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DITTO: Diffusion Inference-Time T-Optimization for Music Generation
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Proposes DITTO, a training-free method that optimizes the initial noise latent of a pre-trained text-to-music diffusion model through the sampler with gradient checkpointing to achieve inpainting, outpainting, looping, intensity, melody, and structure control.
Problem
Adding fine-grained, time-varying control (intensity, melody, structure, in/outpainting) to text-to-music diffusion models usually requires expensive supervised fine-tuning, while inference-time guidance methods are inexact and struggle with fine-grained control.
Approach
Treat the initial noise latent x_T as a free parameter and optimize it through the full differentiable diffusion sampling chain under any differentiable feature-matching loss, using per-step gradient checkpointing to keep memory low; no model weights are updated.
Key results
- DITTO achieves intensity MSE 4.758 vs 38.411 for Music ControlNet and 23.292 for FreeDoM, with CLAP 0.433, without any supervised training.
- On outpainting and looping, DITTO attains the lowest FAD in all settings (e.g., 0.716 at overlap 1 s vs 0.719-0.754 for baselines).
- Versus DOODL on intensity control at 20 sampling steps, DITTO has about 2x lower per-step time (1.859 vs 4.177 s) and about 40% less memory (5.002 vs 8.274 GB) with similar convergence.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| optimize initial noise latents for outpainting rather than use DOODL | DITTO · Outpainting, overlap sizes 1–3 s | FAD: 0.719 -> 0.716; 0.707 -> 0.703; 0.700 -> 0.698 | Helped |
| optimize initial noise latents for inpainting rather than use DOODL | DITTO · Inpainting, gaps 2–4 s | FAD: 0.688 -> 0.686; 0.693 -> 0.688; 0.696 -> 0.690 | Helped |
| use DITTO instead of DOODL | Intensity control · Intensity control | MSE: 4.785 -> 4.758 | Helped |
| use DITTO instead of ControlNet | Intensity control · Intensity control | FAD: 0.637 -> 0.682 | Hurt |
| use DITTO instead of ControlNet | Melody control · Melody control | Accuracy: 81.353 -> 82.625 | Helped |
| use DITTO instead of ControlNet | Melody control · Melody control | FAD: 0.545 -> 0.699 | Hurt |
| use DITTO instead of FreeDoM | Structure control · Structure control | MSE: 0.018 -> 0.024 | Hurt |
| use DITTO instead of DOODL | Structure control · Structure control | FAD: 0.653 -> 0.632 | Helped |
| use DITTO rather than DOODL | Intensity control, 20 sampling steps · Intensity control | MOS (s/step): 4.177 -> 1.859 | Helped |
| use DPM++ instead of DDIM sampling (ablation) | DITTO · Intensity control | MSE: 4.77 -> 6.30 | Hurt |
| use DPM++ instead of DDIM sampling (ablation) | DITTO · Outpainting | FAD: 9.19 -> 9.12 | Helped |
| replace absolute-intensity MSE loss with correlation loss (ablation) | DITTO · Intensity control | Correlation (ρ): 0.6166 -> 0.9040 | Helped |
Surprising findings
- Optimizing only the initial noise latent achieves SOTA control on many music tasks, despite diffusion latents usually being thought to encode little semantic information.
- Music ControlNet reaches high intensity correlation yet poor absolute intensity MSE, suggesting it models correlation rather than absolute dB values.
Limitations
- Optimization is slow per output (inference-time optimization), limiting real-time use.
- Reward hacking / quality degradation can occur with over-optimization (seen in DOODL), and higher learning rates cause instability.
- Multi-objective combinations (e.g., intensity+melody) still perform poorly for all methods.
Experimental setup
Datasets: licensed instrumental music (~1800 h) Wikifonia Lead-Sheet Dataset (synthesized melody references) MusicCaps (evaluation) Free Music Archive (structure transfer)
Benchmarks: MusicCaps
Metrics: FAD (CLAP backbone) MSE accuracy CLAP score MS2C MOS MAM listening test win rate and score difference
Baselines: Naive masking MultiDiffusion FreeDoM Guidance Gradients Music ControlNet DOODL
Builds on: DDIM classifier-free guidance DOODL Music ControlNet gradient checkpointing
Closest papers by meaning
- Presto! Distilling Steps and Layers for Accelerating Music Generation (ICLR 2025 · similarity 0.90)
- Live Music Diffusion Models: Efficient Fine-Tuning and Post-Training of Interactive Diffusion Music Generators (NeurIPS 2026 · similarity 0.89)
- Fast Timing-Conditioned Latent Audio Diffusion (ICML 2024 · similarity 0.89)
- Multi-Source Diffusion Models for Simultaneous Music Generation and Separation (ICLR 2024 · similarity 0.89)
- JEN-1 Composer: A Unified Framework for High-Fidelity Multi-Track Music Generation (AAAI 2025 · similarity 0.88)
- MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners (ICML 2025 · similarity 0.87)
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