Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -0,0 +1,597 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "UEYEdzjgOEOE"
+      },
+      "outputs": [],
+      "source": [
+        "# @title ✳️ Load/initialize values\n",
+        "#Imports\n",
+        "#!pip install safetensors\n",
+        "from safetensors.torch import load_file\n",
+        "import json , os , shelve , torch\n",
+        "import pandas as pd\n",
+        "#----#\n",
+        "\n",
+        "def my_mkdirs(folder):\n",
+        "  if os.path.exists(folder)==False:\n",
+        "    os.makedirs(folder)\n",
+        "\n",
+        "def fix_bad_symbols(txt):\n",
+        "  result = txt\n",
+        "  for symbol in ['^', '}', '{' , ')', '(', '[' , ']' , ':' , '=' ]:\n",
+        "    result = result.replace(symbol,'\\\\' + symbol)\n",
+        "  #------#\n",
+        "  return result;\n",
+        "\n",
+        "\n",
+        "def getPrompts(_path, separator):\n",
+        "\n",
+        "  path = _path + '/text'\n",
+        "  path_enc = _path + '/text_encodings'\n",
+        "  #-----#\n",
+        "  index = 0\n",
+        "  file_index = 0\n",
+        "  prompts = {}\n",
+        "  text_encodings = {}\n",
+        "  _text_encodings = {}\n",
+        "  #-----#\n",
+        "  for filename in os.listdir(f'{path}'):\n",
+        "\n",
+        "    print(f'reading {filename}....')\n",
+        "    _index = 0\n",
+        "    %cd {path}\n",
+        "    with open(f'{filename}', 'r') as f:\n",
+        "      data = json.load(f)\n",
+        "    #------#\n",
+        "    _df = pd.DataFrame({'count': data})['count']\n",
+        "    _prompts = {\n",
+        "        key : value for key, value in _df.items()\n",
+        "    }\n",
+        "    for key in _prompts:\n",
+        "      _index = int(key)\n",
+        "      value = _prompts[key]\n",
+        "\n",
+        "      #Read the 'header' file in the JSON\n",
+        "      if _index <= 0 :\n",
+        "        _NUM_ITEMS = int(value)\n",
+        "        prompts[f'{index}'] = _prompts[f'{_index}'] + separator\n",
+        "        index = index + 1\n",
+        "        continue\n",
+        "      if _index <= 1 :\n",
+        "        _file_name = f'{value}'\n",
+        "        %cd {path_enc}\n",
+        "        _text_encodings = load_file(f'{_file_name}.safetensors')\n",
+        "        #Store text_encodings for the header items\n",
+        "        text_encodings[f'{index-1}'] = _text_encodings[f'{_index-1}']\n",
+        "        text_encodings[f'{index}'] = _text_encodings[f'{_index}']\n",
+        "        #------#\n",
+        "        prompts[f'{index}'] = _prompts[f'{_index}'] + separator\n",
+        "        index = index + 1\n",
+        "        continue\n",
+        "      #------#\n",
+        "      #Read the text_encodings + prompts\n",
+        "      text_encodings[f'{index}'] = _text_encodings[f'{_index}']\n",
+        "      prompts[f'{index}'] = _prompts[f'{_index}'] + separator\n",
+        "      index = index + 1\n",
+        "      continue\n",
+        "      #-------#\n",
+        "    #--------#\n",
+        "    #_text_encodings.close() #close the text_encodings file\n",
+        "    file_index = file_index + 1\n",
+        "  #----------#\n",
+        "  NUM_ITEMS = index -1\n",
+        "  return prompts , text_encodings , NUM_ITEMS\n",
+        "#--------#\n",
+        "\n",
+        "def append_from_url(dictA, tensA , nA , url , separator):\n",
+        "  dictB , tensB, nB = getPrompts(url, separator)\n",
+        "  dictAB = dictA\n",
+        "  tensAB = tensA\n",
+        "  nAB = nA\n",
+        "  for key in dictB:\n",
+        "    nAB = nAB + 1\n",
+        "    dictAB[f'{nA + int(key)}'] = dictB[key]\n",
+        "    tensAB[f'{nA + int(key)}'] = tensB[key]\n",
+        "  #-----#\n",
+        "  return dictAB, tensAB , nAB-1\n",
+        "#-------#\n",
+        "\n",
+        "home_directory = '/content/'\n",
+        "using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n",
+        "if using_Kaggle : home_directory = '/kaggle/working/'\n",
+        "%cd {home_directory}\n",
+        "\n",
+        "#🔸🔹\n",
+        "# Load the data if not already loaded\n",
+        "try:\n",
+        "    loaded\n",
+        "except:\n",
+        "    %cd {home_directory}\n",
+        "    !git clone https://huggingface.co/datasets/codeShare/text-to-image-prompts\n",
+        "    loaded = True\n",
+        "#--------#\n",
+        "\n",
+        "#default NEG values\n",
+        "try: name_NEG\n",
+        "except: name_NEG = ''\n",
+        "try: image_NEG\n",
+        "except: image_NEG = ''\n",
+        "try: strength_image_NEG\n",
+        "except: strength_image_NEG = 1\n",
+        "try: strength_NEG\n",
+        "except: strength_NEG = 1\n",
+        "try: NUM_VOCAB_ITEMS\n",
+        "except: NUM_VOCAB_ITEMS = 0\n",
+        "try: using_NEG\n",
+        "except: using_NEG = False\n",
+        "try: using_image_NEG\n",
+        "except: using_image_NEG = False\n",
+        "#------#\n",
+        "\n",
+        "def getJSON(path , filename):\n",
+        "  %cd {path}\n",
+        "  with open(f'{filename}', 'r') as f:\n",
+        "    data = json.load(f)\n",
+        "  #------#\n",
+        "  print(f'reading {filename}....')\n",
+        "  _df = pd.DataFrame({'count': data})['count']\n",
+        "  _prompts = {\n",
+        "      key : value for key, value in _df.items()\n",
+        "  }\n",
+        "  return _prompts\n",
+        "\n",
+        "#----#\n",
+        "\n",
+        "def getPromptsAndLinks(_path):\n",
+        "  path = _path + '/text'\n",
+        "  path_enc = _path + '/text_encodings'\n",
+        "  #-----#\n",
+        "  path_images = _path + '/images'\n",
+        "  path_enc_images = _path + '/image_encodings'\n",
+        "  #----#\n",
+        "  _file_name = ''\n",
+        "  _file_name_image = ''\n",
+        "  #-----#\n",
+        "  index = 0\n",
+        "  prompts = {}\n",
+        "  _prompts = {}\n",
+        "  #-------#\n",
+        "  urls = {}\n",
+        "  _urls = {}\n",
+        "  #------#\n",
+        "  text_encodings = {}\n",
+        "  _text_encodings = {}\n",
+        "  image_encodings = {}\n",
+        "  _image_encodings = {}\n",
+        "  #-----#\n",
+        "  for filename in os.listdir(f'{path}'):\n",
+        "\n",
+        "    print(f'reading {filename}.json...')\n",
+        "    _index = 0\n",
+        "    %cd {path}\n",
+        "    with open(f'{filename}', 'r') as f:\n",
+        "      data = json.load(f)\n",
+        "      _df = pd.DataFrame({'count': data})['count']\n",
+        "      _prompts = {\n",
+        "          key : value for key, value in _df.items()\n",
+        "      }\n",
+        "\n",
+        "    for key in _prompts:\n",
+        "      _index = int(key)\n",
+        "      value = _prompts[key]\n",
+        "      if _index<=0: continue\n",
+        "      if _index<=1:\n",
+        "        _file_name = f'{value}'\n",
+        "        _file_name_images = _prompts[f'{0}']\n",
+        "        #-------#\n",
+        "        print(f'reading {_file_name_images}.json..')\n",
+        "        %cd {path_images}\n",
+        "        with open(f'{_file_name_images}.json', 'r') as f:\n",
+        "          data = json.load(f)\n",
+        "          _df = pd.DataFrame({'count': data})['count']\n",
+        "          _urls = {\n",
+        "              key : value for key, value in _df.items()\n",
+        "          }\n",
+        "        #--------#\n",
+        "        %cd {path_enc}\n",
+        "        _text_encodings = load_file(f'{_file_name}.safetensors')\n",
+        "        text_encodings[f'{index-1}'] = _text_encodings[f'{_index-1}']\n",
+        "        text_encodings[f'{index}'] = _text_encodings[f'{_index}']\n",
+        "        #-------#\n",
+        "        %cd {path_enc_images}\n",
+        "        _image_encodings = load_file(f'{_file_name_images}.safetensors')\n",
+        "        image_encodings[f'{index-1}'] = _image_encodings[f'{_index-1}']\n",
+        "        image_encodings[f'{index}'] = _image_encodings[f'{_index}']\n",
+        "        #-------#\n",
+        "        prompts[f'{index-1}'] = _prompts[f'{_index-1}']\n",
+        "        urls[f'{index-1}'] = _urls[f'{_index-1}']\n",
+        "        prompts[f'{index}'] = _prompts[f'{_index}']\n",
+        "        urls[f'{index}'] = _urls[f'{_index}']\n",
+        "        #-------#\n",
+        "        index = index + 1\n",
+        "        continue\n",
+        "      #--------#\n",
+        "      #Read the text_encodings + prompts\n",
+        "      text_encodings[f'{index}'] = _text_encodings[f'{_index}']\n",
+        "      image_encodings[f'{index}'] = _image_encodings[f'{_index}']\n",
+        "      prompts[f'{index}'] = _prompts[f'{_index}']\n",
+        "      urls[f'{index}'] = _urls[f'{_index}']\n",
+        "      index = index + 1\n",
+        "      continue\n",
+        "      #-------#\n",
+        "    #--------#\n",
+        "  #----------#\n",
+        "  NUM_ITEMS = index -1\n",
+        "  return prompts , text_encodings , urls , image_encodings , NUM_ITEMS\n",
+        "#--------#\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title 📚 Select items to sample from\n",
+        "\n",
+        "prompt_features = True # @param {\"type\":\"boolean\",\"placeholder\":\"🦜\"}\n",
+        "civitai_blue_set = True # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
+        "suffix = True # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
+        "prefix = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
+        "emojis = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "#------#\n",
+        "\n",
+        "first_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
+        "last_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
+        "full_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "celebs = False # @param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n",
+        "#-------#\n",
+        "danbooru_tags = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎀\"}\n",
+        "lyrics = False # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
+        "tripple_nouns =  True # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
+        "#-----#\n",
+        "female_fullnames = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "debug = False\n",
+        "#------#\n",
+        "prompts = {}\n",
+        "text_encodings = {}\n",
+        "nA = 0\n",
+        "#--------#\n",
+        "\n",
+        "\n",
+        "if tripple_nouns:\n",
+        "  url = '/content/text-to-image-prompts/nouns'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "\n",
+        "if lyrics:\n",
+        "  url = '/content/text-to-image-prompts/lyrics'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "\n",
+        "if danbooru_tags:\n",
+        "  url = '/content/text-to-image-prompts/danbooru'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "#--------#\n",
+        "\n",
+        "if first_names:\n",
+        "  url = '/content/text-to-image-prompts/names/firstnames'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "#--------#\n",
+        "\n",
+        "if last_names:\n",
+        "  url = '/content/text-to-image-prompts/names/lastnames'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "#--------#\n",
+        "\n",
+        "if full_names:\n",
+        "  url = '/content/text-to-image-prompts/names/fullnames'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "#--------#\n",
+        "\n",
+        "if celebs:\n",
+        "  url = '/content/text-to-image-prompts/names/celebs/mixed'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "#--------#\n",
+        "\n",
+        "if celebs_young :\n",
+        "  url = '/content/text-to-image-prompts/names/celebs/young'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "#--------#\n",
+        "\n",
+        "if female_fullnames:\n",
+        "  url = '/content/text-to-image-prompts/names/fullnames'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "#--------#\n",
+        "\n",
+        "\n",
+        "if prompt_features:\n",
+        "  url = '/content/text-to-image-prompts/civitai-prompts/green'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "#--------#\n",
+        "\n",
+        "\n",
+        "if emojis:\n",
+        "  url = '/content/text-to-image-prompts/vocab/text_encodings/emoji'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "#--------#\n",
+        "\n",
+        "\n",
+        "if civitai_blue_set:\n",
+        "  url = '/content/text-to-image-prompts/civitai-prompts/blue'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "#--------#\n",
+        "\n",
+        "if suffix :\n",
+        "  tmp = '/content/text-to-image-prompts/vocab/text_encodings/suffix/'\n",
+        "  for item in ['common','average','rare','weird','exotic'] :\n",
+        "    url =  tmp  + item\n",
+        "    prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "#------#\n",
+        "\n",
+        "if prefix :\n",
+        "  tmp = '/content/text-to-image-prompts/vocab/text_encodings/prefix/'\n",
+        "  for item in ['common','average','rare','weird','exotic'] :\n",
+        "    url =  tmp  + item\n",
+        "    prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '-')\n",
+        "#------#\n",
+        "\n",
+        "if debug:\n",
+        "  index = 0\n",
+        "  for key in prompts: index = index + 1\n",
+        "  print(index)\n",
+        "  index = 0\n",
+        "  for key in text_encodings : index = index + 1\n",
+        "  print(index)\n",
+        "#------#\n",
+        "\n",
+        "NUM_VOCAB_ITEMS = nA\n",
+        "text_tensor = torch.zeros(NUM_VOCAB_ITEMS,768)\n",
+        "for index in range(NUM_VOCAB_ITEMS):\n",
+        "  text_tensor[index] = text_encodings[f'{index}']\n",
+        "#---------#\n"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "CF53WIAKObg3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title \t⚄ Use a pre-encoded prompt + image pair from the fusion gen (note: NSFW!)\n",
+        "\n",
+        "\n",
+        "#image_index = 0 # @param {type:'number'}\n",
+        "# @markdown 📥 Load the data (only required one time)\n",
+        "load_the_data = False # @param {type:\"boolean\"}\n",
+        "\n",
+        "# @markdown 🖼️ Choose a pre-encoded reference\n",
+        "index = 829 # @param {type:\"slider\", min:0, max:1668, step:1}\n",
+        "\n",
+        "# @markdown ⚖️ Set the value for C in the reference  <br> <br> sim = C* text_enc + image_enc*(1-C) <br><br>\n",
+        "\n",
+        "C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+        "\n",
+        "# @markdown 🚫 Penalize similarity to this prompt(optional)\n",
+        "\n",
+        "NEG = '' # @param {type:'string'}\n",
+        "strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.1}\n",
+        "\n",
+        "# @markdown Calculate most similiar items using above settings?\n",
+        "enable = True # @param {type:\"boolean\"}\n",
+        "\n",
+        "if (load_the_data):\n",
+        "  target_prompts , target_text_encodings , urls , target_image_encodings , NUM_ITEMS =  getPromptsAndLinks('/content/text-to-image-prompts/fusion')\n",
+        "  from transformers import AutoTokenizer\n",
+        "  tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
+        "  from transformers import  CLIPProcessor, CLIPModel\n",
+        "  processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n",
+        "  model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
+        "  logit_scale = model.logit_scale.exp() #logit_scale = 100.00000762939453\n",
+        "\n",
+        "from PIL import Image\n",
+        "import requests\n",
+        "prompt = target_prompts[f'{index}']\n",
+        "url = urls[f'{index}']\n",
