How AI Image Generation Actually Works

AI image generation feels almost magical — you type a description and a fully-formed image appears in seconds. Understanding the basics of how this works will make you a significantly better user of these tools.

The Technology: Diffusion Models

Most modern AI image generators use a technology called diffusion models. The core idea:

1. 1.Training: The model learns by looking at hundreds of millions of images paired with text descriptions. It learns the statistical relationship between descriptions and visual patterns.

1. 1.Generation: When you give it a prompt, it starts with random visual noise and progressively refines it — guided by your text description — until it becomes a coherent image.

The "diffusion" refers to the process of gradually removing noise from an image to reveal the final result.

The Major Tools and Their Differences

• [Midjourney](https://www.midjourney.com)
• Accessed via Discord (or via midjourney.com) (or increasingly via midjourney.com)
• Widely considered to produce the most aesthetically impressive results by default
• Particularly strong for photorealistic images, painterly styles, and concept art
• Less controllable than Stable Diffusion but produces stunning results from simpler prompts
• Paid subscription required

• [DALL-E 3](https://openai.com/dall-e-3)
• Built by OpenAI, accessible through ChatGPT Plus
• Strongest at following complex, detailed text prompts accurately
• Excellent for illustrations, diagrams, and images with text
• Integrates naturally into ChatGPT workflows
• Good choice if you already have a ChatGPT Plus subscription

• [Stable Diffusion](https://stability.ai)
• Open-source; can be run locally or via cloud services
• Highest degree of control and customisation
• Large ecosystem of community models fine-tuned for specific styles
• Free if you have adequate hardware; steeper learning curve
• Ideal for users who need specific style control or production pipelines

• [Adobe Firefly](https://firefly.adobe.com)
• Built into Adobe Creative Cloud products
• Trained on licensed images — commercially safe outputs
• Integrates directly into Photoshop and other Adobe tools
• Best choice for commercial creative work within the Adobe ecosystem

What These Tools Can and Cannot Do

• They can:
• Generate photorealistic images from text descriptions
• Create illustrations in virtually any art style
• Produce concept art, mockups, and ideation visuals
• Generate variations of an existing image
• Edit specific parts of an image (inpainting)

• They struggle with:
• Accurate text in images (improving but still inconsistent)
• Consistent character appearance across multiple images
• Precise spatial relationships ("the red ball is to the left of the blue cube")
• Realistic human hands (historically notorious; improving with newer models)
• Highly specific or technical diagrams requiring precision

The Prompt-to-Image Workflow

1. 1.At its simplest:
2. 2.You write a text description (the prompt)
3. 3.The model generates one or more images
4. 4.You select the best result or iterate on the prompt
5. 5.You use variation tools to explore adjacent possibilities

More advanced workflows add style references, negative prompts (things to avoid), seed numbers (for consistency), and aspect ratio controls.

What You Will Learn in This Module

• By the end of this module, you will be able to:
• Write effective image generation prompts
• Choose the right tool for different use cases
• Understand the key controls available in major tools
• Produce consistent, high-quality visuals for different purposes
• Navigate the legal and ethical considerations around AI images