class_docstr: Pipeline for text-guided image-to-image generation using stable unCLIP.
  
  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.).
  
  The pipeline also inherits the following loading methods:
      - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
      - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
      - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
  
  Args:
      feature_extractor ([`CLIPImageProcessor`]):
          Feature extractor for image pre-processing before being encoded.
      image_encoder ([`CLIPVisionModelWithProjection`]):
          CLIP vision model for encoding images.
      image_normalizer ([`StableUnCLIPImageNormalizer`]):
          Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image
          embeddings after the noise has been applied.
      image_noising_scheduler ([`KarrasDiffusionSchedulers`]):
          Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined
          by the `noise_level`.
      tokenizer (`~transformers.CLIPTokenizer`):
          A [`~transformers.CLIPTokenizer`)].
      text_encoder ([`~transformers.CLIPTextModel`]):
          Frozen [`~transformers.CLIPTextModel`] text-encoder.
      unet ([`UNet2DConditionModel`]):
          A [`UNet2DConditionModel`] to denoise the encoded image latents.
      scheduler ([`KarrasDiffusionSchedulers`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents.
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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docstr: Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
  computing decoding in one step.
docstr_tok: ["Disable","sliced","VAE","decoding",".","If","`","enable_vae_slicing","`","was","previously","enabled",",","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 enabled, this method will go back to
      computing decoding in one step.
      """
      self.vae.disable_slicing()
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Union
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
  from ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...models.embeddings import get_timestep_embedding
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
label_desc: Use image node_id 3 for calling the StableUnCLIPImg2ImgPipeline obj's underlying member method code with example usage: obj.disable_vae_slicing() without return types