} 💡 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 start and end noise values for the noise schedule. To predict a slightly less noisy image, pass the following to the scheduler’s step() method: model output, timestep, and current sample.   Copied >>> less_noisy_sample = scheduler.step(model_output=noisy_residual, timestep=2, sample=noisy_sample).prev_sample