Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab 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": [
@@ -91,21 +102,6 @@
         "model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
         "logit_scale = model.logit_scale.exp() #logit_scale = 100.00000762939453\n",
         "\n",
-        "f_add = torch.nn.quantized.FloatFunctional()\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "TC5lMJrS1HCC"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
         "index = 0\n",
         "%cd {home_directory + 'fusion-t2i-generator-data/' + 'vocab'}\n",
         "vocab_encodings = torch.load('vocab_encodings.pt', weights_only=False)\n",
@@ -119,10 +115,11 @@
         "for key in torch.load('reference_text_and_image_encodings.pt', weights_only=False):\n",
         "  index = index + 1;\n",
         "#------#\n",
-        "NUM_REFERENCE_ITEMS = index"
+        "NUM_REFERENCE_ITEMS = index\n",
+        "\n"
       ],
       "metadata": {
-        "id": "Z8x3Y7IsnGlT"
+        "id": "TC5lMJrS1HCC"
       },
       "execution_count": null,
       "outputs": []
@@ -132,7 +129,7 @@
       "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 = 457 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
+        "index = 213 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
         "PROMPT_INDEX = index\n",
         "prompt = target_prompts[f'{PROMPT_INDEX}']\n",
         "url = target_urls[f'{PROMPT_INDEX}']\n",
@@ -140,48 +137,73 @@
         "  image = Image.open(requests.get(url, stream=True).raw)\n",
         "#------#\n",
         "# @markdown ⚖️ 🖼️ encoding <-----?-----> 📝 encoding </div> <br>\n",
-        "C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
-        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
-        "prompt_strength = math.pow(10 ,log_strength-1)\n",
-        "reference = torch.zeros(768)\n",
+        "C = 0.3 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+        "log_strength_1 = 2.17 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "prompt_strength = torch.tensor(math.pow(10 ,log_strength_1-1)).to(dtype = torch.float32)\n",
+        "reference = torch.zeros(768).to(dtype = torch.float32)\n",
         "\n",
         "%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
         "references = torch.load('reference_text_and_image_encodings.pt'  , weights_only=False)\n",
-        "reference = torch.add(reference, C * references[index][0].dequantize())\n",
-        "reference = torch.add(reference, (1-C) * references[index][1].dequantize())\n",
-        "references = ''\n",
+        "reference = torch.add(reference, prompt_strength * C * references[index][0].dequantize().to(dtype = torch.float32))\n",
+        "reference = torch.add(reference, prompt_strength * (1-C) * references[index][1].dequantize().to(dtype = torch.float32))\n",
+        "references = '' # Clear up memory\n",
         "# @markdown -----------\n",
-        "# @markdown 📝➕ Enhance similarity to prompt(s)\n",
+        "# @markdown 📝➕ 1st Enhance similarity to prompt(s)\n",
+        "POS_2 = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
+        "log_strength_2 = 1.03 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "pos_strength = torch.tensor(math.pow(10 ,log_strength_2-1)).to(dtype = torch.float32)\n",
+        "for _POS in POS_2.replace('</w>' , ' ').replace('{' , '').replace('}' , '').replace('|' , ',').split(','):\n",
+        "  inputs = tokenizer(text = _POS.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
+        "  text_features_POS = model.get_text_features(**inputs).to(dtype = torch.float32)\n",
+        "  text_features_POS = text_features_POS/text_features_POS.norm(p=2, dim=-1, keepdim=True)\n",
+        "  reference = torch.add(reference, pos_strength * text_features_POS)\n",
+        "# @markdown -----------\n",
+        "\n",
+        "# @markdown -----------\n",
+        "# @markdown 📝➕ 2nd Enhance similarity to prompt(s)\n",
         "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
-        "log_strength = 1.06 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
-        "pos_strength = math.pow(10 ,log_strength-1)\n",
-        "for _POS in POS.split(','):\n",
+        "log_strength_3 = 1.06 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "pos_strength = torch.tensor(math.pow(10 ,log_strength_3-1)).to(dtype = torch.float32)\n",
+        "for _POS in POS.replace('</w>' , ' ').replace('{' , '').replace('}' , '').replace('|' , ',').split(','):\n",
         "  inputs = tokenizer(text = _POS.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
-        "  text_features_POS = model.get_text_features(**inputs)\n",
+        "  text_features_POS = model.get_text_features(**inputs).to(dtype = torch.float32)\n",
         "  text_features_POS = text_features_POS/text_features_POS.norm(p=2, dim=-1, keepdim=True)\n",
