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(122/122), done.\n", + "Filtering content: 100% (113/113), 355.03 MiB | 43.99 MiB/s, done.\n" + ] + } + ] }, { "cell_type": "code", @@ -136,16 +2896,16 @@ "civitai_blue_set = False # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n", "suffix = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n", "prefix = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n", - "emojis = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", + "emojis = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", "#------#\n", "\n", "#These are borked\n", - "celebs = False # param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n", - "celebs_young = False # param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n", + "celebs = True # @param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n", + "celebs_young = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n", "#-------#\n", "\n", "#-----#\n", - "female_fullnames = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", + "female_fullnames = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", "debug = False\n", "\n", "#------#\n", @@ -214,16 +2974,51 @@ "NUM_VOCAB_ITEMS = nA\n" ], "metadata": { - "id": "ZMG4CThUAmwW" + "id": "ZMG4CThUAmwW", + "outputId": "83b9599d-d72c-4b23-d7f0-c500504b5e10", + "colab": { + "base_uri": "https://localhost:8080/" + } }, - "execution_count": null, - "outputs": [] + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "reading 🆔👨 fusion-t2i-v2-celeb-1-6.json....\n", + "/content/text-to-image-prompts/names/celebs/mixed/text\n", + "/content/text-to-image-prompts/names/celebs/mixed/text_encodings\n", + "reading 🆔👨 fusion-t2i-v2-celeb-1-8.json....\n", + "/content/text-to-image-prompts/names/celebs/mixed/text\n", + "/content/text-to-image-prompts/names/celebs/mixed/text_encodings\n", + "reading 🆔👨 fusion-t2i-v2-celeb-1-5.json....\n", + "/content/text-to-image-prompts/names/celebs/mixed/text\n", + "/content/text-to-image-prompts/names/celebs/mixed/text_encodings\n", + "reading 🆔👨 fusion-t2i-v2-celeb-1-2.json....\n", + "/content/text-to-image-prompts/names/celebs/mixed/text\n", + "/content/text-to-image-prompts/names/celebs/mixed/text_encodings\n", + "reading 🆔👨 fusion-t2i-v2-celeb-1-4.json....\n", + "/content/text-to-image-prompts/names/celebs/mixed/text\n", + "/content/text-to-image-prompts/names/celebs/mixed/text_encodings\n", + "reading 🆔👨 fusion-t2i-v2-celeb-1-3.json....\n", + "/content/text-to-image-prompts/names/celebs/mixed/text\n", + "/content/text-to-image-prompts/names/celebs/mixed/text_encodings\n", + "reading 🆔👨 fusion-t2i-v2-celeb-1-7.json....\n", + "/content/text-to-image-prompts/names/celebs/mixed/text\n", + "/content/text-to-image-prompts/names/celebs/mixed/text_encodings\n", + "reading 🆔👨 fusion-t2i-v2-celeb-1-1.json....\n", + "/content/text-to-image-prompts/names/celebs/mixed/text\n", + "/content/text-to-image-prompts/names/celebs/mixed/text_encodings\n" + ] + } + ] }, { "cell_type": "code", "source": [ "# @title 📝 Get Prompt text_encoding similarity to the pre-calc. text_encodings\n", - "prompt = \"Beyonce and Lady Gaga\" # @param {\"type\":\"string\",\"placeholder\":\"Write a prompt\"}\n", + "prompt = \"Sahara Smith\" # @param {\"type\":\"string\",\"placeholder\":\"Write a prompt\"}\n", "\n", "from transformers import AutoTokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n", @@ -248,10 +3043,218 @@ "sorted , indices = torch.sort(sims,dim=0 , descending=True)" ], "metadata": { - "id": "xc-PbIYF428y" + "id": "xc-PbIYF428y", + "outputId": "6d8d3700-ac2c-42f4-c377-7e7c04f09ff4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 