Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -29,7 +29,8 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "UEYEdzjgOEOE"
+        "id": "UEYEdzjgOEOE",
+        "cellView": "form"
       },
       "outputs": [],
       "source": [
@@ -47,28 +48,22 @@
         "\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('\\\\|','|').replace('  |',' |')\n",
         "  return result;\n",
         "\n",
         "\n",
         "def getPrompts(_path, separator):\n",
-        "\n",
         "  path = _path + '/text'\n",
         "  path_enc = _path + '/text_encodings'\n",
         "  #-----#\n",
         "  index = 0\n",
-        "  file_index = 0\n",
         "  prompts = {}\n",
         "  text_encodings = {}\n",
         "  _text_encodings = {}\n",
         "  #-----#\n",
         "  for filename in os.listdir(f'{path}'):\n",
-        "\n",
         "    print(f'reading {filename}....')\n",
         "    _index = 0\n",
         "    %cd {path}\n",
@@ -79,27 +74,13 @@
         "    _prompts = {\n",
         "        key : value for key, value in _df.items()\n",
         "    }\n",
+        "    _file_name = _prompts[f'{1}']\n",
+        "    %cd {path_enc}\n",
+        "    _text_encodings = load_file(f'{_file_name}.safetensors')\n",
         "    for key in _prompts:\n",
         "      _index = int(key)\n",
         "      value = _prompts[key]\n",
-        "\n",
-        "      #Read the 'header' file in the JSON\n",
-        "      if _index <= 0 :\n",
-        "        _NUM_ITEMS = int(value)\n",
-        "        prompts[f'{index}'] = _prompts[f'{_index}'] + separator\n",
-        "        index = index + 1\n",
-        "        continue\n",
-        "      if _index <= 1 :\n",
-        "        _file_name = f'{value}'\n",
-        "        %cd {path_enc}\n",
-        "        _text_encodings = load_file(f'{_file_name}.safetensors')\n",
-        "        #Store text_encodings for the header items\n",
-        "        text_encodings[f'{index-1}'] = _text_encodings[f'{_index-1}']\n",
-        "        text_encodings[f'{index}'] = _text_encodings[f'{_index}']\n",
-        "        #------#\n",
-        "        prompts[f'{index}'] = _prompts[f'{_index}'] + separator\n",
-        "        index = index + 1\n",
-        "        continue\n",
+        "      if _index<2:continue\n",
         "      #------#\n",
         "      #Read the text_encodings + prompts\n",
         "      text_encodings[f'{index}'] = _text_encodings[f'{_index}']\n",
@@ -108,8 +89,6 @@
         "      continue\n",
         "      #-------#\n",
         "    #--------#\n",
-        "    #_text_encodings.close() #close the text_encodings file\n",
-        "    file_index = file_index + 1\n",
         "  #----------#\n",
         "  NUM_ITEMS = index -1\n",
         "  return prompts , text_encodings , NUM_ITEMS\n",
@@ -255,8 +234,7 @@
         "  #----------#\n",
         "  NUM_ITEMS = index -1\n",
         "  return prompts , text_encodings , urls , image_encodings , NUM_ITEMS\n",
-        "#--------#\n",
-        "\n"
+        "#--------#"
       ]
     },
     {
@@ -264,35 +242,57 @@
       "source": [
         "# @title 📚 Select items to sample from\n",
         "\n",
-        "prompt_features = False # @param {\"type\":\"boolean\",\"placeholder\":\"🦜\"}\n",
-        "civitai_blue_set = False # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
-        "civitai_yellow_set = False # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
-        "artby_prompts = False # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
-        "suffix = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
-        "prefix = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
-        "emojis = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "prompt_features = True # @param {\"type\":\"boolean\",\"placeholder\":\"🦜\"}\n",
+        "civitai_blue_set = True # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n",
+        "suffix = True # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
