Upload sd_token_similarity_calculator.ipynb

sd_token_similarity_calculator.ipynb

@@ -115,27 +115,49 @@
         "    tensAB[f'{nA + int(key)}'] = tensB[key]\n",
         "  #-----#\n",
         "  return dictAB, tensAB , nAB-1\n",
-        "#-------#\n"
+        "#-------#\n",
+        "\n",
+        "#🔸🔹\n",
+        "%cd /content/\n",
+        "!git clone https://huggingface.co/datasets/codeShare/text-to-image-prompts\n"
       ],
       "metadata": {
-        "id": "rUXQ73IbonHY"
+        "id": "rUXQ73IbonHY",
+        "outputId": "9e40d8a1-fbb3-4200-fc80-3d6f32d3667a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "execution_count": 54,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content\n",
+            "Cloning into 'text-to-image-prompts'...\n",
+            "remote: Enumerating objects: 450, done.\u001b[K\n",
+            "remote: Counting objects: 100% (447/447), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (428/428), done.\u001b[K\n",
+            "remote: Total 450 (delta 81), reused 0 (delta 0), pack-reused 3 (from 1)\u001b[K\n",
+            "Receiving objects: 100% (450/450), 998.98 KiB | 3.92 MiB/s, done.\n",
+            "Resolving deltas: 100% (81/81), done.\n",
+            "Filtering content: 100% (95/95), 305.98 MiB | 41.88 MiB/s, done.\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "code",
       "source": [
         "# @title ✳️ Select items for the vocab\n",
-        "prompt_features = True # @param {\"type\":\"boolean\",\"placeholder\":\"🦜\"}\n",
+        "\n",
+        "prompt_features = False # @param {\"type\":\"boolean\",\"placeholder\":\"🦜\"}\n",
+        "civitai_blue_set = True # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
         "suffix = True # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
-        "prefix = True # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
+        "prefix = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
         "debug = False\n",
         "\n",
-        "#🔸🔹\n",
-        "%cd /content/\n",
-        "!git clone https://huggingface.co/datasets/codeShare/text-to-image-prompts\n",
-        "\n",
         "#------#\n",
         "prompts = {}\n",
         "text_encodings = {}\n",
@@ -150,6 +172,14 @@
         "    print(text_encodings[f'{nA}'])\n",
         "#--------#\n",
         "\n",
+        "if civitai_blue_set:\n",
+        "  url = '/content/text-to-image-prompts/civitai-prompts/blue'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "  if debug:\n",
+        "    print(prompts[f'{nA}'])\n",
+        "    print(text_encodings[f'{nA}'])\n",
+        "#--------#\n",
+        "\n",
         "if suffix :\n",
         "  tmp = '/content/text-to-image-prompts/tokens/suffix/'\n",
         "  for item in ['common','average','rare','weird','exotic'] :\n",
@@ -183,121 +213,47 @@
       ],
       "metadata": {
         "id": "ZMG4CThUAmwW",
-        "outputId": "c34b6fcc-e4c5-4fa1-a55b-d8175ccb8f67",
+        "outputId": "dfb5a625-72e7-462e-c118-682f0a45ed12",
         "colab": {
           "base_uri": "https://localhost:8080/"
         }
       },
-      "execution_count": 55,
+      "execution_count": 17,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "/content\n",
-            "fatal: destination path 'text-to-image-prompts' already exists and is not an empty directory.\n",
-            "reading 🦜 fusion-t2i-prompt-features-23.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-9.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-4.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-2.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-25.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-6.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-19.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-33.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-18.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-22.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-31.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-3.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-8.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-7.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-13.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-34.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-14.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-12.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-17.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-21.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-24.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-32.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-5.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-20.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-16.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-28.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-10.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-15.json....\n",
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-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-26.json....\n",
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-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-27.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-1.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-29.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-30.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
