{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "source": [ "This Notebook is a Stable-diffusion tool which allows you to find similiar tokens from the SD 1.5 vocab.json that you can use for text-to-image generation. Try this Free online SD 1.5 generator with the results: https://perchance.org/fusion-ai-image-generator\n", "\n", "Scroll to the bottom of the notebook to see the guide for how this works." ], "metadata": { "id": "L7JTcbOdBPfh" } }, { "cell_type": "code", "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", "\n", "def getPrompts(_path, separator):\n", "\n", " path = _path + '/text'\n", " path_enc = _path + '/text_encodings'\n", " #-----#\n", " index = 0\n", " file_index = 0\n", " prompts = {}\n", " text_encodings = {}\n", " _text_encodings = {}\n", " #-----#\n", " for filename in os.listdir(f'{path}'):\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", " _prompts = {\n", " key : value for key, value in _df.items()\n", " }\n", " for key in _prompts:\n", " _index = int(key)\n", " value = _prompts[key]\n", "\n", " #Read the 'header' file in the JSON\n", " if _index <= 0 :\n", " _NUM_ITEMS = int(value)\n", " prompts[f'{index}'] = _prompts[f'{_index}'] + separator\n", " index = index + 1\n", " continue\n", " if _index <= 1 :\n", " _file_name = f'{value}'\n", " %cd {path_enc}\n", " _text_encodings = load_file(f'{_file_name}.safetensors')\n", " #Store text_encodings for the header items\n", " text_encodings[f'{index-1}'] = _text_encodings[f'{_index-1}']\n", " text_encodings[f'{index}'] = _text_encodings[f'{_index}']\n", " #------#\n", " prompts[f'{index}'] = _prompts[f'{_index}'] + separator\n", " index = index + 1\n", " 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", " #_text_encodings.close() #close the text_encodings file\n", " file_index = file_index + 1\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", "#🔸🔹\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", "#--------#\n", "\n" ], "metadata": { "id": "rUXQ73IbonHY" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title ✳️ Select items for the vocab\n", "\n", "prompt_features = True # @param {\"type\":\"boolean\",\"placeholder\":\"🦜\"}\n", "civitai_blue_set = True # @param {\"type\":\"boolean\",\"placeholder\":\"📘\"}\n", "suffix = True # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n", "prefix = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n", "emojis = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", "#------#\n", "\n", "first_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n", "last_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n", "full_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", "celebs = False # @param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n", "#These are borked\n", "celebs_young = False # param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n", "#-------#\n", "\n", "danbooru_tags = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎀\"}\n", "\n", "lyrics = False # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n", "\n", "tripple_nouns = False # @param {\"type\":\"boolean\",\"placeholder\":\"🎼\"}\n", "\n", "#-----#\n", "female_fullnames = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", "debug = False\n", "\n", "#------#\n", "prompts = {}\n", "text_encodings = {}\n", "nA = 0\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 full_names:\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", "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 celebs_young :\n", " url = '/content/text-to-image-prompts/names/celebs/young'\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" ], "metadata": { "id": "ZMG4CThUAmwW" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title \t⚄ Use a pre-encoded prompt + image pair from the fusion gen (note: NSFW!)\n", "\n", "\n", "#image_index = 0 # @param {type:'number'}\n", "# @markdown Load the data (only required one time)\n", "load_the_data = False # @param {type:\"boolean\"}\n", "\n", "# @markdown Choose an index\n", "index = 829 # @param {type:\"slider\", min:0, max:1668, step:1}\n", "\n", "# @markdown Set the value for C in the reference

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

\n", "\n", "\n", "C = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n", "\n", "\n", "# @markdown Calculate most similiar items using above settings?\n", "enable = True # @param {type:\"boolean\"}\n", "\n", "\n", "\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", " image\n", "\n", " # text-similarity\n", " sims = C * torch.matmul(text_tensor, text_features_A.t())\n", "\n", " # plus image-similarity\n", " sims = sims + (1-C) * torch.matmul(text_tensor, image_features_A.t()) * logit_scale\n", "\n", " # Sort the items\n", " sorted , indices = torch.sort(sims,dim=0 , descending=True)\n", "\n", "\n", " # @title ⚙️📝 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", " # @markdown -----------\n", " # @markdown Printing options\n", " newline_Separator = True # @param {type:\"boolean\"}\n", "\n", " import random\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 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 = (_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", "\n", "#-------#\n", "image\n" ], "metadata": { "id": "7qk3MgPVmApD", "outputId": "4e5a5c06-22cc-48f4-9663-93a6937669d8", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "execution_count": 97, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", "comic art style|\n", "a gorgeous female priest|\n", "beautiful female dragoness face|\n", "cg illustration|\n", "anime cg|\n", "reoen official art|\n", "concept art illustration by artgerm|\n", "moon solo female|\n", "detailed highres|\n", "art station artwork detailed|\n", "seaport_princess |\n", "dawn solo serious|\n", "brown hair highres|\n", "Nani character|\n", "giveaway|\n", "fantasy kiana kaslana white comet|\n", "bare shoulders retro artstyle|\n", "indiedev|\n", "super detailed face|\n", "realistic_skin|\n", "loba_(apex_legends) |\n", "prety woman|\n", "Ciro Marcetti Art|\n", "mujer de ojos rojos y pelo azulado|\n", "highres venusbody|\n", "animation|\n", "1girl Yennifer|\n", "of sexy cyberpunk female mage|\n", "anya_(spy_x_family) |\n", "rodion_(project_moon) |\n", "lumen cinematic contrast lighting|\n", "feminine attractive face|\n", "miss-fortune|\n", "jane|\n", "Highres womanly|\n", "realistic 1woman|\n", "subtle facial animations|\n", "vampire_\\(game\\) |\n", "|\n", "detailed face RTX|\n", "sexbot|\n", "Labyrista 1girl|\n", "Artstation blurry|\n", "animated illustrations|\n", "hyper realisitc texture skin|\n", "cute lips nsfw|\n", "a pretty american female character|\n", "the best female erotic figure|\n", "1 android girl Red|\n", "amada_ken |\n", "vex|\n", "olympia|\n", "Marenka Short hair|\n", "jorge jacinto's artstyle|\n", "arrogant woman21|\n", "cagalli_yula_athha |\n", "Fantasy Character|\n", "videogame cg|\n", "female_detailed girl|\n", "aesthetics|\n", "detailed wallpaper|\n", "unreal engine dof|\n", "detailed cute face|\n", "looks like cirilla from the witcher|\n", "cel shaded devil|\n", "best_quality 1girl|\n", "green eyes highres|\n", "cg illustration cinematic light|\n", "short black hair over one eye|\n", "silence_girl |\n", "d artist name|\n", "Cel shading|\n", "cel shading|\n", "2D female|\n", "trending at cgsociety|\n", "Cel shading Sci-Fi|\n", "detailed skin cute|\n", "eliza|\n", "alpha_signature |\n", "scarletart by artgerm|\n", "character_sheet |\n", "author ; tags female protagonist|\n", "1girl Pharah|\n", "featured art|\n", "art by guweiz|\n", "y cinematic lighting|\n", "qixiong_ruqun |\n", "ray tracing 1girl|\n", "real-time makeup application|\n", "kiana_kaslana_(herrscher_of_finality) |\n", "3d hires model|\n", "dnd character art|\n", "solo aphrodite|\n", "carmen_sandiego_v_shurik|\n", "a sexy female thief on|\n", "evelyn|\n", "ultra_detailed|\n", "beautiful aesthetic face|\n", "vestia_zeta |\n", "concept character art|\n", "dawn solo 1girl|\n", "moon_demon 1girl|\n", "soloanthro female|\n", "by Retros|\n", "aesthetic detailed|\n", "cel shaded style|\n", "humanoid female robot|\n", "leila|\n", "realistic android|\n", "epic fantasy greek priestess|\n", "crescent_rose |\n", "nude detailed face|\n", "very detailed epic|\n", "wallpapers|\n", "game cg roomi|\n", "digital anime art|\n", "beautiful busty slender knight|\n", "8k portrait cass|\n", "super elan_ceres |\n", "beautiful cute sexy anime|\n", "temple portrait|\n", "an stunningly beautiful age half demon|\n", "3D art anime style|\n", "soft-focused realism|\n", "production cinematic character render|\n", "dawn woman sex|\n", "sum_elise|\n", "comic artwork|\n", "suzi_q |\n", "cel shading 1girl|\n", "valkyrie_(apex_legends) |\n", "the most beautiful artwork in the world featuring|\n", "beautiful yellow eyes +|\n", "ultra-detailed CG|\n", "8K UHD female|\n", "solo profile|\n", "a female necromancer|\n", "undone_sarashi |\n", "octane_(apex_legends) |\n", "silent_comic |\n", "86_-eightysix- |\n", "josie|\n", "high resolution face|\n", "beautiful egyptian bombshell black-haired|\n", "|\n", "a.i._voice |\n", "best dynamic lighting|\n", "fiona|\n", "highres gorgeous dark skin noxian|\n", "gorgeous characters|\n", "best quality CG|\n", "best quality cg|\n", "beautiful women complete eyes|\n", "4K artist|\n", "PA7_Portrait-CU|\n", "wattson_(apex_legends) |\n", "a gorgeous beautiful age sexy|\n", "beautiful female face|\n", "lynda|\n", "female human|\n", "alisa_ilinichina_amiella |\n", "alhambra background|\n", "heart_maebari |\n", "she has realistic human|\n", "skin_fang |\n", "cinematic unreal 5|\n", "highres cinematic|\n", "layla|\n", "harmony high-quality skin bump mapping|\n", "bodya high resolution face|\n", "masterpeice 1girl|\n", "by syuro|\n", "iconic fine epic|\n", "garnet_til_alexandros_xvii |\n", "elena_so6|\n", "official_art |\n", "7rtifa elegant|\n", "digital artist|\n", "realistic lesbian|\n", "Escher CGI Artgerm|\n", "wallpaper cinematic lighting sharp focus|\n", "nina|\n", "Detailed lips|\n", "thief|\n", "stylized charming|\n", "by Whooo-Ya|\n", "high-quality skin textures|\n", "eva_beatrice |\n", "fantasy art.HD 3D|\n", "bezier place place character traits lighting|\n", "sexy 8 list|\n", "penelope|\n", "multi-layer skin shading|\n", "streaming|\n", "woman pretty face|\n", "hyperrealism by atey ghailan|\n", "highres cute|\n", "torino_aqua |\n", "guest_art |\n", "masterpiecebust portrait|\n", "2D artstyle|\n", "1girl best quality|\n", "adorable_girl|\n", "prompt lora weight at ->quality loss|\n", "by zackary|\n", "realistic skin1|\n", "stunning fantasy style environment|\n", "Ultra detailed|\n", "Ultra Detailed|\n", "mobile_legends_alice|\n", "detailed_face|\n", "dynamic cinematic portrait medium shot|\n", "absurdres gorgeous noxian|\n", "perfectly composedgorgeous jp-svetf|\n", "dawn solo 1boy|\n", "chloe|\n", "edgtb_woman|\n", "character concept art|\n", "ramudia_(lamyun) |\n", "tall mature female|\n", "nose realistic|\n", "ultra detailed HD|\n", "bliznyashkithetwins_v|\n", "white_thighhighs |\n", "rating_lewd curvy|\n", "super fine illustrations|\n", "source_3Dcartoon|\n", "lucy_(cyberpunk) |\n", "soft diffuse lighting|\n", "pink_hair |\n", "highres black hair|\n", "blue eyes alpha|\n", "Cel shading Wonder|\n", "detailed artgem|\n", "evil AI named Aida|\n", "Realistic face|\n", "realistic face|\n", "RTX 0 shader|\n", "group profile|\n", "beautiful sxz-ashe-ow-|\n", "povsolo 1girl|\n", "cleavage highres|\n", "lips realistic|\n", "adorable female face|\n", "the character the image should be|\n", "jane_doe_(zenless_zone_zero) |\n", "kawakami_rokkaku|\n", "kawakami_rokkaku |\n", "an artist-inspired character|\n", "Detailed background|\n", "Detailed Background|\n", "ziche_fuzhao |\n", "temple cyberpunk soft lighting|\n", "breasts nose_blush|\n", "highres breasts|\n", "natalie|\n", "ultra-detailed eyes|\n", "a beautiful artwork illustration|\n", "Soft Shading|\n", "aquiline_nose |\n", "blush short_hair|\n", "artgerm best quality|\n", "soft_lighting|\n", "1girl kujikawadef|\n", "gundam |\n", "ada_wong resident evil|\n", "Flirty_Shy_Grin|\n", "of beautiful female knight women|\n", "vox|\n", "skadiview-appearance-shiny skin|\n", "detective|\n", "cute solo female|\n", "character request|\n", "fantasy female|\n", "futanari realistic|\n", "dawn solo|\n", "astrosorc22|\n", "luxurious stone castle game|\n", "mignonworks artstyle|\n", "idol|\n", "idol |\n", "fantasy game spell icon|\n", "enya|\n", "moody cinematic epic concept art|\n", "perfect artwork|\n", "age_of_ishtaria |\n", "1boy cute woman|\n", "beatiful face|\n", "female_protagonist_(pokemon_go) |\n", "a pretty caucasian female character|\n", "teresa|\n", "arab escort escort|\n", "telegram|\n", "gorgeous female face|\n", "futuristic setting she should be depicted|\n", "award winning digital painting|\n", "anime woman|\n", "conceptart|\n", "astel_leda |\n", "snow_white |\n", "Epic Digital Art|\n", "epic digital art|\n", "epic Digital art|\n", "heroine|\n", "gorgeous art|\n", "best ai image|\n", "ruyi_jingu_bang |\n", "#explorepage #art #cosplay|\n", "saga|\n", "1girl sfw|\n", "lovely follynobodysd|\n", "marietta|\n", "yen |\n", "banned_artist |\n", "depth-based facial shading|\n", "artstation_sample |\n", "highres dark skin|\n", "unreal engine Maya|\n", "well-lit cinematic|\n", "dark hair pureerosface_v|\n", "masterpiece manhwa|\n", "1girl perfect eyes|\n", "Kyaradin1girl|\n", "john_doe_(jdart) |\n", "official_alternate_eye_color |\n", "character_doll |\n", "epic realistic art|\n", "bronya_zaychik |\n", "human ai hybrid|\n", "JanePorterXLP|\n", "blulust 1girl|\n", "beautiful concept illustration|\n", "Acheron_Blue 1girl|\n", "evie|\n", "lifelike texture dramatic lighting unrealengine|\n", "VichyaIris 1girl|\n", "gwen_(league_of_legends) |\n", "Human woman|\n", "human woman|\n", "woman close up|\n", "Realistic Lustful|\n", "F/ beautiful|\n", "uncensored game cg|\n", "sunlight cinematic lighting|\n", "temple_ruins|\n", "detailed sexy lips|\n", "snapmatic|\n", "milf_saga_face|\n", "by zixiong|\n", "high rating 1girl|\n", "1girl buff female|\n", "nora|\n", "realistic mercy|\n", "HD CGSociety|\n", "HD cgsociety|\n", "snowwhiteWaifu|\n", "kaname_raana |\n", "artist_request |\n", "artist_request|\n", "noir detailed|\n", "polearm |\n", "sex bot fem bot|\n", "lara|\n", "stunning female wendigo|\n", "detailed makeup|\n", "nan|\n", "Detailed person|\n", "character quality|\n", "mahjong_soul |\n", "janet|\n", "fantasy illustration masterpiece|\n", "incredible perfect artwork|\n", "the most beautiful artwork in the worldcute|\n", "beautiful stylized|\n", "ultra detailed eyes|\n", "gwen tennyson|\n", "Gwen Tennyson|\n", "artstation pixiv|\n", "3d shading realism|\n", "kanden_sky |\n", "half human ai hybrid|\n", "animation cinematic|\n", "1girl fair_skin|\n", "perfect rendered face|\n", "comic art|\n", "female_pervert|\n", "female_pervert |\n", "rgb unreal engine|\n", "assassin|\n", "realism image Fill|\n", "masterpiece 2d art|\n", "DETAILED SKIN|\n", "detailed skin|\n", "1girl yor_briar|\n", "3d soft lighting|\n", "little_witch_nobeta |\n", "bisque skin|\n", "perfect face skin|\n", "lcylxsl woman|\n", "gwen_tennyson|\n", "looking at viewercurvy|\n", "uncensored steam|\n", "robot_girl |\n", "1girl monaXL|\n", "captivating digital art|\n", "a beautiful porcelain young slim fit female robot|\n", "k uhd unity wallpaper|\n", "maria-sama_ga_miteru |\n", "justice right bitch heroine|\n", "feminine face profile|\n", "doggsytyle 1girl|\n", "1girl face closeup|\n", "1girl necromancer|\n", "highres 3d cg|\n", "ScarletWitch|\n", "black_widow|\n", "styledreamlikeartredshift|\n", "UHD. AI_girl doll|\n", "lore|\n", "painting stylized|\n", "stunning environmentnsfw|\n", "red_eyes solo|\n", "3D cartoon dreamy|\n", "estheticfutanaritrap|\n", "5 detailed face|\n", "motd|\n", "Cel shading Evil|\n", "helsie|\n", "marissa|\n", "vertical_comic |\n", "commissioner_name |\n", "Ada Wong cartoon|\n", "Epic CG masterpiece|\n", "solo detailed eyes|\n", "Atey Ghailan|\n", "zoe|\n", "cinematic environment|\n", "character_print |\n", "d rendered hentai version|\n", "Beuty lips|\n", "beautiful noelle_silva|\n", "no humans profile|\n", "shiny_skin1|\n", "eli|\n", "hades_1 |\n", "fantasy highres|\n", "Ptylust 1girl|\n", "beautiful yo greek cyborg|\n", "mature female tall|\n", "stunningly beautiful corneo_yor_forger|\n", "scarlet_devil_mansion |\n", "ultra detailed sex|\n", "original character|\n", "towel |\n", "by Sadahide1girl|\n", "ADDCOLfair skin|\n", "nerissa_ravencroft |\n", "mei|\n", "rover_(wuthering_waves) |\n", "vio|\n", "close up of 1girl|\n", "realistic elf girl|\n", "trending cgsociety|\n", "character name|\n", "sexy shy steam|\n", "cornelia_li_britannia |\n", "by pakwan|\n", "perfect eyes best quality|\n", "sumeragi_lee_noriega |\n", "character_signature |\n", "source_comic|\n", "perfect feminine face+|\n", "sci-fi woman|\n", "aya|\n", "lilith_aensland |\n", "vana|\n", "siri|\n", "uncensored shadman|\n", "high_resolution illustration|\n", "profile portrait|\n", "impossibly beautiful egyptian|\n", "superhero game|\n", "ptoto portrait|\n", "elegant cinematic|\n", "very detailed faces|\n", "kaede_johan_nouvel |\n", "cutiesaturday|\n", "female high res|\n", "lip bite blush|\n", "Wealth Highres|\n", "deer_girl |\n", "lily_black |\n", "riven_(league_of_legends) |\n", "a beautiful sexy age chinese|\n", "high detailed skin|\n", "mature female masterpiece|\n", "8k 2d animation|\n", "concept_art |\n", "1>tifa|\n", "red gothic illustration + exquisite|\n", "lili|\n", "paintingsexy femalelooking at viewer|\n", "1girl detail face|\n", "perfect skin 8k|\n", "snow whiteWaifu|\n", "eris_greyrat |\n", "vivian|\n", "solo female artica_sparkle_character|\n", "by wizzikt|\n", "cel shaded 4k|\n", "carmen|\n", "kami_nomi_zo_shiru_sekai |\n", "3d unreal engine|\n", "3D unreal engine|\n", "girl closeup|\n", "by GLaDOS|\n", "red gothic cursed tower|\n", "riot heterochromia|\n", "kata|\n", "lifelike dramatic lighting unrealengine|\n", "alternate_skin_color |\n", "realistic han_juri|\n", "indian mary_winstead eyes|\n", "lulu|\n", "strict_nun_(diva) |\n", "arcadian|\n", "a gorgeous sexy thicc harlequin vampire|\n", "apex_legends |\n", "highres flawless|\n", "official_alternate_hairstyle |\n", "official_alternate_hairstyle|\n", "1 android girl|\n", "bianca|\n", "LydiaV|\n", "best detail 1woman|\n", "RTX moody female|\n", "beautiful elegant face|\n", "comic dark hair|\n", "close-up realistic|\n", "femme fatale|\n", "concept-art|\n", "high skin details|\n", "Full Stylized|\n", "amazing. 3d shading|\n", "fushigi_no_umi_no_nadia |\n", "Sense 1girl|\n", "skin details nude|\n", "game cg sky cloud|\n", "realistic highres|\n", "skin detail NSFW|\n", "Elegant realistic|\n", "sexy sexy 8|\n", "source_furry 1girl|\n", "by Skyrn99|\n", "mo_chengwei |\n", "character description|\n", "kawakami_mai |\n", "art style by artgerm|\n", "thegame|\n", "elizabeth|\n", "super_heroine_boy |\n", "bold eyes fix|\n", "bokeh ray-tracing|\n", "a busty iranian pale ulzzang idol|\n", "Best Quality 1girl|\n", "BEST QUALITY 1GIRL|\n", "best quality1girl|\n", "best quality 1GIRL|\n", "shanghai_doll |\n", "beautiful eyessexy theme|\n", "ultra-detailed1|\n", "n1ll 1girl|\n", "Gwen 1girl solo|\n", "unreal engine rendered|\n", "beautiful face portrait|\n", "submissive_female|\n", "fantasy lifestyle-portrait by mandy jurgens|\n", "kondou_taeko |\n", "alisa|\n", "unreal engine cinematic smooth|\n", "1girl human female|\n", "highres waist-up|\n", "a mature sexy streamer|\n", "high resolution illustrations|\n", "character focus cinematic light|\n", "elegant epic|\n", "Highres elaborate|\n", "human 1girl|\n", "nadia_la_arwall |\n", "seductive mature female|\n", "1girl older|\n", "masterpiece miko|\n", "very detailed skin|\n", "mysterious epic|\n", "watson_amelia |\n", "Visual novel|\n", "hi res detailed|\n", "gumroad username|\n", "Sunrays Raytracing|\n", "1futa 1male|\n", "background characters|\n", "natasha|\n", "Detailed art|\n", "womam skin|\n", "details such as raytracing and|\n", "wcw|\n", "1girl deer woman|\n", "physically-based eye shading|\n", "an attracitve woman|\n", "sexy femme fatale demon babe}\n", "{character design by a character portrait by senior character artist|\n", "eyes by smooth art style|\n", "highres perfect face|\n", "game a pretty arabic female character|\n", "artstation trending|\n", "by artist a character by senior character artist|\n", "stylized detailed_character|\n", "mirage_(apex_legends) |\n", "nsfw_high_detail_character_portrait|\n", "cinematic character design d|\n", "art by artstation womanly|\n", "3d cg woman|\n", "8k profile photo|\n", "soft shadows real-time skin rendering|\n", "detailed 1girl|\n", "cinematic art by hq|\n", "Game art|\n", "flirty Focus on ArtStation|\n", "indiehd detailed character|\n", "Artstation HD|\n", "artstationHD|\n", "beautiful female android face|\n", "cg unity k wallpaper shiny skin|\n", "best crypto_(apex_legends) |\n", "cassandra|\n", "artstation highres|\n", "beautiful female robot|\n", "1girl Annerose|\n", "F/14 environmental Artstation|\n", "artstation|\n", "ilya_kuvshinov |\n", "artist name|\n", "beautiful character design|\n", "artstation smooth|\n", "artstation game assets|\n", "solo mature miko|\n", "ilya kuvshinov style|\n", "sfw detailed face|\n", "stylized goddess|\n", "game icon|\n", "multra detailed face|\n", "SB_Eve_S-|\n", "theresa_apocalypse |\n", "ada|\n", "gwen|\n", "bad_artstation_id |\n", "lifeline_(apex_legends) |\n", "annerose vajra|\n", "assets|\n", "artstation Velvia|\n", "streaming on twitch|\n", "low-poly game art|\n", "8k artstation|\n", "rita_mordio |\n", "a pretty asian female character|\n", "mother_(game) |\n", "a character by senior character portrait artist|\n", "a female character|\n", "ada_wong|\n", "ada_wong |\n", "gameart|\n", "maria_cadenzavna_eve |\n", "lighting place place traits character eyes|\n", "a iranian pale ulzzang madewithunity|\n", "ingrid_brandl_galatea |\n", "sera|\n", "lewd art by riotgames|\n", "perfect female face|\n", "stylized character designs|\n", "k unity cg wallpaper|\n", "game cg highres|\n", "highres detailed|\n", "Highres Detailed|\n", "beautiful_female_skin|\n", "computer game art|\n", "a babylonian female assassin|\n", "artstation winner|\n", "artstyle by helen wells|\n", "indian mary_winstead|\n", "artstation masterpiece|\n", "character art|\n", "a beautiful attractive future k unity engine wallpaper|\n", "game cg supergirl|\n", "1girl sarielx|\n", "artstation female body|\n", "realistic meiko|\n", "3d cg girl|\n", "high resolution skin|\n", "realistic 1girl|\n", "realistic 1 girl|\n", "goddess portrait|\n", "beautiful mature female viera|\n", "artstation contest winner|\n", "detailed skin sfw|\n", "character illustrations|\n", "high detail skin|\n", "a gorgeous female demonologist|\n", "detailed CG|\n", "gamedev|\n", "vanessa|\n", "jp-svetf- as gorgeous supernatural goddess|\n", "artstationextremely|\n", "game_cg |\n", "highres milf|\n", "1girl half body|\n", "character_profile|\n", "character_profile |\n", "alisa_mikhailovna_kujou |\n", "lucia_(scott_malin) |\n", "stylized art|\n", "detailed hot skin|\n", "F/ behance HD|\n", "by Foxovh female|\n", "lunamaria_hawke |\n", "detailed skin UHD|\n", "legends art 1girl|\n", "gorgeous digital artwork|\n", "artstation matte|\n", "hilda_valentine_goneril |\n", "lifelike texture bright lighting unreal engine|\n", "Hentai Cinematic|\n", "k cg unity engine wallpaper|\n", "Concept Art World|\n", "stylized lighting|\n", "indian mary_winstead small|\n", "best_quality woman|\n", "portrait alt girl|\n", "cg 3d girl|\n", "Beuty concept art|\n", "very fine k cg wallpaper|\n", "sexy splash art|\n", "juliet|\n", "3d-realisticstyle|\n", "theresa|\n", "scene art|\n", "CGSociety|\n", "a gorgeous female chronomancer|\n", "cinematic production character rendering|\n", "cleo|\n", "ana|\n", "lifelike texture dcharacter|\n", "trending in art station|\n", "check_artist |\n", "list of characters|\n", "a persian pale ulzzang detailed skin20|\n", "high resolution skin texture|\n", "1girl close-up|\n", "bust wallpaper|\n", "soft lighting3D|\n", "1girl solo alpha|\n", "stylized smooth|\n", "art by unreal engine|\n", "iris_black_games |\n", "nadia|\n", "Artstation.