code: def test_compare_to_diffusers(self, model_arch: str):
      ort_pipeline = self.ORTMODEL_CLASS.from_pretrained(
          MODEL_NAMES[model_arch], export=True
      )
      self.assertIsInstance(
          ort_pipeline.text_encoder, ORTModelTextEncoder
      )
      self.assertIsInstance(
          ort_pipeline.text_encoder_2, ORTModelTextEncoder
      )
      self.assertIsInstance(
          ort_pipeline.vae_decoder, ORTModelVaeDecoder
      )
      self.assertIsInstance(
          ort_pipeline.vae_encoder, ORTModelVaeEncoder
      )
      self.assertIsInstance(ort_pipeline.unet, ORTModelUnet)
      self.assertIsInstance(ort_pipeline.config, Dict)
  
      pipeline = StableDiffusionXLPipeline.from_pretrained(
          MODEL_NAMES[model_arch]
      )
      batch_size, num_images_per_prompt, height, width = 2, 2, 64, 32
      latents = ort_pipeline.prepare_latents(
          batch_size * num_images_per_prompt,
          ort_pipeline.unet.config["in_channels"],
          height,
          width,
          dtype=np.float32,
          generator=np.random.RandomState(0),
      )
  
      kwargs = {
          "prompt": ["sailing ship in storm by Leonardo da Vinci"]
          * batch_size,
          "num_inference_steps": 1,
          "num_images_per_prompt": num_images_per_prompt,
          "height": height,
          "width": width,
          "guidance_rescale": 0.1,
      }
  
      for output_type in ["latent", "np"]:
          ort_outputs = ort_pipeline(
              latents=latents, output_type=output_type, **kwargs
          ).images
          self.assertIsInstance(ort_outputs, np.ndarray)
          with torch.no_grad():
              outputs = pipeline(
                  latents=torch.from_numpy(latents),
                  output_type=output_type,
                  **kwargs,
              ).images
  
          # Compare model outputs
          self.assertTrue(np.allclose(ort_outputs, outputs, atol=1e-4))
          # Compare model devices
          self.assertEqual(pipeline.device, ort_pipeline.device)
  
imports: import random
  import unittest
  from typing import Dict
  import numpy
  import PIL
  import pytest
  import torch
  from diffusers import OnnxStableDiffusionImg2ImgPipeline, StableDiffusionPipeline, StableDiffusionXLPipeline
  from diffusers.utils import load_image
  from diffusers.utils.testing_utils import floats_tensor
  from packaging.version import Version, parse
  from parameterized import parameterized
  from transformers.testing_utils import require_torch_gpu
  from utils_onnxruntime_tests import MODEL_NAMES, SEED, ORTModelTestMixin
  from optimum.onnxruntime import ORTLatentConsistencyModelPipeline, ORTStableDiffusionImg2ImgPipeline, ORTStableDiffusionInpaintPipeline, ORTStableDiffusionPipeline, ORTStableDiffusionXLImg2ImgPipeline, ORTStableDiffusionXLPipeline
  from optimum.onnxruntime.modeling_diffusion import ORTModelTextEncoder, ORTModelUnet, ORTModelVaeDecoder, ORTModelVaeEncoder
  from optimum.pipelines.diffusers.pipeline_utils import VaeImageProcessor
  from optimum.utils.import_utils import _diffusers_version
  from optimum.utils.testing_utils import grid_parameters, require_diffusers, require_ort_rocm
label_desc: Use image node_id 1 for calling the ORTStableDiffusionXLPipelineTest obj's underlying member method code with example usage: obj.test_compare_to_diffusers(model_arch) without return types