{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Try this Free online SD 1.5 generator with the results: https://perchance.org/fusion-ai-image-generator\n",
        "\n",
        " This Notebook is a Stable-diffusion tool which allows you to find similiar prompts to an existing prompt. It uses the Nearest Neighbor decoder method listed here:https://arxiv.org/pdf/2303.03032"
      ],
      "metadata": {
        "id": "cRV2YWomjMBU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "UEYEdzjgOEOE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efa821e0-8c2d-4861-9d99-f08cacb2e85f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "/content\n",
            "Cloning into 'text-to-image-prompts'...\n",
            "remote: Enumerating objects: 4504, done.\u001b[K\n",
            "remote: Counting objects: 100% (4504/4504), done.\u001b[K\n",
            "remote: Compressing objects: 100% (4029/4029), done.\u001b[K\n",
            "remote: Total 4504 (delta 659), reused 2366 (delta 418), pack-reused 0 (from 0)\u001b[K\n",
            "Receiving objects: 100% (4504/4504), 32.32 MiB | 3.25 MiB/s, done.\n",
            "Resolving deltas: 100% (659/659), done.\n",
            "Updating files: 100% (3062/3062), done.\n",
            "Filtering content: 100% (768/768), 2.21 GiB | 48.63 MiB/s, done.\n"
          ]
        }
      ],
      "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",
        "celebs = True # @param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n",
        "#-------#\n",
        "danbooru_tags = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎀\"}\n",
        "lyrics = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
        "tripple_nouns =  True # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
        "#-----#\n",
        "female_fullnames = True # @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 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 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": {
        "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",
        "#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 = 708 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
        "\n",
        "PROMPT_INDEX = index\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 = False # @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 = 7 # @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": {
        "id": "XW3914T8O2uf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The savefile can be used here : https://perchance.org/fusion-ai-image-generator"
      ],
      "metadata": {
        "id": "JldNmWy1iyvK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title \t⚄ Create fusion-generator .json savefile from result\n",
        "filename = 'blank.json'\n",
        "path = '/content/text-to-image-prompts/fusion/'\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",
        "_savefile = {\n",
        "    key : value for key, value in _df.items()\n",
        "}\n",
        "#------#\n",
        "from safetensors.torch import load_file\n",
        "import json , os , torch\n",
        "import pandas as pd\n",
        "#----#\n",
        "def my_mkdirs(folder):\n",
        "  if os.path.exists(folder)==False:\n",
        "    os.makedirs(folder)\n",
        "#------#\n",
        "savefile_prompt = ''\n",
        "for i in range(N) : savefile_prompt = savefile_prompt + ' ' + __prompts\n",
        "_savefile['main'] = savefile_prompt.replace('\\n', ' ').replace('  ', ' ').replace('   ', ' ')\n",
        "#------#\n",
        "save_filename = f'fusion_C05_X7_1000_{PROMPT_INDEX}.json'\n",
        "output_folder = '/content/output/savefiles/'\n",
        "my_mkdirs(output_folder)\n",
        "#-----#\n",
        "%cd {output_folder}\n",
        "print(f'Saving segment {save_filename} to {output_folder}...')\n",
        "with open(save_filename, 'w') as f:\n",
        "    json.dump(_savefile, f)\n"
      ],
      "metadata": {
        "id": "Q7vpNAXQilbf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title \t⚄ Create a savefile-set from the entire range of pre-encoded items\n",
        "\n",
        "#image_index = 0 # @param {type:'number'}\n",
        "# @markdown 📥 Load the data (only required one time)\n",
        "load_the_data = True # @param {type:\"boolean\"}\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",
        "if(load_the_data):\n",
        "  from PIL import Image\n",
        "  import requests\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",
        "\n",
