  We introduce a simple and efficient text-controllable high-fidelity music
generation and editing model. It operates on sequences of continuous latent
representations from a low frame rate 48 kHz stereo variational auto encoder
codec that eliminates the information loss drawback of discrete
representations. Based on a diffusion transformer architecture trained on a
flow-matching objective the model can generate and edit diverse high quality
stereo samples of variable duration, with simple text descriptions. We also
explore a new regularized latent inversion method for zero-shot test-time
text-guided editing and demonstrate its superior performance over naive
denoising diffusion implicit model (DDIM) inversion for variety of music
editing prompts. Evaluations are conducted on both objective and subjective
metrics and demonstrate that the proposed model is not only competitive to the
evaluated baselines on a standard text-to-music benchmark - quality and
efficiency-wise - but also outperforms previous state of the art for music
editing when combined with our proposed latent inversion. Samples are available
at https://melodyflow.github.io.
