DiscoStream.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/WASasquatch/discostream/blob/main/DiscoStream.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TitleTop"
      },
      "source": [
        "# <font size=\"6\">**Disco**Stream v1.2 ![visitors](https://visitor-badge.glitch.me/badge?page_id=DiscoStream&left_color=blue&right_color=orange) [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/WASasquatch/disco-diffusion-portrait-playground/)</font>\n",
        "\n",
        "<hr>\n",
        "\n",
        "#### This Colab is maintained by **WAS**asquatch <font size=\"2\">(<font color=\"7e87f6\">Discord</font>: WAS\\#0263) \n",
        "\n",
        "If you'd like to help support the project and my time, *as well as the compute points required :(*, feel free to buy me some bandwidth (I live rural and pay for bandwidth): https://paypal.me/ThompsonJordan\n",
        "\n",
        "## **Other Tools:**\n",
        "\n",
        "- [Pinegraph | You are a creator](https://pinegraph.com/) - Stable and Disco Diffusion under one sleek web app\n",
        "- [Stability.AI Easy Diffusion](https://rebrand.ly/easy-diffusion) - WAS's Stable Diffusion notebook based on NOP's original notebook\n",
        "- [WAS's Disco Diffusion v5.6-8 - Portrait Playground](https://rebrand.ly/portrait-playground) - A fork of Disco Diffusion that additionally, DiscoStream is forked from.\n",
        "- [OpenAI Guided Diffusion - Model Training / Fine-Tuning](https://rebrand.ly/guided-diffusion) - A implementation of OpenAI's Guided Diffusion in Google Colabs\n",
        "- [Noodle Soup Prompts](https://rebrand.ly/noodle-soup-prompts) - Terminology Database for prompt exploration\n",
        "- [Portrait Generator `init_image` Prep](https://rebrand.ly/dd-image-prep) - Prep portraits other other `init_images` as approximations for better DD improvisation. \n",
        "\n",
        "<br>\n",
        "\n",
        "-----\n",
        "\n",
        "<br> \n",
        "\n",
        "### DiscoStream is based on Disco Diffusion\n",
        "- http://discodiffusion.com/\n",
        "- https://github.com/alembics/disco-diffusion\n",
        "- [LICENSES](https://github.com/WASasquatch/discostream/blob/main/LICENSE)\n",
        "- For issues, join the [Disco Diffusion Discord](https://discord.gg/msEZBy4HxA) and contact **WAS**asquatch <font size=\"2\">(<font color=\"7e87f6\">Discord</font>: WAS\\#0263)</font>. \n",
        "- Please do not contact somnai or gandamu regarding DiscoStream. DiscoStream is a fork of Disco Diffusion.\n",
        "\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **DiscoStream Changelog:**\n",
        "- 8/12/22) Ported WAS's Disco Diffusion 5.6-8 - Portrait Playground\n",
        "- 8/12/22) Patched cutn scheduling\n",
        "- 8/12/22) Go BIG V2 Optional Installation\n",
        "- 8/14/22) Patched NSP installation for local use\n",
        "- 8/14/22) Fixed AdaBins installation falling after disco_xform_utils\n",
        "- 8/14/22) Better installation logic and ordering\n",
        "- 8/14/22) NSP runtime prompts option added\n",
        "- 8/15/22) NSP runtime go big support\n",
        "- 8/17/22) Patched cutn scheduling\n",
        "- 8/17/22) Patched go big scaling without maximize on\n",
        "- 8/19/22) Patch settings loading\n",
        "- 8/21/22) Added [DD Image Prep](https://rebrand.ly/dd-image-prep) to DiscoStream\n",
        "- 8/21/22) Added list functionality to `init_images`. You can now specify a init image for each `n_batch` pass. \n",
        "- 8/21/22) Added author and copyright to metadata\n",
        "- 8/22/22) Added Architecture Diffusion 1.5m Model by jerostephan\n",
        "- 8/30/22) Patched animation print function\n",
        "- 9/15/22) Patched NSP Installation\n",
        "- 9/26/22) Patched DD Image Prep functionlaity\n",
        "- 9/30/22) Patch `open_clip` installation"
      ],
      "metadata": {
        "id": "XODaHHGuM0hf"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SetupTop"
      },
      "source": [
        "# 1. Set Up"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CheckGPU",
        "cellView": "form"
      },
      "source": [
        "#@title 1.1 Check GPU Status\n",
        "\n",
        "# Import future print\n",
        "from __future__ import print_function\n",
        "try:\n",
        "    import __builtin__\n",
        "except ImportError:\n",
        "    import builtins as __builtin__\n",
        "\n",
        "# Emoticon fun!\n",
        "import subprocess\n",
        "try:\n",
        "    import emoji\n",
        "except ImportError:\n",
        "     multipip_res = subprocess.run(['pip', '-q', 'install', 'emoji'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "finally:\n",
        "    import emoji\n",
        "\n",
        "# Override Print Function\n",
        "def print(message, *args, **kwargs):\n",
        "    if 'defaultprint' in kwargs:\n",
        "        kwargs.pop('defaultprint')\n",
        "        return __builtin__.print(message, *args, **kwargs)\n",
        "    else:\n",
        "        return __builtin__.print(emoji.emojize(message), *args, **kwargs)\n",
        "\n",
        "import subprocess, sys\n",
        "\n",
        "simple_nvidia_smi_display = False #@param {type:\"boolean\"}\n",
        "nvidiasmi_simple = subprocess.run(['nvidia-smi', '-L'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "if nvidiasmi_simple.find('NVIDIA-SMI has failed') != -1:\n",
        "    import warnings\n",
        "    warnings.filterwarnings(\"ignore\")\n",
        "    sys.exit('NVIDIA-SMI has failed! No GPU is allocated to this runtime!')\n",
        "if simple_nvidia_smi_display:\n",
        "    print(nvidiasmi_simple, defaultprint=True)\n",
        "else:\n",
        "    nvidiasmi_output = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    nvidiasmi_ecc_note = subprocess.run(['nvidia-smi', '-i', '0', '-e', '0'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    print(nvidiasmi_output, defaultprint=True)\n",
        "    print(nvidiasmi_ecc_note, defaultprint=True)\n",
        "\n",
        "gpu_name = nvidiasmi_simple.split(':')[1].split('(')[0].strip()\n",
        "\n",
        "gpu_warning = None\n",
        "if 'Tesla T4' or 'Tesla P100' in gpu_name:\n",
        "  gpu_warning = \":warning: \\33[33m\\u001b[4mWARNING:\\33[0m \\033[1mYou are using a limited VRAM GPU (\\33[33m\"+gpu_name+\"\\33[0m\\033[1m). \\n         Be cautious when using resolutions above 720p, or heavier models such ViTL14+, RN50x4+, etc.\\33[0m\"\n",
        "  print(gpu_warning)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PrepFolders",
        "cellView": "form"
      },
      "source": [
        "#@title 1.2 Setup DiscoStream Environment\n",
        "\n",
        "# Import future print\n",
        "from __future__ import print_function\n",
        "try:\n",
        "    import __builtin__\n",
        "except ImportError:\n",
        "    import builtins as __builtin__\n",
        "\n",
        "# Emoticon fun!\n",
        "try:\n",
        "    import subprocess\n",
        "    import emoji\n",
        "except ImportError:\n",
        "     multipip_res = subprocess.run(['pip', '-q', 'install', 'emoji'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "finally:\n",
        "    import emoji\n",
        "\n",
        "# Override Print Function\n",
        "def print(message, *args, **kwargs):\n",
        "    if 'defaultprint' in kwargs:\n",
        "        kwargs.pop('defaultprint')\n",
        "        return __builtin__.print(message, *args, **kwargs)\n",
        "    else:\n",
        "        return __builtin__.print(emoji.emojize(message), *args, **kwargs)\n",
        "\n",
        "LINE_UP = '\\033[1A'\n",
        "LINE_CLEAR = '\\033[K'\n",
        "\n",
        "import subprocess, os, sys, ipykernel\n",
        "\n",
        "appname = 'DiscoStream'\n",
        "\n",
        "def gitclone(url, targetdir=None):\n",
        "    if targetdir:\n",
        "        res = subprocess.run(['git', 'clone', '--quiet', url, targetdir], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    else:\n",
        "        res = subprocess.run(['git', 'clone', '--quiet', url], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    print(res, defaultprint=True)\n",
        "\n",
        "def pipi(modulestr):\n",
        "    res = subprocess.run(['pip', 'install', modulestr + ' --quiet'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    print(res, defaultprint=True)\n",
        "\n",
        "def pipie(modulestr):\n",
        "    res = subprocess.run(['git', 'install', '-e', modulestr + ' --quiet'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    print(res, defaultprint=True)\n",
        "\n",
        "def wget(url, outputdir):\n",
        "    res = subprocess.run(['wget', '-q', '--show-progress', url, '-P', f'{outputdir}'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    print(res, defaultprint=True)\n",
        "\n",
        "\n",
        "try:\n",
        "    from google.colab import drive\n",
        "    print(\"Google Colab detected.\")\n",
        "    is_colab = True\n",
        "    #@markdown If you connect your Google Drive, you can save the final image of each run on your drive.\n",
        "    google_drive = True #@param {type:\"boolean\"}\n",
        "    #@markdown Click here if you'd like to save the diffusion model checkpoint file to (and/or load from) your Google Drive:\n",
        "    save_models_to_google_drive = True #@param {type:\"boolean\"}\n",
        "except:\n",
        "    is_colab = False\n",
        "    google_drive = False\n",
        "    save_models_to_google_drive = False\n",
        "    print(\"Google Colab not detected.\")\n",
        "\n",
        "if is_colab:\n",
        "    if google_drive is True:\n",
        "        print(\"\\n:hourglass_not_done: Mounting Google Drive...\")\n",
        "        drive.mount('/content/drive')\n",
        "        root_path = '/content/drive/MyDrive/AI/Disco_Diffusion'\n",
        "    else:\n",
        "        root_path = '/content'\n",
        "else:\n",
        "    root_path = os.getcwd()\n",
        "\n",
        "import os\n",
        "  \n",
        "def createPath(filepath):\n",
        "    os.makedirs(filepath, exist_ok=True)\n",
        "\n",
        "initDirPath = f'{root_path}/init_images'\n",
        "createPath(initDirPath)\n",
        "outDirPath = f'{root_path}/images_out'\n",
        "createPath(outDirPath)\n",
        "\n",
        "if is_colab:\n",
        "    if google_drive and not save_models_to_google_drive or not google_drive:\n",
        "        model_path = '/content/models'\n",
        "        createPath(model_path)\n",
        "    if google_drive and save_models_to_google_drive:\n",
        "        model_path = f'{root_path}/models'\n",
        "        createPath(model_path)\n",
        "else:\n",
        "    model_path = f'{root_path}/models'\n",
        "    createPath(model_path)\n",
        "\n",
        "# libraries = f'{root_path}/libraries'\n",
        "# createPath(libraries)\n",
        "\n",
        "# INSTALL DEPENDENCIES\n",
        "\n",
        "print(f\"\\n--[ Installing {appname} Dependencies ]--\\n\")\n",
        "\n",
        "import pathlib, shutil, sys\n",
        "\n",
        "# There are some reports that with a T4 or V100 on Colab, downgrading to a previous version of PyTorch may be necessary.\n",
        "# .. but there are also reports that downgrading breaks them!  If you're facing issues, you may want to try uncommenting and running this code.\n",
        "# nvidiasmi_output = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "# cards_requiring_downgrade = [\"Tesla T4\", \"V100\"]\n",
        "# if is_colab:\n",
        "#     if any(cardstr in nvidiasmi_output for cardstr in cards_requiring_downgrade):\n",
        "#         print(\"Downgrading pytorch. This can take a couple minutes ...\")\n",
        "#         downgrade_pytorch_result = subprocess.run(['pip', 'install', 'torch==1.10.2', 'torchvision==0.11.3', '-q'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "#         print(\"pytorch downgraded.\")\n",
        "\n",
        "#@markdown Check this if you want to use CPU\n",
        "useCPU = False #@param {type:\"boolean\"}\n",
        "\n",
        "if not is_colab:\n",
        "    # If running locally, there's a good chance your env will need this in order to not crash upon np.matmul() or similar operations.\n",
        "    os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'\n",
        "\n",
        "PROJECT_DIR = os.path.abspath(os.getcwd())\n",
        "USE_ADABINS = True\n",
        "\n",
        "if is_colab:\n",
        "    if not google_drive:\n",
        "        root_path = f'/content'\n",
        "        model_path = '/content/models' \n",
        "else:\n",
        "    root_path = os.getcwd()\n",
        "    model_path = f'{root_path}/models'\n",
        "\n",
        "try:\n",
        "    print(\":hourglass_not_done: Downgrading ipwidgets...\\n\")\n",
        "    ipwidgets_res = subprocess.run(['pip', 'install', 'ipywidgets==7.7.2'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "except subprocess.CalledProcessError as e:\n",
        "    print(\"Unable to downgrade ipwidgets!\\n\", e.output)\n",
        "finally:\n",
        "    if 'installed ipywidgets-7.7.2' in ipwidgets_res:\n",
        "        from google.colab import output\n",
        "        output.enable_custom_widget_manager()\n",
        "        print(\":check_mark_button: \\33[32mInstalled ipwidgets 7 successfully!\\33[0m\\n\")\n",
        "\n",
        "try:\n",
        "    print(\":hourglass_not_done: Installing OS Dependencies...\")\n",
        "    multipip_res = subprocess.run(['pip', '-q', 'install', 'lpips', 'datetime', 'timm', 'ftfy', 'einops', 'pytorch-lightning', 'omegaconf'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    print(multipip_res, defaultprint=True)\n",
        "except BaseException as e:\n",
        "    raise (\"\\r\\rUnable to download OS dependenceis!\\n\").format(e)\n",
        "finally:\n",
        "    print(\"\\r\\r:check_mark_button: \\33[32mInstalled OS dependencies successfully.\\33[0m\\n\")\n",
        "\n",
        "if is_colab:\n",
        "    print(\":hourglass_not_done: Installing ImageMagick...\")\n",
        "    imagemagick_res = subprocess.run(['apt', 'install -qq', 'imagemagick'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    print(imagemagick_res, defaultprint=True)\n",
        "\n",
        "try:\n",
        "    print(\":hourglass_not_done: Installing CLIP...\")\n",
        "    from CLIP import clip\n",
        "except:\n",
        "    if not os.path.exists(\"CLIP\"):\n",
        "        gitclone(\"https://github.com/openai/CLIP\")\n",
        "    sys.path.append(f'{PROJECT_DIR}/CLIP')\n",
        "finally:\n",
        "    from CLIP import clip\n",
        "    if 'CLIP' in sys.modules:\n",
        "        print(\":check_mark_button: \\33[32mInstalled CLIP successfully.\\33[0m\\n\")\n",
        "\n",
        "!pip install open_clip_torch\n",
        "import open_clip\n",
        "\n",
        "try:\n",
        "    print(\":hourglass_not_done: Installing Guided Diffusion...\")\n",
        "    from guided_diffusion.script_util import create_model_and_diffusion\n",
        "except:\n",
        "    if not os.path.exists(\"guided-diffusion\"):\n",
        "        gitclone(\"https://github.com/kostarion/guided-diffusion\")\n",
        "    sys.path.append(f'{PROJECT_DIR}/guided-diffusion')\n",
        "finally:\n",
        "    from guided_diffusion.script_util import create_model_and_diffusion\n",
        "    if 'guided_diffusion.script_util' in sys.modules:\n",
        "        print(\":check_mark_button: \\33[32mInstalled Guided Diffusion successfully.\\33[0m\\n\")\n",
        "\n",
        "try:\n",
        "    print(\":hourglass_not_done: Installing resize_right...\")\n",
        "    from resize_right import resize\n",
        "except:\n",
        "    if not os.path.exists(\"ResizeRight\"):\n",
        "        gitclone(\"https://github.com/assafshocher/ResizeRight.git\")\n",
        "    sys.path.append(f'{PROJECT_DIR}/ResizeRight')\n",
        "finally:\n",
        "    from resize_right import resize\n",
        "    if 'resize_right' in sys.modules:\n",
        "        print(\":check_mark_button: \\33[32mInstalled resize_right successfully.\\33[0m\\n\")\n",
        "\n",
        "try:\n",
        "    print(\":hourglass_not_done: Installing PyTorch3d Lite...\")\n",
        "    import py3d_tools as p3dT\n",
        "except:\n",
        "    if not os.path.exists('pytorch3d-lite'):\n",
        "        gitclone(\"https://github.com/MSFTserver/pytorch3d-lite.git\")\n",
        "    sys.path.append(f'{PROJECT_DIR}/pytorch3d-lite')\n",
        "finally:\n",
        "    import py3d_tools as p3dT\n",
        "    if 'py3d_tools' in sys.modules:\n",
        "        print(\":check_mark_button: \\33[32mInstalled PyTorch3d Lite successfully.\\33[0m\\n\")\n",
        "\n",
        "try:\n",
        "    print(\":hourglass_not_done: Installing Midas Dependenceis...\")\n",
        "    from midas.dpt_depth import DPTDepthModel\n",
        "except:\n",
        "    if not os.path.exists('MiDaS'):\n",
        "        gitclone(\"https://github.com/isl-org/MiDaS.git\")\n",
        "    if not os.path.exists('MiDaS/midas_utils.py'):\n",
        "        shutil.move('MiDaS/utils.py', 'MiDaS/midas_utils.py')\n",
        "    if not os.path.exists(f'{model_path}/dpt_large-midas-2f21e586.pt'):\n",
        "        wget(\"https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt\", model_path)\n",
        "finally:\n",
        "    sys.path.append(f'{PROJECT_DIR}/MiDaS')\n",
        "    from midas.dpt_depth import DPTDepthModel\n",
        "    if 'midas.dpt_depth' in sys.modules:\n",
        "        print(\":check_mark_button: \\33[32mMidas Dependenceis installed.\\33[0m\\n\")\n",
        "\n",
        "# AdaBins stuff\n",
        "if USE_ADABINS:\n",
        "    try:\n",
        "        print(\":hourglass_not_done: Installing AdaBins...\")\n",
        "        from infer import InferenceHelper\n",
        "    except:\n",
        "        if not os.path.exists(\"AdaBins\"):\n",
        "            gitclone(\"https://github.com/shariqfarooq123/AdaBins.git\")\n",
        "        if not os.path.exists(f'{PROJECT_DIR}/pretrained/AdaBins_nyu.pt'):\n",
        "            createPath(f'{PROJECT_DIR}/pretrained')\n",
        "            wget(\"https://cloudflare-ipfs.com/ipfs/Qmd2mMnDLWePKmgfS8m6ntAg4nhV5VkUyAydYBp8cWWeB7/AdaBins_nyu.pt\", f'{PROJECT_DIR}/pretrained')\n",
        "        sys.path.append(f'{PROJECT_DIR}/AdaBins')\n",
        "    finally:\n",
        "        from infer import InferenceHelper\n",
        "        if 'infer' in sys.modules:\n",
        "            print(\":check_mark_button: \\33[32mInstalled AdaBins successfully.\\33[0m\\n\")\n",
        "    MAX_ADABINS_AREA = 500000\n",
        "\n",
        "try:\n",
        "    print(\":hourglass_not_done: Installing Disco Xform Utilities...\")\n",
        "    import disco_xform_utils as dxf\n",
        "    sys.path.append(PROJECT_DIR)\n",
        "except:\n",
        "    if not os.path.exists(\"disco-diffusion\"):\n",
        "        gitclone(\"https://github.com/alembics/disco-diffusion.git\")\n",
        "    if not os.path.exists('disco_xform_utils.py'):\n",
        "        shutil.move('disco-diffusion/disco_xform_utils.py', 'disco_xform_utils.py')\n",
        "    sys.path.append(PROJECT_DIR)\n",
        "finally:\n",
        "    import disco_xform_utils as dxf\n",
        "    if 'disco_xform_utils' in sys.modules:\n",
        "        print(\":check_mark_button: \\33[32mInstalled Disco Xform Utilities successfuly.\\33[0m\\n\")\n",
        "\n",
        "try:\n",
        "    print(\":hourglass_not_done: Initializing general imports...\\n\")\n",
        "    from dataclasses import dataclass\n",
        "    from functools import partial\n",
        "    import cv2, gc, io, math, timm, lpips, requests, json, random, hashlib, time, warnings, pprint, traceback\n",
        "    import pandas as pd\n",
        "    from pprint import *\n",
        "    from IPython import display\n",
        "    from glob import glob\n",
        "    from types import SimpleNamespace\n",
        "    from tqdm.notebook import tqdm\n",
        "    from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults\n",
        "    from datetime import datetime\n",
        "    import numpy as np\n",
        "    import matplotlib.pyplot as plt\n",
        "    from ipywidgets import Output\n",
        "    from functools import partial\n",
        "    if is_colab:\n",
        "        os.chdir('/content')\n",
        "        from google.colab import files\n",
        "    else:\n",
        "        os.chdir(f'{PROJECT_DIR}')\n",
        "    from IPython.display import Image as ipyimg\n",
        "    from numpy import asarray\n",
        "    from einops import rearrange, repeat\n",
        "    from omegaconf import OmegaConf\n",
        "    from os.path import exists\n",
        "    from urllib.parse import urlparse\n",
        "except ImportError:\n",
        "    raise ImportError(\"Unable to load dependency!\")\n",
        "finally:\n",
        "    print(\":check_mark_button: \\33[32mGeneral imports initialized.\\33[0m\\n\")\n",
        "\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
        "\n",
        "# Install easing_functions\n",
        "try:\n",
        "    print(\":hourglass_not_done: Installing Cutn Easing Functions and Dependenceis...\")\n",
        "    multipip_res = subprocess.run(['pip', 'install', 'easing_functions', 'matplotlib', '--quiet'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    print(multipip_res, defaultprint=True)\n",
        "except ImportError:\n",
        "    raise ImportError('Unable to import Cutn Easing Functions and dependenceis.')\n",
        "finally:\n",
        "    from easing_functions import *\n",
        "    if 'easing_functions' in sys.modules:\n",
        "      print(\":check_mark_button: \\33[32mCutn Easing Functions installed successfuly.\\33[0m\\n\")\n",
        "\n",
        "# Noodle Soup prompts\n",
        "nsp = False\n",
        "use_this_prompt = False\n",
        "try:\n",
        "    print(\":hourglass_not_done: Installing Noodle Soup Prompts...\")\n",
        "    wget('https://raw.githubusercontent.com/WASasquatch/noodle-soup-prompts/main/nsp_pantry.py', './')\n",
        "except ImportError:\n",
        "    nsp = False\n",
        "    raise ImportError('Unable to import Noodle Soup Prompts!')\n",
        "finally:\n",
        "    import nsp_pantry\n",
        "    from nsp_pantry import nsp_parse\n",
        "\n",
        "if nsp_parse:\n",
        "    print(\"\\r\\r:check_mark_button: \\33[32mNSP installed successfuly.\\33[0m \\x1B[3mMmm... Noodle Soup.\\x1B[0m\\n\")\n",
        "    nsp = True\n",
        "\n",
        "#@markdown ---\n",
        "# Go Big Support=\n",
        "#@markdown <font color=\"yellow\">**WARNING:**</font> Go BIG v2 requires installing Pillow v9. This requires a restart of the runtime after this cell has completed. Then you must run this cell again. \n",
        "install_go_big_v2_support = False #@param{type: 'boolean'}\n",
        "\n",
        "# Check Pillow Version\n",
        "pil_warning = None\n",
        "if install_go_big_v2_support:\n",
        "    upgrade_pillow = subprocess.run(['pip', '-q', 'install', 'Pillow==9.0.0'],stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    print(upgrade_pillow)\n",
        "    import PIL as PIL\n",
        "    from PIL import Image, ImageFilter, ImageOps, ImageStat, ImageEnhance, ImageDraw\n",
        "    from PIL.PngImagePlugin import PngInfo\n",
        "    if not Image.__version__.startswith('9'):\n",
        "        pil_warning = f\":warning: \\33[33m\\u001b[4mWARNING:\\33[0m \\033[1mYou are using Pillow verison \\33[33m{Image.__version__}\\33[0m\\033[1m, but version \\33[32m9.0.0\\33[0m \\033[1mwas just installed. \\n         Please go to Runtime -> Restart and Run all\\33[0m\"\n",
        "        print(\"\\n\"+pil_warning+\"\\n\")\n",
        "    else: \n",
        "        print(f\":check_mark_button: \\33[32mGo BIG v2 support installed successfuly!\\33[0m\\n\")\n",
        "else:\n",
        "    import PIL as PIL\n",
        "    from PIL import Image, ImageFilter, ImageOps\n",
        "    from PIL.PngImagePlugin import PngInfo\n",
        "    if Image:\n",
        "      pil_warning = f\":warning: \\33[33m\\u001b[4mWARNING:\\33[0m \\033[1mYou are using Pillow verison \\33[33m{Image.__version__}\\33[0m\\033[1m, but version \\33[32m9.0.0\\33[0m \\033[1mis required for Go BIG v2. \\n         If you want to use Go BIG v2, please enable Go BIG v2 Support, Run Setup {appname} Environment again, and go to Runtime -> Restart and Run all\\33[0m\"\n",
        "      print(pil_warning+\"\\n\")\n",
        "\n",
        "# Initialize Torch\n",
        "try:\n",
        "    print(\":hourglass_not_done: Initializing Torch...\")\n",
        "    import torch, torchvision\n",
        "    from torch import nn\n",
        "    from torch.nn import functional as F\n",
        "    import torchvision.transforms as T\n",
        "    import torchvision.transforms.functional as TF\n",
        "except ImportError:\n",
        "    raise ImportError(\"Unable to initialize Torch!\")\n",
        "finally:\n",
        "    print(\":check_mark_button: \\33[32mTorch initialized.\\33[0m\")\n",
        "\n",
        "DEVICE = torch.device('cuda:0' if (torch.cuda.is_available() and not useCPU) else 'cpu')\n",
        "print('Using device:', DEVICE)\n",
        "device = DEVICE # At least one of the modules expects this name..\n",
        "\n",
        "if not useCPU:\n",
        "    if torch.cuda.get_device_capability(DEVICE) == (8,0): ## A100 fix thanks to Emad\n",
        "        print(':warning: Disabling CUDNN for A100 gpu', file=sys.stderr)\n",
        "        torch.backends.cudnn.enabled = False\n",
        "\n",
        "# Sampling Mode Switch\n",
        "def get_resampling_mode():\n",
        "    try:\n",
        "        from PIL import __version__, Image\n",
        "        major_ver = int(__version__.split('.')[0])\n",
        "        if major_ver >= 9:\n",
        "            return Image.Resampling.LANCZOS\n",
        "        else:\n",
        "            return Image.LANCZOS\n",
        "    except Exception as ex:\n",
        "        return 1  # 'Lanczos' irrespective of version.\n",
        "\n",
        "print(\"\\n:check_mark_button: \\033[1mInstallation of Dependencies Complete\\033[0m\")\n",
        "print(\"\\n--[ Initializaing Midas Function ]--\")\n",