-            "reading 🦜 fusion-t2i-prompt-features-11.json....\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text\n",
-            "/content/text-to-image-prompts/civitai-prompts/green/text_encodings\n",
+            "reading 🧿📘 fusion-t2i-civitai-0-20-chars-mix-2.json....\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text_encodings\n",
+            "reading 🧿📘 fusion-t2i-civitai-0-20-chars-mix-10.json....\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text_encodings\n",
+            "reading 🧿📘 fusion-t2i-civitai-0-20-chars-mix-6.json....\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text_encodings\n",
+            "reading 🧿📘 fusion-t2i-civitai-0-20-chars-mix-5.json....\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text_encodings\n",
+            "reading 🧿📘 fusion-t2i-civitai-0-20-chars-mix-4.json....\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text_encodings\n",
+            "reading 🧿📘 fusion-t2i-civitai-0-20-chars-mix-8.json....\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text_encodings\n",
+            "reading 🧿📘 fusion-t2i-civitai-0-20-chars-mix-1.json....\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text_encodings\n",
+            "reading 🧿📘 fusion-t2i-civitai-0-20-chars-mix-7.json....\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text_encodings\n",
+            "reading 🧿📘 fusion-t2i-civitai-0-20-chars-mix-3.json....\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text_encodings\n",
+            "reading 🧿📘 fusion-t2i-civitai-0-20-chars-mix-9.json....\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text\n",
+            "/content/text-to-image-prompts/civitai-prompts/blue/text_encodings\n",
             "reading 🔹 fusion-t2i-sd15-clip-tokens-common-suffix-5 Tokens.json....\n",
             "/content/text-to-image-prompts/tokens/suffix/common/text\n",
             "/content/text-to-image-prompts/tokens/suffix/common/text_encodings\n",
@@ -399,52 +355,7 @@
             "/content/text-to-image-prompts/tokens/suffix/exotic/text_encodings\n",
             "reading 🔹 fusion-t2i-sd15-clip-tokens-exotic-suffix-5 Tokens.json....\n",
             "/content/text-to-image-prompts/tokens/suffix/exotic/text\n",
-            "/content/text-to-image-prompts/tokens/suffix/exotic/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-common-prefix-1 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/common/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/common/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-common-prefix-3 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/common/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/common/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-common-prefix-2 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/common/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/common/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-average-prefix-3 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/average/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/average/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-average-prefix-2 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/average/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/average/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-average-prefix-1 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/average/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/average/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-rare-prefix-1 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/rare/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/rare/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-rare-prefix-3 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/rare/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/rare/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-rare-prefix-2 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/rare/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/rare/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-weird-prefix-1 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/weird/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/weird/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-weird-prefix-3 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/weird/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/weird/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-weird-prefix-2 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/weird/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/weird/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-exotic-prefix-1 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/exotic/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/exotic/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-exotic-prefix-2 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/exotic/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/exotic/text_encodings\n",
-            "reading 🔸 fusion-t2i-sd15-clip-tokens-exotic-prefix-3 Tokens.json....\n",
-            "/content/text-to-image-prompts/tokens/prefix/exotic/text\n",
-            "/content/text-to-image-prompts/tokens/prefix/exotic/text_encodings\n"
+            "/content/text-to-image-prompts/tokens/suffix/exotic/text_encodings\n"
           ]
         }
       ]
@@ -480,7 +391,7 @@
       "metadata": {
         "id": "xc-PbIYF428y"
       },
