code: def test_two_step_model(self):
      pipe = VideoToVideoSDPipeline.from_pretrained(
          "cerspense/zeroscope_v2_576w", torch_dtype=torch.float16
      )
      pipe.enable_model_cpu_offload()
  
      # 10 frames
      generator = torch.Generator(device="cpu").manual_seed(0)
      video = torch.randn((1, 10, 3, 320, 576), generator=generator)
  
      prompt = "Spiderman is surfing"
  
      video_frames = pipe(
          prompt,
          video=video,
          generator=generator,
          num_inference_steps=3,
          output_type="pt",
      ).frames
  
      expected_array = np.array(
          [-0.9770508, -0.8027344, -0.62646484, -0.8334961, -0.7573242]
      )
      output_array = video_frames.cpu().numpy()[0, 0, 0, 0, -5:]
  
      assert (
          numpy_cosine_similarity_distance(expected_array, output_array)
          < 1e-2
      )
  
imports: import random
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, UNet3DConditionModel, VideoToVideoSDPipeline
  from diffusers.utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, is_flaky, nightly, numpy_cosine_similarity_distance, skip_mps, torch_device
  from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the VideoToVideoSDPipelineSlowTests obj's underlying member method code with example usage: obj.test_two_step_model() without return types