You do not need to understand diffusion to generate an image. But understanding it explains almost every frustrating behaviour these tools have, and that makes them much easier to work with.
The short version: the model starts from pure noise and removes it step by step, guided by your prompt, until a picture emerges.
Two consequences follow from that single sentence, and together they account for most of what confuses new users.
Diffusion in one paragraph
During training, the model is shown millions of images with noise added step by step, and it learns to reverse that process — to predict what a slightly less noisy version of an image would look like.
At generation time it runs that learned reversal from the other end. It starts from pure random noise and, over twenty or thirty steps, removes it in the direction your prompt points. What is left is an image.
The number of steps is a setting in some tools. Too few gives a blurry result; past roughly thirty there is usually no visible improvement while generation time doubles.
Why the same prompt gives different results
Because the starting noise is random. Run the same prompt twice and the model begins from two different noise patterns, so it arrives at two different images.
The number that fixes that randomness is called the seed. Note the seed of an image you like and you can regenerate it exactly, with the same prompt and settings.
The real value of a seed is isolating variables: hold it fixed, change one word in your prompt, and you see that word's effect on its own. This is the fastest way to learn what your prompts actually do.
For a series, that randomness is a problem rather than a feature. Style reference — Midjourney's --sref parameter, saved styles in Leonardo and Recraft — is the reliable fix.
Why hands and text still break
The model does not understand objects. It has learned statistical patterns: which shapes tend to appear next to which other shapes, under which words.
It knows the concept "hand" as a cluster of visual patterns. It does not know, as a rule, that a hand has five fingers. So it produces something hand-shaped with a plausible-looking number of fingers, which is sometimes six.
The same applies to letters. The model learned letterforms as shapes, not as writing, which is why short words come out right and longer strings collapse into letter-like scribble.
The latest generation of models — Flux in particular — improved markedly on both. But the underlying limitation has not gone away, which is why zooming in before commercial use is still necessary.
What this means for your prompts
Because the model reads key concepts rather than sentence structure, prompts written as short comma-separated phrases outperform full sentences. Grammar buys you nothing here.
Because it learned from captioned photographs, photographic vocabulary works: focal length, light direction, depth of field. Writing "85mm, soft window light from the left, shallow depth of field" moves the result more than three adjectives would.
And because it has no rules, only patterns, describing a technique gives you more control than naming a style. "Heavy black outlines, flat colour fields, three-colour palette" is both more controllable and more defensible than naming a living artist.
Frequently asked questions
Do I need to understand this to use the tools?
No, but it explains the behaviours that frustrate new users: why results vary between runs, why hands break, and why writing photographic terms works better than writing adjectives.
Why is my image different every time?
The starting noise is random. The number that fixes it is the seed; note the seed of an image you like to reproduce it. For consistency across a series, style reference is a more practical answer than seeds.
Will hands and text ever be fixed?
They have improved markedly — Flux is notably better at both — but the limitation is structural: the model learns patterns rather than rules. Keeping text short and hands out of frame remains the reliable workaround.