code: def test_stable_diffusion_pipeline_with_sequential_cpu_offloading_v_pred(
      self,
  ):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()
  
      pipeline_id = "stabilityai/stable-diffusion-2"
      prompt = "Andromeda galaxy in a bottle"
  
      pipeline = StableDiffusionPipeline.from_pretrained(
          pipeline_id, torch_dtype=torch.float16
      )
      pipeline = pipeline.to(torch_device)
      pipeline.enable_attention_slicing(1)
      pipeline.enable_sequential_cpu_offload()
  
      generator = torch.manual_seed(0)
      _ = pipeline(prompt, generator=generator, num_inference_steps=5)
  
      mem_bytes = torch.cuda.max_memory_allocated()
      # make sure that less than 2.8 GB is allocated
      assert mem_bytes < 2.8 * 10**9
  
imports: import gc
  import time
  import unittest
  import numpy
  import torch
  from huggingface_hub import hf_hub_download
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, EulerDiscreteScheduler, StableDiffusionPipeline, UNet2DConditionModel
  from diffusers.models.attention_processor import AttnProcessor
  from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device
label_desc: Use image node_id 14 for calling the StableDiffusion2VPredictionPipelineIntegrationTests obj's underlying member method code with example usage: obj.test_stable_diffusion_pipeline_with_sequential_cpu_offloading_v_pred() without return types