code: def test_stable_diffusion_negative_prompt(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      components = self.get_dummy_components()
      components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
      ldm3d_pipe = StableDiffusionLDM3DPipeline(**components)
      ldm3d_pipe = ldm3d_pipe.to(device)
      ldm3d_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(device)
      negative_prompt = "french fries"
      output = ldm3d_pipe(**inputs, negative_prompt=negative_prompt)
  
      rgb, depth = output.rgb, output.depth
      rgb_slice = rgb[0, -3:, -3:, -1]
      depth_slice = depth[0, -3:, -1]
  
      assert rgb.shape == (1, 64, 64, 3)
      assert depth.shape == (1, 64, 64)
  
      expected_slice_rgb = np.array(
          [
              0.37044,
              0.71811503,
              0.7223251,
              0.48603675,
              0.5638391,
              0.6364948,
              0.42833704,
              0.4901315,
              0.47926217,
          ]
      )
      expected_slice_depth = np.array([107.84738, 84.62802, 89.962135])
      assert (
          np.abs(rgb_slice.flatten() - expected_slice_rgb).max() < 1e-2
      )
      assert (
          np.abs(depth_slice.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 5 for calling the StableDiffusionLDM3DPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_negative_prompt() without return types