w the scheduler is instantiated from a configuration. Unlike a model, a scheduler does not have trainable weights and is parameter-free!
Some of the most important parameters are:
num_train_timesteps: the length of the denoising process or in other words, the number of timesteps required to process random Gaussian noise into a data sample.
beta_schedule: the type of noise schedule to use for inference and training.
beta_start and beta_end: the 