{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# @title ⚄ 🔄 Initialize\n",
        "\n",
        "import os\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",
        "def fix_bad_symbols(txt):\n",
        "  result = txt\n",
        "  for symbol in ['^', '}', '{' , ')', '(', '[' , ']' , ':' , '=' ]:\n",
        "    result = result.replace(symbol,'\\\\' + symbol)\n",
        "  #------#\n",
        "  return result;\n",
        "\n",
        "def my_mkdirs(folder):\n",
        "  if os.path.exists(folder)==False:\n",
        "    os.makedirs(folder)\n",
        "\n",
        "#🔸🔹\n",
        "# Load the data if not already loaded\n",
        "try:\n",
        "    loaded\n",
        "except:\n",
        "  from safetensors.torch import load_file , save_file\n",
        "  import json , torch , requests , math\n",
        "  import pandas as pd\n",
        "  from PIL import Image\n",
        "  import cv2\n",
        "  from matplotlib import pyplot as plt\n",
        "  #----#\n",
        "  %cd {home_directory}\n",
        "  !git clone https://huggingface.co/datasets/codeShare/fusion-t2i-generator-data\n",
        "  loaded = True\n",
        "  %cd {home_directory + 'fusion-t2i-generator-data/'}\n",
        "  !unzip reference.zip\n",
        "\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",
        "%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
        "with open(f'reference_prompts.json', 'r') as f:\n",
        "  data = json.load(f)\n",
        "  _df = pd.DataFrame({'count': data})['count']\n",
        "  target_prompts = {\n",
        "      key : value for key, value in _df.items()\n",
        "  }\n",
        "#------#\n",
        "with open(f'reference_urls.json', 'r') as f:\n",
        "  data = json.load(f)\n",
        "  _df = pd.DataFrame({'count': data})['count']\n",
        "  target_urls = {\n",
        "      key : value for key, value in _df.items()\n",
        "  }\n",
        "\n",
        "#------#\n",
        "dot_dtype = torch.float32\n",
        "dim = 768\n",
        "ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "\n",
        "# title ⚄  Define parameters for visalizing the reference in a 16x16 grid <br> (the visualization settings has no effect on output)\n",
        "from PIL import Image, ImageDraw\n",
        "SCALE = 0.0002 # param {type:\"slider\", min:0.0001, max:0.001, step:0.00001}\n",
        "ZERO_POINT = 100 # param {type:\"slider\", min:0, max:300, step:1}\n",
        "CELL_SIZE = 16\n",
        "image_size = 0.5 # param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "show_encoding = False # param {type:\"boolean\"}\n",
        "#------#\n",
        "\n",
        "BORDER_THICKNESS = 4\n",
        "\n",
        "def visualize(_ref):\n",
        "  RGB_tensor = (torch.round(_ref/SCALE)+torch.ones(dim)*ZERO_POINT)\n",
        "  cellsize = CELL_SIZE\n",
        "  tick = round(cellsize/2)\n",
        "  border_offset = round(BORDER_THICKNESS/2)\n",
        "  width = 16*cellsize + BORDER_THICKNESS\n",
        "  height = 16*cellsize + BORDER_THICKNESS\n",
        "  image = Image.new('RGB', (width, height), (0, 0, 0))\n",
        "  draw = ImageDraw.Draw(image)\n",
        "  for row in range(16):\n",
        "    for col in range(16):\n",
        "      tmp = 3*row*col\n",
        "      r = max(0,min(255,int(RGB_tensor[tmp].item())))\n",
        "      g = max(0,min(255,int(RGB_tensor[tmp+1].item())))\n",
        "      b = max(0,min(255,int(RGB_tensor[tmp+2].item())))\n",
        "      fillColor = (r,g,b)\n",
        "      x0 = row*cellsize +border_offset\n",
        "      y0 = (15-col)*cellsize +border_offset\n",
        "      x1 = row*cellsize + 2*tick + border_offset\n",
        "      y1 = (15-col)*cellsize + 2*tick + border_offset\n",
        "      shape = [(x0, y0), (x1, y1)]\n",
        "      draw.rectangle(shape, fill=fillColor, outline=(0,0,0))\n",
        "  return (image)\n",
        "\n",
        "num_plots = 1\n",
        "try:\n",
        "  %cd /content/\n",
        "  _ref = load_file('reference.safetensors' )\n",
        "  num_plots = num_plots+1\n",
        "except: _ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "#-----#\n",
        "try: ref\n",
        "except: ref =  torch.zeros(dim).to(dtype = dot_dtype)\n",
        "\n",
        "\n",
        "if show_encoding:\n",
        "  # create figure\n",
        "  fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
        "  fig.patch.set_facecolor((56/255,56/255,56/255))\n",
        "  rows = 1\n",
        "  columns = num_plots\n",
        "  fig.add_subplot(rows, columns, 1)\n",
        "  plt.imshow( visualize(ref))\n",
        "  plt.axis('off')\n",
        "  plt.title( \"Encoding (local variable)\",  color='white',  fontsize=round(20*image_size))\n",
        "  if num_plots>1:\n",
        "    fig.add_subplot(rows, columns, 2)\n",
        "    plt.imshow( visualize( _ref['weights'].to(dot_dtype)))\n",