273, + "referenced_widgets": [ + "6e4bc2c3e92641f59c20a47128d6beb6", + "b04d4d0f88c84c1694382e91813c5779", + "fab6ea6958d84ac9bcdb6cdb6aab8672", + "e168408cf7f54820a6884d05e3005010", + "24ee8f605f2e4e97add0d4128e245cd5", + "f734ee4fa6c74017ba770ed98bdf8b4c", + "4b1b90ce47894a9e91715b2015273314", + "c1c3cd939ab94cdd91d1110e48b831d9", + "4fb48624ea954d16bc48c9919c10cfeb", + "f59335cf2236407a810cd8136c6a5b8b", + "7cc0cde67a974c7393d8a9fed485fd30", + "3d01e5fca66145c4a39268235d84d8ab", + "995a75dd91604a0d8d62b67c9fa4aa82", + "b0e35fd84d024ecf97ba7db3e587291b", + "1f43dd3a7f7c4fc0aeee80bae95a5c3a", + "a92422a2d340494ca1fa175cdf0abd9a", + "a1aa0b111bc04ce9a2f23cacef66dd05", + "996e52c83f27403fa1397c373e163de4", + "7e737bcf20cb4a57a6934bc6fcef544c", + "3faef113257247f1974564a8ac1c08af", + "51c21bb67a9546cc9aecb7b6bf8e11c9", + "ecc314bda12a471dae8870e508c60ff8", + "0d2491b4232543dbbf52f9f0d7cd6d24", + "34fa426779a94a7a80f745f7f2572a46", + "51e4c7ac101f4929b66febd6cf7ff6ca", + "6822d04772b64465915a2f6f1792016d", + "b29d4a051b924816ba26cbe928e8163a", + "5b7a689e81c94f56b76080455e78a2e8", + "f8766ef38f2e434493e0d55767317cdb", + "8f140aa341254e6aa8b2614641796084", + "8373736497fb48c4a702da0ce0eef210", + "6f5ecc195d844d64bf9a305dd4459521", + "c0ebe7d987a341f3a8abdcb2dc914254", + "08ab75cca9de44a2a54e7d00111e0b3c", + "9ff1f18396ba42d39994ecff6e9ff022", + "f3edd7864311426e94f203747f2788c8", + "c4afc9f828e44748a85d849c7f1a6dfb", + "b3425308bdb34d6888e544f85693a8e8", + "c4690c218c2f4f2396c515c909d732b6", + "06ed68db4e984b7691755a03b7030b61", + "e69e85713a2241e182610f023e5ef50e", + "fa788035977d4ed0afaf4d7c9071c216", + "984c26f75ffb44e489312534d8217e00", + "eef67f30ee7a483484882ae652a744ef", + "d88ca4b93bee4e40b846f92df70242a9", + "ebdb37c2133b45bb9f38b92b6acf1b70", + "b8b37800716449d58ca6f236cb126a18", + "5a7eef635b3f45de876239d938d4d0fe", + "90a24b9469d44186b78b48389d2aacf4", + "7858de41113b40418d01a4feda827072", + "c3cd74ef20e64b4698647d14d231d9f6", + "0b63743239544c1c9c6f683bfaac6d1b", + "f32b035318a9475bbdfdcd8645cebd21", + "099f56a34bfd489caa7b430c44e3dcef", + "a295a00f2f0d42f194aee57f9fa9ba4f", + "1e1dc783977d4724902fd1c9ac045776", + "798483b971b14a03b57fc7e991905d38", + "b01991e9b9ae4ed68ebab0abb656c817", + "aa9e8e20a69941e18008b4f932369daf", + "23e3d72c824844fc8ac11abc895b27f4", + "d51b034944c347b497a614a011babbee", + "25598cce7d4a46a8932d4970c5e777c1", + "61a73b0cfd2c4576bb3e2c1ad8e2e06d", + "d96d3f061a124af18a0d79783db0515c", + "85b35dc4aebc47af9c69fdddd92d7ca5", + "184e94db728343a594cee64c702c9071", + "34379f0830a043c1846a1db1793be0c0", + "c1acc4dccefb4f3893d261b18b64ae4e", + "5889110855d14b37a6709f8b709e0ae7", + "91374bab426e4b7fad46225a16ab4b18", + "40ebf1d9ac6d4e8b9544b2037e789723", + "71cdbb4d41b742a3980194a93004c8f8", + "596b6b63272b463a80b9a37167e8ec99", + "15d23891bc1642cf92fb0a61ae5a491a", + "26b05c53460d4915ae85e1c8206d0702", + "ecd7224c93c742e3a9dccaeb33c79120", + "c693e39d6c5e4fc382f7243dad7cfc4c", + "e7ebbc9e9ec04b918db0f3d52c7bad62", + "389e8a492a85418fbc5d78672f2511da", + "41f4da9faa9242998468141e57a24681", + "de7ec9e87899485db62dc60cb474fa9e", + "6913c50a638342939fb8b81b1fb97634", + "16a854d60ad14dd993d775086738f9b2", + "5b64d92af42d4f3d8a57598fd7b7f951", + "2a7a001a9dd14c6da57870c41f56b5ff", + "52542f2bd6514560a57fe3761751810d", + "c46b51a57daa4883b1bc4b915b332139", + "92d936e497eb4935a34b8ee349d777db" + ] + } }, - "execution_count": 9, - "outputs": [] + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/905 [00:00\",\" \") for key, value in _df.items()\n", " }\n", - " NUM_ITEMS = int(prompts[\"0\"])\n", + " index = 