class_docstr: Pipeline for text-to-image generation using Stable Diffusion.
  
  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:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
      text_encoder ([`~transformers.CLIPTextModel`]):
          Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      tokenizer ([`~transformers.CLIPTokenizer`]):
          A `CLIPTokenizer` to tokenize text.
      unet ([`UNet2DConditionModel`]):
          A `UNet2DConditionModel` 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/runwayml/stable-diffusion-v1-5) for more details
          about a model's potential harms.
      feature_extractor ([`~transformers.CLIPImageProcessor`]):
          A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
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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 torch
  import torch.nn.functional
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  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
  from .None import StableDiffusionPipelineOutput
  from .safety_checker import StableDiffusionSafetyChecker
label_desc: Use image node_id 2 for calling the StableDiffusionSAGPipeline obj's underlying member method code with example usage: obj.enable_vae_slicing() without return types