        "    plt.axis('off')\n",
        "    plt.title(\"Encoding (saved file)\",  color='white',  fontsize=round(20*image_size))\n",
        "  #------#\n",
        "\n",
        "print(f'Using settings SCALE = {SCALE} and ZERO_POINT = {ZERO_POINT} for visualizing the text_encoding')"
      ],
      "metadata": {
        "id": "TC5lMJrS1HCC",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ⚄ 📷💭 Use pre-encoded image+prompt pair\n",
        "loaded_ref = False\n",
        "try:\n",
        "  ref\n",
        "  loaded_ref = True\n",
        "except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "if loaded_ref : prev_ref = ref.clone().detach()\n",
        "\n",
        "try:prompt\n",
        "except: prompt = ''\n",
        "\n",
        "# @markdown 🖼️+📝 Choose a pre-encoded reference (note: some results are NSFW!)\n",
        "index = 596 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
        "PROMPT_INDEX = index\n",
        "prompt = target_prompts[f'{PROMPT_INDEX}']\n",
        "url = target_urls[f'{PROMPT_INDEX}']\n",
        "if url.find('perchance')>-1:\n",
        "  image = Image.open(requests.get(url, stream=True).raw)\n",
        "#------#\n",
        "%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
        "references = torch.load('reference_text_and_image_encodings.pt'  , weights_only=False)\n",
        "# @markdown ⚖️ 🖼️ encoding <-----?-----> 📝 encoding </div> <br>\n",
        "C = 0.3 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
        "method = 'Add to existing ref' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
        "image_size = 0.57 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "show_encoding = True # @param {type:\"boolean\"}\n",
        "\n",
        "if(not method == 'Do nothing'):\n",
        "  if method == 'Refresh': ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "  if method == 'Subtract from existing ref':\n",
        "    ref = torch.sub(ref, math.pow(10 ,log_strength-1) * C * references[index][0].dequantize().to(dtype = torch.float32))\n",
        "    ref = torch.sub(ref, math.pow(10 ,log_strength-1) * (1-C) * references[index][1].dequantize().to(dtype = torch.float32))\n",
        "  else:\n",
        "    ref = torch.add(ref, math.pow(10 ,log_strength-1) * C * references[index][0].dequantize().to(dtype = torch.float32))\n",
        "    ref = torch.add(ref, math.pow(10 ,log_strength-1) * (1-C) * references[index][1].dequantize().to(dtype = torch.float32))\n",
        "  #---------#\n",
        "  references = '' # Clear up memory\n",
        "  ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
        "  ref = ref.clone().detach()\n",
        "  #------#\n",
        "  # create figure\n",
        "  fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
        "  fig.patch.set_facecolor((56/255,56/255,56/255))\n",
        "  rows = 1\n",
        "  columns = 1\n",
        "  if show_encoding: columns = columns+1\n",
        "  if show_encoding and loaded_ref : columns = columns+1\n",
        "  fig.add_subplot(rows, columns, 1)\n",
        "  plt.imshow(image)\n",
        "  plt.axis('off')\n",
        "  plt.title(f\"Reference image at index={index}\" ,  color='white' , fontsize=round(20*image_size))\n",
        "  #-----#\n",
        "  if show_encoding and loaded_ref:\n",
        "    fig.add_subplot(rows, columns, columns-1)\n",
        "    plt.imshow( visualize(prev_ref))\n",
        "    plt.axis('off')\n",
        "    plt.title(\"Encoding (before)\" ,  color='white' , fontsize=round(20*image_size))\n",
        "  print(f'Prompt for this image : \\n\\n \"{prompt} \" \\n\\n')\n",
        "\n",
        "  if show_encoding:\n",
        "    fig.add_subplot(rows, columns, columns)\n",
        "    plt.imshow( visualize(ref))\n",
        "    plt.axis('off')\n",
        "    plt.title(\"Encoding (now)\" ,  color='white' , fontsize=round(20*image_size))\n",
        "  #------#\n"
      ],
      "metadata": {
        "id": "BwrEs5zVB0Sb",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Other methods"
      ],
      "metadata": {
        "id": "f9_AcquM7AYZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ⚄ 🧩 Create an encoding\n",
        "# @markdown 📝 Write a text prompt (this will overwrite any savefile already stored)\n",
        "NEW_ENCODING = '' # @param {type:'string' ,placeholder:'write a prompt'}\n",
        "enable = True # @param {type:\"boolean\"}\n",
        "# @markdown -----\n",
        "# @markdown 📝 Enhance/Penalize Similarity and skip items containing word(s)\n",
        "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
        "NEG = ''# @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
        "# @markdown -----\n",
        "# @markdown logarithmic prompt strength x for value 10^(x-1)\n",
        "_POS = 0 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
        "_NEG = 0  # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
        "# @markdown -----\n",
        "# @markdown Check similiarity for this encoding against any written prompt(s)\n",
        "# @title ⚄ Evaluate saved  reference similarity to select items (optional)\n",
        "EVAL = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
        "\n",
        "show_local_reference = True # @param {type:\"boolean\"}\n",
        "show_encoding = True # @param {type:\"boolean\"}\n",
        "\n",
        "try:\n",
        "  %cd /content/\n",
        "  _ref = load_file('reference.safetensors' )\n",
        "  ref = _ref['weights'].to(dot_dtype)\n",
        "except:\n",
        "  ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "  _ref = {}\n",
        "  _ref['weights'] = ref\n",
        "  %cd /content/\n",
        "  save_file(_ref, 'reference.safetensors')\n",
        "#-----#\n",
        "\n",
        "if NEW_ENCODING.strip() != '':\n",
        "  item = NEW_ENCODING.strip()\n",
        "  inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
        "  ref = model.get_text_features(**inputs)[0]\n",
        "  ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
        "#------#\n",
        "\n",
        "try: ref\n",
        "except: ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "\n",
        "if EVAL.strip() != '':\n",
        "  print(\"Saved Reference:\\n\")\n",
        "  for item in EVAL.split(','):\n",
        "    if item.strip()=='':continue\n",
        "    inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
        "    test = model.get_text_features(**inputs)[0]\n",
        "    test = test/test.norm(p=2 , dim = -1 , keepdim = True)\n",
        "    ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
        "    eval = torch.dot(ref , test)\n",
        "    print(f'{item.strip()} : {round(eval.item()*100, 2)}%')\n",
        "  #-----#\n",
        "  if(show_local_reference):\n",
        "    print(\"\\n---------\\nLocal Reference with enchancements added :\\n\")\n",
        "\n",
        "    for _item in POS.split(','):\n",
        "      item = _item.strip()\n",
        "      if item == '':continue\n",
        "      inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
        "      ref = ref + math.pow(10,_POS-1) * model.get_text_features(**inputs)[0]\n",
        "    #-------#\n",
        "\n",
        "    for _item in NEG.split(','):\n",
        "      item = _item.strip()\n",
        "      if item == '':continue\n",
        "      inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
        "      ref = ref + math.pow(10,_NEG-1) * model.get_text_features(**inputs)[0]\n",
        "    #-------#\n",
        "\n",
        "    ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
        "    for item in EVAL.split(','):\n",
        "      if item.strip()=='':continue\n",
        "      inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
        "      test = model.get_text_features(**inputs)[0]\n",
        "      test = test/test.norm(p=2 , dim = -1 , keepdim = True)\n",
        "      eval = torch.dot(ref , test)\n",
        "      print(f'{item.strip()} : {round(eval.item()*100, 2)}%')\n",
        "    #-----#\n",
        "\n",
        "  if show_encoding:\n",
        "    # create figure\n",
        "    fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
        "    fig.patch.set_facecolor((56/255,56/255,56/255))\n",
        "    rows = 1\n",
        "    columns = 3\n",
        "    fig.add_subplot(rows, columns, 1)\n",
        "    plt.imshow( visualize(ref))\n",
        "    plt.axis('off')\n",
        "    plt.title( \"Encoding (local variable)\",  color='white',  fontsize=round(20*image_size))\n",
        "    if num_plots>1:\n",
        "      fig.add_subplot(rows, columns, 2)\n",
        "      plt.imshow( visualize( _ref['weights'].to(dot_dtype)))\n",
        "      plt.axis('off')\n",
        "      plt.title(\"Encoding (saved file)\",  color='white',  fontsize=round(20*image_size))\n",
        "\n",
        "      fig.add_subplot(rows, columns, 3)\n",
        "      plt.imshow( visualize(ref - _ref['weights'].to(dot_dtype)))\n",
        "      plt.axis('off')\n",
        "      plt.title(\"Changes\",  color='white',  fontsize=round(20*image_size))\n",
        "    #------#\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Oxi6nOyrUTAe",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Use an image as a reference via URL (optional)**"
      ],
      "metadata": {
        "id": "KI9Ho6CG7m3Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ⚄ 🌐🖼️ Load an image via URL\n",
        "loaded_ref = False\n",
        "try:\n",
        "  ref\n",
        "  loaded_ref = True\n",
        "except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "if loaded_ref : prev_ref = ref.clone().detach()\n",
        "\n",
        "try:prompt\n",
        "except: prompt = ''\n",
        "\n",
        "# @markdown 🖼️ Upload your own image for use as reference via URL (optional)\n",
        "URL = '' # @param {type:'string' ,placeholder:'paste an url here'}\n",
