Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -412,7 +412,7 @@
         "\n",
         "#image_index = 0 # @param {type:'number'}\n",
         "# @markdown 📥 Load the data (only required one time)\n",
-        "load_the_data = False # @param {type:\"boolean\"}\n",
+        "load_the_data = True # @param {type:\"boolean\"}\n",
         "\n",
         "# @markdown 🖼️ Choose a pre-encoded reference\n",
         "index = 708 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
@@ -429,7 +429,7 @@
         "strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.1}\n",
         "\n",
         "# @markdown Calculate most similiar items using above settings?\n",
-        "enable = True # @param {type:\"boolean\"}\n",
+        "enable = False # @param {type:\"boolean\"}\n",
         "\n",
         "if (load_the_data):\n",
         "  target_prompts , target_text_encodings , urls , target_image_encodings , NUM_ITEMS =  getPromptsAndLinks('/content/text-to-image-prompts/fusion')\n",
@@ -465,10 +465,10 @@
         "    # Get text features for user input\n",
         "    inputs = tokenizer(text = NEG, padding=True, return_tensors=\"pt\")\n",
         "    text_features_NEG = model.get_text_features(**inputs)\n",
-        "    text_features_NEG = text_features_A/text_features_A.norm(p=2, dim=-1, keepdim=True)\n",
+        "    text_features_NEG = text_features_NEG/text_features_NEG.norm(p=2, dim=-1, keepdim=True)\n",
         "\n",
         "    # text-similarity\n",
-        "    neg_sims =  strength*torch.matmul(text_tensor, text_features_NEG.t())\n",
+        "    neg_sims =  torch.matmul(text_tensor, text_features_NEG.t())\n",
         "  #------#\n",
         "\n",
         "  # plus image-similarity\n",
@@ -476,7 +476,7 @@
         "\n",
         "\n",
         "  # minus NEG-similarity\n",
-        "  sims = sims - neg_sims\n",
+        "  sims = sims - strength*neg_sims\n",
         "\n",
         "  # Sort the items\n",
         "  sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
@@ -553,6 +553,77 @@
       "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_Descriptions = True # @param {type:\"boolean\"}\n",
+        "compact_Output = True # @param {type:\"boolean\"}\n",
+        "newline_Separator = False # @param {type:\"boolean\"}\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  = 0 # @param {type:\"slider\", min:0, max:100, step:1}\n",
+        "\n",
+        "# @markdown -----------\n",
+        "# @markdown Repeat output N times\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].item()\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 = fix_bad_symbols(__prompts)\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",
+        "#-------#"
+      ],
+      "metadata": {
+        "id": "EdBiAguJO9aX"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "source": [
@@ -798,77 +869,6 @@
       "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_Descriptions = True # @param {type:\"boolean\"}\n",
-        "compact_Output = True # @param {type:\"boolean\"}\n",
-        "newline_Separator = False # @param {type:\"boolean\"}\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  = 0 # @param {type:\"slider\", min:0, max:100, step:1}\n",
-        "\n",
-        "# @markdown -----------\n",
-        "# @markdown Repeat output N times\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].item()\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 = fix_bad_symbols(__prompts)\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",
-        "#-------#"
-      ],
-      "metadata": {
-        "id": "EdBiAguJO9aX"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
     {
       "cell_type": "code",
       "source": [