        "\n",
        "# DEFINE MIDAS FUNCTION\n",
        "\n",
        "try:\n",
        "    from midas.dpt_depth import DPTDepthModel\n",
        "    from midas.midas_net import MidasNet\n",
        "    from midas.midas_net_custom import MidasNet_small\n",
        "    from midas.transforms import Resize, NormalizeImage, PrepareForNet\n",
        "except ImportError:\n",
        "    raise ImportError(\"Unable to load midas modules!\")\n",
        "\n",
        "# Initialize MiDaS depth model.\n",
        "# It remains resident in VRAM and likely takes around 2GB VRAM.\n",
        "# You could instead initialize it for each frame (and free it after each frame) to save VRAM.. but initializing it is slow.\n",
        "default_models = {\n",
        "    \"midas_v21_small\": f\"{model_path}/midas_v21_small-70d6b9c8.pt\",\n",
        "    \"midas_v21\": f\"{model_path}/midas_v21-f6b98070.pt\",\n",
        "    \"dpt_large\": f\"{model_path}/dpt_large-midas-2f21e586.pt\",\n",
        "    \"dpt_hybrid\": f\"{model_path}/dpt_hybrid-midas-501f0c75.pt\",\n",
        "    \"dpt_hybrid_nyu\": f\"{model_path}/dpt_hybrid_nyu-2ce69ec7.pt\",}\n",
        "\n",
        "\n",
        "def init_midas_depth_model(midas_model_type=\"dpt_large\", optimize=True):\n",
        "    midas_model = None\n",
        "    net_w = None\n",
        "    net_h = None\n",
        "    resize_mode = None\n",
        "    normalization = None\n",
        "\n",
        "    print(f\"Initializing MiDaS '{midas_model_type}' depth model...\")\n",
        "    # load network\n",
        "    midas_model_path = default_models[midas_model_type]\n",
        "\n",
        "    if midas_model_type == \"dpt_large\": # DPT-Large\n",
        "        midas_model = DPTDepthModel(\n",
        "            path=midas_model_path,\n",
        "            backbone=\"vitl16_384\",\n",
        "            non_negative=True,\n",
        "        )\n",
        "        net_w, net_h = 384, 384\n",
        "        resize_mode = \"minimal\"\n",
        "        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
        "    elif midas_model_type == \"dpt_hybrid\": #DPT-Hybrid\n",
        "        midas_model = DPTDepthModel(\n",
        "            path=midas_model_path,\n",
        "            backbone=\"vitb_rn50_384\",\n",
        "            non_negative=True,\n",
        "        )\n",
        "        net_w, net_h = 384, 384\n",
        "        resize_mode=\"minimal\"\n",
        "        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
        "    elif midas_model_type == \"dpt_hybrid_nyu\": #DPT-Hybrid-NYU\n",
        "        midas_model = DPTDepthModel(\n",
        "            path=midas_model_path,\n",
        "            backbone=\"vitb_rn50_384\",\n",
        "            non_negative=True,\n",
        "        )\n",
        "        net_w, net_h = 384, 384\n",
        "        resize_mode=\"minimal\"\n",
        "        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
        "    elif midas_model_type == \"midas_v21\":\n",
        "        midas_model = MidasNet(midas_model_path, non_negative=True)\n",
        "        net_w, net_h = 384, 384\n",
        "        resize_mode=\"upper_bound\"\n",
        "        normalization = NormalizeImage(\n",
        "            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n",
        "        )\n",
        "    elif midas_model_type == \"midas_v21_small\":\n",
        "        midas_model = MidasNet_small(midas_model_path, features=64, backbone=\"efficientnet_lite3\", exportable=True, non_negative=True, blocks={'expand': True})\n",
        "        net_w, net_h = 256, 256\n",
        "        resize_mode=\"upper_bound\"\n",
        "        normalization = NormalizeImage(\n",
        "            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n",
        "        )\n",
        "    else:\n",
        "        print(f\"midas_model_type '{midas_model_type}' not implemented\")\n",
        "        assert False\n",
        "\n",
        "    midas_transform = T.Compose(\n",
        "        [\n",
        "            Resize(\n",
        "                net_w,\n",
        "                net_h,\n",
        "                resize_target=None,\n",
        "                keep_aspect_ratio=True,\n",
        "                ensure_multiple_of=32,\n",
        "                resize_method=resize_mode,\n",
        "                image_interpolation_method=cv2.INTER_CUBIC,\n",
        "            ),\n",
        "            normalization,\n",
        "            PrepareForNet(),\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    midas_model.eval()\n",
        "    \n",
        "    if optimize==True:\n",
        "        if DEVICE == torch.device(\"cuda\"):\n",
        "            midas_model = midas_model.to(memory_format=torch.channels_last)  \n",
        "            midas_model = midas_model.half()\n",
        "\n",
        "    midas_model.to(DEVICE)\n",
        "\n",
        "    print(f\"MiDaS '{midas_model_type}' depth model initialized.\")\n",
        "    return midas_model, midas_transform, net_w, net_h, resize_mode, normalization\n",
        "\n",
        "print(\"\\n:check_mark_button: \\033[1mMidas Function Initialized\\033[0m\")\n",
        "print(\"\\n--[ Initializing Main Functions ]--\")\n",
        "\n",
        "# DEFINE MAIN FUNCTIONS\n",
        "\n",
        "# https://gist.github.com/adefossez/0646dbe9ed4005480a2407c62aac8869\n",
        "\n",
        "# GO BIG GLOBALS\n",
        "current_slice = None\n",
        "go_big_prompts = None\n",
        "runtime_prompts = []\n",
        "original_seed = None\n",
        "last_init = None\n",
        "\n",
        "def interp(t):\n",
        "    return 3 * t**2 - 2 * t ** 3\n",
        "\n",
        "def perlin(width, height, scale=10, device=None):\n",
        "    gx, gy = torch.randn(2, width + 1, height + 1, 1, 1, device=device)\n",
        "    xs = torch.linspace(0, 1, scale + 1)[:-1, None].to(device)\n",
        "    ys = torch.linspace(0, 1, scale + 1)[None, :-1].to(device)\n",
        "    wx = 1 - interp(xs)\n",
        "    wy = 1 - interp(ys)\n",
        "    dots = 0\n",
        "    dots += wx * wy * (gx[:-1, :-1] * xs + gy[:-1, :-1] * ys)\n",
        "    dots += (1 - wx) * wy * (-gx[1:, :-1] * (1 - xs) + gy[1:, :-1] * ys)\n",
        "    dots += wx * (1 - wy) * (gx[:-1, 1:] * xs - gy[:-1, 1:] * (1 - ys))\n",
        "    dots += (1 - wx) * (1 - wy) * (-gx[1:, 1:] * (1 - xs) - gy[1:, 1:] * (1 - ys))\n",
        "    return dots.permute(0, 2, 1, 3).contiguous().view(width * scale, height * scale)\n",
        "\n",
        "def perlin_ms(octaves, width, height, grayscale, device=device):\n",
        "    out_array = [0.5] if grayscale else [0.5, 0.5, 0.5]\n",
        "    # out_array = [0.0] if grayscale else [0.0, 0.0, 0.0]\n",
        "    for i in range(1 if grayscale else 3):\n",
        "        scale = 2 ** len(octaves)\n",
        "        oct_width = width\n",
        "        oct_height = height\n",
        "        for oct in octaves:\n",
        "            p = perlin(oct_width, oct_height, scale, device)\n",
        "            out_array[i] += p * oct\n",
        "            scale //= 2\n",
        "            oct_width *= 2\n",
        "            oct_height *= 2\n",
        "    return torch.cat(out_array)\n",
        "\n",
        "def create_perlin_noise(octaves=[1, 1, 1, 1], width=2, height=2, grayscale=True):\n",
        "    out = perlin_ms(octaves, width, height, grayscale)\n",
        "    if grayscale:\n",
        "        out = TF.resize(size=(side_y, side_x), img=out.unsqueeze(0))\n",
        "        out = TF.to_pil_image(out.clamp(0, 1)).convert('RGB')\n",
        "    else:\n",
        "        out = out.reshape(-1, 3, out.shape[0]//3, out.shape[1])\n",
        "        out = TF.resize(size=(side_y, side_x), img=out)\n",
        "        out = TF.to_pil_image(out.clamp(0, 1).squeeze())\n",
        "\n",
        "    out = ImageOps.autocontrast(out)\n",
        "    return out\n",
        "\n",
        "def regen_perlin():\n",
        "    if perlin_mode == 'color':\n",
        "        init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)\n",
        "        init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, False)\n",
        "    elif perlin_mode == 'gray':\n",
        "        init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, True)\n",
        "        init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)\n",
        "    else:\n",
        "        init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)\n",
        "        init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)\n",
        "\n",
        "    init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device).unsqueeze(0).mul(2).sub(1)\n",
        "    del init2\n",
        "    return init.expand(batch_size, -1, -1, -1)\n",
        "\n",
        "def fetch(url_or_path):\n",
        "    if str(url_or_path).startswith('http://') or str(url_or_path).startswith('https://'):\n",
        "        r = requests.get(url_or_path)\n",
        "        r.raise_for_status()\n",
        "        fd = io.BytesIO()\n",
        "        fd.write(r.content)\n",
        "        fd.seek(0)\n",
        "        return fd\n",
        "    return open(url_or_path, 'rb')\n",
        "\n",
        "def read_image_workaround(path):\n",
        "    \"\"\"OpenCV reads images as BGR, Pillow saves them as RGB. Work around\n",
        "    this incompatibility to avoid colour inversions.\"\"\"\n",
        "    im_tmp = cv2.imread(path)\n",
        "    return cv2.cvtColor(im_tmp, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "def parse_prompt(prompt):\n",
        "    if prompt.startswith('http://') or prompt.startswith('https://'):\n",
        "        vals = prompt.rsplit(':', 2)\n",
        "        vals = [vals[0] + ':' + vals[1], *vals[2:]]\n",
        "    else:\n",
        "        vals = prompt.rsplit(':', 1)\n",
        "    vals = vals + ['', '1'][len(vals):]\n",
        "    vals[1] = eval(vals[1])\n",
        "    if isinstance(vals[1], int) or isinstance(vals[1], float):\n",
        "      return vals[0], [ float(x) for x in [float(vals[1])]*1000 ]\n",
        "    elif isinstance(vals[1], list):\n",
        "      return vals[0], [ float(x) for x in vals[1] ]\n",
        "    else:\n",
        "      raise TypeError(\"Only strings that evaluate to integers, floats and lists are allowed\")\n",
        "\n",
        "def sinc(x):\n",
        "    return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))\n",
        "\n",
        "def lanczos(x, a):\n",
        "    cond = torch.logical_and(-a < x, x < a)\n",
        "    out = torch.where(cond, sinc(x) * sinc(x/a), x.new_zeros([]))\n",
        "    return out / out.sum()\n",
        "\n",
        "def ramp(ratio, width):\n",
        "    n = math.ceil(width / ratio + 1)\n",
        "    out = torch.empty([n])\n",
        "    cur = 0\n",
        "    for i in range(out.shape[0]):\n",
        "        out[i] = cur\n",
        "        cur += ratio\n",
        "    return torch.cat([-out[1:].flip([0]), out])[1:-1]\n",
        "\n",
        "def resample(input, size, align_corners=True):\n",
        "    n, c, h, w = input.shape\n",
        "    dh, dw = size\n",
        "\n",
        "    input = input.reshape([n * c, 1, h, w])\n",
        "\n",
        "    if dh < h:\n",
        "        kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype)\n",
        "        pad_h = (kernel_h.shape[0] - 1) // 2\n",
        "        input = F.pad(input, (0, 0, pad_h, pad_h), 'reflect')\n",
        "        input = F.conv2d(input, kernel_h[None, None, :, None])\n",
        "\n",
        "    if dw < w:\n",
        "        kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype)\n",
        "        pad_w = (kernel_w.shape[0] - 1) // 2\n",
        "        input = F.pad(input, (pad_w, pad_w, 0, 0), 'reflect')\n",
        "        input = F.conv2d(input, kernel_w[None, None, None, :])\n",
        "\n",
        "    input = input.reshape([n, c, h, w])\n",
        "    return F.interpolate(input, size, mode='bicubic', align_corners=align_corners)\n",
        "\n",
        "class MakeCutouts(nn.Module):\n",
        "    def __init__(self, cut_size, cutn, skip_augs=False):\n",
        "        super().__init__()\n",
        "        self.cut_size = cut_size\n",
        "        self.cutn = cutn\n",
        "        self.skip_augs = skip_augs\n",
        "        self.augs = T.Compose([\n",
        "            T.RandomHorizontalFlip(p=0.5),\n",
        "            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "            T.RandomAffine(degrees=15, translate=(0.1, 0.1)),\n",
        "            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "            T.RandomPerspective(distortion_scale=0.4, p=0.7),\n",
        "            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "            T.RandomGrayscale(p=0.15),\n",
        "            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "            # T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),\n",
        "        ])\n",
        "\n",
        "    def forward(self, input):\n",
        "        input = T.Pad(input.shape[2]//4, fill=0)(input)\n",
        "        sideY, sideX = input.shape[2:4]\n",
        "        max_size = min(sideX, sideY)\n",
        "\n",
        "        cutouts = []\n",
        "        for ch in range(self.cutn):\n",
        "            if ch > self.cutn - self.cutn//4:\n",
        "                cutout = input.clone()\n",
        "            else:\n",
        "                size = int(max_size * torch.zeros(1,).normal_(mean=.8, std=.3).clip(float(self.cut_size/max_size), 1.))\n",
        "                offsetx = torch.randint(0, abs(sideX - size + 1), ())\n",
        "                offsety = torch.randint(0, abs(sideY - size + 1), ())\n",
        "                cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]\n",
        "\n",
        "            if not self.skip_augs:\n",
        "                cutout = self.augs(cutout)\n",
        "            cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))\n",
        "            del cutout\n",
        "\n",
        "        cutouts = torch.cat(cutouts, dim=0)\n",
        "        return cutouts\n",
        "\n",
        "cutout_debug = False\n",
        "padargs = {}\n",
        "\n",
        "class MakeCutoutsDango(nn.Module):\n",
        "    def __init__(self, cut_size,\n",
        "                 Overview=4, \n",
        "                 InnerCrop = 0, IC_Size_Pow=0.5, IC_Grey_P = 0.2\n",
        "                 ):\n",
        "        super().__init__()\n",
        "        self.cut_size = cut_size\n",
        "        self.Overview = Overview\n",
        "        self.InnerCrop = InnerCrop\n",
        "        self.IC_Size_Pow = IC_Size_Pow\n",
        "        self.IC_Grey_P = IC_Grey_P\n",
        "        if args.animation_mode == 'None':\n",
        "          self.augs = T.Compose([\n",
        "              T.RandomHorizontalFlip(p=0.5),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomAffine(degrees=10, translate=(0.05, 0.05),  interpolation = T.InterpolationMode.BILINEAR),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomGrayscale(p=0.1),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),\n",
        "          ])\n",
        "        elif args.animation_mode == 'Video Input':\n",
        "          self.augs = T.Compose([\n",
        "              T.RandomHorizontalFlip(p=0.5),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomAffine(degrees=15, translate=(0.1, 0.1)),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomPerspective(distortion_scale=0.4, p=0.7),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomGrayscale(p=0.15),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              # T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),\n",
        "          ])\n",
        "        elif  args.animation_mode == '2D' or args.animation_mode == '3D':\n",
        "          self.augs = T.Compose([\n",
        "              T.RandomHorizontalFlip(p=0.4),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomAffine(degrees=10, translate=(0.05, 0.05),  interpolation = T.InterpolationMode.BILINEAR),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomGrayscale(p=0.1),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.3),\n",
        "          ])\n",
        "          \n",
        "\n",
        "    def forward(self, input):\n",
        "        cutouts = []\n",
        "        gray = T.Grayscale(3)\n",
        "        sideY, sideX = input.shape[2:4]\n",
        "        max_size = min(sideX, sideY)\n",
        "        min_size = min(sideX, sideY, self.cut_size)\n",
        "        l_size = max(sideX, sideY)\n",
        "        output_shape = [1,3,self.cut_size,self.cut_size] \n",
        "        output_shape_2 = [1,3,self.cut_size+2,self.cut_size+2]\n",
        "        pad_input = F.pad(input,((sideY-max_size)//2,(sideY-max_size)//2,(sideX-max_size)//2,(sideX-max_size)//2), **padargs)\n",
        "        cutout = resize(pad_input, out_shape=output_shape)\n",
        "\n",
        "        if self.Overview>0:\n",
        "            if self.Overview<=4:\n",
        "                if self.Overview>=1:\n",
        "                    cutouts.append(cutout)\n",
        "                if self.Overview>=2:\n",
        "                    cutouts.append(gray(cutout))\n",
        "                if self.Overview>=3:\n",
        "                    cutouts.append(TF.hflip(cutout))\n",
        "                if self.Overview==4:\n",
        "                    cutouts.append(gray(TF.hflip(cutout)))\n",
        "            else:\n",
        "                cutout = resize(pad_input, out_shape=output_shape)\n",
        "                for _ in range(self.Overview):\n",
        "                    cutouts.append(cutout)\n",
        "\n",
        "            if cutout_debug:\n",
        "                if is_colab:\n",
        "                    TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save(\"/content/cutout_overview0.jpg\",quality=99)\n",
        "                else:\n",
        "                    TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save(\"cutout_overview0.jpg\",quality=99)\n",
        "\n",
        "                              \n",
        "        if self.InnerCrop >0:\n",
        "            for i in range(self.InnerCrop):\n",
        "                size = int(torch.rand([])**self.IC_Size_Pow * (max_size - min_size) + min_size)\n",
        "                offsetx = torch.randint(0, sideX - size + 1, ())\n",
        "                offsety = torch.randint(0, sideY - size + 1, ())\n",
        "                cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]\n",
        "                if i <= int(self.IC_Grey_P * self.InnerCrop):\n",
        "                    cutout = gray(cutout)\n",
        "                cutout = resize(cutout, out_shape=output_shape)\n",
        "                cutouts.append(cutout)\n",
        "            if cutout_debug:\n",
        "                if is_colab:\n",
        "                    TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save(\"/content/cutout_InnerCrop.jpg\",quality=99)\n",
        "                else:\n",
        "                    TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save(\"cutout_InnerCrop.jpg\",quality=99)\n",
        "        cutouts = torch.cat(cutouts)\n",
        "        if skip_augs is not True: cutouts=self.augs(cutouts)\n",
        "        return cutouts\n",
        "\n",
        "def spherical_dist_loss(x, y):\n",
        "    x = F.normalize(x, dim=-1)\n",
        "    y = F.normalize(y, dim=-1)\n",
        "    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)     \n",
        "\n",
        "def tv_loss(input):\n",
        "    \"\"\"L2 total variation loss, as in Mahendran et al.\"\"\"\n",
        "    input = F.pad(input, (0, 1, 0, 1), 'replicate')\n",
        "    x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]\n",
        "    y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]\n",
        "    return (x_diff**2 + y_diff**2).mean([1, 2, 3])\n",
        "\n",
        "\n",
        "def range_loss(input):\n",
        "    return (input - input.clamp(-1, 1)).pow(2).mean([1, 2, 3])\n",
        "\n",
        "stop_on_next_loop = False  # Make sure GPU memory doesn't get corrupted from cancelling the run mid-way through, allow a full frame to complete\n",
        "TRANSLATION_SCALE = 1.0/200.0\n",
        "\n",
        "def do_3d_step(img_filepath, frame_num, midas_model, midas_transform):\n",
        "  if args.key_frames:\n",
        "    translation_x = args.translation_x_series[frame_num]\n",
        "    translation_y = args.translation_y_series[frame_num]\n",
        "    translation_z = args.translation_z_series[frame_num]\n",
        "    rotation_3d_x = args.rotation_3d_x_series[frame_num]\n",
        "    rotation_3d_y = args.rotation_3d_y_series[frame_num]\n",
        "    rotation_3d_z = args.rotation_3d_z_series[frame_num]\n",
        "    print(\n",
        "        f'translation_x: {translation_x}',\n",
        "        f'translation_y: {translation_y}',\n",
        "        f'translation_z: {translation_z}',\n",
        "        f'rotation_3d_x: {rotation_3d_x}',\n",
        "        f'rotation_3d_y: {rotation_3d_y}',\n",
        "        f'rotation_3d_z: {rotation_3d_z}',\n",
        "    )\n",
        "\n",
        "  translate_xyz = [-translation_x*TRANSLATION_SCALE, translation_y*TRANSLATION_SCALE, -translation_z*TRANSLATION_SCALE]\n",
        "  rotate_xyz_degrees = [rotation_3d_x, rotation_3d_y, rotation_3d_z]\n",
        "  print('translation:',translate_xyz)\n",
        "  print('rotation:',rotate_xyz_degrees)\n",
        "  rotate_xyz = [math.radians(rotate_xyz_degrees[0]), math.radians(rotate_xyz_degrees[1]), math.radians(rotate_xyz_degrees[2])]\n",
        "  rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), \"XYZ\").unsqueeze(0)\n",
        "  print(\"rot_mat: \" + str(rot_mat))\n",
        "  next_step_pil = dxf.transform_image_3d(img_filepath, midas_model, midas_transform, DEVICE,\n",
        "                                          rot_mat, translate_xyz, args.near_plane, args.far_plane,\n",
        "                                          args.fov, padding_mode=args.padding_mode,\n",
        "                                          sampling_mode=args.sampling_mode, midas_weight=args.midas_weight)\n",
        "  return next_step_pil\n",
        "\n",
        "def symmetry_transformation_fn(x):\n",
        "    if args.use_horizontal_symmetry:\n",
        "        [n, c, h, w] = x.size()\n",
        "        x = torch.concat((x[:, :, :, :w//2], torch.flip(x[:, :, :, :w//2], [-1])), -1)\n",
        "        print(\"horizontal symmetry applied\")\n",
        "    if args.use_vertical_symmetry:\n",
        "        [n, c, h, w] = x.size()\n",
        "        x = torch.concat((x[:, :, :h//2, :], torch.flip(x[:, :, :h//2, :], [-2])), -2)\n",
        "        print(\"vertical symmetry applied\")\n",
        "    return x\n",
        "\n",
        "## DD Image Prep Funciton\n",
        "def init_image_prep(img, gblur, noise, nblur, ngrey=False, gdouble=False, pilimg=False):\n",
        "    valid_ext = ['.jpg','.jpeg','.gif','.png']\n",
        "    file_ext = os.path.splitext(img)[1] if type(img) is str else True\n",
        "    if file_ext in valid_ext or file_ext == True :\n",
        "        im1 = img if pilimg else PIL.Image.open(fetch(img)).convert('RGB')\n",
        "        im2 = im1.filter(ImageFilter.GaussianBlur(radius = int(gblur)))\n",
        "        im3 = np.asarray(im2)\n",
        "        im3 = cv2.cvtColor(im3, cv2.COLOR_BGR2RGB)\n",
        "        gn = np.random.normal(0,int(noise),im3.size)\n",
        "        gn = gn.reshape(im3.shape[0],im3.shape[1],im3.shape[2]).astype('uint8')\n",
        "        if gdouble:\n",
        "            gn2 = np.random.normal(0,int(noise),im3.size)\n",
        "            gn2 = gn2.reshape(im3.shape[0],im3.shape[1],im3.shape[2]).astype('uint8')\n",
        "        if ngrey:\n",
        "            gn = cv2.cvtColor(gn, cv2.COLOR_BGR2GRAY)\n",
        "            gn = cv2.cvtColor(gn, cv2.COLOR_BGR2RGB)\n",
        "            if gdouble:\n",
        "                gn2 = cv2.cvtColor(gn2, cv2.COLOR_BGR2GRAY)\n",
        "                gn2 = cv2.cvtColor(gn2, cv2.COLOR_BGR2RGB)\n",
        "        if gdouble:\n",
        "            gn = cv2.add(gn,gn2)\n",
        "        gn = cv2.GaussianBlur(gn,(int(nblur),int(nblur)),cv2.BORDER_DEFAULT)\n",
        "        gi = cv2.add(im3,gn)\n",
        "        gi = cv2.cvtColor(gi, cv2.COLOR_BGR2RGB)\n",
        "        im1 = PIL.Image.fromarray(gi)\n",
        "        #im2 = im1.filter(ImageFilter.GaussianBlur(radius = nblur))\n",
        "        im2 = im2.filter(ImageFilter.SHARPEN)\n",
        "        return im2\n",
        "    else:\n",
        "        return None\n",
        "\n",
        "# Basic image display\n",
        "def displayJsImage(name, img):\n",
        "    from IPython.display import display, Javascript, clear_output\n",
        "    from google.colab.output import eval_js\n",
        "    from base64 import b64encode\n",
        "    from google.colab import files\n",
        "    img = np.asarray(img)\n",
        "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "    ext = os.path.splitext(name)[1].replace('.', '', 1)\n",
        "    ext = ext if ext != 'jpg' else 'jpeg'\n",
        "    js = Javascript('''\n",
        "        async function showImage(name, image, width, height) {\n",
        "        img = document.getElementById(name);\n",
        "        cont = document.getElementById(name+'_container');\n",
        "        if(img == null && cont == null) {\n",
        "            cont = document.createElement('div');\n",
        "            cont.id = name+'_container';\n",
        "            link = document.createElement('a');\n",
        "            link.href = image;\n",
        "            link.target = '_blank';\n",
        "            img = document.createElement('img');\n",
        "            img.id = name;\n",
        "            img.class = \"resultImage\"\n",
        "            cont.style = 'display:inline-block;width:auto;text-align:center;font-size:14px;font-weight:bold;background-color:rgba(0,0,0,0.1);border-radius:5px;padding:2px;margin:2px;'\n",
        "            document.body.appendChild(cont)\n",
        "            cont.innerHTML = '<p style=\"margin:3px auto;width:180px;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;\">'+name+'</p>';\n",
