code: def test_stable_diffusion_xl_offloads(self):
      pipes = []
      components = self.get_dummy_components()
      sd_pipe = self.pipeline_class(**components).to(torch_device)
      pipes.append(sd_pipe)
  
      components = self.get_dummy_components()
      sd_pipe = self.pipeline_class(**components)
      sd_pipe.enable_model_cpu_offload()
      pipes.append(sd_pipe)
  
      components = self.get_dummy_components()
      sd_pipe = self.pipeline_class(**components)
      sd_pipe.enable_sequential_cpu_offload()
      pipes.append(sd_pipe)
  
      image_slices = []
      for pipe in pipes:
          pipe.unet.set_default_attn_processor()
  
          inputs = self.get_dummy_inputs(torch_device)
          image = pipe(**inputs).images
  
          image_slices.append(image[0, -3:, -3:, -1].flatten())
  
      assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
      assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
  
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 9 for calling the ControlNetPipelineSDXLImg2ImgFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_xl_offloads() without return types