Upload sd_token_similarity_calculator.ipynb

Google Colab Notebooks/sd_token_similarity_calculator.ipynb

@@ -271,7 +271,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# @title 🚫 Penalize similarity to Prompt text_encoding (optional)\n",
+        "# @title 📝🚫 Penalize similarity to Prompt text_encoding (optional)\n",
         "neg_prompt = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Write something to avoid\"}\n",
         "\n",
         "neg_strength = 1 # @param {type:\"slider\", min:0, max:5, step:0.01}\n",
@@ -377,8 +377,6 @@
         "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n",
         "model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
         "\n",
-        "if image_NEG != \"\":\n",
-        "\n",
         "# Get text features for user input\n",
         "inputs = tokenizer(text = prompt, padding=True, return_tensors=\"pt\")\n",
         "text_features_A = model.get_text_features(**inputs)\n",
@@ -519,13 +517,14 @@
         "\n",
         "if(not compact_Output):\n",
         "  if(print_Descriptions):\n",
-        "    print(f'The {start_at_index}-{start_at_index + RANGE} most similiar items to prompt : \\n\\n ' + __prompts)\n",
+        "    print(f'The {start_at_index}-{start_at_index + RANGE} most similiar items to prompt : \\n\\n ')\n",
+        "     for i in range(N) : print(__prompts)\n",
         "    print(f'The {start_at_index}-{start_at_index + RANGE} similarity % for items : \\n\\n' + __sims)\n",
         "    print('')\n",
         "  else:\n",
-        "    print(__prompts)\n",
+        "    for i in range(N) : print(__prompts)\n",
         "else:\n",
-        "  print(__prompts)\n",
+        "  for i in range(N) : print(__prompts)\n",
         "#-------#\n",
         "\n",
         "\n",
@@ -704,6 +703,81 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title ⚙️🖼️ Print the results (Advanced)\n",
+        "list_size = 1000 # @param {type:'number'}\n",
+        "start_at_index = 0 # @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",
+        "print_Descriptions = True # @param {type:\"boolean\"}\n",
+        "compact_Output = True # @param {type:\"boolean\"}\n",
+        "newline_Separator = True # @param {type:\"boolean\"}\n",
+        "\n",
+        "\n",
+        "import random\n",
+        "# @markdown -----------\n",
+        "# @markdown Mix with...\n",
+        "list_size2 = 1000 # @param {type:'number'}\n",
+        "start_at_index2 = 10000 # @param {type:'number'}\n",
+        "rate_percent  = 50 # @param {type:\"slider\", min:0, max:100, step:1}\n",
+        "\n",
+        "# @markdown -----------\n",
+        "# @markdown Repeat output N times\n",
+        "\n",
+        "N = 6 # @param {type:\"slider\", min:0, max:10, step:1}\n",
+        "\n",
+        "# title Show the 100 most similiar suffix and prefix text-encodings to the text encoding\n",
+        "RANGE = list_size\n",
+        "separator = '|'\n",
+        "if newline_Separator : separator = separator + '\\n'\n",
+        "\n",
+        "_prompts = '{'\n",
+        "_sims =  '{'\n",
+        "for _index in range(start_at_index + RANGE):\n",
+        "  if _index < start_at_index : continue\n",
+        "  index = indices[_index]\n",
+        "\n",
+        "  prompt = prompts[f'{index}']\n",
+        "  if rate_percent >= random.randint(0,100) : prompt = prompts[f'{random.randint(start_at_index2 , start_at_index2 + list_size2)}']\n",
+        "\n",
+        "  #Remove duplicates\n",
+        "  if _prompts.find(prompt + separator)<=-1:\n",
+        "    _sims = _sims + f'{round(100*sims[index].item(), 2)} %' + separator\n",
+        "  #-------#\n",
+        "  _prompts = _prompts.replace(prompt + separator,'')\n",
+        "  _prompts = _prompts  + prompt + separator\n",
+        "  #------#\n",
+        "#------#\n",
+        "__prompts = (_prompts + '}').replace(separator + '}', '}')\n",
+        "__sims = (_sims + '}').replace(separator + '}', '}')\n",
+        "#------#\n",
+        "\n",
+        "if(not print_Prompts): __prompts = ''\n",
+        "if(not print_Similarity): __sims = ''\n",
+        "\n",
+        "if(not compact_Output):\n",
+        "  if(print_Descriptions):\n",
+        "    print(f'The {start_at_index}-{start_at_index + RANGE} most similiar items to prompt : \\n\\n ')\n",
+        "     for i in range(N) : print(__prompts)\n",
+        "    print(f'The {start_at_index}-{start_at_index + RANGE} similarity % for items : \\n\\n' + __sims)\n",
+        "    print('')\n",
+        "  else:\n",
+        "    for i in range(N) : print(__prompts)\n",
+        "else:\n",
+        "  for i in range(N) : print(__prompts)\n",
+        "#-------#\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "6FEmV02tArrh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [