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MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners

Fang-Duo Tsai, Shih-Lun Wu, Weijaw Lee, Sheng-Ping Yang, Bo-Rui Chen, Hao-Chung Cheng, Yi-Hsuan Yang

arXiv:2506.18729v2cs.SDcs.AIeess.AS

TL;DR

Text-to-music models need more precise control over time-varying musical attributes and reference audio, while existing approaches can be over-parameterized or handle these condition types separately. MuseControlLite uses decoupled cross-attention augmented with positional embeddings as a lightweight fine-tuning mechanism. It improves controllability across musical-attribute control, audio inpainting, and audio outpainting while using substantially fewer trainable parameters.

  • Problem

    Existing text-to-music control methods can be over-parameterized, and little work jointly handles musical-attribute conditions and reference-audio conditions.

  • Method

    MuseControlLite combines rotary positional embeddings with decoupled cross-attention to fine-tune text-to-music models for time-varying musical attributes and reference-audio control.

  • Results

    MuseControlLite demonstrates superior controllability over existing ControlNet-based approaches, including a 4.5% improvement in melody accuracy on the Song Describer benchmark.

  • Takeaways & Limitations

    The method supports musical-attribute control, audio inpainting, and audio outpainting with 85M trainable parameters, providing a lightweight alternative to competing models.

  • Takeaways & Limitations

    MuseControlLite is trained solely on MTG-Jamendo, which contains mostly electronic music, so more diverse fine-tuning data may improve performance on other genres.

Abstract

from arXiv · show

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/.

1. Introduction

MuseControlLite addresses over-parameterization and the limited integration of musical-attribute and reference-audio control in text-to-music generation. It combines decoupled cross-attention with rotary positional embeddings to provide lightweight, flexible control, outperforming existing ControlNet-based approaches in melody control.

  • Existing controllable text-to-music methods include attribute-specific, audio-specific, and broader time-varying control approaches, motivating a more parameter-efficient alternative.The introduction situates MuseControlLite among methods for chords, rhythm, melody, and dynamics.
  • 85M versus 572M trainable parameters gives MuseControlLite nearly an order-of-magnitude reduction relative to ControlNet-based mechanisms.The implementation adds only 8% trainable parameters relative to the diffusion Transformer backbone.
  • Audio conditioning can replicate reference audio at full resolution while retaining partial control for audio inpainting and outpainting.Multiple classifier-free guidance mechanisms regulate time-varying condition strength and help prevent quality degradation from over-fixation.
  • MuseControlLite introduces rotary positional embeddings into decoupled cross-attention for controlling time-varying musical conditions.This is presented as the first investigation of positional embeddings in decoupled cross-attention layers for controllable text-to-music generation.
  • The method jointly supports musical-attribute and reference-audio control, unlike prior fine-tuning methods that generally address either attribute or audio control.This enables combinations such as melody conditioning during audio inpainting or outpainting.
  • MuseControlLite outperforms existing ControlNet-based approaches in melody control, achieving a 4.5% improvement in melody accuracy.This result comes from the public Song Describer evaluation benchmark.

2. Related Work

Related work spans global-condition fine-tuning, local-condition training or fine-tuning, and training-free inference-time optimization. These approaches differ in computational demands, flexibility, and achievable generation quality.

  • Controllable music generation covers global conditions such as text, tempo, instrumentation, timbre, and mood, alongside local time-varying conditions such as chords, rhythm, dynamics, and melody.
  • Direct fine-tuning has been used to add global controls including personalization, chords, tempo, key, and text-based music editing.Examples include DreamBooth, textual inversion, Mustango, Instruct-MusicGen, and MusicGen-style.
  • Local-condition methods support granular controls such as melody, chords, and rhythm, but some require training from scratch and substantial computational resources.MusicGen and JASCO train from scratch, while MusiConGen and Coco-Mulla fine-tune MusicGen for selected conditions.
  • Training-free approaches avoid additional model training and offer computational efficiency and adaptability, but often have quality limitations relative to fully trained models.MuseControlLite addresses similar controllability objectives through training on an open model.

3. MuseControlLite

MuseControlLite adapts a diffusion Transformer for time-varying musical and audio conditions by combining decoupled cross-attention with rotary positional embeddings and lightweight condition-specific adapters. It supports musical-attribute control alongside audio inpainting and outpainting, with separate pipelines that can be combined at inference.

