diff --git "a/Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb" "b/Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb" new file mode 100644--- /dev/null +++ "b/Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb" @@ -0,0 +1,4846 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "Try this Free online SD 1.5 generator with the results: https://perchance.org/fusion-ai-image-generator\n", + "\n", + " This Notebook is a Stable-diffusion tool which allows you to find similiar prompts to an existing prompt. It uses the Nearest Neighbor decoder method listed here:https://arxiv.org/pdf/2303.03032" + ], + "metadata": { + "id": "cRV2YWomjMBU" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "UEYEdzjgOEOE", + "outputId": "1599d06c-8598-4668-d2d0-3da4b888b0c0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n", + "/content\n", + "Cloning into 'text-to-image-prompts'...\n", + "remote: Enumerating objects: 14190, done.\u001b[K\n", + "remote: Counting objects: 100% (7881/7881), done.\u001b[K\n", + "remote: Compressing objects: 100% (7799/7799), done.\u001b[K\n", + "remote: Total 14190 (delta 77), reused 7798 (delta 63), pack-reused 6309 (from 1)\u001b[K\n", + "Receiving objects: 100% (14190/14190), 157.43 MiB | 7.80 MiB/s, done.\n", + "Resolving deltas: 100% (2284/2284), done.\n", + "Updating files: 100% (4449/4449), done.\n", + "Filtering content: 100% (1457/1457), 4.21 GiB | 49.30 MiB/s, done.\n" + ] + } + ], + "source": [ + "# @title ✳️ Load/initialize values\n", + "#Imports\n", + "#!pip install safetensors\n", + "from safetensors.torch import load_file\n", + "import json , os , shelve , torch\n", + "import pandas as pd\n", + "#----#\n", + "\n", + "def my_mkdirs(folder):\n", + " if os.path.exists(folder)==False:\n", + " os.makedirs(folder)\n", + "\n", + "def fix_bad_symbols(txt):\n", + " result = txt\n", + " for symbol in ['^', '}', '{' , ')', '(', '[' , ']' , ':' , '=' ]:\n", + " result = result.replace(symbol,'\\\\' + symbol)\n", + " #------#\n", + " return result;\n", + "\n", + "\n", + "def getPrompts(_path, separator):\n", + " path = _path + '/text'\n", + " path_enc = _path + '/text_encodings'\n", + " #-----#\n", + " index = 0\n", + " prompts = {}\n", + " text_encodings = {}\n", + " _text_encodings = {}\n", + " #-----#\n", + " for filename in os.listdir(f'{path}'):\n", + " print(f'reading {filename}....')\n", + " _index = 0\n", + " %cd {path}\n", + " with open(f'{filename}', 'r') as f:\n", + " data = json.load(f)\n", + " #------#\n", + " _df = pd.DataFrame({'count': data})['count']\n", + " _prompts = {\n", + " key : value for key, value in _df.items()\n", + " }\n", + " _file_name = _prompts[f'{1}']\n", + " %cd {path_enc}\n", + " _text_encodings = load_file(f'{_file_name}.safetensors')\n", + " for key in _prompts:\n", + " _index = int(key)\n", + " value = _prompts[key]\n", + " if _index<2:continue\n", + " #------#\n", + " #Read the text_encodings + prompts\n", + " text_encodings[f'{index}'] = _text_encodings[f'{_index}']\n", + " prompts[f'{index}'] = _prompts[f'{_index}'] + separator\n", + " index = index + 1\n", + " continue\n", + " #-------#\n", + " #--------#\n", + " #----------#\n", + " NUM_ITEMS = index -1\n", + " return prompts , text_encodings , NUM_ITEMS\n", + "#--------#\n", + "\n", + "def append_from_url(dictA, tensA , nA , url , separator):\n", + " dictB , tensB, nB = getPrompts(url, separator)\n", + " dictAB = dictA\n", + " tensAB = tensA\n", + " nAB = nA\n", + " for key in dictB:\n", + " nAB = nAB + 1\n", + " dictAB[f'{nA + int(key)}'] = dictB[key]\n", + " tensAB[f'{nA + int(key)}'] = tensB[key]\n", + " #-----#\n", + " return dictAB, tensAB , nAB-1\n", + "#-------#\n", + "\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", + "#πŸ”ΈπŸ”Ή\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", + "\n", + "#default NEG values\n", + "try: name_NEG\n", + "except: name_NEG = ''\n", + "try: image_NEG\n", + "except: image_NEG = ''\n", + "try: strength_image_NEG\n", + "except: strength_image_NEG = 1\n", + "try: strength_NEG\n", + "except: strength_NEG = 1\n", + "try: NUM_VOCAB_ITEMS\n", + "except: NUM_VOCAB_ITEMS = 0\n", + "try: using_NEG\n", + "except: using_NEG = False\n", + "try: using_image_NEG\n", + "except: using_image_NEG = False\n", + "#------#\n", + "\n", + "def getJSON(path , filename):\n", + " %cd {path}\n", + " with open(f'{filename}', 'r') as f:\n", + " data = json.load(f)\n", + " #------#\n", + " print(f'reading {filename}....')\n", + " _df = pd.DataFrame({'count': data})['count']\n", + " _prompts = {\n", + " key : value for key, value in _df.items()\n", + " }\n", + " return _prompts\n", + "\n", + "#----#\n", + "\n", + "def getPromptsAndLinks(_path):\n", + " path = _path + '/text'\n", + " path_enc = _path + '/text_encodings'\n", + " #-----#\n", + " path_images = _path + '/images'\n", + " path_enc_images = _path + '/image_encodings'\n", + " #----#\n", + " _file_name = ''\n", + " _file_name_image = ''\n", + " #-----#\n", + " index = 0\n", + " prompts = {}\n", + " _prompts = {}\n", + " #-------#\n", + " urls = {}\n", + " _urls = {}\n", + " #------#\n", + " text_encodings = {}\n", + " _text_encodings = {}\n", + " image_encodings = {}\n", + " _image_encodings = {}\n", + " #-----#\n", + " for filename in os.listdir(f'{path}'):\n", + "\n", + " print(f'reading {filename}.json...')