---
title: 'Denoising Diffusion Probabilistic Models'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2006-11239-denoising-diffusion-probabilistic-models.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Jonathan Ho, Ajay Jain, Pieter Abbeel, Denoising Diffusion Probabilistic Models (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: foundations'
tags: arxiv, diffusion, generative-models, foundations
arxiv_id: 2006.11239
alias: 2006.11239-denoising-diffusion-probabilistic-models
type: pointer
format: pointer
sources:
  - id: S1
    title: 'Denoising Diffusion Probabilistic Models'
    publisher: arXiv
    authors: 'Jonathan Ho, Ajay Jain, Pieter Abbeel'
    url: https://arxiv.org/abs/2006.11239
    version_read: 'v2 (16 Dec 2020)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# Denoising Diffusion Probabilistic Models

## Summary
[Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) (2020) introduced the modern denoising-diffusion recipe for generative modeling. A model is trained to reverse a gradual noising process, and that idea became the engine behind most current image and video generators.

What it introduced: a practical training and sampling procedure for denoising diffusion probabilistic models, with strong image-generation results that later systems built on.

What it led to: latent diffusion and Stable Diffusion, plus the diffusion stacks used in current open image tools. Live notes: [hf-diffusers-pipeline-loading](https://iinventedlight.com/library/notes/hf-diffusers-pipeline-loading/) and [hf-diffusers-memory](https://iinventedlight.com/library/notes/hf-diffusers-memory/). The next pointer in this batch is the latent-diffusion paper.

If you want the core diffusion training idea before latent-space systems, this is the paper to read. Where to read it: https://arxiv.org/abs/2006.11239 (v2, last revised 16 Dec 2020).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).