mlsanityData to 7 Oct 2026

An overview of diffusion models for generative artificial intelligence

arXiv 2412.01371 ↗First posted 2 Dec 2024cs.LG cs.AIcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

This article provides a mathematically rigorous introduction to denoising diffusion probabilistic models (DDPMs), sometimes also referred to as diffusion probabilistic models or diffusion models, for generative artificial intelligence. We provide a detailed basic mathematical framework for DDPMs and explain the main ideas behind training and generation procedures. In this overview article we also review selected extensions and improvements of the basic framework from the literature such as improved DDPMs, denoising diffusion implicit models, classifier-free diffusion guidance models, and latent diffusion models.

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 24 of its 55 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

24 of 55 references.

Closest ancestors · 6

Same direction, newest first

  1. 2022Classifier-Free Diffusion Guidance26 Jul
  2. 2021Structured Denoising Diffusion Models in Discrete State-Spaces7 Jul
  3. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  4. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  5. 2020Denoising Diffusion Implicit Models6 Oct
  6. 2020Improved Techniques for Training Score-Based Generative Models16 Jun

Other references · 18

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  5. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  6. 2016Improved Techniques for Training GANscited by 2,938
  7. 2022Video Diffusion Modelscited by 2,280
  8. 2015Variational Inference with Normalizing Flowscited by 2,161
  9. 2018Group Normalizationcited by 1,514
  10. 2016An overview of gradient descent optimization algorithmscited by 1,021
  11. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
  12. 2016Conditional Image Generation with PixelCNN Decoderscited by 859
6 more
  1. 2021DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulationcited by 640
  2. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
  3. 2022Diffusion Probabilistic Modeling for Video Generationcited by 237
  4. 2022Diffusion Models for Medical Anomaly Detectioncited by 231
  5. 2020Understanding SSIMcited by 75
  6. 2023Mathematical Introduction to Deep Learning: Methods, Implementations, and Theorycited by 19

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  1. 2024Denoising Diffusion Probabilistic Models in Six Simple Steps
  2. 2023Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians
  3. 2025Diffusion Models: A Mathematical Introduction
  4. 2024Diffusion Model from Scratch
  5. 2021Learning to Efficiently Sample from Diffusion Probabilistic Models
  6. 2024I-Diff: Structural Regularization for High-Fidelity Diffusion Models
  7. 2022Diffusion Models in Vision: A Survey
  8. 2024Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality