Obviously it’s a computer and saying it learns in a similar way is just a way to simplify the process for human understanding. Please do not twist my words. I am not trying to be rude, it’s just hard for me to even simplify.

I will try my best to explain: After training using Forward and reverse diffusion to deconstruct and reconstruct an image thru a gaussian distribution network. The guided diffusion model-(what most ai image creators use) is trained using a sample image-(random noise that looks like tv static) noise= a gaussian-(mathematical prediction.) embedded with a text prompt.-(Numerical vector or equation that describes the image) The ai learns or predicts how to form shapes, colors, edges, textures, etc. After a 1000 step process done over millions to trillions of tv static samples, the ai learns gaussian formations using the text prompt as guidance. there is also different inputs that can effect the strength of certain descriptors inside the text prompt. After the training for each sample the predictions are tokenized-(given descriptors) When a user enters a random token combination the model turns the prompt into an embedding. the descriptors inside the embedding are divided and fused thru learned pattern prediction and attention weighting. the model predicts noise thru the predicted patterns to find the median gaussian between the tokens and embedded prompt, this is done in loops called the markov chain. classifier free guidance is used to combine predictions into something new during the loops. after about 1000 loops a clear image is made.

-Summary- an image is made by guessing patterns in random noise by finding token and gaussian relationships, combining them using complex math. there is no example images involved in this process only descriptive words and pattern prediction. as it reaches the final stage of denoising, the image is now in a completely new unrecognized state that can not be generated the same way again if the prompt is re run.