|\n", "solo detailed face|\n", "artstation drawing|\n", "beautiful cyberpunk female|\n", "pandora_smith_magister|\n", "fantasy art from dnd extremely beautiful style|\n", "1girl raid_arbiter|\n", "beautiful anthro female|\n", "character_name|\n", "character_name |\n", "Chen Chun game art|\n", "4kdetailed face|\n", "leblanc|\n", "mature female character|\n", "physically-based rendering light on the face|\n", "artstation 4K|\n", "ru_ta artstyle|\n", "askzy 1girl|\n", "a beautiful cyberpunk female|\n", "ramatic lighting unreal engine|\n", "princess_of_moonbrook |\n", "sienna|\n", "ilya kuvshinov|\n", "art station trending|\n", "detailed eyes hd|\n", "best quality woman|\n", "those games where she still|\n", "milla_maxwell |\n", "highres dark hair|\n", "detailed Karyl|\n", "artgerm 1girl|\n", "lupinus_virtual_games |\n", "sola-ui_nuada-re_sophia-ri |\n", "detailed skin nun|\n", "elena|\n", "charming character illustrations|\n", "absurdres game cg|\n", "protagonist|\n", "soft cinematic lighting|\n", "beautiful feminine face+|\n", "every pixiel high resolution|\n", "games|\n", "beautiful sexy adult female face|\n", "cinematic hard lighting eye shading|\n", "beautyful female face|\n", "marina_(blue_archive) |\n", "award winning character concept art of|\n", "by ecmajor|\n", "character portrait|\n", "obsidian-skinned_female|\n", "waifu|\n", "detailed skin solo|\n", "realistic lips|\n", "20s f/|\n", "ultra-fine cg unity k wallpaper|\n", "suic0idegirl|\n", "the character the overall image should have|\n", "skin texture 1lady|\n", "serious aura|\n", "a beautiful android|\n", "the most beautiful anime style art ever seen|\n", "cute anime face cinematic angle|\n", "mysterious look on her face lilith is|\n", "zoomed-in face portrait in background|\n", "gorgeous d rendering by cgsociety|\n", "indian mary_winstead face|\n", "k character concept portrait|\n", "cell shading|\n", "HDR evangeline_a.k._mcdowell |\n", "cuteg 1girl nsfw|\n", "3d cg 16yo girl|\n", "cute breast woman|\n", "game|\n", "WLOP art style|\n", "adora|\n", "beautiful female sex robot|\n", "realistic girl|\n", "mysterious female|\n", "zia|\n", "skin pores & face texture|\n", "xenotr1p 1girl|\n", "characterart|\n", "looking to viewer|\n", "by reysi|\n", "by Reysi|\n", "3d high res 1girl|\n", "anime female|\n", "beret |\n", "enza|\n", "beautiful mature vampire princess|\n", "very aesthetic style face|\n", "Soft_Shading|\n", "by sqoon|\n", "gloomy half body portrait k unity render|\n", "solo Zia|\n", "zoya athene|\n", "8k Concept artist|\n", "ChromaV5 art|\n", "diana|\n", "wallpaper detailed|\n", "cel_shading |\n", "human female|\n", "tia|\n", "artist_name |\n", "ULTRA malenia_blade_of_miquella |\n", "battle_android_bodyguard_female|\n", "illustration highres|\n", "3d cg woman abs|\n", "gorgeous blushing beautiful sexy charming|\n", "beautiful light makeup female sorceress|\n", "beautiful female anthro|\n", "detailed epic|\n", "1girl sex eyes|\n", "celeste|\n", "comic art style|\n", "a gorgeous female priest|\n", "beautiful female dragoness face|\n", "cg illustration|\n", "anime cg|\n", "reoen official art|\n", "concept art illustration by artgerm|\n", "moon solo female|\n", "detailed highres|\n", "art station artwork detailed|\n", "seaport_princess |\n", "dawn solo serious|\n", "brown hair highres|\n", "Nani character|\n", "giveaway|\n", "fantasy kiana kaslana white comet|\n", "bare shoulders retro artstyle|\n", "indiedev|\n", "super detailed face|\n", "realistic_skin|\n", "loba_(apex_legends) |\n", "prety woman|\n", "Ciro Marcetti Art|\n", "mujer de ojos rojos y pelo azulado|\n", "highres venusbody|\n", "animation|\n", "1girl Yennifer|\n", "of sexy cyberpunk female mage|\n", "anya_(spy_x_family) |\n", "rodion_(project_moon) |\n", "lumen cinematic contrast lighting|\n", "feminine attractive face|\n", "miss-fortune|\n", "jane|\n", "Highres womanly|\n", "realistic 1woman|\n", "subtle facial animations|\n", "vampire_\\(game\\) |\n", "|\n", "detailed face RTX|\n", "sexbot|\n", "Labyrista 1girl|\n", "Artstation blurry|\n", "animated illustrations|\n", "hyper realisitc texture skin|\n", "cute lips nsfw|\n", "a pretty american female character|\n", "the best female erotic figure|\n", "1 android girl Red|\n", "amada_ken |\n", "vex|\n", "olympia|\n", "Marenka Short hair|\n", "jorge jacinto's artstyle|\n", "arrogant woman21|\n", "cagalli_yula_athha |\n", "Fantasy Character|\n", "videogame cg|\n", "female_detailed girl|\n", "aesthetics|\n", "detailed wallpaper|\n", "unreal engine dof|\n", "detailed cute face|\n", "looks like cirilla from the witcher|\n", "cel shaded devil|\n", "best_quality 1girl|\n", "green eyes highres|\n", "cg illustration cinematic light|\n", "short black hair over one eye|\n", "silence_girl |\n", "d artist name|\n", "Cel shading|\n", "cel shading|\n", "2D female|\n", "trending at cgsociety|\n", "Cel shading Sci-Fi|\n", "detailed skin cute|\n", "eliza|\n", "alpha_signature |\n", "scarletart by artgerm|\n", "character_sheet |\n", "author ; tags female protagonist|\n", "1girl Pharah|\n", "featured art|\n", "art by guweiz|\n", "y cinematic lighting|\n", "qixiong_ruqun |\n", "ray tracing 1girl|\n", "real-time makeup application|\n", "kiana_kaslana_(herrscher_of_finality) |\n", "3d hires model|\n", "dnd character art|\n", "solo aphrodite|\n", "carmen_sandiego_v_shurik|\n", "a sexy female thief on|\n", "evelyn|\n", "ultra_detailed|\n", "beautiful aesthetic face|\n", "vestia_zeta |\n", "concept character art|\n", "dawn solo 1girl|\n", "moon_demon 1girl|\n", "soloanthro female|\n", "by Retros|\n", "aesthetic detailed|\n", "cel shaded style|\n", "humanoid female robot|\n", "leila|\n", "realistic android|\n", "epic fantasy greek priestess|\n", "crescent_rose |\n", "nude detailed face|\n", "very detailed epic|\n", "wallpapers|\n", "game cg roomi|\n", "digital anime art|\n", "beautiful busty slender knight|\n", "8k portrait cass|\n", "super elan_ceres |\n", "beautiful cute sexy anime|\n", "temple portrait|\n", "an stunningly beautiful age half demon|\n", "3D art anime style|\n", "soft-focused realism|\n", "production cinematic character render|\n", "dawn woman sex|\n", "sum_elise|\n", "comic artwork|\n", "suzi_q |\n", "cel shading 1girl|\n", "valkyrie_(apex_legends) |\n", "the most beautiful artwork in the world featuring|\n", "beautiful yellow eyes +|\n", "ultra-detailed CG|\n", "8K UHD female|\n", "solo profile|\n", "a female necromancer|\n", "undone_sarashi |\n", "octane_(apex_legends) |\n", "silent_comic |\n", "86_-eightysix- |\n", "josie|\n", "high resolution face|\n", "beautiful egyptian bombshell black-haired|\n", "|\n", "a.i._voice |\n", "best dynamic lighting|\n", "fiona|\n", "highres gorgeous dark skin noxian|\n", "gorgeous characters|\n", "best quality CG|\n", "best quality cg|\n", "beautiful women complete eyes|\n", "4K artist|\n", "PA7_Portrait-CU|\n", "wattson_(apex_legends) |\n", "a gorgeous beautiful age sexy|\n", "beautiful female face|\n", "lynda|\n", "female human|\n", "alisa_ilinichina_amiella |\n", "alhambra background|\n", "heart_maebari |\n", "she has realistic human|\n", "skin_fang |\n", "cinematic unreal 5|\n", "highres cinematic|\n", "layla|\n", "harmony high-quality skin bump mapping|\n", "bodya high resolution face|\n", "masterpeice 1girl|\n", "by syuro|\n", "iconic fine epic|\n", "garnet_til_alexandros_xvii |\n", "elena_so6|\n", "official_art |\n", "7rtifa elegant|\n", "digital artist|\n", "realistic lesbian|\n", "Escher CGI Artgerm|\n", "wallpaper cinematic lighting sharp focus|\n", "nina|\n", "Detailed lips|\n", "thief|\n", "stylized charming|\n", "by Whooo-Ya|\n", "high-quality skin textures|\n", "eva_beatrice |\n", "fantasy art.HD 3D|\n", "bezier place place character traits lighting|\n", "sexy 8 list|\n", "penelope|\n", "multi-layer skin shading|\n", "streaming|\n", "woman pretty face|\n", "hyperrealism by atey ghailan|\n", "highres cute|\n", "torino_aqua |\n", "guest_art |\n", "masterpiecebust portrait|\n", "2D artstyle|\n", "1girl best quality|\n", "adorable_girl|\n", "prompt lora weight at ->quality loss|\n", "by zackary|\n", "realistic skin1|\n", "stunning fantasy style environment|\n", "Ultra detailed|\n", "Ultra Detailed|\n", "mobile_legends_alice|\n", "detailed_face|\n", "dynamic cinematic portrait medium shot|\n", "absurdres gorgeous noxian|\n", "perfectly composedgorgeous jp-svetf|\n", "dawn solo 1boy|\n", "chloe|\n", "edgtb_woman|\n", "character concept art|\n", "ramudia_(lamyun) |\n", "tall mature female|\n", "nose realistic|\n", "ultra detailed HD|\n", "bliznyashkithetwins_v|\n", "white_thighhighs |\n", "rating_lewd curvy|\n", "super fine illustrations|\n", "source_3Dcartoon|\n", "lucy_(cyberpunk) |\n", "soft diffuse lighting|\n", "pink_hair |\n", "highres black hair|\n", "blue eyes alpha|\n", "Cel shading Wonder|\n", "detailed artgem|\n", "evil AI named Aida|\n", "Realistic face|\n", "realistic face|\n", "RTX 0 shader|\n", "group profile|\n", "beautiful sxz-ashe-ow-|\n", "povsolo 1girl|\n", "cleavage highres|\n", "lips realistic|\n", "adorable female face|\n", "the character the image should be|\n", "jane_doe_(zenless_zone_zero) |\n", "kawakami_rokkaku|\n", "kawakami_rokkaku |\n", "an artist-inspired character|\n", "Detailed background|\n", "Detailed Background|\n", "ziche_fuzhao |\n", "temple cyberpunk soft lighting|\n", "breasts nose_blush|\n", "highres breasts|\n", "natalie|\n", "ultra-detailed eyes|\n", "a beautiful artwork illustration|\n", "Soft Shading|\n", "aquiline_nose |\n", "blush short_hair|\n", "artgerm best quality|\n", "soft_lighting|\n", "1girl kujikawadef|\n", "gundam |\n", "ada_wong resident evil|\n", "Flirty_Shy_Grin|\n", "of beautiful female knight women|\n", "vox|\n", "skadiview-appearance-shiny skin|\n", "detective|\n", "cute solo female|\n", "character request|\n", "fantasy female|\n", "futanari realistic|\n", "dawn solo|\n", "astrosorc22|\n", "luxurious stone castle game|\n", "mignonworks artstyle|\n", "idol|\n", "idol |\n", "fantasy game spell icon|\n", "enya|\n", "moody cinematic epic concept art|\n", "perfect artwork|\n", "age_of_ishtaria |\n", "1boy cute woman|\n", "beatiful face|\n", "female_protagonist_(pokemon_go) |\n", "a pretty caucasian female character|\n", "teresa|\n", "arab escort escort|\n", "telegram|\n", "gorgeous female face|\n", "futuristic setting she should be depicted|\n", "award winning digital painting|\n", "anime woman|\n", "conceptart|\n", "astel_leda |\n", "snow_white |\n", "Epic Digital Art|\n", "epic digital art|\n", "epic Digital art|\n", "heroine|\n", "gorgeous art|\n", "best ai image|\n", "ruyi_jingu_bang |\n", "#explorepage #art #cosplay|\n", "saga|\n", "1girl sfw|\n", "lovely follynobodysd|\n", "marietta|\n", "yen |\n", "banned_artist |\n", "depth-based facial shading|\n", "artstation_sample |\n", "highres dark skin|\n", "unreal engine Maya|\n", "well-lit cinematic|\n", "dark hair pureerosface_v|\n", "masterpiece manhwa|\n", "1girl perfect eyes|\n", "Kyaradin1girl|\n", "john_doe_(jdart) |\n", "official_alternate_eye_color |\n", "character_doll |\n", "epic realistic art|\n", "bronya_zaychik |\n", "human ai hybrid|\n", "JanePorterXLP|\n", "blulust 1girl|\n", "beautiful concept illustration|\n", "Acheron_Blue 1girl|\n", "evie|\n", "lifelike texture dramatic lighting unrealengine|\n", "VichyaIris 1girl|\n", "gwen_(league_of_legends) |\n", "Human woman|\n", "human woman|\n", "woman close up|\n", "Realistic Lustful|\n", "F/ beautiful|\n", "uncensored game cg|\n", "sunlight cinematic lighting|\n", "temple_ruins|\n", "detailed sexy lips|\n", "snapmatic|\n", "milf_saga_face|\n", "by zixiong|\n", "high rating 1girl|\n", "1girl buff female|\n", "nora|\n", "realistic mercy|\n", "HD CGSociety|\n", "HD cgsociety|\n", "snowwhiteWaifu|\n", "kaname_raana |\n", "artist_request |\n", "artist_request|\n", "noir detailed|\n", "polearm |\n", "sex bot fem bot|\n", "lara|\n", "stunning female wendigo|\n", "detailed makeup|\n", "nan|\n", "Detailed person|\n", "character quality|\n", "mahjong_soul |\n", "janet|\n", "fantasy illustration masterpiece|\n", "incredible perfect artwork|\n", "the most beautiful artwork in the worldcute|\n", "beautiful stylized|\n", "ultra detailed eyes|\n", "gwen tennyson|\n", "Gwen Tennyson|\n", "artstation pixiv|\n", "3d shading realism|\n", "kanden_sky |\n", "half human ai hybrid|\n", "animation cinematic|\n", "1girl fair_skin|\n", "perfect rendered face|\n", "comic art|\n", "female_pervert|\n", "female_pervert |\n", "rgb unreal engine|\n", "assassin|\n", "realism image Fill|\n", "masterpiece 2d art|\n", "DETAILED SKIN|\n", "detailed skin|\n", "1girl yor_briar|\n", "3d soft lighting|\n", "little_witch_nobeta |\n", "bisque skin|\n", "perfect face skin|\n", "lcylxsl woman|\n", "gwen_tennyson|\n", "looking at viewercurvy|\n", "uncensored steam|\n", "robot_girl |\n", "1girl monaXL|\n", "captivating digital art|\n", "a beautiful porcelain young slim fit female robot|\n", "k uhd unity wallpaper|\n", "maria-sama_ga_miteru |\n", "justice right bitch heroine|\n", "feminine face profile|\n", "doggsytyle 1girl|\n", "1girl face closeup|\n", "1girl necromancer|\n", "highres 3d cg|\n", "ScarletWitch|\n", "black_widow|\n", "styledreamlikeartredshift|\n", "UHD. AI_girl doll|\n", "lore|\n", "painting stylized|\n", "stunning environmentnsfw|\n", "red_eyes solo|\n", "3D cartoon dreamy|\n", "estheticfutanaritrap|\n", "5 detailed face|\n", "motd|\n", "Cel shading Evil|\n", "helsie|\n", "marissa|\n", "vertical_comic |\n", "commissioner_name |\n", "Ada Wong cartoon|\n", "Epic CG masterpiece|\n", "solo detailed eyes|\n", "Atey Ghailan|\n", "zoe|\n", "cinematic environment|\n", "character_print |\n", "d rendered hentai version|\n", "Beuty lips|\n", "beautiful noelle_silva|\n", "no humans profile|\n", "shiny_skin1|\n", "eli|\n", "hades_1 |\n", "fantasy highres|\n", "Ptylust 1girl|\n", "beautiful yo greek cyborg|\n", "mature female tall|\n", "stunningly beautiful corneo_yor_forger|\n", "scarlet_devil_mansion |\n", "ultra detailed sex|\n", "original character|\n", "towel |\n", "by Sadahide1girl|\n", "ADDCOLfair skin|\n", "nerissa_ravencroft |\n", "mei|\n", "rover_(wuthering_waves) |\n", "vio|\n", "close up of 1girl|\n", "realistic elf girl|\n", "trending cgsociety|\n", "character name|\n", "sexy shy steam|\n", "cornelia_li_britannia |\n", "by pakwan|\n", "perfect eyes best quality|\n", "sumeragi_lee_noriega |\n", "character_signature |\n", "source_comic|\n", "perfect feminine face+|\n", "sci-fi woman|\n", "aya|\n", "lilith_aensland |\n", "vana|\n", "siri|\n", "uncensored shadman|\n", "high_resolution illustration|\n", "profile portrait|\n", "impossibly beautiful egyptian|\n", "superhero game|\n", "ptoto portrait|\n", "elegant cinematic|\n", "very detailed faces|\n", "kaede_johan_nouvel |\n", "cutiesaturday|\n", "female high res|\n", "lip bite blush|\n", "Wealth Highres|\n", "deer_girl |\n", "lily_black |\n", "riven_(league_of_legends) |\n", "a beautiful sexy age chinese|\n", "high detailed skin|\n", "mature female masterpiece|\n", "8k 2d animation|\n", "concept_art |\n", "1>tifa|\n", "red gothic illustration + exquisite|\n", "lili|\n", "paintingsexy femalelooking at viewer|\n", "1girl detail face|\n", "perfect skin 8k|\n", "snow whiteWaifu|\n", "eris_greyrat |\n", "vivian|\n", "solo female artica_sparkle_character|\n", "by wizzikt|\n", "cel shaded 4k|\n", "carmen|\n", "kami_nomi_zo_shiru_sekai |\n", "3d unreal engine|\n", "3D unreal engine|\n", "girl closeup|\n", "by GLaDOS|\n", "red gothic cursed tower|\n", "riot heterochromia|\n", "kata|\n", "lifelike dramatic lighting unrealengine|\n", "alternate_skin_color |\n", "realistic han_juri|\n", "indian mary_winstead eyes|\n", "lulu|\n", "strict_nun_(diva) |\n", "arcadian|\n", "a gorgeous sexy thicc harlequin vampire|\n", "apex_legends |\n", "highres flawless|\n", "official_alternate_hairstyle |\n", "official_alternate_hairstyle|\n", "1 android girl|\n", "bianca|\n", "LydiaV|\n", "best detail 1woman|\n", "RTX moody female|\n", "beautiful elegant face|\n", "comic dark hair|\n", "close-up realistic|\n", "femme fatale|\n", "concept-art|\n", "high skin details|\n", "Full Stylized|\n", "amazing. 3d shading|\n", "fushigi_no_umi_no_nadia |\n", "Sense 1girl|\n", "skin details nude|\n", "game cg sky cloud|\n", "realistic highres|\n", "skin detail NSFW|\n", "Elegant realistic|\n", "sexy sexy 8|\n", "source_furry 1girl|\n", "by Skyrn99|\n", "mo_chengwei |\n", "character description|\n", "kawakami_mai |\n", "art style by artgerm|\n", "thegame|\n", "elizabeth|\n", "super_heroine_boy |\n", "bold eyes fix|\n", "bokeh ray-tracing|\n", "a busty iranian pale ulzzang idol|\n", "Best Quality 1girl|\n", "BEST QUALITY 1GIRL|\n", "best quality1girl|\n", "best quality 1GIRL|\n", "shanghai_doll |\n", "beautiful eyessexy theme|\n", "ultra-detailed1|\n", "n1ll 1girl|\n", "Gwen 1girl solo|\n", "unreal engine rendered|\n", "beautiful face portrait|\n", "submissive_female|\n", "fantasy lifestyle-portrait by mandy jurgens|\n", "kondou_taeko |\n", "alisa|\n", "unreal engine cinematic smooth|\n", "1girl human female|\n", "highres waist-up|\n", "a mature sexy streamer|\n", "high resolution illustrations|\n", "character focus cinematic light|\n", "elegant epic|\n", "Highres elaborate|\n", "human 1girl|\n", "nadia_la_arwall |\n", "seductive mature female|\n", "1girl older|\n", "masterpiece miko|\n", "very detailed skin|\n", "mysterious epic|\n", "watson_amelia |\n", "Visual novel|\n", "hi res detailed|\n", "gumroad username|\n", "Sunrays Raytracing|\n", "1futa 1male|\n", "background characters|\n", "natasha|\n", "Detailed art|\n", "womam skin|\n", "details such as raytracing and|\n", "wcw|\n", "1girl deer woman|\n", "physically-based eye shading|\n", "an attracitve woman|\n", "sexy femme fatale demon babe}\n", "{character design by a character portrait by senior character artist|\n", "eyes by smooth art style|\n", "highres perfect face|\n", "game a pretty arabic female character|\n", "artstation trending|\n", "by artist a character by senior character artist|\n", "stylized detailed_character|\n", "mirage_(apex_legends) |\n", "nsfw_high_detail_character_portrait|\n", "cinematic character design d|\n", "art by artstation womanly|\n", "3d cg woman|\n", "8k profile photo|\n", "soft shadows real-time skin rendering|\n", "detailed 1girl|\n", "cinematic art by hq|\n", "Game art|\n", "flirty Focus on ArtStation|\n", "indiehd detailed character|\n", "Artstation HD|\n", "artstationHD|\n", "beautiful female android face|\n", "cg unity k wallpaper shiny skin|\n", "best crypto_(apex_legends) |\n", "cassandra|\n", "artstation highres|\n", "beautiful female robot|\n", "1girl Annerose|\n", "F/14 environmental Artstation|\n", "artstation|\n", "ilya_kuvshinov |\n", "artist name|\n", "beautiful character design|\n", "artstation smooth|\n", "artstation game assets|\n", "solo mature miko|\n", "ilya kuvshinov style|\n", "sfw detailed face|\n", "stylized goddess|\n", "game icon|\n", "multra detailed face|\n", "SB_Eve_S-|\n", "theresa_apocalypse |\n", "ada|\n", "gwen|\n", "bad_artstation_id |\n", "lifeline_(apex_legends) |\n", "annerose vajra|\n", "assets|\n", "artstation Velvia|\n", "streaming on twitch|\n", "low-poly game art|\n", "8k artstation|\n", "rita_mordio |\n", "a pretty asian female character|\n", "mother_(game) |\n", "a character by senior character portrait artist|\n", "a female character|\n", "ada_wong|\n", "ada_wong |\n", "gameart|\n", "maria_cadenzavna_eve |\n", "lighting place place traits character eyes|\n", "a iranian pale ulzzang madewithunity|\n", "ingrid_brandl_galatea |\n", "sera|\n", "lewd art by riotgames|\n", "perfect female face|\n", "stylized character designs|\n", "k unity cg wallpaper|\n", "game cg highres|\n", "highres detailed|\n", "Highres Detailed|\n", "beautiful_female_skin|\n", "computer game art|\n", "a babylonian female assassin|\n", "artstation winner|\n", "artstyle by helen wells|\n", "indian mary_winstead|\n", "artstation masterpiece|\n", "character art|\n", "a beautiful attractive future k unity engine wallpaper|\n", "game cg supergirl|\n", "1girl sarielx|\n", "artstation female body|\n", "realistic meiko|\n", "3d cg girl|\n", "high resolution skin|\n", "realistic 1girl|\n", "realistic 1 girl|\n", "goddess portrait|\n", "beautiful mature female viera|\n", "artstation contest winner|\n", "detailed skin sfw|\n", "character illustrations|\n", "high detail skin|\n", "a gorgeous female demonologist|\n", "detailed CG|\n", "gamedev|\n", "vanessa|\n", "jp-svetf- as gorgeous supernatural goddess|\n", "artstationextremely|\n", "game_cg |\n", "highres milf|\n", "1girl half body|\n", "character_profile|\n", "character_profile |\n", "alisa_mikhailovna_kujou |\n", "lucia_(scott_malin) |\n", "stylized art|\n", "detailed hot skin|\n", "F/ behance HD|\n", "by Foxovh female|\n", "lunamaria_hawke |\n", "detailed skin UHD|\n", "legends art 1girl|\n", "gorgeous digital artwork|\n", "artstation matte|\n", "hilda_valentine_goneril |\n", "lifelike texture bright lighting unreal engine|\n", "Hentai Cinematic|\n", "k cg unity engine wallpaper|\n", "Concept Art World|\n", "stylized lighting|\n", "indian mary_winstead small|\n", "best_quality woman|\n", "portrait alt girl|\n", "cg 3d girl|\n", "Beuty concept art|\n", "very fine k cg wallpaper|\n", "sexy splash art|\n", "juliet|\n", "3d-realisticstyle|\n", "theresa|\n", "scene art|\n", "CGSociety|\n", "a gorgeous female chronomancer|\n", "cinematic production character rendering|\n", "cleo|\n", "ana|\n", "lifelike texture dcharacter|\n", "trending in art station|\n", "check_artist |\n", "list of characters|\n", "a persian pale ulzzang detailed skin20|\n", "high resolution skin texture|\n", "1girl close-up|\n", "bust wallpaper|\n", "soft lighting3D|\n", "1girl solo alpha|\n", "stylized smooth|\n", "art by unreal engine|\n", "iris_black_games |\n", "nadia|\n", "Artstation.