change timesteps

Author: zRzRzRzRzRzRzR

src/diffusers/pipelines/cogview4/pipeline_cogview4.py

@@ -74,65 +74,6 @@ def calculate_shift(
 
     return mu
 
-# def retrieve_timesteps(
-#     scheduler,
-#     num_inference_steps: Optional[int] = None,
-#     device: Optional[Union[str, torch.device]] = None,
-#     timesteps: Optional[List[int]] = None,
-#     sigmas: Optional[List[float]] = None,
-#     **kwargs,
-# ):
-#     r"""
-#     Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
-#     custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
-
-#     Args:
-#         scheduler (`SchedulerMixin`):
-#             The scheduler to get timesteps from.
-#         num_inference_steps (`int`):
-#             The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
-#             must be `None`.
-#         device (`str` or `torch.device`, *optional*):
-#             The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-#         timesteps (`List[int]`, *optional*):
-#             Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
-#             `num_inference_steps` and `sigmas` must be `None`.
-#         sigmas (`List[float]`, *optional*):
-#             Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
-#             `num_inference_steps` and `timesteps` must be `None`.
-
-#     Returns:
-#         `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
-#         second element is the number of inference steps.
-#     """
-#     if timesteps is not None and sigmas is not None:
-#         raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
-#     if timesteps is not None:
-#         accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-#         if not accepts_timesteps:
-#             raise ValueError(
-#                 f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-#                 f" timestep schedules. Please check whether you are using the correct scheduler."
-#             )
-#         scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
-#         timesteps = scheduler.timesteps
-#         num_inference_steps = len(timesteps)
-#     elif sigmas is not None:
-#         accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-#         if not accept_sigmas:
-#             raise ValueError(
-#                 f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-#                 f" sigmas schedules. Please check whether you are using the correct scheduler."
-#             )
-#         scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
-#         timesteps = scheduler.timesteps
-#         num_inference_steps = len(timesteps)
-#     else:
-#         scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
-#         timesteps = scheduler.timesteps
-#     return timesteps, num_inference_steps
-
-
 class CogView4Pipeline(DiffusionPipeline):
     r"""
     Pipeline for text-to-image generation using CogView4.