Key Learning Points:

  • The diffusion model is an AI technique that starts with noise and gradually creates meaningful images.
  • It begins by adding noise (a kind of visual static) to an original image, then removes the noise step by step based on patterns the AI has learned, resulting in a new image.
  • While it can produce highly detailed images, challenges include long processing times and potential misuse for spreading false information.

How AI Creates Images from Noise

When you hear that “AI draws pictures,” what kind of process do you imagine? Maybe you think it edits existing photos, or perhaps it combines parts of many images to make something new. That’s a common guess.

But in fact, many of today’s image-generating AIs use a rather unusual method: they start with pure noise and gradually transform it into a meaningful picture. This technique is called a “diffusion model.”

What Is a Diffusion Model? The Process Behind AI-Generated Images

The idea behind diffusion models is quite unique. First, they take a clean image and slowly add noise—like visual static—over and over again. After dozens or even hundreds of steps, the original picture becomes completely buried under the noise. This stage is called “diffusion,” where information is intentionally lost.

Then comes the real magic. From this state—like a screen full of static—the AI begins removing the noise bit by bit. But it doesn’t just erase things randomly. The AI has already learned from huge amounts of data what kinds of noise should be removed in what way. Using that knowledge, it carefully reconstructs an image step by step.

Through this reverse process, the resulting image isn’t just a restored version of the original—it’s something entirely new. In other words, it looks as if the AI is creating visuals out of nothing.

Why the Results Look So Real—and What Issues Remain

Let’s say you ask the AI to “draw a cat sleeping on a sofa.” Based on that prompt (the instruction you give), shapes resembling a cat and sofa gradually emerge from what started as random static. Behind the scenes, hundreds of tiny adjustments are being made to refine each detail until the final image looks natural and realistic.

This process is much like how a sculptor carves form out of stone—starting with raw material (in this case, noise) and revealing meaningful shapes within it.

There are major strengths to this approach. It can generate very high-quality images, and once trained, it can flexibly respond to all kinds of requests. That’s why many cutting-edge image generation tools—such as Stable Diffusion—are built on this technology.

However, there are also challenges. Because so much computation is required, powerful computers or specialized chips (like GPUs) may be necessary. And since these tools can easily create images that look real but aren’t, there’s concern about their use in spreading misinformation or fake news.

AI Creativity That Resembles Human Imagination

But when you think about it, humans do something similar too. We often start with a blank page and shape our vague ideas into clear expressions through trial and error. In that sense, this technology might feel surprisingly familiar—even human-like.

The diffusion model isn’t just a technical method—it reflects a deeper concept: turning chaos into order, or transforming ambiguity into clarity. And this way of thinking is now expanding beyond images into areas like text and audio as well.

“How does AI create something from nothing?” This question will likely continue to draw attention in the future—and diffusion models offer one possible answer.

From within quiet static emerges a single landscape painting. That journey carries with it something deeply human: our desire to create and explore new ideas. In our next article, we’ll take a closer look at another technology that supports this creative power.

Glossary

Diffusion Model: A method used by AI to generate images by starting with noisy data and gradually shaping it into meaningful forms.

Noise: Unwanted distortion or randomness in images or sounds; here it’s used as the starting point for creating new visuals.

Prompt: A command or instruction given to an AI—for example, “Draw an image like this”—to guide what kind of output should be generated.