ensures that generated audio can match its surrounding context, or conform to a class distribution or latent representation specified relative to any suitable pre-trained classifier or embedding model. Audio samples are available at https://machinelearning.apple.com/research/controllable-music

翻译：我们展示了如何利用扩散模型的条件生成能力，通过采样时引导技术解决44.1kHz立体声音乐制作中的多种现实任务。所考虑的场景包括音乐音频的续接、修补与再生、两段不同音轨之间的平滑过渡创建，以及将期望的风格特征迁移至现有音频片段。为实现这一目标，我们在一个支持重建损失与分类损失（或两者任意组合）的简单框架中，于采样时刻应用引导策略。该方法确保生成的音频能够匹配其上下文环境，或符合基于任何合适的预训练分类器或嵌入模型所指定的类别分布或潜在表征。音频样本详见https://machinelearning.apple.com/research/controllable-music。