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
      ```
class_docstr_tok: ["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","``","`"]
docstr: Returns:
      `dict` of attention processors: A dictionary containing all attention processors used in the model with
      indexed by its weight name.
docstr_tok: ["Returns",":","`","dict","`","of","attention","processors",":","A","dictionary","containing","all","attention","processors","used","in","the","model","with","indexed","by","its","weight","name","."]
code: def attn_processors(self) -> Dict[str, AttentionProcessor]:
      r"""
      Returns:
          `dict` of attention processors: A dictionary containing all attention processors used in the model with
          indexed by its weight name.
      """
      # set recursively
      processors = {}
  
      def fn_recursive_add_processors(
          name: str,
          module: torch.nn.Module,
          processors: Dict[str, AttentionProcessor],
      ):
          if hasattr(module, "get_processor"):
              processors[f"{name}.processor"] = module.get_processor(
                  return_deprecated_lora=True
              )
  
          for sub_name, child in module.named_children():
              fn_recursive_add_processors(
                  f"{name}.{sub_name}", child, processors
              )
  
          return processors
  
      for name, module in self.named_children():
          fn_recursive_add_processors(name, module, processors)
  
      return processors
  
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 6 for calling the ConsistencyDecoderVAE obj's underlying member method code with example usage: obj.attn_processors() and returns: processors, processors