{
  "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",
      "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",
        "  #----#\n",
        "  %cd {home_directory}\n",
        "  !git clone https://huggingface.co/datasets/codeShare/fusion-t2i-generator-data\n",
        "  loaded = True\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",
        "%cd {home_directory + 'fusion-t2i-generator-data/'}\n",
        "!unzip reference.zip\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",
        "reference = torch.zeros(dim).to(dtype = dot_dtype)"
      ],
      "metadata": {
        "id": "TC5lMJrS1HCC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feel free to skip these cells if you do not plan on using them\n"
      ],
      "metadata": {
        "id": "Xf9zoq-Za3wi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown 🖼️+📝 Choose a pre-encoded reference (optional)\n",
        "index = 657 # @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",
        "try: reference\n",
        "except: reference = torch.zeros(dim).to(dtype = dot_dtype)\n",
        "if reference == '': reference = torch.zeros(dim).to(dtype = dot_dtype)\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",
        "%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
        "references = torch.load('reference_text_and_image_encodings.pt'  , weights_only=False)\n",
        "reference = torch.add(reference, math.pow(10 ,log_strength-1) * C * references[index][0].dequantize().to(dtype = torch.float32))\n",
        "reference = torch.add(reference, math.pow(10 ,log_strength-1) * (1-C) * references[index][1].dequantize().to(dtype = torch.float32))\n",
        "references = '' # Clear up memory\n",
        "ref = reference.clone().detach()\n",
        "#------#\n",
        "print(f'Prompt for this image : \\n\\n \"{prompt} \" \\n\\n')\n",
        "image"
      ],
      "metadata": {
        "id": "BwrEs5zVB0Sb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown 🖼️ Upload your own image for use as reference via URL (optional)\n",
        "URL = '' # @param {type:'string' ,placeholder:'paste an url here'}\n",
        "image = Image.open(requests.get(URL, stream=True).raw)\n",
        "#---------#\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",
        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
        "ref = ref + math.pow(10,log_strength-1)*image_features\n",
        "image"
      ],
      "metadata": {
        "id": "IqUsiQw2HU2C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @markdown 🖼️ Upload your own image in the /content/ folder for use as reference (optional)\n",
        "FILENAME = '' # @param {type:'string' ,placeholder:'IMG_123.png'}\n",
        "import cv2\n",
        "image = cv2.imread(FILENAME)\n",
        "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "#---------#\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",
        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
        "ref = ref + math.pow(10,log_strength-1)*image_features\n",
        "image"
      ],
      "metadata": {
        "id": "I_-GOwFPKkha"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Save the reference prior to running the Interrogator"
      ],
      "metadata": {
        "id": "zeu6JcM-mk9z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ⚄ Save the reference\n",
        "try: ref\n",
        "except: ref = torch.zeros(dim)\n",
        "_ref = {}\n",
        "_ref['weights'] = ref.to(dot_dtype)\n",
        "%cd /content/\n",
        "save_file(_ref , 'reference.safetensors' )"
      ],
      "metadata": {
        "id": "lOQuTPfBMK82"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ⚄ Run the CLIP interrogator on the saved reference\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",
        "# @markdown -----\n",
        "# @markdown Select vocab\n",
        "general = False # @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",
        "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",
        "#-----#\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",
        "  #------#\n",
        "  try:vocab_loaded\n",
        "  except:\n",
        "    vocab_loaded = 'first'\n",
        "  #-----#\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",
        "\n",
        "\n",
        "  sims = torch.matmul(text_encodings*scale, ref.t())\n",
        "  sorted , indices = torch.sort(sims , dim=0 , descending = True)\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",
        "print(f'\\n\\nProcessed entire list of {total_items} items to find closest match. Saved closest matching indices {START_AT} to {START_AT + LIST_SIZE} as the dict \"similiar_prompts\" with {LIST SIZE} items. \\n\\n')\n"
      ],
      "metadata": {
        "id": "kOYZ8Ajn-DD8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# @title ⚄ Printing results from text corpus\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",
        "\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 {prompt} \\n\\n')\n",
        "  image\n",
        "#-----#\n"
      ],
      "metadata": {
        "id": "XOMkIKc9-wZz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "OTHER STUFF BELOW - Code for the modules below are work-in-progress."
