readme: ```python
  # Download the model weights
  from huggingface_hub import hf_hub_download
  model_path = hf_hub_download(repo_id="johnowhitaker/sac_midu_mini", 
                               filename="midu_model_aesthetic_classifier.pt")
  
  # Load the aesthetic classifier
  m = nn.Sequential(
      nn.Conv2d(1280, 256, kernel_size=3, padding=1), nn.ReLU(),
      nn.MaxPool2d(2, 2),
      nn.Conv2d(256, 128, kernel_size=3, padding=1), nn.ReLU(),
      nn.AdaptiveAvgPool2d(output_size=(2, 2)), nn.Flatten(),
      nn.Linear(128*4, 64), nn.ReLU(), nn.Linear(64, 10)).to(device)
  m.load_state_dict(torch.load(model_path));
  
  # Load the SD pipeline and add a hook
  pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(device)
  pipe.scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
  pipe.scheduler.set_timesteps(30)
  def hook_fn(module, input, output):
      module.output = output 
  pipe.unet.mid_block.register_forward_hook(hook_fn);
  
  # Now after calling the forward pass of the UNET, you can do
  preds = m(pipe.unet.mid_block.output)
  ```