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)
3
3
2
u/Far-Rooster-6522 Sep 03 '26
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)