-      "execution_count": 56,
+      "execution_count": 18,
       "outputs": []
     },
     {
@@ -529,117 +440,117 @@
       ],
       "metadata": {
         "id": "_vnVbxcFf7WV",
-        "outputId": "b9c6a86b-1239-410b-e240-aca1c8301249",
+        "outputId": "47f6617b-752b-4349-a2bd-46fdae985572",
         "colab": {
           "base_uri": "https://localhost:8080/"
         }
       },
-      "execution_count": 50,
+      "execution_count": 19,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "{her car behind on the road</w>|\n",
-            "the race car driver sits behind the wheel</w>|\n",
-            "speed-|\n",
-            "driving-|\n",
-            "highway-|\n",
-            "road-|\n",
-            "fast-|\n",
-            "drive-|\n",
-            "racing car in the background</w>|\n",
-            "traffic-|\n",
-            "driver-|\n",
-            "thats-|\n",
-            "roadto-|\n",
-            "slow-|\n",
-            "cause-|\n",
-            "cruise-|\n",
-            "cyber car looking_upscenery</w>|\n",
-            "cyber car looking_upscenery</w>|\n",
-            "travel-|\n",
-            "truck-|\n",
-            "car-|\n",
-            "that-|\n",
-            "destination-|\n",
-            "way-|\n",
-            "a beautiful</w>|\n",
-            "a beautiful</w>|\n",
-            "just-|\n",
-            "grat-|\n",
-            "rolling-|\n",
-            "simply-|\n",
-            "cruis-|\n",
+            "{Sports Car|\n",
+            "beautiful car|\n",
+            "road nature|\n",
+            "running road|\n",
+            "sport car petite|\n",
+            "it's a gas gas|\n",
+            "The Road Not Taken|\n",
+            "road Horizon|\n",
+            "far away|\n",
             "speed</w>|\n",
-            "something-|\n",
-            "cool-|\n",
             "roadtrip</w>|\n",
-            "onlyin-|\n",
-            "random-|\n",
-            "because-|\n",
-            "carr-|\n",
-            "shre-|\n",
             "driving</w>|\n",
-            "funny-|\n",
-            "street-|\n",
-            "dding-|\n",
-            "aand-|\n",
+            "true to life|\n",
+            "street|\n",
             "ontheroad</w>|\n",
-            "classic-|\n",
-            "beauti-|\n",
-            "quick-|\n",
-            "reall-|\n",
-            "wast-|\n",
-            "wait-|\n",
-            "on the crowded streets</w>|\n",
-            "haun-|\n",
-            "epic-|\n",
-            "lest-|\n",
+            "sharp image|\n",
+            "on a race track|\n",
+            "road construction|\n",
+            "with a soft|\n",
+            "a picture|\n",
             "faster</w>|\n",
-            "stoo-|\n",
-            "quality-|\n",
-            "fantastic-|\n",
-            "seem-|\n",
-            "love-|\n",
-            "yeah-|\n",
-            "well-|\n",
-            "moving-|\n",
-            "nice-|\n",
-            "late-|\n",
-            "camer-|\n",
-            "sure-|\n",
-            "only-|\n",
-            "chasing-|\n",
-            "loving-|\n",
-            "the image should have</w>|\n",
-            "best-|\n",
-            "reali-|\n",
-            "takes off down the track</w>|\n",
-            "and you can't help but feel</w>|\n",
-            "dazz-|\n",
-            "followthe-|\n",
-            "awesome-|\n",
-            "words-|\n",
-            "lifein-|\n",
-            "eng-|\n",
-            "herit-|\n",
-            "oneof-|\n",
-            "but now seems to exude</w>|\n",
-            "then-|\n",
-            "beast-|\n",
-            "thisis-|\n",
-            "goodday-|\n",
-            "thisi-|\n",
-            "right-|\n",
-            "is appropriate for the time period</w>|\n",
-            "amazing-|\n",
-            "race-|\n",
-            "madd-|\n",
-            "coming-|\n",
-            "zoom-|\n",
-            "picture-|\n",
-            "ight-}\n"
+            "Fantastic|\n",
+            "loving|\n",
+            "road architecture|\n",
+            "day scenery|\n",
+            "wonderful|\n",
+            "head back|\n",
+            "photographic style|\n",
+            "alright</w>|\n",
+            "thats</w>|\n",
+            "awesome inspiring|\n",
+            "Know the past|\n",
+            "seems</w>|\n",
+            "as style|\n",
+            "inspired|\n",
+            "lovely|\n",
+            "well</w>|\n",
+            "reminds</w>|\n",
+            "beautiful amazing|\n",
+            "highway</w>|\n",
+            "appears timeless|\n",
+            "that</w>|\n",
+            "beautiful gorgeous|\n",
+            "highway  setting|\n",
+            "Science fiction|\n",
+            "science fiction|\n",
+            "speeding</w>|\n",
+            "in a mountain land|\n",
+            "inspiration|\n",
+            "day time|\n",
+            "busy highway|\n",
+            "really</w>|\n",
+            "Phenomenal|\n",
+            "girl trembling|\n",
+            "beauty|\n",
+            "baby|\n",
+            "top quality|\n",
+            "motorcycle freeway|\n",
+            "very beautiful|\n",
+            "cute beautiful|\n",
+            "beautiful|\n",
+            "beautiful|\n",
+            "Beautiful|\n",
+            "tweeted</w>|\n",
+            "street in city|\n",
+            "exciting|\n",
+            "fire flames|\n",
+            "Memory|\n",