        "            cont.appendChild(link);\n",
        "            link.appendChild(img);\n",
        "        }\n",
        "        img.src = image;\n",
        "        //img.width = (width / 2);\n",
        "        //img.height = (height / 2);\n",
        "        img.style = \"margin: 5px; vertical-align: text-top; max-width: 256px; max-height: 512px;\";\n",
        "        }\n",
        "    ''')\n",
        "    height, width = img.shape[:2]\n",
        "    ret, data = cv2.imencode('.png', img)\n",
        "    data = b64encode(data)\n",
        "    data = data.decode()\n",
        "    data = 'data:image/' + ext + ';base64,' + data\n",
        "    display(js)\n",
        "    eval_js(f'showImage(\"{name}\", \"{data}\", {width}, {height})')\n",
        "\n",
        "def do_run():\n",
        "  seed = args.seed\n",
        "  print(range(args.start_frame, args.max_frames), defaultprint=True)\n",
        "\n",
        "  if (args.animation_mode == \"3D\") and (args.midas_weight > 0.0):\n",
        "      midas_model, midas_transform, midas_net_w, midas_net_h, midas_resize_mode, midas_normalization = init_midas_depth_model(args.midas_depth_model)\n",
        "  for frame_num in range(args.start_frame, args.max_frames):\n",
        "      if stop_on_next_loop:\n",
        "        break\n",
        "      \n",
        "      display.clear_output(wait=True)\n",
        "\n",
        "      # Print Frame progress if animation mode is on\n",
        "      if args.animation_mode != \"None\":\n",
        "        batchBar = tqdm(range(args.max_frames), desc =\"Frames\")\n",
        "        batchBar.n = frame_num\n",
        "        batchBar.refresh()\n",
        "\n",
        "      \n",
        "      # Inits if not video frames\n",
        "      if args.animation_mode != \"Video Input\":\n",
        "        if args.init_image in ['','none', 'None', 'NONE']:\n",
        "          init_image = None\n",
        "        else:\n",
        "          init_image = args.init_image\n",
        "        init_scale = args.init_scale\n",
        "        skip_steps = args.skip_steps\n",
        "\n",
        "      if args.animation_mode == \"2D\":\n",
        "        if args.key_frames:\n",
        "          angle = args.angle_series[frame_num]\n",
        "          zoom = args.zoom_series[frame_num]\n",
        "          translation_x = args.translation_x_series[frame_num]\n",
        "          translation_y = args.translation_y_series[frame_num]\n",
        "          print(\n",
        "              f'angle: {angle}',\n",
        "              f'zoom: {zoom}',\n",
        "              f'translation_x: {translation_x}',\n",
        "              f'translation_y: {translation_y}',\n",
        "          )\n",
        "        \n",
        "        if frame_num > 0:\n",
        "          seed += 1\n",
        "          if resume_run and frame_num == start_frame:\n",
        "            img_0 = cv2.imread(batchFolder+f\"/{batch_name}({batchNum})_{start_frame-1:04}.png\")\n",
        "          else:\n",
        "            img_0 = cv2.imread('prevFrame.png')\n",
        "          center = (1*img_0.shape[1]//2, 1*img_0.shape[0]//2)\n",
        "          trans_mat = np.float32(\n",
        "              [[1, 0, translation_x],\n",
        "              [0, 1, translation_y]]\n",
        "          )\n",
        "          rot_mat = cv2.getRotationMatrix2D( center, angle, zoom )\n",
        "          trans_mat = np.vstack([trans_mat, [0,0,1]])\n",
        "          rot_mat = np.vstack([rot_mat, [0,0,1]])\n",
        "          transformation_matrix = np.matmul(rot_mat, trans_mat)\n",
        "          img_0 = cv2.warpPerspective(\n",
        "              img_0,\n",
        "              transformation_matrix,\n",
        "              (img_0.shape[1], img_0.shape[0]),\n",
        "              borderMode=cv2.BORDER_WRAP\n",
        "          )\n",
        "\n",
        "          cv2.imwrite('prevFrameScaled.png', img_0)\n",
        "          init_image = 'prevFrameScaled.png'\n",
        "          init_scale = args.frames_scale\n",
        "          skip_steps = args.calc_frames_skip_steps\n",
        "\n",
        "      if args.animation_mode == \"3D\":\n",
        "        if frame_num > 0:\n",
        "          seed += 1    \n",
        "          if resume_run and frame_num == start_frame:\n",
        "            img_filepath = batchFolder+f\"/{batch_name}({batchNum})_{start_frame-1:04}.png\"\n",
        "            if turbo_mode and frame_num > turbo_preroll:\n",
        "              shutil.copyfile(img_filepath, 'oldFrameScaled.png')\n",
        "          else:\n",
        "            img_filepath = '/content/prevFrame.png' if is_colab else 'prevFrame.png'\n",
        "\n",
        "          next_step_pil = do_3d_step(img_filepath, frame_num, midas_model, midas_transform)\n",
        "          next_step_pil.save('prevFrameScaled.png')\n",
        "\n",
        "          ### Turbo mode - skip some diffusions, use 3d morph for clarity and to save time\n",
        "          if turbo_mode:\n",
        "            if frame_num == turbo_preroll: #start tracking oldframe\n",
        "              next_step_pil.save('oldFrameScaled.png')#stash for later blending          \n",
        "            elif frame_num > turbo_preroll:\n",
        "              #set up 2 warped image sequences, old & new, to blend toward new diff image\n",
        "              old_frame = do_3d_step('oldFrameScaled.png', frame_num, midas_model, midas_transform)\n",
        "              old_frame.save('oldFrameScaled.png')\n",
        "              if frame_num % int(turbo_steps) != 0: \n",
        "                print('turbo skip this frame: skipping clip diffusion steps')\n",
        "                filename = f'{args.batch_name}({args.batchNum})_{frame_num:04}.png'\n",
        "                blend_factor = ((frame_num % int(turbo_steps))+1)/int(turbo_steps)\n",
        "                print('turbo skip this frame: skipping clip diffusion steps and saving blended frame')\n",
        "                newWarpedImg = cv2.imread('prevFrameScaled.png')#this is already updated..\n",
        "                oldWarpedImg = cv2.imread('oldFrameScaled.png')\n",
        "                blendedImage = cv2.addWeighted(newWarpedImg, blend_factor, oldWarpedImg,1-blend_factor, 0.0)\n",
        "                cv2.imwrite(f'{batchFolder}/{filename}',blendedImage)\n",
        "                next_step_pil.save(f'{img_filepath}') # save it also as prev_frame to feed next iteration\n",
        "                if vr_mode:\n",
        "                  generate_eye_views(TRANSLATION_SCALE,batchFolder,filename,frame_num,midas_model, midas_transform)\n",
        "                continue\n",
        "              else:\n",
        "                #if not a skip frame, will run diffusion and need to blend.\n",
        "                oldWarpedImg = cv2.imread('prevFrameScaled.png')\n",
        "                cv2.imwrite(f'oldFrameScaled.png',oldWarpedImg)#swap in for blending later \n",
        "                print('clip/diff this frame - generate clip diff image')\n",
        "\n",
        "          init_image = 'prevFrameScaled.png'\n",
        "          init_scale = args.frames_scale\n",
        "          skip_steps = args.calc_frames_skip_steps\n",
        "\n",
        "      if  args.animation_mode == \"Video Input\":\n",
        "        init_scale = args.video_init_frames_scale\n",
        "        skip_steps = args.calc_frames_skip_steps\n",
        "        if not video_init_seed_continuity:\n",
        "          seed += 1\n",
        "        if video_init_flow_warp:\n",
        "          if frame_num == 0: \n",
        "            skip_steps = args.video_init_skip_steps\n",
        "            init_image = f'{videoFramesFolder}/{frame_num+1:04}.jpg'\n",
        "          if frame_num > 0: \n",
        "            prev = PIL.Image.open(batchFolder+f\"/{batch_name}({batchNum})_{frame_num-1:04}.png\")\n",
        "            \n",
        "            frame1_path = f'{videoFramesFolder}/{frame_num:04}.jpg'\n",
        "            frame2 = PIL.Image.open(f'{videoFramesFolder}/{frame_num+1:04}.jpg')\n",
        "            flo_path = f\"/{flo_folder}/{frame1_path.split('/')[-1]}.npy\"\n",
        "            \n",
        "            init_image = 'warped.png'\n",
        "            print(video_init_flow_blend, defaultprint=True)\n",
        "            weights_path = None\n",
        "            if video_init_check_consistency:\n",
        "                # TBD\n",
        "                pass\n",
        " \n",
        "            warp(prev, frame2, flo_path, blend=video_init_flow_blend, weights_path=weights_path).save(init_image)\n",
        "            \n",
        "        else:\n",
        "          init_image = f'{videoFramesFolder}/{frame_num+1:04}.jpg'\n",
        "\n",
        "\n",
        "      loss_values = []\n",
        "\n",
        "      #if seed is not None:\n",
        "      #    random.seed(seed)\n",
        "      #    np.random.seed(seed)\n",
        "      #    torch.manual_seed(seed)\n",
        "      \n",
        "      # CLIP init prompts original location...\n",
        "      \n",
        "      run_clip_init = False\n",
        "      cur_t = None\n",
        "  \n",
        "      def cond_fn(x, t, y=None):\n",
        "          with torch.enable_grad():\n",
        "              x_is_NaN = False\n",
        "              x = x.detach().requires_grad_()\n",
        "              n = x.shape[0]\n",
        "              if use_secondary_model is True:\n",
        "                alpha = torch.tensor(diffusion.sqrt_alphas_cumprod[cur_t], device=device, dtype=torch.float32)\n",
        "                sigma = torch.tensor(diffusion.sqrt_one_minus_alphas_cumprod[cur_t], device=device, dtype=torch.float32)\n",
        "                cosine_t = alpha_sigma_to_t(alpha, sigma)\n",
        "                out = secondary_model(x, cosine_t[None].repeat([n])).pred\n",
        "                fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]\n",
        "                x_in = out * fac + x * (1 - fac)\n",
        "                x_in_grad = torch.zeros_like(x_in)\n",
        "              else:\n",
        "                my_t = torch.ones([n], device=device, dtype=torch.long) * cur_t\n",
        "                out = diffusion.p_mean_variance(model, x, my_t, clip_denoised=False, model_kwargs={'y': y})\n",
        "                fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]\n",
        "                x_in = out['pred_xstart'] * fac + x * (1 - fac)\n",
        "                x_in_grad = torch.zeros_like(x_in)\n",
        "              for model_stat in model_stats:\n",
        "                for i in range(args.cutn_batches):\n",
        "                    t_int = int(t.item())+1 #errors on last step without +1, need to find source\n",
        "                    #when using SLIP Base model the dimensions need to be hard coded to avoid AttributeError: 'VisionTransformer' object has no attribute 'input_resolution'\n",
        "                    try:\n",
        "                        input_resolution=model_stat[\"clip_model\"].visual.input_resolution\n",
        "                    except:\n",
        "                        input_resolution=224\n",
        "\n",
        "                    cuts = MakeCutoutsDango(input_resolution,\n",
        "                            Overview= args.cut_overview[1000-t_int], \n",
        "                            InnerCrop = args.cut_innercut[1000-t_int],\n",
        "                            IC_Size_Pow=args.cut_ic_pow[1000-t_int],\n",
        "                            IC_Grey_P = args.cut_icgray_p[1000-t_int]\n",
        "                            )\n",
        "                    clip_in = normalize(cuts(x_in.add(1).div(2)))\n",
        "                    image_embeds = model_stat[\"clip_model\"].encode_image(clip_in).float()\n",
        "                    dists = spherical_dist_loss(image_embeds.unsqueeze(1), model_stat[\"target_embeds\"].unsqueeze(0))\n",
        "                    dists = spherical_dist_loss(image_embeds.unsqueeze(1), model_stat[\"target_embeds\"].unsqueeze(0))\n",
        "                    dists = dists.view([args.cut_overview[1000-t_int]+args.cut_innercut[1000-t_int], n, -1])\n",
        "\n",
        "                    step_weights = []\n",
        "                    for i in range(0, len(model_stat[\"weights\"])):\n",
        "                      step_weights += [model_stat[\"weights\"][i][1000-t_int]]\n",
        "                      \n",
        "                    if np.absolute(np.sum(step_weights)) < 1e-3: raise RuntimeError('The weights must not sum to 0.')\n",
        "                    step_weights_divisor = np.absolute(np.sum(step_weights))\n",
        "                    step_weights = [item / step_weights_divisor for item in step_weights]\n",
        "                    step_weights = torch.tensor(step_weights, device=device)\n",
        "                    losses = dists.mul(step_weights).sum(2).mean(0)\n",
        "                    loss_values.append(losses.sum().item()) # log loss, probably shouldn't do per cutn_batch\n",
        "                    x_in_grad += torch.autograd.grad(losses.sum() * clip_guidance_scale, x_in)[0] / cutn_batches\n",
        "\n",
        "              #print(\"step_weights are \" + str(step_weights))\n",
        "              tv_losses = tv_loss(x_in)\n",
        "              if use_secondary_model is True:\n",
        "                range_losses = range_loss(out)\n",
        "              else:\n",
        "                range_losses = range_loss(out['pred_xstart'])\n",
        "              sat_losses = torch.abs(x_in - x_in.clamp(min=-1,max=1)).mean()\n",
        "              loss = tv_losses.sum() * tv_scale + range_losses.sum() * range_scale + sat_losses.sum() * sat_scale\n",
        "              if init is not None and init_scale:\n",
        "                  init_losses = lpips_model(x_in, init)\n",
        "                  loss = loss + init_losses.sum() * init_scale\n",
        "              x_in_grad += torch.autograd.grad(loss, x_in)[0]\n",
        "              if torch.isnan(x_in_grad).any()==False:\n",
        "                  grad = -torch.autograd.grad(x_in, x, x_in_grad)[0]\n",
        "              else:\n",
        "                # print(\"NaN'd\")\n",
        "                x_is_NaN = True\n",
        "                grad = torch.zeros_like(x)\n",
        "          if args.clamp_grad and x_is_NaN == False:\n",
        "              magnitude = grad.square().mean().sqrt()\n",
        "              return grad * magnitude.clamp(max=args.clamp_max) / magnitude  #min=-0.02, min=-clamp_max, \n",
        "          return grad\n",
        "  \n",
        "      if args.diffusion_sampling_mode == 'ddim':\n",
        "          sample_fn = diffusion.ddim_sample_loop_progressive\n",
        "      else:\n",
        "          sample_fn = diffusion.plms_sample_loop_progressive\n",
        "\n",
        "\n",
        "      image_display = Output()\n",
        "      current_img = None\n",
        "      seed_list = []\n",
        "\n",
        "      for i in range(args.n_batches):\n",
        "\n",
        "          # Increment Batch Seed\n",
        "          if increment_n_batch_seed:\n",
        "              if i == 0:\n",
        "                  global original_seed\n",
        "                  original_seed = seed\n",
        "              seed_list.append(seed)\n",
        "              seed += 1\n",
        "\n",
        "          if seed is not None:\n",
        "              np.random.seed(seed)\n",
        "              random.seed(seed)\n",
        "              torch.manual_seed(seed)\n",
        "              torch.cuda.manual_seed_all(seed)\n",
        "              torch.backends.cudnn.deterministic = True\n",
        "\n",
        "          # CLIP Text Prompt Setup\n",
        "\n",
        "          target_embeds, weights = [], []\n",
        "          \n",
        "          if args.prompts_series is not None and frame_num >= len(args.prompts_series):\n",
        "            frame_prompt = args.prompts_series[-1]\n",
        "          elif args.prompts_series is not None:\n",
        "            frame_prompt = args.prompts_series[frame_num]\n",
        "          else:\n",
        "            frame_prompt = []\n",
        "        \n",
        "          if args.image_prompts_series is not None and frame_num >= len(args.image_prompts_series):\n",
        "            print(args.image_prompts_series, defaultprint=True)\n",
        "            image_prompt = args.image_prompts_series[-1]\n",
        "          elif args.image_prompts_series is not None:\n",
        "            image_prompt = args.image_prompts_series[frame_num]\n",
        "          else:\n",
        "            image_prompt = []\n",
        "\n",
        "          import pprint\n",
        "          from pprint import pprint, pformat\n",
        "\n",
        "          # Parse Runtime Noodle Soup Prompts\n",
        "          if generate_nsp_at_runtime:\n",
        "              frame_prompt = nsp_parse(frame_prompt)\n",
        "              runtime_prompts.append(frame_prompt)\n",
        "\n",
        "          # Go BIG Prompt Recall\n",
        "          if args.go_big:\n",
        "              global go_big_prompts\n",
        "              if go_big_prompts == None:\n",
        "                  go_big_prompts = frame_prompt\n",
        "              else:\n",
        "                  frame_prompt = go_big_prompts\n",
        "\n",
        "          model_stats = []\n",
        "          for clip_model in clip_models:\n",
        "                cutn = 16\n",
        "                model_stat = {\"clip_model\":None,\"target_embeds\":[],\"make_cutouts\":None,\"weights\":[]}\n",
        "                model_stat[\"clip_model\"] = clip_model\n",
        "\n",
        "                for prompt in frame_prompt:\n",
        "\n",
        "                    txt, weight = parse_prompt(prompt)\n",
        "                    txt = clip_model.encode_text(clip.tokenize(prompt).to(device)).float()\n",
        "                    \n",
        "                    if args.fuzzy_prompt:\n",
        "                        for fi in range(25):\n",
        "                            model_stat[\"target_embeds\"].append((txt + torch.randn(txt.shape).cuda() * args.rand_mag).clamp(0,1))\n",
        "                            model_stat[\"weights\"].append(weight)\n",
        "                    else:\n",
        "                        model_stat[\"target_embeds\"].append(txt)\n",
        "                        model_stat[\"weights\"].append(weight)\n",
        "\n",
        "                if image_prompt:\n",
        "                  model_stat[\"make_cutouts\"] = MakeCutouts(clip_model.visual.input_resolution, cutn, skip_augs=skip_augs) \n",
        "                  for prompt in image_prompt:\n",
        "                      path, weight = parse_prompt(prompt)\n",
        "                      img = Image.open(fetch(path)).convert('RGB')\n",
        "                      img = TF.resize(img, min(side_x, side_y, *img.size), T.InterpolationMode.LANCZOS)\n",
        "                      batch = model_stat[\"make_cutouts\"](TF.to_tensor(img).to(device).unsqueeze(0).mul(2).sub(1))\n",
        "                      embed = clip_model.encode_image(normalize(batch)).float()\n",
        "                      if fuzzy_prompt:\n",
        "                          for fi in range(25):\n",
        "                              model_stat[\"target_embeds\"].append((embed + torch.randn(embed.shape).cuda() * rand_mag).clamp(0,1))\n",
        "                              weights.extend([weight / cutn] * cutn)\n",
        "                      else:\n",
        "                          model_stat[\"target_embeds\"].append(embed)\n",
        "                          model_stat[\"weights\"].extend([weight / cutn] * cutn)\n",
        "            \n",
        "                model_stat[\"target_embeds\"] = torch.cat(model_stat[\"target_embeds\"])\n",
        "                model_stats.append(model_stat)\n",
        "    \n",
        "          # if no init_masked is provided, we make one with the render mask\n",
        "          def make_masked_init(image, mask):\n",
        "            image = np.array(image)\n",
        "            image = image.astype(np.float32)/255.0\n",
        "            image = image[None].transpose(0,3,1,2)\n",
        "            image = torch.from_numpy(image)\n",
        "\n",
        "            mask = np.array(mask)\n",
        "            mask = mask.astype(np.float32)/255.0\n",
        "            mask = mask[None,None]\n",
        "            mask = torch.from_numpy(mask)\n",
        "\n",
        "            masked_image = (0+mask)*image\n",
        "            return masked_image\n",
        "\n",
        "          batch_init = False\n",
        "          \n",
        "          # CLIP init Prompts\n",
        "          if not run_clip_init:\n",
        "              init = None\n",
        "              if args.init_image is not None:\n",
        "                  if type(args.init_image) is str:\n",
        "                      init_img = args.init_image\n",
        "                  else:\n",
        "                      init_img = args.init_image[i] if i < len(args.init_image) else args.init_image[-1]\n",
        "                  if type(args.init_image) is list:\n",
        "                      batch_init = f'Working on init_image {i+1}: {init_img}'\n",
        "                  init_img = PIL.Image.open(fetch(init_img)).convert('RGB')\n",
        "                  if args.do_prep_init_image and current_slice is None:\n",
        "                      init_img = init_image_prep(init_img, args.gaussian_blur_radius, args.gaussian_noise_amount, args.gaussian_noise_blur_radius, args.gaussian_noise_greyscale, args.gaussian_noise_double, True)\n",
        "                  init_img = init_img.resize((args.side_x, args.side_y), get_resampling_mode())\n",
        "                  if args.do_go_big_slice_prep and current_slice is not None:\n",
        "                      init_img = init_image_prep(init_img, args.gaussian_blur_radius, args.gaussian_noise_amount, args.gaussian_noise_blur_radius, args.gaussian_noise_greyscale, args.gaussian_noise_double, True)\n",
        "                  if args.init_masked is not None:\n",
        "                    init_masked_img = PIL.Image.open(fetch(args.init_masked)).convert('RGB')\n",
        "                    init = TF.to_tensor(init_masked_img).to(device).unsqueeze(0).mul(2).sub(1)\n",
        "                  else:\n",
        "                    init = TF.to_tensor(init_img).to(device).unsqueeze(0).mul(2).sub(1)\n",
        "                  init_img = init_img.convert('RGBA')\n",
        "                \n",
        "              rmask = None\n",
        "              if args.render_mask is not None:\n",
        "                rmask_img = PIL.Image.open(fetch(args.render_mask)).convert('L')\n",
        "                rmask_img = rmask_img.resize((args.side_x, args.side_y), get_resampling_mode())\n",
        "                rmask = TF.to_tensor(rmask_img).to(device).unsqueeze(0)\n",
        "                if args.init_masked is None:\n",
        "                  init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)\n",
        "                  init = TF.to_pil_image(TF.to_tensor(init).clamp(0, 1).squeeze())\n",
        "                  init_mask = make_masked_init(init, rmask_img).to(device)\n",
        "                  init_mask = TF.to_pil_image(init_mask.clamp(0, 1).squeeze())\n",
        "                  init = TF.to_tensor(init_mask).to(device).unsqueeze(0).mul(2).sub(1)\n",
        "\n",
        "              if (args.perlin_init == True) and (args.init_image == None):\n",
        "                  if args.perlin_mode == 'color':\n",
        "                      init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)\n",
        "                      init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, False)\n",
        "                  elif args.perlin_mode == 'gray':\n",
        "                    init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, True)\n",
        "                    init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)\n",
        "                  else:\n",
        "                    init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)\n",
        "                    init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)\n",
        "                  # init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device)\n",
        "                  init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device).unsqueeze(0).mul(2).sub(1)\n",
        "                  del init2\n",
        "              if args.init_image is None or type(args.init_image) is str:\n",
        "                 run_clip_init = True\n",
        "\n",
        "\n",
        "          if args.animation_mode == 'None':\n",
        "            display.clear_output(wait=True)\n",
        "            barMax = args.n_batches-1 if args.n_batches > 1 else args.n_batches\n",
        "            descType = 'Slices' if current_slice is not None else 'Batches'\n",
        "            batchBar = tqdm(range(barMax), desc=descType)\n",
        "            batchBar.n = i\n",
        "            batchBar.refresh()\n",
        "          print('', defaultprint=True)\n",
        "          display.display(image_display)\n",
        "          import pprint\n",
        "          if current_slice is not None:\n",
        "              print(f\":scissors: \\033[1mSlice {str(current_slice)} :seedling: Seed: {seed}\\033[0m\")\n",
        "          else:\n",
        "              print(f\":card_index_dividers: \\033[1mBatch {str(i)} :seedling: Seed: {seed}\\033[0m\")\n",
        "          print(':black_nib: Prompt:', pprint.pformat(frame_prompt, indent=4))\n",
        "          if batch_init:\n",
        "              print(':framed_picture:', batch_init)\n",
        "          gc.collect()\n",
        "          torch.cuda.empty_cache()\n",
        "          cur_t = diffusion.num_timesteps - skip_steps - 1\n",
        "          total_steps = cur_t\n",
        "\n",
        "          if perlin_init:\n",
        "              init = regen_perlin()\n",
        "\n",
