<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Image Synthesis on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/image-synthesis/</link><description>Recent content in Image Synthesis on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/image-synthesis/index.xml" rel="self" type="application/rss+xml"/><item><title>Stable Diffusion</title><link>https://terms-en.ai-term-hub.com/en/terms/stable_diffusion/</link><pubDate>Sat, 18 Jul 2026 10:16:41 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/stable_diffusion/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Stable Diffusion is a deep learning model that generates detailed images conditioned on text inputs using a latent diffusion process. Unlike pixel-space diffusion models, it operates in a compressed latent space, significantly reducing computational requirements while maintaining high fidelity. Developed by Stability AI and others, it has become a cornerstone of generative AI, enabling users to create diverse visual content from natural language prompts. Its open-source nature has fostered a vast ecosystem of extensions, fine-tunes, and community-driven tools.&lt;/p></description></item><item><title>diffusion-based</title><link>https://terms-en.ai-term-hub.com/en/terms/diffusion_based/</link><pubDate>Sat, 18 Jul 2026 09:38:20 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/diffusion_based/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Diffusion-based models are a class of generative AI that create new data samples by iteratively removing noise from a random distribution. The process begins with a forward phase that slowly adds Gaussian noise to data until it becomes pure randomness, followed by a reverse phase where a neural network learns to predict and remove this noise step-by-step. This method has become highly effective for high-fidelity image, audio, and video generation, surpassing many previous generative adversarial networks in quality and stability.&lt;/p></description></item></channel></rss>