# Generalized Multi-Source Inference for Text Conditioned Music Diffusion Models

arXiv: https://arxiv.org/abs/2403.11706 · HTML: https://mlsanity.com/paper/2403.11706/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 24 of its 27 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Placed in

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

## Builds on (most cited first, 24 of 24)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [Stay on topic with Classifier-Free Guidance](https://mlsanity.com/paper/2306.17806/) (2023)
- [Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity](https://mlsanity.com/paper/1909.08494/) (2019)
- [AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models](https://mlsanity.com/paper/2304.00830/) (2023)
- [Mousai: Text-to-Music Generation with Long-Context Latent Diffusion](https://mlsanity.com/paper/2301.11757/) (2023)
- [Multi-Source Diffusion Models for Simultaneous Music Generation and Separation](https://mlsanity.com/paper/2302.02257/) (2023)
- [VampNet: Music Generation via Masked Acoustic Token Modeling](https://mlsanity.com/paper/2307.04686/) (2023)
- [SingSong: Generating musical accompaniments from singing](https://mlsanity.com/paper/2301.12662/) (2023)
- [Source Separation with Deep Generative Priors](https://mlsanity.com/paper/2002.07942/) (2020)
- [InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models](https://mlsanity.com/paper/2308.14360/) (2023)
- [Learning music audio representations via weak language supervision](https://mlsanity.com/paper/2112.04214/) (2021)
- [Improving Source Separation by Explicitly Modeling Dependencies Between Sources](https://mlsanity.com/paper/2203.15140/) (2022)
- [GASS: Generalizing Audio Source Separation with Large-scale Data](https://mlsanity.com/paper/2310.00140/) (2023)

## Built on by (most cited first, 13 of 13)

- [Diff-A-Riff: Musical Accompaniment Co-creation via Latent Diffusion Models](https://mlsanity.com/paper/2406.08384/) (2024)
- [Taming Data and Transformers for Audio Generation](https://mlsanity.com/paper/2406.19388/) (2024)
- [Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning](https://mlsanity.com/paper/2405.18386/) (2024)
- [COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations](https://mlsanity.com/paper/2404.16969/) (2024)
- [Improving Musical Accompaniment Co-creation via Diffusion Transformers](https://mlsanity.com/paper/2410.23005/) (2024)
- [Naturalistic Music Decoding from EEG Data via Latent Diffusion Models](https://mlsanity.com/paper/2405.09062/) (2024)
- [Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse Problems](https://mlsanity.com/paper/2406.06959/) (2024)
- [Multi-Source Music Generation with Latent Diffusion](https://mlsanity.com/paper/2409.06190/) (2024)
- [MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction](https://mlsanity.com/paper/2505.23305/) (2025)
- [ZeroSep: Separate Anything in Audio with Zero Training](https://mlsanity.com/paper/2505.23625/) (2025)
- [Step-by-Step Video-to-Audio Synthesis via Negative Audio Guidance](https://mlsanity.com/paper/2506.20995/) (2025)
- [The Interpretation Gap in Text-to-Music Generation Models](https://mlsanity.com/paper/2407.10328/) (2024)
- [Soundwich: Video Generation with Layered and Controllable Audio](https://mlsanity.com/paper/2610.00691/) (2026)

## Abstract (arXiv)

Multi-Source Diffusion Models (MSDM) allow for compositional musical generation tasks: generating a set of coherent sources, creating accompaniments, and performing source separation. Despite their versatility, they require estimating the joint distribution over the sources, necessitating pre-separated musical data, which is rarely available, and fixing the number and type of sources at training time. This paper generalizes MSDM to arbitrary time-domain diffusion models conditioned on text embeddings. These models do not require separated data as they are trained on mixtures, can parameterize an arbitrary number of sources, and allow for rich semantic control. We propose an inference procedure enabling the coherent generation of sources and accompaniments. Additionally, we adapt the Dirac separator of MSDM to perform source separation. We experiment with diffusion models trained on Slakh2100 and MTG-Jamendo, showcasing competitive generation and separation results in a relaxed data setting.