        "          if args.diffusion_sampling_mode == 'ddim':\n",
        "              samples = sample_fn(\n",
        "                  model,\n",
        "                  (batch_size, 3, args.side_y, args.side_x),\n",
        "                  clip_denoised=clip_denoised,\n",
        "                  model_kwargs={},\n",
        "                  cond_fn=cond_fn,\n",
        "                  progress=True,\n",
        "                  skip_timesteps=skip_steps,\n",
        "                  init_image=init,\n",
        "                  randomize_class=randomize_class,\n",
        "                  eta=eta,\n",
        "                  transformation_fn=symmetry_transformation_fn,\n",
        "                  transformation_percent=args.transformation_percent\n",
        "              )\n",
        "          else:\n",
        "              samples = sample_fn(\n",
        "                  model,\n",
        "                  (batch_size, 3, args.side_y, args.side_x),\n",
        "                  clip_denoised=clip_denoised,\n",
        "                  model_kwargs={},\n",
        "                  cond_fn=cond_fn,\n",
        "                  progress=True,\n",
        "                  skip_timesteps=skip_steps,\n",
        "                  init_image=init,\n",
        "                  randomize_class=randomize_class,\n",
        "                  order=2,\n",
        "              )\n",
        "          \n",
        "          # with run_display:\n",
        "          #display.clear_output(wait=True)\n",
        "          for j, sample in enumerate(samples):    \n",
        "            cur_t -= 1\n",
        "            intermediateStep = False\n",
        "            if args.steps_per_checkpoint is not None:\n",
        "                if j % steps_per_checkpoint == 0 and j > 0:\n",
        "                  intermediateStep = True\n",
        "            elif j in args.intermediate_saves:\n",
        "              intermediateStep = True\n",
        "            with image_display:\n",
        "              if j % args.display_rate == 0 or cur_t == -1 or intermediateStep == True:\n",
        "                  for k, image in enumerate(sample['pred_xstart']):\n",
        "                      # tqdm.write(f'Batch {i}, step {j}, output {k}:')\n",
        "                      current_time = datetime.now().strftime('%y%m%d-%H%M%S_%f')\n",
        "                      percent = math.ceil(j/total_steps*100)\n",
        "                      filename_figure = f'{args.batch_name}({args.batchNum})_figure.png'\n",
        "                      if args.n_batches > 0:\n",
        "                        #if intermediates are saved to the subfolder, don't append a step or percentage to the name\n",
        "                        if cur_t == -1 and args.intermediates_in_subfolder is True:\n",
        "                          save_num = f'{frame_num:04}' if animation_mode != \"None\" else i\n",
        "                          filename = f'{args.batch_name}({args.batchNum})_{save_num}.png'\n",
        "                          filename_figure = f'{args.batch_name}({args.batchNum})_{save_num}_figure.png'\n",
        "                        else:\n",
        "                          #If we're working with percentages, append it\n",
        "                          if args.steps_per_checkpoint is not None:\n",
        "                            filename = f'{args.batch_name}({args.batchNum})_{i:04}-{percent:02}%.png'\n",
        "                            filename_figure = f'{args.batch_name}({args.batchNum})_{i:04}-{percent:02}%_figure.png'\n",
        "                          # Or else, iIf we're working with specific steps, append those\n",
        "                          else:\n",
        "                            filename = f'{args.batch_name}({args.batchNum})_{i:04}-{j:03}.png'\n",
        "                            filename_figure = f'{args.batch_name}({args.batchNum})_{i:04}-{j:03}_figure.png'\n",
        "\n",
        "                      image = TF.to_pil_image(image.add(1).div(2).clamp(0, 1))\n",
        "                      if j % args.display_rate == 0 or cur_t == -1:\n",
        "                        image.save('progress.png')\n",
        "                        display.clear_output(wait=True)\n",
        "                        display.display(display.Image('progress.png'))\n",
        "                      if args.steps_per_checkpoint is not None:\n",
        "                        if j % args.steps_per_checkpoint == 0 and j > 0:\n",
        "                          if args.intermediates_in_subfolder is True:\n",
        "                            image.save(f'{partialFolder}/{filename}')\n",
        "                          else:\n",
        "                            image.save(f'{batchFolder}/{filename}')\n",
        "                      else:\n",
        "                        if j in args.intermediate_saves:\n",
        "                          if args.intermediates_in_subfolder is True:\n",
        "                            image.save(f'{partialFolder}/{filename}')\n",
        "                          else:\n",
        "                            image.save(f'{batchFolder}/{filename}')\n",
        "                      if cur_t == -1:\n",
        "                        if args.save_metadata:\n",
        "                          metadata = PngInfo()\n",
        "                          if args.meta_author is not None and args.meta_author.lower() not in ['','none']:\n",
        "                            metadata.add_text(\"author\", args.meta_author)\n",
        "                          if args.meta_copyright is not None and args.meta_copyright not in ['','none']:\n",
        "                            metadata.add_text(\"copyright\", args.meta_copyright)\n",
        "                          metadata.add_text(\"software\", appname)\n",
        "                          metadata.add_text(\"prompt\",str(frame_prompt))\n",
        "                          metadata.add_text(\"seed\", str(seed))\n",
        "                          metadata.add_text(\"randomized_prompt\", str(frame_prompt))\n",
        "                        if frame_num == 0:\n",
        "                          save_settings()\n",
        "                        if args.animation_mode != \"None\":\n",
        "                          image.save('prevFrame.png')\n",
        "                        if render_mask:\n",
        "                          image2 = image.copy()\n",
        "                          image2.putalpha(rmask_img)\n",
        "                          image3 = image2.copy()\n",
        "                          image3 = Image.alpha_composite(init_img, image3)\n",
        "                          image = image3.copy()\n",
        "                          image.save('progress.png')\n",
        "                        if args.save_metadata:\n",
        "                          image.save(f'{batchFolder}/{filename}', pnginfo=metadata)\n",
        "                          if save_plot_graph:\n",
        "                              plt.savefig(f'{batchFolder}/{filename_figure}')\n",
        "                          #loss_values = []  \n",
        "                        else:\n",
        "                          print(f'Saving image to: {batchFolder}/{filename}')\n",
        "                          image.save(f'{batchFolder}/{filename}')\n",
        "                          if save_plot_graph:\n",
        "                            plt.savefig(f'{batchFolder}/{filename_figure}')\n",
        "                          #loss_values = []  \n",
        "                        if args.animation_mode == \"3D\":\n",
        "                          # If turbo, save a blended image\n",
        "                          if turbo_mode and frame_num > 0:\n",
        "                            # Mix new image with prevFrameScaled\n",
        "                            blend_factor = (1)/int(turbo_steps)\n",
        "                            newFrame = cv2.imread('prevFrame.png') # This is already updated..\n",
        "                            prev_frame_warped = cv2.imread('prevFrameScaled.png')\n",
        "                            blendedImage = cv2.addWeighted(newFrame, blend_factor, prev_frame_warped, (1-blend_factor), 0.0)\n",
        "                            cv2.imwrite(f'{batchFolder}/{filename}',blendedImage)\n",
        "                            if save_plot_graph:\n",
        "                                plt.savefig(f'{batchFolder}/{filename_figure}')\n",
        "                          else:\n",
        "                            image.save(f'{batchFolder}/{filename}')\n",
        "                            if save_plot_graph:\n",
        "                                plt.savefig(f'{batchFolder}/{filename_figure}')\n",
        "                          if vr_mode:\n",
        "                            generate_eye_views(TRANSLATION_SCALE, batchFolder, filename, frame_num, midas_model, midas_transform)\n",
        "                        \n",
        "                        # if frame_num != args.max_frames-1:\n",
        "                        #   display.clear_output()\n",
        "                        \n",
        "          # All conditions end. Process complete\n",
        "          if seed_list:\n",
        "              print(':seedling: Seed List:', str(seed_list))\n",
        "          plt.plot(np.array(loss_values), 'r')\n",
        "          loss_values = []\n",
        "      seed_list = []\n",
        "\n",
        "def generate_eye_views(trans_scale,batchFolder,filename,frame_num,midas_model, midas_transform):\n",
        "   for i in range(2):\n",
        "      theta = vr_eye_angle * (math.pi/180)\n",
        "      ray_origin = math.cos(theta) * vr_ipd / 2 * (-1.0 if i==0 else 1.0)\n",
        "      ray_rotation = (theta if i==0 else -theta)\n",
        "      translate_xyz = [-(ray_origin)*trans_scale, 0,0]\n",
        "      rotate_xyz = [0, (ray_rotation), 0]\n",
        "      rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), \"XYZ\").unsqueeze(0)\n",
        "      transformed_image = dxf.transform_image_3d(f'{batchFolder}/{filename}', midas_model, midas_transform, DEVICE,\n",
        "                                                      rot_mat, translate_xyz, args.near_plane, args.far_plane,\n",
        "                                                      args.fov, padding_mode=args.padding_mode,\n",
        "                                                      sampling_mode=args.sampling_mode, midas_weight=args.midas_weight,spherical=True)\n",
        "      eye_file_path = batchFolder+f\"/frame_{frame_num:04}\" + ('_l' if i==0 else '_r')+'.png'\n",
        "      transformed_image.save(eye_file_path)\n",
        "\n",
        "def save_settings():\n",
        "    setting_list = {\n",
        "      'generate_nsp_at_runtime': generate_nsp_at_runtime,\n",
        "      'text_prompts': text_prompts,\n",
        "      'runtime_prompts': None if not runtime_prompts else runtime_prompts,\n",
        "      'go_big_prompts': None if not go_big_prompts else go_big_prompts,\n",
        "      'image_prompts': image_prompts,\n",
        "      'clip_guidance_scale': clip_guidance_scale,\n",
        "      'tv_scale': tv_scale,\n",
        "      'range_scale': range_scale,\n",
        "      'sat_scale': sat_scale,\n",
        "      # 'cutn': cutn,\n",
        "      'cutn_batches': cutn_batches,\n",
        "      'max_frames': max_frames,\n",
        "      'interp_spline': interp_spline,\n",
        "      'go_big': go_big,\n",
        "      'go_big_scale': go_big_scale,\n",
        "      'gobig_skip_percent': gobig_skip_percent,\n",
        "      'gobig_overlap': gobig_overlap,\n",
        "      'gobig_maximize': gobig_maximize,\n",
        "      'go_big_init': go_big_init,\n",
        "      'go_big_init_skipresize': go_big_init_skipresize,\n",
        "      'init_masked': init_masked,\n",
        "      'render_mask': render_mask,\n",
        "      # 'rotation_per_frame': rotation_per_frame,\n",
        "      'init_image': init_image,\n",
        "      'init_scale': init_scale,\n",
        "      'skip_steps': skip_steps,\n",
        "      'do_prep_init_image': do_prep_init_image,\n",
        "      'do_go_big_slice_prep': do_go_big_slice_prep,\n",
        "      'gaussian_blur_radius': gaussian_blur_radius,\n",
        "      'gaussian_noise_amount': gaussian_noise_amount,\n",
        "      'gaussian_noise_blur_radius': gaussian_noise_blur_radius,\n",
        "      'gaussian_noise_greyscale': gaussian_noise_greyscale,\n",
        "      'gaussian_noise_double': gaussian_noise_double,\n",
        "      # 'zoom_per_frame': zoom_per_frame,\n",
        "      'frames_scale': frames_scale,\n",
        "      'frames_skip_steps': frames_skip_steps,\n",
        "      'perlin_init': perlin_init,\n",
        "      'perlin_mode': perlin_mode,\n",
        "      'skip_augs': skip_augs,\n",
        "      'randomize_class': randomize_class,\n",
        "      'clip_denoised': clip_denoised,\n",
        "      'clamp_grad': clamp_grad,\n",
        "      'clamp_max': clamp_max,\n",
        "      'seed': seed,\n",
        "      'fuzzy_prompt': fuzzy_prompt,\n",
        "      'rand_mag': rand_mag,\n",
        "      'eta': eta,\n",
        "      'width': width_height[0],\n",
        "      'height': width_height[1],\n",
        "      'diffusion_model': diffusion_model,\n",
        "      'use_secondary_model': use_secondary_model,\n",
        "      'use_checkpoint': use_checkpoint,\n",
        "      'steps': steps,\n",
        "      'diffusion_steps': diffusion_steps,\n",
        "      'diffusion_sampling_mode': diffusion_sampling_mode,\n",
        "      'ViTB32': ViTB32,\n",
        "      'ViTB16': ViTB16,\n",
        "      'ViTL14': ViTL14,\n",
        "      'ViTL14_336px': ViTL14_336px,\n",
        "      'RN101': RN101,\n",
        "      'RN50': RN50,\n",
        "      'RN50x4': RN50x4,\n",
        "      'RN50x16': RN50x16,\n",
        "      'RN50x64': RN50x64,\n",
        "      'ViTB32_laion2b_e16': ViTB32_laion2b_e16,\n",
        "      'ViTB32_laion400m_e31': ViTB32_laion400m_e31,\n",
        "      'ViTB32_laion400m_32': ViTB32_laion400m_32,\n",
        "      'ViTB32quickgelu_laion400m_e31': ViTB32quickgelu_laion400m_e31,\n",
        "      'ViTB32quickgelu_laion400m_e32': ViTB32quickgelu_laion400m_e32,\n",
        "      'ViTB16_laion400m_e31': ViTB16_laion400m_e31,\n",
        "      'ViTB16_laion400m_e32': ViTB16_laion400m_e32,\n",
        "      'RN50_yffcc15m': RN50_yffcc15m,\n",
        "      'RN50_cc12m': RN50_cc12m,\n",
        "      'RN50_quickgelu_yfcc15m': RN50_quickgelu_yfcc15m,\n",
        "      'RN50_quickgelu_cc12m': RN50_quickgelu_cc12m,\n",
        "      'RN101_yfcc15m': RN101_yfcc15m,\n",
        "      'RN101_quickgelu_yfcc15m': RN101_quickgelu_yfcc15m,\n",
        "      'cut_overview': str(cut_overview),\n",
        "      'cut_innercut': str(cut_innercut),\n",
        "      'cut_ic_pow': str(cut_ic_pow),\n",
        "      'cut_icgray_p': str(cut_icgray_p),\n",
        "      'key_frames': key_frames,\n",
        "      'max_frames': max_frames,\n",
        "      'angle': angle,\n",
        "      'zoom': zoom,\n",
        "      'translation_x': translation_x,\n",
        "      'translation_y': translation_y,\n",
        "      'translation_z': translation_z,\n",
        "      'rotation_3d_x': rotation_3d_x,\n",
        "      'rotation_3d_y': rotation_3d_y,\n",
        "      'rotation_3d_z': rotation_3d_z,\n",
        "      'midas_depth_model': midas_depth_model,\n",
        "      'midas_weight': midas_weight,\n",
        "      'near_plane': near_plane,\n",
        "      'far_plane': far_plane,\n",
        "      'fov': fov,\n",
        "      'padding_mode': padding_mode,\n",
        "      'sampling_mode': sampling_mode,\n",
        "      'video_init_path':video_init_path,\n",
        "      'extract_nth_frame':extract_nth_frame,\n",
        "      'video_init_seed_continuity': video_init_seed_continuity,\n",
        "      'turbo_mode':turbo_mode,\n",
        "      'turbo_steps':turbo_steps,\n",
        "      'turbo_preroll':turbo_preroll,\n",
        "      'use_horizontal_symmetry':use_horizontal_symmetry,\n",
        "      'use_vertical_symmetry':use_vertical_symmetry,\n",
        "      'transformation_percent':transformation_percent,\n",
        "      #video init settings\n",
        "      'video_init_steps': video_init_steps,\n",
        "      'video_init_clip_guidance_scale': video_init_clip_guidance_scale,\n",
        "      'video_init_tv_scale': video_init_tv_scale,\n",
        "      'video_init_range_scale': video_init_range_scale,\n",
        "      'video_init_sat_scale': video_init_sat_scale,\n",
        "      'video_init_cutn_batches': video_init_cutn_batches,\n",
        "      'video_init_skip_steps': video_init_skip_steps,\n",
        "      'video_init_frames_scale': video_init_frames_scale,\n",
        "      'video_init_frames_skip_steps': video_init_frames_skip_steps,\n",
        "      #warp settings\n",
        "      'video_init_flow_warp':video_init_flow_warp,\n",
        "      'video_init_flow_blend':video_init_flow_blend,\n",
        "      'video_init_check_consistency':video_init_check_consistency,\n",
        "      'video_init_blend_mode':video_init_blend_mode\n",
        "    }\n",
        "    # print('Settings:', setting_list)\n",
        "    with open(f\"{batchFolder}/{batch_name}({batchNum})_settings.txt\", \"w+\") as f:   #save settings\n",
        "        json.dump(setting_list, f, ensure_ascii=False, indent=4)\n",
        "\n",
        "# DEFINE SECONDARY MODEL\n",
        "\n",
        "print(f\"\\n:check_mark_button: \\033[1mMain Functions Initialized\\033[0m\")\n",
        "print(f\"\\n--[ Initializing Secondary Model ]--\")\n",
        "\n",
        "def append_dims(x, n):\n",
        "    return x[(Ellipsis, *(None,) * (n - x.ndim))]\n",
        "\n",
        "\n",
        "def expand_to_planes(x, shape):\n",
        "    return append_dims(x, len(shape)).repeat([1, 1, *shape[2:]])\n",
        "\n",
        "\n",
        "def alpha_sigma_to_t(alpha, sigma):\n",
        "    return torch.atan2(sigma, alpha) * 2 / math.pi\n",
        "\n",
        "\n",
        "def t_to_alpha_sigma(t):\n",
        "    return torch.cos(t * math.pi / 2), torch.sin(t * math.pi / 2)\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class DiffusionOutput:\n",
        "    v: torch.Tensor\n",
        "    pred: torch.Tensor\n",
        "    eps: torch.Tensor\n",
        "\n",
        "\n",
        "class ConvBlock(nn.Sequential):\n",
        "    def __init__(self, c_in, c_out):\n",
        "        super().__init__(\n",
        "            nn.Conv2d(c_in, c_out, 3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "        )\n",
        "\n",
        "\n",
        "class SkipBlock(nn.Module):\n",
        "    def __init__(self, main, skip=None):\n",
        "        super().__init__()\n",
        "        self.main = nn.Sequential(*main)\n",
        "        self.skip = skip if skip else nn.Identity()\n",
        "\n",
        "    def forward(self, input):\n",
        "        return torch.cat([self.main(input), self.skip(input)], dim=1)\n",
        "\n",
        "\n",
        "class FourierFeatures(nn.Module):\n",
        "    def __init__(self, in_features, out_features, std=1.):\n",
        "        super().__init__()\n",
        "        assert out_features % 2 == 0\n",
        "        self.weight = nn.Parameter(torch.randn([out_features // 2, in_features]) * std)\n",
        "\n",
        "    def forward(self, input):\n",
        "        f = 2 * math.pi * input @ self.weight.T\n",
        "        return torch.cat([f.cos(), f.sin()], dim=-1)\n",
        "\n",
        "\n",
        "class SecondaryDiffusionImageNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        c = 64  # The base channel count\n",
        "\n",
        "        self.timestep_embed = FourierFeatures(1, 16)\n",
        "\n",
        "        self.net = nn.Sequential(\n",
        "            ConvBlock(3 + 16, c),\n",
        "            ConvBlock(c, c),\n",
        "            SkipBlock([\n",
        "                nn.AvgPool2d(2),\n",
        "                ConvBlock(c, c * 2),\n",
        "                ConvBlock(c * 2, c * 2),\n",
        "                SkipBlock([\n",
        "                    nn.AvgPool2d(2),\n",
        "                    ConvBlock(c * 2, c * 4),\n",
        "                    ConvBlock(c * 4, c * 4),\n",
        "                    SkipBlock([\n",
        "                        nn.AvgPool2d(2),\n",
        "                        ConvBlock(c * 4, c * 8),\n",
        "                        ConvBlock(c * 8, c * 4),\n",
        "                        nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),\n",
        "                    ]),\n",
        "                    ConvBlock(c * 8, c * 4),\n",
        "                    ConvBlock(c * 4, c * 2),\n",
        "                    nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),\n",
        "                ]),\n",
        "                ConvBlock(c * 4, c * 2),\n",
        "                ConvBlock(c * 2, c),\n",
        "                nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),\n",
        "            ]),\n",
        "            ConvBlock(c * 2, c),\n",
        "            nn.Conv2d(c, 3, 3, padding=1),\n",
        "        )\n",
        "\n",
        "    def forward(self, input, t):\n",
        "        timestep_embed = expand_to_planes(self.timestep_embed(t[:, None]), input.shape)\n",
        "        v = self.net(torch.cat([input, timestep_embed], dim=1))\n",
        "        alphas, sigmas = map(partial(append_dims, n=v.ndim), t_to_alpha_sigma(t))\n",
        "        pred = input * alphas - v * sigmas\n",
        "        eps = input * sigmas + v * alphas\n",
        "        return DiffusionOutput(v, pred, eps)\n",
        "\n",
        "\n",
        "class SecondaryDiffusionImageNet2(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        c = 64  # The base channel count\n",
        "        cs = [c, c * 2, c * 2, c * 4, c * 4, c * 8]\n",
        "\n",
        "        self.timestep_embed = FourierFeatures(1, 16)\n",
        "        self.down = nn.AvgPool2d(2)\n",
        "        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)\n",
        "\n",
        "        self.net = nn.Sequential(\n",
        "            ConvBlock(3 + 16, cs[0]),\n",
        "            ConvBlock(cs[0], cs[0]),\n",
        "            SkipBlock([\n",
        "                self.down,\n",
        "                ConvBlock(cs[0], cs[1]),\n",
        "                ConvBlock(cs[1], cs[1]),\n",
        "                SkipBlock([\n",
        "                    self.down,\n",
        "                    ConvBlock(cs[1], cs[2]),\n",
        "                    ConvBlock(cs[2], cs[2]),\n",
        "                    SkipBlock([\n",
        "                        self.down,\n",
        "                        ConvBlock(cs[2], cs[3]),\n",
        "                        ConvBlock(cs[3], cs[3]),\n",
        "                        SkipBlock([\n",
        "                            self.down,\n",
        "                            ConvBlock(cs[3], cs[4]),\n",
        "                            ConvBlock(cs[4], cs[4]),\n",
        "                            SkipBlock([\n",
        "                                self.down,\n",
        "                                ConvBlock(cs[4], cs[5]),\n",
        "                                ConvBlock(cs[5], cs[5]),\n",
        "                                ConvBlock(cs[5], cs[5]),\n",
        "                                ConvBlock(cs[5], cs[4]),\n",
        "                                self.up,\n",
        "                            ]),\n",
        "                            ConvBlock(cs[4] * 2, cs[4]),\n",
        "                            ConvBlock(cs[4], cs[3]),\n",
        "                            self.up,\n",
        "                        ]),\n",
        "                        ConvBlock(cs[3] * 2, cs[3]),\n",
        "                        ConvBlock(cs[3], cs[2]),\n",
        "                        self.up,\n",
        "                    ]),\n",
        "                    ConvBlock(cs[2] * 2, cs[2]),\n",
        "                    ConvBlock(cs[2], cs[1]),\n",
        "                    self.up,\n",
        "                ]),\n",
        "                ConvBlock(cs[1] * 2, cs[1]),\n",
        "                ConvBlock(cs[1], cs[0]),\n",
        "                self.up,\n",
        "            ]),\n",
        "            ConvBlock(cs[0] * 2, cs[0]),\n",
        "            nn.Conv2d(cs[0], 3, 3, padding=1),\n",
        "        )\n",
        "\n",
        "    def forward(self, input, t):\n",
        "        timestep_embed = expand_to_planes(self.timestep_embed(t[:, None]), input.shape)\n",
        "        v = self.net(torch.cat([input, timestep_embed], dim=1))\n",
        "        alphas, sigmas = map(partial(append_dims, n=v.ndim), t_to_alpha_sigma(t))\n",
        "        pred = input * alphas - v * sigmas\n",
        "        eps = input * sigmas + v * alphas\n",
        "        return DiffusionOutput(v, pred, eps)\n",
        "\n",
        "print(\"\\n:check_mark_button: \\033[1mSecondary Model Initialized\\33[0m\")\n",
        "print(f\"\\n--[ :confetti_ball::party_popper: \\033[1m\\33[32m{appname} Environtment Setup Complete\\33[0m :party_popper::confetti_ball: ]--\")\n"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "G30IZD4IBLDE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 1.3 Load Disco Diffusion Settings File\n",
        "\n",
        "# LOAD SETTINGS - Implemented by WASasquatch\n",
        "#@markdown ##### **Load Settings from File**\n",
        "#@markdown By default a settings file overrides base diffusion settings, but if you wish, you can select from the following options to overwride more to form crucial parts of, or an entire settings file.\n",
        "settings_file_path = None #@param{type: 'string'}\n",
        "#@markdown Loading a settings file to bypass these settings\n",
        "load_prompts_and_inits = False #@param{type: 'boolean'}\n",
        "#@markdown Load text and image prompts, as well as their related settings*\n",
        "load_diffusion_models = True #@param{type: 'boolean'}\n",
        "#@markdown Load diffusion model settings from file\n",
        "load_CLIP_models = True #@param{type: 'boolean'}\n",