  • Proposed Adapter Design: MuseControlLite introduces rotary positional embeddings into decoupled cross-attention to represent time-varying musical attributes in a diffusion Transformer.The musical-attribute condition is a sequence over time, and ROPE is applied to query, key, and value vectors while duplicated key and value projections are trained.
  • Musical Attribute Control: Using separate channel processing for melody extraction improves melody accuracy to 64.5% after 18,000 training steps.The implementation computes the CQT separately for the left and right audio channels, yielding a finer-grained melody condition.
  • Musical Attribute Control: The model combines separate condition extractors into a cross-attention representation by projecting, length-matching, and concatenating musical attributes.Independent 1D CNNs process the conditions, expand them to Cr/3 channels, interpolate them to the query length, and concatenate them into cattr.
  • Musical Attribute Control: Partial conditioning masks 10% to 90% of each musical attribute independently, allowing specified segments to be controlled while unconditioned segments can be improvised.The masking strategy is applied independently to melody, dynamics, and rhythm during training.
  • Audio Inpainting and Outpainting: The audio pipeline uses VAE-encoded clean latents as conditions for audio inpainting and outpainting, while musical attributes provide more flexible control over masked segments.Because audio conditions carry more information, joint training can cause the model to ignore musical attributes; the resulting design supports partial audio control and musical-attribute control together.

4. Experimental setup

The experiments use open-source music data and benchmark MuseControlLite against controllable text-to-music baselines across melody, attribute control, and audio inpainting/outpainting. Evaluation measures controllability, text adherence, audio realism, and transition smoothness.

  • Dataset: The evaluation uses 586 instrumental Song Describer clips, excluding vocal prompts and removing overlaps between training data and the evaluation set.The training data come from MTG-Jamendo, while Song Describer is reserved for evaluation.
  • Training: The training objective uses v-prediction, where the velocity term combines noise and clean data through time-dependent coefficients to improve training stability.The noise schedule samples t from [0, 1], with α_t and σ_t defined by cosine and sine functions.
  • Baselines and evaluation scope: The experimental comparison includes naïve masking for audio inpainting and outpainting, alongside MusicGen and Stable Audio Open ControlNet baselines.Naïve masking overwrites the reference region after each denoising step with a noisy version of the reference audio.
  • Metrics: Evaluation measures musical attribute controllability, text adherence, and audio realism using melody accuracy, rhythm F1, dynamics correlation, FD_openl3, KL_passt, and CLAPscore.Melody accuracy compares frame-wise pitch classes between reference and generated audio, while the other metrics assess rhythm, dynamics, realism, and semantic alignment.
  • Baselines and evaluation scope: MuseControlLite is compared with MusicGen and Stable Audio Open ControlNet for melody control, with the proposed model reported as achieving the best melody accuracy and acceptable musical quality using fewer trainable parameters and training data.Table 3 covers melody accuracy and musical quality metrics in this comparison.
  • Metrics: Smoothness Value evaluates inpainting and outpainting boundaries using the second finite difference of a novelty curve, with lower values indicating less smooth transitions.The novelty curve is derived from novelty-based segmentation of the generated audio.

5. Result

MuseControlLite achieves strong controllability across melody-conditioned generation, musical-attribute transfer, audio outpainting, and audio inpainting, with subjective performance comparable to Stable Audio Open ControlNet.

  • 5.1. Melody conditioned generation Comparison: Both MuseControlLite-Melody and MuseControlLite-Attr outperform baselines on FD and melody accuracy despite fewer trainable parameters and less training data, while slightly underperforming Stable Audio Open ControlNet on KL and CLAP.The authors attribute the KL and CLAP difference partly to Stable Audio Open ControlNet using more diverse training data than MTG-Jamendo alone.
  • 5.2. Musical attribute control: MuseControlLite-Attr supports arbitrary combinations of musical-attribute controls, with style transfer showing worse FD, KL, and CLAP than non-style transfer while attribute controllability remains approximately unchanged.Melody controls may retain timbre or genre information from the reference audio, whereas rhythm and dynamics produce milder KL and CLAP degradation.
  • 5.3. Audio Outpainting and Inpainting: Text-only audio outpainting outperforms MusicGen-Large on every reported aspect except Rhythm F1, while achieving better objective results than the autoregressive baseline.The results suggest cross-attention can learn to improvise missing segments without an audio condition; naive masking has better CLAP but the lowest smoothness.
  • 5.3. Audio Outpainting and Inpainting: Audio inpainting achieves audio realism and text adherence similar to outpainting, but musical-attribute control is more difficult because the generated segment must make two transitions.Evaluation retains only the first and last 5 seconds of a reference and scores the generated middle portion.
  • 5.4. Subjective Evaluation: Subjective evaluation compares text adherence, melody similarity, and overall preference, with MuseControlLite performing comparably to Stable Audio Open ControlNet.The user-study measures are defined on a 1–5 Likert scale and include outputs from baseline methods.

