# Is Noise Conditioning Necessary for Denoising Generative Models?

arXiv: https://arxiv.org/abs/2502.13129 · HTML: https://mlsanity.com/paper/2502.13129/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Placed in

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

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Stochastic Interpolants: A Unifying Framework for Flows and Diffusions](https://mlsanity.com/paper/2303.08797/) (2023)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Improved Techniques for Training Consistency Models](https://mlsanity.com/paper/2310.14189/) (2023)
- [Noise2Self: Blind Denoising by Self-Supervision](https://mlsanity.com/paper/1901.11365/) (2019)
- [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018)
- [How to Train Your Energy-Based Models](https://mlsanity.com/paper/2101.03288/) (2021)
- [Consistency Models Made Easy](https://mlsanity.com/paper/2406.14548/) (2024)
- [Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis](https://mlsanity.com/paper/2203.13278/) (2022)

## Built on by (most cited first, 30 of 44)

- [Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling](https://mlsanity.com/paper/2504.10612/) (2025)
- [Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model](https://mlsanity.com/paper/2603.21986/) (2026)
- [Equilibrium Matching: Generative Modeling with Implicit Energy-Based Models](https://mlsanity.com/paper/2510.02300/) (2025)
- [H3DP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning](https://mlsanity.com/paper/2505.07819/) (2025)
- [Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis](https://mlsanity.com/paper/2505.10046/) (2025)
- [Uniform Discrete Diffusion with Metric Path for Video Generation](https://mlsanity.com/paper/2510.24717/) (2025)
- [Generalization of Diffusion Models Arises with a Balanced Representation Space](https://mlsanity.com/paper/2512.20963/) (2025)
- [SuperFlow: Training Flow Matching Models with RL on the Fly](https://mlsanity.com/paper/2512.17951/) (2025)
- [Blind denoising diffusion models and the blessings of dimensionality](https://mlsanity.com/paper/2602.09639/) (2026)
- [Scaling Sequence-to-Sequence Generative Neural Rendering](https://mlsanity.com/paper/2510.04236/) (2025)
- [The Geometry of Noise: Why Diffusion Models Don't Need Noise Conditioning](https://mlsanity.com/paper/2602.18428/) (2026)
- [Flow Matching in the Low-Noise Regime: Pathologies and a Contrastive Remedy](https://mlsanity.com/paper/2509.20952/) (2025)
- [WIND: Weather Inverse Diffusion for Zero-Shot Atmospheric Modeling](https://mlsanity.com/paper/2602.03924/) (2026)
- [Discrete Stochastic Localization for Non-autoregressive Generation](https://mlsanity.com/paper/2602.16169/) (2026)
- [Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control](https://mlsanity.com/paper/2603.17834/) (2026)
- [Neural Approaches to SAT Solving: Design Choices and Interpretability](https://mlsanity.com/paper/2504.01173/) (2025)
- [Mixture of States: Routing Token-Level Dynamics for Multimodal Generation](https://mlsanity.com/paper/2511.12207/) (2025)
- [DVD: Discrete Voxel Diffusion for 3D Generation and Editing](https://mlsanity.com/paper/2605.07971/) (2026)
- [What Time Is It? How Data Geometry Makes Time Conditioning Optional for Flow Matching](https://mlsanity.com/paper/2605.08344/) (2026)
- [CrossFlow: One-Step Generation Across Latent and Pixel Spaces](https://mlsanity.com/paper/2606.19970/) (2026)
- [Beckmann Transport Models: From Autonomous Flows to One-Step Maps](https://mlsanity.com/paper/2608.01692/) (2026)
- [SPROUT: A Scalable Diffusion Foundation Model for Multi-Crop Plant Phenotyping](https://mlsanity.com/paper/2603.27519/) (2026)
- [Exploring Time Conditioning in Diffusion Generative Models from Disjoint Noisy Data Manifolds](https://mlsanity.com/paper/2604.25289/) (2026)
- [NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training](https://mlsanity.com/paper/2605.08144/) (2026)
- [Generative Pseudo-Force Fields for Molecular Generation](https://mlsanity.com/paper/2605.19050/) (2026)
- [Time-Unconditional Generative Speech Enhancement via Autonomous Rectified Flow](https://mlsanity.com/paper/2606.20001/) (2026)
- [Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning](https://mlsanity.com/paper/2608.14706/) (2026)
- [Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Models](https://mlsanity.com/paper/2505.22935/) (2025)
- [MAD: Manifold Attracted Diffusion](https://mlsanity.com/paper/2509.24710/) (2025)
- [A Geometric Unification of Generative AI with Manifold-Probabilistic Projection Models](https://mlsanity.com/paper/2510.00666/) (2025)

## Abstract (arXiv)

It is widely believed that noise conditioning is indispensable for denoising diffusion models to work successfully. This work challenges this belief. Motivated by research on blind image denoising, we investigate a variety of denoising-based generative models in the absence of noise conditioning. To our surprise, most models exhibit graceful degradation, and in some cases, they even perform better without noise conditioning. We provide a theoretical analysis of the error caused by removing noise conditioning and demonstrate that our analysis aligns with empirical observations. We further introduce a noise-unconditional model that achieves a competitive FID of 2.23 on CIFAR-10, significantly narrowing the gap to leading noise-conditional models. We hope our findings will inspire the community to revisit the foundations and formulations of denoising generative models.