code: def _test_if_img2img(
      self, pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds
  ):
      # pipeline 1
  
      _start_torch_memory_measurement()
  
      image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(
          torch_device
      )
  
      generator = torch.Generator(device="cpu").manual_seed(0)
  
      output = pipe_1(
          prompt_embeds=prompt_embeds,
          negative_prompt_embeds=negative_prompt_embeds,
          image=image,
          num_inference_steps=2,
          generator=generator,
          output_type="np",
      )
  
      image = output.images[0]
  
      assert image.shape == (64, 64, 3)
  
      mem_bytes = torch.cuda.max_memory_allocated()
      assert mem_bytes < 10 * 10**9
  
      expected_image = load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"
      )
      assert_mean_pixel_difference(image, expected_image)
  
      # pipeline 2
  
      _start_torch_memory_measurement()
  
      generator = torch.Generator(device="cpu").manual_seed(0)
  
      original_image = floats_tensor(
          (1, 3, 256, 256), rng=random.Random(0)
      ).to(torch_device)
      image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(
          torch_device
      )
  
      output = pipe_2(
          prompt_embeds=prompt_embeds,
          negative_prompt_embeds=negative_prompt_embeds,
          image=image,
          original_image=original_image,
          generator=generator,
          num_inference_steps=2,
          output_type="np",
      )
  
      image = output.images[0]
  
      assert image.shape == (256, 256, 3)
  
      mem_bytes = torch.cuda.max_memory_allocated()
      assert mem_bytes < 4 * 10**9
  
      expected_image = load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"
      )
      assert_mean_pixel_difference(image, expected_image)
  
imports: import gc
  import random
  import unittest
  import torch
  from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, IFPipeline, IFSuperResolutionPipeline
  from diffusers.models.attention_processor import AttnAddedKVProcessor
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
  from .None import IFPipelineTesterMixin
label_desc: Use image node_id 4 for calling the IFPipelineSlowTests obj's underlying member method code with example usage: obj._test_if_img2img(pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds) without return types