{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "@id": "https://anchorfact.org/kb/kb-2026-00009",
  "headline": "Diffusion Models",
  "description": "Diffusion models are generative models that create data (images, video, audio, 3D) by learning to reverse a gradual noise-adding process. Starting from pure random noise, they iteratively denoise toward a coherent output. Popularized by DDPM (Ho et al., 2020, UC Berkeley, 15,000+ citations on Google Scholar as of May 2026) and made practical by Stable Diffusion / Latent Diffusion Models (Rombach et al., 2022), diffusion has surpassed GANs as the dominant paradigm for high-quality image generation and has expanded to video (Sora), audio (AudioLDM), 3D (DreamFusion), and biology (AlphaFold 3).",
  "dateCreated": "2026-05-22T14:59:47.490Z",
  "dateModified": "2026-05-22T14:59:47.490Z",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "human_only",
  "citation": []
}