|\n", "solo detailed face|\n", "artstation drawing|\n", "beautiful cyberpunk female|\n", "pandora_smith_magister|\n", "fantasy art from dnd extremely beautiful style|\n", "1girl raid_arbiter|\n", "beautiful anthro female|\n", "character_name|\n", "character_name |\n", "Chen Chun game art|\n", "4kdetailed face|\n", "leblanc|\n", "mature female character|\n", "physically-based rendering light on the face|\n", "artstation 4K|\n", "ru_ta artstyle|\n", "askzy 1girl|\n", "a beautiful cyberpunk female|\n", "ramatic lighting unreal engine|\n", "princess_of_moonbrook |\n", "sienna|\n", "ilya kuvshinov|\n", "art station trending|\n", "detailed eyes hd|\n", "best quality woman|\n", "those games where she still|\n", "milla_maxwell |\n", "highres dark hair|\n", "detailed Karyl|\n", "artgerm 1girl|\n", "lupinus_virtual_games |\n", "sola-ui_nuada-re_sophia-ri |\n", "detailed skin nun|\n", "elena|\n", "charming character illustrations|\n", "absurdres game cg|\n", "protagonist|\n", "soft cinematic lighting|\n", "beautiful feminine face+|\n", "every pixiel high resolution|\n", "games|\n", "beautiful sexy adult female face|\n", "cinematic hard lighting eye shading|\n", "beautyful female face|\n", "marina_(blue_archive) |\n", "award winning character concept art of|\n", "by ecmajor|\n", "character portrait|\n", "obsidian-skinned_female|\n", "waifu|\n", "detailed skin solo|\n", "realistic lips|\n", "20s f/|\n", "ultra-fine cg unity k wallpaper|\n", "suic0idegirl|\n", "the character the overall image should have|\n", "skin texture 1lady|\n", "serious aura|\n", "a beautiful android|\n", "the most beautiful anime style art ever seen|\n", "cute anime face cinematic angle|\n", "mysterious look on her face lilith is|\n", "zoomed-in face portrait in background|\n", "gorgeous d rendering by cgsociety|\n", "indian mary_winstead face|\n", "k character concept portrait|\n", "cell shading|\n", "HDR evangeline_a.k._mcdowell |\n", "cuteg 1girl nsfw|\n", "3d cg 16yo girl|\n", "cute breast woman|\n", "game|\n", "WLOP art style|\n", "adora|\n", "beautiful female sex robot|\n", "realistic girl|\n", "mysterious female|\n", "zia|\n", "skin pores & face texture|\n", "xenotr1p 1girl|\n", "characterart|\n", "looking to viewer|\n", "by reysi|\n", "by Reysi|\n", "3d high res 1girl|\n", "anime female|\n", "beret |\n", "enza|\n", "beautiful mature vampire princess|\n", "very aesthetic style face|\n", "Soft_Shading|\n", "by sqoon|\n", "gloomy half body portrait k unity render|\n", "solo Zia|\n", "zoya athene|\n", "8k Concept artist|\n", "ChromaV5 art|\n", "diana|\n", "wallpaper detailed|\n", "cel_shading |\n", "human female|\n", "tia|\n", "artist_name |\n", "ULTRA malenia_blade_of_miquella |\n", "battle_android_bodyguard_female|\n", "illustration highres|\n", "3d cg woman abs|\n", "gorgeous blushing beautiful sexy charming|\n", "beautiful light makeup female sorceress|\n", "beautiful female anthro|\n", "detailed epic|\n", "1girl sex eyes|\n", "celeste|\n", "comic art style|\n", "a gorgeous female priest|\n", "beautiful female dragoness face|\n", "cg illustration|\n", "anime cg|\n", "reoen official art|\n", "concept art illustration by artgerm|\n", "moon solo female|\n", "detailed highres|\n", "art station artwork detailed|\n", "seaport_princess |\n", "dawn solo serious|\n", "brown hair highres|\n", "Nani character|\n", "giveaway|\n", "fantasy kiana kaslana white comet|\n", "bare shoulders retro artstyle|\n", "indiedev|\n", "super detailed face|\n", "realistic_skin|\n", "loba_(apex_legends) |\n", "prety woman|\n", "Ciro Marcetti Art|\n", "mujer de ojos rojos y pelo azulado|\n", "highres venusbody|\n", "animation|\n", "1girl Yennifer|\n", "of sexy cyberpunk female mage|\n", "anya_(spy_x_family) |\n", "rodion_(project_moon) |\n", "lumen cinematic contrast lighting|\n", "feminine attractive face|\n", "miss-fortune|\n", "jane|\n", "Highres womanly|\n", "realistic 1woman|\n", "subtle facial animations|\n", "vampire_\\(game\\) |\n", "|\n", "detailed face RTX|\n", "sexbot|\n", "Labyrista 1girl|\n", "Artstation blurry|\n", "animated illustrations|\n", "hyper realisitc texture skin|\n", "cute lips nsfw|\n", "a pretty american female character|\n", "the best female erotic figure|\n", "1 android girl Red|\n", "amada_ken |\n", "vex|\n", "olympia|\n", "Marenka Short hair|\n", "jorge jacinto's artstyle|\n", "arrogant woman21|\n", "cagalli_yula_athha |\n", "Fantasy Character|\n", "videogame cg|\n", "female_detailed girl|\n", "aesthetics|\n", "detailed wallpaper|\n", "unreal engine dof|\n", "detailed cute face|\n", "looks like cirilla from the witcher|\n", "cel shaded devil|\n", "best_quality 1girl|\n", "green eyes highres|\n", "cg illustration cinematic light|\n", "short black hair over one eye|\n", "silence_girl |\n", "d artist name|\n", "Cel shading|\n", "cel shading|\n", "2D female|\n", "trending at cgsociety|\n", "Cel shading Sci-Fi|\n", "detailed skin cute|\n", "eliza|\n", "alpha_signature |\n", "scarletart by artgerm|\n", "character_sheet |\n", "author ; tags female protagonist|\n", "1girl Pharah|\n", "featured art|\n", "art by guweiz|\n", "y cinematic lighting|\n", "qixiong_ruqun |\n", "ray tracing 1girl|\n", "real-time makeup application|\n", "kiana_kaslana_(herrscher_of_finality) |\n", "3d hires model|\n", "dnd character art|\n", "solo aphrodite|\n", "carmen_sandiego_v_shurik|\n", "a sexy female thief on|\n", "evelyn|\n", "ultra_detailed|\n", "beautiful aesthetic face|\n", "vestia_zeta |\n", "concept character art|\n", "dawn solo 1girl|\n", "moon_demon 1girl|\n", "soloanthro female|\n", "by Retros|\n", "aesthetic detailed|\n", "cel shaded style|\n", "humanoid female robot|\n", "leila|\n", "realistic android|\n", "epic fantasy greek priestess|\n", "crescent_rose |\n", "nude detailed face|\n", "very detailed epic|\n", "wallpapers|\n", "game cg roomi|\n", "digital anime art|\n", "beautiful busty slender knight|\n", "8k portrait cass|\n", "super elan_ceres |\n", "beautiful cute sexy anime|\n", "temple portrait|\n", "an stunningly beautiful age half demon|\n", "3D art anime style|\n", "soft-focused realism|\n", "production cinematic character render|\n", "dawn woman sex|\n", "sum_elise|\n", "comic artwork|\n", "suzi_q |\n", "cel shading 1girl|\n", "valkyrie_(apex_legends) |\n", "the most beautiful artwork in the world featuring|\n", "beautiful yellow eyes +|\n", "ultra-detailed CG|\n", "8K UHD female|\n", "solo profile|\n", "a female necromancer|\n", "undone_sarashi |\n", "octane_(apex_legends) |\n", "silent_comic |\n", "86_-eightysix- |\n", "josie|\n", "high resolution face|\n", "beautiful egyptian bombshell black-haired|\n", "|\n", "a.i._voice |\n", "best dynamic lighting|\n", "fiona|\n", "highres gorgeous dark skin noxian|\n", "gorgeous characters|\n", "best quality CG|\n", "best quality cg|\n", "beautiful women complete eyes|\n", "4K artist|\n", "PA7_Portrait-CU|\n", "wattson_(apex_legends) |\n", "a gorgeous beautiful age sexy|\n", "beautiful female face|\n", "lynda|\n", "female human|\n", "alisa_ilinichina_amiella |\n", "alhambra background|\n", "heart_maebari |\n", "she has realistic human|\n", "skin_fang |\n", "cinematic unreal 5|\n", "highres cinematic|\n", "layla|\n", "harmony high-quality skin bump mapping|\n", "bodya high resolution face|\n", "masterpeice 1girl|\n", "by syuro|\n", "iconic fine epic|\n", "garnet_til_alexandros_xvii |\n", "elena_so6|\n", "official_art |\n", "7rtifa elegant|\n", "digital artist|\n", "realistic lesbian|\n", "Escher CGI Artgerm|\n", "wallpaper cinematic lighting sharp focus|\n", "nina|\n", "Detailed lips|\n", "thief|\n", "stylized charming|\n", "by Whooo-Ya|\n", "high-quality skin textures|\n", "eva_beatrice |\n", "fantasy art.HD 3D|\n", "bezier place place character traits lighting|\n", "sexy 8 list|\n", "penelope|\n", "multi-layer skin shading|\n", "streaming|\n", "woman pretty face|\n", "hyperrealism by atey ghailan|\n", "highres cute|\n", "torino_aqua |\n", "guest_art |\n", "masterpiecebust portrait|\n", "2D artstyle|\n", "1girl best quality|\n", "adorable_girl|\n", "prompt lora weight at ->quality loss|\n", "by zackary|\n", "realistic skin1|\n", "stunning fantasy style environment|\n", "Ultra detailed|\n", "Ultra Detailed|\n", "mobile_legends_alice|\n", "detailed_face|\n", "dynamic cinematic portrait medium shot|\n", "absurdres gorgeous noxian|\n", "perfectly composedgorgeous jp-svetf|\n", "dawn solo 1boy|\n", "chloe|\n", "edgtb_woman|\n", "character concept art|\n", "ramudia_(lamyun) |\n", "tall mature female|\n", "nose realistic|\n", "ultra detailed HD|\n", "bliznyashkithetwins_v|\n", "white_thighhighs |\n", "rating_lewd curvy|\n", "super fine illustrations|\n", "source_3Dcartoon|\n", "lucy_(cyberpunk) |\n", "soft diffuse lighting|\n", "pink_hair |\n", "highres black hair|\n", "blue eyes alpha|\n", "Cel shading Wonder|\n", "detailed artgem|\n", "evil AI named Aida|\n", "Realistic face|\n", "realistic face|\n", "RTX 0 shader|\n", "group profile|\n", "beautiful sxz-ashe-ow-|\n", "povsolo 1girl|\n", "cleavage highres|\n", "lips realistic|\n", "adorable female face|\n", "the character the image should be|\n", "jane_doe_(zenless_zone_zero) |\n", "kawakami_rokkaku|\n", "kawakami_rokkaku |\n", "an artist-inspired character|\n", "Detailed background|\n", "Detailed Background|\n", "ziche_fuzhao |\n", "temple cyberpunk soft lighting|\n", "breasts nose_blush|\n", "highres breasts|\n", "natalie|\n", "ultra-detailed eyes|\n", "a beautiful artwork illustration|\n", "Soft Shading|\n", "aquiline_nose |\n", "blush short_hair|\n", "artgerm best quality|\n", "soft_lighting|\n", "1girl kujikawadef|\n", "gundam |\n", "ada_wong resident evil|\n", "Flirty_Shy_Grin|\n", "of beautiful female knight women|\n", "vox|\n", "skadiview-appearance-shiny skin|\n", "detective|\n", "cute solo female|\n", "character request|\n", "fantasy female|\n", "futanari realistic|\n", "dawn solo|\n", "astrosorc22|\n", "luxurious stone castle game|\n", "mignonworks artstyle|\n", "idol|\n", "idol |\n", "fantasy game spell icon|\n", "enya|\n", "moody cinematic epic concept art|\n", "perfect artwork|\n", "age_of_ishtaria |\n", "1boy cute woman|\n", "beatiful face|\n", "female_protagonist_(pokemon_go) |\n", "a pretty caucasian female character|\n", "teresa|\n", "arab escort escort|\n", "telegram|\n", "gorgeous female face|\n", "futuristic setting she should be depicted|\n", "award winning digital painting|\n", "anime woman|\n", "conceptart|\n", "astel_leda |\n", "snow_white |\n", "Epic Digital Art|\n", "epic digital art|\n", "epic Digital art|\n", "heroine|\n", "gorgeous art|\n", "best ai image|\n", "ruyi_jingu_bang |\n", "#explorepage #art #cosplay|\n", "saga|\n", "1girl sfw|\n", "lovely follynobodysd|\n", "marietta|\n", "yen |\n", "banned_artist |\n", "depth-based facial shading|\n", "artstation_sample |\n", "highres dark skin|\n", "unreal engine Maya|\n", "well-lit cinematic|\n", "dark hair pureerosface_v|\n", "masterpiece manhwa|\n", "1girl perfect eyes|\n", "Kyaradin1girl|\n", "john_doe_(jdart) |\n", "official_alternate_eye_color |\n", "character_doll |\n", "epic realistic art|\n", "bronya_zaychik |\n", "human ai hybrid|\n", "JanePorterXLP|\n", "blulust 1girl|\n", "beautiful concept illustration|\n", "Acheron_Blue 1girl|\n", "evie|\n", "lifelike texture dramatic lighting unrealengine|\n", "VichyaIris 1girl|\n", "gwen_(league_of_legends) |\n", "Human woman|\n", "human woman|\n", "woman close up|\n", "Realistic Lustful|\n", "F/ beautiful|\n", "uncensored game cg|\n", "sunlight cinematic lighting|\n", "temple_ruins|\n", "detailed sexy lips|\n", "snapmatic|\n", "milf_saga_face|\n", "by zixiong|\n", "high rating 1girl|\n", "1girl buff female|\n", "nora|\n", "realistic mercy|\n", "HD CGSociety|\n", "HD cgsociety|\n", "snowwhiteWaifu|\n", "kaname_raana |\n", "artist_request |\n", "artist_request|\n", "noir detailed|\n", "polearm |\n", "sex bot fem bot|\n", "lara|\n", "stunning female wendigo|\n", "detailed makeup|\n", "nan|\n", "Detailed person|\n", "character quality|\n", "mahjong_soul |\n", "janet|\n", "fantasy illustration masterpiece|\n", "incredible perfect artwork|\n", "the most beautiful artwork in the worldcute|\n", "beautiful stylized|\n", "ultra detailed eyes|\n", "gwen tennyson|\n", "Gwen Tennyson|\n", "artstation pixiv|\n", "3d shading realism|\n", "kanden_sky |\n", "half human ai hybrid|\n", "animation cinematic|\n", "1girl fair_skin|\n", "perfect rendered face|\n", "comic art|\n", "female_pervert|\n", "female_pervert |\n", "rgb unreal engine|\n", "assassin|\n", "realism image Fill|\n", "masterpiece 2d art|\n", "DETAILED SKIN|\n", "detailed skin|\n", "1girl yor_briar|\n", "3d soft lighting|\n", "little_witch_nobeta |\n", "bisque skin|\n", "perfect face skin|\n", "lcylxsl woman|\n", "gwen_tennyson|\n", "looking at viewercurvy|\n", "uncensored steam|\n", "robot_girl |\n", "1girl monaXL|\n", "captivating digital art|\n", "a beautiful porcelain young slim fit female robot|\n", "k uhd unity wallpaper|\n", "maria-sama_ga_miteru |\n", "justice right bitch heroine|\n", "feminine face profile|\n", "doggsytyle 1girl|\n", "1girl face closeup|\n", "1girl necromancer|\n", "highres 3d cg|\n", "ScarletWitch|\n", "black_widow|\n", "styledreamlikeartredshift|\n", "UHD. AI_girl doll|\n", "lore|\n", "painting stylized|\n", "stunning environmentnsfw|\n", "red_eyes solo|\n", "3D cartoon dreamy|\n", "estheticfutanaritrap|\n", "5 detailed face|\n", "motd|\n", "Cel shading Evil|\n", "helsie|\n", "marissa|\n", "vertical_comic |\n", "commissioner_name |\n", "Ada Wong cartoon|\n", "Epic CG masterpiece|\n", "solo detailed eyes|\n", "Atey Ghailan|\n", "zoe|\n", "cinematic environment|\n", "character_print |\n", "d rendered hentai version|\n", "Beuty lips|\n", "beautiful noelle_silva|\n", "no humans profile|\n", "shiny_skin1|\n", "eli|\n", "hades_1 |\n", "fantasy highres|\n", "Ptylust 1girl|\n", "beautiful yo greek cyborg|\n", "mature female tall|\n", "stunningly beautiful corneo_yor_forger|\n", "scarlet_devil_mansion |\n", "ultra detailed sex|\n", "original character|\n", "towel |\n", "by Sadahide1girl|\n", "ADDCOLfair skin|\n", "nerissa_ravencroft |\n", "mei|\n", "rover_(wuthering_waves) |\n", "vio|\n", "close up of 1girl|\n", "realistic elf girl|\n", "trending cgsociety|\n", "character name|\n", "sexy shy steam|\n", "cornelia_li_britannia |\n", "by pakwan|\n", "perfect eyes best quality|\n", "sumeragi_lee_noriega |\n", "character_signature |\n", "source_comic|\n", "perfect feminine face+|\n", "sci-fi woman|\n", "aya|\n", "lilith_aensland |\n", "vana|\n", "siri|\n", "uncensored shadman|\n", "high_resolution illustration|\n", "profile portrait|\n", "impossibly beautiful egyptian|\n", "superhero game|\n", "ptoto portrait|\n", "elegant cinematic|\n", "very detailed faces|\n", "kaede_johan_nouvel |\n", "cutiesaturday|\n", "female high res|\n", "lip bite blush|\n", "Wealth Highres|\n", "deer_girl |\n", "lily_black |\n", "riven_(league_of_legends) |\n", "a beautiful sexy age chinese|\n", "high detailed skin|\n", "mature female masterpiece|\n", "8k 2d animation|\n", "concept_art |\n", "1>tifa|\n", "red gothic illustration + exquisite|\n", "lili|\n", "paintingsexy femalelooking at viewer|\n", "1girl detail face|\n", "perfect skin 8k|\n", "snow whiteWaifu|\n", "eris_greyrat |\n", "vivian|\n", "solo female artica_sparkle_character|\n", "by wizzikt|\n", "cel shaded 4k|\n", "carmen|\n", "kami_nomi_zo_shiru_sekai |\n", "3d unreal engine|\n", "3D unreal engine|\n", "girl closeup|\n", "by GLaDOS|\n", "red gothic cursed tower|\n", "riot heterochromia|\n", "kata|\n", "lifelike dramatic lighting unrealengine|\n", "alternate_skin_color |\n", "realistic han_juri|\n", "indian mary_winstead eyes|\n", "lulu|\n", "strict_nun_(diva) |\n", "arcadian|\n", "a gorgeous sexy thicc harlequin vampire|\n", "apex_legends |\n", "highres flawless|\n", "official_alternate_hairstyle |\n", "official_alternate_hairstyle|\n", "1 android girl|\n", "bianca|\n", "LydiaV|\n", "best detail 1woman|\n", "RTX moody female|\n", "beautiful elegant face|\n", "comic dark hair|\n", "close-up realistic|\n", "femme fatale|\n", "concept-art|\n", "high skin details|\n", "Full Stylized|\n", "amazing. 3d shading|\n", "fushigi_no_umi_no_nadia |\n", "Sense 1girl|\n", "skin details nude|\n", "game cg sky cloud|\n", "realistic highres|\n", "skin detail NSFW|\n", "Elegant realistic|\n", "sexy sexy 8|\n", "source_furry 1girl|\n", "by Skyrn99|\n", "mo_chengwei |\n", "character description|\n", "kawakami_mai |\n", "art style by artgerm|\n", "thegame|\n", "elizabeth|\n", "super_heroine_boy |\n", "bold eyes fix|\n", "bokeh ray-tracing|\n", "a busty iranian pale ulzzang idol|\n", "Best Quality 1girl|\n", "BEST QUALITY 1GIRL|\n", "best quality1girl|\n", "best quality 1GIRL|\n", "shanghai_doll |\n", "beautiful eyessexy theme|\n", "ultra-detailed1|\n", "n1ll 1girl|\n", "Gwen 1girl solo|\n", "unreal engine rendered|\n", "beautiful face portrait|\n", "submissive_female|\n", "fantasy lifestyle-portrait by mandy jurgens|\n", "kondou_taeko |\n", "alisa|\n", "unreal engine cinematic smooth|\n", "1girl human female|\n", "highres waist-up|\n", "a mature sexy streamer|\n", "high resolution illustrations|\n", "character focus cinematic light|\n", "elegant epic|\n", "Highres elaborate|\n", "human 1girl|\n", "nadia_la_arwall |\n", "seductive mature female|\n", "1girl older|\n", "masterpiece miko|\n", "very detailed skin|\n", "mysterious epic|\n", "watson_amelia |\n", "Visual novel|\n", "hi res detailed|\n", "gumroad username|\n", "Sunrays Raytracing|\n", "1futa 1male|\n", "background characters|\n", "natasha|\n", "Detailed art|\n", "womam skin|\n", "details such as raytracing and|\n", "wcw|\n", "1girl deer woman|\n", "physically-based eye shading|\n", "an attracitve woman|\n", "sexy femme fatale demon babe}\n", "{character design by a character portrait by senior character artist|\n", "eyes by smooth art style|\n", "highres perfect face|\n", "game a pretty arabic female character|\n", "artstation trending|\n", "by artist a character by senior character artist|\n", "stylized detailed_character|\n", "mirage_(apex_legends) |\n", "nsfw_high_detail_character_portrait|\n", "cinematic character design d|\n", "art by artstation womanly|\n", "3d cg woman|\n", "8k profile photo|\n", "soft shadows real-time skin rendering|\n", "detailed 1girl|\n", "cinematic art by hq|\n", "Game art|\n", "flirty Focus on ArtStation|\n", "indiehd detailed character|\n", "Artstation HD|\n", "artstationHD|\n", "beautiful female android face|\n", "cg unity k wallpaper shiny skin|\n", "best crypto_(apex_legends) |\n", "cassandra|\n", "artstation highres|\n", "beautiful female robot|\n", "1girl Annerose|\n", "F/14 environmental Artstation|\n", "artstation|\n", "ilya_kuvshinov |\n", "artist name|\n", "beautiful character design|\n", "artstation smooth|\n", "artstation game assets|\n", "solo mature miko|\n", "ilya kuvshinov style|\n", "sfw detailed face|\n", "stylized goddess|\n", "game icon|\n", "multra detailed face|\n", "SB_Eve_S-|\n", "theresa_apocalypse |\n", "ada|\n", "gwen|\n", "bad_artstation_id |\n", "lifeline_(apex_legends) |\n", "annerose vajra|\n", "assets|\n", "artstation Velvia|\n", "streaming on twitch|\n", "low-poly game art|\n", "8k artstation|\n", "rita_mordio |\n", "a pretty asian female character|\n", "mother_(game) |\n", "a character by senior character portrait artist|\n", "a female character|\n", "ada_wong|\n", "ada_wong |\n", "gameart|\n", "maria_cadenzavna_eve |\n", "lighting place place traits character eyes|\n", "a iranian pale ulzzang madewithunity|\n", "ingrid_brandl_galatea |\n", "sera|\n", "lewd art by riotgames|\n", "perfect female face|\n", "stylized character designs|\n", "k unity cg wallpaper|\n", "game cg highres|\n", "highres detailed|\n", "Highres Detailed|\n", "beautiful_female_skin|\n", "computer game art|\n", "a babylonian female assassin|\n", "artstation winner|\n", "artstyle by helen wells|\n", "indian mary_winstead|\n", "artstation masterpiece|\n", "character art|\n", "a beautiful attractive future k unity engine wallpaper|\n", "game cg supergirl|\n", "1girl sarielx|\n", "artstation female body|\n", "realistic meiko|\n", "3d cg girl|\n", "high resolution skin|\n", "realistic 1girl|\n", "realistic 1 girl|\n", "goddess portrait|\n", "beautiful mature female viera|\n", "artstation contest winner|\n", "detailed skin sfw|\n", "character illustrations|\n", "high detail skin|\n", "a gorgeous female demonologist|\n", "detailed CG|\n", "gamedev|\n", "vanessa|\n", "jp-svetf- as gorgeous supernatural goddess|\n", "artstationextremely|\n", "game_cg |\n", "highres milf|\n", "1girl half body|\n", "character_profile|\n", "character_profile |\n", "alisa_mikhailovna_kujou |\n", "lucia_(scott_malin) |\n", "stylized art|\n", "detailed hot skin|\n", "F/ behance HD|\n", "by Foxovh female|\n", "lunamaria_hawke |\n", "detailed skin UHD|\n", "legends art 1girl|\n", "gorgeous digital artwork|\n", "artstation matte|\n", "hilda_valentine_goneril |\n", "lifelike texture bright lighting unreal engine|\n", "Hentai Cinematic|\n", "k cg unity engine wallpaper|\n", "Concept Art World|\n", "stylized lighting|\n", "indian mary_winstead small|\n", "best_quality woman|\n", "portrait alt girl|\n", "cg 3d girl|\n", "Beuty concept art|\n", "very fine k cg wallpaper|\n", "sexy splash art|\n", "juliet|\n", "3d-realisticstyle|\n", "theresa|\n", "scene art|\n", "CGSociety|\n", "a gorgeous female chronomancer|\n", "cinematic production character rendering|\n", "cleo|\n", "ana|\n", "lifelike texture dcharacter|\n", "trending in art station|\n", "check_artist |\n", "list of characters|\n", "a persian pale ulzzang detailed skin20|\n", "high resolution skin texture|\n", "1girl close-up|\n", "bust wallpaper|\n", "soft lighting3D|\n", "1girl solo alpha|\n", "stylized smooth|\n", "art by unreal engine|\n", "iris_black_games |\n", "nadia|\n", "Artstation.