Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -47,9 +47,12 @@
         "\n",
         "def fix_bad_symbols(txt):\n",
         "  result = txt\n",
-        "  for symbol in ['^', '}', '{' , ')', '(', '[' , ']' , ':' , '=' ]:\n",
+        "  for symbol in ['}', '{' , ')', '(', '[' , ']' , ':' , '=' , '^']:\n",
         "    result = result.replace(symbol,'\\\\' + symbol)\n",
+        "  for symbol in ['^']:\n",
+        "    result = result.replace(symbol,'')\n",
         "  #------#\n",
+        "  result = result.replace('\\\\|','|')\n",
         "  return result;\n",
         "\n",
         "\n",
@@ -390,19 +393,27 @@
         "# @markdown 🖼️ Choose a pre-encoded reference\n",
         "index = 708 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
         "PROMPT_INDEX = index\n",
-        "# @markdown ⚖️ Set the value for C in the reference  <br> <br> sim = C* text_enc + image_enc*(1-C) <br>\n",
-        "C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+        "\n",
+        "import math\n",
         "# @markdown -----------\n",
-        "# @markdown 📝 Enhance similarity to this prompt(optional)\n",
-        "POS = '' # @param {type:'string'}\n",
-        "strength = 1 # @param {type:\"slider\", min:-50, max:50, step:0.1}\n",
-        "pos_strength = strength\n",
+        "# @markdown 📝➕ Enhance similarity to prompt(s)\n",
+        "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
+        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "pos_strength = math.pow(10 ,log_strength-1)\n",
         "# @markdown -----------\n",
         "\n",
-        "# @markdown 🚫 Penalize similarity to this prompt(optional)\n",
-        "NEG = '' # @param {type:'string'}\n",
-        "strength = 1 # @param {type:\"slider\", min:-50, max:50, step:0.1}\n",
-        "neg_strength = strength\n",
+        "# @markdown 🚫 Penalize similarity to prompt(s)\n",
+        "NEG = '' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
+        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "neg_strength = math.pow(10 ,log_strength-1)\n",
+        "\n",
+        "# @markdown ⏩ Skip item(s) containing the word\n",
+        "SKIP = '' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
+        "\n",
+        "# @markdown ⚖️ sim_ref = C* text_encoding + image_encoding*(1-C) <br>\n",
+        "C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+        "\n",
+        "blacklist = SKIP\n",
         "# @markdown -----------\n",
         "# @title ⚙️📝 Print the results (Advanced)\n",
         "list_size = 1000 # param {type:'number'}\n",
@@ -436,6 +447,14 @@
         "enable = run_script\n",
         "\n",
         "\n",
+        "\n",
+        "def isBlacklisted(txt):\n",
+        "  if blacklist.strip() == '': return False\n",
+        "  for item in list(blacklist.split(',')):\n",
+        "    if txt.find(item.strip())> -1 : return True\n",
+        "  #------#\n",
+        "  return False\n",
+        "\n",
         "# Load the data if not already loaded\n",
         "try:\n",
         "    loaded2\n",
@@ -471,28 +490,30 @@
         "  # text-similarity\n",
         "  sims =  C * torch.matmul(text_tensor, text_features_A.t())\n",
         "\n",
-        "  neg_sims = 0*sims\n",
+        "  # Calculate negatives\n",
+        "  neg_sims = {}\n",
+        "  neg_sims[f'{0}'] = 0*sims\n",
         "  if(NEG != ''):\n",
-        "\n",
-        "    # 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_NEG/text_features_NEG.norm(p=2, dim=-1, keepdim=True)\n",
-        "\n",
-        "    # text-similarity\n",
-        "    neg_sims =  torch.matmul(text_tensor, text_features_NEG.t())\n",
+        "    _index = 0\n",
+        "    for _NEG in NEG.split(','):\n",
