mlsanityData to 7 Oct 2026

Connective Viewpoints of Signal-to-Noise Diffusion Models

arXiv 2408.04221 ↗First posted 8 Aug 2024cs.CV cs.AI cs.LGcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Diffusion models (DM) have become fundamental components of generative models, excelling across various domains such as image creation, audio generation, and complex data interpolation. Signal-to-Noise diffusion models constitute a diverse family covering most state-of-the-art diffusion models. While there have been several attempts to study Signal-to-Noise (S2N) diffusion models from various perspectives, there remains a need for a comprehensive study connecting different viewpoints and exploring new perspectives. In this study, we offer a comprehensive perspective on noise schedulers, examining their role through the lens of the signal-to-noise ratio (SNR) and its connections to information theory. Building upon this framework, we have developed a generalized backward equation to enhance the performance of the inference process.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 14 of its 16 references that are in the map. First posted on arXiv 8 Aug 2024.

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14 of 16 references.

Closest ancestors · 10

Same direction, newest first

  1. 2023Consistency Models2 Mar
  2. 2023Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation1 Mar
  3. 2022gDDIM: Generalized denoising diffusion implicit models11 Jun
  4. 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
  5. 2022Fast Sampling of Diffusion Models with Exponential Integrator29 Apr
  6. 2021Variational Diffusion Models1 Jul
  7. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  8. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  9. 2020Denoising Diffusion Implicit Models6 Oct
  10. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 4

  1. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  2. 2022Flow Matching for Generative Modelingcited by 5,426
  3. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  4. 2023Improving and generalizing flow-based generative models with minibatch optimal transportcited by 835

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Close in wording, no citation either way

  1. 2024Improved Noise Schedule for Diffusion Training
  2. 2026Elucidating the SNR-t Bias of Diffusion Probabilistic Models
  3. 2023On the Importance of Noise Scheduling for Diffusion Models
  4. 2025Spectral Analysis of Diffusion Models with Application to Schedule Design
  5. 2023Common Diffusion Noise Schedules and Sample Steps are Flawed
  6. 2026Class-frequency Guided Noise Schedule for Diffusion Models
  7. 2025A Fourier Space Perspective on Diffusion Models
  8. 2024GUD: Generation with Unified Diffusion