Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -90,21 +90,7 @@
         "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",
-        "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",
@@ -118,10 +104,11 @@
         "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": []
@@ -131,7 +118,7 @@
       "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",
@@ -139,24 +126,24 @@
         "  image = Image.open(requests.get(url, stream=True).raw)\n",
         "#------#\n",
         "# @markdown ⚖️ 🖼️ encoding <-----?-----> 📝 encoding </div> <br>\n",
-        "C = 1 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
-        "log_strength_1 = 1.06 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
-        "prompt_strength = round(math.pow(10 ,log_strength_1-1),2)\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, prompt_strength * C * references[index][0].dequantize())\n",
-        "reference = torch.add(reference, prompt_strength * (1-C) * references[index][1].dequantize())\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 📝➕ 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 = round(math.pow(10 ,log_strength_2-1),2)\n",
-        "for _POS in POS_2.split(','):\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)\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",
@@ -165,10 +152,10 @@
         "# @markdown 📝➕ 2nd Enhance similarity to prompt(s)\n",
         "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
         "log_strength_3 = 1.06 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
-        "pos_strength = round(math.pow(10 ,log_strength_3-1),2)\n",
-        "for _POS in POS.split(','):\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",
@@ -176,25 +163,30 @@
         "# @markdown 🚫 Penalize similarity to prompt(s)\n",
         "NEG = '' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
         "log_strength_4 = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
-        "neg_strength = round(math.pow(10 ,log_strength_4-1),2)\n",
-        "for _NEG in NEG.split(','):\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.sub(reference, neg_strength * text_features_NEG)\n",
         "# @markdown -----------\n",
         "# @markdown ⏩ Skip item(s) containing the word(s)\n",
         "SKIP = '' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
         "\n",
-        "def isBlacklisted(txt, blacklist):\n",
-        "  if txt.strip().isnumeric(): return True\n",
-        "  if blacklist.strip() == '': return False\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",
         "  found = False\n",
-        "  alphabet = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'\n",
+        "  alphabet = 'abcdefghijklmnopqrstuvxyz'\n",
         "  for letter in alphabet:\n",
         "    found =  txt.find(letter)>-1\n",
         "    if found:break\n",
@@ -213,7 +205,8 @@
         "update_list = True # @param {type:\"boolean\"}\n",
         "\n",
         "calculate_variance  = False # @param {type:\"boolean\"}\n",
-        "enable = update_list\n",
+        "\n",
+        "ne = update_list\n",
         "\n",
         "try: first\n",
         "except:\n",
@@ -253,12 +246,18 @@
         "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",
-        "  output = output + fix_bad_symbols(tmp) + '|'\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": {
@@ -649,7 +648,7 @@
         "cellView": "form",
         "id": "uDzsk02CbMFc"
       },
-      "execution_count": 49,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -681,7 +680,7 @@
         "cellView": "form",
         "id": "Azz1kCza6LB3"
       },
-      "execution_count": 56,
+      "execution_count": null,
       "outputs": []
     }
   ]