code: def test_animatediff(self):
      adapter = MotionAdapter.from_pretrained(
          "guoyww/animatediff-motion-adapter-v1-5-2"
      )
      pipe = AnimateDiffPipeline.from_pretrained(
          "frankjoshua/toonyou_beta6", motion_adapter=adapter
      )
      pipe = pipe.to(torch_device)
      pipe.scheduler = DDIMScheduler(
          beta_start=0.00085,
          beta_end=0.012,
          beta_schedule="linear",
          steps_offset=1,
          clip_sample=False,
      )
      pipe.enable_vae_slicing()
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=None)
  
      prompt = "night, b&w photo of old house, post apocalypse, forest, storm weather, wind, rocks, 8k uhd, dslr, soft lighting, high quality, film grain"
      negative_prompt = "bad quality, worse quality"
  
      generator = torch.Generator("cpu").manual_seed(0)
      output = pipe(
          prompt,
          negative_prompt=negative_prompt,
          num_frames=16,
          generator=generator,
          guidance_scale=7.5,
          num_inference_steps=3,
          output_type="np",
      )
  
      image = output.frames[0]
      assert image.shape == (16, 512, 512, 3)
  
      image_slice = image[0, -3:, -3:, -1]
      expected_slice = np.array(
          [
              0.11357737,
              0.11285847,
              0.11180121,
              0.11084166,
              0.11414117,
              0.09785956,
              0.10742754,
              0.10510018,
              0.08045256,
          ]
      )
      assert (
          numpy_cosine_similarity_distance(
              image_slice.flatten(), expected_slice.flatten()
          )
          < 1e-3
      )
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  import diffusers
  from diffusers import AnimateDiffPipeline, AutoencoderKL, DDIMScheduler, MotionAdapter, UNet2DConditionModel, UNetMotionModel
  from diffusers.utils import is_xformers_available, logging
  from diffusers.utils.testing_utils import numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the AnimateDiffPipelineSlowTests obj's underlying member method code with example usage: obj.test_animatediff() without return types