+        "if url.find('perchance')>-1:\n",
+        "  image = Image.open(requests.get(url, stream=True).raw)\n",
+        "else: print(\"(No image for this ID)\")\n",
+        "\n",
+        "print(\"\")\n",
+        "print(f\"'{prompt}'\")\n",
+        "print(\"\")\n",
+        "\n",
+        "if(enable):\n",
+        "  text_features_A = target_text_encodings[f'{index}']\n",
+        "  image_features_A =  target_image_encodings[f'{index}']\n",
+        "\n",
+        "  # text-similarity\n",
+        "  sims =  C * torch.matmul(text_tensor, text_features_A.t())\n",
+        "\n",
+        "  neg_sims = 0*sims\n",
+        "  if(NEG != ''):\n",
+        "\n",
+        "    # Get text features for user input\n",
+        "    inputs = tokenizer(text = NEG, padding=True, return_tensors=\"pt\")\n",
+        "    text_features_NEG = model.get_text_features(**inputs)\n",
+        "    text_features_NEG = text_features_A/text_features_A.norm(p=2, dim=-1, keepdim=True)\n",
+        "\n",
+        "    # text-similarity\n",
+        "    neg_sims =  strength*torch.matmul(text_tensor, text_features_NEG.t())\n",
+        "  #------#\n",
+        "\n",
+        "  # plus image-similarity\n",
+        "  sims = sims +  (1-C) * torch.matmul(text_tensor, image_features_A.t()) * logit_scale\n",
+        "\n",
+        "\n",
+        "  # minus NEG-similarity\n",
+        "  sims = sims - neg_sims\n",
+        "\n",
+        "  # Sort the items\n",
+        "  sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
+        "\n",
+        "  # @title ⚙️📝 Print the results (Advanced)\n",
+        "  list_size = 1000 # param {type:'number'}\n",
+        "  start_at_index = 0 # param {type:'number'}\n",
+        "  print_Similarity = True # param {type:\"boolean\"}\n",
+        "  print_Prompts = True # param {type:\"boolean\"}\n",
+        "  print_Prefix = True # param {type:\"boolean\"}\n",
+        "  print_Descriptions = True # param {type:\"boolean\"}\n",
+        "  compact_Output = True # param {type:\"boolean\"}\n",
+        "\n",
+        "  # @markdown -----------\n",
+        "  # @markdown ⚙️📝 Printing options\n",
+        "  newline_Separator = True # @param {type:\"boolean\"}\n",
+        "\n",
+        "  import random\n",
+        "  list_size2 = 1000 # param {type:'number'}\n",
+        "  start_at_index2 = 10000 # param {type:'number'}\n",
+        "  rate_percent  = 0 # param {type:\"slider\", min:0, max:100, step:1}\n",
+        "\n",
+        "  # @markdown Repeat output N times\n",
+        "  N = 6 # @param {type:\"slider\", min:0, max:10, step:1}\n",
+        "\n",
+        "  # title Show the 100 most similiar suffix and prefix text-encodings to the text encoding\n",
+        "  RANGE = list_size\n",
+        "  separator = '|'\n",
+        "  if newline_Separator : separator = separator + '\\n'\n",
+        "\n",
+        "  _prompts = ''\n",
+        "  _sims =  ''\n",
+        "  for _index in range(start_at_index + RANGE):\n",
+        "    if _index < start_at_index : continue\n",
+        "    index = indices[_index].item()\n",
+        "\n",
+        "    prompt = prompts[f'{index}']\n",
+        "    if rate_percent >= random.randint(0,100) : prompt = prompts[f'{random.randint(start_at_index2 , start_at_index2 + list_size2)}']\n",
+        "\n",
+        "    #Remove duplicates\n",
+        "    if _prompts.find(prompt + separator)<=-1:\n",
+        "      _sims = _sims + f'{round(100*sims[index].item(), 2)} %' + separator\n",
+        "    #-------#\n",
+        "    _prompts = _prompts.replace(prompt + separator,'')\n",
+        "    _prompts = _prompts  + prompt + separator\n",
+        "    #------#\n",
+        "  #------#\n",
+        "  __prompts = fix_bad_symbols(__prompts)\n",
+        "  __prompts = ('{' + _prompts + '}').replace(separator + '}', '}')\n",
+        "  __sims = ('{' + _sims + '}').replace(separator + '}', '}')\n",
+        "  #------#\n",
+        "\n",
