class_docstr: Pipeline for text-to-image generation using Stable Diffusion and Attend-and-Excite.
  
  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`.
code: def register_attention_control(self):
      attn_procs = {}
      cross_att_count = 0
      for name in self.unet.attn_processors.keys():
          if name.startswith("mid_block"):
              place_in_unet = "mid"
          elif name.startswith("up_blocks"):
              place_in_unet = "up"
          elif name.startswith("down_blocks"):
              place_in_unet = "down"
          else:
              continue
  
          cross_att_count += 1
          attn_procs[name] = AttendExciteAttnProcessor(
              attnstore=self.attention_store,
              place_in_unet=place_in_unet,
          )
  
      self.unet.set_attn_processor(attn_procs)
      self.attention_store.num_att_layers = cross_att_count
  
imports: import inspect
  import math
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from torch.nn import functional
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...models.attention_processor import Attention
  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 16 for calling the StableDiffusionAttendAndExcitePipeline obj's underlying member method code with example usage: obj.register_attention_control() without return types