class_docstr: The consistency decoder used with DALL-E 3.
  
  Examples:
      ```py
      >>> import torch
      >>> from diffusers import StableDiffusionPipeline, ConsistencyDecoderVAE
  
      >>> vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)
      >>> pipe = StableDiffusionPipeline.from_pretrained(
      ...     "runwayml/stable-diffusion-v1-5", vae=vae, torch_dtype=torch.float16
      ... ).to("cuda")
  
      >>> pipe("horse", generator=torch.manual_seed(0)).images
      ```
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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 for saving a large amount of memory and to allow
  processing 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","for","saving","a","large","amount","of","memory","and","to","allow","processing","larger","images","."]
code: def enable_tiling(self, use_tiling: bool = True):
      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 for saving a large amount of memory and to allow
      processing larger images.
      """
      self.use_tiling = use_tiling
  
imports: from dataclasses import dataclass
  from typing import Dict, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...schedulers import ConsistencyDecoderScheduler
  from ...utils import BaseOutput
  from ...utils.accelerate_utils import apply_forward_hook
  from ...utils.torch_utils import randn_tensor
  from ..attention_processor import ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnAddedKVProcessor, AttnProcessor
  from ..modeling_utils import ModelMixin
  from ..unet_2d import UNet2DModel
  from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
label_desc: Use image node_id 2 for calling the ConsistencyDecoderVAE obj's underlying member method code with example usage: obj.enable_tiling(use_tiling) without return types