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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": "markdown",
"source": [
"This notebook processes a JSON file of N items into chunks of 1000 items. The items are stored as a JSON + Safetensor pair. The name of the safestensor file is written within the JSON file at index 1"
],
"metadata": {
"id": "T7pqzVAFcPoK"
}
},
{
"cell_type": "code",
"source": [
"\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",
"# 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",
"#--------#"
],
"metadata": {
"id": "xow5kaB2SgPs"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cskYkw0zXHEm"
},
"outputs": [],
"source": [
"\n",
"# @title Make your own text_encodings .safetensor file for later use (using GPU is recommended to speed things up)\n",
"\n",
"# User input\n",
"target = home_directory + 'text-to-image-prompts/prefix_suffix_pairs/'\n",
"output_folder = home_directory + 'output/prefix_suffix_pairs/'\n",
"root_filename = 'prefix_suffix_pairs'\n",
"NUM_FILES = 1\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",
"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",
" if (NUM_FILES == 1) : filename = f'{root_filename}'\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",
"output_folder = '/content/output'\n",
"#os.remove(f'{home_directory}results.zip')\n",
"zip_dest = f'{home_directory}results.zip'\n",
"!zip -r {zip_dest} '/content/output'"
],
"metadata": {
"id": "cR-ed0CGhekk"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @title Download the text_encodings to google drive as .zip\n",
"from google.colab import drive\n",
"\n",
"output_folder = '/content/output'\n",
"#-----#\n",
"try mounted:\n",
"except:\n",
" mounted = True\n",
" drive.mount('/content/drive')\n",
"#------#\n",
"\n",
"zip_dest = '/content/drive/MyDrive/e621_and_suffixes.zip'\n",
"!zip -r {zip_dest} {output_folder}"
],
"metadata": {
"id": "zTRmgabymGI1"
},
"execution_count": null,
"outputs": []
}
]
} |