code: def test_stable_diffusion_xl_inpaint_2_images(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      components = self.get_dummy_components()
      sd_pipe = self.pipeline_class(**components)
      sd_pipe = sd_pipe.to(device)
      sd_pipe.set_progress_bar_config(disable=None)
  
      # test to confirm if we pass two same image, we will get same output
      inputs = self.get_dummy_inputs(device)
      gen1 = torch.Generator(device=device).manual_seed(0)
      gen2 = torch.Generator(device=device).manual_seed(0)
      for name in ["prompt", "image", "mask_image"]:
          inputs[name] = [inputs[name]] * 2
      inputs["generator"] = [gen1, gen2]
      images = sd_pipe(**inputs).images
  
      assert images.shape == (2, 64, 64, 3)
  
      image_slice1 = images[0, -3:, -3:, -1]
      image_slice2 = images[1, -3:, -3:, -1]
      assert (
          np.abs(image_slice1.flatten() - image_slice2.flatten()).max()
          < 1e-4
      )
  
      # test to confirm that if we pass two different images, we will get different output
      inputs = self.get_dummy_inputs_2images(device)
      images = sd_pipe(**inputs).images
      assert images.shape == (2, 64, 64, 3)
  
      image_slice1 = images[0, -3:, -3:, -1]
      image_slice2 = images[1, -3:, -3:, -1]
      assert (
          np.abs(image_slice1.flatten() - image_slice2.flatten()).max()
          > 1e-2
      )
  
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 20 for calling the StableDiffusionXLInpaintPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_xl_inpaint_2_images() without return types