Upload sd_token_similarity_calculator.ipynb

Google Colab Notebooks/sd_token_similarity_calculator.ipynb

@@ -624,27 +624,10 @@
         "\n"
       ],
       "metadata": {
-        "id": "H3JRx5rhWIEo",
-        "outputId": "df7f400b-1f0a-4c1e-c6c9-4c3db0491545",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
+        "id": "H3JRx5rhWIEo"
       },
-      "execution_count": 26,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "/content\n",
-            "/content\n",
-            "/content/text-to-image-prompts/vocab/raw\n",
-            "/content/text-to-image-prompts/vocab/raw\n",
-            "/content/output/vocab/token_vectors\n",
-            "/content/output/vocab/text\n"
-          ]
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -841,37 +824,27 @@
     {
       "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}"
+        "!zip -r {zip_dest} '/content/text-to-image-prompts/tokens'"
       ],
       "metadata": {
-        "id": "V4YCpmWlkPMG",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "9eafe028-a982-4b67-adf3-0633cbbe81f7"
+        "id": "V4YCpmWlkPMG"
       },
-      "execution_count": 27,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "/content\n",
-            "  adding: content/output/ (stored 0%)\n",
-            "  adding: content/output/vocab/ (stored 0%)\n",
-            "  adding: content/output/vocab/text/ (stored 0%)\n",
-            "  adding: content/output/vocab/text/vocab.json (deflated 71%)\n",
-            "  adding: content/output/vocab/token_vectors/ (stored 0%)\n",
-            "  adding: content/output/vocab/token_vectors/vocab.safetensors (deflated 9%)\n"
-          ]
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -1126,13 +1099,6 @@
         "\n",
         "But you have to set CLIP skip to 1 for this to work is intended.\n",
         "\n",
-        "For example, this is the result from \"photo of a 🧔🏻‍♂️\"\n",
-        "\n",
-        "\n",
-        "<div>\n",
-        "<img src=\"https://lemmy.world/pictrs/image/e2b51aea-6960-4ad0-867e-8ce85f2bd51e.jpeg\" width=\"300\"/>\n",
-        "</div>\n",
-        "\n",
         "A tutorial on stuff you can do with the vocab.list concluded.\n",
         "\n",
         "Anyways, have fun with the notebook.\n",
@@ -1711,8 +1677,7 @@
         "# See this link for additional stuff to do with shelve: https://docs.python.org/3/library/shelve.html"
       ],
       "metadata": {
-        "id": "iWeFnT1gAx6A",
-        "cellView": "form"
+        "id": "iWeFnT1gAx6A"
       },
       "execution_count": null,
       "outputs": []