MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners (2506.18729v1)

Abstract: 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/.

MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners

MuseControlLite introduces a parameter-efficient fine-tuning mechanism for controllable text-to-music generation, targeting precise manipulation of time-varying musical attributes and reference audio signals. The approach is motivated by the need for more accessible, fine-grained control in music generation models, while reducing the computational and memory overhead associated with existing methods such as ControlNet-based architectures.

Technical Contributions

MuseControlLite is built upon a diffusion Transformer backbone (Stable Audio Open), and its core innovation lies in the integration of rotary positional embeddings (RoPE) into decoupled cross-attention layers. This design enables the model to effectively process time-varying conditions—such as melody, rhythm, and dynamics—by encoding temporal information directly into the attention mechanism. The model supports simultaneous conditioning on both musical attributes and reference audio, a capability not present in prior fine-tuning approaches.

Key technical features include:

• Decoupled Cross-Attention with RoPE: By augmenting the cross-attention layers with RoPE, MuseControlLite achieves a significant increase in control accuracy for time-varying conditions, with a 4.5% improvement in melody accuracy over ControlNet-based baselines, while using 6.75 times fewer trainable parameters (85M vs. 572M).
• Lightweight Adapter Design: Only the adapters, feature extractors, and zero-initialized 1D convolution layers are trainable, with the rest of the backbone frozen. This results in an 8% increase in trainable parameters relative to the backbone, enabling efficient fine-tuning on modest hardware (single RTX 3090).
• Multi-Attribute and Audio Conditioning: The architecture supports flexible combinations of text, musical attribute, and audio conditions, enabling applications such as style transfer, audio inpainting, and outpainting.
• Multiple Classifier-Free Guidance: Separate guidance scales for each condition type (text, attribute, audio) allow for fine-grained control over the influence of each conditioning signal during inference.

Implementation Details

The model is fine-tuned on the MTG-Jamendo dataset, with careful preprocessing to exclude vocals and ensure evaluation integrity. Musical attributes are extracted using established signal processing and neural methods (e.g., CQT for melody, Savitzky-Golay smoothing for dynamics, RNN-based beat detection for rhythm). Feature extraction pipelines employ 1D CNNs, and sequence lengths are matched via interpolation.

During training, random masking of conditions encourages the model to disentangle and generalize control signals, supporting partial conditioning and improvisation in unconditioned segments. For audio inpainting/outpainting, complementary masking ensures that the model does not overfit to the more informative audio condition at the expense of attribute control.

The model is trained for 40,000 steps with a batch size of 128, using a v-prediction parameterization for stability. Inference employs 50 denoising steps to generate 47-second audio clips.

Empirical Results

MuseControlLite demonstrates strong performance across multiple tasks:

• Melody Control: Achieves 61.1% melody accuracy, outperforming both MusicGen-Stereo-Large-Melody (43.1%) and Stable Audio Open ControlNet (56.6%), with lower FD and comparable CLAP scores.
• Multi-Attribute Control: Provides significant improvements in controllability metrics (melody accuracy, rhythm F1, dynamics correlation) when the relevant condition is supplied, both in style transfer and non-style transfer settings.
• Audio Inpainting/Outpainting: Outperforms autoregressive and naive masking baselines in audio realism and smoothness of transitions, with the ability to flexibly combine text, attribute, and audio conditions.
• Subjective Evaluation: User studies indicate that MuseControlLite matches the perceptual quality of state-of-the-art ControlNet-based models, despite its reduced parameter count and training data requirements.

Implications and Future Directions

MuseControlLite's lightweight design lowers the barrier for integrating advanced controllability into text-to-music systems, making such capabilities accessible to a broader range of practitioners and applications. The model's ability to handle both attribute and audio conditioning simultaneously enables new creative workflows, such as precise editing, style transfer, and interactive music generation.

From a theoretical perspective, the empirical finding that positional embeddings are critical for time-varying control in cross-attention layers suggests a broader applicability of this technique in other generative domains where temporal or spatial alignment is essential.

Potential future developments include:

• Further Adapter Optimization: Exploring more efficient or dynamic adapter architectures to further reduce training cost and improve control precision.
• Enhanced Feature Extraction: Improving the extraction and representation of musical attributes, especially for genres or attributes not well-captured by current methods.
• Broader Dataset Coverage: Fine-tuning on more diverse datasets to improve generalization across musical styles and genres.
• Real-Time and Interactive Applications: Adapting the model for low-latency, real-time music generation and editing scenarios.

Limitations

• Inference Speed: The use of multiple classifier-free guidance mechanisms introduces some inference overhead due to multi-batch processing.
• Transition Smoothness: In audio inpainting/outpainting, transitions may be less smooth if the text prompt diverges significantly from the reference audio.
• Genre Coverage: Training on MTG-Jamendo limits performance on non-electronic genres; broader datasets are needed for general-purpose deployment.

Conclusion

MuseControlLite establishes a new standard for parameter-efficient, multifunctional control in text-to-music generation. Its architectural innovations and empirical results demonstrate that precise, flexible, and computationally accessible music generation is achievable without the overhead of large-scale fine-tuning. The approach is well-positioned to inform future research and practical systems in controllable generative audio.