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The deal with LLMs is that it's very difficult to say which piece of training material went into which output. Everything gets chopped up and mixed, and it's computationally difficult to run backwards.
My understanding of the image generators is that they operate one pixel at a time too, looking only at neighboring pixels. So in that sense, it's not correct to say they understand the context of anything.
It kinda understands context.
An image generator makes an image of static similar to like a TV does with bad signal. The Ai looks atthe static and sees shapes in it. The prompt influences what it's trying to "see". It starts filling in the static to a full image, it does this in steps, more steps generally means a better quality image.
Also to say a LLM is a Large Language Model and is different from an image generator, though the proccess for them is very similar.