LoRA
A small, efficient fine-tune layered on top of a base generative model to teach it a specific style or subject.
A LoRA (Low-Rank Adaptation) is a small, efficient fine-tune layered on top of a base generative model to teach it a specific style, character, or subject — without retraining the whole model. Because a LoRA only adds a small set of new parameters, it's cheap to train and easy to swap in and out of a base diffusion model.
LoRAs are how creators get a consistent character or brand style out of an otherwise general-purpose model, rather than re-prompting and hoping for consistency each time.
See also: Diffusion Model, ControlNet.
Put these ideas into motion.
DownloadFrequently asked questions
LoRA stands for Low-Rank Adaptation, a technique for fine-tuning a large generative model efficiently by training a small set of additional parameters instead of the entire model.
A LoRA is small — often tens of megabytes instead of gigabytes — quick to train, and can be swapped in and out of a base model, making it a practical way to teach a model a specific character, style, or product without retraining it from scratch.