+        "  if(not print_Prompts): __prompts = ''\n",
+        "  if(not print_Similarity): __sims = ''\n",
+        "\n",
+        "  if(not compact_Output):\n",
+        "    if(print_Descriptions):\n",
+        "      print(f'The {start_at_index}-{start_at_index + RANGE} most similiar items to prompt : \\n\\n ')\n",
+        "      for i in range(N) : print(__prompts)\n",
+        "      print(f'The {start_at_index}-{start_at_index + RANGE} similarity % for items : \\n\\n' + __sims)\n",
+        "      print('')\n",
+        "    else:\n",
+        "      for i in range(N) : print(__prompts)\n",
+        "  else:\n",
+        "    for i in range(N) : print(__prompts)\n",
+        "  #-------#\n",
+        "  #-------#\n",
+        "#-------#\n",
+        "image\n"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "XW3914T8O2uf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title ⚙️📝 Print the results (Advanced)\n",
+        "list_size = 1000 # @param {type:'number'}\n",
+        "start_at_index = 0 # @param {type:'number'}\n",
+        "print_Similarity = True # @param {type:\"boolean\"}\n",
+        "print_Prompts = True # @param {type:\"boolean\"}\n",
+        "print_Descriptions = True # @param {type:\"boolean\"}\n",
+        "compact_Output = True # @param {type:\"boolean\"}\n",
+        "newline_Separator = False # @param {type:\"boolean\"}\n",
+        "\n",
+        "import random\n",
+        "# @markdown -----------\n",
+        "# @markdown Mix with...\n",
+        "list_size2 = 1000 # @param {type:'number'}\n",
+        "start_at_index2 = 10000 # @param {type:'number'}\n",
+        "rate_percent  = 0 # @param {type:\"slider\", min:0, max:100, step:1}\n",
+        "\n",
+        "# @markdown -----------\n",
+        "# @markdown Repeat output N times\n",
+        "N = 6 # @param {type:\"slider\", min:0, max:10, step:1}\n",
+        "\n",
+        "# title Show the 100 most similiar suffix and prefix text-encodings to the text encoding\n",
+        "RANGE = list_size\n",
+        "separator = '|'\n",
+        "if newline_Separator : separator = separator + '\\n'\n",
+        "\n",
+        "_prompts = ''\n",
+        "_sims =  ''\n",
+        "for _index in range(start_at_index + RANGE):\n",
+        "  if _index < start_at_index : continue\n",
+        "  index = indices[_index].item()\n",
+        "\n",
+        "  prompt = prompts[f'{index}']\n",
+        "  if rate_percent >= random.randint(0,100) : prompt = prompts[f'{random.randint(start_at_index2 , start_at_index2 + list_size2)}']\n",
+        "\n",
+        "  #Remove duplicates\n",
+        "  if _prompts.find(prompt + separator)<=-1:\n",
+        "    _sims = _sims + f'{round(100*sims[index].item(), 2)} %' + separator\n",
+        "  #-------#\n",
+        "  _prompts = _prompts.replace(prompt + separator,'')\n",
+        "  _prompts = _prompts  + prompt + separator\n",
+        "  #------#\n",
+        "#------#\n",
+        "__prompts = fix_bad_symbols(__prompts)\n",
+        "__prompts = ('{' + _prompts + '}').replace(separator + '}', '}')\n",
+        "__sims = ('{' + _sims + '}').replace(separator + '}', '}')\n",
+        "#------#\n",
+        "\n",
+        "if(not print_Prompts): __prompts = ''\n",
+        "if(not print_Similarity): __sims = ''\n",
+        "\n",
+        "if(not compact_Output):\n",
+        "  if(print_Descriptions):\n",
+        "    print(f'The {start_at_index}-{start_at_index + RANGE} most similiar items to prompt : \\n\\n ')\n",
+        "    for i in range(N) : print(__prompts)\n",
+        "    print(f'The {start_at_index}-{start_at_index + RANGE} similarity % for items : \\n\\n' + __sims)\n",
+        "    print('')\n",
+        "  else:\n",
+        "    for i in range(N) : print(__prompts)\n",
+        "else:\n",
+        "  for i in range(N) : print(__prompts)\n",
+        "#-------#"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "EdBiAguJO9aX"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}