        "#@markdown Load CLIP model settings from file\n",
        "\n",
        "if settings_file_path in [\"none\",\"\"]:\n",
        "    settings_file_path = None"
      ],
      "metadata": {
        "cellView": "form",
        "id": "TOPRdNXa7Dlu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DiffClipSetTop"
      },
      "source": [
        "# 2. Diffusion and CLIP model settings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ModelSettings",
        "cellView": "form"
      },
      "source": [
        "#@markdown ####**Models Settings (note: For pixel art, the best is pixelartdiffusion_expanded):**\n",
        "diffusion_model = \"custom\" #@param [\"256x256_diffusion_uncond\", \"512x512_diffusion_uncond_finetune_008100\", \"concept_art_generator_v000-1_alpha\", \"concept_art_generator_v000-2_alpha\", \"portrait_generator_v001\", \"portrait_generator_v002\", \"portrait_generator_v003\", \"portrait_generator_v004\", \"portrait_generator_v005\", \"Architecture_Diffusion_1-5m\", \"pixelartdiffusion_expanded\", \"pixel_art_diffusion_hard_256\", \"pixel_art_diffusion_soft_256\", \"pixelartdiffusion4k\", \"watercolordiffusion_2\", \"watercolordiffusion\", \"PulpSciFiDiffusion\", \"Lithography_Diffusion\", \"Medieval_Diffusion\", \"Liminal_Diffusion_v1\", \"Liminal_Diffusion_Source\", \"Floral_Diffusion\", \"FeiArt_Handpainted_CG_Diffusion\", \"Textile_Diffusion\", \"Isometric_Diffusion_Revrart512px\", \"Laproper_Diffusion_Deepspace_256\", \"Schnippi_Diffusion_512x512_V2\", \"custom\"]\n",
        "\n",
        "go_big_diffusion_model = \"Inherit\" #@param [\"Inherit\", \"256x256_diffusion_uncond\", \"512x512_diffusion_uncond_finetune_008100\", \"concept_art_generator_v000-1_alpha\", \"portrait_generator_v001\", \"portrait_generator_v002\", \"portrait_generator_v003\", \"portrait_generator_v004\", \"pixelartdiffusion_expanded\", \"pixel_art_diffusion_hard_256\", \"pixel_art_diffusion_soft_256\", \"pixelartdiffusion4k\", \"watercolordiffusion_2\", \"watercolordiffusion\", \"PulpSciFiDiffusion\", \"Lithography_Diffusion\", \"Medieval_Diffusion\", \"Liminal_Diffusion_v1\", \"Liminal_Diffusion_Source\", \"Floral_Diffusion\", \"FeiArt_Handpainted_CG_Diffusion\", \"Textile_Diffusion\", \"Isometric_Diffusion_Revrart512px\", \"Laproper_Diffusion_Deepspace_256\", \"Schnippi_Diffusion_512x512_V2\", \"custom\"]\n",
        "#@markdown `go_big_diffusion_model`: Leave on `Inherit` if you want Go BIG to use `diffusion_model` instead of a custom selection\n",
        "\n",
        "use_secondary_model = False #@param {type: 'boolean'}\n",
        "diffusion_sampling_mode = 'ddim' #@param ['plms','ddim']\n",
        "#@markdown #####**Custom model:**\n",
        "custom_path = '/content/drive/MyDrive/AI/Checkpoints/concept-art-model/ema_0.9999_040000.pt'#@param {type: 'string'}\n",
        "#@markdown <br>\n",
        "\n",
        "#@markdown ###**CLIP settings:**\n",
        "use_checkpoint = True #@param {type: 'boolean'}\n",
        "ViTB32 = True #@param{type:\"boolean\"}\n",
        "ViTB16 = False #@param{type:\"boolean\"}\n",
        "ViTL14 = True #@param{type:\"boolean\"}\n",
        "ViTL14_336px = False #@param{type:\"boolean\"}\n",
        "RN101 = False #@param{type:\"boolean\"}\n",
        "RN50 = True #@param{type:\"boolean\"}\n",
        "RN50x4 = False #@param{type:\"boolean\"}\n",
        "RN50x16 = False #@param{type:\"boolean\"}\n",
        "RN50x64 = False #@param{type:\"boolean\"}\n",
        "#@markdown <br>\n",
        "\n",
        "#@markdown ###**OpenCLIP settings:**\n",
        "ViTB32_laion2b_e16 = False #@param{type:\"boolean\"}\n",
        "ViTB32_laion400m_e31 = False #@param{type:\"boolean\"}\n",
        "ViTB32_laion400m_32 = False #@param{type:\"boolean\"}\n",
        "ViTB32quickgelu_laion400m_e31 = False #@param{type:\"boolean\"}\n",
        "ViTB32quickgelu_laion400m_e32 = False #@param{type:\"boolean\"}\n",
        "ViTB16_laion400m_e31 = False #@param{type:\"boolean\"}\n",
        "ViTB16_laion400m_e32 = False #@param{type:\"boolean\"}\n",
        "RN50_yffcc15m = False #@param{type:\"boolean\"}\n",
        "RN50_cc12m = False #@param{type:\"boolean\"}\n",
        "RN50_quickgelu_yfcc15m = False #@param{type:\"boolean\"}\n",
        "RN50_quickgelu_cc12m = False #@param{type:\"boolean\"}\n",
        "RN101_yfcc15m = False #@param{type:\"boolean\"}\n",
        "RN101_quickgelu_yfcc15m = False #@param{type:\"boolean\"}\n",
        "#@markdown <br>\n",
        "\n",
        "#@markdown **Note:** If you're having issues with model downloads, check this to compare SHA's:\n",
        "check_model_SHA = False #@param{type:\"boolean\"}\n",
        "\n",
        "if custom_path is not None and custom_path.lower() in ['none', '']:\n",
        "    custom_path = None\n",
        "\n",
        "# LOAD SETTINGS - Implemented by WASasquatch\n",
        "if settings_file_path:\n",
        "\n",
        "    if exists(settings_file_path):\n",
        "\n",
        "        with open(settings_file_path) as json_file:\n",
        "\n",
        "            jsd = json.load(json_file)\n",
        "        \n",
        "            if load_diffusion_models:\n",
        "                print('Loading diffusion model settings from file: '+settings_file_path)\n",
        "                if jsd.__contains__('use_checkpoint'):\n",
        "                    use_checkpoint = jsd['use_checkpoint']\n",
        "                if jsd.__contains__('diffusion_model'):\n",
        "                    diffusion_model = jsd['diffusion_model']\n",
        "                if jsd.__contains__('use_secondary_model'):\n",
        "                    use_secondary_model = jsd['use_secondary_model']\n",
        "            \n",
        "            if load_CLIP_models:\n",
        "                if jsd.__contains__('ViTB32'):\n",
        "                    ViTB32 = jsd['ViTB32']\n",
        "                if jsd.__contains__('ViTB16'):\n",
        "                    ViTB16 = jsd['ViTB16']\n",
        "                if jsd.__contains__('ViTL14'):\n",
        "                    ViTL14 = jsd['ViTL14']\n",
        "                if jsd.__contains__('ViTL14_336px'):\n",
        "                    ViTL14_336px = jsd['ViTL14_336px']\n",
        "                if jsd.__contains__('RN101'):\n",
        "                    RN101 = jsd['RN101']\n",
        "                if jsd.__contains__('RN50'):\n",
        "                    RN50 = jsd['RN50']\n",
        "                if jsd.__contains__('RN50x4'):\n",
        "                    RN50x4 = jsd['RN50x4']\n",
        "                if jsd.__contains__('RN50x16'):\n",
        "                    RN50x16 = jsd['RN50x16']\n",
        "                if jsd.__contains__('RN50x64'):\n",
        "                    RN50x64 = jsd['RN50x64']\n",
        "                if jsd.__contains__('ViTB32_laion2b_e16'):\n",
        "                    ViTB32_laion2b_e16 = jsd['ViTB32_laion2b_e16']\n",
        "                if jsd.__contains__('ViTB32_laion400m_e31'):\n",
        "                    ViTB32_laion400m_e31 = jsd['ViTB32_laion400m_e31']\n",
        "                if jsd.__contains__('ViTB32_laion400m_32'):\n",
        "                    ViTB32_laion400m_32 = jsd['ViTB32_laion400m_32']\n",
        "                if jsd.__contains__('ViTB32quickgelu_laion400m_e31'):\n",
        "                    ViTB32quickgelu_laion400m_e31 = jsd['ViTB32quickgelu_laion400m_e31']\n",
        "                if jsd.__contains__('ViTB32quickgelu_laion400m_e32'):\n",
        "                    ViTB32quickgelu_laion400m_e32 = jsd['ViTB32quickgelu_laion400m_e32']\n",
        "                if jsd.__contains__('ViTB16_laion400m_e31'):\n",
        "                    ViTB16_laion400m_e31 = jsd['ViTB16_laion400m_e31']\n",
        "                if jsd.__contains__('ViTB16_laion400m_e32'):\n",
        "                    ViTB16_laion400m_e32 = jsd['ViTB16_laion400m_e32']\n",
        "                if jsd.__contains__('RN50_yffcc15m'):\n",
        "                    RN50_yffcc15m = jsd['RN50_yffcc15m']\n",
        "                if jsd.__contains__('RN50_cc12m'):\n",
        "                    RN50_cc12m = jsd['RN50_cc12m']\n",
        "                if jsd.__contains__('RN50_quickgelu_yfcc15m'):\n",
        "                    RN50_quickgelu_yfcc15m = jsd['RN50_quickgelu_yfcc15m']\n",
        "                if jsd.__contains__('RN50_quickgelu_cc12m'):\n",
        "                    RN50_quickgelu_cc12m = jsd['RN50_quickgelu_cc12m']\n",
        "                if jsd.__contains__('RN101_yfcc15m'):\n",
        "                    RN101_yfcc15m = jsd['RN101_yfcc15m']\n",
        "                if jsd.__contains__('RN101_quickgelu_yfcc15m'):\n",
        "                    RN101_quickgelu_yfcc15m = jsd['RN101_quickgelu_yfcc15m']\n",
        "\n",
        "diff_model_map = {\n",
        "    '256x256_diffusion_uncond': { 'downloaded': False, 'sha': 'a37c32fffd316cd494cf3f35b339936debdc1576dad13fe57c42399a5dbc78b1', 'uri_list': ['https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt', 'https://www.dropbox.com/s/9tqnqo930mpnpcn/256x256_diffusion_uncond.pt'] },\n",
        "    '512x512_diffusion_uncond_finetune_008100': { 'downloaded': False, 'sha': '9c111ab89e214862b76e1fa6a1b3f1d329b1a88281885943d2cdbe357ad57648', 'uri_list': ['https://huggingface.co/lowlevelware/512x512_diffusion_unconditional_ImageNet/resolve/main/512x512_diffusion_uncond_finetune_008100.pt', 'https://the-eye.eu/public/AI/models/512x512_diffusion_unconditional_ImageNet/512x512_diffusion_uncond_finetune_008100.pt'] },\n",
        "\n",
        "    'concept_art_generator_v000-1_alpha': { 'downloaded': False, 'sha': '0c0394148b4fb56234baee533c36f17202b8f936f28427a957f342aa2f18d040', 'uri_list': ['https://huggingface.co/WAS/concept_art_generator/resolve/main/concept_art_generator_v000-1_alpha.pt'] },\n",
        "    'concept_art_generator_v000-2_alpha': { 'downloaded': False, 'sha': 'a7aee6b8dd73b6f3d377a8c579b99ed2922fc472ead16238074e08bf7517d075', 'uri_list': ['https://huggingface.co/WAS/concept_art_generator/resolve/main/concept_art_generator_v000-2_alpha.pt'] },\n",
        "\n",
        "    'portrait_generator_v001': { 'downloaded': False, 'sha': 'b7e8c747af880d4480b6707006f1ace000b058dd0eac5bb13558ba3752d9b5b9', 'uri_list': ['https://huggingface.co/felipe3dartist/portrait_generator_v001/resolve/main/portrait_generator_v001_ema_0.9999_1MM.pt'] },\n",
        "    'portrait_generator_v002': { 'downloaded': False, 'sha': '3bc39e28fd9690dafbbee83cc08d089a3640eca8ed4d14280c4a9d342c56fd7f', 'uri_list': ['https://huggingface.co/WAS/portrait-diffusion/resolve/main/ema_0.9999_165000.pt'] },\n",
        "    'portrait_generator_v003': { 'downloaded': False, 'sha': 'c0e1739731efe682f6429f2f7f905104b602b415ef8bf0b507034cce6050e8e3', 'uri_list': ['https://huggingface.co/WAS/portrait-diffusion/resolve/main/ema_0.9999_430000.pt'] },\n",
        "    'portrait_generator_v004': { 'downloaded': False, 'sha': '09daed35b70670b8491f27ef172e2a62f08f3a78326aeb88d23cade1fbb878ab', 'uri_list': ['https://huggingface.co/WAS/portrait-diffusion/resolve/main/ema_0.9999_080000.pt'] },\n",
        "    'portrait_generator_v005': { 'downloaded': False, 'sha': '47a9e2cb9ddc9ca9adf9a6bde90c5107c74828b02fe7300bcf0a7d0fb30d5abc', 'uri_list': ['https://huggingface.co/felipe3dartist/Portrait_generator_V2.0/resolve/main/portrait_generator_v2.pt'] },\n",
        "\n",
        "    'Architecture_Diffusion_1-5m': { 'downloaded': False, 'sha': '9a82579a5490e06fde5ff8ac9a37082f66ce10bb03f434b047596f6526ffd95e', 'uri_list': ['https://huggingface.co/jerostephan/Architecture_Diffusion_1.5M/resolve/main/Architecture_Diffusion_1.5M.pt'] },\n",
        "\n",
        "    'pixelartdiffusion_expanded': { 'downloaded': False, 'sha': 'a73b40556634034bf43b5a716b531b46fb1ab890634d854f5bcbbef56838739a', 'uri_list': ['https://huggingface.co/KaliYuga/PADexpanded/resolve/main/PADexpanded.pt'] },\n",
        "    'pixel_art_diffusion_hard_256': { 'downloaded': False, 'sha': 'be4a9de943ec06eef32c65a1008c60ad017723a4d35dc13169c66bb322234161', 'uri_list': ['https://huggingface.co/KaliYuga/pixel_art_diffusion_hard_256/resolve/main/pixel_art_diffusion_hard_256.pt'] },\n",
        "    'pixel_art_diffusion_soft_256': { 'downloaded': False, 'sha': 'd321590e46b679bf6def1f1914b47c89e762c76f19ab3e3392c8ca07c791039c', 'uri_list': ['https://huggingface.co/KaliYuga/pixel_art_diffusion_soft_256/resolve/main/pixel_art_diffusion_soft_256.pt'] },\n",
        "    'pixelartdiffusion4k': { 'downloaded': False, 'sha': 'a1ba4f13f6dabb72b1064f15d8ae504d98d6192ad343572cc416deda7cccac30', 'uri_list': ['https://huggingface.co/KaliYuga/pixelartdiffusion4k/resolve/main/pixelartdiffusion4k.pt'] },\n",
        "    'watercolordiffusion_2': { 'downloaded': False, 'sha': '49c281b6092c61c49b0f1f8da93af9b94be7e0c20c71e662e2aa26fee0e4b1a9', 'uri_list': ['https://huggingface.co/KaliYuga/watercolordiffusion_2/resolve/main/watercolordiffusion_2.pt'] },\n",
        "    'watercolordiffusion': { 'downloaded': False, 'sha': 'a3e6522f0c8f278f90788298d66383b11ac763dd5e0d62f8252c962c23950bd6', 'uri_list': ['https://huggingface.co/KaliYuga/watercolordiffusion/resolve/main/watercolordiffusion.pt'] },\n",
        "    'PulpSciFiDiffusion': { 'downloaded': False, 'sha': 'b79e62613b9f50b8a3173e5f61f0320c7dbb16efad42a92ec94d014f6e17337f', 'uri_list': ['https://huggingface.co/KaliYuga/PulpSciFiDiffusion/resolve/main/PulpSciFiDiffusion.pt'] },\n",
        " \n",
        "    'Liminal_Diffusion_v1': { 'downloaded': False, 'sha': '87c36b544a367fceb0ca127d0028cd8a6f6b6e069e529b22999259d69c14f042', 'uri_list': ['https://huggingface.co/BrainArtLabs/liminal_diffusion/resolve/main/liminal_diffusion_v1.pt'] },\n",
        "    'Liminal_Diffusion_Source': { 'downloaded': False, 'sha': 'ce0064b8cea56c8adb4e5aa0ee2d02f65cd8f1baa905cc6504f462f1aac6d6f4', 'uri_list': ['https://huggingface.co/BrainArtLabs/liminal_diffusion/resolve/main/liminal_diffusion_source.pt'] },\n",
        "    'Medieval_Diffusion': { 'downloaded': False, 'sha': '1b66a0c9749f88b2d9124af7a3ba5c12d2645ffcc33356326269ccda8643a01b', 'uri_list': ['https://huggingface.co/KaliYuga/medievaldiffusion/resolve/main/medievaldiffusion.pt'] },\n",
        "    'Lithography_Diffusion': { 'downloaded': False, 'sha': 'a3e6522f0c8f278f90788298d66383b11ac763dd5e0d62f8252c962c23950bd6', 'uri_list': ['https://huggingface.co/KaliYuga/lithographydiffusion/resolve/main/lithographydiffusion.pt'] },\n",
        "    'Floral_Diffusion': { 'downloaded': False, 'sha': '197e9068f1ca0248fd89f9d6ca1f6f851783e04fddd0cc5b0020bfa655ed3aeb', 'uri_list': ['https://huggingface.co/jags/floraldiffusion/resolve/main/floraldiffusion.pt', 'https://www.dropbox.com/s/i0xrhq28ls1e94g/floraldiffusion.pt'] },\n",
        "    'FeiArt_Handpainted_CG_Diffusion': { 'downloaded': False, 'sha': '85f95f0618f288476ffcec9f48160542ba626f655b3df963543388dcd059f86a', 'uri_list': ['https://huggingface.co/Feiart/FeiArt-Handpainted-CG-Diffusion/resolve/main/FeiArt-Handpainted-CG-Diffusion.pt'] },\n",
        "    'Textile_Diffusion': { 'downloaded': False, 'sha': '82aa9ac10c67a806929b5399f04b933ffaa98f1a2bb0c18103d6ccd8f5bd2dd4', 'uri_list': ['https://huggingface.co/KaliYuga/textilediffusion/resolve/main/textilediffusion.pt'] },\n",
        "    'Isometric_Diffusion_Revrart512px': { 'downloaded': False, 'sha': '649bb7d10ea5170b71bc24adfb17f0305955fde38d2bb1bc5428c6d5baf9811c', 'uri_list': ['https://huggingface.co/Revrart/IsometricDiffusionRevrart512px/resolve/main/IsometricDiffusionRevrart512px.pt'] },\n",
        "    'Laproper_Diffusion_Deepspace_256': { 'downloaded': False, 'sha': '7c0d742d714ee512edda3299b440f22c72e95fda3f32e24c4ba1f03e0c4b0524', 'uri_list': ['https://huggingface.co/laproper/diffusion-deepspace-256/resolve/main/ema_0.9999_102000.pt']},\n",
        "    'Schnippi_Diffusion_512x512_V2': { 'downloaded': False, 'sha': '9c111ab89e214862b76e1fa6a1b3f1d329b1a88281885943d2cdbe357ad57648', 'uri_list': ['https://huggingface.co/shnippi/shnippi_diffusion/resolve/main/512x512_shnippi_V2.pt']},\n",
        "\n",
        "    'secondary': { 'downloaded': False, 'sha': '983e3de6f95c88c81b2ca7ebb2c217933be1973b1ff058776b970f901584613a', 'uri_list': ['https://huggingface.co/spaces/huggi/secondary_model_imagenet_2.pth/resolve/main/secondary_model_imagenet_2.pth', 'https://the-eye.eu/public/AI/models/v-diffusion/secondary_model_imagenet_2.pth', 'https://ipfs.pollinations.ai/ipfs/bafybeibaawhhk7fhyhvmm7x24zwwkeuocuizbqbcg5nqx64jq42j75rdiy/secondary_model_imagenet_2.pth'] },\n",
        "}\n",
        "\n",
        "# Why do we have these unused Kaliyuga lists?\n",
        "kaliyuga_pixel_art_model_names = ['pixelartdiffusion_expanded', 'pixel_art_diffusion_hard_256', 'pixel_art_diffusion_soft_256', 'pixelartdiffusion4k', 'PulpSciFiDiffusion']\n",
        "kaliyuga_watercolor_model_names = ['watercolordiffusion', 'watercolordiffusion_2']\n",
        "kaliyuga_pulpscifi_model_names = ['PulpSciFiDiffusion']\n",
        "diffusion_models_256x256_list = ['256x256_diffusion_uncond'] + kaliyuga_pixel_art_model_names + kaliyuga_watercolor_model_names + kaliyuga_pulpscifi_model_names\n",
        "\n",
        "# Model Functions\n",
        "\n",
        "def configurate_model(diffusion_model, use_checkpoint = False):\n",
        "    model_configurations = {\n",
        "      '512x512_diffusion_uncond_finetune_008100': {\n",
        "          'attention_resolutions': '32, 16, 8',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 250, \n",
        "          'image_size': 512,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 256,\n",
        "          'num_head_channels': 64,\n",
        "          'num_res_blocks': 2,\n",
        "          'resblock_updown': True,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': not useCPU,\n",
        "          'use_scale_shift_norm': True,\n",
        "      },\n",
        "      '256x256_diffusion_uncond': {\n",
        "          'attention_resolutions': '32, 16, 8',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000, \n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 250, \n",
        "          'image_size': 512,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 256,\n",
        "          'num_head_channels': 64,\n",
        "          'num_res_blocks': 2,\n",
        "          'resblock_updown': True,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': not useCPU,\n",
        "          'use_scale_shift_norm': True,\n",
        "      },\n",
        "      '512x512_OpenAI': {\n",
        "          'attention_resolutions': '32, 16, 8',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'rescale_timesteps': True,\n",
        "          'image_size': 512,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 128,\n",
        "          'num_heads': 4,\n",
        "          'num_res_blocks': 2,\n",
        "          'resblock_updown': True,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': True,\n",
        "          'use_scale_shift_norm': True,\n",
        "      },\n",
        "      'Architecture_Diffusion_1-5m': {\n",
        "          'attention_resolutions': '32, 16, 8',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'image_size': 512,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 256,\n",
        "          'num_head_channels': 64,\n",
        "          'num_res_blocks': 2,\n",
        "          'resblock_updown': True,\n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': '250',\n",
        "          'use_scale_shift_norm': True\n",
        "      },\n",
        "      'Liminal_Diffusion': {\n",
        "          'attention_resolutions': '16',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 'ddim100',\n",
        "          'image_size': 256,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 128,\n",
        "          'num_heads': 1,\n",
        "          'num_res_blocks': 2,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': True,\n",
        "          'use_scale_shift_norm': False,\n",
        "      },\n",
        "      'Lithography_Diffusion': {\n",
        "          'attention_resolutions': '16',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 'ddim100',\n",
        "          'image_size': 256,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 128,\n",
        "          'num_heads': 1,\n",
        "          'num_res_blocks': 2,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': True,\n",
        "          'use_scale_shift_norm': False,\n",
        "      },\n",
        "      'Medieval_Diffusion': {\n",
        "          'attention_resolutions': '16',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 'ddim100',\n",
        "          'image_size': 256,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 128,\n",
        "          'num_heads': 1,\n",
        "          'num_res_blocks': 2,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': True,\n",
        "          'use_scale_shift_norm': False,\n",
        "      },\n",
        "      'Floral_Diffusion': {\n",
        "          'attention_resolutions': '16',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 'ddim100',\n",
        "          'image_size': 256,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 128,\n",
        "          'num_heads': 1,        \n",
        "          'num_res_blocks': 2,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': True,\n",
        "          'use_scale_shift_norm': False,\n",
        "      },\n",
        "      'FeiArt_Handpainted_CG_Diffusion': {\n",
        "          'attention_resolutions': '32,16,8',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 'ddim100',\n",
        "          'image_size': 512,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 256,\n",
        "          'num_head_channels': 64,\n",
        "          'num_res_blocks': 2,\n",
        "          'resblock_updown': True,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': True,\n",
        "          'use_scale_shift_norm': True,\n",
        "      },\n",
        "      'Textile_Diffusion': {\n",
        "          'attention_resolutions': '16',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 'ddim100',\n",
        "          'image_size': 256,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 128,\n",
        "          'num_heads': 1,\n",
        "          'num_res_blocks': 2,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': True,\n",
        "          'use_scale_shift_norm': False,\n",
        "      },\n",
        "      'Isometric_Diffusion_Revrart512px': {\n",
        "          'attention_resolutions': '32, 16, 8',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 'ddim100',\n",
        "          'image_size': 512,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 256,\n",
        "          'num_head_channels': 64,\n",
        "          'num_res_blocks': 2,\n",
        "          'resblock_updown': True,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': True,\n",
        "          'use_scale_shift_norm': True,\n",
        "      }, \n",
        "      'Laproper_Diffusion_Deepspace_256': {\n",
        "          'attention_resolutions': '32, 16, 8',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 'ddim100',\n",
        "          'image_size': 256,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 128,\n",
        "          'num_heads': 4,\n",
        "          'num_res_blocks': 2,\n",
        "          'resblock_updown': True,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': True,\n",
        "          'use_scale_shift_norm': True,\n",
        "      },\n",
        "      'Schnippi_Diffusion_512x512_V2': {\n",
        "          'attention_resolutions': '32, 16, 8',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000, \n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 250, \n",
        "          'image_size': 512,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 256,\n",
        "          'num_head_channels': 64,\n",
        "          'num_res_blocks': 2,\n",
        "          'resblock_updown': True,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': not useCPU,\n",
        "          'use_scale_shift_norm': True,\n",
        "      },\n",
        "      'Kaliyuga': {\n",
        "          'attention_resolutions': '16',\n",
        "          'class_cond': False,\n",
        "          'diffusion_steps': 1000,\n",
        "          'rescale_timesteps': True,\n",
        "          'timestep_respacing': 'ddim100',\n",
        "          'image_size': 256,\n",
        "          'learn_sigma': True,\n",
        "          'noise_schedule': 'linear',\n",
        "          'num_channels': 128,\n",
        "          'num_heads': 1,\n",
        "          'num_res_blocks': 2,\n",
        "          'use_checkpoint': use_checkpoint,\n",
        "          'use_fp16': True,\n",
        "          'use_scale_shift_norm': False,\n",
        "        },\n",
        "    }\n",
        "\n",
        "    tmp_model_config = model_and_diffusion_defaults()\n",
        "\n",
        "    if diffusion_model in ['512x512_diffusion_uncond_finetune_008100', \n",
        "                           'Schnippi_Diffusion_512x512_V2']:\n",
        "        tmp_model_config.update(model_configurations['512x512_diffusion_uncond_finetune_008100'])\n",
        "    elif diffusion_model == '256x256_diffusion_uncond':\n",
        "        tmp_model_config.update(model_configurations['256x256_diffusion_uncond'])\n",
        "    elif diffusion_model in ['concept_art_generator_v000-1_alpha',\n",
        "                             'concept_art_generator_v000-2_alpha',\n",
        "                             'portrait_generator_v001', \n",
        "                             'portrait_generator_v002', \n",
        "                             'portrait_generator_v003', \n",
        "                             'portrait_generator_v004',\n",
        "                             'portrait_generator_v005']:\n",
        "        tmp_model_config.update(model_configurations['512x512_OpenAI'])\n",
        "    elif diffusion_model == 'Architecture_Diffusion_1-5m':\n",
        "        tmp_model_config.update(model_configurations['Architecture_Diffusion_1-5m'])\n",
        "    elif diffusion_model in ['Liminal_Diffusion_v1', \n",
        "                             'liminal_diffusion_source']:\n",
        "        tmp_model_config.update(model_configurations['Liminal_Diffusion'])\n",
        "    elif diffusion_model == 'Lithography_Diffusion': \n",
        "        tmp_model_config.update(model_configurations['Lithography_Diffusion'])\n",
        "    elif diffusion_model == 'Medieval_Diffusion': \n",
        "        tmp_model_config.update(model_configurations['Medieval_Diffusion'])\n",