+        "      inputs = tokenizer(text = _NEG, truncation = True  , padding=True, return_tensors=\"pt\")\n",
+        "      text_features_NEG = model.get_text_features(**inputs)\n",
+        "      text_features_NEG = text_features_NEG/text_features_NEG.norm(p=2, dim=-1, keepdim=True)\n",
+        "      # text-similarity\n",
+        "      neg_sims[f'{_index}'] =  torch.matmul(text_tensor, text_features_NEG.t())\n",
         "  #------#\n",
         "\n",
-        "  pos_sims = 0*sims\n",
+        "  # Calculate positives\n",
+        "  pos_sims = {}\n",
+        "  pos_sims[f'{0}'] = 0*sims\n",
         "  if(POS != ''):\n",
-        "\n",
-        "    # Get text features for user input\n",
-        "    inputs = tokenizer(text = POS, padding=True, return_tensors=\"pt\")\n",
-        "    text_features_POS = model.get_text_features(**inputs)\n",
-        "    text_features_POS = text_features_POS/text_features_POS.norm(p=2, dim=-1, keepdim=True)\n",
-        "\n",
-        "    # text-similarity\n",
-        "    pos_sims =  torch.matmul(text_tensor, text_features_POS.t())\n",
+        "    _index = 0\n",
+        "    for _POS in POS.split(','):\n",
+        "      inputs = tokenizer(text = _POS, truncation = True  , padding=True, return_tensors=\"pt\")\n",
+        "      text_features_POS = model.get_text_features(**inputs)\n",
+        "      text_features_POS = text_features_POS/text_features_POS.norm(p=2, dim=-1, keepdim=True)\n",
+        "      # text-similarity\n",
+        "      pos_sims[f'{_index}'] =  torch.matmul(text_tensor, text_features_POS.t())\n",
         "  #------#\n",
         "\n",
         "  # plus image-similarity\n",
@@ -501,11 +522,14 @@
         "\n",
         "\n",
         "  # plus POS-similarity\n",
-        "  sims = sims + pos_strength*pos_sims\n",
-        "\n",
+        "  for key in pos_sims:\n",
+        "    sims = sims + pos_strength*pos_sims[key]\n",
+        "  #------#\n",
         "\n",
         "  # minus NEG-similarity\n",
-        "  sims = sims - neg_strength*neg_sims\n",
+        "  for key in neg_sims:\n",
+        "    sims = sims - neg_strength*neg_sims[key]\n",
+        "  #-------#\n",
         "\n",
         "\n",
         "  # Sort the items\n",
@@ -518,13 +542,28 @@
         "\n",
         "  _prompts = ''\n",
         "  _sims =  ''\n",
+        "  offset = 0\n",
         "  for _index in range(start_at_index + RANGE):\n",
         "    if _index < start_at_index : continue\n",
-        "    index = indices[_index].item()\n",
         "\n",
+        "    for iters in range(10000):\n",
+        "      found = True\n",
+        "      index = indices[_index + offset].item()\n",
+        "      if isBlacklisted(prompts[f'{index}'].lower()):\n",
+        "        offset = offset + 1\n",
+        "        found = False\n",
+        "      if (_index + offset)>NUM_VOCAB_ITEMS : found = True\n",
+        "      if found : break\n",
+        "    #-------#\n",
+        "\n",
+        "    index = indices[_index + offset].item()\n",
         "    prompt = prompts[f'{index}']\n",
+        "\n",
         "    if rate_percent >= random.randint(0,100) : prompt = prompts[f'{random.randint(start_at_index2 , start_at_index2 + list_size2)}']\n",
         "\n",
+        "\n",
+        "    #---------#\n",
+        "\n",
         "    #Remove duplicates\n",
         "    if _prompts.find(prompt + separator)<=-1:\n",
         "      _sims = _sims + f'{round(100*sims[index].item(), 2)} %' + separator\n",
@@ -554,7 +593,10 @@
         "  #-------#\n",
         "  #-------#\n",
         "#-------#\n",
-        "image\n"
+        "image or print('No image found')\n",
+        "\n",
+        "\n",
+        "#------#"
       ],
       "metadata": {
         "id": "XW3914T8O2uf"