EulerAncestralDiscreteScheduler A scheduler that uses ancestral sampling with Euler method steps. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original k-diffusion implementation by Katherine Crowson.  EulerAncestralDiscreteScheduler  class diffusers.EulerAncestralDiscreteScheduler  < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = None prediction_type: str = 'epsilon' timestep_spacing: str = 'linspace' steps_offset: int = 0 rescale_betas_zero_snr: bool = False  )   Parameters   num_train_timesteps (int, defaults to 1000) —