{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Diffusion Model — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"<div style=\"font-family:sans-serif;border:1px solid #e0e0e0;border-radius:8px;padding:16px;max-width:600px;background:#ffffff;color:#111111;\"><p style=\"margin:0 0 4px;font-size:11px;color:#666;\">AI Glossary — gaks.ai</p><h3 style=\"margin:0 0 8px;font-size:16px;\">Diffusion Model</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">A generative model that learns to create data by reversing a gradual noising process. During training, noise is progressively added to real data; the model learns to denoise step by step. At generation time, it starts from pure noise and iteratively refines it into a coherent output. Diffusion models are currently the leading approach for high-quality image and audio generation, and underpin systems like Stable Diffusion and DALL-E.  See also: diffusion, denoising, generative AI.</p><a href=\"https://gaks.ai/glossary/diffusion-model\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/diffusion-model →</a></div>"}