code: def test_negative_conditions(self):
      components = self.get_dummy_components()
      pipe = self.pipeline_class(**components)
      pipe.to(torch_device)
  
      inputs = self.get_dummy_inputs(torch_device)
      image = pipe(**inputs).images
      image_slice_without_neg_cond = image[0, -3:, -3:, -1]
  
      image = pipe(
          **inputs,
          negative_original_size=(512, 512),
          negative_crops_coords_top_left=(0, 0),
          negative_target_size=(1024, 1024),
      ).images
      image_slice_with_neg_cond = image[0, -3:, -3:, -1]
  
      self.assertTrue(
          np.abs(
              image_slice_without_neg_cond - image_slice_with_neg_cond
          ).max()
          > 1e-2
      )
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import AutoencoderKL, ControlNetModel, EulerDiscreteScheduler, LCMScheduler, StableDiffusionXLControlNetPipeline, UNet2DConditionModel
  from diffusers.models.unet_2d_blocks import UNetMidBlock2D
  from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, slow, torch_device
  from diffusers.utils.torch_utils import randn_tensor
  from ..pipeline_params import IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin
label_desc: Use image node_id 8 for calling the StableDiffusionXLMultiControlNetOneModelPipelineFastTests obj's underlying member method code with example usage: obj.test_negative_conditions() without return types