Rather than changing the model's billions of parameters, the technique inserts small matrices alongside them and trains only those. The result is a file that can be megabytes instead of gigabytes, trained from a modest set of example images, and loaded on top of a base model when you want its behaviour and left off when you do not.
That makes it the practical answer to identity at scale. A character you will use for a hundred clips is worth teaching once; after that, the subject is available from a prompt keyword rather than assembled from reference images every single generation.
Two constraints matter. A LoRA is bound to the base model it was trained against and does not transfer to a different one. And several loaded at once compete — stack a character, a style and a lighting adapter and the result is rarely the sum of the three.
In practice
- Small file, trained on a small set, loaded on top of a base model.
- Tied to its base model — a new model version means retraining.
- Stacked adapters interfere; weight them down or use fewer.
The mistake to avoid
Training one on twenty photos from the same shoot. The adapter learns that day's lighting and background as part of the subject and reproduces them everywhere.
Related terms
Fine-tuning
Fine-tuning continues training an existing model on your own examples so it produces your subject or style by default, rather than on request.
Character consistency
Character consistency is whether the same person, mascot or product still looks like itself across separate generations.
Reference image
A reference image is a picture supplied alongside the prompt so the model can copy an identity, product or style from it, without that picture becoming a frame of the output.
Distillation
Distillation trains a smaller or faster model to imitate a larger one's outputs, which is where the fast and turbo variants of familiar models come from.
Diffusion model
A diffusion model generates by starting from random noise and removing a little of it at a time until a picture or clip is left behind.
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