Armonia: Edit-Compatible Diffusion Inversion for Text-Guided Music Editing

Text-guided music editing aims to modify specified attributes of a recording while preserving musical content unrelated to the intended change, enabling flexible music creation. Inversion anchors this editing process to the source music by mapping it to a state that initializes generation under the target text. However, a state that enables faithful source reconstruction is not necessarily a suitable starting point for the requested edit. This distinction motivates us to move part of the editing control into inversion and prepare an initialization that is better suited to the requested edit. To this end, we propose *Armonia*, a training-free method that refines inversion states using output-level attention distillation derived from source- and target-conditioned model responses. To improve refinement efficiency and effectiveness while reducing the risk of off-manifold drift, Armonia uses temporal weighting to focus optimization on selected inversion steps and local projection to limit deviations from each DDIM inversion proposal. The resulting state is then used to initialize the original target-conditioned denoising process, leaving the pretrained diffusion model unchanged. Compared with SOTA, Armonia achieves an average relative improvement of 11.5% in CLAP across three datasets while retaining competitive structural preservation.