+            "Riding|\n",
+            "in first place|\n",
+            "a spaceship|\n",
+            "automobile</w>|\n",
+            "emotional|\n",
+            "retweeted</w>|\n",
+            "handsome|\n",
+            "car</w>|\n",
+            "artistic cool|\n",
+            "it is Furious|\n",
+            "stanning|\n",
+            "trembling|\n",
+            "cool amazing|\n",
+            "smooth|\n",
+            "this</w>|\n",
+            "countryside|\n",
+            "dynamic movement|\n",
+            "beautiful elegant|\n",
+            "stunning|\n",
+            "ethereal fantastic|\n",
+            "gorgeous inspired|\n",
+            "beautiful hot|\n",
+            "street elegant|\n",
+            "heres</w>|\n",
+            "A stylish|\n",
+            "at_day|\n",
+            "evocative image|\n",
+            "hysterical|\n",
+            "dreamlike|\n",
+            ". cute adorable|\n",
+            "Exquisite|\n",
+            "gorgeous}\n"
           ]
         }
       ]
@@ -700,13 +611,13 @@
       ],
       "metadata": {
         "id": "ke6mZ1RZDOeB",
-        "outputId": "53b9280f-b8ba-4036-c720-e679bd0a162c",
+        "outputId": "d8ef4589-8393-4001-ff35-c0c30646a576",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
         }
       },
-      "execution_count": 51,
+      "execution_count": 14,
       "outputs": [
         {
           "output_type": "display_data",
@@ -750,7 +661,7 @@
       "metadata": {
         "id": "rebogpoyOG8k"
       },
-      "execution_count": 52,
+      "execution_count": 15,
       "outputs": []
     },
     {
@@ -758,7 +669,7 @@
       "source": [
         "# @title 🖼️ Print the results\n",
         "list_size = 100 # @param {type:'number'}\n",
-        "start_at_index = 0 # @param {type:'number'}\n",
+        "start_at_index = 100 # @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",
@@ -802,271 +713,118 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "68e866ad-9cc7-43f6-ad67-d72c28fa0b46"
+        "outputId": "2271de2f-6885-4f72-bcd0-3c39a9cfaada"
       },
-      "execution_count": 53,
+      "execution_count": 16,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "{beautiful avatar pictures</w>|\n",
-            "purple hair crowned standing in storm background</w>|\n",
-            "warcraft</w>|\n",
-            "beautiful celebrity futuristic sci-fi</w>|\n",
-            "by magali villeneuve</w>|\n",
-            "visually striking spectacle inspired by the works</w>|\n",
-            "visually striking spectacle inspired by the works</w>|\n",
-            "a beautiful  female warrior</w>|\n",
-            "a sexy scifi warrior</w>|\n",
-            "a sexy scifi warrior</w>|\n",
-            "film still from halo live action adaptation</w>|\n",
-            "cinematic film still from captain marvel</w>|\n",
-            "beautiful female warrior</w>|\n",
-            "beautiful  female warrior</w>|\n",
-            "film still from halo live-action movie adaptation</w>|\n",
-            "outlandish costume design</w>|\n",
-            "defend-|\n",
-            "beautiful indian warrior queen</w>|\n",
-            "genie</w>|\n",
-            "of female space soldier</w>|\n",
-            "vfx</w>|\n",
-            "blue light on her face she appears calm</w>|\n",
-            "a female scifi warrior</w>|\n",
-            "a female scifi warrior</w>|\n",
-            "orian</w>|\n",
-            "nebula in her streak hair</w>|\n",
-            "wall-|\n",
-            "of brown skinned indian warrior queen</w>|\n",
-            "played by young dove cameron</w>|\n",
-            "thora</w>|\n",
-            "has runes on her body</w>|\n",
-            "has runes on her body</w>|\n",
-            "beautiful light makeup female sorceress</w>|\n",
-            "a gorgeous female void thrall</w>|\n",
-            "a gorgeous female void thrall</w>|\n",
-            "beautiful female elf queen</w>|\n",
-            "captivating mystique</w>|\n",
-            "captivating mystique</w>|\n",
-            "symbolizing her role as the goddess</w>|\n",
-            "blu-|\n",
-            "character integrated into the background</w>|\n",
-            "swirling black light around the character</w>|\n",
-            "very beautiful jean grey wearing</w>|\n",
-            "lightly blued metal armor</w>|\n",
-            "multiple different characters in the background</w>|\n",
-            "raid</w>|\n",
-            "tributes</w>|\n",
-            "cinematic still from conan</w>|\n",
-            "yo person as dark elf queen</w>|\n",
-            "descendants</w>|\n",
-            "trending at cgsociety</w>|\n",
-            "exodus</w>|\n",
-            "scarlett</w>|\n",
-            "femaleastronaut exalted human futuristic warrior</w>|\n",
-            "femaleastronaut exalted human futuristic warrior</w>|\n",
-            "indigo</w>|\n",
-            "epic fantasy greek priestess</w>|\n",
-            "epic fantasy greek priestess</w>|\n",
-            "female draenei world</w>|\n",
-            "genetically engineered soldiers</w>|\n",
-            "genetically engineered soldiers</w>|\n",
-            "visually striking scene the lighting</w>|\n",
-            "the female soldier marches in formation</w>|\n",
-            "the female soldier marches in formation</w>|\n",
-            "revealing costume design</w>|\n",
