{"data":{"slug":"diffusion-models","etype":"concept","name":"Diffusion Models","cat":"Core Technical","subtitle":"Generation by denoising — the engine of images and video.","summary":"Diffusion models learn to reverse noise: start from static, subtract noise step by step, arrive at an image (or video, or protein pose) matching the prompt. Stable Diffusion, FLUX, Sora-class video and AlphaFold 3's structure module all use it. Inference takes many denoising steps, which is why image/video generation is so much more compute-hungry per output than text.","attrs":null,"kf":null,"aliases":["diffusion","latent diffusion"],"ticker":null,"exchange":null,"hq":null,"founded":null,"website":null,"status":null,"region":null,"asof":"2026-01-15","sources":[{"url":"https://arxiv.org/abs/2006.11239","label":"DDPM paper"}]},"meta":{"found":true,"count":1,"served_from":"site cache (no live upstream calls)","as_of":"2026-10-14T04:28:57+00:00","disclaimer":"Research and information only — not investment advice. Structured records carry basis labels (vendor spec / reported / estimate); market quotes are delayed/indicative.","attribution":"Records compiled from vendor documentation, official filings and reputable public reporting; market quotes via financial data providers. See /data-sources."}}