mlsanityData to 7 Oct 2026

Can Diffusion Models Disentangle? A Theoretical Perspective

arXiv 2504.00220 ↗First posted 31 Mar 2025cs.LG cs.AI cs.CVcited by 3

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

This paper presents a novel theoretical framework for understanding how diffusion models can learn disentangled representations. Within this framework, we establish identifiability conditions for general disentangled latent variable models, analyze training dynamics, and derive sample complexity bounds for disentangled latent subspace models. To validate our theory, we conduct disentanglement experiments across diverse tasks and modalities, including subspace recovery in latent subspace Gaussian mixture models, image colorization, image denoising, and voice conversion for speech classification. Additionally, our experiments show that training strategies inspired by our theory, such as style guidance regularization, consistently enhance disentanglement performance.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 43 of its 65 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

43 of 65 references.

Closest ancestors · 11

Same direction, newest first

  1. 2024Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory18 Mar
  2. 2023SODA: Bottleneck Diffusion Models for Representation Learning29 Nov
  3. 2023Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data14 Feb
  4. 2022Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions22 Sep
  5. 2022Convergence of denoising diffusion models under the manifold hypothesis10 Aug
  6. 2022Convergence for score-based generative modeling with polynomial complexity13 Jun
  7. 2021Diffusion Schr"odinger Bridge with Applications to Score-Based Generative Modeling1 Jun
  8. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  9. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  10. 2020Denoising Diffusion Probabilistic Models19 Jun
  11. 2020Improved Techniques for Training Score-Based Generative Models16 Jun

Other references · 32

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  4. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  6. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  7. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  8. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  9. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
  10. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 929
  11. 2018Disentangling by Factorisingcited by 614
  12. 2018Gradient Descent Finds Global Minima of Deep Neural Networkscited by 572
20 more
  1. 2018Towards a Definition of Disentangled Representationscited by 294
  2. 2024Seed-TTS: A Family of High-Quality Versatile Speech Generation Modelscited by 293
  3. 2019AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Losscited by 189
  4. 2018Recent Advances in Autoencoder-Based Representation Learningcited by 130
  5. 2017No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysiscited by 127
  6. 2020Generative Modeling with Denoising Auto-Encoders and Langevin Samplingcited by 118
  7. 2022Uncovering the Disentanglement Capability in Text-to-Image Diffusion Modelscited by 116
  8. 2020Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challengecited by 104
  9. 2021Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Schemecited by 99
  10. 2017Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Datacited by 92
  11. 2021A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understandingcited by 82
  12. 2023Voice Conversion With Just Nearest Neighborscited by 67
  13. 2021VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversioncited by 63
  14. 2024EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmarkcited by 39
  15. 2023Diff-HierVC: Diffusion-based Hierarchical Voice Conversion with Robust Pitch Generation and Masked Prior for Zero-shot Speaker Adaptationcited by 30
  16. 2023Self-supervised Predictive Coding Models Encode Speaker and Phonetic Information in Orthogonal Subspacescited by 19
  17. 2023Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Modelscited by 19
  18. 2023Provable benefits of score matchingcited by 18
  19. 2023TriAAN-VC: Triple Adaptive Attention Normalization for Any-to-Any Voice Conversioncited by 10
  20. 2024Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformercited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026StrADiff: A Structured Source-Wise Adaptive Diffusion Framework for Linear and Nonlinear Blind Source Separationcited by 2
  2. 2026CenSynCMB: Centre Maps and Physics-Guided Synthesis for Microbleed Detectioncited by 0
  3. 2026Measured Sliders: Learning Continuous Controls from Differentiable Image Measurementscited by 0

Similar, not cited

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  1. 2025Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states
  2. 2024Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models
  3. 2024Isometric Representation Learning for Disentangled Latent Space of Diffusion Models
  4. 2024Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement
  5. 2023Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods
  6. 2025DiffSDA: Unsupervised Diffusion Sequential Disentanglement Across Modalities
  7. 2023DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models
  8. 2026Disentangled Representation Learning via Flow Matching