class_docstr: This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
  library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
  
  Parameters:
      unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
      scheduler ([`SchedulerMixin`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
          [`DDPMScheduler`], or [`DDIMScheduler`].
code: def __init__(self, unet, scheduler):
      super().__init__()
      self.register_modules(unet=unet, scheduler=scheduler)
  
imports: from typing import Optional, Tuple, Union
  import torch
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 1 to create a new CustomLocalPipeline object from inherited base classes: DiffusionPipeline with example: obj = CustomLocalPipeline(unet, scheduler)