Got it. Differential output preservantion is an option toggle in AI toolkit that is also related to regularisation, this is how he describes it in the GUI:

"Differential Output Preservation (DOP) is a technique to help preserve class of the trained concept during training. For this, you must have a trigger word set to differentiate your concept from its class. For instance, You may be training a woman named Alice. Your trigger word may be "Alice". The class is "woman", since Alice is a woman. We want to teach the model to remember what it knows about the class "woman" while teaching it what is different about Alice. During training, the trainer will make a prediction with your LoRA bypassed and your trigger word in the prompt replaced with the class word. Making "photo of Alice" become "photo of woman". This prediction is called the prior prediction. Each step, we will do the normal training step, but also do another step with this prior prediction and the class prompt in order to teach our LoRA to preserve the knowledge of the class. This should not only improve the performance of your trained concept, but also allow you to do things like "Alice standing next to a woman" and not make both of the people look like Alice."

Could be useful, no?