Upload fusion_t2i_CLIP_interrogator.ipynb

fusion_t2i_CLIP_interrogator.ipynb

@@ -0,0 +1,554 @@
+{
+  "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": [
+        "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",
+        "  %cd {home_directory + 'fusion-t2i-generator-data/'}\n",
+        "  !unzip vocab.zip\n",
+        "  !unzip reference.zip\n",
+        "#------#\n",
+        "%cd {home_directory + 'fusion-t2i-generator-data/' + 'vocab'}\n",
+        "with open(f'prompts.json', '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",
+        "%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",
+        "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",
+        "f_add = torch.nn.quantized.FloatFunctional()\n",
+        "\n",
+        "index = 0\n",
+        "%cd {home_directory + 'fusion-t2i-generator-data/' + 'vocab'}\n",
+        "\n",
+        "vocab_encodings = torch.load('vocab_encodings.pt', weights_only=False)\n",
+        "for key in vocab_encodings:\n",
+        "  index = index + 1;\n",
+        "#------#\n",
+        "NUM_VOCAB_ITEMS = index\n",
+        "\n",
+        "index = 0\n",
+        "%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
+        "for key in torch.load('reference_text_and_image_encodings.pt', weights_only=False):\n",
+        "  index = index + 1;\n",
+        "#------#\n",
+        "NUM_REFERENCE_ITEMS = index\n"
+      ],
+      "metadata": {
+        "id": "TC5lMJrS1HCC"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title \t⚄ Use a pre-encoded prompt + image pair from the fusion gen (note: NSFW!)\n",
+        "# @markdown Choose a pre-encoded reference\n",
+        "index = 457 # @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",
+        "# @markdown ⚖️ 🖼️ encoding <-----?-----> 📝 encoding </div> <br>\n",
+        "C = 0.5 # @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",
+        "prompt_strength = math.pow(10 ,log_strength-1)\n",
+        "reference = torch.zeros(768)\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, C * references[index][0].dequantize())\n",
+        "reference = torch.add(reference, (1-C) * references[index][1].dequantize())\n",
+        "references = ''\n",
+        "# @markdown -----------\n",
+        "# @markdown 📝➕ Enhance similarity to prompt(s)\n",
+        "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
+        "log_strength = 1.06 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "pos_strength = math.pow(10 ,log_strength-1)\n",
+        "for _POS in POS.split(','):\n",
+        "  inputs = tokenizer(text = _POS.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
+        "  text_features_POS = model.get_text_features(**inputs)\n",
+        "  text_features_POS = text_features_POS/text_features_POS.norm(p=2, dim=-1, keepdim=True)\n",
+        "  reference = torch.add(reference, pos_strength * text_features_POS)\n",
+        "# @markdown -----------\n",
+        "\n",
+        "# @markdown 🚫 Penalize similarity to prompt(s)\n",
+        "NEG = '' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
+        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "neg_strength = math.pow(10 ,log_strength-1)\n",
+        "for _NEG in NEG.split(','):\n",
+        "  inputs = tokenizer(text = _NEG.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
+        "  text_features_NEG = model.get_text_features(**inputs)\n",
+        "  text_features_NEG = text_features_NEG/text_features_NEG.norm(p=2, dim=-1, keepdim=True)\n",
+        "  reference = torch.add(reference, (-1) * neg_strength * text_features_NEG)\n",
+        "# @markdown -----------\n",
+        "# @markdown ⏩ Skip item(s) containing the word(s)\n",
+        "SKIP = 'futa ' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
+        "def isBlacklisted(txt):\n",
+        "  if txt.strip().isnumeric(): return True\n",
+        "  if blacklist.strip() == '': return False\n",
+        "  for item in list(blacklist.split(',')):\n",
+        "    if item.strip() == '' : continue\n",
+        "    if txt.find(item.strip())> -1 : return True\n",
+        "  #------#\n",
+        "  return False\n",
+        "# @markdown -----------\n",
+        "# @markdown 🔍 How similar should the results be?\n",
+        "list_size = 1000 # @param {type:'number'}\n",
+        "start_at_index = 1 # @param {type:'number'}\n",
+        "# @markdown -----------\n",
+        "# @markdown Repeat output N times\n",
+        "N = 7 # @param {type:\"slider\", min:0, max:20, step:1}\n",
+        "# @markdown -----------\n",
+        "# @markdown ⚙️ Run the script?\n",
+        "run_script = True # @param {type:\"boolean\"}\n",
+        "enable = run_script\n",
+        "if (enable):\n",
+        "  reference = reference/reference.norm(p=2, dim=-1, keepdim=True)\n",
+        "  %cd {home_directory + 'fusion-t2i-generator-data/' + 'vocab'}\n",
+        "  sims = torch.matmul(vocab_encodings.dequantize(),reference.t())\n",
+        "  sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
+        "\n",
+        "  average = torch.zeros(768)\n",
+        "  for key in range(NUM_VOCAB_ITEMS):\n",
+        "    if (key>=start_at_index and key < start_at_index + list_size):\n",
+        "      average = torch.add(average, vocab_encodings[key].dequantize())\n",
+        "    if (key>=start_at_index + list_size) : break\n",
+        "  average = average * (1/max(1, list_size))\n",
+        "  average = average/average.norm(p=2, dim=-1, keepdim=True)\n",
+        "  print(average.norm(p=2, dim=-1, keepdim=True))\n",
+        "  average = average.clone().detach();\n",
+        "  variance = torch.zeros(1)\n",
+        "  for key in range(NUM_VOCAB_ITEMS):\n",
+        "    if (key>=start_at_index and key < start_at_index + list_size):\n",
+        "      #dot product\n",
+        "\n",
+        "      difference_to_average = 100 * (torch.ones(1) - torch.dot(average[0]\n",
+        "       , vocab_encodings[key].dequantize()[0])/average.norm(p=2, dim=-1, keepdim=True))\n",
+        "\n",
+        "      variance =  torch.add(variance, difference_to_average * difference_to_average)\n",
+        "    if (key>=start_at_index + list_size) : break\n",
+        "  #--------#\n",
+        "  variance =  variance * (1/max(1, list_size))\n",
+        "  variance= variance.clone().detach();\n",
+        "  print(f'The variance for the selected range is {math.sqrt(variance.item())} units from average')\n",
+        "#---#\n",
+        "  output = '{'\n",
+        "  for _index in range(list_size):\n",
+        "    output = output + prompts[f'{indices[min(_index+start_at_index,NUM_VOCAB_ITEMS-1)].item()}'] + '|'\n",
+        "  #---------#\n",
+        "  output = (output + '}').replace('|}' , '}</w>')\n",
+        "  for iter in range(N):\n",
+        "    print(output)\n",
+        "#-------#\n",
+        "image or print('No image found')"
+      ],
+      "metadata": {
+        "id": "NqL_I3ZSrISq"
+      },
+      "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": 113,
+      "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",
+        "cellView": "form"
+      },
+      "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",
+        "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"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}