Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -983,308 +983,6 @@
       },
       "execution_count": null,
       "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "OTHER STUFF BELOW - Code for the modules below are work-in-progress."
-      ],
-      "metadata": {
-        "id": "FRIqYJDEebpf"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "The savefile can be used here : https://perchance.org/fusion-ai-image-generator"
-      ],
-      "metadata": {
-        "id": "JldNmWy1iyvK"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# @title \t⚄ Create fusion-generator .json savefile from result\n",
-        "filename = 'blank.json'\n",
-        "path = '/content/text-to-image-prompts/fusion/'\n",
-        "\n",
-        "print(f'reading {filename}....')\n",
-        "_index = 0\n",
-        "%cd {path}\n",
-        "with open(f'{filename}', 'r') as f:\n",
-        "  data = json.load(f)\n",
-        "#------#\n",
-        "_df = pd.DataFrame({'count': data})['count']\n",
-        "_savefile = {\n",
-        "    key : value for key, value in _df.items()\n",
-        "}\n",
-        "#------#\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",
-        "#------#\n",
-        "save_filename = f'fusion_C05_X7_1000_{PROMPT_INDEX}.json'\n",
-        "output_folder = '/content/output/savefiles/'\n",
-        "my_mkdirs(output_folder)\n",
-        "#-----#\n",
-        "%cd {output_folder}\n",
-        "print(f'Saving segment {save_filename} to {output_folder}...')\n",
-        "with open(save_filename, 'w') as f:\n",
-        "    json.dump(_savefile, f)\n"
-      ],
-      "metadata": {
-        "id": "Q7vpNAXQilbf",
-        "cellView": "form"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# @title \t⚄ Create a savefile-set from the entire range of pre-encoded items\n",
-        "\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",
-        "if(load_the_data):\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",
-        "  from transformers import  CLIPProcessor, CLIPModel\n",
-        "  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",
-        "  logit_scale = model.logit_scale.exp() #logit_scale = 100.00000762939453\n",
-        "#---------#\n",
-        "\n",
-        "filename = 'blank.json'\n",
-        "path = '/content/text-to-image-prompts/fusion/'\n",
-        "print(f'reading {filename}....')\n",
-        "_index = 0\n",
-        "%cd {path}\n",
-        "with open(f'{filename}', 'r') as f:\n",
-        "  data = json.load(f)\n",
-        "#------#\n",
-        "_df = pd.DataFrame({'count': data})['count']\n",
-        "_blank = {\n",
-        "    key : value for key, value in _df.items()\n",
-        "}\n",
-        "#------#\n",
-        "\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",
-        "  prompt = target_prompts[f'{index}']\n",
-        "  url = urls[f'{index}']\n",
-        "  if url.find('perchance')>-1:\n",
-        "    image = Image.open(requests.get(url, stream=True).raw)\n",
-        "  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",
-        "  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",
-        "    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",
-        "  # @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",
-        "    for _index in range(start_at_index + RANGE):\n",
-        "      if _index < start_at_index : continue\n",
-        "      index = indices[_index].item()\n",
-        "      prompt = prompts[f'{index}']\n",
-        "      _prompts = _prompts.replace(prompt + separator,'')\n",
-        "      _prompts = _prompts  + prompt + separator\n",
-        "    #------#\n",
-        "    _prompts = fix_bad_symbols(_prompts)\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",
-        "  print(f'Saving savefile {save_filename} to {output_folder}...')\n",
-        "  with open(save_filename, 'w') as f:\n",
-        "      json.dump(_savefiles, f)\n",
-        "  #---------#\n",
-        "  continue\n",
-        "#-----------#"
-      ],
-      "metadata": {
-        "id": "x1uAVXZEoL0T",
-        "cellView": "form"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Determine if this notebook is running on Colab or Kaggle\n",
-        "#Use https://www.kaggle.com/ if Google Colab GPU is busy\n",
-        "home_directory = '/content/'\n",
-        "using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n",
-        "if using_Kaggle : home_directory = '/kaggle/working/'\n",
-        "%cd {home_directory}\n",
-        "#-------#\n",
-        "\n",
-        "# @title Download the text_encodings as .zip\n",
-        "import os\n",
-        "%cd {home_directory}\n",
-        "#os.remove(f'{home_directory}results.zip')\n",
-        "root_output_folder = home_directory + 'output/'\n",
-        "zip_dest = f'/content/results.zip' #drive/MyDrive\n",
-        "!zip -r {zip_dest} {root_output_folder}"
-      ],
-      "metadata": {
-        "id": "zivBNrw9uSVD",
-        "cellView": "form"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# @title \t⚄ Quick fix for normalizing encoded text corpus tensors\n",
-        "\n",
-        "import os\n",
-        "my_mkdirs('/content/output')\n",
-        "my_mkdirs('/content/output/text_encodings')\n",
-        "\n",
-        "for filename in os.listdir(f'{prompts_folder}'):\n",
-        "  %cd {prompts_folder}\n",
-        "  prompts = {}\n",
-        "  with open(f'{filename}', 'r') as f:\n",
-        "    data = json.load(f).items()\n",
-        "    for key,value in data:\n",
-        "      prompts[key] = value\n",
-        "    #------#\n",
-        "  num_items = int(prompts['num_items'])\n",
-        "\n",
-        "  %cd {encodings_folder}\n",
-        "  enc_filename = filename.replace('json', 'safetensors')\n",
-        "  _text_encodings = load_file(f'{enc_filename}')['weights'].to(torch.uint8)\n",
-        "  text_encodings = torch.zeros(num_items , dim)\n",
-        "  tmp = torch.ones(dim)\n",
-        "  tmp2 = torch.tensor(1/0.0043)\n",
-        "  zero_point = 0\n",
-        "  for index in range(num_items):\n",
-        "    text_encodings[index] = torch.tensor(0.0043) * torch.sub(_text_encodings[index][1:dim+1] , tmp , alpha= _text_encodings[index][0]).to(torch.float32)\n",
-        "    text_encodings[index] = tmp2*text_encodings[index]/text_encodings[index].norm(p=2, dim=-1, keepdim = True)\n",
-        "    test = torch.round( torch.add(text_encodings[index],tmp*zero_point))\n",
-        "    less_than_zero = test<0\n",
-        "    while(torch.any(less_than_zero).item()):\n",
-        "      zero_point = zero_point + 1\n",
-        "      test = torch.round( torch.add(text_encodings[index],tmp*zero_point))\n",
-        "      less_than_zero = test<0\n",
-        "    #------#\n",
-        "    _text_encodings[index][0]  = zero_point\n",
-        "    _text_encodings[index][1:dim+1] = test\n",
-        "  #-------#\n",
-        "  %cd /content/output/text_encodings\n",
-        "\n",
-        "  tmp = {}\n",
-        "  tmp['weights'] =  _text_encodings.to(torch.uint8)\n",
-        "  tmp['num_items'] = torch.tensor(num_items).to(torch.uint8)\n",
-        "  tmp['scale'] = torch.tensor(0.0043)\n",
-        "  save_file(tmp , f'{enc_filename}')\n",
-        "#------#"
-      ],
-      "metadata": {
-        "cellView": "form",
-        "id": "9qgHW1Wr7kZn"
-      },
-      "execution_count": null,
-      "outputs": []
     }
   ]
 }