code: def test_stable_diffusion_img2img_pipeline_default(self):
      init_image = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/img2img/sketch-mountains-input.jpg"
      )
      init_image = init_image.resize((768, 512))
      expected_image = load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"
      )
  
      model_id = "BAAI/AltDiffusion"
      pipe = AltDiffusionImg2ImgPipeline.from_pretrained(
          model_id,
          safety_checker=None,
      )
      pipe.to(torch_device)
      pipe.set_progress_bar_config(disable=None)
      pipe.enable_attention_slicing()
  
      prompt = "A fantasy landscape, trending on artstation"
  
      generator = torch.manual_seed(0)
      output = pipe(
          prompt=prompt,
          image=init_image,
          strength=0.75,
          guidance_scale=7.5,
          generator=generator,
          output_type="np",
      )
      image = output.images[0]
  
      assert image.shape == (512, 768, 3)
      # img2img is flaky across GPUs even in fp32, so using MAE here
      assert np.abs(expected_image - image).max() < 1e-2
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from transformers import XLMRobertaTokenizer
  from diffusers import AltDiffusionImg2ImgPipeline, AutoencoderKL, PNDMScheduler, UNet2DConditionModel
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesConfig, RobertaSeriesModelWithTransformation
  from diffusers.utils import load_image
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_numpy, nightly, require_torch_gpu, torch_device
label_desc: Use image node_id 2 for calling the AltDiffusionImg2ImgPipelineIntegrationTests obj's underlying member method code with example usage: obj.test_stable_diffusion_img2img_pipeline_default() without return types