ou’re using the model for training or inference. During inference, the scheduler generates image from the noise. During training, the scheduler takes a model output - or a sample - from a specific point in the diffusion process and applies noise to the image according to a noise schedule and an update rule. Let’s take a look at the DDPMScheduler and use the add_noise method to add some random noise to the sample_image from before:   Copied >>> 