TL;DRImage generation today is dominated by diffusion models that denoise random noise into pictures.
Modern text-to-image (Stable Diffusion, Midjourney, DALL·E) uses diffusion: start from pure noise and iteratively denoise it toward an image that matches a text prompt (guided by something CLIP-like). It replaced GANs as the dominant approach because it's more stable to train and produces diverse, high-quality results. The text prompt steers the denoising at each step.
Key points
Diffusion denoises noise → image
Text prompt guides the denoising
Replaced GANs (more stable, diverse)
Powers Stable Diffusion, DALL·E, Midjourney
Common mistakes
Confusing diffusion with GANs
Expecting one-shot generation (it’s iterative)
Ignoring prompt’s role in guidance
Try it: Explain the basic idea of generating an image by denoising.
Example code
<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>start: random noise
repeat: denoise a step, guided by the text prompt
end: an image matching the prompt</pre></body></html>