docstr: Test that stable diffusion v-prediction works with fp16
docstr_tok: ["Test","that","stable","diffusion","v-prediction","works","with","fp16"]
code: def test_stable_diffusion_v_pred_fp16(self):
      """Test that stable diffusion v-prediction works with fp16"""
      unet = self.dummy_cond_unet
      scheduler = DDIMScheduler(
          beta_start=0.00085,
          beta_end=0.012,
          beta_schedule="scaled_linear",
          clip_sample=False,
          set_alpha_to_one=False,
          prediction_type="v_prediction",
      )
      vae = self.dummy_vae
      bert = self.dummy_text_encoder
      tokenizer = CLIPTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-clip"
      )
  
      # put models in fp16
      unet = unet.half()
      vae = vae.half()
      bert = bert.half()
  
      # make sure here that pndm scheduler skips prk
      sd_pipe = StableDiffusionPipeline(
          unet=unet,
          scheduler=scheduler,
          vae=vae,
          text_encoder=bert,
          tokenizer=tokenizer,
          safety_checker=None,
          feature_extractor=None,
          requires_safety_checker=False,
      )
      sd_pipe = sd_pipe.to(torch_device)
      sd_pipe.set_progress_bar_config(disable=None)
  
      prompt = "A painting of a squirrel eating a burger"
      generator = torch.manual_seed(0)
      image = sd_pipe(
          [prompt],
          generator=generator,
          num_inference_steps=2,
          output_type="np",
      ).images
  
      assert image.shape == (1, 64, 64, 3)
  
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 7 for calling the StableDiffusion2VPredictionPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_v_pred_fp16() without return types