How Do AI Image Generators Work? The Answer Will Surprise You!

Type any prompt you can think of into an AI image generator, and you’ll get a high-quality, completely original graphic back in just a few seconds–but how does it all work, and what happens behind the scenes when you’re sitting back and waiting for the results?

In this post, we’ll explain how AI image creation tools work, why their training is so important, and how a neural network can interpret your input text and transform it into a detailed, complex image in any style of your choosing. 

The Basics of AI Image Generators

Let’s start with a recap of what an AI art generation model does. In short, these tools allow users to select any phrase, description, style, object, or landscape and enter their text string or sentence into the platform. The AI takes that input away, evaluates what the user is asking for, and scours its knowledge bank to determine the right graphic.

This process works much the same way if you’re looking into how to AI upscale image files. AI models process your original graphic and guess what needs to go in each missing pixel to bring the image up to a higher resolution.

The training phase is crucial because this provides the AI with the raw data–in vast volumes–that it needs to grasp what a castle, dog, pen, or pair of sunglasses are, the multiple variations they may have, and how one object or background should look in relation to another. Text-to-image AI models also come in various different types as the functionality of AI (and our understanding of how to use it) evolve.

The first image generators used generative adversarial networks, or GANs, which means two neural networks operate concurrently, one producing images and the other judging the accuracy of the output. The ‘discriminating’ network would attempt to differentiate between AI-generated and real graphics, whereas the ‘generating’ network would create graphics from random noise.

Each network is trained simultaneously through adversarial training, during which the generator’s task is to convince the discriminator that the data it produces is real. However, newer diffusion models are now the leading form of AI leveraged to produce high-quality images.

Diffusion AI Models in Text-to-Image Generators

Diffusion models work very differently from GANs and are trained in literally billions of images; each one is captioned to describe what is being depicted. The AI model extracts contextual knowledge about how words are linked to images and how different styles of artwork compare.

Once trained, a diffusion AI model can process a text input and begins by developing a basic, low-res graphic. From there, it adds extra details one layer at a time before producing the final image. Of course, all of this happens in a fraction of the time it would take a person to undertake the same process!

The interesting aspect is that a diffusion model isn’t copying an existing graphic or taking pre-existing components of images it has been trained on. Instead, it develops every element of the image from scratch, using that earlier training to inform the AI’s understanding of what each word in your text input means.

For example, if you ask for the colour blue, a teddy bear, rainy weather, a cute narwhal, or a countryside landscape, the AI will determine what you would like your graphic to look like based on its knowledge of what each of those things means.

The Advantages of Using Diffusion Model AI Image Generators

Diffusion-based AI generators are more advanced than previous models because they are easier to train and can produce realistic and photorealistic graphics, while the user can control the outputs with more detailed and exact text prompts. In the same way that a person will consider their knowledge of any object to sketch it, the AI can access its data banks to incorporate as many instructions as you wish to provide, from the style and theme of the graphic to the objects, people, landscapes and buildings within it.

For example, you could instruct the text-to-image generator to develop an image in the style of a watercolour painting, a fine art illustration, a professional photograph, or a picture painted onto canvas, and it could accurately recognise what this style looks like and what makes it different from the countless other options.