Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -130,7 +130,7 @@
           "height": 889
         }
       },
-      "execution_count": 22,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -225,7 +225,7 @@
       "source": [
         "# @title ⚄ Set range\n",
         "LIST_SIZE = 1000  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
-        "START_AT = 2000  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
+        "START_AT = 0  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
         "# @markdown -----\n",
         "# @markdown Select vocab\n",
         "general = True # @param {type:\"boolean\"}\n",
@@ -323,9 +323,19 @@
         "    for key,value in data:\n",
         "      prompts[key] = value\n",
         "  num_items = int(prompts['num_items'])\n",
+        "\n",
         "  #------#\n",
         "  try:vocab_loaded\n",
-        "  except:vocab_loaded = ''\n",
+        "  except:\n",
+        "    vocab_loaded = 'general'\n",
+        "    _text_encodings = load_file(f'{root_filename}.safetensors')['weights'].to(torch.uint8)\n",
+        "    text_encodings = torch.zeros(num_items , dim)\n",
+        "    tmp = torch.ones(dim).to(dot_dtype)\n",
+        "    for index in range(num_items):\n",
+        "      text_encodings[index] = torch.sub(_text_encodings[index][1:dim+1].to(dot_dtype) , tmp , alpha= _text_encodings[index][0].to(dot_dtype))\n",
+        "    vocab_loaded = vocab_to_load\n",
+        "  #-----#\n",
+        "\n",
         "  if vocab_loaded != vocab_to_load and not multi:\n",
         "    %cd {encodings_folder}\n",
         "    _text_encodings = load_file(f'{root_filename}.safetensors')['weights'].to(torch.uint8)\n",
@@ -337,12 +347,12 @@
         "    #------#\n",
         "  sims = torch.matmul(text_encodings*scale, ref.t())\n",
         "  sorted , indices = torch.sort(sims , dim=0 , descending = True)\n",
-        "\n",
         "  #-----#\n",
         "  for index in range(LIST_SIZE + START_AT):\n",
         "    if index<START_AT: continue\n",
         "    key = indices[index].item()\n",
-        "    prompt = prompts[f'{key}']\n",
+        "    try:prompt = prompts[f'{key}']\n",
+        "    except:continue\n",
         "    if(isBlacklisted(prompt)):continue\n",
         "    #-------#\n",
         "    similiar_sims[_index] = torch.tensor(round(sims[key].item(), 5))\n",
@@ -383,11 +393,11 @@
         "    for index in range(LIST_SIZE):\n",
         "      key = indices[index].item()\n",
         "      sim = similiar_sims[key].item()\n",
-        "      prompt = prompt + similiar_prompts[f'{key}'] + '|'\n",
+        "      prompt = prompt + fix_bad_symbols(similiar_prompts[f'{key}']) + '|'\n",
         "    #-----#\n",
         "    prompt = (prompt + '}').replace('|}',  '} ')\n",
         "  #------#\n",
-        "  print(f'\\n\\n{prompt}\\n\\n')\n",
+        "  print(f'\\n\\n {prompt} \\n\\n')\n",
         "#-----#\n"
       ],
       "metadata": {
@@ -397,7 +407,7 @@
           "base_uri": "https://localhost:8080/"
         }
       },
-      "execution_count": 31,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",