+        "prefix = True # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
+        "emojis = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
         "#------#\n",
-        "suffix_pairs = True # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
-        "first_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
-        "last_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
-        "celebs = False # @param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n",
+        "\n",
+        "first_names = True # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n",
+        "last_names = True # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n",
+        "celebs = True # @param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n",
         "#-------#\n",
         "danbooru_tags = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎀\"}\n",
-        "lyrics = False # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
-        "tripple_nouns =  False # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
+        "lyrics = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
+        "tripple_nouns =  True # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n",
         "#-----#\n",
-        "female_fullnames = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "female_fullnames = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
         "debug = False\n",
+        "\n",
+        "civitai_red_set = True # @param {\"type\":\"boolean\",\"placeholder\":\"📕\"}\n",
+        "e621 = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "prefix_suffix_pairs = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "suffix_tripple = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
+        "suffix_quad = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n",
         "#------#\n",
         "prompts = {}\n",
         "text_encodings = {}\n",
         "nA = 0\n",
         "#--------#\n",
         "\n",
-        "if suffix_pairs:\n",
-        "  url = '/content/text-to-image-prompts/suffix_pairs'\n",
+        "if civitai_red_set:\n",
+        "  url = '/content/text-to-image-prompts/civitai-prompts/red'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "\n",
+        "if e621:\n",
+        "  url = '/content/text-to-image-prompts/e621'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "\n",
+        "if prefix_suffix_pairs:\n",
+        "  url = '/content/text-to-image-prompts/prefix_suffix_pairs'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "\n",
+        "if suffix_tripple:\n",
+        "  url = '/content/text-to-image-prompts/suffix_tripple'\n",
         "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
         "\n",
+        "if suffix_quad:\n",
+        "  url = '/content/text-to-image-prompts/suffix_quad'\n",
+        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
+        "\n",
+        "\n",
+        "\n",
         "if tripple_nouns:\n",
         "  url = '/content/text-to-image-prompts/nouns'\n",
         "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
@@ -344,16 +344,6 @@
         "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
         "#--------#\n",
         "\n",
-        "if civitai_yellow_set:\n",
-        "  url = '/content/text-to-image-prompts/civitai-prompts/yellow'\n",
-        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
-        "#--------#\n",
-        "\n",
-        "if artby_prompts:\n",
-        "  url = '/content/text-to-image-prompts/artby'\n",
-        "  prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
-        "#--------#\n",
-        "\n",
         "if suffix :\n",
         "  tmp = '/content/text-to-image-prompts/vocab/text_encodings/suffix/'\n",
         "  for item in ['common','average','rare','weird','exotic'] :\n",
@@ -381,10 +371,13 @@
         "text_tensor = torch.zeros(NUM_VOCAB_ITEMS,768)\n",
         "for index in range(NUM_VOCAB_ITEMS):\n",
         "  text_tensor[index] = text_encodings[f'{index}']\n",
