code: def test_alt_diffusion_fast_ddim(self):
      scheduler = DDIMScheduler.from_pretrained(
          "BAAI/AltDiffusion", subfolder="scheduler"
      )
  
      alt_pipe = AltDiffusionPipeline.from_pretrained(
          "BAAI/AltDiffusion", scheduler=scheduler, safety_checker=None
      )
      alt_pipe = alt_pipe.to(torch_device)
      alt_pipe.set_progress_bar_config(disable=None)
  
      prompt = "A painting of a squirrel eating a burger"
      generator = torch.manual_seed(0)
  
      output = alt_pipe(
          [prompt],
          generator=generator,
          num_inference_steps=2,
          output_type="numpy",
      )
      image = output.images
  
      image_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 512, 512, 3)
      expected_slice = np.array(
          [
              0.4019,
              0.4052,
              0.3810,
              0.4119,
              0.3916,
              0.3982,
              0.4651,
              0.4195,
              0.5323,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
  from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNet2DConditionModel
  from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesConfig, RobertaSeriesModelWithTransformation
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 3 for calling the AltDiffusionPipelineIntegrationTests obj's underlying member method code with example usage: obj.test_alt_diffusion_fast_ddim() without return types