Noodle_Soup_Prompts_Prompt_Terminology_Generator_v3_0.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/WASasquatch/noodle-soup-prompts/blob/main/Noodle_Soup_Prompts_Prompt_Terminology_Generator_v3_0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <img 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\"> <font size=\"6\">**Noodle Soup Prompts** v3.0</font> <font size=\"5\">- Prompt Terminology Generator</font> ![visitors](https://visitor-badge.glitch.me/badge?page_id=Noodle-Soup-Prompts&left_color=blue&right_color=orange) <img 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\">\n",
        "\n",
        "This tool contains a *growing* database of terminology to help build interesting prompts for [Disco Diffusion](https://discodiffusion.com/). **Give it a try!**\n",
        "\n",
        "Artist names gathered by **MisterRuffian** <font size=\"2\">(<font color=\"7e87f6\">Discord</font> Misterruffian#2891)</font> on his amazing [Latent Artist & Modifier Encyclopedia](https://docs.google.com/spreadsheets/d/1_jgQ9SyvUaBNP1mHHEzZ6HhL_Es1KwBKQtnpnmWW82I/).\n",
        "\n",
        "Terminology Database created by **WAS**asquatch <font size=\"2\">(<font color=\"7e87f6\">Discord</font>: WAS\\#0263)</font>\n",
        "\n",
        "\n",
        "## Other Tools\n",
        "\n",
        "- [Easy Diffusion](https://rebrand.ly/easy-diffusion) - A fork of [NOP's Stable Diffusion notebook](https://colab.research.google.com/drive/1jUwJ0owjigpG-9m6AI_wEStwimisUE17#scrollTo=_ekR-LW6trWG), packed full of features. \n",
        "- [DiscoStream](https://rebrand.ly/discostream) - A new fork of Disco Diffusion designed to be a little easier to use, with advanced functionality. It also includes GO BIG v2!\n",
        "- [OpenAI Guided Diffusion - Model Training / Fine-Tuning](https://rebrand.ly/guided-diffusion) - A implementation of OpenAI's Guided Diffusion on Google Colabs\n",
        "- [Portrait Playground](https://rebrand.ly/portrait-playground) - A version of Disco Diffusion with all current Portrait Generator Models.\n",
        "- [Portrait Generator `init_image` Prep](https://rebrand.ly/dd-image-prep) - Prep portraits or other `init_images` as approximations for better DD improvisation.  \n"
      ],
      "metadata": {
        "id": "kLh-sLUHfdrU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Process Text Prompts"
      ],
      "metadata": {
        "id": "zUhk7BO2n1Yk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from genericpath import exists\n",
        "#@markdown ## [Terminology Noodle Wiki](https://github.com/WASasquatch/noodle-soup-prompts/wiki/Terminology-Reference)\n",
        "#@title <font size=\"\">3) Cook up some Noodle Soup</font>\n",
        "#@markdown *Run this cell to return your processed prompt*<br>\n",
        "#@markdown Use `|` to deliminate a new list element *(only valid for Disco Diffusion / DiscoStream)*\n",
        "import random, subprocess, time, json\n",
        "\n",
        "class log:\n",
        "    f = lambda color: lambda string: print(color + string + \"\\33[0m\")\n",
        "    black = f(\"\\33[30m\")\n",
        "    red = f(\"\\33[31m\")\n",
        "    green = f(\"\\33[32m\")\n",
        "    yellow = f(\"\\33[33m\")\n",
        "    blue = f(\"\\33[34m\")\n",
        "    megenta = f(\"\\33[35m\")\n",
        "    cyan = f(\"\\33[36m\")\n",
        "    white = f(\"\\33[37m\")\n",
        "\n",
        "try:\n",
        "    import nsp_pantry\n",
        "    from nsp_pantry import nsp_parse\n",
        "except ImportError:\n",
        "    print(subprocess.run(['wget', '-q', '--show-progress', '--no-cache', '--backups=1', 'https://raw.githubusercontent.com/WASasquatch/noodle-soup-prompts/main/nsp_pantry.py'], stdout=subprocess.PIPE).stdout.decode('utf-8'))\n",
        "finally:\n",
        "    import nsp_pantry\n",
        "    from nsp_pantry import nsp_parse\n",
        "\n",
        "#@markdown ---\n",