        "filename = 'blank.json'\n",
        "path = '/content/text-to-image-prompts/fusion/'\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",
        "_blank = {\n",
        "    key : value for key, value in _df.items()\n",
        "}\n",
        "#------#\n",
        "\n",
        "root_savefile_name = 'fusion_C05_X7_1000_'\n",
        "output_folder = '/content/output/savefiles/'\n",
        "my_mkdirs(output_folder)\n",
        "NEG = '' # @param {type:'string'}\n",
        "strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.1}\n",
        "\n",
        "for index in range(1667):\n",
        "\n",
        "  PROMPT_INDEX = index\n",
        "\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: continue #print(\"(No image for this ID)\")\n",
        "\n",
        "  print(f\"no. {PROMPT_INDEX} : '{prompt}'\")\n",
        "\n",
        "\n",
        "  if(True):\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",
        "    # 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 = False # @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 = 7 # @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",
        "  _savefile = _blank\n",
        "  from safetensors.torch import load_file\n",
        "  import json , os , torch\n",
        "  import pandas as pd\n",
        "  #----#\n",
        "  def my_mkdirs(folder):\n",
        "    if os.path.exists(folder)==False:\n",
        "      os.makedirs(folder)\n",
        "  #------#\n",
        "  savefile_prompt = ''\n",
        "  for i in range(N) : savefile_prompt = savefile_prompt + ' ' + __prompts\n",
        "  _savefile['main'] = savefile_prompt.replace('\\n', ' ').replace('  ', ' ').replace('   ', ' ')\n",
        "  #------#\n",
        "  save_filename = f'{root_savefile_name}{PROMPT_INDEX}.json'\n",
        "  #-----#\n",
        "  %cd {output_folder}\n",
        "  print(f'Saving savefile {save_filename} to {output_folder}...')\n",
        "  with open(save_filename, 'w') as f:\n",
        "      json.dump(_savefile, f)\n",
        "  #---------#\n",
        "  continue\n",
        "#-----------#\n",
        "\n"
      ],
      "metadata": {
        "id": "x1uAVXZEoL0T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Determine if this notebook is running on Colab or Kaggle\n",
        "#Use https://www.kaggle.com/ if Google Colab GPU is busy\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",
        "# @title Download the text_encodings as .zip\n",
        "import os\n",
        "%cd {home_directory}\n",
        "#os.remove(f'{home_directory}results.zip')\n",
        "root_output_folder = home_directory + 'output/'\n",
        "zip_dest = f'{home_directory}results.zip'\n",
        "!zip -r {zip_dest} {root_output_folder}"
      ],
      "metadata": {
        "id": "zivBNrw9uSVD"
      },
      "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": {
        "id": "EdBiAguJO9aX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_folder = '/content/output/fusion-gen-savefiles/'\n",
        "index = 0\n",
        "path = '/content/text-to-image-prompts/fusion-gen-savefiles'\n",
        "\n",
        "def my_mkdirs(folder):\n",
        "  if os.path.exists(folder)==False:\n",
        "    os.makedirs(folder)\n",
        "\n",
        "my_mkdirs(output_folder)\n",
        "for filename in os.listdir(f'{path}'):\n",
        "  if filename.find('fusion_C05_X7_1000_')<=-1: continue\n",
        "  print(f'reading {filename}...')\n",
        "  %cd {path}\n",
        "  with open(f'{filename}', 'r') as f:\n",
        "    data = json.load(f)\n",
        "    _df = pd.DataFrame({'count': data})['count']\n",
        "    _savefile = {\n",
        "        key : value for key, value in _df.items()\n",
        "    }\n",
        "\n",
        "    _savefile2 = {}\n",
        "\n",
        "    for key in _savefile:\n",
        "      _savefile2[key] = _savefile[key]\n",
        "      if(key == \"_main\") :\n",
        "        _savefile2[key] =  \"Prompt input only ✏️\"\n",
        "        print(\"changed\")\n",
        "    #----------#\n",
        "\n",
        "  save_filename = f'fusion_C05_X7_1000_{index}.json'\n",
        "  index = index + 1\n",
        "\n",
        "  %cd {output_folder}\n",
        "  print(f'Saving savefile {save_filename} to {output_folder}...')\n",
        "  with open(save_filename, 'w') as f:\n",
        "      json.dump(_savefile2, f)"
      ],
      "metadata": {
        "id": "A3ASDnO3IzSL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}