tomize the default components of any pipeline with another compatible component. Customization is important because: Changing the scheduler is important for exploring the trade-off between generation speed and quality. Different components of a model are typically trained independently and you can swap out a component with a better-performing one. During finetuning, usually only some components - like the UNet or text encoder - are trained. To 