How a neural network works, in plain words
In short: it does not search for ready-made images and does not copy them from the internet. It assembles the image anew from noise, step by step fitting it to your description.
Three things that explain almost all of its behavior
- The model does not remember specific images, it has learned patterns.
- The result is assembled anew every time, so two runs do not match.
- Whatever is not in the description, the model fills in on its own, relying on what is most common.
Where the image comes from
During training the model saw a huge number of images together with their descriptions. It did not memorize them whole, it learned connections: what a “mug” looks like, what “soft light” means, how a “close-up” differs from a “wide shot”.
When you send a request, the model starts from random noise and gradually turns it into an image, checking at every step whether what is coming out looks like what you described. Hence the familiar behavior: the image seems to “develop”.
Why the same request gives different images
Because the starting noise is new every time. It is not a glitch and not “the model made a mistake”. It is how it is built. To make shots come out alike, describe the light, background and angle the same way: the fewer decisions are left to the model, the closer the results are to each other.
Why you get “extra fingers” and crooked lettering
The model works with the overall composition, not with the rules of anatomy or the alphabet. It has learned that a hand usually has several fingers, but it does not count them. Text in the frame behaves the same way: letters are an element of the image for it, not a sequence of characters. So lettering comes out looking like text but often meaningless. Newer models are noticeably better at this, but it is not fully solved.
How models differ from each other
- What they were trained on: hence the difference in styles and in what the model “knows”.
- How closely they follow the description: some stick to the text, others take liberties.
- How much they compute: heavy models process the scene longer and cost more.
- What they accept as input: only text, or reference images as well.