      ],
      "metadata": {
        "id": "FRIqYJDEebpf"
      }
    },
    {
      "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",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title \t⚄ Create a savefile-set from the entire range of pre-encoded items\n",
        "\n",
        "# @markdown 📥 Load the data (only required one time)\n",
        "load_the_data = True # @param {type:\"boolean\"}\n",
        "\n",
        "import math\n",
        "from safetensors.torch import load_file\n",
        "import json , os , torch\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import requests\n",
        "\n",
        "def my_mkdirs(folder):\n",
        "  if os.path.exists(folder)==False:\n",
        "    os.makedirs(folder)\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",
        "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",
        "\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'\n",
        "\n",
        "%cd /content/\n",
        "output_folder = '/content/output/savefiles/'\n",
        "my_mkdirs(output_folder)\n",
        "my_mkdirs('/content/output2/savefiles/')\n",
        "my_mkdirs('/content/output3/savefiles/')\n",
        "my_mkdirs('/content/output4/savefiles/')\n",
        "my_mkdirs('/content/output5/savefiles/')\n",
        "my_mkdirs('/content/output6/savefiles/')\n",
        "my_mkdirs('/content/output7/savefiles/')\n",
        "my_mkdirs('/content/output8/savefiles/')\n",
        "my_mkdirs('/content/output9/savefiles/')\n",
        "my_mkdirs('/content/output10/savefiles/')\n",
        "my_mkdirs('/content/output11/savefiles/')\n",
        "my_mkdirs('/content/output12/savefiles/')\n",
        "my_mkdirs('/content/output13/savefiles/')\n",
        "\n",
        "\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",
        "  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",
        "  text_features_A = target_text_encodings[f'{index}']\n",
        "  image_features_A =  target_image_encodings[f'{index}']\n",
        "  # text-similarity\n",
        "  sims =  C * torch.matmul(text_tensor, text_features_A.t())\n",
        "\n",
        "  neg_sims = 0*sims\n",
        "  if(NEG != ''):\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",
        "    # 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",
        "  # @markdown Repeat output N times\n",
        "  RANGE = 1000\n",
        "  NUM_CHUNKS = 10+\n",
        "  separator = '|'\n",
        "  _savefiles = {}\n",
        "  #-----#\n",
        "  for chunk in range(NUM_CHUNKS):\n",
        "    if chunk=<10:continue\n",
        "    start_at_index = chunk * RANGE\n",
        "    _prompts = ''\n",
        "    for _index in range(start_at_index + RANGE):\n",
        "      if _index < start_at_index : continue\n",
        "      index = indices[_index].item()\n",
        "      prompt = prompts[f'{index}']\n",
        "      _prompts = _prompts.replace(prompt + separator,'')\n",
        "      _prompts = _prompts  + prompt + separator\n",
        "    #------#\n",
        "    _prompts = fix_bad_symbols(_prompts)\n",
        "    _prompts = ('{' + _prompts + '}').replace(separator + '}', '}')\n",
        "    _savefiles[f'{chunk}'] = _prompts\n",
        "  #---------#\n",
        "  save_filename = f'{root_savefile_name}_{start_at_index + RANGE}_{PROMPT_INDEX}.json'\n",
        "\n",
        "\n",
        "  if (chunk=<20 && chunk>10): %cd '/content/output2/savefiles/'\n",
        "  if (chunk<=30 && chunk>20): %cd '/content/output3/savefiles/'\n",
        "  if (chunk=<40 && chunk>30): %cd '/content/output4/savefiles/'\n",
        "  if (chunk<=50 && chunk>40): %cd '/content/output5/savefiles/'\n",
        "  if (chunk=<60 && chunk>50): %cd '/content/output6/savefiles/'\n",
        "  if (chunk<=70 && chunk>60): %cd '/content/output7/savefiles/'\n",
