code: def test_compare_diffusers_pipeline(self, model_arch: str):
      model_args = {"test_name": model_arch, "model_arch": model_arch}
      self._setup(model_args)
      height, width = 128, 128
      pipeline = self.ORTMODEL_CLASS.from_pretrained(
          self.onnx_model_dirs[model_arch]
      )
      inputs = self.generate_inputs(height=height, width=width)
      inputs["prompt"] = "A painting of a squirrel eating a burger"
      inputs["image"] = floats_tensor(
          (1, 3, height, width), rng=random.Random(SEED)
      )
  
      output = pipeline(
          **inputs, generator=np.random.RandomState(0)
      ).images[0, -3:, -3:, -1]
      # https://github.com/huggingface/diffusers/blob/v0.17.1/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py#L71
      expected_slice = np.array(
          [
              0.69643,
              0.58484,
              0.50314,
              0.58760,
              0.55368,
              0.59643,
              0.51529,
              0.41217,
              0.49087,
          ]
      )
      self.assertTrue(
          np.allclose(output.flatten(), expected_slice, atol=1e-1)
      )
  
      # Verify it can be loaded with ORT diffusers pipeline
      diffusers_pipeline = (
          OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
              self.onnx_model_dirs[model_arch]
          )
      )
      diffusers_output = diffusers_pipeline(
          **inputs, generator=np.random.RandomState(0)
      ).images[0, -3:, -3:, -1]
      self.assertTrue(np.allclose(output, diffusers_output, atol=1e-2))
  
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 ORTStableDiffusionImg2ImgPipelineTest obj's underlying member method code with example usage: obj.test_compare_diffusers_pipeline(model_arch) without return types