By ‘learned’ I mean, the images that it was trained on i.e. the Dataset of images that the model is trained on.
It doesn’t actually ‘retain’ information from succeeding generations but it ‘tries’ to match the ‘word’ to an image that the model is trained to.
I’m not sure what ‘generation traits’ do you mean but there is an option to add an ‘Image Seed’ which essentially locks the ‘noise map’ that the image is generated from (although it doesn’t really lock it due to the sampler used in the generation). Here is a simple example of it.
Settings:
Prompt: beautiful clever draconic magical
Image Seed: 1
Resolution : 512x512
Guidance Scale: 7
Negative Prompt: bad
All of them have the same settings which means that it would have the same ‘noise map’ where the image is generated from. Due to the Sampler (Euler A) used (see this website for a visualization) it would still have slight variations even if the image seed is the same.
As for creating a local setup, and by what I mean ‘local setup’ is downloading your own AI model and using it in your own computer. It might need quite a powerful computer to be fast in generating but it has more features than the Perchance one can provide. See this video tutorial.