class_docstr: Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
class_docstr_tok: ["Inspired","by",":","https",":","\/\/github.com\/haofanwang\/ControlNet-for-Diffusers\/"]
docstr: Enable sliced VAE decoding.
  
  When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
  steps. This is useful to save some memory and allow larger batch sizes.
docstr_tok: ["Enable","sliced","VAE","decoding",".","When","this","option","is","enabled",",","the","VAE","will","split","the","input","tensor","in","slices","to","compute","decoding","in","several","steps",".","This","is","useful","to","save","some","memory","and","allow","larger","batch","sizes","."]
code: def enable_vae_slicing(self):
      r"""
      Enable sliced VAE decoding.
  
      When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
      steps. This is useful to save some memory and allow larger batch sizes.
      """
      self.vae.enable_slicing()
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, ControlNetModel, DiffusionPipeline, UNet2DConditionModel, logging
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import PIL_INTERPOLATION, is_accelerate_available, is_accelerate_version, replace_example_docstring
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 2 for calling the StableDiffusionControlNetInpaintImg2ImgPipeline obj's underlying member method code with example usage: obj.enable_vae_slicing() without return types