code: def test_stable_diffusion_ddim(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
  
      components = self.get_dummy_components()
      ldm3d_pipe = StableDiffusionLDM3DPipeline(**components)
      ldm3d_pipe = ldm3d_pipe.to(torch_device)
      ldm3d_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(device)
      output = ldm3d_pipe(**inputs)
      rgb, depth = output.rgb, output.depth
  
      image_slice_rgb = rgb[0, -3:, -3:, -1]
      image_slice_depth = depth[0, -3:, -1]
  
      assert rgb.shape == (1, 64, 64, 3)
      assert depth.shape == (1, 64, 64)
  
      expected_slice_rgb = np.array(
          [
              0.37338176,
              0.70247,
              0.74203193,
              0.51643604,
              0.58256793,
              0.60932136,
              0.4181095,
              0.48355877,
              0.46535262,
          ]
      )
      expected_slice_depth = np.array([103.46727, 85.812004, 87.849236])
  
      assert (
          np.abs(image_slice_rgb.flatten() - expected_slice_rgb).max()
          < 1e-2
      )
      assert (
          np.abs(
              image_slice_depth.flatten() - expected_slice_depth
          ).max()
          < 1e-2
      )
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, PNDMScheduler, StableDiffusionLDM3DPipeline, UNet2DConditionModel
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
label_desc: Use image node_id 3 for calling the StableDiffusionLDM3DPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_ddim() without return types