Module 1 of 5 · Beginner · 6 min read
How image models work
From noise to picture: diffusion, autoregressive models and why text in images finally works.
- 1Diffusion models start from noise and refine it step by step towards an image matching your prompt.
- 2Newer models built into chat assistants understand prompts far better.
- 3Text rendering went from garbled to reliable between 2024 and 2026.
01Diffusion in one paragraph
A diffusion model learns by watching images get gradually buried in noise, then learning to reverse the process. To generate, it starts from pure noise and removes it step by step, guided by your text prompt, until an image emerges.
Models like FLUX, Midjourney and Seedream use variations of this approach, often with transformer backbones.
02Image models inside language models
Models like GPT Image 2 and Nano Banana are tightly connected to large language models. They understand long, nuanced prompts, follow complex instructions, render text accurately and can edit images through conversation.
That is why you can now say "move the lamp to the left and make it evening" instead of re-rolling a prompt dozens of times.
03Choosing a model
Different models have different strengths. Check the image races for current leaders.
- Photorealism and editing: GPT Image 2, Nano Banana.
- Aesthetic and art direction: Midjourney.
- Typography and design: Ideogram, Recraft.
- Open weights: FLUX, Qwen-Image.
Finished reading?
Mark it complete to track your progress, then test yourself with the Image & Video AI quiz.