Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -25,6 +25,17 @@
         "id": "cRV2YWomjMBU"
       }
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "THIS IS AN OLD VERSION OF THE CLIP INTERROGATOR.\n",
+        "\n",
+        "YOU WILL FIND THE UP TO DATE VERSION HERE:https://huggingface.co/datasets/codeShare/fusion-t2i-generator-data/tree/main/Google%20Colab%20Jupyter%20Notebooks"
+      ],
+      "metadata": {
+        "id": "9slWHq0JIX6D"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -57,7 +68,18 @@
         "  #----#\n",
         "  %cd {home_directory}\n",
         "  !git clone https://huggingface.co/datasets/codeShare/fusion-t2i-generator-data\n",
-        "  loaded = True\n",
+        "  loaded = True"
+      ],
+      "metadata": {
+        "id": "A30Xl4BswyEr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
         "  %cd {home_directory + 'fusion-t2i-generator-data/'}\n",
         "  !unzip vocab.zip\n",
         "  !unzip reference.zip\n",
@@ -104,8 +126,7 @@
         "for key in torch.load('reference_text_and_image_encodings.pt', weights_only=False):\n",
         "  index = index + 1;\n",
         "#------#\n",
-        "NUM_REFERENCE_ITEMS = index\n",
-        "\n"
+        "NUM_REFERENCE_ITEMS = index"
       ],
       "metadata": {
         "id": "TC5lMJrS1HCC"
@@ -261,26 +282,136 @@
         "image or print('No image found')"
       ],
       "metadata": {
-        "id": "NqL_I3ZSrISq"
+        "id": "NqL_I3ZSrISq",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "ouE3KYiJefac"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
-        "# Check the average value for this set\n",
-        "sims = torch.matmul(vocab_encodings.dequantize(),average.t())\n",
-        "sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
-        "for index in range(10):\n",
-        "  print(prompts[f'{indices[index].item()}'])"
+        "# @title ⚄ New interrogator code using quantized text corpus\n",
+        "ref = '' # @param {type:'string' , placeholder:'type a single prompt to match'}\n",
+        "LIST_SIZE = 1000  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
+        "\n",
+        "# @markdown Select vocab\n",
+        "fanfic = False # @param {type:\"boolean\"}\n",
+        "civitai = True # @param {type:\"boolean\"}\n",
+        "names = True # @param {type:\"boolean\"}\n",
+        "r34 = True # @param {type:\"boolean\"}\n",
+        "\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",
+        "dot_dtype = torch.float32\n",
+        "inputs = tokenizer(text = ref.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
+        "ref = model.get_text_features(**inputs)[0]\n",
+        "ref = (ref/ref.norm(p=2, dim=-1, keepdim=True)).to(dtype = dot_dtype)\n",
+        "#-----#\n",
+        "prompts_folder = f'{home_directory}fusion-t2i-generator-data/vocab/text'\n",
+        "encodings_folder = f'{home_directory}fusion-t2i-generator-data/vocab/text_encodings'\n",
+        "#----#\n",
+        "dim = 768\n",
+        "scale = 0.0043\n",
+        "size = 0\n",
+        "#------#\n",
+        "for filename in os.listdir(prompts_folder):\n",
+        "  if (not civitai and filename.find('civitai')>-1):continue\n",
+        "  if (not fanfic and filename.find('fanfic')>-1):continue\n",
+        "  if (not r34 and filename.find('r34')>-1):continue\n",
+        "  if (not names and filename.find('names')>-1):continue\n",
+        "  size = size + LIST_SIZE\n",
+        "#-------#\n",
+        "similiar_sims = torch.zeros(size)\n",
+        "similiar_prompts = {}\n",
+        "_index = 0\n",
+        "#-------#\n",
+        "similiar_encodings = {}\n",
+        "for filename in os.listdir(prompts_folder):\n",
+        "  if (not civitai and filename.find('civitai')>-1):continue\n",
+        "  if (not fanfic and filename.find('fanfic')>-1):continue\n",
+        "  if (not r34 and filename.find('r34')>-1):continue\n",
+        "  if (not names and filename.find('names')>-1):continue\n",
+        "  #------#\n",
+        "  root_filename = filename.replace('.json', '')\n",
+        "  %cd {prompts_folder}\n",
+        "  prompts = {}\n",
+        "  with open(f'{root_filename}.json', 'r') as f:\n",
+        "    data = json.load(f).items()\n",
+        "    for key,value in data:\n",
+        "      prompts[key] = value\n",
+        "  num_items = int(prompts['num_items'])\n",
+        "  #------#\n",
+        "  %cd {encodings_folder}\n",
+        "  _text_encodings = load_file(f'{root_filename}.safetensors')['weights'].to(torch.uint8)\n",
+        "\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",
+        "  #------#\n",
+        "  sims = torch.matmul(text_encodings*scale, ref.t())\n",
+        "  sorted , indices = torch.sort(sims , dim=0 , descending = True)\n",
+        "  for index in range(LIST_SIZE):\n",
+        "    key = indices[index].item()\n",
+        "    prompt = prompts[f'{key}']\n",
+        "    #-------#\n",
+        "    similiar_sims[_index] = torch.tensor(round(sims[key].item(), 5))\n",
+        "    similiar_prompts[f'{_index}'] = prompt\n",
+        "    _index = _index + 1\n",
+        "  #-------#\n",
+        "  continue\n",
+        "#---------#\n"
       ],
       "metadata": {
