code: def test_stable_diffusion_xl_negative_conditions(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      components = self.get_dummy_components()
      sd_pipe = StableDiffusionXLPipeline(**components)
      sd_pipe = sd_pipe.to(device)
      sd_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(device)
      image = sd_pipe(**inputs).images
      image_slice_with_no_neg_cond = image[0, -3:, -3:, -1]
  
      image = sd_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_with_no_neg_cond - image_slice_with_neg_cond
          ).max()
          > 1e-2
      )
  
imports: import copy
  import tempfile
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, StableDiffusionXLImg2ImgPipeline, StableDiffusionXLPipeline, UNet2DConditionModel, UniPCMultistepScheduler
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 13 for calling the StableDiffusionXLPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_xl_negative_conditions() without return types