code: def init_embedder_options(
      keys, init_dict, prompt=None, negative_prompt=None
  ):
      value_dict = {}
      for key in keys:
          if key == "txt":
              value_dict["prompt"] = prompt
              value_dict["negative_prompt"] = ""
  
          if key == "original_size_as_tuple":
              orig_width = init_dict["orig_width"]
              orig_height = init_dict["orig_height"]
  
              value_dict["orig_width"] = orig_width
              value_dict["orig_height"] = orig_height
  
          if key == "crop_coords_top_left":
              crop_coord_top = 0
              crop_coord_left = 0
  
              value_dict["crop_coords_top"] = crop_coord_top
              value_dict["crop_coords_left"] = crop_coord_left
  
          if key == "aesthetic_score":
              value_dict["aesthetic_score"] = 6.0
              value_dict["negative_aesthetic_score"] = 2.5
  
          if key == "target_size_as_tuple":
              value_dict["target_width"] = init_dict["target_width"]
              value_dict["target_height"] = init_dict["target_height"]
  
      return value_dict
  
file_path: generative-models/scripts/demo/turbo.py
imports: from streamlit_helpers import *
  from st_keyup import st_keyup
  from sgm.modules.diffusionmodules.sampling import EulerAncestralSampler
label_desc: Use image node_id 2 for calling a global function with example usage: init_embedder_options(keys, init_dict, prompt, negative_prompt) and returns: value_dict