Generating one good image of a character is straightforward, and generating fifty images of the same character is a different problem. The techniques that solve it work by adding something to the model rather than by describing harder.
Description cannot specify a person
A prompt names attributes, and attributes describe a type rather than an individual.
Two images matching a detailed description of hair, age, clothing and expression will still show two different people, because identity rests on proportions that words do not reach.
This is why prompt refinement plateaus quickly on consistency while continuing to improve other qualities.
Fine-tuning teaches the model a new token
The established approach trains on a small set of images of the subject, binding them to an identifier the model did not previously use.
That identifier then works like any other concept in the prompt, and can be combined with poses, settings and styles the training images never showed.
The quality of the result depends heavily on variety in the training set: images shot in one location and one pose teach the location and the pose along with the face.
Adapters keep the base model intact
Rather than altering the whole model, low-rank adapters train a small set of additional weights that modify its behaviour.
These files are small, can be shared, and several can be combined, which is why community character and style libraries are distributed in this form.
Combining them is imperfect, since two adapters trained separately can interfere, and the failure usually shows as one influence overwhelming the other.
Reference conditioning avoids training entirely
A faster route supplies reference images at generation time, with an encoder extracting identity features that steer the output.
This works immediately with no training step, which suits one-off use, and it holds identity less firmly than a trained adapter across large changes of angle.
The distinction that matters in practice is how many images are needed: reference conditioning for a handful, training for a body of work.
Overfitting is the common failure
Training too long on too few images produces a model that reproduces the training photographs rather than the character.
The symptom is recognisable: prompts asking for new poses return something close to one of the inputs, and the character cannot be placed anywhere new.
Stopping earlier gives a weaker likeness with far more flexibility, and most working setups deliberately accept the weaker likeness because a character that cannot move is not usable in a sequence.