0\n", + " for key in prompts:\n", + " index = index + 1\n", + " #----------#\n", + " NUM_ITEMS = index\n", " #------#\n", "\n", " # Calculate text_encoding for .json file contents and results as .db file\n", - "\n", - " %cd {output_folder}\n", + " names_dict = {}\n", " text_encoding_dict = {}\n", - " offset = 0;\n", + " segments = {}\n", " index = 0;\n", " subby = 1;\n", + " NUM_HEADERS = 2\n", + " CHUNKS_SIZE = 1000\n", + " _filename = ''\n", " for _index in range(NUM_ITEMS):\n", - " index = _index - offset\n", - " inputs = tokenizer(text = '' + prompts[f'{index}'], padding=True, return_tensors=\"pt\").to(device)\n", - " text_features = model.get_text_features(**inputs).to(device)\n", - " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", - " text_encoding_dict[f'{index}'] = text_features.to(torch.device('cpu'))\n", - " if (index % 100 == 0) :\n", - " print(index)\n", - " if (index % 1000 == 0) :\n", - " save_file(text_encoding_dict, f'{filename}-{subby}.safetensors')\n", - " offset = offset + index\n", - " subby = subby + 1\n", - " text_encoding_dict = {}\n", - " continue\n", - " save_file(text_encoding_dict, f'{filename}.safetensors')\n", + " if (index % 100 == 0) : print(index)\n", + " if (index == 0 and _index>0) : index = index + 2 #make space for headers\n", + " if (_index % (CHUNKS_SIZE-NUM_HEADERS) == 0 and _index > 0) :\n", + "\n", + " # Write headers in the .json\n", + " names_dict[f'{0}'] = f'{_index}'\n", + " names_dict[f'{1}'] = f'{filename}-{subby}'\n", + "\n", + " # Encode the headers into text_encoding\n", + " inputs = tokenizer(text = '' + names_dict[f'{0}'], padding=True, return_tensors=\"pt\").to(device)\n", + " text_features = model.get_text_features(**inputs).to(device)\n", + " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", + " text_encoding_dict[f'{0}'] = text_features.to(torch.device('cpu'))\n", + " inputs = tokenizer(text = '' + names_dict[f'{1}'], padding=True, return_tensors=\"pt\").to(device)\n", + " text_features = model.get_text_features(**inputs).to(device)\n", + " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", + " text_encoding_dict[f'{1}'] = text_features.to(torch.device('cpu'))\n", + " #-------#\n", + "\n", + " # Write .json\n", + " _filename = f'{filename}-{subby}.json'\n", + " %cd {output_folder_text}\n", + " print(f'Saving segment {_filename} to {output_folder_text}...')\n", + " with open(_filename, 'w') as f:\n", + " json.dump(names_dict, f)\n", + " #-------#\n", + "\n", + " # Write .safetensors\n", + " _filename = f'{filename}-{subby}.safetensors'\n", + " %cd {output_folder_text_encodings}\n", + " print(f'Saving segment {_filename} to {output_folder_text_encodings}...')\n", + " save_file(text_encoding_dict, _filename)\n", + " #--------#\n", + "\n", + " #Iterate\n", + " subby = subby + 1\n", + " segments[f'{subby}'] = _filename\n", + " text_encoding_dict = {}\n", + " names_dict = {}\n", + " index = 0\n", + " #------#\n", + " #------#\n", + " else: index = index + 1\n", + " #--------#\n", + " inputs = tokenizer(text = '' + prompts[f'{_index}'], padding=True, return_tensors=\"pt\").to(device)\n", + " text_features = model.get_text_features(**inputs).to(device)\n", + " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", + " text_encoding_dict[f'{index}'] = text_features.to(torch.device('cpu'))\n", + " names_dict[f'{index}'] = prompts[f'{_index}']\n", + " continue\n", + " #-----#\n", " #-----#\n", + " # Write headers in the .json\n", + " names_dict[f'{0}'] = f'{_index}'\n", + " names_dict[f'{1}'] = f'{filename}-{subby}'\n", + "\n", + " # Encode the headers into text_encoding\n", + " inputs = tokenizer(text = '' + names_dict[f'{0}'], padding=True, return_tensors=\"pt\").to(device)\n", + " text_features = model.get_text_features(**inputs).to(device)\n", + " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", + " text_encoding_dict[f'{0}'] = text_features.to(torch.device('cpu'))\n", + " inputs = tokenizer(text = '' + names_dict[f'{1}'], padding=True, return_tensors=\"pt\").to(device)\n", + " text_features = model.get_text_features(**inputs).to(device)\n", + " text_features = text_features/text_features.norm(p=2, dim=-1, keepdim=True)\n", + " text_encoding_dict[f'{1}'] = text_features.to(torch.device('cpu'))\n", + " #-------#\n", + "\n", + " # Write .json\n", + " _filename = f'{filename}-{subby}.json'\n", + " %cd {output_folder_text}\n", + " print(f'Saving segment {_filename} to {output_folder_text}...')\n", + " with open(_filename, 'w') as f:\n", + " json.dump(names_dict, f)\n", + " #-------#\n", + "\n", + " # Write .safetensors\n", + " _filename = f'{filename}-{subby}.safetensors'\n", + " %cd {output_folder_text_encodings}\n", + " print(f'Saving segment {_filename} to {output_folder_text_encodings}...')\n", + " save_file(text_encoding_dict, _filename)\n", + " #--------#\n", "\n", + " #Iterate\n", + " subby = subby + 1\n", + " segments[f'{subby}'] = _filename\n", + " text_encoding_dict = {}\n", + " names_dict = {}\n", + " index = 0\n", + " #------#\n", " #----#\n", "\n", - "#from safetensors.torch import load_file\n", - "#%cd /content/text_encodings\n", - "#loaded = load_file('🦜 fusion-t2i-prompt-features-1.safetensors')\n", - "#print(loaded[\"325\"])" - ], - "metadata": { - "id": "9ZiTsF9jV0TV" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ "# @title Download the text_encodings as .zip\n", - "%cd /content/\n", - "!zip -r /content/text_encodings.zip /content/text_encodings/" - ], - "metadata": { - "id": "gX-sHZPWj4Lt" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# @title Merge text_encodings\n", - "\n", - "import json\n", - "import pandas as pd\n", "import os\n", - "import shelve\n", - "import torch\n", - "from safetensors.torch import save_file , load_file\n", - "\n", - "def my_mkdirs(folder):\n", - " if os.path.exists(folder)==False:\n", - " os.makedirs(folder)\n", - "\n", - "\n", - "using_Kaggle = False #Use https://www.kaggle.com/ if Google Colab GPU is busy\n", - "\n", - "home_directory = '/content/'\n", - "if using_Kaggle : home_directory = '/kaggle/working/'\n", - "output_folder = home_directory +'merged_text_encodings/'\n", - "my_mkdirs(f'{output_folder}')\n", - "\n", - "target = home_directory + 'text_encodings'\n", - "_filename = f'🆔👨 fusion-t2i-v2-celeb-1'\n", - "result = {}\n", - "#------#\n", - "%cd {target}\n", - "for _index in range(5):\n", - " if _index <1:continue\n", - " filename = _filename + f'-{_index}'\n", - " prompts = load_file(filename + '.safetensors')\n", - " for key in prompts:\n", - " index = _index*1000 + int(key)\n", - " result[f'{index}'] = prompts[key]\n", - " #------#\n", - "#--------3\n", - "\n", - "%cd {output_folder}\n", - "save_file(result, f'{_filename}.safetensors')\n", - "\n", - "\n", - "\n" - ], - "metadata": { - "id": "DNb_iJtLiuOL" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# @title Download the created text_encodings as .zip file\n", - "%cd /content/\n", - "!zip -r /content/text-encodings.zip /content/text_encodings" + "%cd {home_directory}\n", + "os.remove(f'{home_directory}results.zip')\n", + "zip_dest = f'{home_directory}results.zip'\n", + "!zip -r {zip_dest} {output_folder}" ], "metadata": { - "id": "b3DUPYfskAIc" + "id": "9ZiTsF9jV0TV" }, "execution_count": null, "outputs": []