        "    elif diffusion_model == 'Floral_Diffusion': \n",
        "        tmp_model_config.update(model_configurations['Floral_Diffusion'])\n",
        "    elif diffusion_model == 'FeiArt_Handpainted_CG_Diffusion':\n",
        "        tmp_model_config.update(model_configurations['FeiArt_Handpainted_CG_Diffusion'])\n",
        "    elif diffusion_model == 'Textile_Diffusion':\n",
        "        tmp_model_config.update(model_configurations['Textile_Diffusion'])\n",
        "    elif diffusion_model == 'Isometric_Diffusion_Revrart512px':\n",
        "        tmp_model_config.update(model_configurations['Isometric_Diffusion_Revrart512px'])\n",
        "    elif diffusion_model == 'Laproper_Diffusion_Deepspace_256':\n",
        "        tmp_model_config.update(model_configurations['Laproper_Diffusion_Deepspace_256'])\n",
        "    else:\n",
        "        tmp_model_config.update(model_configurations['Kaliyuga'])\n",
        "\n",
        "    return tmp_model_config\n",
        "\n",
        "def get_model_filename(diffusion_model_name):\n",
        "  model_uri = diff_model_map[diffusion_model_name]['uri_list'][0]\n",
        "  model_filename = os.path.basename(urlparse(model_uri).path)\n",
        "  return model_filename\n",
        "\n",
        "def check_sha_hash(diffusion_model_name, model_local_path):\n",
        "  with open(model_local_path, \"rb\") as f: return True if hashlib.sha256(f.read()).hexdigest() == diff_model_map[diffusion_model_name]['sha'] else False\n",
        "\n",
        "def download_model(diffusion_model_name, uri_index=0):\n",
        "  if diffusion_model_name is 'custom':\n",
        "    return\n",
        "  model_filename = get_model_filename(diffusion_model_name)\n",
        "  model_local_path = os.path.join(model_path, model_filename)\n",
        "  print(\"Downloading Diffusion Model: \", diffusion_model_name)\n",
        "  if os.path.exists(model_local_path):\n",
        "    if check_model_SHA:\n",
        "      print(f\"Checking {model_filename} SHA256 Hash...\")\n",
        "      if check_sha_hash(diffusion_model_name, model_local_path):\n",
        "        print(f'{diffusion_model_name} SHA256 matches.')\n",
        "        diff_model_map[diffusion_model_name]['downloaded'] = True\n",
        "      else:\n",
        "        print(f\"{diffusion_model_name} SHA doesn't match. Will redownload it...\")\n",
        "        os.remove(model_local_path)\n",
        "        return download_model(diffusion_model_name)\n",
        "    print(f\"{model_filename} \\033[32malready downloaded.\\033[0;0m\")\n",
        "  else:\n",
        "    for model_uri in diff_model_map[diffusion_model_name]['uri_list']:\n",
        "      print(f\"Downloading {diffusion_model_name} from: {model_uri}\")\n",
        "      #!wget -q --show-progress --no-cache $model_uri -P $model_path\n",
        "      wget(model_uri, model_path)\n",
        "      if os.path.exists(model_local_path):\n",
        "        if check_model_SHA:\n",
        "          print(f\"Checking {model_filename} SHA256 Hash...\")\n",
        "          if check_sha_hash(diffusion_model_name, model_local_path):\n",
        "            print(f'{diffusion_model_name} SHA256 matches.')\n",
        "            diff_model_map[diffusion_model_name]['downloaded'] = True\n",
        "            print(f\"{diffusion_model_name} \\033[32mdownloaded successfully.\\033[0;0m\")\n",
        "            return\n",
        "          else:\n",
        "            print(f\"{diffusion_model_name} \\033[31mSHA doesn't match!\\033[0;0m Attempting to redownload\")\n",
        "            os.remove(model_local_path)\n",
        "            return download_model(diffusion_model_name)\n",
        "        else:\n",
        "          diff_model_map[diffusion_model_name]['downloaded'] = True\n",
        "          print(f\"{diffusion_model_name} \\033[32mdownloaded successfully.\\033[0;0m\")\n",
        "          return\n",
        "      else:\n",
        "        print(f\"\\033[31mFailed to download\\033[0;0m {diffusion_model_name} from: {model_uri}\")\n",
        "        if len(diff_model_map[diffusion_model_name]['uri_list']) > 1:\n",
        "          print(\"Attempting to download from another source...\")\n",
        "        else:\n",
        "          print(f\"No additional download resources available for {diffusion_model_name}\")\n",
        "\n",
        "\n",
        "# Download the diffusion model(s)\n",
        "download_model(diffusion_model)\n",
        "if use_secondary_model:\n",
        "    download_model('secondary')\n",
        "if go_big_diffusion_model != \"Inherit\":\n",
        "    download_model(go_big_diffusion_model)\n",
        "\n",
        "# Configurate diffusion model\n",
        "if diffusion_model is not 'custom':\n",
        "    model_config = configurate_model(diffusion_model, use_checkpoint)\n",
        "if go_big_diffusion_model is not ['Inherit', 'custom']:\n",
        "    go_big_model_config = configurate_model(go_big_diffusion_model, use_checkpoint)\n",
        "if use_secondary_model:\n",
        "    secondary_model = SecondaryDiffusionImageNet2()\n",
        "    secondary_model.load_state_dict(torch.load(f'{model_path}/secondary_model_imagenet_2.pth', map_location='cpu'))\n",
        "    secondary_model.eval().requires_grad_(False).to(device)\n",
        "\n",
        "clip_models = []\n",
        "if ViTB32: print(\"Downloading CLIP Model: ViT-B/32\"); clip_models.append(clip.load('ViT-B/32', jit=False)[0].eval().requires_grad_(False).to(device))\n",
        "if ViTB16: print(\"Downloading CLIP Model: ViT-B/16\"); clip_models.append(clip.load('ViT-B/16', jit=False)[0].eval().requires_grad_(False).to(device))\n",
        "if ViTL14: print(\"Downloading CLIP Model: ViT-L/14\"); clip_models.append(clip.load('ViT-L/14', jit=False)[0].eval().requires_grad_(False).to(device))\n",
        "if ViTL14_336px: print(\"Downloading CLIP Model: ViT-L/14@336px\"); clip_models.append(clip.load('ViT-L/14@336px', jit=False)[0].eval().requires_grad_(False).to(device))\n",
        "if RN50: print(\"Downloading CLIP Model: RN50\"); clip_models.append(clip.load('RN50', jit=False)[0].eval().requires_grad_(False).to(device))\n",
        "if RN50x4: print(\"Downloading CLIP Model: RN50x4\"); clip_models.append(clip.load('RN50x4', jit=False)[0].eval().requires_grad_(False).to(device))\n",
        "if RN50x16: print(\"Downloading CLIP Model: RN50x16\"); clip_models.append(clip.load('RN50x16', jit=False)[0].eval().requires_grad_(False).to(device))\n",
        "if RN50x64: print(\"Downloading CLIP Model: RN50x64\"); clip_models.append(clip.load('RN50x64', jit=False)[0].eval().requires_grad_(False).to(device))\n",
        "if RN101: print(\"Downloading CLIP Model: RN101\");clip_models.append(clip.load('RN101', jit=False)[0].eval().requires_grad_(False).to(device))\n",
        "\n",
        "if ViTB32_laion2b_e16: print(\"Downloading CLIP Model: ViT-B/32 laion2b_e16\"); clip_models.append(open_clip.create_model('ViT-B-32', pretrained='laion2b_e16').eval().requires_grad_(False).to(device))\n",
        "if ViTB32_laion400m_e31: print(\"Downloading CLIP Model: ViT-B-32 laion400m_e31\"); clip_models.append(open_clip.create_model('ViT-B-32', pretrained='laion400m_e31').eval().requires_grad_(False).to(device))\n",
        "if ViTB32_laion400m_32: print(\"Downloading CLIP Model: ViT-B/32 laion400m_e32\"); clip_models.append(open_clip.create_model('ViT-B-32', pretrained='laion400m_e32').eval().requires_grad_(False).to(device))\n",
        "if ViTB32quickgelu_laion400m_e31: print(\"Downloading CLIP Model: ViT-B-32-quickgelu laion400m_e31\"); clip_models.append(open_clip.create_model('ViT-B-32-quickgelu', pretrained='laion400m_e31').eval().requires_grad_(False).to(device))\n",
        "if ViTB32quickgelu_laion400m_e32: print(\"Downloading CLIP Model: ViT-B-32-quickgelu laion400m_e32\"); clip_models.append(open_clip.create_model('ViT-B-32-quickgelu', pretrained='laion400m_e32').eval().requires_grad_(False).to(device))\n",
        "if ViTB16_laion400m_e31: print(\"Downloading CLIP Model: ViT-B-16 laion400m_e31\"); clip_models.append(open_clip.create_model('ViT-B-16', pretrained='laion400m_e31').eval().requires_grad_(False).to(device))\n",
        "if ViTB16_laion400m_e32: print(\"Downloading CLIP Model: ViT-B-16 laion400m_e32\"); clip_models.append(open_clip.create_model('ViT-B-16', pretrained='laion400m_e32').eval().requires_grad_(False).to(device))\n",
        "if RN50_yffcc15m: print(\"Downloading CLIP Model: RN50 yfcc15m\"); clip_models.append(open_clip.create_model('RN50', pretrained='yfcc15m').eval().requires_grad_(False).to(device))\n",
        "if RN50_cc12m: print(\"Downloading CLIP Model: RN50 yfcc15m\"); clip_models.append(open_clip.create_model('RN50', pretrained='cc12m').eval().requires_grad_(False).to(device))\n",
        "if RN50_quickgelu_yfcc15m: print(\"Downloading CLIP Model: RN50-quickgelu yfcc15m\"); clip_models.append(open_clip.create_model('RN50-quickgelu', pretrained='yfcc15m').eval().requires_grad_(False).to(device))\n",
        "if RN50_quickgelu_cc12m: print(\"Downloading CLIP Model: RN50-quickgelu cc12m\"); clip_models.append(open_clip.create_model('RN50-quickgelu', pretrained='cc12m').eval().requires_grad_(False).to(device))\n",
        "if RN101_yfcc15m: print(\"Downloading CLIP Model: RN101 yfcc15m\"); clip_models.append(open_clip.create_model('RN101', pretrained='yfcc15m').eval().requires_grad_(False).to(device))\n",
        "if RN101_quickgelu_yfcc15m: print(\"Downloading CLIP Model: RN101=quickgelu yfcc15m\"); clip_models.append(open_clip.create_model('RN101-quickgelu', pretrained='yfcc15m').eval().requires_grad_(False).to(device))\n",
        "\n",
        "normalize = T.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])\n",
        "lpips_model = lpips.LPIPS(net='vgg').to(device)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TutorialTop"
      },
      "source": [
        "# <a name=\"reference_guide\">Reference</a> ([View on Github](https://github.com/WASasquatch/discostream/wiki/Quick-Reference-Wiki))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DiffusionSet"
      },
      "source": [
        "**Diffusion Settings Reference**\n",
        "---\n",
        "Disco Diffusion is complex, and continually evolving with new features.  The most current documentation on on Disco Diffusion settings can be found in the unofficial guidebook:\n",
        "\n",
        "[Zippy's Disco Diffusion Cheatsheet](https://docs.google.com/document/d/1l8s7uS2dGqjztYSjPpzlmXLjl5PM3IGkRWI3IiCuK7g/edit)\n",
        "\n",
        "We also encourage users to join the [Disco Diffusion User Discord](https://discord.gg/XGZrFFCRfN) to learn from the active user community.\n",
        "\n",
        "This section below is outdated as of v2\n",
        "\n",
        "Setting | Description | Default\n",
        "--- | --- | ---\n",
        "**Your vision:**\n",
        "`text_prompts` | A description of what you'd like the machine to generate. Think of it like writing the caption below your image on a website. | `N/A`\n",
        "`image_prompts` | Think of these images more as a description of their contents. | `N/A`\n",
        "**Image quality:**\n",
        "`clip_guidance_scale`  | Controls how much the image should look like the prompt. | `1000`\n",
        "`tv_scale` | Controls the smoothness of the final output. | `150`\n",
        "`range_scale` | Controls how far out of range RGB values are allowed to be. | `150`\n",
        "`sat_scale` | Controls how much saturation is allowed. From nshepperd's JAX notebook. | `0`\n",
        "`cutn` | Controls how many crops to take from the image. | `16`\n",
        "`cutn_batches` | Accumulate CLIP gradient from multiple batches of cuts. | `2`\n",
        "**Go BIG Settings:**\n",
        "`go_big` | A boolean to enable Go BIG tiled upscale diffusion. | `False`\n",
        "`go_big_scale` | The Go BIG multiplier factor | `2`+\n",
        "`gobig_skip_perent` | Percentage of steps to skip when doing Go BIG tiled diffusion runs. | `85`\n",
        "`go_big_init` | A path or URL to a Go BIG init image. This will skip the diffusion run. | `None`\n",
        "`go_big_init_skipresize` | Whether to skip resizing the go BIG init image | `False`\n",
        "`render_mask` | A alpha mask of areas to diffuse (white), and not diffuse (black) | `None`\n",
        "`init_masked` | A alpha mask that can compliement the render mask in Go BIG runs. | `None`\n",
        "**Init settings:**\n",
        "`init_image` | URL or local path | `None`\n",
        "`init_scale` | This enhances the effect of the init image, a good value is 1000 | `0`\n",
        "`skip_steps` | Controls the starting point along the diffusion timesteps | `0`\n",
        "`perlin_init` | Option to start with random perlin noise | `False`\n",
        "`perlin_mode` | (`gray`, `color`) | `mixed`\n",
        "**Advanced:**\n",
        "`skip_augs` | Controls whether to skip torchvision augmentations | `False`\n",
        "`randomize_class` | Controls whether the imagenet class is randomly changed each iteration | `True`\n",
        "`clip_denoised` | Determines whether CLIP discriminates a noisy or denoised image | `False`\n",
        "`clamp_grad` | Experimental: Using adaptive clip grad in the cond_fn | `True`\n",
        "`seed`  | Choose a random seed and print it at end of run for reproduction | `random_seed`\n",
        "`fuzzy_prompt` | Controls whether to add multiple noisy prompts to the prompt losses | `False`\n",
        "`rand_mag` | Controls the magnitude of the random noise | `0.1`\n",
        "`eta` | DDIM hyperparameter | `0.5`\n",
        "`use_vertical_symmetry` | Enforce symmetry over x axis of the image on [`tr_st`*`steps` for `tr_st` in `transformation_steps`] steps of the diffusion process | `False`\n",
        "`use_horizontal_symmetry` | Enforce symmetry over y axis of the image on [`tr_st`*`steps` for `tr_st` in `transformation_steps`] steps of the diffusion process | `False`\n",
        "`transformation_steps` | Steps (expressed in percentages) in which the symmetry is enforced | `[0.01]`\n",
        "`video_init_flow_warp` | Flow warp enabled | True\n",
        "`video_init_flow_blend` | 0 - you get raw input, 1 - you get warped diffused previous frame  | `0.999`\n",
        "`video_init_check_consistency` | TBD check forward-backward flow consistency (uncheck unless there are too many warping artifacts) | `False`\n",
        "\n",
        "<br>\n",
        "\n",
        "<font size=\"5\">**Model settings**</font>\n",
        "<hr>\n",
        "\n",
        "Setting | Description | Default\n",
        "--- | --- | ---\n",
        "**Diffusion:**\n",
        "`timestep_respacing` | Modify this value to decrease the number of timesteps. | ddim100\n",
        "`diffusion_steps` || 1000\n",
        "**Diffusion:**\n",
        "`clip_models` | Models of CLIP to load. Typically the more, the better but they all come at a hefty VRAM cost. | ViT-B/32, ViT-B/16, RN50x4\n",
        "\n",
        "## Noodle Soup Prompts\n",
        "\n",
        "To use a term database, simply use one of keys below. \n",
        "\n",
        "For example if you wanted beauty adjective, you would write `_adj-beauty_` in your prompt. \n",
        "\n",
        "## Terminology Keys\n",
        "\n",
        "### Please refer to the [Noodle Terminology Wiki](https://github.com/WASasquatch/noodle-soup-prompts/wiki/Terminology-Reference)\n",
        "\n",
        "<br>\n",
        "\n",
        "## **Easing Function Aliases**\n",
        " - Easing Integer: `easeint(st=40, en=1, s=800, fn=cut_easing_fn)` \n",
        " - Easing Float: `easeflt(st=40, en=1, s=800, fn=cut_easing_fn)`\n",
        "<br><br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CustModelTop"
      },
      "source": [
        "# Custom model settings \n",
        "Modify in accordance with your training settings and run the cell"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CustModel",
        "cellView": "form"
      },
      "source": [
        "#@markdown ####**Custom Model Settings:**\n",
        "\n",
        "#@markdown This is only relevant if you're using a \"custom\" model. Define these settings according to your custom model.\n",
        "\n",
        "# Default Settings\n",
        "custom_attention_resolutions = '32, 16, 8' #@param{type: 'string'}\n",
        "custom_class_cond = False #@param{type: 'boolean'}\n",
        "custom_diffusion_steps = 1000 #@param{type: 'number'}\n",
        "custom_rescale_timesteps = True #@param{type: 'boolean'}\n",
        "custom_image_size = 512 #@param{type: 'number'}\n",
        "custom_learn_sigma = True #@param{type: 'boolean'}\n",
        "custom_noise_schedule = 'linear' #@param{type: 'string'}\n",
        "custom_num_channels = 128 #@param{type: 'number'}\n",
        "custom_num_heads = 4 #@param{type: 'number'}\n",
        "custom_num_res_blocks = 2 #@param{type: 'number'}\n",
        "custom_resblock_updown = True #@param{type: 'boolean'}\n",
        "custom_use_checkpoint = True #@param{type: 'boolean'}\n",
        "custom_use_fp16 = True #@param{type: 'boolean'}\n",
        "custom_use_scale_shift_norm = True #@param{type: 'boolean'}\n",
        "#@markdown <br>\n",
        "\n",
        "custom_model_config = {\n",
        "          'attention_resolutions': custom_attention_resolutions,\n",
        "          'class_cond': custom_class_cond,\n",
        "          'diffusion_steps': custom_diffusion_steps,\n",
        "          'rescale_timesteps': custom_rescale_timesteps,\n",
        "          'image_size': custom_image_size,\n",
        "          'learn_sigma': custom_learn_sigma,\n",
        "          'noise_schedule': custom_noise_schedule,\n",
        "          'num_channels': custom_num_channels,\n",
        "          'num_heads': custom_num_heads,\n",
        "          'num_res_blocks': custom_num_res_blocks,\n",
        "          'resblock_updown': custom_resblock_updown,\n",
        "          'use_checkpoint': custom_use_checkpoint,\n",
        "          'use_fp16': custom_use_fp16,\n",
        "          'use_scale_shift_norm': custom_use_scale_shift_norm,\n",
        "      }\n",
        "\n",
        "custom_model_settings_override = \"\" #@param{type: 'string'}\n",
        "#@markdown Here you can paste the dictionary settings of your model.\n",
        "#@markdown **Example:**\n",
        "#@markdown `{ 'attention_resolutions': '32, 16, 8', 'class_cond': False, 'diffusion_steps': 1000, 'rescale_timesteps': True, 'image_size': 512, 'learn_sigma': True, 'noise_schedule': 'linear', 'num_channels': 128, 'num_heads': 4, 'num_res_blocks': 2, 'resblock_updown': True, 'use_checkpoint': True, 'use_fp16': True, 'use_scale_shift_norm': True }`\n",
        "if custom_model_settings_override.lower() not in [None, \"none\", \"\"]:\n",
        "    custom_model_config = eval(custom_model_settings_override)\n",
        "\n",
        "custom_model = model_and_diffusion_defaults()\n",
        "if diffusion_model is 'custom' or go_big_diffusion_model is 'custom':\n",
        "    custom_model.update(custom_model_config)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SettingsTop"
      },
      "source": [
        "# 3. Settings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BasicSettings",
        "cellView": "form"
      },
      "source": [
        "#@markdown ##**Basic Settings:** <br><br>\n",
        "\n",
        "#@markdown ### **Basic Diffusion Settings** \n",
        "batch_name = 'ConceptModelTesting' #@param{type: 'string'}\n",
        "steps =  150#@param [25,50,100,150,250,500,1000]{type: 'raw', allow-input: true}\n",
        "width_height_for_512x512_models = [512,640] #@param{type: 'raw'}\n",
        "clip_guidance_scale =  1500#@param{type: 'number'}\n",
        "tv_scale = 1000#@param{type: 'number'}\n",
        "range_scale = 150#@param{type: 'number'}\n",
        "sat_scale = 10#@param{type: 'number'}\n",
        "cutn_batches = 1#@param{type: 'number'}\n",
        "skip_augs = False#@param{type: 'boolean'}\n",
        "\n",
        "#@markdown ####**Image dimensions to be used for 256x256 models (e.g. pixelart models):**\n",
        "width_height_for_256x256_models = [469,485] #@param{type: 'raw'}\n",
        "#@markdown --- \n",
        "#@markdown <br><br>\n",
        "\n",
        "#@markdown ### **Go <font color=\"00a000\">BIG</font> Settings**\n",
        "#@markdown #### <font size=\"4\" color=\"CC0000\">*WARNING*</font>*: go_big_scale is a multipler* - if you go above **4** you might be waiting a long time\n",
        "#@markdown **NOTE**: If you have go_big_init set it will start with that image and skip the initial render\n",
        "\n",
        "go_big = False #@param{type: 'boolean'}\n",
        "go_big_scale = 2 #@param{type: 'number'}\n",
        "gobig_skip_percent = 80#@param{type: 'number'}\n",
        "gobig_overlap = 64 #@param{type: 'number'}\n",
        "gobig_maximize = False #@param{type: 'boolean'}\n",
        "#@markdown (maximize extends the gobig canvas to use areas that would otherwise be wasted, resulting in a larger final image at no additional render time cost)\n",
        "go_big_init = None #@param{type: 'string'}\n",
        "#@markdown Example: /content/drive/MyDrive/AI/Disco_Diffusion/init_images/image.png\n",
        "go_big_init_skipresize = False #@param{type: 'boolean'}\n",
        "#@markdown Check the above if you already resized the image. Please make sure go_big_scale is set to the multiplier you used.\n",
        "render_mask = None #@param{type: 'string'}\n",
        "#@markdown render_mask is a grayscale image that tells us where to draw (white) and not draw (black).\n",
        "#@markdown useful when using an init for go_big to avoid adding unwanted details in empty areas\n",
        "init_masked = None #@param{type: 'string'}\n",
        "#@markdown init_masked is an alternate init image that can be used in conjunction with render_mask.\n",
        "#@markdown When using a render_mask, the white areas will be rendered with init_masked instead of init_image\n",
        "\n",
        "\n",
        "#@markdown --- \n",
        "#@markdown <br><br>\n",
        "\n",
        "#@markdown ###**Video Init Basic Settings:**\n",
        "video_init_steps = 100 #@param [25,50,100,150,250,500,1000]{type: 'raw', allow-input: true}\n",
        "video_init_clip_guidance_scale = 1000 #@param{type: 'number'}\n",
        "video_init_tv_scale = 0.1#@param{type: 'number'}\n",
        "video_init_range_scale = 150#@param{type: 'number'}\n",
        "video_init_sat_scale = 300#@param{type: 'number'}\n",
        "video_init_cutn_batches = 4#@param{type: 'number'}\n",
        "video_init_skip_steps = 50 #@param{type: 'integer'}\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown <br><br>\n",
        "\n",
        "#@markdown ###**Init Image Settings:**\n",
        "#@markdown Init Image is either a string, or list, you must include the correct syntax.<br>**Example:**<br> - `None` (capitalized)<br> - `\"path/to/my/init_image.png\"` (with quotation marks)<br> - `[\"path/to/my/init_image1.png\", \"path/to/my/init_image2.png\"]` (with brackets, quotation marks, and commas)\n",
        "init_image = None #@param{type: 'raw'}\n",
        "init_scale = 0 #@param{type: 'integer'}\n",
        "skip_steps =  0 #@param{type: 'integer'}\n",
        "#@markdown *Make sure you set skip_steps to ~50% of your steps if you want to use an init image.* \n",
        "#@markdown <br><br>\n",
        "#@markdown ###**Init Image Prep**\n",
        "#@markdown Prep a init_image for easier improvisation by guided diffusion.\n",
        "do_prep_init_image = True #@param{type: 'boolean'}\n",
        "do_go_big_slice_prep = False #@param{type: 'boolean'}\n",
        "gaussian_blur_radius = 4 #@param{type: 'number'}\n",
        "#@markdown Gaussian Blur Radius in Pixels\n",
        "gaussian_noise_amount = 1 #@param{type: 'number'}\n",
        "#@markdown Gaussian Noise Amount: `0` - `1`+\n",
        "gaussian_noise_blur_radius = 1.25 #@param{type: 'number'}\n",
        "#@markdown Gaussiean Noise Blur Radius in Pixels\n",
        "gaussian_noise_greyscale = False #@param{type: 'boolean'}\n",
        "#@markdown Gaussian Noise Greyscale Mode\n",
        "gaussian_noise_double = False #@param{type: 'boolean'}\n",
        "#@markdown Double the Gaussian Noise amount (good for low noise level)\n",
        "do_preview_init_prep = True #@param{type: 'boolean'}\n",
        "#@markdown Do not display init prep previews\n",
        "\n",
        "width_height = width_height_for_256x256_models if diffusion_model in diffusion_models_256x256_list else width_height_for_512x512_models\n",
        "\n",
        "#Get corrected sizes\n",
        "side_x = (width_height[0]//64)*64;\n",
        "side_y = (width_height[1]//64)*64;\n",
        "if side_x != width_height[0] or side_y != width_height[1]:\n",
        "    print(f'Changing output size to {side_x}x{side_y}. Dimensions must by multiples of 64.')\n",
        "\n",
        "#Make folder for batch\n",
        "batchFolder = f'{outDirPath}/{batch_name}'\n",
        "createPath(batchFolder)\n",
        "\n",
        "if go_big_init != None and go_big_init.lower() in ['none','']:\n",
        "    go_big_init = None\n",
        "if render_mask != None and render_mask.lower() in ['none','']:\n",
        "    render_mask = None\n",
        "if init_masked != None and init_masked.lower() in ['none','']:\n",
        "    init_masked = None\n",
        "if init_image != None and ( type(init_image) is not list and init_image.lower() in ['none',''] ):\n",
        "    init_image = None\n",
        "elif init_image != None and type(init_image) is list:\n",
        "    new_init_list = []\n",
        "    for init in init_image:\n",
        "        if init != None and init.lower() not in ['none','']:\n",
        "            new_init_list.append(init)\n",
        "    if new_init_list:\n",
        "        init_image = new_init_list\n",
        "\n",
        "if skip_steps >= steps:\n",
        "    raise Exception(f\"Skip steps cannot equal, or exceed steps. Invalid value: {skip_steps} where steps: {steps}\")\n",
        "\n",
        "if not install_go_big_v2_support and go_big:\n",
        "    print(pil_warning+\"\\n\")\n",
        "    print(\"\\33[31mDisabling Go BIG!\\33[0m\")\n",
        "    go_big = False\n",
        "\n",
        "# Go BIG Calculations\n",
        "if go_big:\n",
        "\n",
        "  if go_big_init_skipresize == True and go_big_init == None:\n",
        "    go_big_init_skipresize = False\n",
        "    raise Exception('You enabled skipresize but did not provide a go_big_init image')\n",
        "\n",
        "  if go_big_init != None:\n",
        "    print(f'side_x was {side_x}, side_y was {side_y}')\n",
