class_docstr: Pipeline for text-to-image generation using Retrieval Augmented Diffusion.
  
  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.
      clip ([`CLIPModel`]):
          Frozen CLIP model. Retrieval Augmented Diffusion uses the CLIP model, 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`].
      feature_extractor ([`CLIPFeatureExtractor`]):
          Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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docstr: Enable tiled VAE decoding.
  
  When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
  several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
docstr_tok: ["Enable","tiled","VAE","decoding",".","When","this","option","is","enabled",",","the","VAE","will","split","the","input","tensor","into","tiles","to","compute","decoding","and","encoding","in","several","steps",".","This","is","useful","to","save","a","large","amount","of","memory","and","to","allow","the","processing","of","larger","images","."]
code: def enable_vae_tiling(self):
      r"""
      Enable tiled VAE decoding.
  
      When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
      several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
      """
      self.vae.enable_tiling()
  
imports: import inspect
  from typing import Callable, List, Optional, Union
  import torch
  from PIL import Image
  from retriever import Retriever, normalize_images, preprocess_images
  from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, ImagePipelineOutput, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel, logging
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.utils import is_accelerate_available, randn_tensor
label_desc: Use image node_id 6 for calling the RDMPipeline obj's underlying member method code with example usage: obj.enable_vae_tiling() without return types