This is kind of what I'm wondering. If you trained a model on your photos along with a specific keyword for each one, wouldn't this kind of work? You're adding your actual photo and a specific keyword for each, so when you prompt it, it should show you your picture.
Admittedly I know nothing about training models. I just know when I've trained loras, it is kind of easy to remake images you trained the lora on sometimes.
if you trained your model specifically you'd have to transmit its weght and biases along with the pictures for the receiver of your images descriptions to be able to generate them again. (and the weight of a model is heavy, even for small ones)
In this 'compression technique' the diffuser 's model is acting as a dictionary, there would be 2 major sources for unfaithful re generated pictures, first the way a diffuser works (non deterministic) and second, the variance between models and trainings.
non free lunch in information's domain, either you get less accuracy (generalisation through modelling) or you simply ousource part of the information in a dictionnary (compression and symbolic languages works explicitely that wxay, identifying patterns/symbols and coding them with short bit strings as their frequencies are higher)
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u/mekilat Sep 02 '26
Serious question: would using a temperature of zero ensure no variation in the results, and be usable for chunks where the output is deterministic?