Prior preservation loss is a method that uses a model's own generated samples to help it learn how to generate more diverse images. Because these generated sample images belong to the same class as the images you provided, they help the model retain what it has learned about the class and how it can use what it already knows about the class to make new compositions.  
Many of the parameters for prior preservation loss are described in the [DreamBooth](dreambooth#prior-preservation-loss) training guide.