        "prompt = 'Portrait of a _adj-beauty_ _noun-emote_ _body-fit_ _nationality_ woman from _pop-culture_ in _pop-location_  by _artist_, _site_ | Intricate Detail, Elaborate Desings, HD, 8k resolution' #@param{type: 'string'}\n",
        "bowls_of_soup = 5 #@param {type: 'number'}\n",
        "#@markdown &nbsp;&nbsp;&nbsp;&nbsp;**bowls_of_soup:** *How many prompt iterations should we run?*\n",
        "output_mode = 'Stable Diffusion' #@param['Stable Diffusion', 'Disco Diffusion / DiscoStream']\n",
        "save_text_file = True #@param{type: 'boolean'}\n",
        "\n",
        "text_file_list = []\n",
        "\n",
        "displayPrint = True\n",
        "if output_mode == 'Disco Diffusion / DiscoStream':\n",
        "    prompt = [x.strip() for x in prompt.split('|')]\n",
        "    if type(prompt) is list:\n",
        "        displayPrint = False\n",
        "        text_file_list = {}\n",
        "\n",
        "log.green(\"TEXT PROMPTS CODE:\")\n",
        "for i in range(1, bowls_of_soup+1):\n",
        "    log.blue(\"\\n\\033[1mTEXT PROMPT \"+str(i)+\":\\033[0m\")\n",
        "    if displayPrint:\n",
        "        pprompt = nsp_parse(prompt)\n",
        "        if save_text_file:\n",
        "            text_file_list.append(pprompt)\n",
        "        print(pprompt)\n",
        "    else:\n",
        "        prompt = nsp_parse(prompt)\n",
        "        if save_text_file:\n",
        "            text_file_list[i] = prompt\n",
        "        print('[')\n",
        "        for i in range(len(prompt)):\n",
        "            deliminator = ',' if i != len(prompt)-1 else ''\n",
        "            print(f'    \\'{prompt[i]}\\'{deliminator}')\n",
        "        print(']')\n",
        "\n",
        "\n",
        "if save_text_file:\n",
        "    if displayPrint:\n",
        "        with open(f'prompts_{str(int(time.time()))}.txt', 'w') as f:\n",
        "            for p in text_file_list:\n",
        "                f.write(p)\n",
        "                f.write('\\n')\n",
        "    else:\n",
        "        with open(f'prompts_{str(int(time.time()))}.txt', 'w') as f:\n",
        "            f.write(json.dumps(text_file_list, indent=4))\n"
      ],
      "metadata": {
        "id": "jxcwZwnZ4UxR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26b93fb4-6c7f-4c50-fc80-ab0d0c4a6b5e",
        "cellView": "form"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32mTEXT PROMPTS CODE:\u001b[0m\n",
            "\u001b[34m\n",
            "\u001b[1mTEXT PROMPT 1:\u001b[0m\u001b[0m\n",
            "Portrait of a appealing Bored sound Papua New Guinean woman from Code Geass in Vancouver, Canada  by Bertil Nilsson, trending on Unsplash | Intricate Detail, Elaborate Desings, HD, 8k resolution\n",
            "\u001b[34m\n",
            "\u001b[1mTEXT PROMPT 2:\u001b[0m\u001b[0m\n",
            "Portrait of a good-looking Optimistic sinewy Icelandic woman from WarCraft in Machu Picchu, Peru  by Miriam Schapiro, trending on Behance | Intricate Detail, Elaborate Desings, HD, 8k resolution\n",
            "\u001b[34m\n",
            "\u001b[1mTEXT PROMPT 3:\u001b[0m\u001b[0m\n",
            "Portrait of a radiant Disgusted developed Nepalese woman from Pulp Fiction in Tokyo, Japan  by Anton Corbijn, trending on Saatchi Art | Intricate Detail, Elaborate Desings, HD, 8k resolution\n",
            "\u001b[34m\n",
            "\u001b[1mTEXT PROMPT 4:\u001b[0m\u001b[0m\n",
            "Portrait of a alluring Optimistic strong British Virgin Islander woman from The Simpsons in Matterhorn, Switzerland  by Donato Giancola, trending on Flickr | Intricate Detail, Elaborate Desings, HD, 8k resolution\n",
            "\u001b[34m\n",
            "\u001b[1mTEXT PROMPT 5:\u001b[0m\u001b[0m\n",
            "Portrait of a studly Indifferent Herculean Guyanese woman from Earthworm Jim in Hong Kong  by Rockwell Kent, trending on Sketchfab | Intricate Detail, Elaborate Desings, HD, 8k resolution\n"
          ]
        }
      ]
    }
  ]
}