-        "#---------#\n"
+        "#---------#\n",
+        "print(f\"Done loading vocabulary for the interrogator\")\n",
+        "print(f\"Vocab size is : {NUM_VOCAB_ITEMS} items\")"
       ],
       "metadata": {
-        "id": "CF53WIAKObg3"
+        "id": "CF53WIAKObg3",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
@@ -394,7 +387,7 @@
       "source": [
         "# @title \t⚄ Use a pre-encoded prompt + image pair from the fusion gen (note: NSFW!)\n",
         "# @markdown 🖼️ Choose a pre-encoded reference\n",
-        "index = 617 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
+        "index = 682 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
         "PROMPT_INDEX = index\n",
         "\n",
         "import math\n",
@@ -411,7 +404,7 @@
         "neg_strength = math.pow(10 ,log_strength-1)\n",
         "\n",
         "# @markdown ⏩ Skip item(s) containing the word\n",
-        "SKIP = '_ass , ass_' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
+        "SKIP = 'futa ' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
         "\n",
         "# @markdown ⚖️ How important is the prompt vs. the image? <br> <br>\n",
         "# @markdown <div style = \"font-size: 10px; \"> sim_ref = (10^(log_strength-1)) * ( C* text_encoding + image_encoding*(1-C) ) </div> <br>\n",
@@ -609,7 +602,8 @@
         "#------#"
       ],
       "metadata": {
-        "id": "XW3914T8O2uf"
+        "id": "XW3914T8O2uf",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
@@ -680,7 +674,8 @@
         "#-------#"
       ],
       "metadata": {
-        "id": "EdBiAguJO9aX"
+        "id": "EdBiAguJO9aX",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
@@ -734,7 +729,8 @@
         "    json.dump(_savefile, f)\n"
       ],
       "metadata": {
-        "id": "Q7vpNAXQilbf"
+        "id": "Q7vpNAXQilbf",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
@@ -744,19 +740,26 @@
       "source": [
         "# @title \t⚄ Create a savefile-set from the entire range of pre-encoded items\n",
         "\n",
-        "#image_index = 0 # @param {type:'number'}\n",
         "# @markdown 📥 Load the data (only required one time)\n",
         "load_the_data = True # @param {type:\"boolean\"}\n",
         "\n",
+        "import math\n",
+        "from safetensors.torch import load_file\n",
+        "import json , os , torch\n",
+        "import pandas as pd\n",
+        "from PIL import Image\n",
+        "import requests\n",
+        "\n",
+        "def my_mkdirs(folder):\n",
+        "  if os.path.exists(folder)==False:\n",
+        "    os.makedirs(folder)\n",
+        "\n",
         "# @markdown ⚖️ Set the value for C in the reference  <br> <br> sim = C* text_enc + image_enc*(1-C) <br><br>\n",
         "\n",
         "C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
         "\n",
         "# @markdown 🚫 Penalize similarity to this prompt(optional)\n",
-        "\n",
         "if(load_the_data):\n",
-        "  from PIL import Image\n",
-        "  import requests\n",
         "  target_prompts , target_text_encodings , urls , target_image_encodings , NUM_ITEMS =  getPromptsAndLinks('/content/text-to-image-prompts/fusion')\n",
         "  from transformers import AutoTokenizer\n",
         "  tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
@@ -780,16 +783,31 @@
         "}\n",
         "#------#\n",
         "\n",
-        "root_savefile_name = 'fusion_C05_X7_1000_'\n",
+        "root_savefile_name = 'fusion_C05_X7'\n",
+        "\n",
+        "%cd /content/\n",
         "output_folder = '/content/output/savefiles/'\n",
         "my_mkdirs(output_folder)\n",
+        "my_mkdirs('/content/output2/savefiles/')\n",
+        "my_mkdirs('/content/output3/savefiles/')\n",
+        "my_mkdirs('/content/output4/savefiles/')\n",
+        "my_mkdirs('/content/output5/savefiles/')\n",
+        "my_mkdirs('/content/output6/savefiles/')\n",
+        "my_mkdirs('/content/output7/savefiles/')\n",
+        "my_mkdirs('/content/output8/savefiles/')\n",
