# DITTO: Diffusion Inference-Time T-Optimization for Music Generation

arXiv: https://arxiv.org/abs/2401.12179 · HTML: https://mlsanity.com/paper/2401.12179/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 67 of its 85 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 30 of 67)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Diffusion Posterior Sampling for General Noisy Inverse Problems](https://mlsanity.com/paper/2209.14687/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Training Diffusion Models with Reinforcement Learning](https://mlsanity.com/paper/2305.13301/) (2023)
- [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022)
- [Training Deep Nets with Sublinear Memory Cost](https://mlsanity.com/paper/1604.06174/) (2016)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)

## Built on by (most cited first, 30 of 73)

- [Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps](https://mlsanity.com/paper/2501.09732/) (2025)
- [ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization](https://mlsanity.com/paper/2406.04312/) (2024)
- [Long-form music generation with latent diffusion](https://mlsanity.com/paper/2404.10301/) (2024)
- [Inference-Time Alignment of Diffusion Models with Direct Noise Optimization](https://mlsanity.com/paper/2405.18881/) (2024)
- [Noise Hypernetworks: Amortizing Test-Time Compute in Diffusion Models](https://mlsanity.com/paper/2508.09968/) (2025)
- [Diff-A-Riff: Musical Accompaniment Co-creation via Latent Diffusion Models](https://mlsanity.com/paper/2406.08384/) (2024)
- [MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models](https://mlsanity.com/paper/2402.06178/) (2024)
- [DITTO-2: Distilled Diffusion Inference-Time T-Optimization for Music Generation](https://mlsanity.com/paper/2405.20289/) (2024)
- [Aligning Text-to-Music Evaluation with Human Preferences](https://mlsanity.com/paper/2503.16669/) (2025)
- [Presto! Distilling Steps and Layers for Accelerating Music Generation](https://mlsanity.com/paper/2410.05167/) (2024)
- [PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing](https://mlsanity.com/paper/2409.10831/) (2024)
- [High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching](https://mlsanity.com/paper/2407.03648/) (2024)
- [Editing Music with Melody and Text: Using ControlNet for Diffusion Transformer](https://mlsanity.com/paper/2410.05151/) (2024)
- [Source-Guided Flow Matching](https://mlsanity.com/paper/2508.14807/) (2025)
- [CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects](https://mlsanity.com/paper/2505.21437/) (2025)
- [Improving Musical Accompaniment Co-creation via Diffusion Transformers](https://mlsanity.com/paper/2410.23005/) (2024)
- [It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models](https://mlsanity.com/paper/2601.00090/) (2025)
- [Live Music Models](https://mlsanity.com/paper/2508.04651/) (2025)
- [SoundCTM: Unifying Score-based and Consistency Models for Full-band Text-to-Sound Generation](https://mlsanity.com/paper/2405.18503/) (2024)
- [MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners](https://mlsanity.com/paper/2506.18729/) (2025)
- [MEDIC: Zero-shot Music Editing with Disentangled Inversion Control](https://mlsanity.com/paper/2407.13220/) (2024)
- [DiTSE: High-Fidelity Generative Speech Enhancement via Latent Diffusion Transformers](https://mlsanity.com/paper/2504.09381/) (2025)
- [Steering Autoregressive Music Generation with Recursive Feature Machines](https://mlsanity.com/paper/2510.19127/) (2025)
- [Stable Audio 3](https://mlsanity.com/paper/2605.17991/) (2026)
- [Futga: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation](https://mlsanity.com/paper/2407.20445/) (2024)
- [Multi-Track MusicLDM: Towards Versatile Music Generation with Latent Diffusion Model](https://mlsanity.com/paper/2409.02845/) (2024)
- [MOSAIC: Generating Consistent, Privacy-Preserving Scenes from Multiple Depth Views in Multi-Room Environments](https://mlsanity.com/paper/2503.13816/) (2025)
- [A Survey on Music Generation from Single-Modal, Cross-Modal, and Multi-Modal Perspectives](https://mlsanity.com/paper/2504.00837/) (2025)
- [DRAGON: Distributional Rewards Optimize Diffusion Generative Models](https://mlsanity.com/paper/2504.15217/) (2025)
- [ZeroSep: Separate Anything in Audio with Zero Training](https://mlsanity.com/paper/2505.23625/) (2025)

## Abstract (arXiv)

We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-work for controlling pre-trained text-to-music diffusion models at inference-time via optimizing initial noise latents. Our method can be used to optimize through any differentiable feature matching loss to achieve a target (stylized) output and leverages gradient checkpointing for memory efficiency. We demonstrate a surprisingly wide-range of applications for music generation including inpainting, outpainting, and looping as well as intensity, melody, and musical structure control - all without ever fine-tuning the underlying model. When we compare our approach against related training, guidance, and optimization-based methods, we find DITTO achieves state-of-the-art performance on nearly all tasks, including outperforming comparable approaches on controllability, audio quality, and computational efficiency, thus opening the door for high-quality, flexible, training-free control of diffusion models. Sound examples can be found at https://DITTO-Music.github.io/web/.