MuseControlLite: A Lightweight Approach to Controllable Music Generation

MuseControlLite is a lightweight, multifunctional control mechanism for text-to-music generation that enables fine-grained, time-varying conditioning through musical attributes and reference audio, with a parameter-efficient adapter design. The approach is characterized by its integration of rotary positional embeddings (RoPE) into decoupled cross-attention layers, substantially enhancing temporal control accuracy for diverse music attributes while decreasing fine-tuning cost and resource requirements. MuseControlLite targets high-controllability tasks such as melody-conditioned generation, audio inpainting/outpainting, and multimodal attribute mixing, and demonstrates state-of-the-art control capability at a fraction of the parameter count of previous control frameworks. Source code and evaluation benchmarks are accessible at https://MuseControlLite.github.io/web/.

1. Mechanism and Architecture

MuseControlLite augments a pre-trained diffusion Transformer backbone (notably Stable Audio Open) with lightweight, decoupled cross-attention modules designed to accept time-varying control signals, including musical attributes (e.g., melody, rhythm, dynamics) and reference audio. This is achieved without duplicating the backbone or employing large parameter sets as in ControlNet-based methods.

The core technical innovation is the use of rotary positional embeddings (RoPE) in the cross-attention between the model's main sequence and control signals. For a sequence position $m$ in the main input and $n$ in the conditioner:

$$q_m = R_{\theta, m} W_q X_m \qquad k_n = R_{\theta, n} W'_k C_n \qquad v_n = R_{\theta, n} W'_v C_n$$

where $R_{\theta, m}$ is the RoPE matrix, $X_m$ is the backbone feature at position $m$, and $C_n$ is the control signal at $n$. Conditioned outputs are combined as:

$$X = \mathrm{ZCNN}( X_\mathrm{text} + X_\mathrm{attr} )$$

where $\mathrm{ZCNN}$ is a zero-initialized 1D convolution for stabilizing adapter output.

This mechanism enables conditioning on time-aligned signals by encoding both absolute and relative temporal positions in both the backbone and control signals. The lightweight adapters and RoPE implementation allow the addition of advanced control without sacrificing efficiency or requiring full backbone duplication.

2. Time-Varying Musical Attribute and Audio Conditioning

MuseControlLite supports multiple types of control signals:

• Melody: Time-aligned vector representations extracted from audio or symbolic source; RoPE enables aligning the conditional signal to its correct musical position.
• Rhythm and Dynamics: Similar temporal embeddings, supporting per-frame or per-bar control of accent and loudness.
• Reference Audio: For audio inpainting (filling in missing music) and outpainting (generating continuations), partial audio features are provided as conditioners, again positionally aligned.
• Multimodal Mixing: The system can accept arbitrary combinations of text, attribute, or audio controls; e.g., text prompt plus melody track plus partial audio segment.

By design, any subset of control signals can be supplied at inference, enabling creative workflows—users can fix melody while leaving rhythm unconstrained, perform local audio repairs, or combine attributes from multiple sources.

3. Parameter Efficiency and Computational Cost

MuseControlLite utilizes approximately 85 million trainable parameters for its adapters and extraction layers, which is 6.75 times fewer than ControlNet-style adaptations (which require 572M parameters for the same backbone and control granularity). The additional parameter load is about 8% of the full Stable Audio Open backbone's size, maintaining model capacity without substantial resource demands.

Empirical results demonstrate that, compared to both MusicGen-Large (full retraining, 3.3B parameters) and Stable Audio Open ControlNet (adapter duplication, 572M parameters), MuseControlLite provides superior control fidelity using a much smaller and more tractable parameter budget. This efficiency makes advanced music control practical on standard hardware.

4. Experimental Evaluation and Controllability

Comprehensive benchmarks on the Song Describer dataset and related music-generation tasks establish MuseControlLite's superior performance in several control-focused metrics. For melody-conditioned generation:

In ablation studies, the addition of RoPE to cross-attention adapters increased melody control from 10.7% to 58.6%, demonstrating the necessity of positional conditioning for time-dependent tasks. Further, the model achieves high accuracy on rhythm and dynamics controls, and objective evaluations of audio inpainting/outpainting show fewer boundary artifacts and higher overall smoothness compared to prior baselines.

User studies indicate that the perceived similarity and musicality of generated outputs are at least on par with larger, more resource-intensive models.

5. Audio Inpainting, Outpainting, and Boundary Handling

MuseControlLite provides robust support for both audio inpainting and outpainting through reference audio conditioning. For inpainting, partial audio is encoded and provided via the attribute adapter with precise position encoding. Outpainting extends generation seamlessly from a finished segment; boundary quality is enhanced over naive masking due to the model’s explicit temporal alignment.

Objective tests report strong FAD, CLAP, and user listening metrics for both one-boundary and two-boundary restoration, validating practical fidelity for music editing, rearrangement, and style transfer applications.

6. Applications and Practical Implications

MuseControlLite is suited for:

• Controllable text-to-music generation: Direct specification of melody, rhythm, dynamics, or mixed conditioning signals during generation, at arbitrary granularity.
• Multi-modal music editing: Inpainting/outpainting, style transfer with mixed text/audio/attribute inputs.
• Creative and educational tools: Enabling both expert and non-expert users to generate music fulfilling explicit constraints or reference signals.
• Rapid prototyping and adaptation: Low fine-tuning cost supports quick adaptation to new tasks, genres, or datasets.

Control signals can be derived automatically (from MIDI, audio segmentation, or external symbolic analysis) or input interactively by users, supporting diverse user cohorts and workflows.

7. Resources and Accessibility

Code, model weights, and demonstration audio are hosted at https://MuseControlLite.github.io/web/, with dataset creation scripts at https://github.com/fundwotsai2001/Text-to-music-dataset-preparation. Evaluation protocols and metrics (including FAD, CLAP, and controllability indices) are open-sourced to facilitate benchmarking and reproducibility.

MuseControlLite’s design makes fine-grained, time-varying control in music generation accessible on standard research infrastructure, with sufficient flexibility to serve as a foundation for further developments in controllable neural music modeling.