-        "id": "XNHz0hfhHRUu"
+        "cellView": "form",
+        "id": "w2dfozFY5IwM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title ⚄ Printing results from text corpus\n",
+        "sorted , indices = torch.sort(similiar_sims , dim=0 , descending = True)\n",
+        "\n",
+        "include_similiarity = False # @param {type:\"boolean\"}\n",
+        "for index in range(LIST_SIZE):\n",
+        "  key = indices[index].item()\n",
+        "  sim = similiar_sims[key].item()\n",
+        "  prompt = similiar_prompts[f'{key}']\n",
+        "  #-------#\n",
+        "  if include_similiarity :print(f'{prompt}  - {round(sim*100,1)} %')\n",
+        "  else: print(f'{prompt}')"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "E3kfOKXITDI9"
       },
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "OTHER STUFF BELOW"
+      ],
+      "metadata": {
+        "id": "FRIqYJDEebpf"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -594,59 +725,54 @@
     {
       "cell_type": "code",
       "source": [
-        "# @title \t⚄ New code (work in progress)\n",
-        "\n",
-        "def get_num_vocab_items(_url):\n",
-        "  num_vocab_items = 0\n",
-        "  for item in _url.split('_'):\n",
-        "    if item.find('safetensors')>-1: num_vocab_items = int(item.replace('.safetensors', ''))\n",
-        "  #------#\n",
-        "  return num_vocab_items-1\n",
+        "# @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",
-        "def get_similiar(_ref , urls, _LIST_SIZE):\n",
-        "  dot_dtype = torch.float16\n",
-        "  _SCALE = torch.tensor(0.0043).to(dot_dtype)\n",
-        "  _DIM = 768\n",
-        "  _vocab = {}\n",
-        "  #----#\n",
-        "  inputs = tokenizer(text = _ref.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
-        "  ref = model.get_text_features(**inputs)[0]\n",
-        "  ref = (ref/ref.norm(p=2, dim=-1, keepdim=True)).to(dtype = dot_dtype)\n",
-        "  #-----#\n",
-        "  num_vocab_items = 0\n",
-        "  for url in urls:\n",
-        "    num_vocab_items = num_vocab_items + get_num_vocab_items(url)\n",
-        "  #------#\n",
-        "  vocab = torch.zeros(num_vocab_items , _DIM).to(torch.uint8)\n",
+        "for filename in os.listdir(f'{prompts_folder}'):\n",
+        "  %cd {prompts_folder}\n",
         "  prompts = {}\n",
-        "  index = 0\n",
-        "  for url in urls:\n",
-        "    __vocab = load_file(url)\n",
-        "    for key in load_file(url):\n",
-        "      vocab[index] = __vocab[key][1:_DIM+1] - __vocab[key][0]*torch.ones(_DIM).t()\n",
-        "      prompts[f'{index}'] = key\n",
-        "      index = index + 1\n",
-        "    #-------#\n",
-        "    __vocab = {}\n",
-        "  #-------#\n",
-        "  sims = torch.matmul((vocab*_SCALE).to(dot_dtype) , ref.t())\n",
-        "  sorted , indices = torch.sort(sims, dim = 0 , descending = True)\n",
-        "  return indices , prompts , sims\n",
-        "  _prompts = {}\n",
-        "  for index in range(num_vocab_items):\n",
-        "    key = prompts[f'{indices[index]}']\n",
-        "    _prompts[f'{key}'] = sims[key].item()\n",
-        "    index = index + 1\n",
-        "    if index>_LIST_SIZE:break\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",
-        "  return _prompts\n",
-        "#-------#\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": "uDzsk02CbMFc"
+        "id": "9qgHW1Wr7kZn"
       },
       "execution_count": null,
       "outputs": []
@@ -654,31 +780,14 @@
     {
       "cell_type": "code",
       "source": [
-        "vocab = {}\n",
-        "# @title \t⚄ New code (work in progress)\n",
-        "ref = 'impressionist painting by luis royo' # @param {type:'string' , placeholder:'type a single prompt to match'}\n",
-        "LIST_SIZE = 1000  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
-        "urls = [ '/content/fusion-t2i-generator-data/civitai_vocab_q0043_203663.safetensors' ,]\n",
-        "\n",
-        "         #'/content/fusion-t2i-generator-data/clip_vocab_q0043_541291.safetensors' , '/content/fusion-t2i-generator-data/lyrics_vocab_q0043_41905.safetensors' , '/content/fusion-t2i-generator-data/names_vocab_q0043_162977.safetensors' , '/content/fusion-t2i-generator-data/r34_vocab_q0043_96166.safetensors' ]\n",
-        "\n",
-        "indices , prompts , sims = get_similiar(ref , urls , LIST_SIZE)\n",
-        "\n",
-        "index = 0\n",
-        "_prompts = {}\n",
-        "for index in range(203662):\n",
-        "  try:\n",
-        "    key = prompts[f'{indices[index].item()}']\n",
-        "    print(key)\n",
-        "  except: print('Not found!')\n",
-        "  #_prompts[f'{key}'] = sims[key].item()\n",
-        "  index = index + 1\n",
-        "  if index>LIST_SIZE:break\n",
-        "\n"
+        "# Check the average value for this set\n",
+        "sims = torch.matmul(vocab_encodings.dequantize(),average.t())\n",
+        "sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
+        "for index in range(10):\n",
+        "  print(prompts[f'{indices[index].item()}'])"
       ],
       "metadata": {
-        "cellView": "form",
-        "id": "Azz1kCza6LB3"
+        "id": "XNHz0hfhHRUu"
       },
       "execution_count": null,
       "outputs": []