code: def test_two_step_model(self):
      expected_video = load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"
      )
  
      pipe = TextToVideoSDPipeline.from_pretrained(
          "damo-vilab/text-to-video-ms-1.7b"
      )
      pipe = pipe.to(torch_device)
  
      prompt = "Spiderman is surfing"
      generator = torch.Generator(device="cpu").manual_seed(0)
  
      video_frames = pipe(
          prompt,
          generator=generator,
          num_inference_steps=2,
          output_type="pt",
      ).frames
      video = video_frames.cpu().numpy()
  
      assert np.abs(expected_video - video).mean() < 5e-2
  
imports: import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, TextToVideoSDPipeline, UNet3DConditionModel
  from diffusers.utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, skip_mps, 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 1 for calling the TextToVideoSDPipelineSlowTests obj's underlying member method code with example usage: obj.test_two_step_model() without return types