# DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

arXiv: https://arxiv.org/abs/2301.13721 · HTML: https://mlsanity.com/paper/2301.13721/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 38 of its 50 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 30 of 38)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (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)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021)
- [ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2108.02938/) (2021)
- [Disentangling by Factorising](https://mlsanity.com/paper/1802.05983/) (2018)
- [GANSpace: Discovering Interpretable GAN Controls](https://mlsanity.com/paper/2004.02546/) (2020)
- [Improving Diffusion Models for Inverse Problems using Manifold Constraints](https://mlsanity.com/paper/2206.00941/) (2022)
- [Diffusion Autoencoders: Toward a Meaningful and Decodable Representation](https://mlsanity.com/paper/2111.15640/) (2021)
- [Diffusion Models already have a Semantic Latent Space](https://mlsanity.com/paper/2210.10960/) (2022)
- [Towards a Definition of Disentangled Representations](https://mlsanity.com/paper/1812.02230/) (2018)
- [Unsupervised Discovery of Interpretable Directions in the GAN Latent Space](https://mlsanity.com/paper/2002.03754/) (2020)
- [CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information](https://mlsanity.com/paper/2006.12013/) (2020)
- [More Control for Free! Image Synthesis with Semantic Diffusion Guidance](https://mlsanity.com/paper/2112.05744/) (2021)
- [High-Fidelity GAN Inversion for Image Attribute Editing](https://mlsanity.com/paper/2109.06590/) (2021)
- [Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2212.08698/) (2022)
- [On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset](https://mlsanity.com/paper/1906.03292/) (2019)

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

- [Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement](https://mlsanity.com/paper/2402.09712/) (2024)
- [ChatFace: Chat-Guided Real Face Editing via Diffusion Latent Space Manipulation](https://mlsanity.com/paper/2305.14742/) (2023)
- [Learning Semantic Latent Directions for Accurate and Controllable Human Motion Prediction](https://mlsanity.com/paper/2407.11494/) (2024)
- [Flow Factorized Representation Learning](https://mlsanity.com/paper/2309.13167/) (2023)
- [Disentangling representations of retinal images with generative models](https://mlsanity.com/paper/2402.19186/) (2024)
- [Diffusion Bridge AutoEncoders for Unsupervised Representation Learning](https://mlsanity.com/paper/2405.17111/) (2024)
- [Do Diffusion Models Learn Semantically Meaningful and Efficient Representations?](https://mlsanity.com/paper/2402.03305/) (2024)
- [Modular Machine Learning: An Indispensable Path towards New-Generation Large Language Models](https://mlsanity.com/paper/2504.20020/) (2025)
- [FreeSliders: Training-Free, Modality-Agnostic Concept Sliders for Fine-Grained Diffusion Control in Images, Audio, and Video](https://mlsanity.com/paper/2511.00103/) (2025)
- [Unsupervised Representation Learning from Sparse Transformation Analysis](https://mlsanity.com/paper/2410.05564/) (2024)
- [DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks](https://mlsanity.com/paper/2503.09679/) (2025)
- [A Controllable Appearance Representation for Flexible Transfer and Editing](https://mlsanity.com/paper/2504.15028/) (2025)
- [Feature-Based Lie Group Transformer for Real-World Applications](https://mlsanity.com/paper/2506.04668/) (2025)
- [Disentangled Representation Learning via Flow Matching](https://mlsanity.com/paper/2602.05214/) (2026)
- [StrADiff: A Structured Source-Wise Adaptive Diffusion Framework for Linear and Nonlinear Blind Source Separation](https://mlsanity.com/paper/2604.04973/) (2026)
- [Transferring disentangled representations: bridging the gap between synthetic and real images](https://mlsanity.com/paper/2409.18017/) (2024)
- [On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning](https://mlsanity.com/paper/2506.00136/) (2025)
- [Disentangled Representation Learning via Modular Compositional Bias](https://mlsanity.com/paper/2510.21402/) (2025)
- [Representation Disentaglement via Regularization by Causal Identification](https://mlsanity.com/paper/2303.00128/) (2023)
- [Independence Constrained Disentangled Representation Learning from Epistemological Perspective](https://mlsanity.com/paper/2409.02672/) (2024)
- [Disentanglement as Identifiable Pushforward Factorisation](https://mlsanity.com/paper/2410.22559/) (2024)
- [Progressive Monitoring of Generative Model Training Evolution](https://mlsanity.com/paper/2412.12755/) (2024)
- [Enhancing Interpretability of Sparse Latent Representations with Class Information](https://mlsanity.com/paper/2505.14476/) (2025)
- [Disentangled representations of microscopy images](https://mlsanity.com/paper/2506.20649/) (2025)
- [CLOAK: Contrastive Guidance for Latent Diffusion-Based Data Obfuscation](https://mlsanity.com/paper/2512.12086/) (2025)
- [Disentangled representations via score-based variational autoencoders](https://mlsanity.com/paper/2512.17127/) (2025)
- [XFACTORS: Disentangled Information Bottleneck via Contrastive Supervision](https://mlsanity.com/paper/2601.21688/) (2026)
- [CenSynCMB: Centre Maps and Physics-Guided Synthesis for Microbleed Detection](https://mlsanity.com/paper/2607.05325/) (2026)
- [Decafs: Disentangled Conditional adversarial Flows](https://mlsanity.com/paper/2607.18755/) (2026)
- [Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation](https://mlsanity.com/paper/2608.25862/) (2026)

## Abstract (arXiv)

Targeting to understand the underlying explainable factors behind observations and modeling the conditional generation process on these factors, we connect disentangled representation learning to Diffusion Probabilistic Models (DPMs) to take advantage of the remarkable modeling ability of DPMs. We propose a new task, disentanglement of (DPMs): given a pre-trained DPM, without any annotations of the factors, the task is to automatically discover the inherent factors behind the observations and disentangle the gradient fields of DPM into sub-gradient fields, each conditioned on the representation of each discovered factor. With disentangled DPMs, those inherent factors can be automatically discovered, explicitly represented, and clearly injected into the diffusion process via the sub-gradient fields. To tackle this task, we devise an unsupervised approach named DisDiff, achieving disentangled representation learning in the framework of DPMs. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of DisDiff.