code: def test_stable_diffusion_xl_controlnet_img2img_guess(self):
      device = "cpu"
  
      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)
  
      inputs = self.get_dummy_inputs(device)
      inputs["guess_mode"] = True
  
      output = sd_pipe(**inputs)
      image_slice = output.images[0, -3:, -3:, -1]
      assert output.images.shape == (1, 64, 64, 3)
  
      expected_slice = np.array(
          [
              0.5557202,
              0.46418434,
              0.46983826,
              0.623529,
              0.5557242,
              0.49262643,
              0.6070508,
              0.5702978,
              0.43777135,
          ]
      )
  
      # make sure that it's equal
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
  
imports: import random
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import AutoencoderKL, ControlNetModel, EulerDiscreteScheduler, StableDiffusionXLControlNetImg2ImgPipeline, UNet2DConditionModel
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
  from ..pipeline_params import IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 4 for calling the ControlNetPipelineSDXLImg2ImgFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_xl_controlnet_img2img_guess() without return types