# Diffusion models

Record: term-diffusion-models · Type: term · Edition: 0.20.0 · Evidence cutoff: 2026-09-15

[Read in the atlas](https://theaiatlas.org/ideas/diffusion-models/) · [Complete evidence](https://theaiatlas.org/evidence.html#idea-diffusion-models) · [JSON](https://theaiatlas.org/records/term-diffusion-models.json) · [Pinned complete dataset](https://theaiatlas.org/editions/e74392d479c0e7da8636a7d6a0454d03510df86ffca9931eb86babd952665ca5/data.json)

Dataset pointer: `/glossary/121`. Reviewed: 2026-09-15.

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## Publication dates and source age

Source is over 18 months old.

Newest dated source: 2020-06-19. Assessed at this edition’s evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Publication age does not establish validity or a new source-reading date. Unknown dates and month/year precision remain explicit in the JSON record.

## /summary

Generative models that can build an image through repeated removal of noise. Generating a picture does not establish that the pictured event happened.

Claim: claim-term-diffusion-models-1a5192ba3d7825ca26b24a72. Annotation: synthesis.

[glossary-wide-diffusion](https://arxiv.org/html/2006.11239v2)

## /definition

In the denoising approach, training examples are corrupted with noise and a model is trained to reverse that process. Generation starts with noise and applies learned steps to produce an output.

Claim: claim-term-diffusion-models-1e4c26398ee834b2e16dc7b8. Annotation: synthesis.

[glossary-wide-diffusion](https://arxiv.org/html/2006.11239v2)

## /placement

Map context: this is a way to generate content. Its use does not establish a view about catastrophic risk or faster development.

Claim: claim-term-diffusion-models-25f52a21ad9f2e54a62ed1b2. Annotation: editorial.

[glossary-wide-diffusion](https://arxiv.org/html/2006.11239v2)

## /distinction

The classic diffusion approach refines a noisy representation over successive steps. That differs from a language model generating text one token at a time; both are computational methods.

Claim: claim-term-diffusion-models-09422ce4d5c74cb753a9bb98. Annotation: synthesis.

[glossary-wide-diffusion](https://arxiv.org/html/2006.11239v2)

## Source provenance

### glossary-wide-diffusion

[Denoising Diffusion Probabilistic Models](https://arxiv.org/html/2006.11239v2)

Jonathan Ho, Ajay Jain and Pieter Abbeel / arXiv · First-hand source (primary) · Published: 2020-06-19 · Updated: 2020-12-16 · Material last read: 2026-09-15 · Verification: read

Read scope is described in the source note.

Read the abstract, introduction, forward/reverse process and sampling algorithm. The entry explains the denoising approach in this paper, without treating its benchmark results as current performance.

No archive check recorded.