         "  reference = torch.add(reference, pos_strength * text_features_POS)\n",
         "# @markdown -----------\n",
         "\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",
-        "for _NEG in NEG.split(','):\n",
+        "log_strength_4 = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "neg_strength = torch.tensor(math.pow(10 ,log_strength_4-1)).to(dtype = torch.float32)\n",
+        "for _NEG in NEG.replace('</w>' , ' ').replace('{' , '').replace('}' , '').replace('|' , ',').split(','):\n",
         "  inputs = tokenizer(text = _NEG.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
-        "  text_features_NEG = model.get_text_features(**inputs)\n",
+        "  text_features_NEG = model.get_text_features(**inputs).to(dtype = torch.float32)\n",
         "  text_features_NEG = text_features_NEG/text_features_NEG.norm(p=2, dim=-1, keepdim=True)\n",
-        "  reference = torch.add(reference, (-1) * neg_strength * text_features_NEG)\n",
+        "  reference = torch.sub(reference, neg_strength * text_features_NEG)\n",
         "# @markdown -----------\n",
         "# @markdown ⏩ Skip item(s) containing the word(s)\n",
-        "SKIP = 'futa ' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
-        "def isBlacklisted(txt):\n",
-        "  if txt.strip().isnumeric(): return True\n",
-        "  if blacklist.strip() == '': return False\n",
+        "SKIP = '' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
+        "\n",
+        "min_wordcount = 0 # @param {type:\"slider\", min:0, max:20, step:1}\n",
+        "\n",
+        "def isBlacklisted(_txt, _blacklist):\n",
+        "  blacklist =  _blacklist.lower().replace('</w>' , ' ').replace('{' , '').replace('}' , '').replace('|' , ',').strip()\n",
+        "  txt = _txt.lower().strip()\n",
+        "  if len(txt)<min_wordcount: return True\n",
+        "  if txt.isnumeric(): return True\n",
+        "  if blacklist == '': return False\n",
         "  for item in list(blacklist.split(',')):\n",
         "    if item.strip() == '' : continue\n",
         "    if txt.find(item.strip())> -1 : return True\n",
         "  #------#\n",
-        "  return False\n",
+        "  found = False\n",
+        "  alphabet = 'abcdefghijklmnopqrstuvxyz'\n",
+        "  for letter in alphabet:\n",
+        "    found =  txt.find(letter)>-1\n",
+        "    if found:break\n",
+        "  #------#\n",
+        "  return not found\n",
+        "\n",
         "# @markdown -----------\n",
         "# @markdown 🔍 How similar should the results be?\n",
         "list_size = 1000 # @param {type:'number'}\n",
@@ -191,46 +213,62 @@
         "N = 7 # @param {type:\"slider\", min:0, max:20, step:1}\n",
         "# @markdown -----------\n",
         "# @markdown ⚙️ Run the script?\n",
-        "run_script = True # @param {type:\"boolean\"}\n",
-        "enable = run_script\n",
+        "update_list = True # @param {type:\"boolean\"}\n",
+        "\n",
+        "calculate_variance  = False # @param {type:\"boolean\"}\n",
+        "\n",
+        "ne = update_list\n",
+        "\n",
+        "try: first\n",
+        "except:\n",
+        "  enable = True\n",
+        "  first = True\n",
+        "\n",
         "if (enable):\n",
         "  reference = reference/reference.norm(p=2, dim=-1, keepdim=True)\n",
         "  %cd {home_directory + 'fusion-t2i-generator-data/' + 'vocab'}\n",
         "  sims = torch.matmul(vocab_encodings.dequantize(),reference.t())\n",
         "  sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
         "\n",
-        "  average = torch.zeros(768)\n",
-        "  for key in range(NUM_VOCAB_ITEMS):\n",
-        "    if (key>=start_at_index and key < start_at_index + list_size):\n",
-        "      average = torch.add(average, vocab_encodings[key].dequantize())\n",
-        "    if (key>=start_at_index + list_size) : break\n",
-        "  average = average * (1/max(1, list_size))\n",
-        "  average = average/average.norm(p=2, dim=-1, keepdim=True)\n",
-        "  print(average.norm(p=2, dim=-1, keepdim=True))\n",
-        "  average = average.clone().detach();\n",
-        "  variance = torch.zeros(1)\n",
-        "  for key in range(NUM_VOCAB_ITEMS):\n",
-        "    if (key>=start_at_index and key < start_at_index + list_size):\n",
-        "      #dot product\n",
-        "\n",
-        "      difference_to_average = 100 * (torch.ones(1) - torch.dot(average[0]\n",
-        "       , vocab_encodings[key].dequantize()[0])/average.norm(p=2, dim=-1, keepdim=True))\n",
-        "\n",
-        "      variance =  torch.add(variance, difference_to_average * difference_to_average)\n",
-        "    if (key>=start_at_index + list_size) : break\n",
+        "  if calculate_variance:\n",
+        "    average = torch.zeros(768)\n",
+        "    for key in range(NUM_VOCAB_ITEMS):\n",
+        "      if (key>=start_at_index and key < start_at_index + list_size):\n",
