code: def init_embedder_options(
      keys, init_dict, prompt=None, negative_prompt=None
  ):
      # Hardcoded demo settings; might undergo some changes in the future
  
      value_dict = {}
      for key in keys:
          if key == "txt":
              if prompt is None:
                  prompt = "A professional photograph of an astronaut riding a pig"
              if negative_prompt is None:
                  negative_prompt = ""
  
              prompt = st.text_input("Prompt", prompt)
              negative_prompt = st.text_input(
                  "Negative prompt", negative_prompt
              )
  
              value_dict["prompt"] = prompt
              value_dict["negative_prompt"] = negative_prompt
  
          if key == "original_size_as_tuple":
              orig_width = st.number_input(
                  "orig_width",
                  value=init_dict["orig_width"],
                  min_value=16,
              )
              orig_height = st.number_input(
                  "orig_height",
                  value=init_dict["orig_height"],
                  min_value=16,
              )
  
              value_dict["orig_width"] = orig_width
              value_dict["orig_height"] = orig_height
  
          if key == "crop_coords_top_left":
              crop_coord_top = st.number_input(
                  "crop_coords_top", value=0, min_value=0
              )
              crop_coord_left = st.number_input(
                  "crop_coords_left", value=0, min_value=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"]
  
          if key in ["fps_id", "fps"]:
              fps = st.number_input("fps", value=6, min_value=1)
  
              value_dict["fps"] = fps
              value_dict["fps_id"] = fps - 1
  
          if key == "motion_bucket_id":
              mb_id = st.number_input(
                  "motion bucket id", 0, 511, value=127
              )
              value_dict["motion_bucket_id"] = mb_id
  
          if key == "pool_image":
              st.text("Image for pool conditioning")
              image = load_img(
                  key="pool_image_input",
                  size=224,
                  center_crop=True,
              )
              if image is None:
                  st.info("Need an image here")
                  image = torch.zeros(1, 3, 224, 224)
              value_dict["pool_image"] = image
  
      return value_dict
  
imports: import copy
  import math
  import os
  from glob import glob
  from typing import Dict, List, Optional, Tuple, Union
  import cv2
  import numpy
  import streamlit
  import torch
  import torch.nn
  import torchvision.transforms
  from einops import rearrange, repeat
  from imwatermark import WatermarkEncoder
  from omegaconf import ListConfig, OmegaConf
  from PIL import Image
  from safetensors.torch import load_file
  from torch import autocast
  from torchvision import transforms
  from torchvision.utils import make_grid, save_image
  from scripts.demo.discretization import Img2ImgDiscretizationWrapper, Txt2NoisyDiscretizationWrapper
  from scripts.util.detection.nsfw_and_watermark_dectection import DeepFloydDataFiltering
  from sgm.inference.helpers import embed_watermark
  from sgm.modules.diffusionmodules.guiders import LinearPredictionGuider, VanillaCFG
  from sgm.modules.diffusionmodules.sampling import DPMPP2MSampler, DPMPP2SAncestralSampler, EulerAncestralSampler, EulerEDMSampler, HeunEDMSampler, LinearMultistepSampler
  from sgm.util import append_dims, default, instantiate_from_config
label_desc: Use image node_id 8 for calling a global function with example usage: init_embedder_options(keys, init_dict, prompt, negative_prompt) and returns: value_dict