|\n", "solo detailed face|\n", "artstation drawing|\n", "beautiful cyberpunk female|\n", "pandora_smith_magister|\n", "fantasy art from dnd extremely beautiful style|\n", "1girl raid_arbiter|\n", "beautiful anthro female|\n", "character_name|\n", "character_name |\n", "Chen Chun game art|\n", "4kdetailed face|\n", "leblanc|\n", "mature female character|\n", "physically-based rendering light on the face|\n", "artstation 4K|\n", "ru_ta artstyle|\n", "askzy 1girl|\n", "a beautiful cyberpunk female|\n", "ramatic lighting unreal engine|\n", "princess_of_moonbrook |\n", "sienna|\n", "ilya kuvshinov|\n", "art station trending|\n", "detailed eyes hd|\n", "best quality woman|\n", "those games where she still|\n", "milla_maxwell |\n", "highres dark hair|\n", "detailed Karyl|\n", "artgerm 1girl|\n", "lupinus_virtual_games |\n", "sola-ui_nuada-re_sophia-ri |\n", "detailed skin nun|\n", "elena|\n", "charming character illustrations|\n", "absurdres game cg|\n", "protagonist|\n", "soft cinematic lighting|\n", "beautiful feminine face+|\n", "every pixiel high resolution|\n", "games|\n", "beautiful sexy adult female face|\n", "cinematic hard lighting eye shading|\n", "beautyful female face|\n", "marina_(blue_archive) |\n", "award winning character concept art of|\n", "by ecmajor|\n", "character portrait|\n", "obsidian-skinned_female|\n", "waifu|\n", "detailed skin solo|\n", "realistic lips|\n", "20s f/|\n", "ultra-fine cg unity k wallpaper|\n", "suic0idegirl|\n", "the character the overall image should have|\n", "skin texture 1lady|\n", "serious aura|\n", "a beautiful android|\n", "the most beautiful anime style art ever seen|\n", "cute anime face cinematic angle|\n", "mysterious look on her face lilith is|\n", "zoomed-in face portrait in background|\n", "gorgeous d rendering by cgsociety|\n", "indian mary_winstead face|\n", "k character concept portrait|\n", "cell shading|\n", "HDR evangeline_a.k._mcdowell |\n", "cuteg 1girl nsfw|\n", "3d cg 16yo girl|\n", "cute breast woman|\n", "game|\n", "WLOP art style|\n", "adora|\n", "beautiful female sex robot|\n", "realistic girl|\n", "mysterious female|\n", "zia|\n", "skin pores & face texture|\n", "xenotr1p 1girl|\n", "characterart|\n", "looking to viewer|\n", "by reysi|\n", "by Reysi|\n", "3d high res 1girl|\n", "anime female|\n", "beret |\n", "enza|\n", "beautiful mature vampire princess|\n", "very aesthetic style face|\n", "Soft_Shading|\n", "by sqoon|\n", "gloomy half body portrait k unity render|\n", "solo Zia|\n", "zoya athene|\n", "8k Concept artist|\n", "ChromaV5 art|\n", "diana|\n", "wallpaper detailed|\n", "cel_shading |\n", "human female|\n", "tia|\n", "artist_name |\n", "ULTRA malenia_blade_of_miquella |\n", "battle_android_bodyguard_female|\n", "illustration highres|\n", "3d cg woman abs|\n", "gorgeous blushing beautiful sexy charming|\n", "beautiful light makeup female sorceress|\n", "beautiful female anthro|\n", "detailed epic|\n", "1girl sex eyes|\n", "celeste|\n", "comic art style|\n", "a gorgeous female priest|\n", "beautiful female dragoness face|\n", "cg illustration|\n", "anime cg|\n", "reoen official art|\n", "concept art illustration by artgerm|\n", "moon solo female|\n", "detailed highres|\n", "art station artwork detailed|\n", "seaport_princess |\n", "dawn solo serious|\n", "brown hair highres|\n", "Nani character|\n", "giveaway|\n", "fantasy kiana kaslana white comet|\n", "bare shoulders retro artstyle|\n", "indiedev|\n", "super detailed face|\n", "realistic_skin|\n", "loba_(apex_legends) |\n", "prety woman|\n", "Ciro Marcetti Art|\n", "mujer de ojos rojos y pelo azulado|\n", "highres venusbody|\n", "animation|\n", "1girl Yennifer|\n", "of sexy cyberpunk female mage|\n", "anya_(spy_x_family) |\n", "rodion_(project_moon) |\n", "lumen cinematic contrast lighting|\n", "feminine attractive face|\n", "miss-fortune|\n", "jane|\n", "Highres womanly|\n", "realistic 1woman|\n", "subtle facial animations|\n", "vampire_\\(game\\) |\n", "|\n", "detailed face RTX|\n", "sexbot|\n", "Labyrista 1girl|\n", "Artstation blurry|\n", "animated illustrations|\n", "hyper realisitc texture skin|\n", "cute lips nsfw|\n", "a pretty american female character|\n", "the best female erotic figure|\n", "1 android girl Red|\n", "amada_ken |\n", "vex|\n", "olympia|\n", "Marenka Short hair|\n", "jorge jacinto's artstyle|\n", "arrogant woman21|\n", "cagalli_yula_athha |\n", "Fantasy Character|\n", "videogame cg|\n", "female_detailed girl|\n", "aesthetics|\n", "detailed wallpaper|\n", "unreal engine dof|\n", "detailed cute face|\n", "looks like cirilla from the witcher|\n", "cel shaded devil|\n", "best_quality 1girl|\n", "green eyes highres|\n", "cg illustration cinematic light|\n", "short black hair over one eye|\n", "silence_girl |\n", "d artist name|\n", "Cel shading|\n", "cel shading|\n", "2D female|\n", "trending at cgsociety|\n", "Cel shading Sci-Fi|\n", "detailed skin cute|\n", "eliza|\n", "alpha_signature |\n", "scarletart by artgerm|\n", "character_sheet |\n", "author ; tags female protagonist|\n", "1girl Pharah|\n", "featured art|\n", "art by guweiz|\n", "y cinematic lighting|\n", "qixiong_ruqun |\n", "ray tracing 1girl|\n", "real-time makeup application|\n", "kiana_kaslana_(herrscher_of_finality) |\n", "3d hires model|\n", "dnd character art|\n", "solo aphrodite|\n", "carmen_sandiego_v_shurik|\n", "a sexy female thief on|\n", "evelyn|\n", "ultra_detailed|\n", "beautiful aesthetic face|\n", "vestia_zeta |\n", "concept character art|\n", "dawn solo 1girl|\n", "moon_demon 1girl|\n", "soloanthro female|\n", "by Retros|\n", "aesthetic detailed|\n", "cel shaded style|\n", "humanoid female robot|\n", "leila|\n", "realistic android|\n", "epic fantasy greek priestess|\n", "crescent_rose |\n", "nude detailed face|\n", "very detailed epic|\n", "wallpapers|\n", "game cg roomi|\n", "digital anime art|\n", "beautiful busty slender knight|\n", "8k portrait cass|\n", "super elan_ceres |\n", "beautiful cute sexy anime|\n", "temple portrait|\n", "an stunningly beautiful age half demon|\n", "3D art anime style|\n", "soft-focused realism|\n", "production cinematic character render|\n", "dawn woman sex|\n", "sum_elise|\n", "comic artwork|\n", "suzi_q |\n", "cel shading 1girl|\n", "valkyrie_(apex_legends) |\n", "the most beautiful artwork in the world featuring|\n", "beautiful yellow eyes +|\n", "ultra-detailed CG|\n", "8K UHD female|\n", "solo profile|\n", "a female necromancer|\n", "undone_sarashi |\n", "octane_(apex_legends) |\n", "silent_comic |\n", "86_-eightysix- |\n", "josie|\n", "high resolution face|\n", "beautiful egyptian bombshell black-haired|\n", "|\n", "a.i._voice |\n", "best dynamic lighting|\n", "fiona|\n", "highres gorgeous dark skin noxian|\n", "gorgeous characters|\n", "best quality CG|\n", "best quality cg|\n", "beautiful women complete eyes|\n", "4K artist|\n", "PA7_Portrait-CU|\n", "wattson_(apex_legends) |\n", "a gorgeous beautiful age sexy|\n", "beautiful female face|\n", "lynda|\n", "female human|\n", "alisa_ilinichina_amiella |\n", "alhambra background|\n", "heart_maebari |\n", "she has realistic human|\n", "skin_fang |\n", "cinematic unreal 5|\n", "highres cinematic|\n", "layla|\n", "harmony high-quality skin bump mapping|\n", "bodya high resolution face|\n", "masterpeice 1girl|\n", "by syuro|\n", "iconic fine epic|\n", "garnet_til_alexandros_xvii |\n", "elena_so6|\n", "official_art |\n", "7rtifa elegant|\n", "digital artist|\n", "realistic lesbian|\n", "Escher CGI Artgerm|\n", "wallpaper cinematic lighting sharp focus|\n", "nina|\n", "Detailed lips|\n", "thief|\n", "stylized charming|\n", "by Whooo-Ya|\n", "high-quality skin textures|\n", "eva_beatrice |\n", "fantasy art.HD 3D|\n", "bezier place place character traits lighting|\n", "sexy 8 list|\n", "penelope|\n", "multi-layer skin shading|\n", "streaming|\n", "woman pretty face|\n", "hyperrealism by atey ghailan|\n", "highres cute|\n", "torino_aqua |\n", "guest_art |\n", "masterpiecebust portrait|\n", "2D artstyle|\n", "1girl best quality|\n", "adorable_girl|\n", "prompt lora weight at ->quality loss|\n", "by zackary|\n", "realistic skin1|\n", "stunning fantasy style environment|\n", "Ultra detailed|\n", "Ultra Detailed|\n", "mobile_legends_alice|\n", "detailed_face|\n", "dynamic cinematic portrait medium shot|\n", "absurdres gorgeous noxian|\n", "perfectly composedgorgeous jp-svetf|\n", "dawn solo 1boy|\n", "chloe|\n", "edgtb_woman|\n", "character concept art|\n", "ramudia_(lamyun) |\n", "tall mature female|\n", "nose realistic|\n", "ultra detailed HD|\n", "bliznyashkithetwins_v|\n", "white_thighhighs |\n", "rating_lewd curvy|\n", "super fine illustrations|\n", "source_3Dcartoon|\n", "lucy_(cyberpunk) |\n", "soft diffuse lighting|\n", "pink_hair |\n", "highres black hair|\n", "blue eyes alpha|\n", "Cel shading Wonder|\n", "detailed artgem|\n", "evil AI named Aida|\n", "Realistic face|\n", "realistic face|\n", "RTX 0 shader|\n", "group profile|\n", "beautiful sxz-ashe-ow-|\n", "povsolo 1girl|\n", "cleavage highres|\n", "lips realistic|\n", "adorable female face|\n", "the character the image should be|\n", "jane_doe_(zenless_zone_zero) |\n", "kawakami_rokkaku|\n", "kawakami_rokkaku |\n", "an artist-inspired character|\n", "Detailed background|\n", "Detailed Background|\n", "ziche_fuzhao |\n", "temple cyberpunk soft lighting|\n", "breasts nose_blush|\n", "highres breasts|\n", "natalie|\n", "ultra-detailed eyes|\n", "a beautiful artwork illustration|\n", "Soft Shading|\n", "aquiline_nose |\n", "blush short_hair|\n", "artgerm best quality|\n", "soft_lighting|\n", "1girl kujikawadef|\n", "gundam |\n", "ada_wong resident evil|\n", "Flirty_Shy_Grin|\n", "of beautiful female knight women|\n", "vox|\n", "skadiview-appearance-shiny skin|\n", "detective|\n", "cute solo female|\n", "character request|\n", "fantasy female|\n", "futanari realistic|\n", "dawn solo|\n", "astrosorc22|\n", "luxurious stone castle game|\n", "mignonworks artstyle|\n", "idol|\n", "idol |\n", "fantasy game spell icon|\n", "enya|\n", "moody cinematic epic concept art|\n", "perfect artwork|\n", "age_of_ishtaria |\n", "1boy cute woman|\n", "beatiful face|\n", "female_protagonist_(pokemon_go) |\n", "a pretty caucasian female character|\n", "teresa|\n", "arab escort escort|\n", "telegram|\n", "gorgeous female face|\n", "futuristic setting she should be depicted|\n", "award winning digital painting|\n", "anime woman|\n", "conceptart|\n", "astel_leda |\n", "snow_white |\n", "Epic Digital Art|\n", "epic digital art|\n", "epic Digital art|\n", "heroine|\n", "gorgeous art|\n", "best ai image|\n", "ruyi_jingu_bang |\n", "#explorepage #art #cosplay|\n", "saga|\n", "1girl sfw|\n", "lovely follynobodysd|\n", "marietta|\n", "yen |\n", "banned_artist |\n", "depth-based facial shading|\n", "artstation_sample |\n", "highres dark skin|\n", "unreal engine Maya|\n", "well-lit cinematic|\n", "dark hair pureerosface_v|\n", "masterpiece manhwa|\n", "1girl perfect eyes|\n", "Kyaradin1girl|\n", "john_doe_(jdart) |\n", "official_alternate_eye_color |\n", "character_doll |\n", "epic realistic art|\n", "bronya_zaychik |\n", "human ai hybrid|\n", "JanePorterXLP|\n", "blulust 1girl|\n", "beautiful concept illustration|\n", "Acheron_Blue 1girl|\n", "evie|\n", "lifelike texture dramatic lighting unrealengine|\n", "VichyaIris 1girl|\n", "gwen_(league_of_legends) |\n", "Human woman|\n", "human woman|\n", "woman close up|\n", "Realistic Lustful|\n", "F/ beautiful|\n", "uncensored game cg|\n", "sunlight cinematic lighting|\n", "temple_ruins|\n", "detailed sexy lips|\n", "snapmatic|\n", "milf_saga_face|\n", "by zixiong|\n", "high rating 1girl|\n", "1girl buff female|\n", "nora|\n", "realistic mercy|\n", "HD CGSociety|\n", "HD cgsociety|\n", "snowwhiteWaifu|\n", "kaname_raana |\n", "artist_request |\n", "artist_request|\n", "noir detailed|\n", "polearm |\n", "sex bot fem bot|\n", "lara|\n", "stunning female wendigo|\n", "detailed makeup|\n", "nan|\n", "Detailed person|\n", "character quality|\n", "mahjong_soul |\n", "janet|\n", "fantasy illustration masterpiece|\n", "incredible perfect artwork|\n", "the most beautiful artwork in the worldcute|\n", "beautiful stylized|\n", "ultra detailed eyes|\n", "gwen tennyson|\n", "Gwen Tennyson|\n", "artstation pixiv|\n", "3d shading realism|\n", "kanden_sky |\n", "half human ai hybrid|\n", "animation cinematic|\n", "1girl fair_skin|\n", "perfect rendered face|\n", "comic art|\n", "female_pervert|\n", "female_pervert |\n", "rgb unreal engine|\n", "assassin|\n", "realism image Fill|\n", "masterpiece 2d art|\n", "DETAILED SKIN|\n", "detailed skin|\n", "1girl yor_briar|\n", "3d soft lighting|\n", "little_witch_nobeta |\n", "bisque skin|\n", "perfect face skin|\n", "lcylxsl woman|\n", "gwen_tennyson|\n", "looking at viewercurvy|\n", "uncensored steam|\n", "robot_girl |\n", "1girl monaXL|\n", "captivating digital art|\n", "a beautiful porcelain young slim fit female robot|\n", "k uhd unity wallpaper|\n", "maria-sama_ga_miteru |\n", "justice right bitch heroine|\n", "feminine face profile|\n", "doggsytyle 1girl|\n", "1girl face closeup|\n", "1girl necromancer|\n", "highres 3d cg|\n", "ScarletWitch|\n", "black_widow|\n", "styledreamlikeartredshift|\n", "UHD. AI_girl doll|\n", "lore|\n", "painting stylized|\n", "stunning environmentnsfw|\n", "red_eyes solo|\n", "3D cartoon dreamy|\n", "estheticfutanaritrap|\n", "5 detailed face|\n", "motd|\n", "Cel shading Evil|\n", "helsie|\n", "marissa|\n", "vertical_comic |\n", "commissioner_name |\n", "Ada Wong cartoon|\n", "Epic CG masterpiece|\n", "solo detailed eyes|\n", "Atey Ghailan|\n", "zoe|\n", "cinematic environment|\n", "character_print |\n", "d rendered hentai version|\n", "Beuty lips|\n", "beautiful noelle_silva|\n", "no humans profile|\n", "shiny_skin1|\n", "eli|\n", "hades_1 |\n", "fantasy highres|\n", "Ptylust 1girl|\n", "beautiful yo greek cyborg|\n", "mature female tall|\n", "stunningly beautiful corneo_yor_forger|\n", "scarlet_devil_mansion |\n", "ultra detailed sex|\n", "original character|\n", "towel |\n", "by Sadahide1girl|\n", "ADDCOLfair skin|\n", "nerissa_ravencroft |\n", "mei|\n", "rover_(wuthering_waves) |\n", "vio|\n", "close up of 1girl|\n", "realistic elf girl|\n", "trending cgsociety|\n", "character name|\n", "sexy shy steam|\n", "cornelia_li_britannia |\n", "by pakwan|\n", "perfect eyes best quality|\n", "sumeragi_lee_noriega |\n", "character_signature |\n", "source_comic|\n", "perfect feminine face+|\n", "sci-fi woman|\n", "aya|\n", "lilith_aensland |\n", "vana|\n", "siri|\n", "uncensored shadman|\n", "high_resolution illustration|\n", "profile portrait|\n", "impossibly beautiful egyptian|\n", "superhero game|\n", "ptoto portrait|\n", "elegant cinematic|\n", "very detailed faces|\n", "kaede_johan_nouvel |\n", "cutiesaturday|\n", "female high res|\n", "lip bite blush|\n", "Wealth Highres|\n", "deer_girl |\n", "lily_black |\n", "riven_(league_of_legends) |\n", "a beautiful sexy age chinese|\n", "high detailed skin|\n", "mature female masterpiece|\n", "8k 2d animation|\n", "concept_art |\n", "1>tifa|\n", "red gothic illustration + exquisite|\n", "lili|\n", "paintingsexy femalelooking at viewer|\n", "1girl detail face|\n", "perfect skin 8k|\n", "snow whiteWaifu|\n", "eris_greyrat |\n", "vivian|\n", "solo female artica_sparkle_character|\n", "by wizzikt|\n", "cel shaded 4k|\n", "carmen|\n", "kami_nomi_zo_shiru_sekai |\n", "3d unreal engine|\n", "3D unreal engine|\n", "girl closeup|\n", "by GLaDOS|\n", "red gothic cursed tower|\n", "riot heterochromia|\n", "kata|\n", "lifelike dramatic lighting unrealengine|\n", "alternate_skin_color |\n", "realistic han_juri|\n", "indian mary_winstead eyes|\n", "lulu|\n", "strict_nun_(diva) |\n", "arcadian|\n", "a gorgeous sexy thicc harlequin vampire|\n", "apex_legends |\n", "highres flawless|\n", "official_alternate_hairstyle |\n", "official_alternate_hairstyle|\n", "1 android girl|\n", "bianca|\n", "LydiaV|\n", "best detail 1woman|\n", "RTX moody female|\n", "beautiful elegant face|\n", "comic dark hair|\n", "close-up realistic|\n", "femme fatale|\n", "concept-art|\n", "high skin details|\n", "Full Stylized|\n", "amazing. 3d shading|\n", "fushigi_no_umi_no_nadia |\n", "Sense 1girl|\n", "skin details nude|\n", "game cg sky cloud|\n", "realistic highres|\n", "skin detail NSFW|\n", "Elegant realistic|\n", "sexy sexy 8|\n", "source_furry 1girl|\n", "by Skyrn99|\n", "mo_chengwei |\n", "character description|\n", "kawakami_mai |\n", "art style by artgerm|\n", "thegame|\n", "elizabeth|\n", "super_heroine_boy |\n", "bold eyes fix|\n", "bokeh ray-tracing|\n", "a busty iranian pale ulzzang idol|\n", "Best Quality 1girl|\n", "BEST QUALITY 1GIRL|\n", "best quality1girl|\n", "best quality 1GIRL|\n", "shanghai_doll |\n", "beautiful eyessexy theme|\n", "ultra-detailed1|\n", "n1ll 1girl|\n", "Gwen 1girl solo|\n", "unreal engine rendered|\n", "beautiful face portrait|\n", "submissive_female|\n", "fantasy lifestyle-portrait by mandy jurgens|\n", "kondou_taeko |\n", "alisa|\n", "unreal engine cinematic smooth|\n", "1girl human female|\n", "highres waist-up|\n", "a mature sexy streamer|\n", "high resolution illustrations|\n", "character focus cinematic light|\n", "elegant epic|\n", "Highres elaborate|\n", "human 1girl|\n", "nadia_la_arwall |\n", "seductive mature female|\n", "1girl older|\n", "masterpiece miko|\n", "very detailed skin|\n", "mysterious epic|\n", "watson_amelia |\n", "Visual novel|\n", "hi res detailed|\n", "gumroad username|\n", "Sunrays Raytracing|\n", "1futa 1male|\n", "background characters|\n", "natasha|\n", "Detailed art|\n", "womam skin|\n", "details such as raytracing and|\n", "wcw|\n", "1girl deer woman|\n", "physically-based eye shading|\n", "an attracitve woman|\n", "sexy femme fatale demon babe}\n", "{character design by a character portrait by senior character artist|\n", "eyes by smooth art style|\n", "highres perfect face|\n", "game a pretty arabic female character|\n", "artstation trending|\n", "by artist a character by senior character artist|\n", "stylized detailed_character|\n", "mirage_(apex_legends) |\n", "nsfw_high_detail_character_portrait|\n", "cinematic character design d|\n", "art by artstation womanly|\n", "3d cg woman|\n", "8k profile photo|\n", "soft shadows real-time skin rendering|\n", "detailed 1girl|\n", "cinematic art by hq|\n", "Game art|\n", "flirty Focus on ArtStation|\n", "indiehd detailed character|\n", "Artstation HD|\n", "artstationHD|\n", "beautiful female android face|\n", "cg unity k wallpaper shiny skin|\n", "best crypto_(apex_legends) |\n", "cassandra|\n", "artstation highres|\n", "beautiful female robot|\n", "1girl Annerose|\n", "F/14 environmental Artstation|\n", "artstation|\n", "ilya_kuvshinov |\n", "artist name|\n", "beautiful character design|\n", "artstation smooth|\n", "artstation game assets|\n", "solo mature miko|\n", "ilya kuvshinov style|\n", "sfw detailed face|\n", "stylized goddess|\n", "game icon|\n", "multra detailed face|\n", "SB_Eve_S-|\n", "theresa_apocalypse |\n", "ada|\n", "gwen|\n", "bad_artstation_id |\n", "lifeline_(apex_legends) |\n", "annerose vajra|\n", "assets|\n", "artstation Velvia|\n", "streaming on twitch|\n", "low-poly game art|\n", "8k artstation|\n", "rita_mordio |\n", "a pretty asian female character|\n", "mother_(game) |\n", "a character by senior character portrait artist|\n", "a female character|\n", "ada_wong|\n", "ada_wong |\n", "gameart|\n", "maria_cadenzavna_eve |\n", "lighting place place traits character eyes|\n", "a iranian pale ulzzang madewithunity|\n", "ingrid_brandl_galatea |\n", "sera|\n", "lewd art by riotgames|\n", "perfect female face|\n", "stylized character designs|\n", "k unity cg wallpaper|\n", "game cg highres|\n", "highres detailed|\n", "Highres Detailed|\n", "beautiful_female_skin|\n", "computer game art|\n", "a babylonian female assassin|\n", "artstation winner|\n", "artstyle by helen wells|\n", "indian mary_winstead|\n", "artstation masterpiece|\n", "character art|\n", "a beautiful attractive future k unity engine wallpaper|\n", "game cg supergirl|\n", "1girl sarielx|\n", "artstation female body|\n", "realistic meiko|\n", "3d cg girl|\n", "high resolution skin|\n", "realistic 1girl|\n", "realistic 1 girl|\n", "goddess portrait|\n", "beautiful mature female viera|\n", "artstation contest winner|\n", "detailed skin sfw|\n", "character illustrations|\n", "high detail skin|\n", "a gorgeous female demonologist|\n", "detailed CG|\n", "gamedev|\n", "vanessa|\n", "jp-svetf- as gorgeous supernatural goddess|\n", "artstationextremely|\n", "game_cg |\n", "highres milf|\n", "1girl half body|\n", "character_profile|\n", "character_profile |\n", "alisa_mikhailovna_kujou |\n", "lucia_(scott_malin) |\n", "stylized art|\n", "detailed hot skin|\n", "F/ behance HD|\n", "by Foxovh female|\n", "lunamaria_hawke |\n", "detailed skin UHD|\n", "legends art 1girl|\n", "gorgeous digital artwork|\n", "artstation matte|\n", "hilda_valentine_goneril |\n", "lifelike texture bright lighting unreal engine|\n", "Hentai Cinematic|\n", "k cg unity engine wallpaper|\n", "Concept Art World|\n", "stylized lighting|\n", "indian mary_winstead small|\n", "best_quality woman|\n", "portrait alt girl|\n", "cg 3d girl|\n", "Beuty concept art|\n", "very fine k cg wallpaper|\n", "sexy splash art|\n", "juliet|\n", "3d-realisticstyle|\n", "theresa|\n", "scene art|\n", "CGSociety|\n", "a gorgeous female chronomancer|\n", "cinematic production character rendering|\n", "cleo|\n", "ana|\n", "lifelike texture dcharacter|\n", "trending in art station|\n", "check_artist |\n", "list of characters|\n", "a persian pale ulzzang detailed skin20|\n", "high resolution skin texture|\n", "1girl close-up|\n", "bust wallpaper|\n", "soft lighting3D|\n", "1girl solo alpha|\n", "stylized smooth|\n", "art by unreal engine|\n", "iris_black_games |\n", "nadia|\n", "Artstation.|\n", "solo detailed face|\n", "artstation drawing|\n", "beautiful cyberpunk female|\n", "pandora_smith_magister|\n", "fantasy art from dnd extremely beautiful style|\n", "1girl raid_arbiter|\n", "beautiful anthro female|\n", "character_name|\n", "character_name |\n", "Chen Chun game art|\n", "4kdetailed face|\n", "leblanc|\n", "mature female character|\n", "physically-based rendering light on the face|\n", "artstation 4K|\n", "ru_ta artstyle|\n", "askzy 1girl|\n", "a beautiful cyberpunk female|\n", "ramatic lighting unreal engine|\n", "princess_of_moonbrook |\n", "sienna|\n", "ilya kuvshinov|\n", "art station trending|\n", "detailed eyes hd|\n", "best quality woman|\n", "those games where she still|\n", "milla_maxwell |\n", "highres dark hair|\n", "detailed Karyl|\n", "artgerm 1girl|\n", "lupinus_virtual_games |\n", "sola-ui_nuada-re_sophia-ri |\n", "detailed skin nun|\n", "elena|\n", "charming character illustrations|\n", "absurdres game cg|\n", "protagonist|\n", "soft cinematic lighting|\n", "beautiful feminine face+|\n", "every pixiel high resolution|\n", "games|\n", "beautiful sexy adult female face|\n", "cinematic hard lighting eye shading|\n", "beautyful female face|\n", "marina_(blue_archive) |\n", "award winning character concept art of|\n", "by ecmajor|\n", "character portrait|\n", "obsidian-skinned_female|\n", "waifu|\n", "detailed skin solo|\n", "realistic lips|\n", "20s f/|\n", "ultra-fine cg unity k wallpaper|\n", "suic0idegirl|\n", "the character the overall image should have|\n", "skin texture 1lady|\n", "serious aura|\n", "a beautiful android|\n", "the most beautiful anime style art ever seen|\n", "cute anime face cinematic angle|\n", "mysterious look on her face lilith is|\n", "zoomed-in face portrait in background|\n", "gorgeous d rendering by cgsociety|\n", "indian mary_winstead face|\n", "k character concept portrait|\n", "cell shading|\n", "HDR evangeline_a.k._mcdowell |\n", "cuteg 1girl nsfw|\n", "3d cg 16yo girl|\n", "cute breast woman|\n", "game|\n", "WLOP art style|\n", "adora|\n", "beautiful female sex robot|\n", "realistic girl|\n", "mysterious female|\n", "zia|\n", "skin pores & face texture|\n", "xenotr1p 1girl|\n", "characterart|\n", "looking to viewer|\n", "by reysi|\n", "by Reysi|\n", "3d high res 1girl|\n", "anime female|\n", "beret |\n", "enza|\n", "beautiful mature vampire princess|\n", "very aesthetic style face|\n", "Soft_Shading|\n", "by sqoon|\n", "gloomy half body portrait k unity render|\n", "solo Zia|\n", "zoya athene|\n", "8k Concept artist|\n", "ChromaV5 art|\n", "diana|\n", "wallpaper detailed|\n", "cel_shading |\n", "human female|\n", "tia|\n", "artist_name |\n", "ULTRA malenia_blade_of_miquella |\n", "battle_android_bodyguard_female|\n", "illustration highres|\n", "3d cg woman abs|\n", "gorgeous blushing beautiful sexy charming|\n", "beautiful light makeup female sorceress|\n", "beautiful female anthro|\n", "detailed epic|\n", "1girl sex eyes|\n", "celeste|\n", "comic art style|\n", "a gorgeous female priest|\n", "beautiful female dragoness face|\n", "cg illustration|\n", "anime cg|\n", "reoen official art|\n", "concept art illustration by artgerm|\n", "moon solo female|\n", "detailed highres|\n", "art station artwork detailed|\n", "seaport_princess |\n", "dawn solo serious|\n", "brown hair highres|\n", "Nani character|\n", "giveaway|\n", "fantasy kiana kaslana white comet|\n", "bare shoulders retro artstyle|\n", "indiedev|\n", "super detailed face|\n", "realistic_skin|\n", "loba_(apex_legends) |\n", "prety woman|\n", "Ciro Marcetti Art|\n", "mujer de ojos rojos y pelo azulado|\n", "highres venusbody|\n", "animation|\n", "1girl Yennifer|\n", "of sexy cyberpunk female mage|\n", "anya_(spy_x_family) |\n", "rodion_(project_moon) |\n", "lumen cinematic contrast lighting|\n", "feminine attractive face|\n", "miss-fortune|\n", "jane|\n", "Highres womanly|\n", "realistic 1woman|\n", "subtle facial animations|\n", "vampire_\\(game\\) |\n", "|\n", "detailed face RTX|\n", "sexbot|\n", "Labyrista 1girl|\n", "Artstation blurry|\n", "animated illustrations|\n", "hyper realisitc texture skin|\n", "cute lips nsfw|\n", "a pretty american female character|\n", "the best female erotic figure|\n", "1 android girl Red|\n", "amada_ken |\n", "vex|\n", "olympia|\n", "Marenka Short hair|\n", "jorge jacinto's artstyle|\n", "arrogant woman21|\n", "cagalli_yula_athha |\n", "Fantasy Character|\n", "videogame cg|\n", "female_detailed girl|\n", "aesthetics|\n", "detailed wallpaper|\n", "unreal engine dof|\n", "detailed cute face|\n", "looks like cirilla from the witcher|\n", "cel shaded devil|\n", "best_quality 1girl|\n", "green eyes highres|\n", "cg illustration cinematic light|\n", "short black hair over one eye|\n", "silence_girl |\n", "d artist name|\n", "Cel shading|\n", "cel shading|\n", "2D female|\n", "trending at cgsociety|\n", "Cel shading Sci-Fi|\n", "detailed skin cute|\n", "eliza|\n", "alpha_signature |\n", "scarletart by artgerm|\n", "character_sheet |\n", "author ; tags female protagonist|\n", "1girl Pharah|\n", "featured art|\n", "art by guweiz|\n", "y cinematic lighting|\n", "qixiong_ruqun |\n", "ray tracing 1girl|\n", "real-time makeup application|\n", "kiana_kaslana_(herrscher_of_finality) |\n", "3d hires model|\n", "dnd character art|\n", "solo aphrodite|\n", "carmen_sandiego_v_shurik|\n", "a sexy female thief on|\n", "evelyn|\n", "ultra_detailed|\n", "beautiful aesthetic face|\n", "vestia_zeta |\n", "concept character art|\n", "dawn solo 1girl|\n", "moon_demon 1girl|\n", "soloanthro female|\n", "by Retros|\n", "aesthetic detailed|\n", "cel shaded style|\n", "humanoid female robot|\n", "leila|\n", "realistic android|\n", "epic fantasy greek priestess|\n", "crescent_rose |\n", "nude detailed face|\n", "very detailed epic|\n", "wallpapers|\n", "game cg roomi|\n", "digital anime art|\n", "beautiful busty slender knight|\n", "8k portrait cass|\n", "super elan_ceres |\n", "beautiful cute sexy anime|\n", "temple portrait|\n", "an stunningly beautiful age half demon|\n", "3D art anime style|\n", "soft-focused realism|\n", "production cinematic character render|\n", "dawn woman sex|\n", "sum_elise|\n", "comic artwork|\n", "suzi_q |\n", "cel shading 1girl|\n", "valkyrie_(apex_legends) |\n", "the most beautiful artwork in the world featuring|\n", "beautiful yellow eyes +|\n", "ultra-detailed CG|\n", "8K UHD female|\n", "solo profile|\n", "a female necromancer|\n", "undone_sarashi |\n", "octane_(apex_legends) |\n", "silent_comic |\n", "86_-eightysix- |\n", "josie|\n", "high resolution face|\n", "beautiful egyptian bombshell black-haired|\n", "|\n", "a.i._voice |\n", "best dynamic lighting|\n", "fiona|\n", "highres gorgeous dark skin noxian|\n", "gorgeous characters|\n", "best quality CG|\n", "best quality cg|\n", "beautiful women complete eyes|\n", "4K artist|\n", "PA7_Portrait-CU|\n", "wattson_(apex_legends) |\n", "a gorgeous beautiful age sexy|\n", "beautiful female face|\n", "lynda|\n", "female human|\n", "alisa_ilinichina_amiella |\n", "alhambra background|\n", "heart_maebari |\n", "she has realistic human|\n", "skin_fang |\n", "cinematic unreal 5|\n", "highres cinematic|\n", "layla|\n", "harmony high-quality skin bump mapping|\n", "bodya high resolution face|\n", "masterpeice 1girl|\n", "by syuro|\n", "iconic fine epic|\n", "garnet_til_alexandros_xvii |\n", "elena_so6|\n", "official_art |\n", "7rtifa elegant|\n", "digital artist|\n", "realistic lesbian|\n", "Escher CGI Artgerm|\n", "wallpaper cinematic lighting sharp focus|\n", "nina|\n", "Detailed lips|\n", "thief|\n", "stylized charming|\n", "by Whooo-Ya|\n", "high-quality skin textures|\n", "eva_beatrice |\n", "fantasy art.HD 3D|\n", "bezier place place character traits lighting|\n", "sexy 8 list|\n", "penelope|\n", "multi-layer skin shading|\n", "streaming|\n", "woman pretty face|\n", "hyperrealism by atey ghailan|\n", "highres cute|\n", "torino_aqua |\n", "guest_art |\n", "masterpiecebust portrait|\n", "2D artstyle|\n", "1girl best quality|\n", "adorable_girl|\n", "prompt lora weight at ->quality loss|\n", "by zackary|\n", "realistic skin1|\n", "stunning fantasy style environment|\n", "Ultra detailed|\n", "Ultra Detailed|\n", "mobile_legends_alice|\n", "detailed_face|\n", "dynamic cinematic portrait medium shot|\n", "absurdres gorgeous noxian|\n", "perfectly composedgorgeous jp-svetf|\n", "dawn solo 1boy|\n", "chloe|\n", "edgtb_woman|\n", "character concept art|\n", "ramudia_(lamyun) |\n", "tall mature female|\n", "nose realistic|\n", "ultra detailed HD|\n", "bliznyashkithetwins_v|\n", "white_thighhighs |\n", "rating_lewd curvy|\n", "super fine illustrations|\n", "source_3Dcartoon|\n", "lucy_(cyberpunk) |\n", "soft diffuse lighting|\n", "pink_hair |\n", "highres black hair|\n", "blue eyes alpha|\n", "Cel shading Wonder|\n", "detailed artgem|\n", "evil AI named Aida|\n", "Realistic face|\n", "realistic face|\n", "RTX 0 shader|\n", "group profile|\n", "beautiful sxz-ashe-ow-|\n", "povsolo 1girl|\n", "cleavage highres|\n", "lips realistic|\n", "adorable female face|\n", "the character the image should be|\n", "jane_doe_(zenless_zone_zero) |\n", "kawakami_rokkaku|\n", "kawakami_rokkaku |\n", "an artist-inspired character|\n", "Detailed background|\n", "Detailed Background|\n", "ziche_fuzhao |\n", "temple cyberpunk soft lighting|\n", "breasts nose_blush|\n", "highres breasts|\n", "natalie|\n", "ultra-detailed eyes|\n", "a beautiful artwork illustration|\n", "Soft Shading|\n", "aquiline_nose |\n", "blush short_hair|\n", "artgerm best quality|\n", "soft_lighting|\n", "1girl kujikawadef|\n", "gundam |\n", "ada_wong resident evil|\n", "Flirty_Shy_Grin|\n", "of beautiful female knight women|\n", "vox|\n", "skadiview-appearance-shiny skin|\n", "detective|\n", "cute solo female|\n", "character request|\n", "fantasy female|\n", "futanari realistic|\n", "dawn solo|\n", "astrosorc22|\n", "luxurious stone castle game|\n", "mignonworks artstyle|\n", "idol|\n", "idol |\n", "fantasy game spell icon|\n", "enya|\n", "moody cinematic epic concept art|\n", "perfect artwork|\n", "age_of_ishtaria |\n", "1boy cute woman|\n", "beatiful face|\n", "female_protagonist_(pokemon_go) |\n", "a pretty caucasian female character|\n", "teresa|\n", "arab escort escort|\n", "telegram|\n", "gorgeous female face|\n", "futuristic setting she should be depicted|\n", "award winning digital painting|\n", "anime woman|\n", "conceptart|\n", "astel_leda |\n", "snow_white |\n", "Epic Digital Art|\n", "epic digital art|\n", "epic Digital art|\n", "heroine|\n", "gorgeous art|\n", "best ai image|\n", "ruyi_jingu_bang |\n", "#explorepage #art #cosplay|\n", "saga|\n", "1girl sfw|\n", "lovely follynobodysd|\n", "marietta|\n", "yen |\n", "banned_artist |\n", "depth-based facial shading|\n", "artstation_sample |\n", "highres dark skin|\n", "unreal engine Maya|\n", "well-lit cinematic|\n", "dark hair pureerosface_v|\n", "masterpiece manhwa|\n", "1girl perfect eyes|\n", "Kyaradin1girl|\n", "john_doe_(jdart) |\n", "official_alternate_eye_color |\n", "character_doll |\n", "epic realistic art|\n", "bronya_zaychik |\n", "human ai hybrid|\n", "JanePorterXLP|\n", "blulust 1girl|\n", "beautiful concept illustration|\n", "Acheron_Blue 1girl|\n", "evie|\n", "lifelike texture dramatic lighting unrealengine|\n", "VichyaIris 1girl|\n", "gwen_(league_of_legends) |\n", "Human woman|\n", "human woman|\n", "woman close up|\n", "Realistic Lustful|\n", "F/ beautiful|\n", "uncensored game cg|\n", "sunlight cinematic lighting|\n", "temple_ruins|\n", "detailed sexy lips|\n", "snapmatic|\n", "milf_saga_face|\n", "by zixiong|\n", "high rating 1girl|\n", "1girl buff female|\n", "nora|\n", "realistic mercy|\n", "HD CGSociety|\n", "HD cgsociety|\n", "snowwhiteWaifu|\n", "kaname_raana |\n", "artist_request |\n", "artist_request|\n", "noir detailed|\n", "polearm |\n", "sex bot fem bot|\n", "lara|\n", "stunning female wendigo|\n", "detailed makeup|\n", "nan|\n", "Detailed person|\n", "character quality|\n", "mahjong_soul |\n", "janet|\n", "fantasy illustration masterpiece|\n", "incredible perfect artwork|\n", "the most beautiful artwork in the worldcute|\n", "beautiful stylized|\n", "ultra detailed eyes|\n", "gwen tennyson|\n", "Gwen Tennyson|\n", "artstation pixiv|\n", "3d shading realism|\n", "kanden_sky |\n", "half human ai hybrid|\n", "animation cinematic|\n", "1girl fair_skin|\n", "perfect rendered face|\n", "comic art|\n", "female_pervert|\n", "female_pervert |\n", "rgb unreal engine|\n", "assassin|\n", "realism image Fill|\n", "masterpiece 2d art|\n", "DETAILED SKIN|\n", "detailed skin|\n", "1girl yor_briar|\n", "3d soft lighting|\n", "little_witch_nobeta |\n", "bisque skin|\n", "perfect face skin|\n", "lcylxsl woman|\n", "gwen_tennyson|\n", "looking at viewercurvy|\n", "uncensored steam|\n", "robot_girl |\n", "1girl monaXL|\n", "captivating digital art|\n", "a beautiful porcelain young slim fit female robot|\n", "k uhd unity wallpaper|\n", "maria-sama_ga_miteru |\n", "justice right bitch heroine|\n", "feminine face profile|\n", "doggsytyle 1girl|\n", "1girl face closeup|\n", "1girl necromancer|\n", "highres 3d cg|\n", "ScarletWitch|\n", "black_widow|\n", "styledreamlikeartredshift|\n", "UHD. AI_girl doll|\n", "lore|\n", "painting stylized|\n", "stunning environmentnsfw|\n", "red_eyes solo|\n", "3D cartoon dreamy|\n", "estheticfutanaritrap|\n", "5 detailed face|\n", "motd|\n", "Cel shading Evil|\n", "helsie|\n", "marissa|\n", "vertical_comic |\n", "commissioner_name |\n", "Ada Wong cartoon|\n", "Epic CG masterpiece|\n", "solo detailed eyes|\n", "Atey Ghailan|\n", "zoe|\n", "cinematic environment|\n", "character_print |\n", "d rendered hentai version|\n", "Beuty lips|\n", "beautiful noelle_silva|\n", "no humans profile|\n", "shiny_skin1|\n", "eli|\n", "hades_1 |\n", "fantasy highres|\n", "Ptylust 1girl|\n", "beautiful yo greek cyborg|\n", "mature female tall|\n", "stunningly beautiful corneo_yor_forger|\n", "scarlet_devil_mansion |\n", "ultra detailed sex|\n", "original character|\n", "towel |\n", "by Sadahide1girl|\n", "ADDCOLfair skin|\n", "nerissa_ravencroft |\n", "mei|\n", "rover_(wuthering_waves) |\n", "vio|\n", "close up of 1girl|\n", "realistic elf girl|\n", "trending cgsociety|\n", "character name|\n", "sexy shy steam|\n", "cornelia_li_britannia |\n", "by pakwan|\n", "perfect eyes best quality|\n", "sumeragi_lee_noriega |\n", "character_signature |\n", "source_comic|\n", "perfect feminine face+|\n", "sci-fi woman|\n", "aya|\n", "lilith_aensland |\n", "vana|\n", "siri|\n", "uncensored shadman|\n", "high_resolution illustration|\n", "profile portrait|\n", "impossibly beautiful egyptian|\n", "superhero game|\n", "ptoto portrait|\n", "elegant cinematic|\n", "very detailed faces|\n", "kaede_johan_nouvel |\n", "cutiesaturday|\n", "female high res|\n", "lip bite blush|\n", "Wealth Highres|\n", "deer_girl |\n", "lily_black |\n", "riven_(league_of_legends) |\n", "a beautiful sexy age chinese|\n", "high detailed skin|\n", "mature female masterpiece|\n", "8k 2d animation|\n", "concept_art |\n", "1>tifa|\n", "red gothic illustration + exquisite|\n", "lili|\n", "paintingsexy femalelooking at viewer|\n", "1girl detail face|\n", "perfect skin 8k|\n", "snow whiteWaifu|\n", "eris_greyrat |\n", "vivian|\n", "solo female artica_sparkle_character|\n", "by wizzikt|\n", "cel shaded 4k|\n", "carmen|\n", "kami_nomi_zo_shiru_sekai |\n", "3d unreal engine|\n", "3D unreal engine|\n", "girl closeup|\n", "by GLaDOS|\n", "red gothic cursed tower|\n", "riot heterochromia|\n", "kata|\n", "lifelike dramatic lighting unrealengine|\n", "alternate_skin_color |\n", "realistic han_juri|\n", "indian mary_winstead eyes|\n", "lulu|\n", "strict_nun_(diva) |\n", "arcadian|\n", "a gorgeous sexy thicc harlequin vampire|\n", "apex_legends |\n", "highres flawless|\n", "official_alternate_hairstyle |\n", "official_alternate_hairstyle|\n", "1 android girl|\n", "bianca|\n", "LydiaV|\n", "best detail 1woman|\n", "RTX moody female|\n", "beautiful elegant face|\n", "comic dark hair|\n", "close-up realistic|\n", "femme fatale|\n", "concept-art|\n", "high skin details|\n", "Full Stylized|\n", "amazing. 3d shading|\n", "fushigi_no_umi_no_nadia |\n", "Sense 1girl|\n", "skin details nude|\n", "game cg sky cloud|\n", "realistic highres|\n", "skin detail NSFW|\n", "Elegant realistic|\n", "sexy sexy 8|\n", "source_furry 1girl|\n", "by Skyrn99|\n", "mo_chengwei |\n", "character description|\n", "kawakami_mai |\n", "art style by artgerm|\n", "thegame|\n", "elizabeth|\n", "super_heroine_boy |\n", "bold eyes fix|\n", "bokeh ray-tracing|\n", "a busty iranian pale ulzzang idol|\n", "Best Quality 1girl|\n", "BEST QUALITY 1GIRL|\n", "best quality1girl|\n", "best quality 1GIRL|\n", "shanghai_doll |\n", "beautiful eyessexy theme|\n", "ultra-detailed1|\n", "n1ll 1girl|\n", "Gwen 1girl solo|\n", "unreal engine rendered|\n", "beautiful face portrait|\n", "submissive_female|\n", "fantasy lifestyle-portrait by mandy jurgens|\n", "kondou_taeko |\n", "alisa|\n", "unreal engine cinematic smooth|\n", "1girl human female|\n", "highres waist-up|\n", "a mature sexy streamer|\n", "high resolution illustrations|\n", "character focus cinematic light|\n", "elegant epic|\n", "Highres elaborate|\n", "human 1girl|\n", "nadia_la_arwall |\n", "seductive mature female|\n", "1girl older|\n", "masterpiece miko|\n", "very detailed skin|\n", "mysterious epic|\n", "watson_amelia |\n", "Visual novel|\n", "hi res detailed|\n", "gumroad username|\n", "Sunrays Raytracing|\n", "1futa 1male|\n", "background characters|\n", "natasha|\n", "Detailed art|\n", "womam skin|\n", "details such as raytracing and|\n", "wcw|\n", "1girl deer woman|\n", "physically-based eye shading|\n", "an attracitve woman|\n", "sexy femme fatale demon babe}\n", "{character design by a character portrait by senior character artist|\n", "eyes by smooth art style|\n", "highres perfect face|\n", "game a pretty arabic female character|\n", "artstation trending|\n", "by artist a character by senior character artist|\n", "stylized detailed_character|\n", "mirage_(apex_legends) |\n", "nsfw_high_detail_character_portrait|\n", "cinematic character design d|\n", "art by artstation womanly|\n", "3d cg woman|\n", "8k profile photo|\n", "soft shadows real-time skin rendering|\n", "detailed 1girl|\n", "cinematic art by hq|\n", "Game art|\n", "flirty Focus on ArtStation|\n", "indiehd detailed character|\n", "Artstation HD|\n", "artstationHD|\n", "beautiful female android face|\n", "cg unity k wallpaper shiny skin|\n", "best crypto_(apex_legends) |\n", "cassandra|\n", "artstation highres|\n", "beautiful female robot|\n", "1girl Annerose|\n", "F/14 environmental Artstation|\n", "artstation|\n", "ilya_kuvshinov |\n", "artist name|\n", "beautiful character design|\n", "artstation smooth|\n", "artstation game assets|\n", "solo mature miko|\n", "ilya kuvshinov style|\n", "sfw detailed face|\n", "stylized goddess|\n", "game icon|\n", "multra detailed face|\n", "SB_Eve_S-|\n", "theresa_apocalypse |\n", "ada|\n", "gwen|\n", "bad_artstation_id |\n", "lifeline_(apex_legends) |\n", "annerose vajra|\n", "assets|\n", "artstation Velvia|\n", "streaming on twitch|\n", "low-poly game art|\n", "8k artstation|\n", "rita_mordio |\n", "a pretty asian female character|\n", "mother_(game) |\n", "a character by senior character portrait artist|\n", "a female character|\n", "ada_wong|\n", "ada_wong |\n", "gameart|\n", "maria_cadenzavna_eve |\n", "lighting place place traits character eyes|\n", "a iranian pale ulzzang madewithunity|\n", "ingrid_brandl_galatea |\n", "sera|\n", "lewd art by riotgames|\n", "perfect female face|\n", "stylized character designs|\n", "k unity cg wallpaper|\n", "game cg highres|\n", "highres detailed|\n", "Highres Detailed|\n", "beautiful_female_skin|\n", "computer game art|\n", "a babylonian female assassin|\n", "artstation winner|\n", "artstyle by helen wells|\n", "indian mary_winstead|\n", "artstation masterpiece|\n", "character art|\n", "a beautiful attractive future k unity engine wallpaper|\n", "game cg supergirl|\n", "1girl sarielx|\n", "artstation female body|\n", "realistic meiko|\n", "3d cg girl|\n", "high resolution skin|\n", "realistic 1girl|\n", "realistic 1 girl|\n", "goddess portrait|\n", "beautiful mature female viera|\n", "artstation contest winner|\n", "detailed skin sfw|\n", "character illustrations|\n", "high detail skin|\n", "a gorgeous female demonologist|\n", "detailed CG|\n", "gamedev|\n", "vanessa|\n", "jp-svetf- as gorgeous supernatural goddess|\n", "artstationextremely|\n", "game_cg |\n", "highres milf|\n", "1girl half body|\n", "character_profile|\n", "character_profile |\n", "alisa_mikhailovna_kujou |\n", "lucia_(scott_malin) |\n", "stylized art|\n", "detailed hot skin|\n", "F/ behance HD|\n", "by Foxovh female|\n", "lunamaria_hawke |\n", "detailed skin UHD|\n", "legends art 1girl|\n", "gorgeous digital artwork|\n", "artstation matte|\n", "hilda_valentine_goneril |\n", "lifelike texture bright lighting unreal engine|\n", "Hentai Cinematic|\n", "k cg unity engine wallpaper|\n", "Concept Art World|\n", "stylized lighting|\n", "indian mary_winstead small|\n", "best_quality woman|\n", "portrait alt girl|\n", "cg 3d girl|\n", "Beuty concept art|\n", "very fine k cg wallpaper|\n", "sexy splash art|\n", "juliet|\n", "3d-realisticstyle|\n", "theresa|\n", "scene art|\n", "CGSociety|\n", "a gorgeous female chronomancer|\n", "cinematic production character rendering|\n", "cleo|\n", "ana|\n", "lifelike texture dcharacter|\n", "trending in art station|\n", "check_artist |\n", "list of characters|\n", "a persian pale ulzzang detailed skin20|\n", "high resolution skin texture|\n", "1girl close-up|\n", "bust wallpaper|\n", "soft lighting3D|\n", "1girl solo alpha|\n", "stylized smooth|\n", "art by unreal engine|\n", "iris_black_games |\n", "nadia|\n", "Artstation.|\n", "solo detailed face|\n", "artstation drawing|\n", "beautiful cyberpunk female|\n", "pandora_smith_magister|\n", "fantasy art from dnd extremely beautiful style|\n", "1girl raid_arbiter|\n", "beautiful anthro female|\n", "character_name|\n", "character_name |\n", "Chen Chun game art|\n", "4kdetailed face|\n", "leblanc|\n", "mature female character|\n", "physically-based rendering light on the face|\n", "artstation 4K|\n", "ru_ta artstyle|\n", "askzy 1girl|\n", "a beautiful cyberpunk female|\n", "ramatic lighting unreal engine|\n", "princess_of_moonbrook |\n", "sienna|\n", "ilya kuvshinov|\n", "art station trending|\n", "detailed eyes hd|\n", "best quality woman|\n", "those games where she still|\n", "milla_maxwell |\n", "highres dark hair|\n", "detailed Karyl|\n", "artgerm 1girl|\n", "lupinus_virtual_games |\n", "sola-ui_nuada-re_sophia-ri |\n", "detailed skin nun|\n", "elena|\n", "charming character illustrations|\n", "absurdres game cg|\n", "protagonist|\n", "soft cinematic lighting|\n", "beautiful feminine face+|\n", "every pixiel high resolution|\n", "games|\n", "beautiful sexy adult female face|\n", "cinematic hard lighting eye shading|\n", "beautyful female face|\n", "marina_(blue_archive) |\n", "award winning character concept art of|\n", "by ecmajor|\n", "character portrait|\n", "obsidian-skinned_female|\n", "waifu|\n", "detailed skin solo|\n", "realistic lips|\n", "20s f/|\n", "ultra-fine cg unity k wallpaper|\n", "suic0idegirl|\n", "the character the overall image should have|\n", "skin texture 1lady|\n", "serious aura|\n", "a beautiful android|\n", "the most beautiful anime style art ever seen|\n", "cute anime face cinematic angle|\n", "mysterious look on her face lilith is|\n", "zoomed-in face portrait in background|\n", "gorgeous d rendering by cgsociety|\n", "indian mary_winstead face|\n", "k character concept portrait|\n", "cell shading|\n", "HDR evangeline_a.k._mcdowell |\n", "cuteg 1girl nsfw|\n", "3d cg 16yo girl|\n", "cute breast woman|\n", "game|\n", "WLOP art style|\n", "adora|\n", "beautiful female sex robot|\n", "realistic girl|\n", "mysterious female|\n", "zia|\n", "skin pores & face texture|\n", "xenotr1p 1girl|\n", "characterart|\n", "looking to viewer|\n", "by reysi|\n", "by Reysi|\n", "3d high res 1girl|\n", "anime female|\n", "beret |\n", "enza|\n", "beautiful mature vampire princess|\n", "very aesthetic style face|\n", "Soft_Shading|\n", "by sqoon|\n", "gloomy half body portrait k unity render|\n", "solo Zia|\n", "zoya athene|\n", "8k Concept artist|\n", "ChromaV5 art|\n", "diana|\n", "wallpaper detailed|\n", "cel_shading |\n", "human female|\n", "tia|\n", "artist_name |\n", "ULTRA malenia_blade_of_miquella |\n", "battle_android_bodyguard_female|\n", "illustration highres|\n", "3d cg woman abs|\n", "gorgeous blushing beautiful sexy charming|\n", "beautiful light makeup female sorceress|\n", "beautiful female anthro|\n", "detailed epic|\n", "1girl sex eyes|\n", "celeste|\n", "comic art style|\n", "a gorgeous female priest|\n", "beautiful female dragoness face|\n", "cg illustration|\n", "anime cg|\n", "reoen official art|\n", "concept art illustration by artgerm|\n", "moon solo female|\n", "detailed highres|\n", "art station artwork detailed|\n", "seaport_princess |\n", "dawn solo serious|\n", "brown hair highres|\n", "Nani character|\n", "giveaway|\n", "fantasy kiana kaslana white comet|\n", "bare shoulders retro artstyle|\n", "indiedev|\n", "super detailed face|\n", "realistic_skin|\n", "loba_(apex_legends) |\n", "prety woman|\n", "Ciro Marcetti Art|\n", "mujer de ojos rojos y pelo azulado|\n", "highres venusbody|\n", "animation|\n", "1girl Yennifer|\n", "of sexy cyberpunk female mage|\n", "anya_(spy_x_family) |\n", "rodion_(project_moon) |\n", "lumen cinematic contrast lighting|\n", "feminine attractive face|\n", "miss-fortune|\n", "jane|\n", "Highres womanly|\n", "realistic 1woman|\n", "subtle facial animations|\n", "vampire_\\(game\\) |\n", "|\n", "detailed face RTX|\n", "sexbot|\n", "Labyrista 1girl|\n", "Artstation blurry|\n", "animated illustrations|\n", "hyper realisitc texture skin|\n", "cute lips nsfw|\n", "a pretty american female character|\n", "the best female erotic figure|\n", "1 android girl Red|\n", "amada_ken |\n", "vex|\n", "olympia|\n", "Marenka Short hair|\n", "jorge jacinto's artstyle|\n", "arrogant woman21|\n", "cagalli_yula_athha |\n", "Fantasy Character|\n", "videogame cg|\n", "female_detailed girl|\n", "aesthetics|\n", "detailed wallpaper|\n", "unreal engine dof|\n", "detailed cute face|\n", "looks like cirilla from the witcher|\n", "cel shaded devil|\n", "best_quality 1girl|\n", "green eyes highres|\n", "cg illustration cinematic light|\n", "short black hair over one eye|\n", "silence_girl |\n", "d artist name|\n", "Cel shading|\n", "cel shading|\n", "2D female|\n", "trending at cgsociety|\n", "Cel shading Sci-Fi|\n", "detailed skin cute|\n", "eliza|\n", "alpha_signature |\n", "scarletart by artgerm|\n", "character_sheet |\n", "author ; tags female protagonist|\n", "1girl Pharah|\n", "featured art|\n", "art by guweiz|\n", "y cinematic lighting|\n", "qixiong_ruqun |\n", "ray tracing 1girl|\n", "real-time makeup application|\n", "kiana_kaslana_(herrscher_of_finality) |\n", "3d hires model|\n", "dnd character art|\n", "solo aphrodite|\n", "carmen_sandiego_v_shurik|\n", "a sexy female thief on|\n", "evelyn|\n", "ultra_detailed|\n", "beautiful aesthetic face|\n", "vestia_zeta |\n", "concept character art|\n", "dawn solo 1girl|\n", "moon_demon 1girl|\n", "soloanthro female|\n", "by Retros|\n", "aesthetic detailed|\n", "cel shaded style|\n", "humanoid female robot|\n", "leila|\n", "realistic android|\n", "epic fantasy greek priestess|\n", "crescent_rose |\n", "nude detailed face|\n", "very detailed epic|\n", "wallpapers|\n", "game cg roomi|\n", "digital anime art|\n", "beautiful busty slender