Upload sd_token_similarity_calculator.ipynb

sd_token_similarity_calculator.ipynb

@@ -124,7 +124,7 @@
           "base_uri": "https://localhost:8080/"
         }
       },
-      "execution_count": 1,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -151,7 +151,7 @@
         "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 = \" blanket \" # @param {type:'string',\"placeholder\":\"leave empty for random value token\"}\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",
@@ -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 = 1000 # @param {type:\"slider\", min:10, max: 1000, step:10}\n",
+        "search_range = 1220 # @param {type:\"slider\", min:10, max: 2000, step:10}\n",
         "\n",
-        "samples_per_iter = 10 # @param {type:\"slider\", min:10, max: 100, step:10}\n",
+        "samples_per_iter = 20 # @param {type:\"slider\", min:10, max: 100, step:10}\n",
         "\n",
-        "iterations = 5 # @param {type:\"slider\", min:1, max: 20, step:0}\n",
+        "iterations = 20 # @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",
@@ -406,7 +406,7 @@
         "RANGE =  min(search_range , max(1,NUM_TOKENS - start_search_at_ID))\n",
         "#-----#\n",
         "import math, random\n",
-        "NUM_PERMUTATIONS = 4\n",
+        "NUM_PERMUTATIONS = 6\n",
         "ITERS = iterations\n",
         "#-----#\n",
         "#LOOP START\n",
@@ -443,7 +443,7 @@
         "  _start = START + iter*RANGE\n",
         "\n",
         "  for index in range(samples_per_iter):\n",
-        "    id_C = min(_start + index, NUM_TOKENS) + random.randint(0,RANGE)\n",
+        "    id_C = min(_start + index*RANGE, NUM_TOKENS) + random.randint(0,RANGE)\n",
         "    name_C = db_vocab[f'{id_C}']\n",
         "    is_Prefix = 0\n",
         "    #Skip if non-AZ characters are found\n",
@@ -522,6 +522,7 @@
         "  print_Similarity = True # @param {type:\"boolean\"}\n",
         "  print_Name = True # @param {type:\"boolean\"}\n",
         "  print_Divider = True # @param {type:\"boolean\"}\n",
+        "  print_Suggestions = False # @param {type:\"boolean\"}\n",
         "  #----#\n",
         "  if (print_Divider):\n",
         "    print('//---//')\n",
@@ -571,10 +572,12 @@
         "  trails = (trails + \"&&&&\").replace(\"|&&&&\", \"}\").replace(\"</w>\", \" \").replace(\"{&&&&\", \"\")\n",
         "  aheads = (aheads + \"&&&&\").replace(\"|&&&&\", \"}\").replace(\"</w>\", \" \").replace(\"{&&&&\", \"\")\n",
         "  #-----#\n",
-        "  print(f\"place these items ahead of prompt :  {aheads}\")\n",
-        "  print(\"\")\n",
-        "  print(f\"place these items behind the prompt : {trails}\")\n",
-        "  print(\"\")\n",
+        "\n",
+        "  if(print_Suggestions):\n",
+        "    print(f\"place these items ahead of prompt :  {aheads}\")\n",
+        "    print(\"\")\n",
+        "    print(f\"place these items behind the prompt : {trails}\")\n",
+        "    print(\"\")\n",
         "\n",
         "  tmp = must_start_with + ' ' + max_name_ahead + name_B + ' ' + must_end_with\n",
         "  tmp = tmp.strip().replace('</w>', ' ')\n",
@@ -591,8 +594,10 @@
         "    name_inner = ''\n",
         "    if index == 0 : name_inner = name_B\n",
         "    if index == 1: name_inner = max_name_ahead\n",
-        "    if index == 2: name_inner = name_B + max_name_trail\n",
-        "    if index == 3: name_inner = max_name_ahead + name_B + max_name_trail\n",
+        "    if index == 2: name_inner = max_name_trail\n",
+        "    if index == 3: name_inner = name_B + max_name_trail\n",
+        "    if index == 4: name_inner = max_name_ahead + name_B\n",
+        "    if index == 5: name_inner = max_name_ahead + name_B + max_name_trail\n",
         "    if name_inner == '': name_inner = max_name_ahead + name_B + max_name_trail\n",
         "\n",
         "    name = must_start_with  + name_inner + must_end_with\n",
@@ -616,9 +621,23 @@
         "    results_sim[iter*NUM_PERMUTATIONS + index] = sim\n",
         "    results_name_B[iter*NUM_PERMUTATIONS + index] = name_inner.replace('</w>',' ')\n",
         "  #------#\n",
-        "  name_B  = results_name_B[iter*NUM_PERMUTATIONS + random.randint(0,3)]\n",
-        "#--------#\n",
+        "  #name_B  = results_name_B[iter*NUM_PERMUTATIONS + random.randint(0,3)]\n",
+        "  tmp = iter*NUM_PERMUTATIONS\n",
+        "  _name_B=''\n",
+        "  if results_sim[tmp+1]>results_sim[tmp+2]: _name_B = results_name_B[tmp + 3]\n",
+        "  if results_sim[tmp+2]>results_sim[tmp+1]: _name_B = results_name_B[tmp + 4]\n",
+        "\n",
+        "  if _name_B != name_B:\n",
+        "    name_B=_name_B\n",
+        "  else:\n",
+        "    name_B = results_name_B[tmp + 5]\n",
         "\n",
+        "#--------#\n",
+        "print('')\n",
+        "if(use == '🖼️image_encoding from image'):\n",
+        "  from google.colab.patches import cv2_imshow\n",
+        "  cv2_imshow(image_A)\n",
+        "#-----#\n",
         "print('')\n",
         "sorted, indices = torch.sort(results_sim,dim=0 , descending=True)\n",
         "\n",
@@ -637,6 +656,34 @@
       "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": [