        "if URL.strip() != '':\n",
        "  image = Image.open(requests.get(URL, stream=True).raw)\n",
        "  log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
        "  method = 'Add to existing ref' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
        "  image_size = 0.79 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "  show_encoding = True # @param {type:\"boolean\"}\n",
        "  #---------#\n",
        "  if(not method == 'Do nothing'):\n",
        "    # Get image features\n",
        "    inputs = processor(images=image, return_tensors=\"pt\")\n",
        "    image_features = model.get_image_features(**inputs)\n",
        "    image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\n",
        "    #-------#\n",
        "    if method == 'Refresh':\n",
        "      ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "    if method == 'Subtract from existing ref':\n",
        "      ref = ref - math.pow(10,log_strength-1)*image_features\n",
        "    else: ref = ref + math.pow(10,log_strength-1)*image_features\n",
        "    #-----#\n",
        "    ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
        "    ref = ref[0]\n",
        "    ref = ref.clone().detach()\n",
        "    #------#\n",
        "    # create figure\n",
        "    fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
        "    fig.patch.set_facecolor((56/255,56/255,56/255))\n",
        "    rows = 1\n",
        "    columns = 1\n",
        "    if show_encoding: columns = 2\n",
        "    if show_encoding and loaded_ref : columns = 3\n",
        "    fig.add_subplot(rows, columns, 1)\n",
        "    plt.imshow(image)\n",
        "    plt.axis('off')\n",
        "    plt.title(\"Reference image from URL\" ,  color='white' , fontsize=round(20*image_size))\n",
        "    #-----#\n",
        "    if show_encoding and loaded_ref:\n",
        "      fig.add_subplot(rows, columns, columns-1)\n",
        "      plt.imshow( visualize(prev_ref))\n",
        "      plt.axis('off')\n",
        "      plt.title(\"Encoding (before)\" ,  color='white' , fontsize=round(20*image_size))\n",
        "    if show_encoding:\n",
        "      fig.add_subplot(rows, columns, columns)\n",
        "      plt.imshow( visualize(ref))\n",
        "      plt.axis('off')\n",
        "      plt.title(\"Encoding (now)\" ,  color='white' , fontsize=round(20*image_size))\n",
        "    #------#"
      ],
      "metadata": {
        "id": "IqUsiQw2HU2C",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Use an image as a reference via uploading it to the /content/ folder (optional)**"
      ],
      "metadata": {
        "id": "MBPi7F8S7tg3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ⚄ 📂🖼️ Use an uploaded image as reference\n",
        "loaded_ref = False\n",
        "try:\n",
        "  ref\n",
        "  loaded_ref = True\n",
        "except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "if loaded_ref : prev_ref = ref.clone().detach()\n",
        "\n",
        "try:prompt\n",
        "except: prompt = ''\n",
        "\n",
        "# @markdown 🖼️ Upload your own image for use as reference via URL (optional)\n",
        "FILENAME = '' # @param {type:'string' ,placeholder:'IMG_123.png'}\n",
        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
        "method = 'Add to existing ref' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
        "image_size = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "show_encoding = True # @param {type:\"boolean\"}\n",
        "\n",
        "if FILENAME.strip() != '':\n",
        "  %cd /content/\n",
        "  image = cv2.imread(FILENAME)\n",
        "  b,g,r = cv2.split(image)\n",
        "  image = cv2.merge([r,g,b])\n",
        "  #---------#\n",
        "  if(not method == 'Do nothing'):\n",
        "    # Get image features\n",
        "    inputs = processor(images=image, return_tensors=\"pt\")\n",
        "    image_features = model.get_image_features(**inputs)\n",
        "    image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\n",
        "    #-------#\n",
        "    if method == 'Refresh':\n",
        "      ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "    if method == 'Subtract from existing ref':\n",
        "      ref = ref - math.pow(10,log_strength-1)*image_features\n",
        "    else: ref = ref + math.pow(10,log_strength-1)*image_features\n",
        "    #-----#\n",
        "    ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
        "    ref = ref[0]\n",
        "    ref = ref.clone().detach()\n",
        "    #------#\n",
        "    # create figure\n",
        "    fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
        "    fig.patch.set_facecolor((56/255,56/255,56/255))\n",
        "    rows = 1\n",
        "    columns = 1\n",
        "    if show_encoding: columns = 2\n",
        "    if show_encoding and loaded_ref : columns = 3\n",
