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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cskYkw0zXHEm"
},
"outputs": [],
"source": [
"# @title Make your own text_encodings .safetensor file for later use (using GPU is recommended to speed things up)\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\n",
"import json\n",
"\n",
"# Determine if this notebook is running on Colab or Kaggle\n",
"#Use https://www.kaggle.com/ if Google Colab GPU is busy\n",
"home_directory = '/content/'\n",
"using_Kaggle = os.environ.get('KAGGLE_URL_BASE','')\n",
"if using_Kaggle : home_directory = '/kaggle/working/'\n",
"%cd {home_directory}\n",
"#-------#\n",
"\n",
"# User input\n",
"target = home_directory + 'text-to-image-prompts/names/fullnames/'\n",
"output_folder = home_directory + 'output/fullnames/'\n",
"root_filename = 'names_fullnames_text_👱_♀️female_fullnames'\n",
"NUM_FILES = 9\n",
"#--------#\n",
"\n",
"# Setup environment\n",
"def my_mkdirs(folder):\n",
" if os.path.exists(folder)==False:\n",
" os.makedirs(folder)\n",
"#--------#\n",
"output_folder_text = output_folder + 'text/'\n",
"output_folder_text = output_folder + 'text/'\n",
"output_folder_text_encodings = output_folder + 'text_encodings/'\n",
"target_raw = target + 'raw/'\n",
"%cd {home_directory}\n",
"my_mkdirs(output_folder)\n",
"my_mkdirs(output_folder_text)\n",
"my_mkdirs(output_folder_text_encodings)\n",
"#-------#\n",
"\n",
"# Load the data if not already loaded\n",
"try:\n",
" loaded\n",
"except:\n",
" %cd {home_directory}\n",
" !git clone https://huggingface.co/datasets/codeShare/text-to-image-prompts\n",
" loaded = True\n",
"#--------#\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\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\").to(device)\n",
"#---------#\n",
"for file_index in range(NUM_FILES + 1):\n",
" if (file_index < 1): continue\n",
" filename = f'{root_filename}-{file_index}'\n",
"\n",
" # Read {filename}.json\n",
" %cd {target_raw}\n",
" with open(filename + '.json', 'r') as f:\n",
" data = json.load(f)\n",
" _df = pd.DataFrame({'count': data})['count']\n",
" prompts = {\n",
" key : value.replace(\"</w>\",\" \") for key, value in _df.items()\n",
" }\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",
" names_dict = {}\n",
" text_encoding_dict = {}\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",
" 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",
" #----#"
]
},
{
"cell_type": "code",
"source": [
"# @title Download the text_encodings as .zip\n",
"import os\n",
"%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": "cR-ed0CGhekk"
},
"execution_count": null,
"outputs": []
}
]
} |