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.).
  
  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: 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 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 2 for calling the StableUnCLIPImg2ImgPipeline obj's underlying member method code with example usage: obj.enable_vae_slicing() without return types