        "    fig.add_subplot(rows, columns, 1)\n",
        "    plt.imshow(image)\n",
        "    plt.axis('off')\n",
        "    plt.title(f\"Reference image from uploaded image {FILENAME}\" ,  color='white' , fontsize=round(20*image_size))\n",
        "    #-----#\n",
        "    if show_encoding and loaded_ref:\n",
        "      fig.add_subplot(rows, columns, columns-1)\n",
        "      plt.imshow( visualize(prev_ref))\n",
        "      plt.axis('off')\n",
        "      plt.title(\"Encoding (before)\" ,  color='white' , fontsize=round(20*image_size))\n",
        "    if show_encoding:\n",
        "      fig.add_subplot(rows, columns, columns)\n",
        "      plt.imshow( visualize(ref))\n",
        "      plt.axis('off')\n",
        "      plt.title(\"Encoding (now)\" ,  color='white' , fontsize=round(20*image_size))\n",
        "    #------#"
      ],
      "metadata": {
        "id": "I_-GOwFPKkha",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Search prompts using CLIP"
      ],
      "metadata": {
        "id": "UqrYOkhlEQdM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ⚄ 💾 Save the reference\n",
        "\n",
        "loaded_ref = False\n",
        "try:\n",
        "  ref\n",
        "  loaded_ref = True\n",
        "except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "if loaded_ref : prev_ref = ref.clone().detach()\n",
        "\n",
        "try:prompt\n",
        "except: prompt = ''\n",
        "\n",
        "reset_everything = False # @param {type:\"boolean\"}\n",
        "_ref = {}\n",
        "ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
        "if (reset_everything) : ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "_ref['weights'] = ref.to(dot_dtype)\n",
        "%cd /content/\n",
        "save_file(_ref , 'reference.safetensors' )\n",
        "image_size = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "show_encoding = True # @param {type:\"boolean\"}\n",
        "#------#\n",
        "print(\"Saved local encoding to reference.safetensors\")\n",
        "if show_encoding:\n",
        "  # create figure\n",
        "  fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
        "  fig.patch.set_facecolor((56/255,56/255,56/255))\n",
        "  rows = 1\n",
        "  columns = num_plots\n",
        "  fig.add_subplot(rows, columns, 1)\n",
        "  plt.imshow( visualize(ref))\n",
        "  plt.axis('off')\n",
        "  plt.title( \"Encoding (local variable)\",  color='white',  fontsize=round(20*image_size))\n",
        "  if num_plots>1:\n",
        "    fig.add_subplot(rows, columns, 2)\n",
        "    plt.imshow( visualize( _ref['weights'].to(dot_dtype)))\n",
        "    plt.axis('off')\n",
        "    plt.title(\"Encoding (saved file)\",  color='white',  fontsize=round(20*image_size))\n",
        "  #------#"
      ],
      "metadata": {
        "id": "lOQuTPfBMK82",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Run the interrogator**\n",
        "\n",
        " Since the list of items is large (>1 million items) you will need to select a range within the sorted results to print."
      ],
      "metadata": {
        "id": "ROKsoZrt7zMe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ⚄ 🕵️‍♂️ Run the CLIP Interrogator\n",
        "LIST_SIZE = 1000  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
        "_START_AT = '0' # @param [\"0\", \"10000\", \"50000\"] {allow-input: true}\n",
        "START_AT = 0\n",
        "#-----#\n",
        "if _START_AT.find('K')>-1:\n",
        "  START_AT = _START_AT.replace('K','')\n",
        "  if START_AT.isnumeric(): START_AT = int(START_AT)*1000\n",
        "#------#\n",
        "else:\n",
        "  if _START_AT.isnumeric(): START_AT = int(_START_AT)\n",
        "#----#\n",
        "\n",
        "output_folder = home_directory + 'results/'\n",
        "output_folder_sims = home_directory + 'results/sims/'\n",
        "my_mkdirs(output_folder)\n",
        "my_mkdirs(output_folder_sims)\n",
        "\n",
        "# @markdown -----\n",
        "# @markdown Select vocab\n",
        "general = True # @param {type:\"boolean\"}\n",
        "civit9 = True # @param {type:\"boolean\"}\n",
        "fanfic1 = False # @param {type:\"boolean\"}\n",
        "fanfic2 = False # @param {type:\"boolean\"}\n",
        "# @markdown -----\n",
        "# @title ⚄ New interrogator code using quantized text corpus\n",
        "%cd /content/\n",
        "_ref = load_file('reference.safetensors' )\n",
        "ref = _ref['weights'].to(dot_dtype)\n",
        "# @markdown 📝 Enhance/Penalize Similarity and skip items containing word(s)\n",
        "POS1 = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
        "POS2 = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
        "NEG = ''# @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
        "SKIP = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
        "min_wordcount = 0 # @param {type:\"slider\", min:0, max:20, step:1}\n",
