class_docstr: A class that supports merging diffusion models based on the discussion here:
  https://github.com/huggingface/diffusers/issues/877
  
  Example usage:-
  
  pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger.py")
  
  merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","prompthero/openjourney"], interp = 'inv_sigmoid', alpha = 0.8, force = True)
  
  merged_pipe.to('cuda')
  
  prompt = "An astronaut riding a unicycle on Mars"
  
  results = merged_pipe(prompt)
  
  ## For more details, see the docstring for the merge method.
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code: def __init__(self):
      self.register_to_config()
      super().__init__()
  
file_path: diffusers/examples/community/checkpoint_merger.py
imports: import glob
  import os
  from typing import Dict, List, Union
  import safetensors.torch
  import torch
  from huggingface_hub import snapshot_download
  from huggingface_hub.utils import validate_hf_hub_args
  from diffusers import DiffusionPipeline, __version__
  from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
  from diffusers.utils import CONFIG_NAME, ONNX_WEIGHTS_NAME, WEIGHTS_NAME
label_desc: Use image node_id 1 to create a new CheckpointMergerPipeline object from inherited base classes: DiffusionPipeline with example: obj = CheckpointMergerPipeline()