Diffusion model
Also called latent diffusion, diffusion AI
What is a diffusion model?
A diffusion model is a type of generative AI that creates images, video or audio by starting from random noise and removing it step by step until a clear result appears. During training it learns to reverse the process of adding noise to real data. Most well-known AI image and video generators are built on diffusion.
Most modern systems are latent diffusion models: they work in a compressed version of the image rather than on every pixel, which makes generation much faster. A text encoder turns the prompt into guidance, and each denoising step nudges the image toward that description.
Settings you may see in tools come from this process. Steps is how many denoising passes run. Seed is the starting noise, so the same seed and prompt give a similar image. Guidance (or CFG scale) controls how strictly the image follows the prompt. A negative prompt pushes the result away from things you do not want.
Why a diffusion model matters
You do not need to understand the math to use AI images, but knowing how diffusion works explains common behavior: why results vary each time, why a fixed seed helps keep a series consistent, and why small text and fine details are hard.
That understanding helps you pick the right tool and write better prompts, which saves credits and time when you make visuals for posts and ads.
Diffusion model example
A designer making a set of five promotional backdrops for a coffee shop keeps the same seed and changes only the subject in the prompt. The lighting and style stay similar across the set, so the posts look like one campaign.
Diffusion model FAQ
How do diffusion models work in simple terms?
They learn to clean up noisy images. To generate, they start with pure noise and clean it up many times, guided by your prompt, until a picture emerges.
What is the difference between a diffusion model and an LLM?
An LLM predicts text one token at a time. A diffusion model refines a whole image (or clip, or audio) at once over many steps. Some newer systems mix both ideas.
Related terms
Generative AI is artificial intelligence that creates new text, images, audio or video from a prompt, based on patterns learned from training data.
Text to image is AI that turns a written description into a picture. You type a prompt and the model generates matching images.
An AI image generator is a tool that creates images from a text description, and sometimes from a reference image, using a trained AI model.
Text to video is AI that creates video from a written prompt or script, either as generated footage or as an edited video with scenes and voice.
A negative prompt tells an AI image or video model what to leave out, such as text, blur, extra fingers or a watermark.