code: def test_stable_diffusion_xl_multi_prompts(self):
      components = self.get_dummy_components()
      sd_pipe = self.pipeline_class(**components).to(torch_device)
  
      # forward with single prompt
      inputs = self.get_dummy_inputs(torch_device)
      inputs["num_inference_steps"] = 5
      output = sd_pipe(**inputs)
      image_slice_1 = output.images[0, -3:, -3:, -1]
  
      # forward with same prompt duplicated
      inputs = self.get_dummy_inputs(torch_device)
      inputs["num_inference_steps"] = 5
      inputs["prompt_2"] = inputs["prompt"]
      output = sd_pipe(**inputs)
      image_slice_2 = output.images[0, -3:, -3:, -1]
  
      # ensure the results are equal
      assert (
          np.abs(
              image_slice_1.flatten() - image_slice_2.flatten()
          ).max()
          < 1e-4
      )
  
      # forward with different prompt
      inputs = self.get_dummy_inputs(torch_device)
      inputs["num_inference_steps"] = 5
      inputs["prompt_2"] = "different prompt"
      output = sd_pipe(**inputs)
      image_slice_3 = output.images[0, -3:, -3:, -1]
  
      # ensure the results are not equal
      assert (
          np.abs(
              image_slice_1.flatten() - image_slice_3.flatten()
          ).max()
          > 1e-4
      )
  
      # manually set a negative_prompt
      inputs = self.get_dummy_inputs(torch_device)
      inputs["num_inference_steps"] = 5
      inputs["negative_prompt"] = "negative prompt"
      output = sd_pipe(**inputs)
      image_slice_1 = output.images[0, -3:, -3:, -1]
  
      # forward with same negative_prompt duplicated
      inputs = self.get_dummy_inputs(torch_device)
      inputs["num_inference_steps"] = 5
      inputs["negative_prompt"] = "negative prompt"
      inputs["negative_prompt_2"] = inputs["negative_prompt"]
      output = sd_pipe(**inputs)
      image_slice_2 = output.images[0, -3:, -3:, -1]
  
      # ensure the results are equal
      assert (
          np.abs(
              image_slice_1.flatten() - image_slice_2.flatten()
          ).max()
          < 1e-4
      )
  
      # forward with different negative_prompt
      inputs = self.get_dummy_inputs(torch_device)
      inputs["num_inference_steps"] = 5
      inputs["negative_prompt"] = "negative prompt"
      inputs["negative_prompt_2"] = "different negative prompt"
      output = sd_pipe(**inputs)
      image_slice_3 = output.images[0, -3:, -3:, -1]
  
      # ensure the results are not equal
      assert (
          np.abs(
              image_slice_1.flatten() - image_slice_3.flatten()
          ).max()
          > 1e-4
      )
  
imports: import copy
  import random
  import unittest
  import numpy
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionConfig, CLIPVisionModelWithProjection
  from diffusers import AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, LCMScheduler, StableDiffusionXLInpaintPipeline, UNet2DConditionModel, UniPCMultistepScheduler
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, slow, torch_device
  from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS, TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS
  from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 17 for calling the StableDiffusionXLInpaintPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_xl_multi_prompts() without return types