{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Denoising — 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;\">Denoising</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">A core process in diffusion models where a model learns to progressively remove noise from a corrupted input to recover a clean output. During training, noise is added to data in steps; during generation, the model reverses this process, starting from pure noise and gradually refining it into a coherent image, audio clip, or other output.  See also: diffusion model, diffusion, generative AI.</p><a href=\"https://gaks.ai/glossary/denoising\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/denoising →</a></div>"}