What Is AI Image Generation? A Beginner-Friendly Explanation from Zero

“Just type some text and an image appears” — AI image generation is a technology that has advanced at a remarkable pace over the past few years. Yet many people still don’t fully understand how it works.

This article explains the technology behind AI image generation in a way that’s accessible even if you have zero technical background.

What Is AI Image Generation?

AI image generation is a technology that uses artificial intelligence to automatically create new images from inputs like text or other images.

What’s particularly noteworthy is Text-to-Image generation — you type a natural language prompt like “a cat standing by the seaside at sunset,” and an image matching that description is generated.

In the past, creating images required specialized skills in illustration or photography. AI image generation makes it possible to obtain images simply by describing what you have in mind.

How Text Becomes an Image

Today’s mainstream AI image generation is based on a mechanism called a diffusion model.

The Basic Idea Behind Diffusion Models

The diffusion model learning process consists of two major steps:

1. Adding noise (forward diffusion): A clean image gradually has random noise (like TV static) added to it until it becomes completely noisy.
2. Removing noise (reverse diffusion): The AI is trained to learn how to remove the noise step by step, restoring the original clean image from complete noise.

Once training is complete, the AI can start from random noise and progressively remove it to “draw” an image. By providing text information as a condition, images matching the specified content are generated.

Key Technical Components

An AI image generation system using diffusion models is primarily made up of three components.

Text Encoder

A component that converts input text (prompts) into numerical sequences (vectors) that the AI can understand. A well-known example is OpenAI’s CLIP. CLIP learns the relationship between text and images from large datasets, encoding “cat” as text and images of cats in numerically similar ways.

The performance of the text encoder directly affects how accurately prompts are interpreted. For more details, see the CLIP paper explanation.

Denoising Network (U-Net / Transformer)

The core of the diffusion model, performing step-by-step noise removal. Earlier models used a convolutional neural network structure called U-Net, while more recent models increasingly adopt Transformer structures — proven in natural language processing.

While referencing information received from the text encoder, it removes noise so the result becomes “an image matching this text.”

VAE (Variational Autoencoder)

Processing images directly would be computationally expensive, so the processing is done in a compressed “latent space.” VAE handles the conversion between images and latent space.

• Encoder: Compresses the image into latent space
• Decoder: Restores latent space information back into an image

Because noise removal happens in this latent space, even high-resolution images can be generated relatively efficiently.

Major AI Image Generation Models

Many AI image generation models are now publicly available or offered as services. Here are the notable ones:

Stable Diffusion

An open-source model developed by Stability AI. The model weights (trained parameters) are publicly available, free to download and use by anyone. Highly customizable, with an abundance of community extensions (LoRA, ControlNet, etc.).

Midjourney

A service known for producing high-quality art-style images. Used via Discord or a web app. Uses a proprietary model that is not publicly released.

DALL-E

A model developed by OpenAI, integrated into ChatGPT — you can request image generation directly in conversation. Safety filters are set strictly.

Flux / z-image and other newer-generation models

Relatively new-generation models with improved faithfulness to text instructions and reported improvements in finger depiction.

3 Ways to Get Started with AI Image Generation

There are primarily three ways to start using AI image generation. Choose based on your goals and budget.

1. Use a Cloud Service

Use web services that let you generate images directly from a browser. No high-performance PC required.

• Pros: No environment setup needed; start immediately
• Cons: Monthly fees; customization options are limited

For a comparison of representative services, see Cloud GPU Comparison.

2. Run It Locally on Your PC

Download models to your own PC and run them locally. NVIDIA GPU recommended (VRAM 6–24 GB depending on the model). AMD GPUs can work in some environments, but compatibility varies by tool.

• Pros: Free and unlimited generation; fully customizable
• Cons: Requires a high-performance GPU; initial setup requires some technical knowledge

Tools like ComfyUI or Stable Diffusion WebUI are commonly used.

3. API Access

Generate images through an API from code. Well-suited for automation and large-volume generation.

• Pros: Easy to automate; can integrate with other systems
• Cons: Requires programming knowledge; often pay-per-use

Summary and Next Steps

AI image generation is a technology where text encoders, denoising networks, and VAEs work together on a diffusion model foundation. Since you only need to provide text instructions to get images, it can be used without specialized image creation skills.

Once you understand how it works, learning how to write prompts effectively is a productive next step. Prompt Basics covers techniques for generating images that match your intent.

Hands-on practice is the fastest route to improvement. Start by generating a single image with a service or tool you’re curious about.