code: def test_compare_diffusers_pipeline(self, model_arch: str):
      model_args = {"test_name": model_arch, "model_arch": model_arch}
      self._setup(model_args)
      ort_pipeline = self.ORTMODEL_CLASS.from_pretrained(
          self.onnx_model_dirs[model_arch]
      )
      height, width = 64, 64
      latents_shape = (
          1,
          ort_pipeline.vae_decoder.config["latent_channels"],
          height // ort_pipeline.vae_scale_factor,
          width // ort_pipeline.vae_scale_factor,
      )
      latents = np.random.randn(*latents_shape).astype(np.float32)
      inputs = self.generate_inputs(height=height, width=width)
      inputs["image"] = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo.png"
      ).resize((width, height))
  
      inputs["mask_image"] = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
      ).resize((width, height))
  
      outputs = ort_pipeline(**inputs, latents=latents).images
      self.assertEqual(outputs.shape, (1, height, width, 3))
      expected_slice = np.array(
          [
              0.5442,
              0.3002,
              0.5665,
              0.6485,
              0.4421,
              0.6441,
              0.5778,
              0.5076,
              0.5612,
          ]
      )
      self.assertTrue(
          np.allclose(
              outputs[0, -3:, -3:, -1].flatten(),
              expected_slice,
              atol=1e-4,
          )
      )
  
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 ORTStableDiffusionInpaintPipelineTest obj's underlying member method code with example usage: obj.test_compare_diffusers_pipeline(model_arch) without return types