+        "        average = torch.add(average, vocab_encodings[key].dequantize())\n",
+        "      if (key>=start_at_index + list_size) : break\n",
+        "    average = average * (1/max(1, list_size))\n",
+        "    average = average/average.norm(p=2, dim=-1, keepdim=True)\n",
+        "    average = average.clone().detach();\n",
+        "    variance = torch.zeros(1)\n",
+        "    for key in range(NUM_VOCAB_ITEMS):\n",
+        "      if (key>=start_at_index and key < start_at_index + list_size):\n",
+        "        #dot product\n",
+        "        difference_to_average = 100 * (torch.ones(1) - torch.dot(average[0]\n",
+        "        , vocab_encodings[key].dequantize()[0])/average.norm(p=2, dim=-1, keepdim=True))\n",
+        "        variance =  torch.add(variance, difference_to_average * difference_to_average)\n",
+        "      if (key>=start_at_index + list_size) : break\n",
+        "    #--------#\n",
+        "    variance =  variance * (1/max(1, list_size))\n",
+        "    variance= variance.clone().detach();\n",
+        "    print(f'The variance for the selected range is {math.sqrt(variance.item())} units from average')\n",
         "  #--------#\n",
-        "  variance =  variance * (1/max(1, list_size))\n",
-        "  variance= variance.clone().detach();\n",
-        "  print(f'The variance for the selected range is {math.sqrt(variance.item())} units from average')\n",
         "#---#\n",
-        "  output = '{'\n",
-        "  for _index in range(list_size):\n",
-        "    output = output + prompts[f'{indices[min(_index+start_at_index,NUM_VOCAB_ITEMS-1)].item()}'] + '|'\n",
-        "  #---------#\n",
-        "  output = (output + '}').replace('|}' , '} ')\n",
-        "  for iter in range(N):\n",
-        "    print(output)\n",
+        "output = '{'\n",
+        "for _index in range(list_size):\n",
+        "  tmp = prompts[f'{indices[min(_index+start_at_index,NUM_VOCAB_ITEMS-1)].item()}']\n",
+        "  if isBlacklisted(tmp , SKIP): continue\n",
+        "  tmp = fix_bad_symbols(tmp)\n",
+        "  if output.find(tmp)>-1:continue\n",
+        "  output = output + tmp + '|'\n",
+        "#---------#\n",
+        "output = (output + '}').replace('|}' , '} ')\n",
+        "print('')\n",
+        "print('')\n",
+        "for iter in range(N):\n",
+        "  print(output)\n",
         "#-------#\n",
+        "print('')\n",
+        "print('')\n",
         "image or print('No image found')"
       ],
       "metadata": {
@@ -558,7 +596,8 @@
         "!zip -r {zip_dest} {root_output_folder}"
       ],
       "metadata": {
-        "id": "zivBNrw9uSVD"
+        "id": "zivBNrw9uSVD",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
@@ -566,44 +605,91 @@
     {
       "cell_type": "code",
       "source": [
-        "\n",
         "# @title \t⚄ New code (work in progress)\n",
         "\n",
-        "LIST_SIZE = 1000\n",
-        "SCALE = 0.0043\n",
-        "DIM = 768\n",
-        "\n",
-        "from safetensors.torch import load_file\n",
-        "\n",
-        "def get_most_similiar_items_to(ref , url , num_items):\n",
-        "  vocab =  load_file(url)\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",
         "\n",
-        "  def similarity(item):\n",
-        "    key = item[0]\n",
-        "    value = item[1]\n",
-        "    tmp = torch.sub(value[1:DIM+1] , torch.ones(DIM) , alpha = value[0].item()).to(dtype=torch.uint8)\n",
-        "    return torch.dot(tmp,ref).item()\n",
-        "  #--------#\n",
         "\n",
-        "  return dict(sorted(vocab.items(), key=similarity))\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",
+        "  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",
+        "  #-------#\n",
+        "  return _prompts\n",
+        "#-------#\n",
+        "\n"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "uDzsk02CbMFc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "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",
-        "ref = torch.rand(DIM)\n",
-        "ref = (1/SCALE) * ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
-        "ref = torch.round(ref).to(dtype=torch.uint8)\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",
-        "url = '/content/fusion-t2i-generator-data/lyrics_vocab_q0043_41905.safetensors'\n",
-        "test = get_most_similiar_items_to(ref , url , LIST_SIZE)\n",
+        "indices , prompts , sims = get_similiar(ref , urls , LIST_SIZE)\n",
         "\n",
         "index = 0\n",
-        "for key in test:\n",
-        "  print(key)\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>=10:break\n",
-        "#-----#"
+        "  if index>LIST_SIZE:break\n",
+        "\n"
       ],
       "metadata": {
-        "id": "PGyLzCmYqCPg"
+        "cellView": "form",
+        "id": "Azz1kCza6LB3"
       },
       "execution_count": null,
       "outputs": []