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Google Colab Notebooks/sd_token_similarity_calculator.ipynb

@@ -259,33 +259,25 @@
     {
       "cell_type": "code",
       "source": [
-        "# @title ✳️ Select items for the vocab\n",
+        "# @title 📚 Select items to sample from\n",
         "\n",
         "prompt_features = True # @param {\"type\":\"boolean\",\"placeholder\":\"🦜\"}\n",
         "civitai_blue_set = True # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
         "suffix = True # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
         "prefix = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
-        "emojis = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "emojis = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
         "#------#\n",
         "\n",
         "first_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
         "last_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
-        "full_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
-        "celebs = False # @param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n",
-        "#These are borked\n",
-        "celebs_young = False # param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
+        "celebs = True # @param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n",
         "#-------#\n",
-        "\n",
         "danbooru_tags = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎀\"}\n",
-        "\n",
-        "lyrics = False # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
-        "\n",
-        "tripple_nouns =  False # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
-        "\n",
+        "lyrics = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
+        "tripple_nouns =  True # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
         "#-----#\n",
-        "female_fullnames = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "female_fullnames = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
         "debug = False\n",
-        "\n",
         "#------#\n",
         "prompts = {}\n",
         "text_encodings = {}\n",
@@ -316,21 +308,11 @@
         "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
         "#--------#\n",
         "\n",
-        "if full_names:\n",
-        "  url = '/content/text-to-image-prompts/names/fullnames'\n",
-        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
-        "#--------#\n",
-        "\n",
         "if celebs:\n",
         "  url = '/content/text-to-image-prompts/names/celebs/mixed'\n",
         "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
         "#--------#\n",
         "\n",
-        "if celebs_young :\n",
-        "  url = '/content/text-to-image-prompts/names/celebs/young'\n",
-        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
-        "#--------#\n",
-        "\n",
         "if female_fullnames:\n",
         "  url = '/content/text-to-image-prompts/names/fullnames'\n",
         "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
@@ -394,13 +376,14 @@
       "source": [
         "# @title \t⚄ Use a pre-encoded prompt + image pair from the fusion gen (note: NSFW!)\n",
         "\n",
-        "\n",
         "#image_index = 0 # @param {type:'number'}\n",
         "# @markdown 📥 Load the data (only required one time)\n",
         "load_the_data = False # @param {type:\"boolean\"}\n",
         "\n",
         "# @markdown 🖼️ Choose a pre-encoded reference\n",
-        "index = 829 # @param {type:\"slider\", min:0, max:1668, step:1}\n",
+        "index = 708 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
+        "\n",
+        "PROMPT_INDEX = index\n",
         "\n",
         "# @markdown ⚖️ Set the value for C in the reference  <br> <br> sim = C* text_enc + image_enc*(1-C) <br><br>\n",
         "\n",
@@ -475,7 +458,7 @@
         "\n",
         "  # @markdown -----------\n",
         "  # @markdown ⚙️📝 Printing options\n",
-        "  newline_Separator = True # @param {type:\"boolean\"}\n",
