0.02
beta_schedule: str = 'linear'
trained_betas: typing.Optional[numpy.ndarray] = None
solver_order: int = 2
prediction_type: str = 'epsilon'
thresholding: bool = False
dynamic_thresholding_ratio: float = 0.995
sample_max_value: float = 1.0
algorithm_type: str = 'deis'
solver_type: str = 'logrho'
lower_order_final: bool = True

)


Parameters 

num_train_timesteps (int) — number of diffusion steps used to train the model.


beta_start (float) 