-            "pandora_smith_magister</w>|\n",
-            "pandora_smith_magister</w>|\n",
-            "his sorceress in the back ground</w>|\n",
-            "tali</w>|\n",
-            "gorgeous muscular elven ukrainian</w>|\n",
-            "gorgeous muscular elven ukrainian</w>|\n",
-            "womenshistorymonth</w>|\n",
-            "water elemental officer jenny</w>|\n",
-            "ontari-|\n",
-            "bleedblue</w>|\n",
-            "norse female goddess</w>|\n",
-            "matte fantasy painting</w>|\n",
-            "thanos</w>|\n",
-            "periwinkle purple skin</w>|\n",
-            "xmen</w>|\n",
-            "of norse female goddess</w>|\n",
-            "mujer de ojos rojos y pelo azulado</w>|\n",
-            "strength the battle scene around her</w>|\n",
-            "a beautiful young redhead warrior</w>|\n",
-            "a beautiful young redhead warrior</w>|\n",
-            "moody cinematic epic concept art</w>|\n",
-            "hypnotically beautiful wood elf in</w>|\n",
-            "hypnotically beautiful wood elf in</w>|\n",
-            "loraemmawatsonlora_v</w>|\n",
-            "alphonse mucha cinematic epic + rule</w>|\n",
-            "alphonse mucha cinematic epic + rule</w>|\n",
-            "an actress standing behind</w>|\n",
-            "fking_scifi_v amazing</w>|\n",
-            "shine like sapphires</w>|\n",
-            "defence</w>|\n",
-            "female elemental water wizard</w>|\n",
-            "female elemental water wizard</w>|\n",
-            "beautiful character design</w>|\n",
-            "horde</w>|\n",
-            "female warriors protecting an underwater temple</w>}\n"
+            "{reimagined</w>|\n",
+            "movie</w>|\n",
+            "4k vivid colors|\n",
+            "movie still|\n",
+            "Movie still|\n",
+            "heroine</w>|\n",
+            "amazon</w>|\n",
+            "taun-|\n",
+            "alliance</w>|\n",
+            "reminis-|\n",
+            "premiere</w>|\n",
+            "honor-|\n",
+            "artemis</w>|\n",
+            "blue archive|\n",
+            "guarding</w>|\n",
+            "purple-|\n",
+            "protectors</w>|\n",
+            "Concept art|\n",
+            "concept art|\n",
+            "mags</w>|\n",
+            "cinematic still|\n",
+            "Cinematic still|\n",
+            "epic fantasy|\n",
+            "athena</w>|\n",
+            "ragnarok</w>|\n",
+            "bloo-|\n",
+            "special effects|\n",
+            "rained</w>|\n",
+            "vibrant arthouse|\n",
+            "clones</w>|\n",
+            "cinema art|\n",
+            "elves</w>|\n",
+            "movie texture|\n",
+            "anarch-|\n",
+            "oxi-|\n",
+            "sura-|\n",
+            "widow</w>|\n",
+            "vibrant Concept art|\n",
+            "goddess</w>|\n",
+            "Masterpiece Sci-Fi|\n",
+            "recruited</w>|\n",
+            "terra</w>|\n",
+            "sirens</w>|\n",
+            "defiance</w>|\n",
+            "sprite</w>|\n",
+            "soaked</w>|\n",
+            "kavan-|\n",
+            "holocau-|\n",
+            "soldiers</w>|\n",
+            "artstation|\n",
+            "valor</w>|\n",
+            "etty</w>|\n",
+            "marshals</w>|\n",
+            "clint</w>|\n",
+            "hd 8k masterpiece|\n",
+            "bluec-|\n",
+            "poppins</w>|\n",
+            "deeps darks|\n",
+            "hera</w>|\n",
+            "marvel 1girl|\n",
+            "guardian</w>|\n",
+            "references</w>|\n",
+            "woman solo|\n",
+            "4K 2girl|\n",
+            "characters</w>|\n",
+            "resolve</w>|\n",
+            "hail</w>|\n",
+            "sarmy</w>|\n",
+            "watched</w>|\n",
+            "drow-|\n",
+            "absurdres highres|\n",
+            "ogue</w>|\n",
+            "eq-|\n",
+            "snapped</w>|\n",
+            "atrix</w>|\n",
+            "navis</w>|\n",
+            "bodypaint|\n",
+            "striking</w>|\n",
+            "in that scene|\n",
+            "legion-|\n",
+            "hue-|\n",
+            "empowered</w>|\n",
+            "faction</w>|\n",
+            "widows</w>|\n",
+            "1girl vast|\n",
+            "destiny</w>|\n",
+            "visually</w>|\n",
+            "aspirations</w>|\n",
+            "tson</w>|\n",
+            "highres ultrares|\n",
+            "tali-|\n",
+            "swoon</w>|\n",
+            "aroo</w>|\n",
+            "oxi</w>|\n",
+            "blue filter|\n",
+            "blue theme|\n",
+            "women</w>|\n",
+            "orah</w>|\n",
+            "backlash</w>|\n",
+            "legendof-}\n"
           ]
         }
       ]
     },
-    {
-      "cell_type": "code",
-      "source": [
-        "# @title ⚡ Get similiar tokens (not updated yet)\n",
-        "import torch\n",
-        "from transformers import AutoTokenizer\n",
-        "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
-        "\n",
-        "# @markdown Write name of token to match against\n",
-        "token_name = \"banana \" # @param {type:'string',\"placeholder\":\"leave empty for random value token\"}\n",
-        "\n",
-        "prompt = token_name\n",
-        "# @markdown (optional) Mix the token with something else\n",
-        "mix_with = \"\" # @param {\"type\":\"string\",\"placeholder\":\"leave empty for random value token\"}\n",
-        "mix_method = \"None\" # @param [\"None\" , \"Average\", \"Subtract\"] {allow-input: true}\n",
-        "w = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
-        "# @markdown Limit char size of included token\n",
-        "\n",
-        "min_char_size = 0 # param {type:\"slider\", min:0, max: 50, step:1}\n",
-        "char_range = 50 # param {type:\"slider\", min:0, max: 50, step:1}\n",
-        "\n",
-        "tokenizer_output = tokenizer(text = prompt)\n",
-        "input_ids = tokenizer_output['input_ids']\n",
-        "id_A = input_ids[1]\n",
-        "A = torch.tensor(token[id_A])\n",
-        "A = A/A.norm(p=2, dim=-1, keepdim=True)\n",
-        "#-----#\n",
-        "tokenizer_output = tokenizer(text = mix_with)\n",
-        "input_ids = tokenizer_output['input_ids']\n",
-        "id_C = input_ids[1]\n",
-        "C = torch.tensor(token[id_C])\n",
-        "C = C/C.norm(p=2, dim=-1, keepdim=True)\n",
-        "#-----#\n",
-        "sim_AC = torch.dot(A,C)\n",
-        "#-----#\n",
-        "print(input_ids)\n",
-        "#-----#\n",
-        "\n",
-        "#if no imput exists we just randomize the entire thing\n",
-        "if (prompt == \"\"):\n",
-        "  id_A = -1\n",
-        "  print(\"Tokenized prompt tensor A is a random valued tensor with no ID\")\n",
-        "  R = torch.rand(A.shape)\n",
-        "  R = R/R.norm(p=2, dim=-1, keepdim=True)\n",
-        "  A = R\n",
-        "  name_A = 'random_A'\n",
-        "\n",
-        "#if no imput exists we just randomize the entire thing\n",
-        "if (mix_with == \"\"):\n",
-        "  id_C = -1\n",
-        "  print(\"Tokenized prompt  'mix_with' tensor C is a random valued tensor with no ID\")\n",
-        "  R = torch.rand(A.shape)\n",
-        "  R = R/R.norm(p=2, dim=-1, keepdim=True)\n",
-        "  C = R\n",
-        "  name_C = 'random_C'\n",
-        "\n",
-        "name_A = \"A of random type\"\n",
-        "if (id_A>-1):\n",
-        "  name_A = vocab(id_A)\n",
-        "\n",
-        "name_C = \"token C of random type\"\n",
-        "if (id_C>-1):\n",
-        "  name_C = vocab(id_C)\n",
-        "\n",
-        "print(f\"The similarity between A '{name_A}' and C '{name_C}' is {round(sim_AC.item()*100,2)} %\")\n",
-        "\n",
-        "if (mix_method ==  \"None\"):\n",
-        "  print(\"No operation\")\n",
-        "\n",
-        "if (mix_method ==  \"Average\"):\n",
-        "  A = w*A + (1-w)*C\n",
-        "  _A = LA.vector_norm(A, ord=2)\n",
-        "  print(f\"Tokenized prompt tensor A '{name_A}' token has been recalculated as A = w*A + (1-w)*C , where C is '{name_C}' token , for w = {w}  \")\n",
-        "\n",
-        "if (mix_method ==  \"Subtract\"):\n",
-        "  tmp =  w*A - (1-w)*C\n",
-        "  tmp = tmp/tmp.norm(p=2, dim=-1, keepdim=True)\n",
-        "  A = tmp\n",
-        "  #//---//\n",
-        "  print(f\"Tokenized prompt tensor A '{name_A}' token has been recalculated as A = _A*norm(w*A  - (1-w)*C) , where C is '{name_C}' token , for w = {w} \")\n",
-        "\n",
-        "#OPTIONAL : Add/subtract + normalize above result with another token. Leave field empty to get a random value tensor\n",
-        "\n",
-        "dots = torch.zeros(NUM_TOKENS)\n",
-        "for index in range(NUM_TOKENS):\n",
-        "  id_B = index\n",
-        "  B = torch.tensor(token[id_B])\n",
-        "  B = B/B.norm(p=2, dim=-1, keepdim=True)\n",
-        "  sim_AB = torch.dot(A,B)\n",
-        "  dots[index] = sim_AB\n",
-        "\n",
-        "\n",
-        "sorted, indices = torch.sort(dots,dim=0 , descending=True)\n",
-        "#----#\n",
-        "if (mix_method ==  \"Average\"):\n",
-        "  print(f'Calculated all cosine-similarities between the average of token {name_A} and {name_C} with Id_A = {id_A} and mixed Id_C = {id_C} as a 1x{sorted.shape[0]} tensor')\n",
-        "if (mix_method ==  \"Subtract\"):\n",
-        "  print(f'Calculated all cosine-similarities between the subtract of token {name_A} and {name_C} with Id_A = {id_A} and mixed Id_C = {id_C} as a 1x{sorted.shape[0]} tensor')\n",
-        "if (mix_method ==  \"None\"):\n",
-        "  print(f'Calculated all cosine-similarities between the token {name_A} with Id_A = {id_A} with the the rest of the {NUM_TOKENS} tokens as a 1x{sorted.shape[0]} tensor')\n",
-        "\n",
-        "#Produce a list id IDs that are most similiar to the prompt ID at positiion 1 based on above result\n",
-        "\n",
-        "# @markdown Set print options\n",
-        "list_size = 100 # @param {type:'number'}\n",
-        "print_ID = False # @param {type:\"boolean\"}\n",
-        "print_Similarity = True # @param {type:\"boolean\"}\n",
-        "print_Name = True # @param {type:\"boolean\"}\n",
-        "print_Divider = True # @param {type:\"boolean\"}\n",
-        "\n",
-        "\n",
-        "if (print_Divider):\n",
-        "  print('//---//')\n",
-        "\n",
-        "print('')\n",
-        "print('Here is the result : ')\n",
-        "print('')\n",
-        "\n",
-        "for index in range(list_size):\n",
-        "  id = indices[index].item()\n",
-        "  if (print_Name):\n",
-        "    print(f'{vocab(id)}') # vocab item\n",
-        "  if (print_ID):\n",
-        "    print(f'ID = {id}') # IDs\n",
-        "  if (print_Similarity):\n",
-        "    print(f'similiarity = {round(sorted[index].item()*100,2)} %')\n",
