AI Image Generation Basics

Welcome to Making Things Shine - Practice and Learn Image Generation with AI! Have you ever imagined describing a scene and watching it come to life as an image? AI image generators make this possible. In this lesson, you'll discover how these tools transform words into visuals, explore popular platforms, and learn how to tap into their creative potential.

AI image generators work like artists with a twist — they learn from millions of images and their descriptions. When you type a prompt like "a futuristic city with neon lights and flying cars," the AI:

1. Analyzes Patterns: Recognizes relationships between words (e.g., neon lights = glowing colors, flying cars = futuristic vehicles).
2. Generates Layer by Layer: Builds the image layer by layer, blending shapes, colors, and textures.
3. Refines Outputs: Adjusts details until the result matches the prompt's intent.

An actual algorithm behind it might be different and sometime counterintuitive. The model doesn't draw an image how a person would do it. However, the general idea is always the same: the AI model reproduces patterns from it's training data, matching the provided text description with shapes and colors.

Note: Many modern AI image generators are based on diffusion models, which systematically transform random noise into a coherent image based on the prompt's guidance.

AI image generation is changing at lightning speed. New tools appear, older ones get upgraded, and models leapfrog each other in quality month after month. What’s considered “the best” today might be old news tomorrow.

That’s why, in this course, we’ll focus on core principles and universal techniques that work across any AI model—now and in the rapidly evolving future. By understanding the fundamentals, you’ll be ready to experiment with whatever tool or trend comes next.

With this in mind, let's still overview the most popular image generators in the beginning of 2026.

Rather than trying to identify which model is "the best," we will highlight the specific features of today's leading tools to help you find the right fit for your needs.

• Nano Banana: A Google model that gained significant attention in 2025, users often find it particularly effective for adjustments and variations. It is frequently noted for producing realistic, lifelike results when modifying existing images.
• Qwen: Qwen has built a reputation as a solid, balanced model. While it may not be discussed as having a single "killer feature," many users rely on it for consistent, high-quality performance across a wide range of subjects.
• Reve: Emerging from an independent research collective, Reve is often cited as a state-of-the-art option for projects requiring character consistency. Users frequently highlight its ability to maintain a character's appearance across scenes and its flexible editing tools.
• ChatGPT: Powered by OpenAI, this tool is widely used for its accessibility. Some users observe that it tends to produce specific artistic styles that can look more "AI-generated," and it is often noted for struggling with text rendering more than some of its newer competitors.
• Flux: Flux is frequently highlighted for its ability to handle text within images. Many users find it more consistent than other models when a prompt requires specific, legible words.
• Midjourney: Developed by an independent design lab, Midjourney is often recognized for its focus on artistic expression and emotional context. While some users find other models more technically photorealistic, it remains a popular choice for evocative illustrations and creative storytelling.

Note: For the remainder of this course, we encourage you to pick one of these tools (or more!) to experiment with. Hands-on testing with different prompts is the best way to uncover how AI image generation can enhance your creativity.