        "    print(f'width_height was {width_height}')\n",
        "    try:\n",
        "      temp_image = PIL.Image.open(fetch(go_big_init)).convert(\"RGB\")\n",
        "    except AttributeError as e:\n",
        "        if \"'PIL.TiffTags' has no attribute 'IFD'\" in str(e):\n",
        "          raise Exception(\"\\33[33m\\u001b[4mWARNING:\\33[0m \\033[1mPIL.TiffTags requires a restart. Please select Runtime -> Restart Runtime, and then run all the cells again.\\033[0m\")\n",
        "          \n",
        "    side_x, side_y = temp_image.size\n",
        "    print(f'side_x is now {side_x}, side_y is now {side_y}')\n",
        "    width_height[0] = side_x\n",
        "    width_height[1] = side_y\n",
        "    print(f'width_height is now {width_height}')\n",
        "    temp_image.close\n",
        "    side_x = (width_height[0]//64)*64;\n",
        "    side_y = (width_height[1]//64)*64;\n",
        "    if side_x != width_height[0] or side_y != width_height[1]:\n",
        "      print('ERROR: Your go big init resolution was NOT a multiple of 64.')\n",
        "      raise Exception('Exiting due to improperly sized go big init.')\n",
        "\n",
        "if do_prep_init_image and do_preview_init_prep:\n",
        "    if init_image is not None:\n",
        "        print(\":hourglass_not_done: Loading preview(s)...\", LINE_UP, end=LINE_CLEAR, flush=True)\n",
        "        \n",
        "        if type(init_image) is str:\n",
        "            temp_init = init_image_prep(init_image, gaussian_blur_radius, gaussian_noise_amount, gaussian_noise_blur_radius, gaussian_noise_greyscale, gaussian_noise_double)\n",
        "            displayJsImage('', temp_init)\n",
        "        elif type(init_image) is list:\n",
        "            for init in init_image:\n",
        "                temp_init = init_image_prep(init, gaussian_blur_radius, gaussian_noise_amount, gaussian_noise_blur_radius, gaussian_noise_greyscale, gaussian_noise_double)\n",
        "                displayJsImage(os.path.basename(init), temp_init)\n",
        "        print(\":check_mark_button:\")\n",
        "\n",
        "\n"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AnimSetTop"
      },
      "source": [
        "### Animation Settings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AnimSettings",
        "cellView": "form"
      },
      "source": [
        "#@markdown ####**Animation Mode:**\n",
        "animation_mode = 'None' #@param ['None', '2D', '3D', 'Video Input'] {type:'string'}\n",
        "#@markdown *For animation, you probably want to turn `cutn_batches` to 1 to make it quicker.*\n",
        "\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ####**Video Input Settings:**\n",
        "if is_colab:\n",
        "    video_init_path = \"/content/drive/MyDrive/init.mp4\" #@param {type: 'string'}\n",
        "else:\n",
        "    video_init_path = \"init.mp4\" #@param {type: 'string'}\n",
        "extract_nth_frame = 2 #@param {type: 'number'}\n",
        "persistent_frame_output_in_batch_folder = True #@param {type: 'boolean'}\n",
        "video_init_seed_continuity = False #@param {type: 'boolean'}\n",
        "#@markdown #####**Video Optical Flow Settings:**\n",
        "video_init_flow_warp = True #@param {type: 'boolean'}\n",
        "# Call optical flow from video frames and warp prev frame with flow\n",
        "video_init_flow_blend =  0.999#@param {type: 'number'} #0 - take next frame, 1 - take prev warped frame\n",
        "video_init_check_consistency = False #Insert param here when ready\n",
        "video_init_blend_mode = \"optical flow\" #@param ['None', 'linear', 'optical flow']\n",
        "# Call optical flow from video frames and warp prev frame with flow\n",
        "if animation_mode == \"Video Input\":\n",
        "    if persistent_frame_output_in_batch_folder or (not is_colab): #suggested by Chris the Wizard#8082 at discord\n",
        "        videoFramesFolder = f'{batchFolder}/videoFrames'\n",
        "    else:\n",
        "        videoFramesFolder = f'/content/videoFrames'\n",
        "    createPath(videoFramesFolder)\n",
        "    print(f\"Exporting Video Frames (1 every {extract_nth_frame})...\")\n",
        "    try:\n",
        "        for f in pathlib.Path(f'{videoFramesFolder}').glob('*.jpg'):\n",
        "            f.unlink()\n",
        "    except:\n",
        "        print('')\n",
        "    vf = f'select=not(mod(n\\,{extract_nth_frame}))'\n",
        "    if os.path.exists(video_init_path):\n",
        "        subprocess.run(['ffmpeg', '-i', f'{video_init_path}', '-vf', f'{vf}', '-vsync', 'vfr', '-q:v', '2', '-loglevel', 'error', '-stats', f'{videoFramesFolder}/%04d.jpg'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "    else: \n",
        "        print(f'\\nWARNING!\\n\\nVideo not found: {video_init_path}.\\nPlease check your video path.\\n')\n",
        "    #!ffmpeg -i {video_init_path} -vf {vf} -vsync vfr -q:v 2 -loglevel error -stats {videoFramesFolder}/%04d.jpg\n",
        "\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ####**2D Animation Settings:**\n",
        "#@markdown `zoom` is a multiplier of dimensions, 1 is no zoom.\n",
        "#@markdown All rotations are provided in degrees.\n",
        "\n",
        "key_frames = True #@param {type:\"boolean\"}\n",
        "max_frames = 10000#@param {type:\"number\"}\n",
        "\n",
        "if animation_mode == \"Video Input\":\n",
        "    max_frames = len(glob(f'{videoFramesFolder}/*.jpg'))\n",
        "\n",
        "interp_spline = 'Linear' #Do not change, currently will not look good. param ['Linear','Quadratic','Cubic']{type:\"string\"}\n",
        "angle = \"0:(0)\"#@param {type:\"string\"}\n",
        "zoom = \"0: (1), 10: (1.05)\"#@param {type:\"string\"}\n",
        "translation_x = \"0: (0)\"#@param {type:\"string\"}\n",
        "translation_y = \"0: (0)\"#@param {type:\"string\"}\n",
        "translation_z = \"0: (10.0)\"#@param {type:\"string\"}\n",
        "rotation_3d_x = \"0: (0)\"#@param {type:\"string\"}\n",
        "rotation_3d_y = \"0: (0)\"#@param {type:\"string\"}\n",
        "rotation_3d_z = \"0: (0)\"#@param {type:\"string\"}\n",
        "midas_depth_model = \"dpt_large\"#@param {type:\"string\"}\n",
        "midas_weight = 0.3#@param {type:\"number\"}\n",
        "near_plane = 200#@param {type:\"number\"}\n",
        "far_plane = 10000#@param {type:\"number\"}\n",
        "fov = 40#@param {type:\"number\"}\n",
        "padding_mode = 'border'#@param {type:\"string\"}\n",
        "sampling_mode = 'bicubic'#@param {type:\"string\"}\n",
        "\n",
        "#======= TURBO MODE\n",
        "#@markdown ---\n",
        "#@markdown ####**Turbo Mode (3D anim only):**\n",
        "#@markdown (Starts after frame 10,) skips diffusion steps and just uses depth map to warp images for skipped frames.\n",
        "#@markdown Speeds up rendering by 2x-4x, and may improve image coherence between frames.\n",
        "#@markdown For different settings tuned for Turbo Mode, refer to the original Disco-Turbo Github: https://github.com/zippy731/disco-diffusion-turbo\n",
        "\n",
        "turbo_mode = False #@param {type:\"boolean\"}\n",
        "turbo_steps = \"3\" #@param [\"2\",\"3\",\"4\",\"5\",\"6\"] {type:\"string\"}\n",
        "turbo_preroll = 10 # frames\n",
        "\n",
        "#insist turbo be used only w 3d anim.\n",
        "if turbo_mode and animation_mode != '3D':\n",
        "    print('=====')\n",
        "    print('Turbo mode only available with 3D animations. Disabling Turbo.')\n",
        "    print('=====')\n",
        "    turbo_mode = False\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ####**Coherency Settings:**\n",
        "#@markdown `frame_scale` tries to guide the new frame to looking like the old one. A good default is 1500.\n",
        "frames_scale = 1500 #@param{type: 'integer'}\n",
        "#@markdown `frame_skip_steps` will blur the previous frame - higher values will flicker less but struggle to add enough new detail to zoom into.\n",
        "frames_skip_steps = '60%' #@param ['40%', '50%', '60%', '70%', '80%'] {type: 'string'}\n",
        "\n",
        "#@markdown ####**Video Init Coherency Settings:**\n",
        "#@markdown `frame_scale` tries to guide the new frame to looking like the old one. A good default is 1500.\n",
        "video_init_frames_scale = 15000 #@param{type: 'integer'}\n",
        "#@markdown `frame_skip_steps` will blur the previous frame - higher values will flicker less but struggle to add enough new detail to zoom into.\n",
        "video_init_frames_skip_steps = '70%' #@param ['40%', '50%', '60%', '70%', '80%'] {type: 'string'}\n",
        "\n",
        "#======= VR MODE\n",
        "#@markdown ---\n",
        "#@markdown ####**VR Mode (3D anim only):**\n",
        "#@markdown Enables stereo rendering of left/right eye views (supporting Turbo) which use a different (fish-eye) camera projection matrix.   \n",
        "#@markdown Note the images you're prompting will work better if they have some inherent wide-angle aspect\n",
        "#@markdown The generated images will need to be combined into left/right videos. These can then be stitched into the VR180 format.\n",
        "#@markdown Google made the VR180 Creator tool but subsequently stopped supporting it. It's available for download in a few places including https://www.patrickgrunwald.de/vr180-creator-download\n",
        "#@markdown The tool is not only good for stitching (videos and photos) but also for adding the correct metadata into existing videos, which is needed for services like YouTube to identify the format correctly.\n",
        "#@markdown Watching YouTube VR videos isn't necessarily the easiest depending on your headset. For instance Oculus have a dedicated media studio and store which makes the files easier to access on a Quest https://creator.oculus.com/manage/mediastudio/\n",
        "#@markdown \n",
        "#@markdown The command to get ffmpeg to concat your frames for each eye is in the form: `ffmpeg -framerate 15 -i frame_%4d_l.png l.mp4` (repeat for r)\n",
        "\n",
        "vr_mode = False #@param {type:\"boolean\"}\n",
        "#@markdown `vr_eye_angle` is the y-axis rotation of the eyes towards the center\n",
        "vr_eye_angle = 0.5 #@param{type:\"number\"}\n",
        "#@markdown interpupillary distance (between the eyes)\n",
        "vr_ipd = 5.0 #@param{type:\"number\"}\n",
        "\n",
        "#insist VR be used only w 3d anim.\n",
        "if vr_mode and animation_mode != '3D':\n",
        "    print('=====')\n",
        "    print('VR mode only available with 3D animations. Disabling VR.')\n",
        "    print('=====')\n",
        "    vr_mode = False\n",
        "\n",
        "\n",
        "def parse_key_frames(string, prompt_parser=None):\n",
        "    \"\"\"Given a string representing frame numbers paired with parameter values at that frame,\n",
        "    return a dictionary with the frame numbers as keys and the parameter values as the values.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    string: string\n",
        "        Frame numbers paired with parameter values at that frame number, in the format\n",
        "        'framenumber1: (parametervalues1), framenumber2: (parametervalues2), ...'\n",
        "    prompt_parser: function or None, optional\n",
        "        If provided, prompt_parser will be applied to each string of parameter values.\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    dict\n",
        "        Frame numbers as keys, parameter values at that frame number as values\n",
        "\n",
        "    Raises\n",
        "    ------\n",
        "    RuntimeError\n",
        "        If the input string does not match the expected format.\n",
        "    \n",
        "    Examples\n",
        "    --------\n",
        "    >>> parse_key_frames(\"10:(Apple: 1| Orange: 0), 20: (Apple: 0| Orange: 1| Peach: 1)\")\n",
        "    {10: 'Apple: 1| Orange: 0', 20: 'Apple: 0| Orange: 1| Peach: 1'}\n",
        "\n",
        "    >>> parse_key_frames(\"10:(Apple: 1| Orange: 0), 20: (Apple: 0| Orange: 1| Peach: 1)\", prompt_parser=lambda x: x.lower()))\n",
        "    {10: 'apple: 1| orange: 0', 20: 'apple: 0| orange: 1| peach: 1'}\n",
        "    \"\"\"\n",
        "    import re\n",
        "    pattern = r'((?P<frame>[0-9]+):[\\s]*[\\(](?P<param>[\\S\\s]*?)[\\)])'\n",
        "    frames = dict()\n",
        "    for match_object in re.finditer(pattern, string):\n",
        "        frame = int(match_object.groupdict()['frame'])\n",
        "        param = match_object.groupdict()['param']\n",
        "        if prompt_parser:\n",
        "            frames[frame] = prompt_parser(param)\n",
        "        else:\n",
        "            frames[frame] = param\n",
        "\n",
        "    if frames == {} and len(string) != 0:\n",
        "        raise RuntimeError('Key Frame string not correctly formatted')\n",
        "    return frames\n",
        "\n",
        "def get_inbetweens(key_frames, integer=False):\n",
        "    \"\"\"Given a dict with frame numbers as keys and a parameter value as values,\n",
        "    return a pandas Series containing the value of the parameter at every frame from 0 to max_frames.\n",
        "    Any values not provided in the input dict are calculated by linear interpolation between\n",
        "    the values of the previous and next provided frames. If there is no previous provided frame, then\n",
        "    the value is equal to the value of the next provided frame, or if there is no next provided frame,\n",
        "    then the value is equal to the value of the previous provided frame. If no frames are provided,\n",
        "    all frame values are NaN.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    key_frames: dict\n",
        "        A dict with integer frame numbers as keys and numerical values of a particular parameter as values.\n",
        "    integer: Bool, optional\n",
        "        If True, the values of the output series are converted to integers.\n",
        "        Otherwise, the values are floats.\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    pd.Series\n",
        "        A Series with length max_frames representing the parameter values for each frame.\n",
        "    \n",
        "    Examples\n",
        "    --------\n",
        "    >>> max_frames = 5\n",
        "    >>> get_inbetweens({1: 5, 3: 6})\n",
        "    0    5.0\n",
        "    1    5.0\n",
        "    2    5.5\n",
        "    3    6.0\n",
        "    4    6.0\n",
        "    dtype: float64\n",
        "\n",
        "    >>> get_inbetweens({1: 5, 3: 6}, integer=True)\n",
        "    0    5\n",
        "    1    5\n",
        "    2    5\n",
        "    3    6\n",
        "    4    6\n",
        "    dtype: int64\n",
        "    \"\"\"\n",
        "    key_frame_series = pd.Series([np.nan for a in range(max_frames)])\n",
        "\n",
        "    for i, value in key_frames.items():\n",
        "        key_frame_series[i] = value\n",
        "    key_frame_series = key_frame_series.astype(float)\n",
        "    \n",
        "    interp_method = interp_spline\n",
        "\n",
        "    if interp_method == 'Cubic' and len(key_frames.items()) <=3:\n",
        "      interp_method = 'Quadratic'\n",
        "    \n",
        "    if interp_method == 'Quadratic' and len(key_frames.items()) <= 2:\n",
        "      interp_method = 'Linear'\n",
        "      \n",
        "    \n",
        "    key_frame_series[0] = key_frame_series[key_frame_series.first_valid_index()]\n",
        "    key_frame_series[max_frames-1] = key_frame_series[key_frame_series.last_valid_index()]\n",
        "    # key_frame_series = key_frame_series.interpolate(method=intrp_method,order=1, limit_direction='both')\n",
        "    key_frame_series = key_frame_series.interpolate(method=interp_method.lower(),limit_direction='both')\n",
        "    if integer:\n",
        "        return key_frame_series.astype(int)\n",
        "    return key_frame_series\n",
        "\n",
        "def split_prompts(prompts):\n",
        "    prompt_series = pd.Series([np.nan for a in range(max_frames)])\n",
        "    for i, prompt in prompts.items():\n",
        "        prompt_series[i] = prompt\n",
        "    # prompt_series = prompt_series.astype(str)\n",
        "    prompt_series = prompt_series.ffill().bfill()\n",
        "    return prompt_series\n",
        "\n",
        "if key_frames:\n",
        "    try:\n",
        "        angle_series = get_inbetweens(parse_key_frames(angle))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `angle` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `angle` as \"\n",
        "            f'\"0: ({angle})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        angle = f\"0: ({angle})\"\n",
        "        angle_series = get_inbetweens(parse_key_frames(angle))\n",
        "\n",
        "    try:\n",
        "        zoom_series = get_inbetweens(parse_key_frames(zoom))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `zoom` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `zoom` as \"\n",
        "            f'\"0: ({zoom})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        zoom = f\"0: ({zoom})\"\n",
        "        zoom_series = get_inbetweens(parse_key_frames(zoom))\n",
        "\n",
        "    try:\n",
        "        translation_x_series = get_inbetweens(parse_key_frames(translation_x))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `translation_x` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `translation_x` as \"\n",
        "            f'\"0: ({translation_x})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        translation_x = f\"0: ({translation_x})\"\n",
        "        translation_x_series = get_inbetweens(parse_key_frames(translation_x))\n",
        "\n",
        "    try:\n",
        "        translation_y_series = get_inbetweens(parse_key_frames(translation_y))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `translation_y` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `translation_y` as \"\n",
        "            f'\"0: ({translation_y})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        translation_y = f\"0: ({translation_y})\"\n",
        "        translation_y_series = get_inbetweens(parse_key_frames(translation_y))\n",
        "\n",
        "    try:\n",
        "        translation_z_series = get_inbetweens(parse_key_frames(translation_z))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `translation_z` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `translation_z` as \"\n",
        "            f'\"0: ({translation_z})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        translation_z = f\"0: ({translation_z})\"\n",
        "        translation_z_series = get_inbetweens(parse_key_frames(translation_z))\n",
        "\n",
        "    try:\n",
        "        rotation_3d_x_series = get_inbetweens(parse_key_frames(rotation_3d_x))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `rotation_3d_x` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `rotation_3d_x` as \"\n",
        "            f'\"0: ({rotation_3d_x})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        rotation_3d_x = f\"0: ({rotation_3d_x})\"\n",
        "        rotation_3d_x_series = get_inbetweens(parse_key_frames(rotation_3d_x))\n",
        "\n",
        "    try:\n",
        "        rotation_3d_y_series = get_inbetweens(parse_key_frames(rotation_3d_y))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `rotation_3d_y` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `rotation_3d_y` as \"\n",
        "            f'\"0: ({rotation_3d_y})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        rotation_3d_y = f\"0: ({rotation_3d_y})\"\n",
        "        rotation_3d_y_series = get_inbetweens(parse_key_frames(rotation_3d_y))\n",
        "\n",
        "    try:\n",
        "        rotation_3d_z_series = get_inbetweens(parse_key_frames(rotation_3d_z))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `rotation_3d_z` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `rotation_3d_z` as \"\n",
        "            f'\"0: ({rotation_3d_z})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        rotation_3d_z = f\"0: ({rotation_3d_z})\"\n",
        "        rotation_3d_z_series = get_inbetweens(parse_key_frames(rotation_3d_z))\n",
        "\n",
        "else:\n",
        "    angle = float(angle)\n",
        "    zoom = float(zoom)\n",
        "    translation_x = float(translation_x)\n",
        "    translation_y = float(translation_y)\n",
        "    translation_z = float(translation_z)\n",
        "    rotation_3d_x = float(rotation_3d_x)\n",
        "    rotation_3d_y = float(rotation_3d_y)\n",
        "    rotation_3d_z = float(rotation_3d_z)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InstallRAFT",
        "cellView": "form"
      },
      "source": [
        "#@title Install RAFT for Video input animation mode only\n",
        "#@markdown Run once per session. Doesn't download again if model path exists.\n",
        "#@markdown Use force download to reload raft models if needed\n",
        "force_download = False #@param {type:'boolean'}\n",
        "if animation_mode == 'Video Input':\n",
        "    try:\n",
        "        from raft import RAFT\n",
        "    except:\n",
        "        if not os.path.exists(os.path.join(PROJECT_DIR, 'RAFT')):\n",
        "            gitclone('https://github.com/princeton-vl/RAFT', os.path.join(PROJECT_DIR, 'RAFT'))\n",
        "        sys.path.append(f'{PROJECT_DIR}/RAFT')\n",
        "\n",
        "    if (not (os.path.exists(f'{root_path}/RAFT/models'))) or force_download:\n",
        "        createPath(f'{root_path}/RAFT')\n",
        "        os.chdir(f'{root_path}/RAFT')\n",
        "        sub_p_res = subprocess.run(['bash', f'{PROJECT_DIR}/RAFT/download_models.sh'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "        print(sub_p_res)\n",
        "        os.chdir(PROJECT_DIR)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlowFns1",
        "cellView": "form"
      },
      "source": [
        "#@title Define optical flow functions for Video input animation mode only\n",
        "if animation_mode == 'Video Input':\n",
        "    in_path = videoFramesFolder\n",
        "    flo_folder = f'{in_path}/out_flo_fwd'\n",
        "    path = f'{PROJECT_DIR}/RAFT/core'\n",
        "    import sys\n",
        "    sys.path.append(f'{PROJECT_DIR}/RAFT/core')\n",
        "    os.chdir(f'{PROJECT_DIR}/RAFT/core')\n",
        "    print(os.getcwd())\n",
        "  \n",
        "    print(\"Renaming RAFT core's utils.utils to raftutils.utils (to avoid a naming conflict with AdaBins)\")\n",
        "    if not os.path.exists(f'{PROJECT_DIR}/RAFT/core/raftutils'):\n",
        "        os.rename(f'{PROJECT_DIR}/RAFT/core/utils', f'{PROJECT_DIR}/RAFT/core/raftutils')\n",
        "        sub_p_res = subprocess.run(['sed', '-i', 's/from utils.utils/from raftutils.utils/g', f'{PROJECT_DIR}/RAFT/core/corr.py'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "        sub_p_res = subprocess.run(['sed', '-i', 's/from utils.utils/from raftutils.utils/g', f'{PROJECT_DIR}/RAFT/core/raft.py'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "\n",
        "    from raftutils.utils import InputPadder\n",
        "    from raft import RAFT\n",
        "    from raftutils import flow_viz\n",
        "    import numpy as np\n",
        "    import argparse, PIL, cv2\n",
        "    from PIL import Image, ImageOps, ImageStat, ImageEnhance, ImageDraw\n",
        "    from tqdm.notebook import tqdm\n",
        "    from glob import glob\n",
        "    import torch\n",
        "  \n",
        "    args2 = argparse.Namespace()\n",
        "    args2.small = False\n",
        "    args2.mixed_precision = True\n",
        "  \n",
        "  \n",
        "    TAG_CHAR = np.array([202021.25], np.float32)\n",
        "  \n",
        "    def writeFlow(filename,uv,v=None):\n",
        "        \"\"\" \n",
        "        https://github.com/NVIDIA/flownet2-pytorch/blob/master/utils/flow_utils.py\n",
        "        Copyright 2017 NVIDIA CORPORATION\n",
        "  \n",
        "        Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "        you may not use this file except in compliance with the License.\n",
        "        You may obtain a copy of the License at\n",
        "  \n",
        "            http://www.apache.org/licenses/LICENSE-2.0\n",
        "  \n",
        "        Unless required by applicable law or agreed to in writing, software\n",
        "        distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "        WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "        See the License for the specific language governing permissions and\n",
        "        limitations under the License.\n",
        "        \n",
        "        Write optical flow to file.\n",
        "        \n",
        "        If v is None, uv is assumed to contain both u and v channels,\n",
        "        stacked in depth.\n",
        "        Original code by Deqing Sun, adapted from Daniel Scharstein.\n",
        "        \"\"\"\n",
        "        nBands = 2\n",
        "  \n",
        "        if v is None:\n",
        "            assert(uv.ndim == 3)\n",
        "            assert(uv.shape[2] == 2)\n",
        "            u = uv[:,:,0]\n",
        "            v = uv[:,:,1]\n",
        "        else:\n",
        "            u = uv\n",
        "  \n",
        "        assert(u.shape == v.shape)\n",
        "        height,width = u.shape\n",
        "        f = open(filename,'wb')\n",
        "        # write the header\n",
        "        f.write(TAG_CHAR)\n",
        "        np.array(width).astype(np.int32).tofile(f)\n",
        "        np.array(height).astype(np.int32).tofile(f)\n",
        "        # arrange into matrix form\n",
        "        tmp = np.zeros((height, width*nBands))\n",
        "        tmp[:,np.arange(width)*2] = u\n",
        "        tmp[:,np.arange(width)*2 + 1] = v\n",
        "        tmp.astype(np.float32).tofile(f)\n",
        "        f.close()\n",
        "  \n",
        "    def load_img(img, size):\n",
        "        img = Image.open(img).convert('RGB').resize(size)\n",
        "        return torch.from_numpy(np.array(img)).permute(2,0,1).float()[None,...].cuda()\n",
        "  \n",
        "    def get_flow(frame1, frame2, model, iters=20):\n",
        "        padder = InputPadder(frame1.shape)\n",
        "        frame1, frame2 = padder.pad(frame1, frame2)\n",
        "        _, flow12 = model(frame1, frame2, iters=iters, test_mode=True)\n",
        "        flow12 = flow12[0].permute(1, 2, 0).detach().cpu().numpy()\n",