-        "  if (print_Divider):\n",
-        "    print('--------')\n",
-        "\n",
-        "#Print the sorted list from above result\n",
-        "\n",
-        "#The prompt will be enclosed with the <|start-of-text|> and <|end-of-text|> tokens, which is why output will be [49406, ... , 49407].\n",
-        "\n",
-        "#You can leave the 'prompt' field empty to get a random value tensor. Since the tensor is random value, it will not correspond to any tensor in the vocab.json list , and this it will have no ID.\n",
-        "\n",
-        "# Save results as .db file\n",
-        "import shelve\n",
-        "VOCAB_FILENAME = 'tokens_most_similiar_to_' + name_A.replace('</w>','').strip()\n",
-        "d = shelve.open(VOCAB_FILENAME)\n",
-        "#NUM TOKENS == 49407\n",
-        "for index in range(NUM_TOKENS):\n",
-        "  #print(d[f'{index}']) #<-----Use this to read values from the .db file\n",
-        "  d[f'{index}']= vocab(indices[index].item()) #<---- write values to .db file\n",
-        "#----#\n",
-        "d.close() #close the file\n",
-        "# See this link for additional stuff to do with shelve: https://docs.python.org/3/library/shelve.html"
-      ],
-      "metadata": {
-        "id": "iWeFnT1gAx6A"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
     {
       "cell_type": "code",
       "source": [
@@ -1383,20 +1141,6 @@
       "execution_count": null,
       "outputs": []
     },
-    {
-      "cell_type": "code",
-      "source": [
-        "# @title (Optional) ⚡Actively set which Vocab list to use for the interrogator\n",
-        "token_name = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Write a token_name used earlier\"}\n",
-        "VOCAB_FILENAME = 'tokens_most_similiar_to_' + token_name.replace('</w>','').strip()\n",
-        "print(f'Using a vocab ordered to most similiar to the token {token_name}')"
-      ],
-      "metadata": {
-        "id": "FYa96UCQuE1U"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
     {
       "cell_type": "code",
       "source": [
@@ -1436,6 +1180,159 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title ⚡ Get similiar tokens (not updated yet)\n",
+        "import torch\n",
+        "from transformers import AutoTokenizer\n",
+        "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
+        "\n",
+        "# @markdown Write name of token to match against\n",
+        "token_name = \"banana \" # @param {type:'string',\"placeholder\":\"leave empty for random value token\"}\n",
+        "\n",
+        "prompt = token_name\n",
+        "# @markdown (optional) Mix the token with something else\n",
+        "mix_with = \"\" # @param {\"type\":\"string\",\"placeholder\":\"leave empty for random value token\"}\n",
+        "mix_method = \"None\" # @param [\"None\" , \"Average\", \"Subtract\"] {allow-input: true}\n",
+        "w = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+        "# @markdown Limit char size of included token\n",
+        "\n",
+        "min_char_size = 0 # param {type:\"slider\", min:0, max: 50, step:1}\n",
+        "char_range = 50 # param {type:\"slider\", min:0, max: 50, step:1}\n",
+        "\n",
+        "tokenizer_output = tokenizer(text = prompt)\n",
+        "input_ids = tokenizer_output['input_ids']\n",
+        "id_A = input_ids[1]\n",
+        "A = torch.tensor(token[id_A])\n",
+        "A = A/A.norm(p=2, dim=-1, keepdim=True)\n",
+        "#-----#\n",
+        "tokenizer_output = tokenizer(text = mix_with)\n",
+        "input_ids = tokenizer_output['input_ids']\n",
+        "id_C = input_ids[1]\n",
+        "C = torch.tensor(token[id_C])\n",
+        "C = C/C.norm(p=2, dim=-1, keepdim=True)\n",
+        "#-----#\n",
+        "sim_AC = torch.dot(A,C)\n",
+        "#-----#\n",
+        "print(input_ids)\n",
+        "#-----#\n",
+        "\n",
+        "#if no imput exists we just randomize the entire thing\n",
+        "if (prompt == \"\"):\n",
+        "  id_A = -1\n",
+        "  print(\"Tokenized prompt tensor A is a random valued tensor with no ID\")\n",
+        "  R = torch.rand(A.shape)\n",
+        "  R = R/R.norm(p=2, dim=-1, keepdim=True)\n",
+        "  A = R\n",
+        "  name_A = 'random_A'\n",
+        "\n",
+        "#if no imput exists we just randomize the entire thing\n",
+        "if (mix_with == \"\"):\n",
+        "  id_C = -1\n",
+        "  print(\"Tokenized prompt  'mix_with' tensor C is a random valued tensor with no ID\")\n",
+        "  R = torch.rand(A.shape)\n",
+        "  R = R/R.norm(p=2, dim=-1, keepdim=True)\n",
+        "  C = R\n",
+        "  name_C = 'random_C'\n",
+        "\n",
+        "name_A = \"A of random type\"\n",
+        "if (id_A>-1):\n",
+        "  name_A = vocab(id_A)\n",
+        "\n",
+        "name_C = \"token C of random type\"\n",
+        "if (id_C>-1):\n",
+        "  name_C = vocab(id_C)\n",
+        "\n",
+        "print(f\"The similarity between A '{name_A}' and C '{name_C}' is {round(sim_AC.item()*100,2)} %\")\n",
+        "\n",
+        "if (mix_method ==  \"None\"):\n",
+        "  print(\"No operation\")\n",
+        "\n",
+        "if (mix_method ==  \"Average\"):\n",
+        "  A = w*A + (1-w)*C\n",
+        "  _A = LA.vector_norm(A, ord=2)\n",
+        "  print(f\"Tokenized prompt tensor A '{name_A}' token has been recalculated as A = w*A + (1-w)*C , where C is '{name_C}' token , for w = {w}  \")\n",
+        "\n",