        "def isBlacklisted(_txt):\n",
        "  blacklist =  SKIP.lower().replace('</w>' , ' ').replace('{' , '').replace('}' , '').replace('|' , ',').strip()\n",
        "  if blacklist == '': return False\n",
        "  txt = _txt.lower().strip()\n",
        "  if len(txt)<min_wordcount: return True\n",
        "  if txt.isnumeric(): return True\n",
        "  #-----#\n",
        "  for item in list(blacklist.split(',')):\n",
        "    if item.strip() == '' : continue\n",
        "    if txt.find(item.strip())> -1 : return True\n",
        "  #------#\n",
        "  found = False\n",
        "  alphabet = 'abcdefghijklmnopqrstuvxyz'\n",
        "  for letter in alphabet:\n",
        "    found =  txt.find(letter)>-1\n",
        "    if found:break\n",
        "  #------#\n",
        "  return not found\n",
        "# @markdown -----\n",
        "# @markdown logarithmic prompt strength x for value 10^(x-1)\n",
        "_POS1 = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
        "_POS2 = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
        "_NEG = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
        "# @markdown -----\n",
        "# @markdown Save similarity as a list for later review (this will slow down the code)\n",
        "save_similiarity = True # @param {type:\"boolean\"}\n",
        "# @markdown -----\n",
        "include_similiarity = False # @param {type:\"boolean\"}\n",
        "print_as_list = False # @param {type:\"boolean\"}\n",
        "N = 7 # @param {type:\"slider\", min:0, max:10, step:1}\n",
        "#-----#\n",
        "for _item in POS1.split(','):\n",
        "  item = _item.strip()\n",
        "  if item == '':continue\n",
        "  inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
        "  ref = ref + math.pow(10,_POS1-1) * model.get_text_features(**inputs)[0]\n",
        "#-------#\n",
        "for _item in POS2.split(','):\n",
        "  item = _item.strip()\n",
        "  if item == '':continue\n",
        "  inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
        "  ref = ref + math.pow(10,_POS2-1) * model.get_text_features(**inputs)[0]\n",
        "#-------#\n",
        "for _item in NEG.split(','):\n",
        "  item = _item.strip()\n",
        "  if item == '':continue\n",
        "  inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
        "  ref = ref + math.pow(10,_NEG-1) * model.get_text_features(**inputs)[0]\n",
        "#------#\n",
        "ref = (ref/ref.norm(p=2, dim=-1, keepdim=True)).to(dtype = dot_dtype)\n",
        "vocab_to_load = ''\n",
        "if (general): vocab_to_load = vocab_to_load + 'general , '\n",
        "if (civit9): vocab_to_load = vocab_to_load + 'civit9 , '\n",
        "if (fanfic1): vocab_to_load = vocab_to_load + 'fanfic1 , '\n",
        "if (fanfic2): vocab_to_load = vocab_to_load + 'fanfic2 , '\n",
        "vocab_to_load = (vocab_to_load +'}').replace(' , }' , '')\n",
        "multi = vocab_to_load.find(',')>-1\n",
        "#-----#\n",
        "prompts_folder = f'{home_directory}fusion-t2i-generator-data/vocab-v2/text'\n",
        "encodings_folder = f'{home_directory}fusion-t2i-generator-data/vocab-v2/text_encodings'\n",
        "#----#\n",
        "scale = 0.0043\n",
        "size = 0\n",
        "#------#\n",
        "total_items = 0\n",
        "for filename in os.listdir(prompts_folder):\n",
        "  if (not general and filename.find('general')>-1):continue\n",
        "  if (not civit9 and filename.find('civit9')>-1):continue\n",
        "  if (not fanfic1 and filename.find('fanfic1')>-1):continue\n",
        "  if (not fanfic2 and filename.find('fanfic2')>-1):continue\n",
        "  size = size + LIST_SIZE\n",
        "#-------#\n",
        "similiar_sims = torch.zeros(size)\n",
        "similiar_prompts = {}\n",
        "_index = 0\n",
        "#-------#\n",
        "similiar_encodings = {}\n",
        "for filename in os.listdir(prompts_folder):\n",
        "  if (not general and filename.find('general')>-1):continue\n",
        "  if (not civit9 and filename.find('civit9')>-1):continue\n",
        "  if (not fanfic1 and filename.find('fanfic1')>-1):continue\n",
        "  if (not fanfic2 and filename.find('fanfic2')>-1):continue\n",
        "  #------#\n",
        "  root_filename = filename.replace('.json', '')\n",
        "  %cd {prompts_folder}\n",
        "  prompts = {}\n",
        "  with open(f'{root_filename}.json', 'r') as f:\n",
        "    data = json.load(f).items()\n",
        "    for key,value in data:\n",
        "      prompts[key] = value\n",
        "  num_items = int(prompts['num_items'])\n",
        "  total_items = total_items + num_items\n",
        "  #------#\n",
        "  try:vocab_loaded\n",
        "  except:\n",
        "    vocab_loaded = 'first'\n",
        "  #-----#\n",
        "  if  vocab_loaded == 'first' or (vocab_loaded != vocab_to_load and not multi):\n",
        "    %cd {encodings_folder}\n",
