code: def test_stable_diffusion_k_lms(self):
      pipe = StableDiffusionPipeline.from_pretrained(
          "stabilityai/stable-diffusion-2-base"
      )
      pipe.scheduler = LMSDiscreteScheduler.from_config(
          pipe.scheduler.config
      )
      pipe.to(torch_device)
      pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_inputs(torch_device)
      image = pipe(**inputs).images
      image_slice = image[0, -3:, -3:, -1].flatten()
  
      assert image.shape == (1, 512, 512, 3)
      expected_slice = np.array(
          [
              0.10440,
              0.13115,
              0.11100,
              0.10141,
              0.11440,
              0.07215,
              0.11332,
              0.09693,
              0.10006,
          ]
      )
      assert np.abs(image_slice - expected_slice).max() < 3e-3
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel, logging
  from diffusers.utils.testing_utils import CaptureLogger, backend_empty_cache, enable_full_determinism, load_numpy, nightly, numpy_cosine_similarity_distance, require_torch_accelerator, require_torch_gpu, skip_mps, slow, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 5 for calling the StableDiffusion2PipelineSlowTests obj's underlying member method code with example usage: obj.test_stable_diffusion_k_lms() without return types