knight|\n", "8k portrait cass|\n", "super elan_ceres |\n", "beautiful cute sexy anime|\n", "temple portrait|\n", "an stunningly beautiful age half demon|\n", "3D art anime style|\n", "soft-focused realism|\n", "production cinematic character render|\n", "dawn woman sex|\n", "sum_elise|\n", "comic artwork|\n", "suzi_q |\n", "cel shading 1girl|\n", "valkyrie_(apex_legends) |\n", "the most beautiful artwork in the world featuring|\n", "beautiful yellow eyes +|\n", "ultra-detailed CG|\n", "8K UHD female|\n", "solo profile|\n", "a female necromancer|\n", "undone_sarashi |\n", "octane_(apex_legends) |\n", "silent_comic |\n", "86_-eightysix- |\n", "josie|\n", "high resolution face|\n", "beautiful egyptian bombshell black-haired|\n", "|\n", "a.i._voice |\n", "best dynamic lighting|\n", "fiona|\n", "highres gorgeous dark skin noxian|\n", "gorgeous characters|\n", "best quality CG|\n", "best quality cg|\n", "beautiful women complete eyes|\n", "4K artist|\n", "PA7_Portrait-CU|\n", "wattson_(apex_legends) |\n", "a gorgeous beautiful age sexy|\n", "beautiful female face|\n", "lynda|\n", "female human|\n", "alisa_ilinichina_amiella |\n", "alhambra background|\n", "heart_maebari |\n", "she has realistic human|\n", "skin_fang |\n", "cinematic unreal 5|\n", "highres cinematic|\n", "layla|\n", "harmony high-quality skin bump mapping|\n", "bodya high resolution face|\n", "masterpeice 1girl|\n", "by syuro|\n", "iconic fine epic|\n", "garnet_til_alexandros_xvii |\n", "elena_so6|\n", "official_art |\n", "7rtifa elegant|\n", "digital artist|\n", "realistic lesbian|\n", "Escher CGI Artgerm|\n", "wallpaper cinematic lighting sharp focus|\n", "nina|\n", "Detailed lips|\n", "thief|\n", "stylized charming|\n", "by Whooo-Ya|\n", "high-quality skin textures|\n", "eva_beatrice |\n", "fantasy art.HD 3D|\n", "bezier place place character traits lighting|\n", "sexy 8 list|\n", "penelope|\n", "multi-layer skin shading|\n", "streaming|\n", "woman pretty face|\n", "hyperrealism by atey ghailan|\n", "highres cute|\n", "torino_aqua |\n", "guest_art |\n", "masterpiecebust portrait|\n", "2D artstyle|\n", "1girl best quality|\n", "adorable_girl|\n", "prompt lora weight at ->quality loss|\n", "by zackary|\n", "realistic skin1|\n", "stunning fantasy style environment|\n", "Ultra detailed|\n", "Ultra Detailed|\n", "mobile_legends_alice|\n", "detailed_face|\n", "dynamic cinematic portrait medium shot|\n", "absurdres gorgeous noxian|\n", "perfectly composedgorgeous jp-svetf|\n", "dawn solo 1boy|\n", "chloe|\n", "edgtb_woman|\n", "character concept art|\n", "ramudia_(lamyun) |\n", "tall mature female|\n", "nose realistic|\n", "ultra detailed HD|\n", "bliznyashkithetwins_v|\n", "white_thighhighs |\n", "rating_lewd curvy|\n", "super fine illustrations|\n", "source_3Dcartoon|\n", "lucy_(cyberpunk) |\n", "soft diffuse lighting|\n", "pink_hair |\n", "highres black hair|\n", "blue eyes alpha|\n", "Cel shading Wonder|\n", "detailed artgem|\n", "evil AI named Aida|\n", "Realistic face|\n", "realistic face|\n", "RTX 0 shader|\n", "group profile|\n", "beautiful sxz-ashe-ow-|\n", "povsolo 1girl|\n", "cleavage highres|\n", "lips realistic|\n", "adorable female face|\n", "the character the image should be|\n", "jane_doe_(zenless_zone_zero) |\n", "kawakami_rokkaku|\n", "kawakami_rokkaku |\n", "an artist-inspired character|\n", "Detailed background|\n", "Detailed Background|\n", "ziche_fuzhao |\n", "temple cyberpunk soft lighting|\n", "breasts nose_blush|\n", "highres breasts|\n", "natalie|\n", "ultra-detailed eyes|\n", "a beautiful artwork illustration|\n", "Soft Shading|\n", "aquiline_nose |\n", "blush short_hair|\n", "artgerm best quality|\n", "soft_lighting|\n", "1girl kujikawadef|\n", "gundam |\n", "ada_wong resident evil|\n", "Flirty_Shy_Grin|\n", "of beautiful female knight women|\n", "vox|\n", "skadiview-appearance-shiny skin|\n", "detective|\n", "cute solo female|\n", "character request|\n", "fantasy female|\n", "futanari realistic|\n", "dawn solo|\n", "astrosorc22|\n", "luxurious stone castle game|\n", "mignonworks artstyle|\n", "idol|\n", "idol |\n", "fantasy game spell icon|\n", "enya|\n", "moody cinematic epic concept art|\n", "perfect artwork|\n", "age_of_ishtaria |\n", "1boy cute woman|\n", "beatiful face|\n", "female_protagonist_(pokemon_go) |\n", "a pretty caucasian female character|\n", "teresa|\n", "arab escort escort|\n", "telegram|\n", "gorgeous female face|\n", "futuristic setting she should be depicted|\n", "award winning digital painting|\n", "anime woman|\n", "conceptart|\n", "astel_leda |\n", "snow_white |\n", "Epic Digital Art|\n", "epic digital art|\n", "epic Digital art|\n", "heroine|\n", "gorgeous art|\n", "best ai image|\n", "ruyi_jingu_bang |\n", "#explorepage #art #cosplay|\n", "saga|\n", "1girl sfw|\n", "lovely follynobodysd|\n", "marietta|\n", "yen |\n", "banned_artist |\n", "depth-based facial shading|\n", "artstation_sample |\n", "highres dark skin|\n", "unreal engine Maya|\n", "well-lit cinematic|\n", "dark hair pureerosface_v|\n", "masterpiece manhwa|\n", "1girl perfect eyes|\n", "Kyaradin1girl|\n", "john_doe_(jdart) |\n", "official_alternate_eye_color |\n", "character_doll |\n", "epic realistic art|\n", "bronya_zaychik |\n", "human ai hybrid|\n", "JanePorterXLP|\n", "blulust 1girl|\n", "beautiful concept illustration|\n", "Acheron_Blue 1girl|\n", "evie|\n", "lifelike texture dramatic lighting unrealengine|\n", "VichyaIris 1girl|\n", "gwen_(league_of_legends) |\n", "Human woman|\n", "human woman|\n", "woman close up|\n", "Realistic Lustful|\n", "F/ beautiful|\n", "uncensored game cg|\n", "sunlight cinematic lighting|\n", "temple_ruins|\n", "detailed sexy lips|\n", "snapmatic|\n", "milf_saga_face|\n", "by zixiong|\n", "high rating 1girl|\n", "1girl buff female|\n", "nora|\n", "realistic mercy|\n", "HD CGSociety|\n", "HD cgsociety|\n", "snowwhiteWaifu|\n", "kaname_raana |\n", "artist_request |\n", "artist_request|\n", "noir detailed|\n", "polearm |\n", "sex bot fem bot|\n", "lara|\n", "stunning female wendigo|\n", "detailed makeup|\n", "nan|\n", "Detailed person|\n", "character quality|\n", "mahjong_soul |\n", "janet|\n", "fantasy illustration masterpiece|\n", "incredible perfect artwork|\n", "the most beautiful artwork in the worldcute|\n", "beautiful stylized|\n", "ultra detailed eyes|\n", "gwen tennyson|\n", "Gwen Tennyson|\n", "artstation pixiv|\n", "3d shading realism|\n", "kanden_sky |\n", "half human ai hybrid|\n", "animation cinematic|\n", "1girl fair_skin|\n", "perfect rendered face|\n", "comic art|\n", "female_pervert|\n", "female_pervert |\n", "rgb unreal engine|\n", "assassin|\n", "realism image Fill|\n", "masterpiece 2d art|\n", "DETAILED SKIN|\n", "detailed skin|\n", "1girl yor_briar|\n", "3d soft lighting|\n", "little_witch_nobeta |\n", "bisque skin|\n", "perfect face skin|\n", "lcylxsl woman|\n", "gwen_tennyson|\n", "looking at viewercurvy|\n", "uncensored steam|\n", "robot_girl |\n", "1girl monaXL|\n", "captivating digital art|\n", "a beautiful porcelain young slim fit female robot|\n", "k uhd unity wallpaper|\n", "maria-sama_ga_miteru |\n", "justice right bitch heroine|\n", "feminine face profile|\n", "doggsytyle 1girl|\n", "1girl face closeup|\n", "1girl necromancer|\n", "highres 3d cg|\n", "ScarletWitch|\n", "black_widow|\n", "styledreamlikeartredshift|\n", "UHD. AI_girl doll|\n", "lore|\n", "painting stylized|\n", "stunning environmentnsfw|\n", "red_eyes solo|\n", "3D cartoon dreamy|\n", "estheticfutanaritrap|\n", "5 detailed face|\n", "motd|\n", "Cel shading Evil|\n", "helsie|\n", "marissa|\n", "vertical_comic |\n", "commissioner_name |\n", "Ada Wong cartoon|\n", "Epic CG masterpiece|\n", "solo detailed eyes|\n", "Atey Ghailan|\n", "zoe|\n", "cinematic environment|\n", "character_print |\n", "d rendered hentai version|\n", "Beuty lips|\n", "beautiful noelle_silva|\n", "no humans profile|\n", "shiny_skin1|\n", "eli|\n", "hades_1 |\n", "fantasy highres|\n", "Ptylust 1girl|\n", "beautiful yo greek cyborg|\n", "mature female tall|\n", "stunningly beautiful corneo_yor_forger|\n", "scarlet_devil_mansion |\n", "ultra detailed sex|\n", "original character|\n", "towel |\n", "by Sadahide1girl|\n", "ADDCOLfair skin|\n", "nerissa_ravencroft |\n", "mei|\n", "rover_(wuthering_waves) |\n", "vio|\n", "close up of 1girl|\n", "realistic elf girl|\n", "trending cgsociety|\n", "character name|\n", "sexy shy steam|\n", "cornelia_li_britannia |\n", "by pakwan|\n", "perfect eyes best quality|\n", "sumeragi_lee_noriega |\n", "character_signature |\n", "source_comic|\n", "perfect feminine face+|\n", "sci-fi woman|\n", "aya|\n", "lilith_aensland |\n", "vana|\n", "siri|\n", "uncensored shadman|\n", "high_resolution illustration|\n", "profile portrait|\n", "impossibly beautiful egyptian|\n", "superhero game|\n", "ptoto portrait|\n", "elegant cinematic|\n", "very detailed faces|\n", "kaede_johan_nouvel |\n", "cutiesaturday|\n", "female high res|\n", "lip bite blush|\n", "Wealth Highres|\n", "deer_girl |\n", "lily_black |\n", "riven_(league_of_legends) |\n", "a beautiful sexy age chinese|\n", "high detailed skin|\n", "mature female masterpiece|\n", "8k 2d animation|\n", "concept_art |\n", "1>tifa|\n", "red gothic illustration + exquisite|\n", "lili|\n", "paintingsexy femalelooking at viewer|\n", "1girl detail face|\n", "perfect skin 8k|\n", "snow whiteWaifu|\n", "eris_greyrat |\n", "vivian|\n", "solo female artica_sparkle_character|\n", "by wizzikt|\n", "cel shaded 4k|\n", "carmen|\n", "kami_nomi_zo_shiru_sekai |\n", "3d unreal engine|\n", "3D unreal engine|\n", "girl closeup|\n", "by GLaDOS|\n", "red gothic cursed tower|\n", "riot heterochromia|\n", "kata|\n", "lifelike dramatic lighting unrealengine|\n", "alternate_skin_color |\n", "realistic han_juri|\n", "indian mary_winstead eyes|\n", "lulu|\n", "strict_nun_(diva) |\n", "arcadian|\n", "a gorgeous sexy thicc harlequin vampire|\n", "apex_legends |\n", "highres flawless|\n", "official_alternate_hairstyle |\n", "official_alternate_hairstyle|\n", "1 android girl|\n", "bianca|\n", "LydiaV|\n", "best detail 1woman|\n", "RTX moody female|\n", "beautiful elegant face|\n", "comic dark hair|\n", "close-up realistic|\n", "femme fatale|\n", "concept-art|\n", "high skin details|\n", "Full Stylized|\n", "amazing. 3d shading|\n", "fushigi_no_umi_no_nadia |\n", "Sense 1girl|\n", "skin details nude|\n", "game cg sky cloud|\n", "realistic highres|\n", "skin detail NSFW|\n", "Elegant realistic|\n", "sexy sexy 8|\n", "source_furry 1girl|\n", "by Skyrn99|\n", "mo_chengwei |\n", "character description|\n", "kawakami_mai |\n", "art style by artgerm|\n", "thegame|\n", "elizabeth|\n", "super_heroine_boy |\n", "bold eyes fix|\n", "bokeh ray-tracing|\n", "a busty iranian pale ulzzang idol|\n", "Best Quality 1girl|\n", "BEST QUALITY 1GIRL|\n", "best quality1girl|\n", "best quality 1GIRL|\n", "shanghai_doll |\n", "beautiful eyessexy theme|\n", "ultra-detailed1|\n", "n1ll 1girl|\n", "Gwen 1girl solo|\n", "unreal engine rendered|\n", "beautiful face portrait|\n", "submissive_female|\n", "fantasy lifestyle-portrait by mandy jurgens|\n", "kondou_taeko |\n", "alisa|\n", "unreal engine cinematic smooth|\n", "1girl human female|\n", "highres waist-up|\n", "a mature sexy streamer|\n", "high resolution illustrations|\n", "character focus cinematic light|\n", "elegant epic|\n", "Highres elaborate|\n", "human 1girl|\n", "nadia_la_arwall |\n", "seductive mature female|\n", "1girl older|\n", "masterpiece miko|\n", "very detailed skin|\n", "mysterious epic|\n", "watson_amelia |\n", "Visual novel|\n", "hi res detailed|\n", "gumroad username|\n", "Sunrays Raytracing|\n", "1futa 1male|\n", "background characters|\n", "natasha|\n", "Detailed art|\n", "womam skin|\n", "details such as raytracing and|\n", "wcw|\n", "1girl deer woman|\n", "physically-based eye shading|\n", "an attracitve woman|\n", "sexy femme fatale demon babe}\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ], "image/png": 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\n", 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\n" }, "metadata": {}, "execution_count": 97 } ] }, { "cell_type": "code", "source": [ "# @title ⚙️📝 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", "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 = (_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": { "cellView": "form", "id": "Qz05kRtU236V" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title 📝 Get Prompt text_encoding similarity to the pre-calc. text_encodings\n", "prompt = \"pixar animation\" # @param {\"type\":\"string\",\"placeholder\":\"Write a prompt\"}\n", "\n", "use_negatives = False # @param {type:\"boolean\"}\n", "\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()\n", "\n", "# Get text features for user input\n", "inputs = tokenizer(text = prompt, padding=True, return_tensors=\"pt\")\n", "text_features_A = model.get_text_features(**inputs)\n", "text_features_A = text_features_A/text_features_A.norm(p=2, dim=-1, keepdim=True)\n", "name_A = prompt\n", "#------#\n", "\n", "penalty_NEG = 0\n", "image_penalty_NEG = 0\n", "\n", "#------#\n", "try: strength_NEG\n", "except: strength_NEG = 1\n", "\n", "try: strength_image_NEG\n", "except: strength_image_NEG = 1\n", "#------#\n", "\n", "if using_NEG and use_negatives:\n", " penalty_NEG = strength_NEG* torch.nn.functional.cosine_similarity(text_features_A, text_features_NEG)\n", "if using_image_NEG and use_negatives:\n", " torch.matmul(text_features_A, image_features_NEG.t()) * logit_scale\n", " image_penalty_NEG = strength_image_NEG* torch.nn.functional.cosine_similarity(text_features_A, image_features_NEG)\n", "#-------#\n", "\n", "sims = torch.zeros(NUM_VOCAB_ITEMS)\n", "for index in range(NUM_VOCAB_ITEMS):\n", " if index<2: continue\n", " text_features = text_encodings[f'{index}']\n", " sims[index] = torch.nn.functional.cosine_similarity(text_features, text_features_A) - penalty_NEG - image_penalty_NEG\n", " #------#\n", "\n", "#------#\n", "\n", "sorted , indices = torch.sort(sims,dim=0 , descending=True)" ], "metadata": { "id": "xc-PbIYF428y", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title ⚙️📝 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", "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]\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 = (_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": "ifblBRcXoB6t", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title 📝🚫 Penalize similarity to Prompt text_encoding (optional)\n", "neg_prompt = \"a drawing of a cat \" # @param {\"type\":\"string\",\"placeholder\":\"Write something to avoid\"}\n", "\n", "neg_strength = 1 # @param {type:\"slider\", min:0, max:5, step:0.01}\n", "\n", "enable = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", "\n", "using_NEG = enable\n", "\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", "\n", "\n", "name_NEG = ''\n", "strength_NEG = 1\n", "if enable:\n", " # Get text features for user input\n", " inputs = tokenizer(text = neg_prompt, 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", " name_NEG = neg_prompt\n", " strength_NEG = neg_strength\n", " #------#" ], "metadata": { "id": "sX2JGqOH5B8g", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title 🖼️🚫 Penalize similarity to Prompt image_encoding (optional)\n", "from google.colab import files\n", "def upload_files():\n", " from google.colab import files\n", " uploaded = files.upload()\n", " for k, v in uploaded.items():\n", " open(k, 'wb').write(v)\n", " return list(uploaded.keys())\n", "\n", "\n", "neg_strength = 1 # @param {type:\"slider\", min:0, max:5, step:0.01}\n", "enable = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", "using_image_NEG = enable\n", "\n", "\n", "colab_image_folder = '/content/text-to-image-prompts/images/'\n", "#Get image\n", "# You can use \"http://images.cocodataset.org/val2017/000000039769.jpg\" for testing\n", "image_url = \"\" # @param {\"type\":\"string\",\"placeholder\":\"leave empty for local upload (scroll down to see it)\"}\n", "colab_image_path = \"imperial.png\" # @param {\"type\":\"string\",\"placeholder\": \"eval. as '/content/sd_tokens/' + **your input**\"}\n", "# @markdown --------------------------\n", "\n", "image_path = \"\"\n", "\n", "from PIL import Image\n", "import requests\n", "image_NEG = \"\"\n", "image_features_NEG = \"\"\n", "strength_image_NEG = 1\n", "\n", "#----#\n", "if enable :\n", " strength_image_NEG = neg_strength\n", " if image_url == \"\":\n", " import cv2\n", " from google.colab.patches import cv2_imshow\n", " # Open the image.\n", " if colab_image_path == \"\":\n", " keys = upload_files()\n", " for key in keys:\n", " image_NEG = cv2.imread(colab_image_folder + key)\n", " colab_image_path = colab_image_folder + key\n", " image_path = colab_image_folder + key\n", " else:\n", " image_NEG = cv2.imread(colab_image_folder + colab_image_path)\n", " else:\n", " image_NEG = Image.open(requests.get(image_url, stream=True).raw)\n", " #------#\n", " from google.colab.patches import cv2_imshow\n", " cv2_imshow(image_NEG)\n", "\n", " inputs = processor(images=image_NEG, return_tensors=\"pt\")\n", " image_features_NEG = model.get_image_features(**inputs)\n", " image_features_NEG = image_features_NEG / image_features_NEG.norm(p=2, dim=-1, keepdim=True)" ], "metadata": { "id": "oCJ97b-B7927", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title 📝 Print the results\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", "newline_Separator = True # @param {type:\"boolean\"}\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]\n", " #Remove duplicates\n", " if _prompts.find(prompts[f'{index}'] + separator)<=-1:\n", " _sims = _sims + f'{round(100*sims[index].item(), 2)} %' + separator\n", " #-------#\n", " _prompts = _prompts.replace(prompts[f'{index}'] + separator,'')\n", " _prompts = _prompts + prompts[f'{index}'] + separator\n", " #------#\n", "#------#\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 ' + __prompts)\n", " print(f'The {start_at_index}-{start_at_index + RANGE} similarity % for items : \\n\\n' + __sims)\n", " print('')\n", " else:\n", " print(__prompts)\n", "else:\n", " print(__prompts)\n", "#-------#" ], "metadata": { "id": "_vnVbxcFf7WV", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Below are the Image interrogators" ], "metadata": { "id": "qZvLkJCtGC89" } }, { "cell_type": "code", "source": [ "# @title 🖼️ Upload an image\n", "def upload_files():\n", " from google.colab import files\n", " uploaded = files.upload()\n", " for k, v in uploaded.items():\n", " open(k, 'wb').write(v)\n", " return list(uploaded.keys())\n", "\n", "\n", "colab_image_folder = '/content/text-to-image-prompts/images/'\n", "#Get image\n", "# You can use \"http://images.cocodataset.org/val2017/000000039769.jpg\" for testing\n", "image_url = \"\" # @param {\"type\":\"string\",\"placeholder\":\"leave empty for local upload (scroll down to see it)\"}\n", "colab_image_path = \"imperial.png\" # @param {\"type\":\"string\",\"placeholder\": \"eval. as '/content/sd_tokens/' + **your input**\"}\n", "# @markdown --------------------------\n", "\n", "image_path = \"\"\n", "\n", "from PIL import Image\n", "import requests\n", "image_A = \"\"\n", "#----#\n", "if image_url == \"\":\n", " import cv2\n", " from google.colab.patches import cv2_imshow\n", " # Open the image.\n", " if colab_image_path == \"\":\n", " keys = upload_files()\n", " for key in keys:\n", " image_A = cv2.imread(colab_image_folder + key)\n", " colab_image_path = colab_image_folder + key\n", " image_path = colab_image_folder + key\n", " else:\n", " image_A = cv2.imread(colab_image_folder + colab_image_path)\n", " #---------#\n", "else:\n", " image_A = Image.open(requests.get(image_url, stream=True).raw)\n", " image_A\n", "#------#\n", "if image_url == \"\":\n", " from google.colab.patches import cv2_imshow\n", " cv2_imshow(image_A)\n", "#------#\n", "image_A\n", "\n" ], "metadata": { "id": "ke6mZ1RZDOeB", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "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", "\n", "# Get image features\n", "inputs = processor(images=image_A, return_tensors=\"pt\")\n", "image_features = model.get_image_features(**inputs)\n", "image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\n", "name_A = \"the image\"\n", "#-----#" ], "metadata": { "id": "gAqsRQaZVf1A" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#'/content/text-to-image-prompts/fusion/image_encodings/links-1.safetensors'\n", "path = '/content/text-to-image-prompts/fusion/image_encodings/'\n", "filename = 'links-1'\n", "#------#\n", "from safetensors.torch import load_file\n", "import json , os , shelve , torch\n", "import pandas as pd\n", "\n", "\n", "%cd {path}\n", "_image_encodings = load_file(f'{filename}.safetensors')\n", "#Store text_encodings for the header items" ], "metadata": { "id": "SEPUbRwpVwRQ", "outputId": "b058be19-2fe5-4de2-ff3c-3e821043a177", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content/text-to-image-prompts/fusion/image_encodings\n" ] } ] }, { "cell_type": "code", "source": [ "_image_encoding = _image_encodings[f'{16}']\n", "sim = torch.nn.functional.cosine_similarity(image_features, _image_encoding)\n", "print(sim.item())" ], "metadata": { "id": "5oXvYS1aXdjt", "outputId": "00491826-4329-4c02-d038-bc3b221937b1", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "1.0\n" ] } ] }, { "cell_type": "code", "source": [ "# @title 🖼️ Get image_encoding similarity to the pre-calc. text_encodings\n", "\n", "use_negatives = False # @param {type:\"boolean\"}\n", "\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", "\n", "# Get image features\n", "inputs = processor(images=image_A, return_tensors=\"pt\")\n", "image_features = model.get_image_features(**inputs)\n", "image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\n", "name_A = \"the image\"\n", "#-----#\n", "\n", "sims = torch.zeros(NUM_VOCAB_ITEMS)\n", "logit_scale = model.logit_scale.exp()\n", "for index in range(NUM_VOCAB_ITEMS):\n", " text_features = text_encodings[f'{index}']\n", "\n", " torch.matmul(text_features, image_features.t()) * logit_scale\n", " sims[index] = torch.nn.functional.cosine_similarity(text_features, image_features)\n", " if using_NEG and use_negatives :\n", " torch.matmul(text_features_NEG, image_features.t()) * logit_scale\n", "\n", " sims[index] = sims[index] - neg_strength* torch.nn.functional.cosine_similarity(text_features_NEG, image_features)\n", " if using_image_NEG and use_negatives :\n", " sims[index] = sims[index] - neg_strength* torch.nn.functional.cosine_similarity(image_features, image_features_NEG)\n", "#-------#\n", "sorted , indices = torch.sort(sims,dim=0 , descending=True)" ], "metadata": { "id": "rebogpoyOG8k" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title 🖼️ Print the results\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", "newline_Separator = True # @param {type:\"boolean\"}\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]\n", " _prompts = _prompts + prompts[f'{index}'] + separator\n", " _sims = _sims + f'{round(100*sims[index].item(), 2)} %' + separator\n", "#------#\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 ' + __prompts)\n", " print(f'The {start_at_index}-{start_at_index + RANGE} similarity % for items : \\n\\n' + __sims)\n", " print('')\n", " if name_NEG != '': print(f'Using negatives at {strength_NEG} strength for this text : {name_NEG}')\n", " else:\n", " print(__prompts)\n", "else:\n", " print(__prompts)\n", "#-------#" ], "metadata": { "id": "JkzncP8SgKtS" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title ⚙️🖼️ 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", "newline_Separator = True # @param {type:\"boolean\"}\n", "\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 = 50 # @param {type:\"slider\", min:0, max:100, step:1}\n", "\n", "# @markdown -----------\n", "# @markdown Repeat output N times\n", "\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]\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 = (_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" ], "metadata": { "id": "6FEmV02tArrh" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title 💫 Compare Text encodings\n", "prompt_A = \"banana\" # @param {\"type\":\"string\",\"placeholder\":\"Write a prompt\"}\n", "prompt_B = \"bike \" # @param {\"type\":\"string\",\"placeholder\":\"Write a prompt\"}\n", "use_token_padding = True # param {type:\"boolean\"} <----- Enabled by default\n", "#-----#\n", "from transformers import AutoTokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\",\n", "clean_up_tokenization_spaces = False)\n", "#-----#\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", "#----#\n", "inputs = tokenizer(text = prompt_A, padding=True, return_tensors=\"pt\")\n", "text_features_A = model.get_text_features(**inputs)\n", "text_features_A = text_features_A / text_features_A.norm(p=2, dim=-1, keepdim=True)\n", "name_A = prompt_A\n", "#----#\n", "inputs = tokenizer(text = prompt_B, padding=True, return_tensors=\"pt\")\n", "text_features_B = model.get_text_features(**inputs)\n", "text_features_B = text_features_B / text_features_B.norm(p=2, dim=-1, keepdim=True)\n", "name_B = prompt_B\n", "#----#\n", "import torch\n", "sim_AB = torch.nn.functional.cosine_similarity(text_features_A, text_features_B)\n", "#----#\n", "print(f'The similarity between the text_encoding for A:\"{prompt_A}\" and B: \"{prompt_B}\" is {round(sim_AB.item()*100,2)} %')" ], "metadata": { "id": "QQOjh5BvnG8M", "collapsed": true, "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title Quick Fix\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", "\n", "def doFixPrompts(_path):\n", " output_folder = '/content/outputs/text'\n", " my_mkdirs(output_folder)\n", " path = _path + '/text'\n", " #-----#\n", " index = 0\n", " file_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", " #-----#\n", " text_encoding_filename = _prompts['1']\n", " links_encoding_filename = _prompts['1'].replace('prompts','links')\n", " _prompts['0'] = links_encoding_filename\n", " #-----#\n", " %cd {output_folder}\n", " print(f'Saving segment {filename} to {output_folder}...')\n", " with open(filename, 'w') as f:\n", " json.dump(_prompts, f)\n", " #-------#\n", " #--------#\n", "#----------#\n", "\n", "\n", "def doFixLinks(_path):\n", " output_folder = '/content/outputs/images'\n", " my_mkdirs(output_folder)\n", " path = _path + '/images'\n", " #-----#\n", " index = 0\n", " file_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", " _links = {\n", " key : value for key, value in _df.items()\n", " }\n", " #-----#\n", " links_encoding_filename = _links['1']\n", " text_encoding_filename = _links['1'].replace('links','prompts')\n", " _links['0'] = links_encoding_filename\n", " _links['1'] = text_encoding_filename\n", " #-----#\n", " %cd {output_folder}\n", " print(f'Saving segment {filename} to {output_folder}...')\n", " with open(filename, 'w') as f:\n", " json.dump(_links, f)\n", " #-------#\n", " #--------#" ], "metadata": { "cellView": "form", "id": "Cbt78mgJYHgr" }, "execution_count": 28, "outputs": [] }, { "cell_type": "markdown", "source": [ "You can write an url or upload a file locally from your device to use as reference. The image will by saved in the 'sd_tokens' folder. Note that the 'sd_tokens' folder will be deleted upon exiting this runtime." ], "metadata": { "id": "hyK423TQCRup" } }, { "cell_type": "code", "source": [ "# @title Process the raw vocab into json + .safetensor pair\n", "\n", "# NOTE : although they have 1x768 dimension , these are not text_encodings , but token vectors\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", "import json\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", "\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", "# User input\n", "target = home_directory + 'text-to-image-prompts/vocab/'\n", "root_output_folder = home_directory + 'output/'\n", "output_folder = root_output_folder + 'vocab/'\n", "root_filename = 'vocab'\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_token_vectors = output_folder + 'token_vectors/'\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_token_vectors)\n", "#-------#\n", "\n", "%cd {target_raw}\n", "tokens = torch.load(f'{root_filename}.pt' , weights_only=True)\n", "tokens = model.clone().detach()\n", "\n", "\n", "%cd {target_raw}\n", "with open(f'{root_filename}.json', 'r') as f:\n", " data = json.load(f)\n", "_df = pd.DataFrame({'count': data})['count']\n", "#reverse key and value in the dict\n", "vocab = {\n", " value : key for key, value in _df.items()\n", "}\n", "#------#\n", "\n", "\n", "tensors = {}\n", "for key in vocab:\n", " name = vocab[key]\n", " token = tokens[int(key)]\n", " tensors[key] = token\n", "#-----#\n", "\n", "%cd {output_folder_token_vectors}\n", "save_file(tensors, \"vocab.safetensors\")\n", "\n", "%cd {output_folder_text}\n", "with open('vocab.json', 'w') as f:\n", " json.dump(vocab, f)\n", "\n", "\n" ], "metadata": { "id": "H3JRx5rhWIEo", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title Do the same but for image encodings (if urls exist)\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", "from PIL import Image\n", "import requests\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", "# 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", "# User input\n", "target = home_directory + 'text-to-image-prompts/fusion/'\n", "root_output_folder = home_directory + 'output/'\n", "output_folder = root_output_folder + 'fusion/'\n", "root_filename = 'prompts'\n", "root_filename_links = 'links'\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_images = output_folder + 'images/'\n", "output_folder_text_encodings = output_folder + 'text_encodings/'\n", "output_folder_image_encodings = output_folder + 'image_encodings/'\n", "target_raw_text = target + 'raw/text/'\n", "target_raw_images = target + 'raw/images/'\n", "%cd {home_directory}\n", "my_mkdirs(output_folder)\n", "my_mkdirs(output_folder_text)\n", "my_mkdirs(output_folder_images)\n", "my_mkdirs(output_folder_text_encodings)\n", "my_mkdirs(output_folder_image_encodings)\n", "#-------#\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", "\n", " # Assign name of JSON file to read\n", " filename = f'{root_filename}{file_index}'\n", " if NUM_FILES == 1 : filename = f'{root_filename}'\n", " #--------#\n", "\n", " # Assign name of JSON file to read\n", " filename_links = f'{root_filename_links}{file_index}'\n", " if NUM_FILES == 1 : filename_links = f'{root_filename_links}'\n", " #--------#\n", "\n", " # Read {filename}.json\n", " %cd {target_raw_text}\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(\"\",\" \") 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", " # Read image_urls\n", " %cd {target_raw_images}\n", " with open(filename_links + '.json', 'r') as f:\n", " data = json.load(f)\n", " _df = pd.DataFrame({'count': data})['count']\n", " image_urls = {\n", " key : value.replace(\"\",\" \") for key, value in _df.items()\n", " }\n", " index = 0\n", " for key in image_urls:\n", " index = index + 1\n", " #----------#\n", " NUM_ITEMS2 = index\n", " #------#\n", "\n", " if (NUM_ITEMS != NUM_ITEMS2) :\n", " print(f\"NUM_ITEMS (text) : {NUM_ITEMS}\")\n", " print(f\"NUM_ITEMS (links) : {NUM_ITEMS2}\")\n", "\n", " # Calculate text_encoding for .json file contents and results as .db file\n", " NUM_HEADERS = 2\n", " CHUNKS_SIZE = 20\n", " START_AT = 0 #<---Use this is job was aborted and you wish to continue where you left of. Set the value to 0 otherwise\n", " #--------#\n", " names_dict = {}\n", " image_encoding_dict = {}\n", " text_encoding_dict = {}\n", " segments = {}\n", " index = 0;\n", " subby = 1;\n", " _filename = ''\n", "\n", " print(f'processing batch no {subby}....')\n", " print(f'----------')\n", " for _index in range(NUM_ITEMS2):\n", " if not (f'{_index}' in prompts) : continue\n", " if (prompts[f'{_index}']==\"SKIP\") : continue\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 or _index <= 0) :\n", " index = index + 1\n", " else:\n", " if index\",\" \") 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", " # Read image_urls\n", " %cd {target_raw_images}\n", " with open(filename_links + '.json', 'r') as f:\n", " data = json.load(f)\n", " _df = pd.DataFrame({'count': data})['count']\n", " image_urls = {\n", " key : value.replace(\"\",\" \") for key, value in _df.items()\n", " }\n", " index = 0\n", " for key in image_urls:\n", " index = index + 1\n", " #----------#\n", " NUM_ITEMS2 = index\n", " #------#\n", "\n", " if (NUM_ITEMS != NUM_ITEMS2) :\n", " print(f\"NUM_ITEMS (text) : {NUM_ITEMS}\")\n", " print(f\"NUM_ITEMS (links) : {NUM_ITEMS2}\")\n", "\n", " # Calculate text_encoding for .json file contents and results as .db file\n", " NUM_HEADERS = 2\n", " CHUNKS_SIZE = 20\n", " START_AT = 0 #<---Use this is job was aborted and you wish to continue where you left of. Set the value to 0 otherwise\n", " #--------#\n", " names_dict = {}\n", " image_encoding_dict = {}\n", " segments = {}\n", " index = 0;\n", " subby = 1;\n", " _filename = ''\n", "\n", " print(f'processing batch no {subby}....')\n", " print(f'----------')\n", " for _index in range(NUM_ITEMS2):\n", " if not (f'{_index}' in prompts) : continue\n", " if (prompts[f'{_index}']==\"SKIP\") : continue\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 or _index <= 0) :\n", " index = index + 1\n", " else:\n", " if index\",\" \") 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", "\n", "\n", " # Read image_urls\n", " %cd {target_raw_images}\n", " with open('links.json', 'r') as f:\n", " data = json.load(f)\n", " _df = pd.DataFrame({'count': data})['count']\n", " image_urls = {\n", " key : value.replace(\"\",\" \") for key, value in _df.items()\n", " }\n", " index = 0\n", " for key in image_urls:\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", " image_encoding_dict = {}\n", " segments = {}\n", " index = 0;\n", " subby = 1;\n", " NUM_HEADERS = 2\n", " CHUNKS_SIZE = 500\n", " _filename = ''\n", " for _index in range(NUM_ITEMS):\n", " if not (f'{_index}' in prompts) : continue\n", " if (prompts[f'{_index}']==\"SKIP\") : continue\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,truncation=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", " image_encoding_dict[f'{0}'] = text_features.to(torch.device('cpu'))\n", " inputs = tokenizer(text = '' + names_dict[f'{1}'], padding=True,truncation=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", " image_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_images}\n", " print(f'Saving segment {_filename} to {output_folder_images}...')\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_image_encodings}\n", " print(f'Saving segment {_filename} to {output_folder_image_encodings}...')\n", " save_file(image_encoding_dict, _filename)\n", " #--------#\n", "\n", " #Iterate\n", " subby = subby + 1\n", " segments[f'{subby}'] = _filename\n", " image_encoding_dict = {}\n", " names_dict = {}\n", " index = 0\n", " #------#\n", " #------#\n", " else: index = index + 1\n", " #--------#\n", "\n", "\n", " inputs = tokenizer(text = '' + prompts[f'{_index}'], padding=True,truncation=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", "\n", "\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,truncation=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,truncation=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", " #----#" ], "metadata": { "id": "Sy5K7c-IDcic", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "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'{home_directory}results.zip'\n", "!zip -r {zip_dest} {root_output_folder}" ], "metadata": { "id": "V4YCpmWlkPMG" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title Extract tags from the Danbooru website (AI tags)\n", "\n", "import requests\n", "import re\n", "import json\n", "\n", "prompts = {}\n", "index = 0\n", "for url_index in range(10):\n", " url = f'https://danbooru.donmai.us/ai_tags?commit=Search&mode=table&page={url_index}&search%5Bis_posted%5D=true&search%5Border%5D=media_asset_id'\n", " r = requests.get(url)\n", " #-----#\n", " matches = re.findall(\"data-tag-name=.*.* href\", r.text)\n", " for x in matches:\n", " prompts[f'{index}'] = x.replace('data-tag-name=\"','').replace('\" href','')\n", " index = index + 1\n", "\n", "#-------#\n", "with open('danbooru_ai_tags.json', 'w') as f:\n", " json.dump(prompts, f)" ], "metadata": { "cellView": "form", "id": "tBbJnlA5pjd2" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title Extract tags from the Danbooru website (Normal tags)\n", "prompts = {}\n", "index = 0\n", "for url_index in range(1000):\n", " url = f'https://danbooru.donmai.us/tags?commit=Search&page={url_index}&search%5Bhide_empty%5D=yes&search%5Border%5D=count'\n", " r = requests.get(url)\n", " #-----#\n", " matches = re.findall('%5D=.*.*\">Related tags', r.text)\n", " for x in matches:\n", " prompts[f'{index}'] = x.replace('\\\">Related tags','').replace('%5D=','')\n", " index = index + 1\n", "\n", "#-------#\n", "with open('danbooru_tags.json', 'w') as f:\n", " json.dump(prompts, f)" ], "metadata": { "cellView": "form", "id": "l8t-4GmsviJt" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#Remove URL Encoding from the fetched Danbooru tags\n", "danboorus = getJSON('/content/text-to-image-prompts/danbooru/raw/','🎀 fusion-t2i-danbooru-tags.json')\n", "from urllib.parse import unquote\n", "for key in danboorus:\n", " danboorus[key] = unquote(danboorus[key])\n", "%cd /content/\n", "with open(f'🎀 fusion-t2i-danbooru-tags', 'w') as f:\n", " json.dump(danboorus, f)" ], "metadata": { "id": "AjSf585hWWMB" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title Download nouns - import data\n", "import os\n", "import json\n", "\n", "# Setup environment\n", "def my_mkdirs(folder):\n", " if os.path.exists(folder)==False:\n", " os.makedirs(folder)\n", "#--------#\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", "root_output_folder = home_directory + 'outputs/'\n", "\n", "# @title Extract nouns\n", "my_mkdirs(root_output_folder)\n", "%cd {root_output_folder}\n", "\n", "!pip install datasets\n", "\n", "from datasets import load_dataset\n", "\n", "ds = load_dataset(\"bartoszmaj/nouns_one\")\n", "#ds2 = load_dataset(\"bartoszmaj/nouns_two\")\n", "#ds3 = load_dataset(\"bartoszmaj/nouns_three\")\n", "#ds4 = load_dataset(\"bartoszmaj/nouns_four\")\n", "\n" ], "metadata": { "cellView": "form", "id": "HC72wZW9llzw" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title Download nouns - pick three items at random and write in JSONs\n", "import random\n", "my_mkdirs(root_output_folder)\n", "%cd {root_output_folder}\n", "for file_index in range(21):\n", " if file_index <=0: continue\n", " tripple_nouns = {}\n", " for index in range (10000):\n", " word = \"\"\n", " for its in range(3):\n", " _index = random.randint(0,1000000-1)\n", " words = list(ds['train'][_index]['nouns'])\n", " if len(words)>0:\n", " _word = random.choice(words)\n", " word = word + ' ' + _word\n", " #---------#\n", " tripple_nouns[f'{index}'] = word\n", " #--------#\n", " with open(f'tripple_nouns_{file_index}.json', 'w') as f:\n", " json.dump(tripple_nouns, f)\n", " #----------#\n", "\n" ], "metadata": { "cellView": "form", "id": "CWlWk0KpuX55" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "\n", "\n", "# How does this notebook work?\n", "\n", "Similiar vectors = similiar output in the SD 1.5 / SDXL / FLUX model\n", "\n", "CLIP converts the prompt text to vectors (“tensors”) , with float32 values usually ranging from -1 to 1.\n", "\n", "Dimensions are \\[ 1x768 ] tensors for SD 1.5 , and a \\[ 1x768 , 1x1024 ] tensor for SDXL and FLUX.\n", "\n", "The SD models and FLUX converts these vectors to an image.\n", "\n", "This notebook takes an input string , tokenizes it and matches the first token against the 49407 token vectors in the vocab.json : [https://huggingface.co/black-forest-labs/FLUX.1-dev/tree/main/tokenizer](https://www.google.com/url?q=https%3A%2F%2Fhuggingface.co%2Fblack-forest-labs%2FFLUX.1-dev%2Ftree%2Fmain%2Ftokenizer)\n", "\n", "It finds the “most similiar tokens” in the list. Similarity is the theta angle between the token vectors.\n", "\n", "
\n", "\n", "
\n", "\n", "The angle is calculated using cosine similarity , where 1 = 100% similarity (parallell vectors) , and 0 = 0% similarity (perpendicular vectors).\n", "\n", "Negative similarity is also possible.\n", "\n", "# How can I use it?\n", "\n", "If you are bored of prompting “girl” and want something similiar you can run this notebook and use the “chick” token at 21.88% similarity , for example\n", "\n", "You can also run a mixed search , like “cute+girl”/2 , where for example “kpop” has a 16.71% similarity\n", "\n", "There are some strange tokens further down the list you go. Example: tokens similiar to the token \"pewdiepie\" (yes this is an actual token that exists in CLIP)\n", "\n", "
\n", "\n", "
\n", "\n", "Each of these correspond to a unique 1x768 token vector.\n", "\n", "The higher the ID value , the less often the token appeared in the CLIP training data.\n", "\n", "To reiterate; this is the CLIP model training data , not the SD-model training data.\n", "\n", "So for certain models , tokens with high ID can give very consistent results , if the SD model is trained to handle them.\n", "\n", "Example of this can be anime models , where japanese artist names can affect the output greatly. \n", "\n", "Tokens with high ID will often give the \"fun\" output when used in very short prompts.\n", "\n", "# What about token vector length?\n", "\n", "If you are wondering about token magnitude,\n", "Prompt weights like (banana:1.2) will scale the magnitude of the corresponding 1x768 tensor(s) by 1.2 . So thats how prompt token magnitude works.\n", "\n", "Source: [https://huggingface.co/docs/diffusers/main/en/using-diffusers/weighted\\_prompts](https://www.google.com/url?q=https%3A%2F%2Fhuggingface.co%2Fdocs%2Fdiffusers%2Fmain%2Fen%2Fusing-diffusers%2Fweighted_prompts)\\*\n", "\n", "So TLDR; vector direction = “what to generate” , vector magnitude = “prompt weights”\n", "\n", "# How prompting works (technical summary)\n", "\n", " 1. There is no correct way to prompt.\n", "\n", "2. Stable diffusion reads your prompt left to right, one token at a time, finding association _from_ the previous token _to_ the current token _and to_ the image generated thus far (Cross Attention Rule)\n", "\n", "3. Stable Diffusion is an optimization problem that seeks to maximize similarity to prompt and minimize similarity to negatives (Optimization Rule)\n", "\n", "Reference material (covers entire SD , so not good source material really, but the info is there) : https://youtu.be/sFztPP9qPRc?si=ge2Ty7wnpPGmB0gi\n", "\n", "# The SD pipeline\n", "\n", "For every step (20 in total by default) for SD1.5 :\n", "\n", "1. Prompt text => (tokenizer)\n", "2. => Nx768 token vectors =>(CLIP model) =>\n", "3. 1x768 encoding => ( the SD model / Unet ) =>\n", "4. => _Desired_ image per Rule 3 => ( sampler)\n", "5. => Paint a section of the image => (image)\n", "\n", "# Disclaimer /Trivia\n", "\n", "This notebook should be seen as a \"dictionary search tool\" for the vocab.json , which is the same for SD1.5 , SDXL and FLUX. Feel free to verify this by checking the 'tokenizer' folder under each model.\n", "\n", "vocab.json in the FLUX model , for example (1 of 2 copies) : https://huggingface.co/black-forest-labs/FLUX.1-dev/tree/main/tokenizer\n", "\n", "I'm using Clip-vit-large-patch14 , which is used in SD 1.5 , and is one among the two tokenizers for SDXL and FLUX : https://huggingface.co/openai/clip-vit-large-patch14/blob/main/README.md\n", "\n", "This set of tokens has dimension 1x768. \n", "\n", "SDXL and FLUX uses an additional set of tokens of dimension 1x1024.\n", "\n", "These are not included in this notebook. Feel free to include them yourselves (I would appreciate that).\n", "\n", "To do so, you will have to download a FLUX and/or SDXL model\n", "\n", ", and copy the 49407x1024 tensor list that is stored within the model and then save it as a .pt file.\n", "\n", "//---//\n", "\n", "I am aware it is actually the 1x768 text_encoding being processed into an image for the SD models + FLUX.\n", "\n", "As such , I've included text_encoding comparison at the bottom of the Notebook.\n", "\n", "I am also aware thar SDXL and FLUX uses additional encodings , which are not included in this notebook.\n", "\n", "* Clip-vit-bigG for SDXL: https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/blob/main/README.md\n", "\n", "* And the T5 text encoder for FLUX. I have 0% understanding of FLUX T5 text_encoder.\n", "\n", "//---//\n", "\n", "If you want them , feel free to include them yourself and share the results (cuz I probably won't) :)!\n", "\n", "That being said , being an encoding , I reckon the CLIP Nx768 => 1x768 should be \"linear\" (or whatever one might call it)\n", "\n", "So exchange a few tokens in the Nx768 for something similiar , and the resulting 1x768 ought to be kinda similar to 1x768 we had earlier. Hopefully.\n", "\n", "I feel its important to mention this , in case some wonder why the token-token similarity don't match the text-encoding to text-encoding similarity.\n", "\n", "# Note regarding CLIP text encoding vs. token\n", "\n", "*To make this disclaimer clear; Token-to-token similarity is not the same as text_encoding similarity.*\n", "\n", "I have to say this , since it will otherwise get (even more) confusing , as both the individual tokens , and the text_encoding have dimensions 1x768.\n", "\n", "They are separate things. Separate results. etc.\n", "\n", "As such , you will not get anything useful if you start comparing similarity between a token , and a text-encoding. So don't do that :)!\n", "\n", "# What about the CLIP image encoding?\n", "\n", "The CLIP model can also do an image_encoding of an image, where the output will be a 1x768 tensor. These _can_ be compared with the text_encoding.\n", "\n", "Comparing CLIP image_encoding with the CLIP text_encoding for a bunch of random prompts until you find the \"highest similarity\" , is a method used in the CLIP interrogator : https://huggingface.co/spaces/pharmapsychotic/CLIP-Interrogator\n", "\n", "List of random prompts for CLIP interrogator can be found here, for reference : https://github.com/pharmapsychotic/clip-interrogator/tree/main/clip_interrogator/data\n", "\n", "The CLIP image_encoding is not included in this Notebook.\n", "\n", "If you spot errors / ideas for improvememts; feel free to fix the code in your own notebook and post the results.\n", "\n", "I'd appreciate that over people saying \"your math is wrong you n00b!\" with no constructive feedback.\n", "\n", "//---//\n", "\n", "Regarding output\n", "\n", "# What are the symbols?\n", "\n", "The whitespace symbol indicate if the tokenized item ends with whitespace ( the suffix \"banana\" => \"banana \" ) or not (the prefix \"post\" in \"post-apocalyptic \")\n", "\n", "For ease of reference , I call them prefix-tokens and suffix-tokens.\n", "\n", "Sidenote:\n", "\n", "Prefix tokens have the unique property in that they \"mutate\" suffix tokens\n", "\n", "Example: \"photo of a #prefix#-banana\"\n", "\n", "where #prefix# is a randomly selected prefix-token from the vocab.json\n", "\n", "The hyphen \"-\" exists to guarantee the tokenized text splits into the written #prefix# and #suffix# token respectively. The \"-\" hypen symbol can be replaced by any other special character of your choosing.\n", "\n", " Capital letters work too , e.g \"photo of a #prefix#Abanana\" since the capital letters A-Z are only listed once in the entire vocab.json.\n", "\n", "You can also choose to omit any separator and just rawdog it with the prompt \"photo of a #prefix#banana\" , however know that this may , on occasion , be tokenized as completely different tokens of lower ID:s.\n", "\n", "Curiously , common NSFW terms found online have in the CLIP model have been purposefully fragmented into separate #prefix# and #suffix# counterparts in the vocab.json. Likely for PR-reasons.\n", "\n", "You can verify the results using this online tokenizer: https://sd-tokenizer.rocker.boo/\n", "\n", "