+        "my_mkdirs('/content/output9/savefiles/')\n",
+        "my_mkdirs('/content/output10/savefiles/')\n",
+        "my_mkdirs('/content/output11/savefiles/')\n",
+        "my_mkdirs('/content/output12/savefiles/')\n",
+        "my_mkdirs('/content/output13/savefiles/')\n",
+        "\n",
+        "\n",
         "NEG = '' # @param {type:'string'}\n",
         "strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.1}\n",
         "\n",
         "for index in range(1667):\n",
         "\n",
         "  PROMPT_INDEX = index\n",
-        "\n",
         "  prompt = target_prompts[f'{index}']\n",
         "  url = urls[f'{index}']\n",
         "  if url.find('perchance')>-1:\n",
@@ -797,110 +815,79 @@
         "  else: continue #print(\"(No image for this ID)\")\n",
         "\n",
         "  print(f\"no. {PROMPT_INDEX} : '{prompt}'\")\n",
+        "  text_features_A = target_text_encodings[f'{index}']\n",
+        "  image_features_A =  target_image_encodings[f'{index}']\n",
+        "  # text-similarity\n",
+        "  sims =  C * torch.matmul(text_tensor, text_features_A.t())\n",
         "\n",
-        "\n",
-        "  if(True):\n",
-        "    text_features_A = target_text_encodings[f'{index}']\n",
-        "    image_features_A =  target_image_encodings[f'{index}']\n",
-        "\n",
+        "  neg_sims = 0*sims\n",
+        "  if(NEG != ''):\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_A/text_features_A.norm(p=2, dim=-1, keepdim=True)\n",
         "    # text-similarity\n",
-        "    sims =  C * torch.matmul(text_tensor, text_features_A.t())\n",
-        "\n",
-        "    neg_sims = 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_A/text_features_A.norm(p=2, dim=-1, keepdim=True)\n",
-        "\n",
-        "      # text-similarity\n",
-        "      neg_sims =  strength*torch.matmul(text_tensor, text_features_NEG.t())\n",
-        "    #------#\n",
-        "\n",
-        "    # plus image-similarity\n",
-        "    sims = sims +  (1-C) * torch.matmul(text_tensor, image_features_A.t()) * logit_scale\n",
-        "\n",
-        "    # minus NEG-similarity\n",
-        "    sims = sims - neg_sims\n",
-        "\n",
-        "    # Sort the items\n",
-        "    sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
-        "\n",
-        "    # @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",
-        "\n",
-        "    # @markdown -----------\n",
-        "    # @markdown ⚙️📝 Printing options\n",
-        "    newline_Separator = False # @param {type:\"boolean\"}\n",
+        "    neg_sims =  strength*torch.matmul(text_tensor, text_features_NEG.t())\n",
+        "  #------#\n",
         "\n",
-        "    import random\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",
+        "  # plus image-similarity\n",
+        "  sims = sims +  (1-C) * torch.matmul(text_tensor, image_features_A.t()) * logit_scale\n",
         "\n",
-        "    # @markdown Repeat output N times\n",
-        "    N = 7 # @param {type:\"slider\", min:0, max:10, step:1}\n",
+        "  # minus NEG-similarity\n",
+        "  sims = sims - neg_sims\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",
+        "  # Sort the items\n",
+        "  sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
         "\n",
+        "  # @markdown Repeat output N times\n",
+        "  RANGE = 1000\n",
+        "  NUM_CHUNKS = 10+\n",
+        "  separator = '|'\n",
+        "  _savefiles = {}\n",
+        "  #-----#\n",
+        "  for chunk in range(NUM_CHUNKS):\n",
+        "    if chunk=<10:continue\n",
+        "    start_at_index = chunk * RANGE\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",
-        "  _savefile = _blank\n",
-        "  from safetensors.torch import load_file\n",
-        "  import json , os , torch\n",
-        "  import pandas as pd\n",
-        "  #----#\n",
-        "  def my_mkdirs(folder):\n",
-        "    if os.path.exists(folder)==False:\n",
-        "      os.makedirs(folder)\n",
