Polyffusion: A Diffusion Model for Polyphonic Score Generation with
  Internal and External Controls
Lejun Min Junyan Jiang Gus Xia Jingwei Zhao
We propose Polyffusion, a diffusion model that generates polyphonic music
scores by regarding music as image-like piano roll representations. The model
is capable of controllable music generation with two paradigms: internal
control and external control. Internal control refers to the process in which
users pre-define a part of the music and then let the model infill the rest,
similar to the task of masked music generation (or music inpainting). External
control conditions the model with external yet related information, such as
chord, texture, or other features, via the cross-attention mechanism. We show
that by using internal and external controls, Polyffusion unifies a wide range
of music creation tasks, including melody generation given accompaniment,
accompaniment generation given melody, arbitrary music segment inpainting, and
music arrangement given chords or textures. Experimental results show that our
model significantly outperforms existing Transformer and sampling-based
baselines, and using pre-trained disentangled representations as external
conditions yields more effective controls.