Upload sd_token_similarity_calculator.ipynb

sd_token_similarity_calculator.ipynb

@@ -387,11 +387,11 @@
         "start_search_at_index = 0 # @param {type:\"slider\", min:0, max: 49407, step:100}\n",
         "# @markdown The lower the start_index, the more similiar the sampled tokens will be to the target token assigned in the '⚡ Get similiar tokens' cell\". If the cell was not run, then it will use tokens ordered by similarity to the \"girl\\</w>\" token\n",
         "start_search_at_ID = start_search_at_index\n",
-        "search_range = 1220 # @param {type:\"slider\", min:10, max: 2000, step:10}\n",
+        "search_range = 1000 # @param {type:\"slider\", min:10, max: 2000, step:10}\n",
         "\n",
-        "samples_per_iter = 20 # @param {type:\"slider\", min:10, max: 100, step:10}\n",
+        "samples_per_iter = 10 # @param {type:\"slider\", min:10, max: 100, step:10}\n",
         "\n",
-        "iterations = 20 # @param {type:\"slider\", min:1, max: 20, step:0}\n",
+        "iterations = 5 # @param {type:\"slider\", min:1, max: 20, step:0}\n",
         "restrictions = 'None' # @param [\"None\", \"Suffix only\", \"Prefix only\"]\n",
         "#markdown Limit char size of included token <----- Disabled\n",
         "min_char_size = 0 #param {type:\"slider\", min:0, max: 20, step:1}\n",
@@ -436,14 +436,14 @@
         "results_name = {}\n",
         "#-----#\n",
         "for iter in range(ITERS):\n",
-        "  dots = torch.zeros(RANGE)\n",
-        "  is_trail = torch.zeros(RANGE)\n",
-        "  import re\n",
+        "  dots = torch.zeros(min(list_size,RANGE))\n",
+        "  is_trail = torch.zeros(min(list_size,RANGE))\n",
+        "\n",
         "  #-----#\n",
-        "  _start = START + iter*RANGE\n",
         "\n",
         "  for index in range(samples_per_iter):\n",
-        "    id_C = min(_start + index*RANGE, NUM_TOKENS) + random.randint(0,RANGE)\n",
+        "    _start = START + iter*RANGE\n",
+        "    id_C = random.randint(_start , _start + RANGE)\n",
         "    name_C = db_vocab[f'{id_C}']\n",
         "    is_Prefix = 0\n",
         "    #Skip if non-AZ characters are found\n",
@@ -546,7 +546,7 @@
         "  max_name_trail = ''\n",
         "  #----#\n",
         "  for index in range(min(list_size,RANGE)):\n",
-        "    id = START + indices[index].item()\n",
+        "    id = _start + indices[index].item()\n",
         "    name = db_vocab[f'{id}']\n",
         "    #-----#\n",
         "    if (name.find('</w>')<=-1):\n",
@@ -634,7 +634,7 @@
         "\n",
         "#--------#\n",
         "print('')\n",
-        "if(use == '🖼️image_encoding from image'):\n",
+        "if(use == '🖼️image_encoding from image'  and colab_image_path != \"\"):\n",
         "  from google.colab.patches import cv2_imshow\n",
         "  cv2_imshow(image_A)\n",
         "#-----#\n",
@@ -656,34 +656,6 @@
       "execution_count": null,
       "outputs": []
     },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "5XN2pM5NAfS5",
-        "outputId": "df4eefe6-12e7-416e-dc2d-b6df22a14d69",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 321
-        }
-      },
-      "execution_count": 25,
-      "outputs": [
-        {
-          "output_type": "error",
-          "ename": "AttributeError",
-          "evalue": "clip",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-25-2eb0ffbc049b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muse\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'🖼️image_encoding from image'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m   \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatches\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcv2_imshow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m   \u001b[0mcv2_imshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_A\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/patches/__init__.py\u001b[0m in \u001b[0;36mcv2_imshow\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m     16\u001b[0m       \u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0man\u001b[0m \u001b[0mNxM\u001b[0m \u001b[0mBGRA\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m   \"\"\"\n\u001b[0;32m---> 18\u001b[0;31m   \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m255\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'uint8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m   \u001b[0;31m# cv2 stores colors as BGR; convert to RGB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    527\u001b[0m             \u001b[0mdeprecate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Image categories\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"is_animated\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplural\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    528\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_category\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 529\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mAttributeError\u001b[0m: clip"
-          ]
-        }
-      ]
-    },
     {
       "cell_type": "code",
       "source": [