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MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners
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Proposes MuseControlLite, a lightweight adapter that adds RoPE to decoupled cross-attention in Stable Audio Open's diffusion Transformer, enabling time-varying musical attribute (melody, rhythm, dynamics) and audio inpainting/outpainting control with 85M trainable parameters.
Problem
Adding precise time-varying musical attribute and reference-audio controls to text-to-music diffusion models usually requires heavy ControlNet-style duplication of the backbone, and prior work has not jointly handled attribute and audio conditions.
Approach
Apply IP-Adapter-style decoupled cross-attention to a diffusion Transformer, with rotary positional embeddings applied to the query, key and value of the decoupled attribute branch so that time-varying conditions are aligned with the audio timeline; audio conditions use separate adapters with multi-scale classifier-free guidance.
Key results
- Adding RoPE to decoupled cross-attention raises melody accuracy from 56.6% (Stable Audio Open ControlNet) to 61.1% (MuseControlLite-Melody) with 85M trainable parameters vs 572M.
- Without RoPE, the adapter fails to learn melody control (10.7% melody accuracy, FD 113.13).
- MuseControlLite-Melody achieves the best FD (76.42) but slightly lower KL and CLAP than Stable Audio Open ControlNet.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add RoPE to the decoupled cross-attention layers (ablation) | MuseControlLite · Song Describer melody control | Mel acc.: 10.7% -> 58.6% | Helped |
| Add RoPE to the decoupled cross-attention layers (ablation) | MuseControlLite · Song Describer melody control | FD: 113.13 -> ~~78.50 | Helped |
| Add RoPE to the decoupled cross-attention layers (ablation) | MuseControlLite · Song Describer melody control | CLAP: 0.41 -> 0.38 | Hurt |
| Add a 1D-CNN condition extractor (ablation) | MuseControlLite · Song Describer melody control | Mel acc.: 57.2% -> 58.6% | Helped |
| Add zero-initialized 1D-CNN layers to sum cross-attention outputs (ablation) | MuseControlLite · Song Describer melody control | Mel acc.: 56.7% -> 58.6% | Helped |
| Double attention heads by scaling W'k and W'v (ablation) | MuseControlLite · Song Describer melody control | Mel acc.: 58.6% -> 58.4% | Hurt |
| Provide melody control | MuseControlLite-Attr · Song Describer style-transfer control | Mel acc.: 0.09 -> 0.60 | Helped |
| Provide rhythm control | MuseControlLite-Attr · Song Describer style-transfer control | Rhy F1: 0.21 -> 0.89 | Helped |
| Provide dynamics control | MuseControlLite-Attr · Song Describer style-transfer control | Dyn cor.: 0.05 -> 0.92 | Helped |
| Use melody-only conditioning instead of text condition only | MuseControlLite outpainting · Song Describer outpainting | Mel acc.: 0.14 -> 0.57 | Helped |
| Use rhythm-only conditioning instead of text condition only | MuseControlLite outpainting · Song Describer outpainting | Rhy F1: 0.45 -> 0.95 | Helped |
| Use dynamics-only conditioning instead of text condition only | MuseControlLite outpainting · Song Describer outpainting | Dyn cor.: 0.14 -> 0.77 | Helped |
Surprising findings
- Without RoPE the decoupled cross-attention adapter essentially fails to learn melody control (10.7% melody accuracy), despite positional embeddings being rarely used for text conditions.
- Training with both attribute and audio conditions simultaneously caused the model to ignore the attribute condition, requiring separate adapters and complementary masking.
Limitations
- Multiple classifier-free guidance slightly slows inference due to multi-batch processing.
- Smooth transitions are hard when the text prompt deviates strongly from the reference audio.
- Trained only on MTG-Jamendo, which is mostly electronic music, limiting genre generality.
Experimental setup
Datasets: MTG-Jamendo Song Describer Dataset
Benchmarks: Song Describer Dataset (instrumental subset, 586 clips)
Metrics: FD_openl3 KL_passt CLAP score melody accuracy rhythm F1 dynamics correlation smoothness value
Baselines: MusicGen-Stereo-Large-Melody Stable Audio Open ControlNet Naive masking MusicGen-Stereo-Large
Builds on: IP-Adapter Stable Audio Open ControlNet Music ControlNet classifier-free guidance
Closest papers by meaning
- MIDI-GPT: A Controllable Generative Model for Computer-Assisted Multitrack Music Composition (AAAI 2025 · similarity 0.89)
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