6. Limitations

The method has limitations involving inference speed, transition smoothness under mismatched prompts, and training-data coverage.

  • Multiple classifier-free guidance mechanisms slightly slow inference because they require multi-batch processing.
  • When text prompts deviate substantially from reference audio, audio inpainting and outpainting may produce nonsmooth transitions.
  • Training solely on the mostly electronic MTG-Jamendo dataset limits performance on other genres without fine-tuning on more diverse data.

7. Conclusion

The paper introduces MuseControlLite as a lightweight method for musical-attribute control plus audio inpainting and outpainting, and reports superior results against competing ControlNet-based methods.

  • MuseControlLite enables precise musical-attribute control alongside text-only or attribute-controlled audio inpainting and outpainting.
  • The method is benchmarked against state-of-the-art ControlNet-based approaches for structural control and demonstrates superior results in the reported comparisons.
  • Future work includes modifying attention for more efficient training and more precise control, plus improving control over conditions that current feature extractors cannot accurately extract.

Impact Statement

MuseControlLite is presented as a lower-barrier tool for precise time-varying music control, while its broader use raises intellectual-property, cultural-expression, and creator-role concerns.

  • Fewer trainable parameters may make precise time-varying control more accessible to artists, hobbyists, researchers, and resource-constrained developers.
  • The technology could support customized soundtracks, music-learning tools, artistic experimentation, and commercial prototyping while reducing technical overhead.
  • Responsible use requires attention to intellectual property, cultural expression, changing creator roles, transparent data governance, copyright, and cultural context.

A. Separated guidance scale formulation

The formulation generalizes classifier-free guidance to multiple conditions by decomposing the conditional score with Bayes’ rule, logarithms, differentiation, and condition-specific scaling.

  • A. Separated guidance scale formulation: The method starts from a joint conditional probability and applies Bayes’ rule to expand guidance from one condition to multiple conditions.The derivation proceeds from the general conditional distribution before transforming its terms.
  • A. Separated guidance scale formulation: Taking logarithms converts probability multiplications into additions, making the multi-condition score decomposable.
  • A. Separated guidance scale formulation: Differentiating with respect to x removes the constant term and yields the conditional score as an unconditional score plus condition-dependent terms.
  • A. Separated guidance scale formulation: Each condition term is scaled by its own guidance λ_i, allowing control strength to differ across conditions.The resulting expression leads to Equation (9) in Section 3.5.

B. Ablation study for all key module in MuseControlLite

The ablation study shows that RoPE is essential for melody control, while the condition extractor and zero-initialized cross-attention summation layers support overall performance; attention maps indicate effective use of partial audio.

  • B. Ablation study for all key module in MuseControlLite: Without RoPE, the model cannot perform melody control, despite achieving the highest CLAP score.This identifies positional information as critical for the time-varying melody-control task.
  • B. Ablation study for all key module in MuseControlLite: Removing the 1D-CNN condition extractor causes all metrics to drop, demonstrating its importance across evaluated controls.
  • B. Ablation study for all key module in MuseControlLite: Omitting the zero-initialized 1D-CNN layers that sum cross-attention outputs similarly degrades performance.
  • B. Ablation study for all key module in MuseControlLite: Table 9 reports performance for all combinations of controls using conditions extracted from the Song Describer Dataset.
  • B. Ablation study for all key module in MuseControlLite: After training, attention becomes partly diagonal under full audio and references previous keys under partial audio for inpainting and outpainting.These patterns indicate use of the supplied audio condition in both complete and partial settings.
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