\n", + " _index = 0\n", + " %cd {path}\n", + " with open(f'{filename}', 'r') as f:\n", + " data = json.load(f)\n", + " _df = pd.DataFrame({'count': data})['count']\n", + " _prompts = {\n", + " key : value for key, value in _df.items()\n", + " }\n", + "\n", + " for key in _prompts:\n", + " _index = int(key)\n", + " value = _prompts[key]\n", + " if _index<=0: continue\n", + " if _index<=1:\n", + " _file_name = f'{value}'\n", + " _file_name_images = _prompts[f'{0}']\n", + " #-------#\n", + " print(f'reading {_file_name_images}.json..')\n", + " %cd {path_images}\n", + " with open(f'{_file_name_images}.json', 'r') as f:\n", + " data = json.load(f)\n", + " _df = pd.DataFrame({'count': data})['count']\n", + " _urls = {\n", + " key : value for key, value in _df.items()\n", + " }\n", + " #--------#\n", + " %cd {path_enc}\n", + " _text_encodings = load_file(f'{_file_name}.safetensors')\n", + " text_encodings[f'{index-1}'] = _text_encodings[f'{_index-1}']\n", + " text_encodings[f'{index}'] = _text_encodings[f'{_index}']\n", + " #-------#\n", + " %cd {path_enc_images}\n", + " _image_encodings = load_file(f'{_file_name_images}.safetensors')\n", + " image_encodings[f'{index-1}'] = _image_encodings[f'{_index-1}']\n", + " image_encodings[f'{index}'] = _image_encodings[f'{_index}']\n", + " #-------#\n", + " prompts[f'{index-1}'] = _prompts[f'{_index-1}']\n", + " urls[f'{index-1}'] = _urls[f'{_index-1}']\n", + " prompts[f'{index}'] = _prompts[f'{_index}']\n", + " urls[f'{index}'] = _urls[f'{_index}']\n", + " #-------#\n", + " index = index + 1\n", + " continue\n", + " #--------#\n", + " #Read the text_encodings + prompts\n", + " text_encodings[f'{index}'] = _text_encodings[f'{_index}']\n", + " image_encodings[f'{index}'] = _image_encodings[f'{_index}']\n", + " prompts[f'{index}'] = _prompts[f'{_index}']\n", + " urls[f'{index}'] = _urls[f'{_index}']\n", + " index = index + 1\n", + " continue\n", + " #-------#\n", + " #--------#\n", + " #----------#\n", + " NUM_ITEMS = index -1\n", + " return prompts , text_encodings , urls , image_encodings , NUM_ITEMS\n", + "#--------#" + ] + }, + { + "cell_type": "code", + "source": [ + "# @title πŸ“š Select items to sample from\n", + "\n", + "prompt_features = True # @param {\"type\":\"boolean\",\"placeholder\":\"🦜\"}\n", + "civitai_blue_set = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ“˜\"}\n", + "suffix = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ”Ή\"}\n", + "prefix = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ”Έ\"}\n", + "emojis = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ˜ƒ\"}\n", + "#------#\n", + "\n", + "first_names = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ”Ή\"}\n", + "last_names = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ”Έ\"}\n", + "celebs = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ†”πŸ‘¨\"}\n", + "#-------#\n", + "danbooru_tags = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸŽ€\"}\n", + "lyrics = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n", + "tripple_nouns = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n", + "#-----#\n", + "female_fullnames = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ˜ƒ\"}\n", + "debug = False\n", + "\n", + "civitai_red_set = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ“•\"}\n", + "e621 = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ˜ƒ\"}\n", + "prefix_suffix_pairs = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ˜ƒ\"}\n", + "suffix_tripple = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ˜ƒ\"}\n", + "suffix_quad = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ˜ƒ\"}\n", + "\n", + "fusion = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ˜ƒ\"}\n", + "manga = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ˜ƒ\"}\n", + "movies = True # @param {\"type\":\"boolean\",\"placeholder\":\"πŸ˜ƒ\"}\n", + "#------#\n", + "prompts = {}\n", + "text_encodings = {}\n", + "nA = 0\n", + "#--------#\n", + "\n", + "if manga:\n", + " url = '/content/text-to-image-prompts/manga'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "\n", + "if movies:\n", + " url = '/content/text-to-image-prompts/movies'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "\n", + "if fusion:\n", + " url = '/content/text-to-image-prompts/fusion'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "\n", + "if civitai_red_set:\n", + " url = '/content/text-to-image-prompts/civitai-prompts/red'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "\n", + "if e621:\n", + " url = '/content/text-to-image-prompts/e621'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "\n", + "if prefix_suffix_pairs:\n", + " url = '/content/text-to-image-prompts/prefix_suffix_pairs'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "\n", + "if suffix_tripple:\n", + " url = '/content/text-to-image-prompts/suffix_tripple'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "\n", + "if suffix_quad:\n", + " url = '/content/text-to-image-prompts/suffix_quad'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "\n", + "\n", + "\n", + "if tripple_nouns:\n", + " url = '/content/text-to-image-prompts/nouns'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "\n", + "if lyrics:\n", + " url = '/content/text-to-image-prompts/lyrics'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "\n", + "if danbooru_tags:\n", + " url = '/content/text-to-image-prompts/danbooru'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "#--------#\n", + "\n", + "if first_names:\n", + " url = '/content/text-to-image-prompts/names/firstnames'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "#--------#\n", + "\n", + "if last_names:\n", + " url = '/content/text-to-image-prompts/names/lastnames'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "#--------#\n", + "\n", + "if celebs:\n", + " url = '/content/text-to-image-prompts/names/celebs/mixed'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "#--------#\n", + "\n", + "if female_fullnames:\n", + " url = '/content/text-to-image-prompts/names/fullnames'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "#--------#\n", + "\n", + "\n", + "if prompt_features:\n", + " url = '/content/text-to-image-prompts/civitai-prompts/green'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "#--------#\n", + "\n", + "\n", + "if emojis:\n", + " url = '/content/text-to-image-prompts/vocab/text_encodings/emoji'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "#--------#\n", + "\n", + "\n", + "if civitai_blue_set:\n", + " url = '/content/text-to-image-prompts/civitai-prompts/blue'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "#--------#\n", + "\n", + "if suffix :\n", + " tmp = '/content/text-to-image-prompts/vocab/text_encodings/suffix/'\n", + " for item in ['common','average','rare','weird','exotic'] :\n", + " url = tmp + item\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "#------#\n", + "\n", + "if prefix :\n", + " tmp = '/content/text-to-image-prompts/vocab/text_encodings/prefix/'\n", + " for item in ['common','average','rare','weird','exotic'] :\n", + " url = tmp + item\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '-')\n", + "#------#\n", + "\n", + "if debug:\n", + " index = 0\n", + " for key in prompts: index = index + 1\n", + " print(index)\n", + " index = 0\n", + " for key in text_encodings : index = index + 1\n", + " print(index)\n", + "#------#\n", + "\n", + "NUM_VOCAB_ITEMS = nA\n", + "text_tensor = torch.zeros(NUM_VOCAB_ITEMS,768)\n", + "for index in range(NUM_VOCAB_ITEMS):\n", + " text_tensor[index] = text_encodings[f'{index}']\n", + "#---------#\n", + "print(f\"Done loading vocabulary for the interrogator\")\n", + "print(f\"Vocab size is : {NUM_VOCAB_ITEMS} items\")" + ], + "metadata": { + "id": "CF53WIAKObg3", + "outputId": "431ca8ef-6362-4198-ea57-f57e672da3ab", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "cellView": "form" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "reading 🈹 fusion-t2i-manga-2.json....\n", + "/content/text-to-image-prompts/manga/text\n", + "/content/text-to-image-prompts/manga/text_encodings\n", + "reading 🈹 fusion-t2i-manga-1.json....\n", + "/content/text-to-image-prompts/manga/text\n", + "/content/text-to-image-prompts/manga/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-13.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-16.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-20.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-1.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-17.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-9.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-7.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-19.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-6.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-10.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-11.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-21.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-12.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-4.json....\n", + "/content/text-to-image-prompts/movies/text\n", + "/content/text-to-image-prompts/movies/text_encodings\n", + "reading 🎬🍿 fusion-t2i-movies-15.json....\n", + "/content/text-to-image-prompts/movies/text\n", + 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+ "reading tripple_nouns_13-2.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_14-8.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_14-9.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_13-7.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_15-2.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_12-9.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_17-6.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + 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"/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_11-11.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_17-3.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_15-4.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_16-6.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_13-4.