        "    _text_encodings = load_file(f'{root_filename}.safetensors')['weights'].to(torch.uint8)\n",
        "    text_encodings = torch.zeros(num_items , dim)\n",
        "    tmp = torch.ones(dim).to(dot_dtype)\n",
        "    for index in range(num_items):\n",
        "      text_encodings[index] = torch.sub(_text_encodings[index][1:dim+1].to(dot_dtype) , tmp , alpha= _text_encodings[index][0].to(dot_dtype))\n",
        "    vocab_loaded = vocab_to_load\n",
        "  #------#\n",
        "  sims = torch.matmul(text_encodings*scale, ref.t())\n",
        "  sorted , indices = torch.sort(sims , dim=0 , descending = True)\n",
        "  tmp = {}\n",
        "  tmp['weights'] = sorted\n",
        "  %cd {output_folder_sims}\n",
        "  save_file(tmp, root_filename + '_sims.safetensors')\n",
        "  tmp={}\n",
        "  #-----#\n",
        "  for index in range(LIST_SIZE + START_AT):\n",
        "    if index<START_AT: continue\n",
        "    key = indices[index].item()\n",
        "    try:prompt = prompts[f'{key}']\n",
        "    except:continue\n",
        "    if(isBlacklisted(prompt)):continue\n",
        "    #-------#\n",
        "    similiar_sims[_index] = torch.tensor(round(sims[key].item(), 5))\n",
        "    similiar_prompts[f'{_index}'] = prompt\n",
        "    _index = _index + 1\n",
        "  #-------#\n",
        "  continue\n",
        "#---------#\n",
        "total_items = total_items + num_items+1\n",
        "#-------#\n",
        "print(f'\\nProcessed entire list of {total_items} items to find closest match.\\nSaved closest matching indices {START_AT} to {START_AT + LIST_SIZE} as the dict \"similiar_prompts\" with {LIST_SIZE} items.\\n')\n",
        "\n",
        "# Print results\n",
        "sorted , indices = torch.sort(similiar_sims , dim=0 , descending = True)\n",
        "if(print_as_list):\n",
        "  for index in range(LIST_SIZE):\n",
        "    key = indices[index].item()\n",
        "    sim = similiar_sims[key].item()\n",
        "    prompt = similiar_prompts[f'{key}']\n",
        "    if include_similiarity :print(f'{prompt}  - {round(sim*100,1)} %')\n",
        "    else: print(f'{prompt}')\n",
        "#-------#\n",
        "else:\n",
        "  prompt = ''\n",
        "  for iter in range(N):\n",
        "    prompt = prompt + '{'\n",
        "    for index in range(LIST_SIZE):\n",
        "      key = indices[index].item()\n",
        "      sim = similiar_sims[key].item()\n",
        "      prompt = prompt + fix_bad_symbols(similiar_prompts[f'{key}']) + '|'\n",
        "    #-----#\n",
        "    prompt = (prompt + '}').replace('|}',  '} ')\n",
        "  #------#\n",
        "  print(f'Similiar prompts: \\n\\n\\n{prompt} \\n\\n\\n//----//')\n",
        "#-----#\n",
        "\n",
        "#Clear memory\n",
        "_text_encodings = {}\n",
        "prompts = {}\n",
        "#-----#\n",
        "\n",
        "image\n"
      ],
      "metadata": {
        "id": "kOYZ8Ajn-DD8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluate Similarities**\n",
        "\n",
        "Run this cell to see how far down the list you can go before similarity to the reference is lost."
      ],
      "metadata": {
        "id": "yl1DYzUn8YCC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ⚄ 🔍 Test how unique the encoding is\n",
        "%cd {output_folder_sims}\n",
        "index = 0\n",
        "for filename in os.listdir(output_folder_sims):\n",
        "  _sims = load_file(filename)\n",
        "  _sims = _sims['weights']\n",
        "  for _sim in _sims.tolist():\n",
        "    index = index + 1\n",
        "  #-------#\n",
        "total_items = index\n",
        "sims = torch.zeros(total_items)\n",
        "index = 0\n",
        "for filename in os.listdir(output_folder_sims):\n",
        "  _sims = load_file(filename)\n",
        "  _sims = _sims['weights']\n",
        "  for sim in _sims.tolist():\n",
        "    sims[index] = sim\n",
        "    index = index + 1\n",
        "  #-------#\n",
        "#---------------#\n",
        "_sorted , indices  = torch.sort(sims , dim=0 , descending = True)\n",
        "SCALE = 0.001\n",
        "sorted = torch.round(_sorted/SCALE)\n",
        "ZERO_POINT = sorted[total_items-1].item()\n",
        "sorted = (sorted - torch.ones(total_items)*ZERO_POINT)\n",
        "densities = torch.bincount(sorted.to(dtype = torch.int64))\n",
        "yy = densities.tolist()\n",
        "top = (sorted[0] + ZERO_POINT).to(dtype = torch.int64).item()\n",
        "num_coords = round(top - ZERO_POINT)\n",
        "xx = [round((ZERO_POINT + x)*100*SCALE,2) for x in range(num_coords+1)]\n",
        "index = 0\n",
        "for item in xx:\n",
        "  if item>0:break\n",
        "  index = index + 1\n",
        "#----#\n",
        "positive_bound = index\n",
        "ss =list(xx)\n",
        "tmp = 0\n",
        "chunk = 1\n",
        "CHUNK_SIZE = 1000\n",
        "index = 0\n",
        "for num in reversed(yy):\n",