+        "  newline_Separator = False # @param {type:\"boolean\"}\n",
         "\n",
         "  import random\n",
         "  list_size2 = 1000 # param {type:'number'}\n",
@@ -483,7 +466,7 @@
         "  rate_percent  = 0 # param {type:\"slider\", min:0, max:100, step:1}\n",
         "\n",
         "  # @markdown Repeat output N times\n",
-        "  N = 6 # @param {type:\"slider\", min:0, max:10, step:1}\n",
+        "  N = 7 # @param {type:\"slider\", min:0, max:10, step:1}\n",
         "\n",
         "  # title Show the 100 most similiar suffix and prefix text-encodings to the text encoding\n",
         "  RANGE = list_size\n",
@@ -507,7 +490,7 @@
         "    _prompts = _prompts  + prompt + separator\n",
         "    #------#\n",
         "  #------#\n",
-        "  __prompts = fix_bad_symbols(__prompts)\n",
+        "  _prompts = fix_bad_symbols(_prompts)\n",
         "  __prompts = ('{' + _prompts + '}').replace(separator + '}', '}')\n",
         "  __sims = ('{' + _sims + '}').replace(separator + '}', '}')\n",
         "  #------#\n",
@@ -527,8 +510,6 @@
         "    for i in range(N) : print(__prompts)\n",
         "  #-------#\n",
         "  #-------#\n",
-        "\n",
-        "\n",
         "#-------#\n",
         "image\n"
       ],
@@ -538,6 +519,196 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title \t⚄ Create a savefile-set from the entire range of pre-encoded items\n",
+        "\n",
+        "#image_index = 0 # @param {type:'number'}\n",
+        "# @markdown 📥 Load the data (only required one time)\n",
+        "load_the_data = True # @param {type:\"boolean\"}\n",
+        "\n",
+        "# @markdown ⚖️ Set the value for C in the reference  <br> <br> sim = C* text_enc + image_enc*(1-C) <br><br>\n",
+        "\n",
+        "C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+        "\n",
+        "# @markdown 🚫 Penalize similarity to this prompt(optional)\n",
+        "\n",
+        "if(load_the_data):\n",
+        "  from PIL import Image\n",
+        "  import requests\n",
+        "  target_prompts , target_text_encodings , urls , target_image_encodings , NUM_ITEMS =  getPromptsAndLinks('/content/text-to-image-prompts/fusion')\n",
+        "  from transformers import AutoTokenizer\n",
+        "  tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
+        "  from transformers import  CLIPProcessor, CLIPModel\n",
+        "  processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n",
+        "  model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
+        "  logit_scale = model.logit_scale.exp() #logit_scale = 100.00000762939453\n",
+        "#---------#\n",
+        "\n",
+        "filename = 'blank.json'\n",
+        "path = '/content/text-to-image-prompts/fusion/'\n",
+        "print(f'reading {filename}....')\n",
+        "_index = 0\n",
+        "%cd {path}\n",
+        "with open(f'{filename}', 'r') as f:\n",
+        "  data = json.load(f)\n",
+        "#------#\n",
+        "_df = pd.DataFrame({'count': data})['count']\n",
+        "_blank = {\n",
+        "    key : value for key, value in _df.items()\n",
+        "}\n",
+        "#------#\n",
+        "\n",
+        "root_savefile_name = 'fusion_C05_X7_1000_'\n",
+        "output_folder = '/content/output/savefiles/'\n",
+        "my_mkdirs(output_folder)\n",
+        "NEG = '' # @param {type:'string'}\n",
+        "strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.1}\n",
+        "\n",
+        "for index in range(1667):\n",
+        "\n",
+        "  PROMPT_INDEX = index\n",
+        "\n",
+        "  prompt = target_prompts[f'{index}']\n",
+        "  url = urls[f'{index}']\n",
+        "  if url.find('perchance')>-1:\n",
+        "    image = Image.open(requests.get(url, stream=True).raw)\n",
+        "  else: continue #print(\"(No image for this ID)\")\n",
+        "\n",
+        "  print(f\"no. {PROMPT_INDEX} : '{prompt}'\")\n",
+        "\n",
+        "\n",
+        "  if(True):\n",
