Introduction
DDPM demonstrates high-quality image synthesis with diffusion probabilistic models. In contrast with a GAN's direct generation, diffusion constructs an image through an iterative process of reversing noise. This approach subsequently becomes a major technical basis for text-to-image systems after 2022.
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Ho, Jain & Abbeel · original paper · Image rights remain with their respective holders; no open licence is asserted
Source · Denoising Diffusion Probabilistic Models ↗RESEARCH ACCOUNT
Constructing images through successive denoising.
ANTI-AI ARCHIVE · Revised 2026-10-02
Method and contribution
DDPM organises generation around learning to reverse progressive corruption by noise. The paper connects diffusion probabilistic models, denoising score matching and sampling, reporting image experiments on datasets including CIFAR-10 and LSUN. It is neither a text-to-image product nor evidence that diffusion methods originated only in 2020.
Section sources: Denoising Diffusion Probabilistic Models
Art-historical interpretation
The framework treats image formation as a sequence of revisions and provides context for later conditioning, latent diffusion and sampling acceleration. Analysing an artwork therefore requires attention to the sampling process as well as the final prompt: steps, conditions and random initialisation participate in image formation.
Section sources: Denoising Diffusion Probabilistic Models
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Compare the methods, forms of control or presentation contexts of this entry and Diffusion Models Beat GANs.
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Dates & version record
Date displayed for this node: 2020 · Historical date recorded for the source: 2020
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Original sources & further reading
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Denoising Diffusion Probabilistic Models
https://arxiv.org/abs/2006.11239
Current cited URL
Provenance, translation & verification
Timeline entry #110 · Historical node
Research materials & supplement references · 1
AI Art Genealogy Research Master · p. 21 · 069
Date as recorded: 2020
Denoising Diffusion Probabilistic Models ↗
Denoising Diffusion Probabilistic Models
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Cite this node
ANTI-AI ARCHIVE. Denoising Diffusion Probabilistic Models. Art-history node #110. https://salondesrefuses.cn/en/art-history/069
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