>>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config) Try generating an image with the new scheduler and see if you notice a difference! In the next section, you’ll take a closer look at the components - the model and scheduler - that make up the DiffusionPipeline and learn how to use these components to generate an image of a cat.  Models Most models take a noisy sample, and at each timestep it predicts the noise residual (other models learn to predict the previous sample directly or the velocity or v-prediction), the difference between a less noisy image and the input image. You can mix and match models to create other diffusion systems. Models are initiated with the from_pretrained() method which also locally caches the model weights so it is faster the next time you load the model. For the quicktour, you’ll load the UNet2DModel, a basic unconditional image generation model with a checkpoint trained on cat images:   Copied >>> from diffusers import UNet2DModel