+        "if (mix_method ==  \"Subtract\"):\n",
+        "  tmp =  w*A - (1-w)*C\n",
+        "  tmp = tmp/tmp.norm(p=2, dim=-1, keepdim=True)\n",
+        "  A = tmp\n",
+        "  #//---//\n",
+        "  print(f\"Tokenized prompt tensor A '{name_A}' token has been recalculated as A = _A*norm(w*A  - (1-w)*C) , where C is '{name_C}' token , for w = {w} \")\n",
+        "\n",
+        "#OPTIONAL : Add/subtract + normalize above result with another token. Leave field empty to get a random value tensor\n",
+        "\n",
+        "dots = torch.zeros(NUM_TOKENS)\n",
+        "for index in range(NUM_TOKENS):\n",
+        "  id_B = index\n",
+        "  B = torch.tensor(token[id_B])\n",
+        "  B = B/B.norm(p=2, dim=-1, keepdim=True)\n",
+        "  sim_AB = torch.dot(A,B)\n",
+        "  dots[index] = sim_AB\n",
+        "\n",
+        "\n",
+        "sorted, indices = torch.sort(dots,dim=0 , descending=True)\n",
+        "#----#\n",
+        "if (mix_method ==  \"Average\"):\n",
+        "  print(f'Calculated all cosine-similarities between the average of token {name_A} and {name_C} with Id_A = {id_A} and mixed Id_C = {id_C} as a 1x{sorted.shape[0]} tensor')\n",
+        "if (mix_method ==  \"Subtract\"):\n",
+        "  print(f'Calculated all cosine-similarities between the subtract of token {name_A} and {name_C} with Id_A = {id_A} and mixed Id_C = {id_C} as a 1x{sorted.shape[0]} tensor')\n",
+        "if (mix_method ==  \"None\"):\n",
+        "  print(f'Calculated all cosine-similarities between the token {name_A} with Id_A = {id_A} with the the rest of the {NUM_TOKENS} tokens as a 1x{sorted.shape[0]} tensor')\n",
+        "\n",
+        "#Produce a list id IDs that are most similiar to the prompt ID at positiion 1 based on above result\n",
+        "\n",
+        "# @markdown Set print options\n",
+        "list_size = 100 # @param {type:'number'}\n",
+        "print_ID = False # @param {type:\"boolean\"}\n",
+        "print_Similarity = True # @param {type:\"boolean\"}\n",
+        "print_Name = True # @param {type:\"boolean\"}\n",
+        "print_Divider = True # @param {type:\"boolean\"}\n",
+        "\n",
+        "\n",
+        "if (print_Divider):\n",
+        "  print('//---//')\n",
+        "\n",
+        "print('')\n",
+        "print('Here is the result : ')\n",
+        "print('')\n",
+        "\n",
+        "for index in range(list_size):\n",
+        "  id = indices[index].item()\n",
+        "  if (print_Name):\n",
+        "    print(f'{vocab(id)}') # vocab item\n",
+        "  if (print_ID):\n",
+        "    print(f'ID = {id}') # IDs\n",
+        "  if (print_Similarity):\n",
+        "    print(f'similiarity = {round(sorted[index].item()*100,2)} %')\n",
+        "  if (print_Divider):\n",
+        "    print('--------')\n",
+        "\n",
+        "#Print the sorted list from above result\n",
+        "\n",
+        "#The prompt will be enclosed with the <|start-of-text|> and <|end-of-text|> tokens, which is why output will be [49406, ... , 49407].\n",
+        "\n",
+        "#You can leave the 'prompt' field empty to get a random value tensor. Since the tensor is random value, it will not correspond to any tensor in the vocab.json list , and this it will have no ID.\n",
+        "\n",
+        "# Save results as .db file\n",
+        "import shelve\n",
+        "VOCAB_FILENAME = 'tokens_most_similiar_to_' + name_A.replace('</w>','').strip()\n",
+        "d = shelve.open(VOCAB_FILENAME)\n",
+        "#NUM TOKENS == 49407\n",
+        "for index in range(NUM_TOKENS):\n",
+        "  #print(d[f'{index}']) #<-----Use this to read values from the .db file\n",
+        "  d[f'{index}']= vocab(indices[index].item()) #<---- write values to .db file\n",
+        "#----#\n",
+        "d.close() #close the file\n",
+        "# See this link for additional stuff to do with shelve: https://docs.python.org/3/library/shelve.html"
+      ],
+      "metadata": {
+        "id": "iWeFnT1gAx6A"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "source": [
@@ -1485,16 +1382,16 @@
         "my_mkdirs('/content/text_encodings/')\n",
         "filename = ''\n",
         "\n",
-        "NUM_FILES = 7\n",
+        "NUM_FILES = 10\n",
         "\n",
         "for  file_index in range(NUM_FILES + 1):\n",
         "  if file_index <1: continue\n",
         "  #if file_index >4: break\n",
-        "  filename = f'🔹 fusion-t2i-sd15-clip-tokens-exotic-suffix-{file_index} Tokens'\n",
+        "  filename = f'🧿📘 fusion-t2i-civitai-0-20-chars-mix-{file_index}'\n",
         "  #🦜 fusion-t2i-prompt-features-1.json\n",
         "\n",
         "  # Read suffix.json\n",
-        "  %cd /content/text-to-image-prompts/tokens/suffix/exotic/text\n",
+        "  %cd /content/text-to-image-prompts/civitai-prompts/blue/text\n",
         "  with open(filename + '.json', 'r') as f:\n",
         "      data = json.load(f)\n",
         "  _df = pd.DataFrame({'count': data})['count']\n",
@@ -1530,9 +1427,9 @@
     {
       "cell_type": "code",
       "source": [
-        "# @title Download the created text_encodings as .zip file\n",
+        "# @title Download the created JSON as .zip file\n",
         "%cd /content/\n",
-        "!zip -r /content/tokens.zip /content/text-to-image-prompts/tokens"
+        "!zip -r /content/blue.zip /content/text-to-image-prompts/civitai-prompts/blue/text"
       ],
       "metadata": {
         "id": "gX-sHZPWj4Lt"