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_1-10.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading 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"/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_17-9.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_17-7.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_10-10.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_10-9.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_11-4.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_13-8.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading 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"/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_15-5.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_15-10.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_15-6.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_17-10.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_16-5.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading tripple_nouns_15-3.json....\n", + "/content/text-to-image-prompts/nouns/text\n", + "/content/text-to-image-prompts/nouns/text_encodings\n", + "reading 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"/content/text-to-image-prompts/vocab/text_encodings/prefix/rare/text_encodings\n", + "reading πŸ”Έ fusion-t2i-sd15-clip-tokens-rare-prefix-1 Tokens.json....\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/rare/text\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/rare/text_encodings\n", + "reading πŸ”Έ fusion-t2i-sd15-clip-tokens-rare-prefix-3 Tokens.json....\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/rare/text\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/rare/text_encodings\n", + "reading πŸ”Έ fusion-t2i-sd15-clip-tokens-weird-prefix-3 Tokens.json....\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/weird/text\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/weird/text_encodings\n", + "reading πŸ”Έ fusion-t2i-sd15-clip-tokens-weird-prefix-2 Tokens.json....\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/weird/text\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/weird/text_encodings\n", + "reading πŸ”Έ fusion-t2i-sd15-clip-tokens-weird-prefix-1 Tokens.json....\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/weird/text\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/weird/text_encodings\n", + "reading πŸ”Έ fusion-t2i-sd15-clip-tokens-exotic-prefix-3 Tokens.json....\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/exotic/text\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/exotic/text_encodings\n", + "reading πŸ”Έ fusion-t2i-sd15-clip-tokens-exotic-prefix-2 Tokens.json....\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/exotic/text\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/exotic/text_encodings\n", + "reading πŸ”Έ fusion-t2i-sd15-clip-tokens-exotic-prefix-1 Tokens.json....\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/exotic/text\n", + "/content/text-to-image-prompts/vocab/text_encodings/prefix/exotic/text_encodings\n", + "Done loading vocabulary for the interrogator\n", + "Vocab size is : 1073905 items\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# @title \tβš„ Use a pre-encoded prompt + image pair from the fusion gen (note: NSFW!)\n", + "# @markdown πŸ–ΌοΈ Choose a pre-encoded reference\n", + "index = 682 # @param {type:\"slider\", min:0, max:1666, step:1}\n", + "PROMPT_INDEX = index\n", + "\n", + "import math\n", + "# @markdown -----------\n", + "# @markdown πŸ“βž• Enhance similarity to prompt(s)\n", + "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n", + "log_strength = 1.06 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n", + "pos_strength = math.pow(10 ,log_strength-1)\n", + "# @markdown -----------\n", + "\n", + "# @markdown 🚫 Penalize similarity to prompt(s)\n", + "NEG = '' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n", + "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n", + "neg_strength = math.pow(10 ,log_strength-1)\n", + "\n", + "# @markdown ⏩ Skip item(s) containing the word\n", + "SKIP = 'futa ' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n", + "\n", + "# @markdown βš–οΈ How important is the prompt vs. the image?

\n", + "# @markdown
sim_ref = (10^(log_strength-1)) * ( C* text_encoding + image_encoding*(1-C) )

\n", + "C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n", + "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n", + "prompt_strength = math.pow(10 ,log_strength-1)\n", + "\n", + "blacklist = SKIP\n", + "# @markdown -----------\n", + "# @title βš™οΈπŸ“ Print the results (Advanced)\n", + "list_size = 1000 # param {type:'number'}\n", + "start_at_index = 0 # param {type:'number'}\n", + "print_Similarity = True # param {type:\"boolean\"}\n", + "print_Prompts = True # param {type:\"boolean\"}\n", + "print_Prefix = True # param {type:\"boolean\"}\n", + "print_Descriptions = True # param {type:\"boolean\"}\n", + "compact_Output = True # param {type:\"boolean\"}\n", + "\n", + "\n", + "# markdown Printing options\n", + "newline_Separator = False # param {type:\"boolean\"}\n", + "\n", + "import