code: def test_img_variation_dpm(self):
      sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(
          "fusing/sd-image-variations-diffusers"
      )
      sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(
          sd_pipe.scheduler.config
      )
      sd_pipe.to(torch_device)
      sd_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_inputs(torch_device)
      inputs["num_inference_steps"] = 25
      image = sd_pipe(**inputs).images[0]
  
      expected_image = load_numpy(
          "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
          "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"
      )
      max_diff = np.abs(expected_image - image).max()
      assert max_diff < 1e-3
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection
  from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PNDMScheduler, StableDiffusionImageVariationPipeline, UNet2DConditionModel
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_image, load_numpy, nightly, numpy_cosine_similarity_distance, print_tensor_test, require_torch_gpu, slow, torch_device
  from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 4 for calling the StableDiffusionImageVariationPipelineNightlyTests obj's underlying member method code with example usage: obj.test_img_variation_dpm() without return types