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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=self.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 = torch.empty(0, dtype=torch.float32)

step_index property 

step_index: int | None

timesteps instance-attribute 

timesteps = torch.empty(0, dtype=torch.float32)

timesteps_ori instance-attribute 

timesteps_ori = torch.empty(0, dtype=torch.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