Upload sd_token_similarity_calculator.ipynb

Google Colab Notebooks/sd_token_similarity_calculator.ipynb

@@ -365,6 +365,17 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install datasets"
+      ],
+      "metadata": {
+        "id": "mNZOQYq5h1Sk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [
@@ -1123,9 +1134,9 @@
         "import os\n",
         "%cd {home_directory}\n",
         "#os.remove(f'{home_directory}results.zip')\n",
-        "root_output_folder = home_directory + 'output/'\n",
+        "root_output_folder = home_directory + 'outputs/'\n",
         "zip_dest = f'{home_directory}results.zip'\n",
-        "!zip -r {zip_dest} '/content/text-to-image-prompts/tokens'"
+        "!zip -r {zip_dest} {root_output_folder}"
       ],
       "metadata": {
         "id": "V4YCpmWlkPMG"
@@ -1133,6 +1144,57 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# @title Make your own text_encodings .safetensor file for later use (using GPU is recommended to speed things up , but not required)\n",
+        "import json\n",
+        "import pandas as pd\n",
+        "import os\n",
+        "import shelve\n",
+        "import torch\n",
+        "from safetensors.torch import save_file\n",
+        "import json\n",
+        "\n",
+        "# 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",
+        "# Load the data if not already loaded\n",
+        "try:\n",
+        "    loaded\n",
+        "except:\n",
+        "    %cd {home_directory}\n",
+        "    !git clone https://huggingface.co/datasets/codeShare/text-to-image-prompts\n",
+        "    loaded = True\n",
+        "#--------#\n",
+        "\n",
+        "# User input\n",
+        "target = home_directory + 'text-to-image-prompts/danbooru/'\n",
+        "root_output_folder = home_directory + 'output/'\n",
+        "output_folder = root_output_folder  + 'danbooru/'\n",
+        "root_filename = '🎀 fusion-t2i-danbooru-tags'\n",
+        "NUM_FILES = 1\n",
+        "#--------#\n",
+        "\n",
+        "# Setup environment\n",
+        "def my_mkdirs(folder):\n",
+        "    if os.path.exists(folder)==False:\n",
+        "        os.makedirs(folder)\n",
+        "#------------#\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "JCt-xelIkl1a"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [
@@ -1190,6 +1252,95 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title Download nouns - import data\n",
+        "import os\n",
+        "import json\n",
+        "\n",
+        "# Setup environment\n",
+        "def my_mkdirs(folder):\n",
+        "    if os.path.exists(folder)==False:\n",
+        "        os.makedirs(folder)\n",
+        "#--------#\n",
+        "\n",
+        "# 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",
+        "root_output_folder = home_directory + 'outputs/'\n",
+        "\n",
+        "# @title Extract nouns\n",
+        "my_mkdirs(root_output_folder)\n",
+        "%cd {root_output_folder}\n",
+        "\n",
+        "!pip install datasets\n",
+        "\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "ds = load_dataset(\"bartoszmaj/nouns_one\")\n",
+        "#ds2 = load_dataset(\"bartoszmaj/nouns_two\")\n",
+        "#ds3 = load_dataset(\"bartoszmaj/nouns_three\")\n",
+        "#ds4 = load_dataset(\"bartoszmaj/nouns_four\")\n",
+        "\n"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "HC72wZW9llzw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title Download nouns - pick three items at random and write in JSONs\n",
+        "import random\n",
+        "my_mkdirs(root_output_folder)\n",
+        "%cd {root_output_folder}\n",
+        "for file_index in range(21):\n",
+        "  if file_index <=0: continue\n",
+        "  tripple_nouns = {}\n",
+        "  for index in range (10000):\n",
+        "    word = \"\"\n",
+        "    for its in range(3):\n",
+        "      _index = random.randint(0,1000000-1)\n",
+        "      words  = list(ds['train'][_index]['nouns'])\n",
+        "      if len(words)>0:\n",
+        "        _word = random.choice(words)\n",
+        "        word = word + ' ' + _word\n",
+        "    #---------#\n",
+        "    tripple_nouns[f'{index}'] = word\n",
+        "  #--------#\n",
+        "  with open(f'tripple_nouns_{file_index}.json', 'w') as f:\n",
+        "    json.dump(tripple_nouns, f)\n",
+        "  #----------#\n",
+        "\n"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "CWlWk0KpuX55",
+        "outputId": "418a74c3-f83c-4cfd-8514-437974a84601",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/outputs\n"
+          ]
+        }
+      ]
+    },
     {
       "cell_type": "markdown",
       "source": [
@@ -1964,7 +2115,8 @@
         "# See this link for additional stuff to do with shelve: https://docs.python.org/3/library/shelve.html"
       ],
       "metadata": {
-        "id": "iWeFnT1gAx6A"
+        "id": "iWeFnT1gAx6A",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []