code: def test_stable_diffusion_img2img_default_case(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      unet = self.dummy_cond_unet
      scheduler = PNDMScheduler(skip_prk_steps=True)
      vae = self.dummy_vae
      bert = self.dummy_text_encoder
      tokenizer = XLMRobertaTokenizer.from_pretrained(
          "hf-internal-testing/tiny-xlm-roberta"
      )
      tokenizer.model_max_length = 77
  
      init_image = self.dummy_image.to(device)
      init_image = init_image / 2 + 0.5
  
      # make sure here that pndm scheduler skips prk
      alt_pipe = AltDiffusionImg2ImgPipeline(
          unet=unet,
          scheduler=scheduler,
          vae=vae,
          text_encoder=bert,
          tokenizer=tokenizer,
          safety_checker=None,
          feature_extractor=self.dummy_extractor,
      )
      alt_pipe.image_processor = VaeImageProcessor(
          vae_scale_factor=alt_pipe.vae_scale_factor, do_normalize=True
      )
      alt_pipe = alt_pipe.to(device)
      alt_pipe.set_progress_bar_config(disable=None)
  
      prompt = "A painting of a squirrel eating a burger"
      generator = torch.Generator(device=device).manual_seed(0)
      output = alt_pipe(
          [prompt],
          generator=generator,
          guidance_scale=6.0,
          num_inference_steps=2,
          output_type="np",
          image=init_image,
      )
  
      image = output.images
  
      generator = torch.Generator(device=device).manual_seed(0)
      image_from_tuple = alt_pipe(
          [prompt],
          generator=generator,
          guidance_scale=6.0,
          num_inference_steps=2,
          output_type="np",
          image=init_image,
          return_dict=False,
      )[0]
  
      image_slice = image[0, -3:, -3:, -1]
      image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
  
      assert image.shape == (1, 32, 32, 3)
      expected_slice = np.array(
          [
              0.4427,
              0.3731,
              0.4249,
              0.4941,
              0.4546,
              0.4148,
              0.4193,
              0.4666,
              0.4499,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
      assert (
          np.abs(
              image_from_tuple_slice.flatten() - expected_slice
          ).max()
          < 5e-3
      )
  
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 7 for calling the AltDiffusionImg2ImgPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_img2img_default_case() without return types