Blind denoising diffusion models and the blessings of dimensionality

• URL: http://arxiv.org/abs/2602.09639v1
• Date: Tue, 10 Feb 2026 10:38:16 GMT
• Title: Blind denoising diffusion models and the blessings of dimensionality
• Authors: Zahra Kadkhodaie, Aram-Alexandre Pooladian, Sinho Chewi, Eero Simoncelli,
• Abstract summary: We analyze, theoretically and empirically, the performance of generative diffusion models based on blind denoisers.<n>Remarkably, we observe that schedule-free BDDMs produce samples of higher quality compared to their non-blind counterparts.
• Score: 14.525157313875459
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We analyze, theoretically and empirically, the performance of generative diffusion models based on \emph{blind denoisers}, in which the denoiser is not given the noise amplitude in either the training or sampling processes. Assuming that the data distribution has low intrinsic dimensionality, we prove that blind denoising diffusion models (BDDMs), despite not having access to the noise amplitude, \emph{automatically} track a particular \emph{implicit} noise schedule along the reverse process. Our analysis shows that BDDMs can accurately sample from the data distribution in polynomially many steps as a function of the intrinsic dimension. Empirical results corroborate these mathematical findings on both synthetic and image data, demonstrating that the noise variance is accurately estimated from the noisy image. Remarkably, we observe that schedule-free BDDMs produce samples of higher quality compared to their non-blind counterparts. We provide evidence that this performance gain arises because BDDMs correct the mismatch between the true residual noise (of the image) and the noise assumed by the schedule used in non-blind diffusion models.

Related papers

• Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance [54.88271057438763]
  Noise Awareness Guidance (NAG) is a correction method that explicitly steers sampling trajectories to remain consistent with the pre-defined noise schedule.<n>NAG consistently mitigates noise shift and substantially improves the generation quality of mainstream diffusion models.
  arXiv  Detail & Related papers  (2025-10-14T13:31:34Z)
• Noise Conditional Variational Score Distillation [60.38982038894823]
  Noise Conditional Variational Score Distillation (NCVSD) is a novel method for distilling pretrained diffusion models into generative denoisers.<n>By integrating this insight into the Variational Score Distillation framework, we enable scalable learning of generative denoisers.
  arXiv  Detail & Related papers  (2025-06-11T06:01:39Z)
• Non-stationary Diffusion For Probabilistic Time Series Forecasting [3.7687375904925484]
  We develop a diffusion-based probabilistic forecasting framework, termed Non-stationary Diffusion (NsDiff)<n>NsDiff combines a denoising diffusion-based conditional generative model with a pre-trained conditional mean and variance estimator.<n>Experiments conducted on nine real-world and synthetic datasets demonstrate the superior performance of NsDiff compared to existing approaches.
  arXiv  Detail & Related papers  (2025-05-07T09:29:39Z)
• VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference [5.852077003870417]
  We show that our VIPaint method significantly outperforms previous approaches in both the plausibility and diversity of imputations.<n>We show that our VIPaint method significantly outperforms previous approaches in both the plausibility and diversity of imputations.
  arXiv  Detail & Related papers  (2024-11-28T05:35:36Z)
• Ultrasound Image Enhancement with the Variance of Diffusion Models [7.360352432782388]
  Enhancing ultrasound images requires a delicate balance between contrast, resolution, and speckle preservation. This paper introduces a novel approach that integrates adaptive beamforming with denoising diffusion-based variance imaging.
  arXiv  Detail & Related papers  (2024-09-17T17:29:33Z)
• Listening to the Noise: Blind Denoising with Gibbs Diffusion [4.310554658046964]
  We develop a Gibbs algorithm that alternates sampling steps from a conditional diffusion model trained to map the signal prior to the family of noise distributions. Our theoretical analysis highlights potential pitfalls, guides diagnostic usage, and quantifies errors in the Gibbs stationary distribution. We showcase our method for 1) blind denoising of natural images involving colored noises with unknown amplitude and spectral index, and 2) a cosmology problem, where Bayesian inference of "noise" parameters means constraining models of the evolution of the Universe.
  arXiv  Detail & Related papers  (2024-02-29T18:50:11Z)
• Blue noise for diffusion models [50.99852321110366]
  We introduce a novel and general class of diffusion models taking correlated noise within and across images into account. Our framework allows introducing correlation across images within a single mini-batch to improve gradient flow. We perform both qualitative and quantitative evaluations on a variety of datasets using our method.
  arXiv  Detail & Related papers  (2024-02-07T14:59:25Z)
• Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples [58.68233326265417]
  Non-image data are prevalent in real applications and tend to be noisy. Risk-sensitive SDE is a type of differential equation (SDE) parameterized by the risk vector. We conduct systematic studies for both Gaussian and non-Gaussian noise distributions.
  arXiv  Detail & Related papers  (2024-02-03T08:41:51Z)
• Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models [76.46246743508651]
  We show that current diffusion models actually have an expressive bottleneck in backward denoising. We introduce soft mixture denoising (SMD), an expressive and efficient model for backward denoising.
  arXiv  Detail & Related papers  (2023-09-25T12:03:32Z)
• VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation [88.49030739715701]
  This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation.
  arXiv  Detail & Related papers  (2023-03-15T02:16:39Z)
• Denoising Diffusion Samplers [41.796349001299156]
  Denoising diffusion models are a popular class of generative models providing state-of-the-art results in many domains. We explore a similar idea to sample approximately from unnormalized probability density functions and estimate their normalizing constants. While score matching is not applicable in this context, we can leverage many of the ideas introduced in generative modeling for Monte Carlo sampling.
  arXiv  Detail & Related papers  (2023-02-27T14:37:16Z)
• Markup-to-Image Diffusion Models with Scheduled Sampling [111.30188533324954]
  Building on recent advances in image generation, we present a data-driven approach to rendering markup into images. The approach is based on diffusion models, which parameterize the distribution of data using a sequence of denoising operations. We conduct experiments on four markup datasets: mathematical formulas (La), table layouts (HTML), sheet music (LilyPond), and molecular images (SMILES)
  arXiv  Detail & Related papers  (2022-10-11T04:56:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.