random\n", + "list_size2 = 0 # param {type:'number'}\n", + "start_at_index2 = NUM_VOCAB_ITEMS # param {type:'number'}\n", + "rate_percent = -1 # param {type:\"slider\", min:0, max:100, step:1}\n", + "\n", + "# @markdown Repeat output N times\n", + "N = 7 # @param {type:\"slider\", min:0, max:10, step:1}\n", + "\n", + "#image_index = 0 # @param {type:'number'}\n", + "# @markdown πŸ“₯ Reload vocab (required if you change the vocab)\n", + "reload_vocab = False # @param {type:\"boolean\"}\n", + "_load_the_data = reload_vocab\n", + "\n", + "#image_index = 0 # @param {type:'number'}\n", + "# @markdown βš™οΈ Do dot product calculation (disable if you only want to browse images)\n", + "run_script = True # @param {type:\"boolean\"}\n", + "enable = run_script\n", + "\n", + "\n", + "\n", + "def isBlacklisted(txt):\n", + " if txt.strip().isnumeric(): return True\n", + " if blacklist.strip() == '': 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", + " return False\n", + "\n", + "# Load the data if not already loaded\n", + "try:\n", + " loaded2\n", + "except:\n", + " _load_the_data = True\n", + " loaded2 = True\n", + "\n", + "if (_load_the_data):\n", + " target_prompts , target_text_encodings , urls , target_image_encodings , NUM_ITEMS = getPromptsAndLinks('/content/text-to-image-prompts/fusion')\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\")\n", + " logit_scale = model.logit_scale.exp() #logit_scale = 100.00000762939453\n", + "\n", + "from PIL import Image\n", + "import requests\n", + "prompt = target_prompts[f'{index}']\n", + "url = urls[f'{index}']\n", + "if url.find('perchance')>-1:\n", + " image = Image.open(requests.get(url, stream=True).raw)\n", + "else: print(\"(No image for this ID)\")\n", + "\n", + "print(\"\")\n", + "print(f\"'{prompt}'\")\n", + "print(\"\")\n", + "\n", + "if(enable):\n", + " text_features_A = target_text_encodings[f'{index}']\n", + " image_features_A = target_image_encodings[f'{index}']\n", + "\n", + " # text-similarity\n", + " sims = prompt_strength * C * torch.matmul(text_tensor, text_features_A.t())\n", + "\n", + " # Calculate negatives\n", + " neg_sims = {}\n", + " neg_sims[f'{0}'] = 0*sims\n", + " if(NEG != ''):\n", + " _index = 0\n", + " for _NEG in NEG.split(','):\n", + " inputs = tokenizer(text = _NEG, 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", + " # text-similarity\n", + " neg_sims[f'{_index}'] = torch.matmul(text_tensor, text_features_NEG.t())\n", + " #------#\n", + "\n", + " # Calculate positives\n", + " pos_sims = {}\n", + " pos_sims[f'{0}'] = 0*sims\n", + " if(POS != ''):\n", + " _index = 0\n", + " for _POS in POS.split(','):\n", + " inputs = tokenizer(text = _POS, truncation = True , padding=True, return_tensors=\"pt\")\n", + " text_features_POS = model.get_text_features(**inputs)\n", + " text_features_POS = text_features_POS/text_features_POS.norm(p=2, dim=-1, keepdim=True)\n", + " # text-similarity\n", + " pos_sims[f'{_index}'] = torch.matmul(text_tensor, text_features_POS.t())\n", + " #------#\n", + "\n", + " # plus image-similarity\n", + " img_sims = torch.matmul(text_tensor, image_features_A.t()) * logit_scale\n", + " sims = sims + prompt_strength * (1-C) * img_sims\n", + "\n", + "\n", + " # plus POS-similarity\n", + " for key in pos_sims:\n", + " sims = sims + pos_strength*pos_sims[key]\n", + " #------#\n", + "\n", + " # minus NEG-similarity\n", + " for key in neg_sims:\n", + " sims = sims - neg_strength*neg_sims[key]\n", + " #-------#\n", + "\n", + "\n", + " # Sort the items\n", + " sorted , indices = torch.sort(sims,dim=0 , descending=True)\n", + "\n", + " # title Show the 100 most similiar suffix and prefix text-encodings to the text encoding\n", + " RANGE = list_size\n", + " separator = ' |'\n", + " if newline_Separator : separator = separator + '\\n'\n", + "\n", + " _prompts = ''\n", + " _sims = ''\n", + " offset = 0\n", + " for _index in range(start_at_index + RANGE):\n", + " if _index < start_at_index : continue\n", + "\n", + " for iters in range(1000):\n", + " found = True\n", + " index = indices[min(_index + offset,NUM_VOCAB_ITEMS-1)].item()\n", + " if isBlacklisted(prompts[f'{index}'].lower()):\n", + " offset = offset + 1\n", + " found = False\n", + " if (_index + offset)>=NUM_VOCAB_ITEMS-2 :\n", + " found = True\n", + " offset = NUM_VOCAB_ITEMS - _index -1\n", + " if found : break\n", + " #-------#\n", + "\n", + " index = indices[_index + offset].item()\n", + " prompt = prompts[f'{index}']\n", + "\n", + " if rate_percent >= random.randint(0,100) : prompt = prompts[f'{random.randint(start_at_index2 , start_at_index2 + list_size2)}']\n", + "\n", + "\n", + " #---------#\n", + "\n", + " #Remove duplicates\n", + " if _prompts.find(prompt + separator)<=-1:\n", + " _sims = _sims + f'{round(100*sims[index].item(), 2)} %' + separator\n", + " #-------#\n", + " _prompts = _prompts.replace(prompt + separator,'')\n", + " _prompts = _prompts + prompt + separator\n", + " #------#\n", + " #------#\n", + " _prompts = fix_bad_symbols(_prompts)\n", + " __prompts = ('{' + _prompts + '}').replace(separator + '}', '}')\n", + " __sims = ('{' + _sims + '}').replace(separator + '}', '}')\n", + " #------#\n", + "\n", + " if(not print_Prompts): __prompts = ''\n", + " if(not print_Similarity): __sims = ''\n", + "\n", + " if(not compact_Output):\n", + " if(print_Descriptions):\n", + " print(f'The {start_at_index}-{start_at_index + RANGE} most similiar items to prompt : \\n\\n ')\n", + " for i in range(N) : print(__prompts)\n", + " print(f'The {start_at_index}-{start_at_index + RANGE} similarity % for items : \\n\\n' + __sims)\n", + " print('')\n", + " else:\n", + " for i in range(N) : print(__prompts)\n", + " else:\n", + " for i in range(N) : print(__prompts)\n", + " #-------#\n", + " #-------#\n", + "#-------#\n", + "image or print('No image found')\n", + "\n", + "\n", + "#------#" + ], + "metadata": { + "id": "XW3914T8O2uf", + "cellView": "form" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# @title βš™οΈπŸ“ Print the results (Advanced)\n", + "list_size = 1000 # @param {type:'number'}\n", + "start_at_index = 0 # @param {type:'number'}\n", + "print_Similarity = True # @param {type:\"boolean\"}\n", + "print_Prompts = True # @param {type:\"boolean\"}\n", + "print_Descriptions = True # @param {type:\"boolean\"}\n", + "compact_Output = True # @param {type:\"boolean\"}\n", + "newline_Separator = False # @param {type:\"boolean\"}\n", + "\n", + "import random\n", + "# @markdown -----------\n", + "# @markdown Mix with...\n", + "list_size2 = 1000 # @param {type:'number'}\n", + "start_at_index2 = 10000 # @param {type:'number'}\n", + "rate_percent = 0 # @param {type:\"slider\", min:0, max:100, step:1}\n", + "\n", + "# @markdown -----------\n", + "# @markdown Repeat output N times\n", + "N = 6 # @param {type:\"slider\", min:0, max:10, step:1}\n", + "\n", + "# title Show the 100 most similiar suffix and prefix text-encodings to the text encoding\n", + "RANGE = list_size\n", + "separator = '|'\n", + "if newline_Separator : separator = separator + '\\n'\n", + "\n", + "_prompts = ''\n", + "_sims = ''\n", + "for _index in range(start_at_index + RANGE):\n", + " if _index < start_at_index : continue\n", + " index = indices[_index].item()\n", + "\n", + " prompt = prompts[f'{index}']\n", + " if rate_percent >= random.randint(0,100) : prompt = prompts[f'{random.randint(start_at_index2 , start_at_index2 + list_size2)}']\n", + "\n", + " #Remove duplicates\n", + " if _prompts.find(prompt + separator)<=-1:\n", + " _sims = _sims + f'{round(100*sims[index].item(), 2)} %' + separator\n", + " #-------#\n", + " _prompts = _prompts.replace(prompt + separator,'')\n", + " _prompts = _prompts + prompt + separator\n", + " #------#\n", + "#------#\n", + "__prompts = fix_bad_symbols(__prompts)\n", + "__prompts = ('{' + _prompts + '}').replace(separator + '}', '}')\n", + "__sims = ('{' + _sims + '}').replace(separator + '}', '}')\n", + "#------#\n", + "\n", + "if(not print_Prompts): __prompts = ''\n", + "if(not print_Similarity): __sims = ''\n", + "\n", + "if(not compact_Output):\n", + " if(print_Descriptions):\n", + " print(f'The {start_at_index}-{start_at_index + RANGE} most similiar items to prompt : \\n\\n ')\n", + " for i in range(N) : print(__prompts)\n", + " print(f'The {start_at_index}-{start_at_index + RANGE} similarity % for items : \\n\\n' + __sims)\n", + " print('')\n", + " else:\n", + " for i in range(N) : print(__prompts)\n", + "else:\n", + " for i in range(N) : print(__prompts)\n", + "#-------#" + ], + "metadata": { + "id": "EdBiAguJO9aX", + "cellView": "form" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The savefile can be used here : https://perchance.org/fusion-ai-image-generator" + ], + "metadata": { + "id": "JldNmWy1iyvK" + } + }, + { + "cell_type": "code", + "source": [ + "# @title \tβš„ Create fusion-generator .json savefile from result\n", + "filename = 'blank.json'\n", + "path = '/content/text-to-image-prompts/fusion/'\n", + "\n", + "print(f'reading {filename}....')\n", + "_index = 0\n", + "%cd {path}\n", + "with open(f'{filename}', 'r') as f:\n", + " data = json.load(f)\n", + "#------#\n", + "_df = pd.DataFrame({'count': data})['count']\n", + "_savefile = {\n", + " key : value for key, value in _df.items()\n", + "}\n", + "#------#\n", + "from safetensors.torch import load_file\n", + "import json , os , torch\n", + "import pandas as pd\n", + "#----#\n", + "def my_mkdirs(folder):\n", + " if os.path.exists(folder)==False:\n", + " os.makedirs(folder)\n", + "#------#\n", + "savefile_prompt = ''\n", + "for i in range(N) : savefile_prompt = savefile_prompt + ' ' + __prompts\n", + "_savefile['main'] = savefile_prompt.replace('\\n', ' ').replace(' ', ' ').replace(' ', ' ')\n", + "#------#\n", + "save_filename = f'fusion_C05_X7_1000_{PROMPT_INDEX}.json'\n", + "output_folder = '/content/output/savefiles/'\n", + "my_mkdirs(output_folder)\n", + "#-----#\n", + "%cd {output_folder}\n", + "print(f'Saving segment {save_filename} to {output_folder}...')\n", + "with open(save_filename, 'w') as f:\n", + " json.dump(_savefile, f)\n" + ], + "metadata": { + "id": "Q7vpNAXQilbf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# @title \tβš„ Create a savefile-set from the entire range of pre-encoded items\n", + "\n", + "# @markdown πŸ“₯ Load the data (only required one time)\n", + "load_the_data = True # @param {type:\"boolean\"}\n", + "\n", + "import math\n", + "from safetensors.torch import load_file\n", + "import json , os , torch\n", + "import pandas as pd\n", + "from PIL import Image\n", + "import requests\n", + "\n", + "def my_mkdirs(folder):\n", + " if os.path.exists(folder)==False:\n", + " os.makedirs(folder)\n", + "\n", + "# @markdown βš–οΈ Set the value for C in the reference

