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"1d43ca87431346fa9a52d5530393727e", - "2f863f2f920b46ef87f239fbe4762dfd", - "1d6c6f06e0fb4cf48e26f29efd5d6167", - "7c2b86deb50041a1af7fae7e495bdc3b", - "61fc79819d2e4ce8b7fb25ce7e39d0e7", - "009a65724c76480f95615fb0f16561d2", - "263543aba19a49e7a718be71f3263976", - "eac9a4bdf08e47ba9d5ed1203cebbe45" - ] - } + "id": "TC5lMJrS1HCC" }, "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content\n", - "/content\n", - "Cloning into 'fusion-t2i-generator-data'...\n", - "remote: Enumerating objects: 55, done.\u001b[K\n", - "remote: Counting objects: 100% (52/52), done.\u001b[K\n", - "remote: Compressing objects: 100% (49/49), done.\u001b[K\n", - "remote: Total 55 (delta 14), reused 0 (delta 0), pack-reused 3 (from 1)\u001b[K\n", - "Unpacking objects: 100% (55/55), 32.76 KiB | 541.00 KiB/s, done.\n", - "Filtering content: 100% (8/8), 2.56 GiB | 30.07 MiB/s, done.\n", - "/content/fusion-t2i-generator-data\n", - "Archive: vocab.zip\n", - " inflating: vocab/prompts.json \n", - " inflating: vocab/vocab_encodings.pt \n", - "Archive: reference.zip\n", - " inflating: reference/reference_prompts.json \n", - " inflating: reference/reference_text_and_image_encodings.pt \n", - " inflating: reference/reference_urls.json \n", - "/content/fusion-t2i-generator-data/vocab\n", - "/content/fusion-t2i-generator-data/reference\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/905 [00:00 📝 encoding
\n", - "C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n", - "log_strength_1 = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n", - "prompt_strength = math.pow(10 ,log_strength_1-1)\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", "\n", "%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n", @@ -3126,8 +152,8 @@ "# @markdown -----------\n", "# @markdown 📝➕ 1st Enhance similarity to prompt(s)\n", "POS_2 = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n", - "log_strength_2 = 1.06 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n", - "pos_strength = math.pow(10 ,log_strength_2-1)\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", " inputs = tokenizer(text = _POS.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n", " text_features_POS = model.get_text_features(**inputs)\n", @@ -3139,7 +165,7 @@ "# @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 = math.pow(10 ,log_strength_3-1)\n", + "pos_strength = round(math.pow(10 ,log_strength_3-1),2)\n", "for _POS in POS.split(','):\n", " inputs = tokenizer(text = _POS.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n", " text_features_POS = model.get_text_features(**inputs)\n", @@ -3150,15 +176,15 @@ "# @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 = math.pow(10 ,log_strength_4-1)\n", + "neg_strength = round(math.pow(10 ,log_strength_4-1),2)\n", "for _NEG in NEG.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 = 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", + "SKIP = '' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n", "\n", "def isBlacklisted(txt, blacklist):\n", " if txt.strip().isnumeric(): return True\n", @@ -3167,7 +193,13 @@ " if item.strip() == '' : continue\n", " if txt.find(item.strip())> -1 : return True\n", " #------#\n", - " return False\n", + " found = False\n", + " alphabet = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'\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", @@ -3178,47 +210,54 @@ "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", + "enable = 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", - " tmp = prompts[f'{indices[min(_index+start_at_index,NUM_VOCAB_ITEMS-1)].item()}']\n", - " if isBlacklisted(tmp , SKIP): continue\n", - " output = output + tmp + '|'\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", + " output = output + fix_bad_symbols(tmp) + '|'\n", + "#---------#\n", + "output = (output + '}').replace('|}' , '} ')\n", + "for iter in range(N):\n", + " print(output)\n", "#-------#\n", "image or print('No image found')" ], @@ -3547,7 +586,8 @@ "!zip -r {zip_dest} {root_output_folder}" ], "metadata": { - "id": "zivBNrw9uSVD" + "id": "zivBNrw9uSVD", + "cellView": "form" }, "execution_count": null, "outputs": [] @@ -3555,53 +595,93 @@ { "cell_type": "code", "source": [ - "\n", "# @title \t⚄ New code (work in progress)\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", - "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", - " output = torch.dot(tmp,__ref).item()\n", - " return output\n", - " #--------#\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 key in dict(sorted(vocab.items(), key=similarity)):\n", - " print(key)\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>=10:break\n", - " #------#\n", - "\n", - " return 1\n", - "#----------#\n", - "\n", - "inputs = tokenizer(text = _ref.strip(), truncation = True , padding=True, return_tensors=\"pt\")\n", - "ref = model.get_text_features(**inputs)[0]\n", - "\n", - "ref = (1/SCALE) * ref/ref.norm(p=2, dim=-1, keepdim=True)\n", - "ref = torch.round(ref).to(dtype=torch.uint8)\n", + " if index>_LIST_SIZE:break\n", + " #-------#\n", + " return _prompts\n", + "#-------#\n", + "\n" + ], + "metadata": { + "cellView": "form", + "id": "uDzsk02CbMFc" + }, + "execution_count": 49, + "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", - "url = '/content/fusion-t2i-generator-data/civitai_vocab_q0043_203663.safetensors'\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", - "vocab = load_file(url)\n", + "indices , prompts , sims = get_similiar(ref , urls , LIST_SIZE)\n", "\n", - "#get_most_similiar_items_to(ref , url , LIST_SIZE)" + "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" ], "metadata": { - "id": "PGyLzCmYqCPg" + "cellView": "form", + "id": "Azz1kCza6LB3" }, - "execution_count": null, + "execution_count": 56, "outputs": [] } ]