+        "    text_features_A = target_text_encodings[f'{index}']\n",
+        "    image_features_A =  target_image_encodings[f'{index}']\n",
+        "\n",
+        "    # text-similarity\n",
+        "    sims =  C * torch.matmul(text_tensor, text_features_A.t())\n",
+        "\n",
+        "    neg_sims = 0*sims\n",
+        "    if(NEG != ''):\n",
+        "\n",
+        "      # Get text features for user input\n",
+        "      inputs = tokenizer(text = NEG, padding=True, return_tensors=\"pt\")\n",
+        "      text_features_NEG = model.get_text_features(**inputs)\n",
+        "      text_features_NEG = text_features_A/text_features_A.norm(p=2, dim=-1, keepdim=True)\n",
+        "\n",
+        "      # text-similarity\n",
+        "      neg_sims =  strength*torch.matmul(text_tensor, text_features_NEG.t())\n",
+        "    #------#\n",
+        "\n",
+        "    # plus image-similarity\n",
+        "    sims = sims +  (1-C) * torch.matmul(text_tensor, image_features_A.t()) * logit_scale\n",
+        "\n",
+        "    # minus NEG-similarity\n",
+        "    sims = sims - neg_sims\n",
+        "\n",
+        "    # Sort the items\n",
+        "    sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
+        "\n",
+        "    # @title ⚙️📝 Print the results (Advanced)\n",
+        "    list_size = 1000 # param {type:'number'}\n",
+        "    start_at_index = 0 # param {type:'number'}\n",
+        "    print_Similarity = True # param {type:\"boolean\"}\n",
+        "    print_Prompts = True # param {type:\"boolean\"}\n",
+        "    print_Prefix = True # param {type:\"boolean\"}\n",
+        "    print_Descriptions = True # param {type:\"boolean\"}\n",
+        "    compact_Output = True # param {type:\"boolean\"}\n",
+        "\n",
+        "    # @markdown -----------\n",
+        "    # @markdown ⚙️📝 Printing options\n",
+        "    newline_Separator = False # @param {type:\"boolean\"}\n",
+        "\n",
+        "    import random\n",
+        "    list_size2 = 1000 # param {type:'number'}\n",
+        "    start_at_index2 = 10000 # param {type:'number'}\n",
+        "    rate_percent  = 0 # param {type:\"slider\", min:0, max:100, step:1}\n",
+        "\n",
+        "    # @markdown Repeat output N times\n",
+        "    N = 7 # @param {type:\"slider\", min:0, max:10, step:1}\n",
+        "\n",
+        "    # title Show the 100 most similiar suffix and prefix text-encodings to the text encoding\n",
+        "    RANGE = list_size\n",
+        "    separator = '|'\n",
+        "    if newline_Separator : separator = separator + '\\n'\n",
+        "\n",
+        "    _prompts = ''\n",
+        "    _sims =  ''\n",
+        "    for _index in range(start_at_index + RANGE):\n",
+        "      if _index < start_at_index : continue\n",
+        "      index = indices[_index].item()\n",
+        "\n",
+        "      prompt = prompts[f'{index}']\n",
+        "      if rate_percent >= random.randint(0,100) : prompt = prompts[f'{random.randint(start_at_index2 , start_at_index2 + list_size2)}']\n",
+        "\n",
+        "      #Remove duplicates\n",
+        "      if _prompts.find(prompt + separator)<=-1:\n",
+        "        _sims = _sims + f'{round(100*sims[index].item(), 2)} %' + separator\n",
+        "      #-------#\n",
+        "      _prompts = _prompts.replace(prompt + separator,'')\n",
+        "      _prompts = _prompts  + prompt + separator\n",
+        "      #------#\n",
+        "    #------#\n",
+        "    _prompts = fix_bad_symbols(_prompts)\n",
+        "    __prompts = ('{' + _prompts + '}').replace(separator + '}', '}')\n",
+        "    __sims = ('{' + _sims + '}').replace(separator + '}', '}')\n",
+        "    #------#\n",
+        "  #--------#\n",
+        "  _savefile = _blank\n",
+        "  from safetensors.torch import load_file\n",
+        "  import json , os , torch\n",
+        "  import pandas as pd\n",
+        "  #----#\n",
+        "  def my_mkdirs(folder):\n",
+        "    if os.path.exists(folder)==False:\n",