\n", "\n", "\n", "\n", "
\n", "\n", "# What is that gibberish tokens that show up?\n", "\n", "The gibberish tokens like \"ðŁĺħ\\\" are actually emojis!\n", "\n", "Try writing some emojis in this online tokenizer to see the results: https://sd-tokenizer.rocker.boo/\n", "\n", "It is a bit borked as it can't process capital letters properly.\n", "\n", "Also note that this is not reversible.\n", "\n", "If tokenization \"😅\" => ðŁĺħ\n", "\n", "Then you can't prompt \"ðŁĺħ\" and expect to get the same result as the tokenized original emoji , \"😅\".\n", "\n", "SD 1.5 models actually have training for Emojis.\n", "\n", "But you have to set CLIP skip to 1 for this to work is intended.\n", "\n", "A tutorial on stuff you can do with the vocab.list concluded.\n", "\n", "Anyways, have fun with the notebook.\n", "\n", "There might be some updates in the future with features not mentioned here.\n", "\n", "//---//\n", "\n", "https://codeandlife.com/2023/01/26/mastering-the-huggingface-clip-model-how-to-extract-embeddings-and-calculate-similarity-for-text-and-images/\n", "\n", "https://arxiv.org/pdf/2303.03032" ], "metadata": { "id": "njeJx_nSSA8H" } }, { "cell_type": "code", "source": [ "\n", "# @title Create random names from firstname and lastnames\n", "import random\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", "\n", "def my_mkdirs(folder):\n", " if os.path.exists(folder)==False:\n", " os.makedirs(folder)\n", "\n", "\n", "my_mkdirs('/content/female_full_names/')\n", "filename = ''\n", "\n", "filename = '🆔👩_🦰 fusion-t2i-girl-firstname-1'\n", "%cd /content/text-to-image-prompts/names/firstnames/text\n", "with open(filename + '.json', 'r') as f:\n", " data = json.load(f)\n", "_df = pd.DataFrame({'count': data})['count']\n", "firstname = {\n", " key : value for key, value in _df.items()\n", "}\n", "\n", "NUM_FIRSTNAME = 100901\n", "\n", "\n", "NUM_FILES = 9\n", "for file_index in range(NUM_FILES + 1):\n", " if file_index <1: continue\n", " #if file_index >4: break\n", " filename = f'👱_♀️ fusion-t2i-lastnames-{file_index} plugin'\n", " #🦜 fusion-t2i-prompt-features-1.json\n", "\n", " # Read suffix.json\n", " %cd /content/text-to-image-prompts/names/lastnames/text\n", " with open(filename + '.json', 'r') as f:\n", " data = json.load(f)\n", " _df = pd.DataFrame({'count': data})['count']\n", " names = {\n", " key : firstname[f'{random.randint(2,NUM_FIRSTNAME)}'] + ' ' f'{value}' + ' ' for key, value in _df.items()\n", " }\n", "\n", " index = 0\n", "\n", " for key in names:\n", " index = index + 1\n", " #-----#\n", " RANGE = min(index,1000)\n", " output = {}\n", "\n", " for index in range(RANGE):\n", " if index >1000: break\n", " output[f'{index}'] = names[f'{index}']\n", " #-----#\n", " output[f'{1}'] = f'👱_♀️female_fullnames-{file_index}'\n", " output[f'{0}'] = f'{RANGE}'\n", " txt_filename = f'👱_♀️female_fullnames-{file_index}'\n", " %cd /content/female_full_names/\n", " with open(txt_filename + '.txt', 'w') as f:\n", " f.write(str(output))\n", "\n", " #files.download(f'fullnames-{file_index}.txt')\n", "\n", "#firstname[f'{random.randint(2,NUM_FIRSTNAME)}'] + f'{value}'\n", "\n", " #------#\n", "\n", "\n" ], "metadata": { "id": "JR0wl2ecj6RJ", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title Download the created text_encodings as .zip file\n", "%cd /content/\n", "!zip -r /content/female_full_names.zip /content/female_full_names/" ], "metadata": { "id": "IBenvYVrofil", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title ⚡+🖼️ -> 📝 Token-Sampling Image interrogator (work in progress)\n", "#-----#\n", "NUM_TOKENS = 49407\n", "import shelve\n", "db_vocab = shelve.open(VOCAB_FILENAME)\n", "print(f'using the tokens found in {VOCAB_FILENAME}.db as the vocab')\n", "# @markdown # What do you want to to mimic?\n", "use = '🖼️image_encoding from image' # @param ['📝text_encoding from prompt', '🖼️image_encoding from image']\n", "# @markdown --------------------------\n", "use_token_padding = True # param {type:\"boolean\"} <---- Enabled by default\n", "prompt = \"photo of a banana\" # @param {\"type\":\"string\",\"placeholder\":\"Write a prompt\"}\n", "#-----#\n", "prompt_A = prompt\n", "if(image_path != \"\") : image_A = cv2.imread(\"/content/sd_tokens/\" + image_path)\n", "#-----#\n", "\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", "#-----#\n", "if(use == '🖼️image_encoding from image'):\n", " # Get image features\n", " inputs = processor(images=image_A, return_tensors=\"pt\")\n", " image_features = model.get_image_features(**inputs)\n", " image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\n", " name_A = \"the image\"\n", "#-----#\n", "if(use == '📝text_encoding from prompt'):\n", " # Get text features\n", " inputs = tokenizer(text = prompt, padding=True, return_tensors=\"pt\")\n", " text_features_A = model.get_text_features(**inputs)\n", " name_A = prompt\n", "#-----#\n", "# @markdown # The output...\n", "must_start_with = \"\" # @param {\"type\":\"string\",\"placeholder\":\"write a text\"}\n", "must_contain = \"\" # @param {\"type\":\"string\",\"placeholder\":\"write a text\"}\n", "must_end_with = \"\" # @param {\"type\":\"string\",\"placeholder\":\"write a text\"}\n", "# @markdown -----\n", "# @markdown # Use a range of tokens from the vocab.json (slow method)\n", "start_search_at_index = 0 # @param {type:\"slider\", min:0, max: 49407, step:100}\n", "# @markdown The lower the start_index, the more similiar the sampled tokens will be to the target token assigned in the '⚡ Get similiar tokens' cell\". If the cell was not run, then it will use tokens ordered by similarity to the \"girl\\\" token\n", "start_search_at_ID = start_search_at_index\n", "search_range = 1000 # @param {type:\"slider\", min:100, max:49407, step:100}\n", "\n", "samples_per_iter = 10 # @param {type:\"slider\", min:10, max: 100, step:10}\n", "\n", "iterations = 5 # @param {type:\"slider\", min:1, max: 20, step:0}\n", "restrictions = 'None' # @param [\"None\", \"Suffix only\", \"Prefix only\"]\n", "#markdown Limit char size of included token <----- Disabled\n", "min_char_size = 0 #param {type:\"slider\", min:0, max: 20, step:1}\n", "char_range = 50 #param {type:\"slider\", min:0, max: 20, step:1}\n", "# markdown # ...or paste prompt items\n", "# markdown Format must be {item1|item2|...}. You can aquire prompt items using the Randomizer in the fusion gen: https://perchance.org/fusion-ai-image-generator\n", "_enable = False # param {\"type\":\"boolean\"}\n", "prompt_items = \"\" # param {\"type\":\"string\",\"placeholder\":\"{item1|item2|...}\"}\n", "#-----#\n", "#-----#\n", "START = start_search_at_ID\n", "RANGE = min(search_range , max(1,NUM_TOKENS - start_search_at_ID))\n", "#-----#\n", "import math, random\n", "NUM_PERMUTATIONS = 6\n", "ITERS = iterations\n", "#-----#\n", "#LOOP START\n", "#-----#\n", "# Check if original solution is best\n", "best_sim = 0\n", "name = must_start_with + must_contain + must_end_with\n", "ids = processor.tokenizer(text=name, padding=use_token_padding, return_tensors=\"pt\")\n", "text_features = model.get_text_features(**ids)\n", "text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", "#------#\n", "sim = 0\n", "if(use == '🖼️image_encoding from image'):\n", " logit_scale = model.logit_scale.exp()\n", " torch.matmul(text_features, image_features.t()) * logit_scale\n", " sim = torch.nn.functional.cosine_similarity(text_features, image_features) * logit_scale\n", "#-----#\n", "if(use == '📝text_encoding from prompt'):\n", " sim = torch.nn.functional.cosine_similarity(text_features, text_features_A)\n", "#-----#\n", "best_sim = sim\n", "best_name = name\n", "name_B = must_contain\n", "#------#\n", "results_sim = torch.zeros(ITERS*NUM_PERMUTATIONS)\n", "results_name_B = {}\n", "results_name = {}\n", "#-----#\n", "for iter in range(ITERS):\n", " dots = torch.zeros(min(list_size,RANGE))\n", " is_trail = torch.zeros(min(list_size,RANGE))\n", "\n", " #-----#\n", "\n", " for index in range(samples_per_iter):\n", " _start = START\n", " id_C = random.randint(_start , _start + RANGE)\n", " name_C = db_vocab[f'{id_C}']\n", " is_Prefix = 0\n", " #Skip if non-AZ characters are found\n", " #???\n", " #-----#\n", " # Decide if we should process prefix/suffix tokens\n", " if name_C.find('')<=-1:\n", " is_Prefix = 1\n", " if restrictions != \"Prefix only\":\n", " continue\n", " else:\n", " if restrictions == \"Prefix only\":\n", " continue\n", " #-----#\n", " # Decide if char-size is within range\n", " if len(name_C) < min_char_size:\n", " continue\n", " if len(name_C) > min_char_size + char_range:\n", " continue\n", " #-----#\n", " name_CB = must_start_with + name_C + name_B + must_end_with\n", " if is_Prefix>0:\n", " name_CB = must_start_with + ' ' + name_C + '-' + name_B + ' ' + must_end_with\n", " #-----#\n", " if(use == '🖼️image_encoding from image'):\n", " ids_CB = processor.tokenizer(text=name_CB, padding=use_token_padding, return_tensors=\"pt\")\n", " text_features = model.get_text_features(**ids_CB)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " logit_scale = model.logit_scale.exp()\n", " torch.matmul(text_features, image_features.t()) * logit_scale\n", " sim_CB = torch.nn.functional.cosine_similarity(text_features, image_features) * logit_scale\n", " #-----#\n", " if(use == '📝text_encoding from prompt'):\n", " ids_CB = processor.tokenizer(text=name_CB, padding=use_token_padding, return_tensors=\"pt\")\n", " text_features = model.get_text_features(**ids_CB)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " sim_CB = torch.nn.functional.cosine_similarity(text_features, text_features_A)\n", " #-----#\n", " #-----#\n", " if restrictions == \"Prefix only\":\n", " result = sim_CB\n", " result = result.item()\n", " dots[index] = result\n", " continue\n", " #-----#\n", " if(use == '🖼️image_encoding from image'):\n", " name_BC = must_start_with + name_B + name_C + must_end_with\n", " ids_BC = processor.tokenizer(text=name_BC, padding=use_token_padding, return_tensors=\"pt\")\n", " text_features = model.get_text_features(**ids_BC)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " logit_scale = model.logit_scale.exp()\n", " torch.matmul(text_features, image_features.t()) * logit_scale\n", " sim_BC = torch.nn.functional.cosine_similarity(text_features, image_features) * logit_scale\n", " #-----#\n", " if(use == '📝text_encoding from prompt'):\n", " name_BC = must_start_with + name_B + name_C + must_end_with\n", " ids_BC = processor.tokenizer(text=name_BC, padding=use_token_padding, return_tensors=\"pt\")\n", " text_features = model.get_text_features(**ids_BC)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " sim_BC = torch.nn.functional.cosine_similarity(text_features, text_features_A)\n", " #-----#\n", " result = sim_CB\n", " if(sim_BC > sim_CB):\n", " is_trail[index] = 1\n", " result = sim_BC\n", " #-----#\n", " #result = absolute_value(result.item())\n", " result = result.item()\n", " dots[index] = result\n", " #----#\n", " sorted, indices = torch.sort(dots,dim=0 , descending=True)\n", " # @markdown ----------\n", " # @markdown # Print options\n", " list_size = 100 # param {type:'number'}\n", " print_ID = False # @param {type:\"boolean\"}\n", " print_Similarity = True # @param {type:\"boolean\"}\n", " print_Name = True # @param {type:\"boolean\"}\n", " print_Divider = True # @param {type:\"boolean\"}\n", " print_Suggestions = False # @param {type:\"boolean\"}\n", " #----#\n", " if (print_Divider):\n", " print('//---//')\n", " #----#\n", " print('')\n", "\n", " used_reference = f'the text_encoding for {prompt_A}'\n", " if(use == '🖼️image_encoding from image'):\n", " used_reference = 'the image input'\n", " print(f'These token pairings within the range ID = {_start} to ID = {_start + RANGE} most closely match {used_reference}: ')\n", " print('')\n", " #----#\n", " aheads = \"{\"\n", " trails = \"{\"\n", " tmp = \"\"\n", " #----#\n", " max_sim_ahead = 0\n", " max_sim_trail = 0\n", " sim = 0\n", " max_name_ahead = ''\n", " max_name_trail = ''\n", " #----#\n", " for index in range(min(list_size,RANGE)):\n", " id = _start + indices[index].item()\n", " name = db_vocab[f'{id}']\n", " #-----#\n", " if (name.find('')<=-1):\n", " name = name + '-'\n", " if(is_trail[index]>0):\n", " trails = trails + name + \"|\"\n", " else:\n", " aheads = aheads + name + \"|\"\n", " #----#\n", " sim = sorted[index].item()\n", " #----#\n", " if(is_trail[index]>0):\n", " if sim>max_sim_trail:\n", " max_sim_trail = sim\n", " max_name_trail = name\n", " max_name_trail = max_name_trail.strip()\n", "\n", " else:\n", " if sim>max_sim_ahead:\n", " max_sim_ahead = sim\n", " max_name_ahead = name\n", " #------#\n", " trails = (trails + \"&&&&\").replace(\"|&&&&\", \"}\").replace(\"\", \" \").replace(\"{&&&&\", \"\")\n", " aheads = (aheads + \"&&&&\").replace(\"|&&&&\", \"}\").replace(\"\", \" \").replace(\"{&&&&\", \"\")\n", " #-----#\n", "\n", " if(print_Suggestions):\n", " print(f\"place these items ahead of prompt : {aheads}\")\n", " print(\"\")\n", " print(f\"place these items behind the prompt : {trails}\")\n", " print(\"\")\n", "\n", " tmp = must_start_with + ' ' + max_name_ahead + name_B + ' ' + must_end_with\n", " tmp = tmp.strip().replace('', ' ')\n", " print(f\"max_similarity_ahead = {round(max_sim_ahead,2)} % when using '{tmp}' \")\n", " print(\"\")\n", " tmp = must_start_with + ' ' + name_B + max_name_trail + ' ' + must_end_with\n", " tmp = tmp.strip().replace('', ' ')\n", " print(f\"max_similarity_trail = {round(max_sim_trail,2)} % when using '{tmp}' \")\n", " #-----#\n", " #STEP 2\n", " import random\n", " #-----#\n", " for index in range(NUM_PERMUTATIONS):\n", " name_inner = ''\n", " if index == 0 : name_inner = name_B\n", " if index == 1: name_inner = max_name_ahead\n", " if index == 2: name_inner = max_name_trail\n", " if index == 3: name_inner = name_B + max_name_trail\n", " if index == 4: name_inner = max_name_ahead + name_B\n", " if index == 5: name_inner = max_name_ahead + name_B + max_name_trail\n", " if name_inner == '': name_inner = max_name_ahead + name_B + max_name_trail\n", "\n", " name = must_start_with + name_inner + must_end_with\n", " #----#\n", " ids = processor.tokenizer(text=name, padding=use_token_padding, return_tensors=\"pt\")\n", " #----#\n", " sim = 0\n", " if(use == '🖼️image_encoding from image'):\n", " text_features = model.get_text_features(**ids)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " logit_scale = model.logit_scale.exp()\n", " torch.matmul(text_features, image_features.t()) * logit_scale\n", " sim = torch.nn.functional.cosine_similarity(text_features, image_features) * logit_scale\n", " #-----#\n", " if(use == '📝text_encoding from prompt'):\n", " text_features = model.get_text_features(**ids)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " sim = torch.nn.functional.cosine_similarity(text_features, text_features_A)\n", " #-----#\n", " results_name[iter*NUM_PERMUTATIONS + index] = name\n", " results_sim[iter*NUM_PERMUTATIONS + index] = sim\n", " results_name_B[iter*NUM_PERMUTATIONS + index] = name_inner.replace('',' ')\n", " #------#\n", " #name_B = results_name_B[iter*NUM_PERMUTATIONS + random.randint(0,3)]\n", " tmp = iter*NUM_PERMUTATIONS\n", " _name_B=''\n", " if results_sim[tmp+1]>results_sim[tmp+2]: _name_B = results_name_B[tmp + 3]\n", " if results_sim[tmp+2]>results_sim[tmp+1]: _name_B = results_name_B[tmp + 4]\n", "\n", " if _name_B != name_B:\n", " name_B=_name_B\n", " else:\n", " name_B = results_name_B[tmp + 5]\n", "\n", "#--------#\n", "print('')\n", "if(use == '🖼️image_encoding from image' and colab_image_path != \"\"):\n", " from google.colab.patches import cv2_imshow\n", " cv2_imshow(image_A)\n", "#-----#\n", "print('')\n", "sorted, indices = torch.sort(results_sim,dim=0 , descending=True)\n", "\n", "for index in range(ITERS*NUM_PERMUTATIONS):\n", " name_inner = results_name[indices[index].item()]\n", " print(must_start_with + name_inner + must_end_with)\n", " print(f'similiarity = {round(sorted[index].item(),2)} %')\n", " print('------')\n", "#------#\n", "db_vocab.close() #close the file" ], "metadata": { "collapsed": true, "id": "fi0jRruI0-tu", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title ⚡ Get similiar tokens (not updated yet)\n", "import torch\n", "from transformers import AutoTokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n", "\n", "# @markdown Write name of token to match against\n", "token_name = \"banana \" # @param {type:'string',\"placeholder\":\"leave empty for random value token\"}\n", "\n", "prompt = token_name\n", "# @markdown (optional) Mix the token with something else\n", "mix_with = \"\" # @param {\"type\":\"string\",\"placeholder\":\"leave empty for random value token\"}\n", "mix_method = \"None\" # @param [\"None\" , \"Average\", \"Subtract\"] {allow-input: true}\n", "w = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n", "# @markdown Limit char size of included token\n", "\n", "min_char_size = 0 # param {type:\"slider\", min:0, max: 50, step:1}\n", "char_range = 50 # param {type:\"slider\", min:0, max: 50, step:1}\n", "\n", "tokenizer_output = tokenizer(text = prompt)\n", "input_ids = tokenizer_output['input_ids']\n", "id_A = input_ids[1]\n", "A = torch.tensor(token[id_A])\n", "A = A/A.norm(p=2, dim=-1, keepdim=True)\n", "#-----#\n", "tokenizer_output = tokenizer(text = mix_with)\n", "input_ids = tokenizer_output['input_ids']\n", "id_C = input_ids[1]\n", "C = torch.tensor(token[id_C])\n", "C = C/C.norm(p=2, dim=-1, keepdim=True)\n", "#-----#\n", "sim_AC = torch.dot(A,C)\n", "#-----#\n", "print(input_ids)\n", "#-----#\n", "\n", "#if no imput exists we just randomize the entire thing\n", "if (prompt == \"\"):\n", " id_A = -1\n", " print(\"Tokenized prompt tensor A is a random valued tensor with no ID\")\n", " R = torch.rand(A.shape)\n", " R = R/R.norm(p=2, dim=-1, keepdim=True)\n", " A = R\n", " name_A = 'random_A'\n", "\n", "#if no imput exists we just randomize the entire thing\n", "if (mix_with == \"\"):\n", " id_C = -1\n", " print(\"Tokenized prompt 'mix_with' tensor C is a random valued tensor with no ID\")\n", " R = torch.rand(A.shape)\n", " R = R/R.norm(p=2, dim=-1, keepdim=True)\n", " C = R\n", " name_C = 'random_C'\n", "\n", "name_A = \"A of random type\"\n", "if (id_A>-1):\n", " name_A = vocab(id_A)\n", "\n", "name_C = \"token C of random type\"\n", "if (id_C>-1):\n", " name_C = vocab(id_C)\n", "\n", "print(f\"The similarity between A '{name_A}' and C '{name_C}' is {round(sim_AC.item()*100,2)} %\")\n", "\n", "if (mix_method == \"None\"):\n", " print(\"No operation\")\n", "\n", "if (mix_method == \"Average\"):\n", " A = w*A + (1-w)*C\n", " _A = LA.vector_norm(A, ord=2)\n", " print(f\"Tokenized prompt tensor A '{name_A}' token has been recalculated as A = w*A + (1-w)*C , where C is '{name_C}' token , for w = {w} \")\n", "\n", "if (mix_method == \"Subtract\"):\n", " tmp = w*A - (1-w)*C\n", " tmp = tmp/tmp.norm(p=2, dim=-1, keepdim=True)\n", " A = tmp\n", " #//---//\n", " print(f\"Tokenized prompt tensor A '{name_A}' token has been recalculated as A = _A*norm(w*A - (1-w)*C) , where C is '{name_C}' token , for w = {w} \")\n", "\n", "#OPTIONAL : Add/subtract + normalize above result with another token. Leave field empty to get a random value tensor\n", "\n", "dots = torch.zeros(NUM_TOKENS)\n", "for index in range(NUM_TOKENS):\n", " id_B = index\n", " B = torch.tensor(token[id_B])\n", " B = B/B.norm(p=2, dim=-1, keepdim=True)\n", " sim_AB = torch.dot(A,B)\n", " dots[index] = sim_AB\n", "\n", "\n", "sorted, indices = torch.sort(dots,dim=0 , descending=True)\n", "#----#\n", "if (mix_method == \"Average\"):\n", " print(f'Calculated all cosine-similarities between the average of token {name_A} and {name_C} with Id_A = {id_A} and mixed Id_C = {id_C} as a 1x{sorted.shape[0]} tensor')\n", "if (mix_method == \"Subtract\"):\n", " print(f'Calculated all cosine-similarities between the subtract of token {name_A} and {name_C} with Id_A = {id_A} and mixed Id_C = {id_C} as a 1x{sorted.shape[0]} tensor')\n", "if (mix_method == \"None\"):\n", " print(f'Calculated all cosine-similarities between the token {name_A} with Id_A = {id_A} with the the rest of the {NUM_TOKENS} tokens as a 1x{sorted.shape[0]} tensor')\n", "\n", "#Produce a list id IDs that are most similiar to the prompt ID at positiion 1 based on above result\n", "\n", "# @markdown Set print options\n", "list_size = 100 # @param {type:'number'}\n", "print_ID = False # @param {type:\"boolean\"}\n", "print_Similarity = True # @param {type:\"boolean\"}\n", "print_Name = True # @param {type:\"boolean\"}\n", "print_Divider = True # @param {type:\"boolean\"}\n", "\n", "\n", "if (print_Divider):\n", " print('//---//')\n", "\n", "print('')\n", "print('Here is the result : ')\n", "print('')\n", "\n", "for index in range(list_size):\n", " id = indices[index].item()\n", " if (print_Name):\n", " print(f'{vocab(id)}') # vocab item\n", " if (print_ID):\n", " print(f'ID = {id}') # IDs\n", " if (print_Similarity):\n", " print(f'similiarity = {round(sorted[index].item()*100,2)} %')\n", " if (print_Divider):\n", " print('--------')\n", "\n", "#Print the sorted list from above result\n", "\n", "#The prompt will be enclosed with the <|start-of-text|> and <|end-of-text|> tokens, which is why output will be [49406, ... , 49407].\n", "\n", "#You can leave the 'prompt' field empty to get a random value tensor. Since the tensor is random value, it will not correspond to any tensor in the vocab.json list , and this it will have no ID.\n", "\n", "# Save results as .db file\n", "import shelve\n", "VOCAB_FILENAME = 'tokens_most_similiar_to_' + name_A.replace('','').strip()\n", "d = shelve.open(VOCAB_FILENAME)\n", "#NUM TOKENS == 49407\n", "for index in range(NUM_TOKENS):\n", " #print(d[f'{index}']) #<-----Use this to read values from the .db file\n", " d[f'{index}']= vocab(indices[index].item()) #<---- write values to .db file\n", "#----#\n", "d.close() #close the file\n", "# See this link for additional stuff to do with shelve: https://docs.python.org/3/library/shelve.html" ], "metadata": { "id": "iWeFnT1gAx6A", "cellView": "form" }, "execution_count": null, "outputs": [] } ] }