MuseControlLite: Multifunctional Music Generation with Lightweight
  Conditioners
Authors: name: Fang-Duo Tsai, name: Shih-Lun Wu, name: Weijaw Lee, name: Sheng-Ping Yang, name: Bo-Rui Chen, name: Hao-Chung Cheng, name: Yi-Hsuan Yang

We propose MuseControlLite, a lightweight mechanism designed to fine-tune
text-to-music generation models for precise conditioning using various
time-varying musical attributes and reference audio signals. The key finding is
that positional embeddings, which have been seldom used by text-to-music
generation models in the conditioner for text conditions, are critical when the
condition of interest is a function of time. Using melody control as an
example, our experiments show that simply adding rotary positional embeddings
to the decoupled cross-attention layers increases control accuracy from 56.6%
to 61.1%, while requiring 6.75 times fewer trainable parameters than
state-of-the-art fine-tuning mechanisms, using the same pre-trained diffusion
Transformer model of Stable Audio Open. We evaluate various forms of musical
attribute control, audio inpainting, and audio outpainting, demonstrating
improved controllability over MusicGen-Large and Stable Audio Open ControlNet
at a significantly lower fine-tuning cost, with only 85M trainble parameters.
Source code, model checkpoints, and demo examples are available at:
https://musecontrollite.github.io/web/.