MusicMorpher: Music Morphing via Style Tokens

Continuous-strength instruction editing lets users specify what to change and by how much. Such methods provide this control for images, but remain underdeveloped for music because they require source-target examples or morph paths for supervision, which are hard to obtain. For example, image-based Kontinuous Kontext derives such trajectories from FreeMorph outputs, but requires post-filtering since interpolating zero-noise latents is non-smooth. We find image-based methods transfer poorly to music: they can act akin to a volume slider and ignore musical traits like tempo and beat alignment. We therefore identify music morphing, rather than editor fine-tuning, as the upstream bottleneck. We introduce MusicMorpher, a music morphing method that generates instruction-conditioned morph paths between a source and target. We do so by learning a per-song style token, applied through modulation so that it shifts global style without touching temporal structure, and interpolating it together with each song's inverted structural latent, each in its own space. At inference, we tempo- and beat-align songs before style-token computation to prevent rhythmic discontinuities. We compare MusicMorpher against SDEdit, RF-Inversion, FreeMorph, and FlowMorph on morph quality and smoothness across varying rhythmic alignment, introducing trajectory-level measures absent from prior single-sample metrics. Results show our method produces higher-quality, smoother morphs, and, without supervised instruct-edit training, extends to continuous-strength semantic edits reaching quality-alignment trade-offs beyond baseline sweeps; our analyses explain prior methods' inconsistent results. Examples at https://musicmorpher.github.io/.