-        "  #------#\n",
-        "  savefile_prompt = ''\n",
-        "  for i in range(N) : savefile_prompt = savefile_prompt + ' ' + __prompts\n",
-        "  _savefile['main'] = savefile_prompt.replace('\\n', ' ').replace('  ', ' ').replace('   ', ' ')\n",
+        "    _prompts = ('{' + _prompts + '}').replace(separator + '}', '}')\n",
+        "    _savefiles[f'{chunk}'] = _prompts\n",
+        "  #---------#\n",
+        "  save_filename = f'{root_savefile_name}_{start_at_index + RANGE}_{PROMPT_INDEX}.json'\n",
+        "\n",
+        "\n",
+        "  if (chunk=<20 && chunk>10): %cd '/content/output2/savefiles/'\n",
+        "  if (chunk<=30 && chunk>20): %cd '/content/output3/savefiles/'\n",
+        "  if (chunk=<40 && chunk>30): %cd '/content/output4/savefiles/'\n",
+        "  if (chunk<=50 && chunk>40): %cd '/content/output5/savefiles/'\n",
+        "  if (chunk=<60 && chunk>50): %cd '/content/output6/savefiles/'\n",
+        "  if (chunk<=70 && chunk>60): %cd '/content/output7/savefiles/'\n",
+        "  if (chunk=<80 && chunk>70): %cd '/content/output8/savefiles/'\n",
+        "  if (chunk<=90 && chunk>80): %cd '/content/output9/savefiles/'\n",
+        "  if (chunk=<100 && chunk>90): %cd '/content/output10/savefiles/'\n",
+        "  if (chunk<=110 && chunk>100): %cd '/content/output11/savefiles/'\n",
+        "  if (chunk=<120 && chunk>110): %cd '/content/output12/savefiles/'\n",
+        "  if (chunk<=130 && chunk>120): %cd '/content/output13/savefiles/'\n",
+        "\n",
+        "\n",
         "  #------#\n",
-        "  save_filename = f'{root_savefile_name}{PROMPT_INDEX}.json'\n",
-        "  #-----#\n",
-        "  %cd {output_folder}\n",
         "  print(f'Saving savefile {save_filename} to {output_folder}...')\n",
         "  with open(save_filename, 'w') as f:\n",
-        "      json.dump(_savefile, f)\n",
+        "      json.dump(_savefiles, f)\n",
         "  #---------#\n",
         "  continue\n",
-        "#-----------#\n",
-        "\n"
+        "#-----------#"
       ],
       "metadata": {
-        "id": "x1uAVXZEoL0T"
+        "id": "x1uAVXZEoL0T",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
@@ -925,53 +912,8 @@
         "!zip -r {zip_dest} {root_output_folder}"
       ],
       "metadata": {
-        "id": "zivBNrw9uSVD"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "output_folder = '/content/output/fusion-gen-savefiles/'\n",
-        "index = 0\n",
-        "path = '/content/text-to-image-prompts/fusion-gen-savefiles'\n",
-        "\n",
-        "def my_mkdirs(folder):\n",
-        "  if os.path.exists(folder)==False:\n",
-        "    os.makedirs(folder)\n",
-        "\n",
-        "my_mkdirs(output_folder)\n",
-        "for filename in os.listdir(f'{path}'):\n",
-        "  if filename.find('fusion_C05_X7_1000_')<=-1: continue\n",
-        "  print(f'reading {filename}...')\n",
-        "  %cd {path}\n",
-        "  with open(f'{filename}', 'r') as f:\n",
-        "    data = json.load(f)\n",
-        "    _df = pd.DataFrame({'count': data})['count']\n",
-        "    _savefile = {\n",
-        "        key : value for key, value in _df.items()\n",
-        "    }\n",
-        "\n",
-        "    _savefile2 = {}\n",
-        "\n",
-        "    for key in _savefile:\n",
-        "      _savefile2[key] = _savefile[key]\n",
-        "      if(key == \"_main\") :\n",
-        "        _savefile2[key] =  \"Prompt input only ✏️\"\n",
-        "        print(\"changed\")\n",
-        "    #----------#\n",
-        "\n",
-        "  save_filename = f'fusion_C05_X7_1000_{index}.json'\n",
-        "  index = index + 1\n",
-        "\n",
-        "  %cd {output_folder}\n",
-        "  print(f'Saving savefile {save_filename} to {output_folder}...')\n",
-        "  with open(save_filename, 'w') as f:\n",
-        "      json.dump(_savefile2, f)"
-      ],
-      "metadata": {
-        "id": "A3ASDnO3IzSL"
+        "id": "zivBNrw9uSVD",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []