class_docstr: Pipeline for text-to-video generation.
  
  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.).
  
  The pipeline also inherits the following loading methods:
      - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
      - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
      - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
      - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
      text_encoder ([`CLIPTextModel`]):
          Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      tokenizer (`CLIPTokenizer`):
          A [`~transformers.CLIPTokenizer`] to tokenize text.
      unet ([`UNet2DConditionModel`]):
          A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
      motion_adapter ([`MotionAdapter`]):
          A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video 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`].
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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 dataclasses import dataclass
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
  from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
  from diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel, UNetMotionModel
  from diffusers.models.lora import adjust_lora_scale_text_encoder
  from diffusers.models.unet_motion_model import MotionAdapter
  from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.schedulers import DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
  from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
label_desc: Use image node_id 5 for calling the AnimateDiffControlNetPipeline obj's underlying member method code with example usage: obj.enable_vae_slicing() without return types