class_docstr: Pipeline for image generation.
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
  implemented for all pipelines (downloading, saving, running on a particular device, etc.).
  
  Parameters:
      unet ([`UNet2DModel`]):
          A `UNet2DModel` to denoise the encoded image latents.
      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__()
  
      # make sure scheduler can always be converted to DDIM
      scheduler = DDIMScheduler.from_config(scheduler.config)
  
      self.register_modules(unet=unet, scheduler=scheduler)
  
file_path: llmga/llmga/diffusers/src/diffusers/pipelines/ddim/pipeline_ddim.py
imports: from typing import List, Optional, Tuple, Union
  import torch
  from ...schedulers import DDIMScheduler
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 1 to create a new DDIMPipeline object from inherited base classes: DiffusionPipeline with example: obj = DDIMPipeline(unet, scheduler)