vllm_omni.diffusion.models.wan2_2.scheduling_wan_euler ¶ WanEulerScheduler ¶ begin_index property ¶ begin_index: int | None config instance-attribute ¶ config = SimpleNamespace( num_train_timesteps=num_train_timesteps ) device instance-attribute ¶ device = device init_noise_sigma instance-attribute ¶ init_noise_sigma = 1.0 num_train_timesteps instance-attribute ¶ num_train_timesteps = int(num_train_timesteps) order class-attribute instance-attribute ¶ order = 1 sigmas instance-attribute ¶ sigmas = empty(0, dtype=float32) step_index property ¶ step_index: int | None timesteps instance-attribute ¶ timesteps = empty(0, dtype=float32) timesteps_ori instance-attribute ¶ timesteps_ori = empty(0, dtype=float32) index_for_timestep ¶ index_for_timestep(timestep: Tensor) -> int scale_model_input ¶ scale_model_input( sample: Tensor, timestep: int | None = None ) -> Tensor set_begin_index ¶ set_begin_index(begin_index: int = 0) -> None set_shift ¶ set_shift(shift: float = 1.0) -> None set_timesteps ¶ set_timesteps( num_inference_steps: int, device: device | str | int | None = None, **kwargs, ) -> None step ¶ step( model_output: FloatTensor, timestep: float | FloatTensor, sample: FloatTensor, return_dict: bool = True, **kwargs, ) -> WanEulerSchedulerOutput | tuple[FloatTensor] WanEulerSchedulerOutput dataclass ¶ prev_sample instance-attribute ¶ prev_sample: FloatTensor