  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
