class_docstr: Pipeline for imagic image editing.
  See paper here: https://arxiv.org/pdf/2210.09276.pdf
  
  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.)
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
      text_encoder ([`CLIPTextModel`]):
          Frozen text-encoder. Stable Diffusion uses the text portion of
          [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
          the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
      tokenizer (`CLIPTokenizer`):
          Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
      scheduler ([`SchedulerMixin`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
          [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
      safety_checker ([`StableDiffusionSafetyChecker`]):
          Classification module that estimates whether generated images could be considered offsensive or harmful.
          Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
      feature_extractor ([`CLIPImageProcessor`]):
          Model that extracts features from generated images to be used as inputs for the `safety_checker`.
docstr: Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
  back to computing attention in one step.
docstr_tok: ["Disable","sliced","attention","computation",".","If","`","enable_attention_slicing","`","was","previously","invoked",",","this","method","will","go","back","to","computing","attention","in","one","step","."]
code: def disable_attention_slicing(self):
      r"""
      Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
      back to computing attention in one step.
      """
      # set slice_size = `None` to disable `attention slicing`
      self.enable_attention_slicing(None)
  
imports: import inspect
  import warnings
  from typing import List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from accelerate import Accelerator
  from packaging import version
  from tqdm.auto import tqdm
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers import DiffusionPipeline
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.utils import logging
label_desc: Use image node_id 3 for calling the ImagicStableDiffusionPipeline obj's underlying member method code with example usage: obj.disable_attention_slicing() without return types