sim = C* text_enc + image_enc*(1-C)

\n", + "\n", + "C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n", + "\n", + "# @markdown 🚫 Penalize similarity to this prompt(optional)\n", + "if(load_the_data):\n", + " target_prompts , target_text_encodings , urls , target_image_encodings , NUM_ITEMS = getPromptsAndLinks('/content/text-to-image-prompts/fusion')\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\")\n", + " logit_scale = model.logit_scale.exp() #logit_scale = 100.00000762939453\n", + "#---------#\n", + "\n", + "filename = 'blank.json'\n", + "path = '/content/text-to-image-prompts/fusion/'\n", + "print(f'reading {filename}....')\n", + "_index = 0\n", + "%cd {path}\n", + "with open(f'{filename}', 'r') as f:\n", + " data = json.load(f)\n", + "#------#\n", + "_df = pd.DataFrame({'count': data})['count']\n", + "_blank = {\n", + " key : value for key, value in _df.items()\n", + "}\n", + "#------#\n", + "\n", + "root_savefile_name = 'fusion_C05_X7'\n", + "\n", + "%cd /content/\n", + "output_folder = '/content/output/savefiles/'\n", + "my_mkdirs(output_folder)\n", + "my_mkdirs('/content/output2/savefiles/')\n", + "my_mkdirs('/content/output3/savefiles/')\n", + "my_mkdirs('/content/output4/savefiles/')\n", + "my_mkdirs('/content/output5/savefiles/')\n", + "my_mkdirs('/content/output6/savefiles/')\n", + "my_mkdirs('/content/output7/savefiles/')\n", + "my_mkdirs('/content/output8/savefiles/')\n", + "my_mkdirs('/content/output9/savefiles/')\n", + "my_mkdirs('/content/output10/savefiles/')\n", + "my_mkdirs('/content/output11/savefiles/')\n", + "my_mkdirs('/content/output12/savefiles/')\n", + "my_mkdirs('/content/output13/savefiles/')\n", + "\n", + "\n", + "NEG = '' # @param {type:'string'}\n", + "strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.1}\n", + "\n", + "for index in range(1667):\n", + "\n", + " PROMPT_INDEX = index\n", + " prompt = target_prompts[f'{index}']\n", + " url = urls[f'{index}']\n", + " if url.find('perchance')>-1:\n", + " image = Image.open(requests.get(url, stream=True).raw)\n", + " else: continue #print(\"(No image for this ID)\")\n", + "\n", + " print(f\"no. {PROMPT_INDEX} : '{prompt}'\")\n", + " text_features_A = target_text_encodings[f'{index}']\n", + " image_features_A = target_image_encodings[f'{index}']\n", + " # text-similarity\n", + " sims = C * torch.matmul(text_tensor, text_features_A.t())\n", + "\n", + " neg_sims = 0*sims\n", + " if(NEG != ''):\n", + " # Get text features for user input\n", + " inputs = tokenizer(text = NEG, padding=True, return_tensors=\"pt\")\n", + " text_features_NEG = model.get_text_features(**inputs)\n", + " text_features_NEG = text_features_A/text_features_A.norm(p=2, dim=-1, keepdim=True)\n", + " # text-similarity\n", + " neg_sims = strength*torch.matmul(text_tensor, text_features_NEG.t())\n", + " #------#\n", + "\n", + " # plus image-similarity\n", + " sims = sims + (1-C) * torch.matmul(text_tensor, image_features_A.t()) * logit_scale\n", + "\n", + " # minus NEG-similarity\n", + " sims = sims - neg_sims\n", + "\n", + " # Sort the items\n", + " sorted , indices = torch.sort(sims,dim=0 , descending=True)\n", + "\n", + " # @markdown Repeat output N times\n", + " RANGE = 1000\n", + " NUM_CHUNKS = 10+\n", + " separator = '|'\n", + " _savefiles = {}\n", + " #-----#\n", + " for chunk in range(NUM_CHUNKS):\n", + " if chunk=<10:continue\n", + " start_at_index = chunk * RANGE\n", + " _prompts = ''\n", + " for _index in range(start_at_index + RANGE):\n", + " if _index < start_at_index : continue\n", + " index = indices[_index].item()\n", + " prompt = prompts[f'{index}']\n", + " _prompts = _prompts.replace(prompt + separator,'')\n", + " _prompts = _prompts + prompt + separator\n", + " #------#\n", + " _prompts = fix_bad_symbols(_prompts)\n", + " _prompts = ('{' + _prompts + '}').replace(separator + '}', '}')\n", + " _savefiles[f'{chunk}'] = _prompts\n", + " #---------#\n", + " save_filename = f'{root_savefile_name}_{start_at_index + RANGE}_{PROMPT_INDEX}.json'\n", + "\n", + "\n", + " if (chunk=<20 && chunk>10): %cd '/content/output2/savefiles/'\n", + " if (chunk<=30 && chunk>20): %cd '/content/output3/savefiles/'\n", + " if (chunk=<40 && chunk>30): %cd '/content/output4/savefiles/'\n", + " if (chunk<=50 && chunk>40): %cd '/content/output5/savefiles/'\n", + " if (chunk=<60 && chunk>50): %cd '/content/output6/savefiles/'\n", + " if (chunk<=70 && chunk>60): %cd '/content/output7/savefiles/'\n", + " if (chunk=<80 && chunk>70): %cd '/content/output8/savefiles/'\n", + " if (chunk<=90 && chunk>80): %cd '/content/output9/savefiles/'\n", + " if (chunk=<100 && chunk>90): %cd '/content/output10/savefiles/'\n", + " if (chunk<=110 && chunk>100): %cd '/content/output11/savefiles/'\n", + " if (chunk=<120 && chunk>110): %cd '/content/output12/savefiles/'\n", + " if (chunk<=130 && chunk>120): %cd '/content/output13/savefiles/'\n", + "\n", + "\n", + " #------#\n", + " print(f'Saving savefile {save_filename} to {output_folder}...')