+        "      os.makedirs(folder)\n",
+        "  #------#\n",
+        "  savefile_prompt = ''\n",
+        "  for i in range(N) : savefile_prompt = savefile_prompt + ' ' + __prompts\n",
+        "  _savefile['main'] = savefile_prompt.replace('\\n', ' ').replace('  ', ' ').replace('   ', ' ')\n",
+        "  #------#\n",
+        "  save_filename = f'{root_savefile_name}{PROMPT_INDEX}.json'\n",
+        "  #-----#\n",
+        "  %cd {output_folder}\n",
+        "  print(f'Saving savefile {save_filename} to {output_folder}...')\n",
+        "  with open(save_filename, 'w') as f:\n",
+        "      json.dump(_savefile, f)\n",
+        "  #---------#\n",
+        "  continue\n",
+        "#-----------#"
+      ],
+      "metadata": {
+        "id": "NZy2HrkZ1Rto"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Determine if this notebook is running on Colab or Kaggle\n",
+        "#Use https://www.kaggle.com/ if Google Colab GPU is busy\n",
+        "home_directory = '/content/'\n",
+        "using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n",
+        "if using_Kaggle : home_directory = '/kaggle/working/'\n",
+        "%cd {home_directory}\n",
+        "#-------#\n",
+        "\n",
+        "# @title Download the text_encodings as .zip\n",
+        "import os\n",
+        "%cd {home_directory}\n",
+        "#os.remove(f'{home_directory}results.zip')\n",
+        "root_output_folder = home_directory + 'output/'\n",
+        "zip_dest = f'{home_directory}results.zip'\n",
+        "!zip -r {zip_dest} {root_output_folder}"
+      ],
+      "metadata": {
+        "id": "DaV1ynRs1XeS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [
@@ -609,6 +780,53 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title Quick fix to created json files above\n",
+        "output_folder = '/content/output/fusion-gen-savefiles/'\n",
+        "index = 0\n",
+        "path = '/content/text-to-image-prompts/fusion-gen-savefiles'\n",
+        "\n",
+        "def my_mkdirs(folder):\n",
+        "  if os.path.exists(folder)==False:\n",
+        "    os.makedirs(folder)\n",
+        "\n",
+        "my_mkdirs(output_folder)\n",
+        "for filename in os.listdir(f'{path}'):\n",
+        "  if filename.find('fusion_C05_X7_1000_')<=-1: continue\n",
+        "  print(f'reading {filename}...')\n",
+        "  %cd {path}\n",
+        "  with open(f'{filename}', 'r') as f:\n",
+        "    data = json.load(f)\n",
+        "    _df = pd.DataFrame({'count': data})['count']\n",
+        "    _savefile = {\n",
+        "        key : value for key, value in _df.items()\n",
+        "    }\n",
+        "\n",
+        "    _savefile2 = {}\n",
+        "\n",
+        "    for key in _savefile:\n",
+        "      _savefile2[key] = _savefile[key]\n",
+        "      if(key == \"_main\") :\n",
+        "        _savefile2[key] =  \"Prompt input only ✏️\"\n",
+        "        print(\"changed\")\n",
+        "    #----------#\n",
+        "\n",
+        "  save_filename = f'fusion_C05_X7_1000_{index}.json'\n",
+        "  index = index + 1\n",
+        "\n",
+        "  %cd {output_folder}\n",
+        "  print(f'Saving savefile {save_filename} to {output_folder}...')\n",
+        "  with open(save_filename, 'w') as f:\n",
+        "      json.dump(_savefile2, f)"
+      ],
+      "metadata": {
+        "id": "mRhTZ6wS1g0m"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [
@@ -1006,7 +1224,7 @@
         "id": "SEPUbRwpVwRQ",
         "outputId": "b058be19-2fe5-4de2-ff3c-3e821043a177"
       },
-      "execution_count": 9,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -1031,7 +1249,7 @@
         "id": "5oXvYS1aXdjt",
         "outputId": "00491826-4329-4c02-d038-bc3b221937b1"
       },
-      "execution_count": 14,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -1340,7 +1558,7 @@
         "cellView": "form",
         "id": "Cbt78mgJYHgr"
       },
-      "execution_count": 28,
+      "execution_count": null,
       "outputs": []
     },
     {