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`].
docstr: Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
  computing decoding in one step.
docstr_tok: ["Disable","sliced","VAE","decoding",".","If","`","enable_vae_slicing","`","was","previously","enabled",",","this","method","will","go","back","to","computing","decoding","in","one","step","."]
code: def disable_vae_slicing(self):
      r"""
      Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
      computing decoding in one step.
      """
      self.vae.disable_slicing()
  
imports: import inspect
  from dataclasses import dataclass
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
  from ...image_processor import PipelineImageInput, VaeImageProcessor
  from ...loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...models.unet_motion_model import MotionAdapter
  from ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler
  from ...utils import USE_PEFT_BACKEND, BaseOutput, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
label_desc: Use image node_id 6 for calling the AnimateDiffPipeline obj's underlying member method code with example usage: obj.disable_vae_slicing() without return types