class_docstr: Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
  weighting in prompt.
  
  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 offensive 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`.
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docstr: Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
  computing decoding in one step.
docstr_tok: ["Disable","sliced","VAE","decoding",".","If","`","enable_vae_slicing","`","was","previously","invoked",",","this","method","will","go","back","to","computing","decoding","in","one","step","."]
code: def disable_vae_slicing(self):
      r"""
      Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
      computing decoding in one step.
      """
      self.vae.disable_slicing()
  
imports: import inspect
  import re
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from packaging import version
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers import DiffusionPipeline
  from diffusers.configuration_utils import FrozenDict
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import PIL_INTERPOLATION, deprecate, is_accelerate_available, is_accelerate_version, logging
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 3 for calling the StableDiffusionLongPromptWeightingPipeline obj's underlying member method code with example usage: obj.disable_vae_slicing() without return types