How AI Image Generation Actually Works (There Are Only 2 Ways)

2026-06-07 · Source: What's AI by Louis-François Bouchard · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Novice, long

Summary

AI image generation operates via two fundamental approaches: refinement from noise (diffusion models) or sequential token-by-token construction (auto-regressive models). Contrary to common belief, these models do not stitch images from a database but learn statistical maps of visual structures and their relation to text within a compressed "latent space." Diffusion models, exemplified by Flux, progressively denoise an image, often leveraging U-Nets or Transformer architectures. Auto-regressive models, such as Nano Banana, build images sequentially, similar to how large language models generate text, processing 1,290 tokens per image. Both families utilize attention mechanisms to steer generation based on text prompts, differentiating between text-to-image generation and image editing by conditioning on text alone or text plus an existing image.

Key takeaway

For AI engineers and professional users optimizing image generation workflows, understanding the two core model families is crucial. If you need precise structural control and text placement, auto-regressive models like Nano Banana offer sequential token generation akin to LLMs. For iterative refinement and broader compositional flexibility, diffusion models like Flux, which sculpt from noise, are often more forgiving. Tailor your prompt engineering and model choice to the specific generation paradigm for superior results.

Key insights

AI image generation relies on two core methods: diffusion (noise sculpting) or auto-regressive (sequential token building) within a latent space.

Principles

• AI models learn statistical image patterns, not by database recombination.
• Latent space compression is key for scalable image generation.
• Prompt specificity directly enhances image generation quality.

Method

Diffusion models progressively denoise images from random noise. Auto-regressive models convert images to tokens, then predict these tokens sequentially, building the image piece by piece.

In practice

• Use one strong input photo for consistent results.
• Craft concise, detail-packed prompts for better control.
• Restart generation or a new chat for varied outputs.

Topics

• AI Image Generation
• Diffusion Models
• Auto-regressive Models
• Latent Space
• Prompt Engineering
• Transformer Architectures

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by What's AI by Louis-François Bouchard.