        "  if (chunk=<80 && chunk>70): %cd '/content/output8/savefiles/'\n",
        "  if (chunk<=90 && chunk>80): %cd '/content/output9/savefiles/'\n",
        "  if (chunk=<100 && chunk>90): %cd '/content/output10/savefiles/'\n",
        "  if (chunk<=110 && chunk>100): %cd '/content/output11/savefiles/'\n",
        "  if (chunk=<120 && chunk>110): %cd '/content/output12/savefiles/'\n",
        "  if (chunk<=130 && chunk>120): %cd '/content/output13/savefiles/'\n",
        "\n",
        "\n",
        "  #------#\n",
        "  print(f'Saving savefile {save_filename} to {output_folder}...')\n",
        "  with open(save_filename, 'w') as f:\n",
        "      json.dump(_savefiles, f)\n",
        "  #---------#\n",
        "  continue\n",
        "#-----------#"
      ],
      "metadata": {
        "id": "x1uAVXZEoL0T",
        "cellView": "form"
      },
      "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'/content/results.zip' #drive/MyDrive\n",
        "!zip -r {zip_dest} {root_output_folder}"
      ],
      "metadata": {
        "id": "zivBNrw9uSVD",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title \t⚄ Quick fix for normalizing encoded text corpus tensors\n",
        "\n",
        "import os\n",
        "my_mkdirs('/content/output')\n",
        "my_mkdirs('/content/output/text_encodings')\n",
        "\n",
        "for filename in os.listdir(f'{prompts_folder}'):\n",
        "  %cd {prompts_folder}\n",
        "  prompts = {}\n",
        "  with open(f'{filename}', 'r') as f:\n",
        "    data = json.load(f).items()\n",
        "    for key,value in data:\n",
        "      prompts[key] = value\n",
        "    #------#\n",
        "  num_items = int(prompts['num_items'])\n",
        "\n",
        "  %cd {encodings_folder}\n",
        "  enc_filename = filename.replace('json', 'safetensors')\n",
        "  _text_encodings = load_file(f'{enc_filename}')['weights'].to(torch.uint8)\n",
        "  text_encodings = torch.zeros(num_items , dim)\n",
        "  tmp = torch.ones(dim)\n",
        "  tmp2 = torch.tensor(1/0.0043)\n",
        "  zero_point = 0\n",
        "  for index in range(num_items):\n",
        "    text_encodings[index] = torch.tensor(0.0043) * torch.sub(_text_encodings[index][1:dim+1] , tmp , alpha= _text_encodings[index][0]).to(torch.float32)\n",
        "    text_encodings[index] = tmp2*text_encodings[index]/text_encodings[index].norm(p=2, dim=-1, keepdim = True)\n",
        "    test = torch.round( torch.add(text_encodings[index],tmp*zero_point))\n",
        "    less_than_zero = test<0\n",
        "    while(torch.any(less_than_zero).item()):\n",
        "      zero_point = zero_point + 1\n",
        "      test = torch.round( torch.add(text_encodings[index],tmp*zero_point))\n",
        "      less_than_zero = test<0\n",
        "    #------#\n",
        "    _text_encodings[index][0]  = zero_point\n",
        "    _text_encodings[index][1:dim+1] = test\n",
        "  #-------#\n",
        "  %cd /content/output/text_encodings\n",
        "\n",
        "  tmp = {}\n",
        "  tmp['weights'] =  _text_encodings.to(torch.uint8)\n",
        "  tmp['num_items'] = torch.tensor(num_items).to(torch.uint8)\n",
        "  tmp['scale'] = torch.tensor(0.0043)\n",
        "  save_file(tmp , f'{enc_filename}')\n",
        "#------#"
      ],
      "metadata": {
        "cellView": "form",
        "id": "9qgHW1Wr7kZn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the average value for this set\n",
        "sims = torch.matmul(vocab_encodings.dequantize(),average.t())\n",
        "sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
        "for index in range(10):\n",
        "  print(prompts[f'{indices[index].item()}'])"
      ],
      "metadata": {
        "id": "XNHz0hfhHRUu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}