es using Guided Diffusion Models

DDIMInverseScheduler
	

class diffusers.DDIMInverseScheduler

<
source
>
(
num_train_timesteps: int = 1000
beta_start: float = 0.0001
beta_end: float = 0.02
beta_schedule: str = 'linear'
trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None
clip_sample: bool = True
set_alpha_to_zero: bool = True
steps_offset: int = 0
prediction_type: str = 'epsilon'
clip_sample_range: float = 1.0
**kwa