\n", + " with open(save_filename, 'w') as f:\n", + " json.dump(_savefiles, f)\n", + " #---------#\n", + " continue\n", + "#-----------#" + ], + "metadata": { + "id": "x1uAVXZEoL0T", + "cellView": "form" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from safetensors.torch import save_file , load_file\n" + ], + "metadata": { + "id": "3hYMnX1qxiKJ" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "text_encodings_tensor = torch.zeros([NUM_VOCAB_ITEMS+1, 1, 768])\n", + "for key in text_encodings:\n", + " _key = int(key)\n", + " text_encodings_tensor[_key] = text_encodings[key]\n", + " if (_key % 1000 == 0): print(key)\n", + "#------#\n", + "print(text_encodings_tensor.shape)" + ], + "metadata": { + "id": "tu4hwdt_iyyi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from torch.quantization.observer import MinMaxObserver, MovingAverageMinMaxObserver, HistogramObserver\n", + "observers = [MinMaxObserver(), MovingAverageMinMaxObserver(), HistogramObserver()]\n", + "for obs in observers:\n", + " for x in text_encodings_tensor: obs(x)\n", + " print(obs.__class__.__name__, obs.calculate_qparams())" + ], + "metadata": { + "id": "WtvW1biE1fq1", + "outputId": "7d55cdfe-f624-4fcd-efb8-b6667d36b826", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MinMaxObserver (tensor([0.0051]), tensor([133], dtype=torch.int32))\n", + "MovingAverageMinMaxObserver (tensor([0.0043]), tensor([136], dtype=torch.int32))\n", + "HistogramObserver (tensor([0.0050]), tensor([136], dtype=torch.int32))\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "WxehvbQwl8Gl", + "outputId": "dac29ae3-d904-496c-9873-46fb3302eaa8", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "48.0" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "M-3Q8xvuic1Y" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "x = torch.quantize_per_tensor(text_encodings_tensor, torch.tensor(0.0048), torch.tensor(136), torch.quint8)" + ], + "metadata": { + "id": "iXLH3hpNhkYf" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "x[3].shape" + ], + "metadata": { + "id": "2nu7ZDd7nUh1", + "outputId": "06512f88-4b43-488f-becc-a1960fd4cc8a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "torch.Size([1, 768])" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "source": [ + "output = {}\n", + "for index in range(1073906):\n", + " output[f'{index}'] = x[index].to(dtype = torch.float32)\n" + ], + "metadata": { + "id": "uYC16twqiC3Z", + "outputId": "0864ac48-0415-49b0-adbe-b58335f3c9cb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 176 + } + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "Creation of quantized tensor requires quantized dtype like torch.quint8", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1073906\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf'{index}'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m: Creation of quantized tensor requires quantized dtype like torch.quint8" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from safetensors.torch import save_file , load_file\n", + "# @title Download the text_encodings as .zip\n", + "import os\n", + "%cd {home_directory}\n", + "#os.remove(f'{home_directory}results.zip')\n", + "root_output_folder = home_directory + 'output/'\n", + "\n", + "my_mkdirs(root_output_folder)\n", + "torch.save( output, 'text_encodings.pt')" + ], + "metadata": { + "id": "DFPNZbEZmTyc", + "outputId": "094b2eda-f147-47eb-f234-5903dee718c3", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "save_filename = 'prompts.json'\n", + "%cd {home_directory}\n", + "print(f'Saving segment {save_filename} to {home_directory}...')\n", + "with open(save_filename, 'w') as f:\n", + " json.dump(prompts, f)" + ], + "metadata": { + "id": "EBr2qBKsoah4", + "outputId": "7deff758-0e0b-4465-dd8c-417844781a90", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n", + "Saving segment prompts.json to /content/...\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "id": "PPCGsZ05o-ZY", + "outputId": "73d71f50-d5da-446f-9114-4fb515a82710", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 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", + "# @title Download the text_encodings as .zip\n", + "import os\n", + "%cd {home_directory}\n", + "#os.remove(f'{home_directory}results.zip')\n", + "root_output_folder = home_directory + 'output/'\n", + "zip_dest = f'/content/drive/MyDrive/results.zip'\n", + "!zip -r {zip_dest} {root_output_folder}" + ], + "metadata": { + "id": "zivBNrw9uSVD", + "outputId": "4ecabf7b-f67c-4240-c7ba-6ca43f989c35", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n", + "/content\n", + " adding: content/output/ (stored 0%)\n", + " adding: content/output/prompts.json (deflated 68%)\n", + " adding: content/output/text_encodings.pt (deflated 42%)\n" + ] + } + ] + } + ] +} \ No newline at end of file