        "  tmp = tmp + num\n",
        "  if(tmp>CHUNK_SIZE):\n",
        "    _tmp = math.floor(tmp/CHUNK_SIZE)\n",
        "    chunk = chunk + _tmp\n",
        "    tmp = tmp - CHUNK_SIZE * _tmp\n",
        "  ss[num_coords - index] = chunk\n",
        "  index = index + 1\n",
        "#------#\n",
        "fig, ax = plt.subplots()\n",
        "fig.canvas.draw()\n",
        "plt.plot(ss[positive_bound:], xx[positive_bound:])\n",
        "plt.xlabel ('Search depth')\n",
        "plt.ylabel ('Similarity')\n",
        "plt.title ('Similarity to index')\n",
        "plt.grid()\n",
        "indices_depth = [item.get_text() for item in ax.get_xticklabels()]\n",
        "sim_pcnts = [item.get_text() for item in ax.get_yticklabels()]\n",
        "\n",
        "index = 0\n",
        "for index_depth in indices_depth:\n",
        "  indices_depth[index] = index_depth + 'K'\n",
        "  index = index + 1\n",
        "#-------#\n",
        "\n",
        "index = 0\n",
        "for sim_pcnt in sim_pcnts:\n",
        "  sim_pcnts[index] = sim_pcnt + '%'\n",
        "  index = index + 1\n",
        "#-------#\n",
        "ax.set_xticklabels(indices_depth)\n",
        "ax.set_yticklabels(sim_pcnts)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ln6DsZPG99ez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ⚄ Save the results\n",
        "\n",
        "def mkdir(folder):\n",
        "  if os.path.exists(folder)==False:\n",
        "    os.makedirs(folder)\n",
        "#-----#\n",
        "output_folder = home_directory + 'results'\n",
        "mkdir(output_folder)\n",
        "#-----#\n",
        "try: similiar_prompts\n",
        "except:similiar_prompts = {}\n",
        "%cd {output_folder}\n",
        "print(f'Saving similiar_prompts.json to {output_folder}...')\n",
        "with open('similiar_prompts.json', 'w') as f:\n",
        "  json.dump(similiar_prompts, f)\n",
        "#-----#\n",
        "try: similiar_sims\n",
        "except: similiar_sims = torch.zeros(dim).to(dot_dtype)\n",
        "#-------#\n",
        "_similiar_sims = {}\n",
        "_similiar_sims['weights'] = similiar_sims.to(dot_dtype)\n",
        "%cd {output_folder}\n",
        "print(f'Saving similiar_sims.safetensors to {output_folder}...')\n",
        "save_file(_similiar_sims, 'similiar_sims.safetensors')\n"
      ],
      "metadata": {
        "id": "m-N553nXz9Jd",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# @title ⚄ Print results\n",
        "sorted , indices = torch.sort(similiar_sims , dim=0 , descending = True)\n",
        "include_similiarity = False # @param {type:\"boolean\"}\n",
        "print_as_list = False # @param {type:\"boolean\"}\n",
        "N = 7 # @param {type:\"slider\", min:0, max:10, step:1}\n",
        "FILENAME = '' # @param {type:'string' ,placeholder:'write .json file to load (optional)'}\n",
        "_FILENAME  = FILENAME.replace('.json' , '')\n",
        "if _FILENAME.strip() == '': _FILENAME = 'similiar_prompts'\n",
        "#------#\n",
        "%cd {output_folder}\n",
        "with open(f'{_FILENAME}.json', 'r') as f:\n",
        "  data = json.load(f)\n",
        "  _df = pd.DataFrame({'count': data})['count']\n",
        "  similiar_prompts = {\n",
        "      key : value for key, value in _df.items()\n",
        "  }\n",
        "#-------#\n",
        "_similiar_sims = load_file('similiar_sims.safetensors')\n",
        "similiar_sims = _similiar_sims['weights'].to(dot_dtype)\n",
        "\n",
        "# @title ⚄ Run the CLIP interrogator on the saved reference\n",
        "\n",
        "# @markdown Select which values within the saved list to print\n",
        "LIST_SIZE = 1000  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
        "START_AT = 0  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
        "\n",
        "if(print_as_list):\n",
        "  for index in range(LIST_SIZE + START_AT):\n",
        "    if index<START_AT:continue\n",
        "    key = indices[index].item()\n",
        "    sim = similiar_sims[key].item()\n",
        "    prompt = similiar_prompts[f'{key}']\n",
        "    if include_similiarity :print(f'{prompt}  - {round(sim*100,1)} %')\n",
        "    else: print(f'{prompt}')\n",
        "#-------#\n",
        "else:\n",
        "  prompt = ''\n",
        "  for iter in range(N):\n",
        "    prompt = prompt + '{'\n",
        "    for index in range(LIST_SIZE + START_AT):\n",
        "      if index<START_AT:continue\n",
        "      key = indices[index].item()\n",
        "      sim = similiar_sims[key].item()\n",
        "      prompt = prompt + fix_bad_symbols(similiar_prompts[f'{key}']) + '|'\n",
        "    #-----#\n",
        "    prompt = (prompt + '}').replace('|}',  '} ')\n",
        "  #------#\n",
        "  print(f'Similiar prompts: \\n\\n {prompt} \\n\\n')\n",
        "image\n",
        "#-----#\n"
      ],
      "metadata": {
        "id": "XOMkIKc9-wZz",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}