        "  \n",
        "        return flow12\n",
        "  \n",
        "    def warp_flow(img, flow):\n",
        "        h, w = flow.shape[:2]\n",
        "        flow = flow.copy()\n",
        "        flow[:, :, 0] += np.arange(w)\n",
        "        flow[:, :, 1] += np.arange(h)[:, np.newaxis]\n",
        "        res = cv2.remap(img, flow, None, cv2.INTER_LINEAR)\n",
        "        return res\n",
        "  \n",
        "    def makeEven(_x):\n",
        "        return _x if (_x % 2 == 0) else _x+1\n",
        "  \n",
        "    def fit(img,maxsize=512):\n",
        "        maxdim = max(*img.size)\n",
        "        if maxdim>maxsize:\n",
        "            # if True:\n",
        "            ratio = maxsize/maxdim\n",
        "            x,y = img.size\n",
        "            size = (makeEven(int(x*ratio)),makeEven(int(y*ratio))) \n",
        "            img = img.resize(size)\n",
        "        return img\n",
        "  \n",
        "    def warp(frame1, frame2, flo_path, blend=0.5, weights_path=None):\n",
        "        flow21 = np.load(flo_path)\n",
        "        frame1pil = np.array(frame1.convert('RGB').resize((flow21.shape[1],flow21.shape[0])))\n",
        "        frame1_warped21 = warp_flow(frame1pil, flow21)\n",
        "        # frame2pil = frame1pil\n",
        "        frame2pil = np.array(frame2.convert('RGB').resize((flow21.shape[1],flow21.shape[0])))\n",
        "    \n",
        "        if weights_path:\n",
        "            # TBD\n",
        "            pass\n",
        "        else:\n",
        "            blended_w = frame2pil*(1-blend) + frame1_warped21*(blend)\n",
        "  \n",
        "        return  PIL.Image.fromarray(blended_w.astype('uint8'))\n",
        "  \n",
        "    in_path = videoFramesFolder\n",
        "    flo_folder = f'{in_path}/out_flo_fwd'\n",
        "  \n",
        "    temp_flo = in_path+'/temp_flo'\n",
        "    flo_fwd_folder = in_path+'/out_flo_fwd'\n",
        "    # TBD flow backwards!\n",
        "  \n",
        "    os.chdir(PROJECT_DIR)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlowFns2",
        "cellView": "form"
      },
      "source": [
        "#@title Generate optical flow and consistency maps\n",
        "#@markdown Run once per init video\n",
        "\n",
        "if animation_mode == \"Video Input\":\n",
        "    import gc\n",
        "\n",
        "    force_flow_generation = False #@param {type:'boolean'}\n",
        "    in_path = videoFramesFolder\n",
        "    flo_folder = f'{in_path}/out_flo_fwd'\n",
        "\n",
        "    if not video_init_flow_warp:\n",
        "        print('video_init_flow_warp not set, skipping')\n",
        "\n",
        "    if (animation_mode == 'Video Input') and (video_init_flow_warp):\n",
        "        flows = glob(flo_folder+'/*.*')\n",
        "        if (len(flows)>0) and not force_flow_generation:\n",
        "            print(f'Skipping flow generation:\\nFound {len(flows)} existing flow files in current working folder: {flo_folder}.\\nIf you wish to generate new flow files, check force_flow_generation and run this cell again.')\n",
        "    \n",
        "        if (len(flows)==0) or force_flow_generation:\n",
        "            frames = sorted(glob(in_path+'/*.*'));\n",
        "            if len(frames)<2: \n",
        "                print(f'WARNING!\\nCannot create flow maps: Found {len(frames)} frames extracted from your video input.\\nPlease check your video path.')\n",
        "            if len(frames)>=2:\n",
        "        \n",
        "                raft_model = torch.nn.DataParallel(RAFT(args2))\n",
        "                raft_model.load_state_dict(torch.load(f'{root_path}/RAFT/models/raft-things.pth'))\n",
        "                raft_model = raft_model.module.cuda().eval()\n",
        "        \n",
        "                for f in pathlib.Path(f'{flo_fwd_folder}').glob('*.*'):\n",
        "                    f.unlink()\n",
        "        \n",
        "                temp_flo = in_path+'/temp_flo'\n",
        "                flo_fwd_folder = in_path+'/out_flo_fwd'\n",
        "        \n",
        "                createPath(flo_fwd_folder)\n",
        "                createPath(temp_flo)\n",
        "        \n",
        "                # TBD Call out to a consistency checker?\n",
        "        \n",
        "                framecount = 0\n",
        "                for frame1, frame2 in tqdm(zip(frames[:-1], frames[1:]), total=len(frames)-1):\n",
        "        \n",
        "                    out_flow21_fn = f\"{flo_fwd_folder}/{frame1.split('/')[-1]}\"\n",
        "            \n",
        "                    frame1 = load_img(frame1, width_height)\n",
        "                    frame2 = load_img(frame2, width_height)\n",
        "            \n",
        "                    flow21 = get_flow(frame2, frame1, raft_model)\n",
        "                    np.save(out_flow21_fn, flow21)\n",
        "            \n",
        "                    if video_init_check_consistency:\n",
        "                        # TBD\n",
        "                        pass\n",
        "\n",
        "                del raft_model \n",
        "                gc.collect()"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ExtraSetTop"
      },
      "source": [
        "### Extra Settings\n",
        "#### **Partial Saves | Advanced Settings | Cutn Scheduling | Symmetry**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExtraSettings",
        "cellView": "form"
      },
      "source": [
        "#@markdown ###**Saving:**\n",
        "\n",
        "intermediate_saves = [100,125,140]#@param{type: 'raw'}\n",
        "intermediates_in_subfolder = False #@param{type: 'boolean'}\n",
        "#@markdown Intermediate steps will save a copy at your specified intervals. You can either format it as a single integer or a list of specific steps \n",
        "\n",
        "#@markdown A value of `2` will save a copy at 33% and 66%. 0 will save none.\n",
        "\n",
        "#@markdown A value of `[5, 9, 34, 45]` will save at steps 5, 9, 34, and 45. (Make sure to include the brackets)\n",
        "\n",
        "\n",
        "if type(intermediate_saves) is not list:\n",
        "    if intermediate_saves:\n",
        "        steps_per_checkpoint = math.floor((steps - skip_steps - 1) // (intermediate_saves+1))\n",
        "        steps_per_checkpoint = steps_per_checkpoint if steps_per_checkpoint > 0 else 1\n",
        "        print(f'Will save every {steps_per_checkpoint} steps')\n",
        "    else:\n",
        "        steps_per_checkpoint = steps+10\n",
        "else:\n",
        "    steps_per_checkpoint = None\n",
        "\n",
        "if intermediate_saves and intermediates_in_subfolder is True:\n",
        "    partialFolder = f'{batchFolder}/partials'\n",
        "    createPath(partialFolder)\n",
        "\n",
        "#@markdown <br>\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ###**Advanced Settings:**\n",
        "#@markdown *There are a few extra advanced settings available if you double click this cell.*\n",
        "\n",
        "#@markdown *Perlin init will replace your init, so uncheck if using one.*\n",
        "\n",
        "perlin_init = False  #@param{type: 'boolean'}\n",
        "perlin_mode = 'mixed' #@param ['mixed', 'color', 'gray']\n",
        "set_seed = 'random_seed' #@param{type: 'string'}\n",
        "increment_n_batch_seed = True #@param {type: 'boolean'}\n",
        "eta = 0.8 #@param{type: 'number'}\n",
        "clamp_grad = True #@param{type: 'boolean'}\n",
        "clamp_max = 0.05 #@param{type: 'number'}\n",
        "\n",
        "#@markdown <br>\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "### EXTRA ADVANCED SETTINGS:\n",
        "#@markdown #### **Advanced Extra Settings:**\n",
        "randomize_class = True #@param{type: 'boolean'}\n",
        "clip_denoised = False #@param{type: 'boolean'}\n",
        "fuzzy_prompt = False #@param{type: 'boolean'}\n",
        "rand_mag = 0.05 #@param{type: 'number'}\n",
        "\n",
        "#@markdown <br>\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ### **Cutn Scheduling**\n",
        "\n",
        "#@markdown To use easing, for cut_overview use the following format `[40]*100+dd_easing_fw(start=40, end=1, steps=800)+[1]*100`\n",
        "#@markdown and for cut_innercut use the following format `[1]*100+dd_easing_bw(start=1, end=40, steps=800)+[40]*100`\n",
        "#@markdown info on easing functions: https://github.com/semitable/easing-functions/blob/master/docs/cubic.png?raw=true\n",
        "#@markdown more info on easing functions: https://github.com/semitable/easing-functions\n",
        "\n",
        "def easeint(st, en, s, fn): return dd_easing_int(start=st, end=en, steps=s, easing_fn=fn)\n",
        "def easefloat(st, en, s, fn): return dd_easing_float(start=st, end=en, steps=s, easing_fn=fn)\n",
        "\n",
        "cut_easing_fn = \"ExponentialEaseInOut\" #@param [\"LinearInOut\", \"QuadEaseInOut\", \"CubicEaseInOut\", \"QuarticEaseInOut\", \"QuinticEaseInOut\", \"SineEaseInOut\", \"CircularEaseInOut\", \"ExponentialEaseInOut\", \"ElasticEaseInOut\", \"BackEaseInOut\", \"BounceEaseInOut\"]\n",
        "cut_overview = \"dd_easing_int(start=22, end=15, steps=250, easing_fn='ExponentialEaseIn')+dd_easing_int(start=15, end=0, steps=900, easing_fn='ExponentialEaseOut')+[0]*100\" #@param {type: 'string'}\n",
        "cut_overview_preset = \"None\" #@param [\"None\", \"DEFAULT -> [22]*200+dd_easing_int(start=22, end=11, steps=350, easing_fn='ExponentialEaseIn')+dd_easing_int(start=11, end=0, steps=350, easing_fn='ExponentialEaseOut')+[0]*100\", \"DD DEFAULT -> [12]*400+[4]*600\"]\n",
        "cut_innercut = \"[22-x for x in eval(cut_overview)]\" #@param {type: 'string'}\n",
        "cut_innercut_preset = \"None\" #@param [\"None\", \"DEFAULT -> [22-x for x in eval(cut_overview)]\", \"DD DEFAULT -> [4]*400+[12]*600\"]\n",
        "cut_ic_pow = \"[0.05]*500+dd_easing_float(start=0, end=2.5, steps=250, easing_fn=cut_easing_fn)+dd_easing_float(start=2.5, end=5, steps=250, easing_fn=cut_easing_fn)\" #@param {type: 'string'}\n",
        "cut_ic_pow_preset = \"PORTRAIT DEFAULT -> [0.05]*1000\" #@param [\"None\", \"DEFAULT -> [0]*500+dd_easing_float(start=0, end=16, steps=500, easing_fn=cut_easing_fn)\", \"DD DEFAULT -> [1]*1000\", \"PORTRAIT DEFAULT -> [0.05]*1000\"]\n",
        "cut_icgray_p = \"[0.2]*400+[0]*600\" #@param {type: 'string'}\n",
        "cut_icgray_p_preset = \"None\" #@param [\"None\", \"DEFAULT -> [0.6]*100+[0.2]*100+[0.1]*100+[0.05]*100+[0]*600\", \"DD DEFAULT ->[0.2]*400+[0]*600\", \"[0.2]*400+[0.1]*600\"]\n",
        "\n",
        "\n",
        "# Overwrite settings with presets if selected\n",
        "if cut_overview_preset != \"None\": cut_overview = cut_overview_preset.split(' -> ')[1] if ' -> ' in cut_overview_preset else cut_overview_preset\n",
        "if cut_innercut_preset != \"None\": cut_innercut = cut_innercut_preset.split(' -> ')[1] if ' -> ' in cut_innercut_preset else cut_innercut_preset\n",
        "if cut_ic_pow_preset != \"None\": cut_ic_pow = cut_ic_pow_preset.split(' -> ')[1] if ' -> ' in cut_ic_pow_preset else cut_ic_pow_preset\n",
        "if cut_icgray_p_preset != \"None\": cut_icgray_p = cut_icgray_p.split(' -> ')[1] if ' -> ' in cut_icgray_p else cut_icgray_p\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "def easing_normalize(arr, t_min, t_max):\n",
        "  norm_arr = []\n",
        "  for i in arr:\n",
        "    norm_arr.append((((i - min(arr))*(t_max - t_min))/(max(arr) - min(arr))) + t_min)\n",
        "  return norm_arr\n",
        "\n",
        "def dd_easing_int(start=1, end=9, steps=1000, easing_fn=\"ExponentialEaseInOut\"): return list(np.rint(easing_normalize(list(map(eval(easing_fn + \"(start=start, end=end, duration=steps)\"), np.arange(0, steps, 1))), min([start, end]), max([start, end]))).astype(int))\n",
        "\n",
        "def dd_easing_float(start=1, end=9, steps=1000, easing_fn=\"ExponentialEaseInOut\"): return list(easing_normalize(list(map(eval(easing_fn + \"(start=start, end=end, duration=steps)\"), np.arange(0, steps, 1))), min([start, end]), max([start, end])))\n",
        "\n",
        "plt.plot(np.arange(0, 1000, 1), eval(cut_overview)[0:1000], label='overview cuts')\n",
        "plt.plot(np.arange(0, 1000, 1), eval(cut_innercut)[0:1000], label='inner cuts')\n",
        "\n",
        "total_cuts = []\n",
        "cut_overview_evaled = eval(cut_overview)\n",
        "cut_innercut_evaled = eval(cut_innercut)\n",
        "for i in np.arange(0, 1000, 1):\n",
        "  total_cuts = total_cuts + [cut_overview_evaled[i] + cut_innercut_evaled[i]]\n",
        "\n",
        "plt.plot(np.arange(0, 1000, 1), total_cuts[0:1000], label='total cuts')\n",
        "plt.plot(np.arange(0, 1000, 1), eval(cut_ic_pow)[0:1000], label='ic_pow')\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n",
        "\n",
        "#@markdown <br>\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ### **KaliYuga Cutn Scheduling:**\n",
        "\n",
        "#@markdown KaliYuga model settings. Refer to [cut_ic_pow](https://ezcharts.miraheze.org/wiki/Category:Cut_ic_pow) as a guide. Values between 1 and 100 all work.\n",
        "pad_or_pulp_cut_overview = \"[15]*100+[15]*100+[12]*100+[12]*100+[6]*100+[4]*100+[2]*200+[0]*200\" #@param {type: 'string'}\n",
        "pad_or_pulp_cut_innercut = \"[1]*100+[1]*100+[4]*100+[4]*100+[8]*100+[8]*100+[10]*200+[10]*200\" #@param {type: 'string'}\n",
        "pad_or_pulp_cut_ic_pow = \"[12]*300+[12]*100+[12]*50+[12]*50+[10]*100+[10]*100+[10]*300\" #@param {type: 'string'}\n",
        "pad_or_pulp_cut_icgray_p = \"[0.87]*100+[0.78]*50+[0.73]*50+[0.64]*60+[0.56]*40+[0.50]*50+[0.33]*100+[0.19]*150+[0]*400\" #@param {type: 'string'}\n",
        "\n",
        "watercolor_cut_overview = \"[14]*200+[12]*200+[4]*400+[0]*200\" #@param {type: 'string'}\n",
        "watercolor_cut_innercut = \"[2]*200+[4]*200+[12]*400+[12]*200\" #@param {type: 'string'}\n",
        "watercolor_cut_ic_pow = \"[12]*300+[12]*100+[12]*50+[12]*50+[10]*100+[10]*100+[10]*300\" #@param {type: 'string'}\n",
        "watercolor_cut_icgray_p = \"[0.7]*100+[0.6]*100+[0.45]*100+[0.3]*100+[0]*600\" #@param {type: 'string'}\n",
        "\n",
        "if (diffusion_model in kaliyuga_pixel_art_model_names) or (diffusion_model in kaliyuga_pulpscifi_model_names):\n",
        "    cut_overview = pad_or_pulp_cut_overview\n",
        "    cut_innercut = pad_or_pulp_cut_innercut\n",
        "    cut_ic_pow = pad_or_pulp_cut_ic_pow\n",
        "    cut_icgray_p = pad_or_pulp_cut_icgray_p\n",
        "elif diffusion_model in kaliyuga_watercolor_model_names:\n",
        "    cut_overview = watercolor_cut_overview\n",
        "    cut_innercut = watercolor_cut_innercut\n",
        "    cut_ic_pow = watercolor_cut_ic_pow\n",
        "    cut_icgray_p = watercolor_cut_icgray_p\n",
        "\n",
        "#@markdown <br>\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ### **Symmetry Transformation Settings:**\n",
        "use_vertical_symmetry = False #@param {type:\"boolean\"}\n",
        "use_horizontal_symmetry = False #@param {type:\"boolean\"}\n",
        "transformation_percent = [0.09] #@param"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Prompt Setup\n",
        "\n",
        "`animation_mode: None` will only use the first set. `animation_mode: 2D / Video` will run through them per the set frames and hold on to the last one.\n",
        "\n",
        "For help with Noodle Soup Prompts see the <a href=\"#reference_guide\">Reference section</a>"
      ],
      "metadata": {
        "id": "Ys8uUbuWc0bt"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Prompts"
      },
      "source": [
        "# Note: If using a pixelart diffusion model, try adding \"#pixelart\" to the end of the prompt for a stronger effect. It'll tend to work a lot better!\n",
        "# Note: Prompts now support scheduling. Ex: \"My awesome Disco Diffusion Prompt:dd_easing_float(start=15, end=0, steps=400, easing_fn=cut_easing_fn)\"\n",
        "\n",
        "\n",
        "text_prompts = {\n",
        "\t0: [\n",
        "\t\t\t'!dream \"beautiful portrait of a woman in plate armor with symmetrical features and solemn expression:3',\n",
        "\t\t\t'intricate detail, elaborate, regal, royal relief, swirling clouds, glowing particles, etched relief, radiant vibrant colors:5'\n",
        "\t\t\t'fantasy, trending on artstation, volumetric lighting, micro details, texture, intricate, ornate, royally decorated, organic, growth, whirling nebulas:2',\n",
        "\t\t\t'jpeg, watermark, text, asymmetry:-1'\n",
        "\t]\n",
        "}\n",
        "\n",
        "\n",
        "image_prompts = {\n",
        "    # 0:['ImagePromptsWorkButArentVeryGood.png:2',],\n",
        "}\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "### PROMPT PREVIEW CODE - No need to edit\n",
        "\n",
        "#@title Preview Prompts\n",
        "use_this_prompt = False #@param{type: 'boolean'}\n",
        "#@markdown Use this random prompt instead of generating randomly on diffusion run.<br>**Note:** The parsed random prompt will be saved to the settings file, not your noodle soup keys.\n",
        "generate_nsp_at_runtime = True #@param{type: 'boolean'}\n",
        "#@markdown Use random prompts for each `n_batch`. **Note:** this will disable `use_this_prompt`\n",
        "new_prompts = None\n",
        "if nsp: new_prompts = nsp_parse(text_prompts) \n",
        "if generate_nsp_at_runtime: print(\"\\033[1mThe following text prompts are just an example. Generating prompts at runtime...\\033[0m\\n\")\n",
        "if new_prompts: import pprint; print(pprint.pformat(new_prompts))"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DiffuseTop"
      },
      "source": [
        "# 5. Render Images / Frames"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoTheRun",
        "cellView": "form"
      },
      "source": [
        "#@title **Render Diffusion Still Image**\n",
        "display_rate = 10 #@param{type: 'number'}\n",
        "#@markdown ---\n",
        "#@markdown ####**Batch Settings**\n",
        "\n",
        "n_batches = 5 #@param{type: 'number'}\n",
        "#@markdown **Note:** `n_batches` ignored with animation modes.<br>\n",
        "#@markdown **Note:** `n_batches` must be `1` for Go BIG. \n",
        "\n",
        "if gpu_warning:\n",
        "  print(gpu_warning+\"\\n\")\n",
        "\n",
        "if pil_warning:\n",
        "  print(pil_warning+\"\\n\")\n",
        "\n",
        "if animation_mode == 'Video Input':\n",
        "    steps = video_init_steps\n",
        "\n",
        "#Update Model Settings\n",
        "timestep_respacing = f'ddim{steps}'\n",
        "diffusion_steps = (1000//steps)*steps if steps < 1000 else steps\n",
        "if diffusion_model is not 'custom':\n",
        "    model_config.update({\n",
        "        'timestep_respacing': timestep_respacing,\n",
        "        'diffusion_steps': diffusion_steps,\n",
        "    })\n",
        "else:\n",
        "    custom_model.update({\n",
        "        'timestep_respacing': timestep_respacing,\n",
        "        'diffusion_steps': diffusion_steps,\n",
        "    })\n",
        "if go_big_diffusion_model is not 'Inherit':\n",
        "    if go_big_diffusion_model is not 'custom':\n",
        "        go_big_model_config.update({\n",
        "            'timestep_respacing': timestep_respacing,\n",
        "            'diffusion_steps': diffusion_steps,\n",
        "        })\n",
        "    else:\n",
        "        custom_model.update({\n",
        "            'timestep_respacing': timestep_respacing,\n",
        "            'diffusion_steps': diffusion_steps,\n",
        "        })\n",
        "\n",
        "batch_size = 1 \n",
        "\n",
        "def move_files(start_num, end_num, old_folder, new_folder):\n",
        "    for i in range(start_num, end_num):\n",
        "        old_file = old_folder + f'/{batch_name}({batchNum})_{i:04}.png'\n",
        "        new_file = new_folder + f'/{batch_name}({batchNum})_{i:04}.png'\n",
        "        os.rename(old_file, new_file)\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown #### **Resume Render**\n",
        "\n",
        "resume_run = False #@param{type: 'boolean'}\n",
        "run_to_resume = 'latest' #@param{type: 'string'}\n",
        "resume_from_frame = 'latest' #@param{type: 'string'}\n",
        "retain_overwritten_frames = False #@param{type: 'boolean'}\n",
        "if retain_overwritten_frames:\n",
        "    retainFolder = f'{batchFolder}/retained'\n",
        "    createPath(retainFolder)\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown #### **Extra Save Settings**\n",
        "\n",
        "save_metadata = False #@param{type: 'boolean'}\n",
        "meta_author = \"None\" #@param{type: 'string'}\n",
        "meta_copyright = \"None\" #@param{type: 'string'}\n",
        "save_plot_graph = False#@param{type: 'boolean'}\n",
        "\n",
        "if meta_author is not None and meta_author.lower() in ['','none']:\n",
        "    meta_author = None\n",
        "if meta_copyright is not None and meta_copyright.lower() in ['','none']:\n",
        "    meta_copyright = None\n",
        "\n",
        "# Parse Noodle Soup Prompts\n",
        "text_prompts = new_prompts if (use_this_prompt == True and generate_nsp_at_runtime == False) else text_prompts\n",
        "\n",
        "\n",
        "skip_step_ratio = int(frames_skip_steps.rstrip(\"%\")) / 100\n",
        "calc_frames_skip_steps = math.floor(steps * skip_step_ratio)\n",
        "\n",
        "if animation_mode == 'Video Input':\n",
        "    frames = sorted(glob(in_path+'/*.*'));\n",
        "    if len(frames)==0: \n",
        "        sys.exit(\"ERROR: 0 frames found.\\nPlease check your video input path and rerun the video settings cell.\")\n",
        "    flows = glob(flo_folder+'/*.*')\n",
        "    if (len(flows)==0) and video_init_flow_warp:\n",
        "        sys.exit(\"ERROR: 0 flow files found.\\nPlease rerun the flow generation cell.\")\n",
        "\n",
        "if steps <= calc_frames_skip_steps:\n",
        "    sys.exit(\"ERROR: You can't skip more steps than your total steps\")\n",
        "\n",
        "if resume_run:\n",
        "    if run_to_resume == 'latest':\n",
        "        try:\n",
        "            batchNum\n",
        "        except:\n",
        "            batchNum = len(glob(f\"{batchFolder}/{batch_name}(*)_settings.txt\"))-1\n",
        "    else:\n",
        "        batchNum = int(run_to_resume)\n",
        "    if resume_from_frame == 'latest':\n",
        "        start_frame = len(glob(batchFolder+f\"/{batch_name}({batchNum})_*.png\"))\n",
        "        if animation_mode != '3D' and turbo_mode == True and start_frame > turbo_preroll and start_frame % int(turbo_steps) != 0:\n",
        "            start_frame = start_frame - (start_frame % int(turbo_steps))\n",
        "    else:\n",
        "        start_frame = int(resume_from_frame)+1\n",
        "        if animation_mode != '3D' and turbo_mode == True and start_frame > turbo_preroll and start_frame % int(turbo_steps) != 0:\n",
        "            start_frame = start_frame - (start_frame % int(turbo_steps))\n",
        "        if retain_overwritten_frames is True:\n",
        "            existing_frames = len(glob(batchFolder+f\"/{batch_name}({batchNum})_*.png\"))\n",
        "            frames_to_save = existing_frames - start_frame\n",
        "            print(f'Moving {frames_to_save} frames to the Retained folder')\n",
        "            move_files(start_frame, existing_frames, batchFolder, retainFolder)\n",
        "else:\n",
        "    start_frame = 0\n",
        "    batchNum = len(glob(batchFolder+\"/*.txt\"))\n",
        "    while os.path.isfile(f\"{batchFolder}/{batch_name}({batchNum})_settings.txt\") or os.path.isfile(f\"{batchFolder}/{batch_name}-{batchNum}_settings.txt\"):\n",
        "        batchNum += 1\n",
        "\n",
        "if go_big:\n",
        "    print(f'Starting Run: {batch_name}({batchNum}) at frame {start_frame} with Go BIG!')\n",
        "else:\n",
        "    print(f'Starting Run: {batch_name}({batchNum}) at frame {start_frame}')\n",
        "\n",
        "if set_seed == 'random_seed':\n",
        "    random.seed()\n",
        "    seed = random.randint(0, 2**32)\n",
        "    # print(f'Using seed: {seed}')\n",
        "else:\n",
        "    seed = int(set_seed)\n",
        "\n",
        "# LOAD SETTINGS - Implemented by WASasquatch\n",
        "if settings_file_path:\n",
        "\n",
        "    import json\n",
        "    from os.path import exists\n",
        "\n",
        "    if exists(settings_file_path):\n",
        "\n",
        "        with open(settings_file_path) as json_file:\n",
        "\n",
        "            jsd = json.load(json_file)\n",
        "\n",
        "            if jsd:\n",
        "                print('Loading diffusion settings from file: '+settings_file_path)\n",
        "            if load_prompts_and_inits:\n",
        "                print('Loading text, image, and resolution prompt settings from file: '+settings_file_path)\n",
        "\n",
        "            if jsd.__contains__('clip_guidance_scale'):\n",
        "                clip_guidance_scale = jsd['clip_guidance_scale']\n",
        "            if jsd.__contains__('tv_scale'):\n",
        "                tv_scale = jsd['tv_scale']\n",
        "            if jsd.__contains__('range_scale'):\n",
        "                range_scale =  jsd['range_scale']\n",
        "            if jsd.__contains__('sat_scale'):\n",
        "                sat_scale = jsd['sat_scale']\n",
        "            if jsd.__contains__('cutn_batches'):\n",
        "                cutn_batches = jsd['cutn_batches']\n",
        "            if jsd.__contains__('interp_spline'):\n",
        "                interp_spline = jsd['interp_spline']\n",
        "            if jsd.__contains__('randomize_class'):\n",
        "                randomize_class = jsd['randomize_class']\n",
        "            if jsd.__contains__('clip_denoised'):\n",
        "                clip_denoised = jsd['clip_denoised']\n",
        "            if jsd.__contains__('clamp_gras'):\n",
        "                clamp_grad = jsd['clamp_gras']\n",
        "            if jsd.__contains__('clamp_max'):\n",
        "                clamp_max = jsd['clamp_max']\n",
        "            if jsd.__contains__('fuzzy_prompt'):\n",
        "                fuzzy_prompt = jsd['fuzzy_prompt']\n",
        "            if jsd.__contains__('max_frames'):\n",
        "                max_frames = jsd['max_frames']\n",
        "            if jsd.__contains__('rand_mag'):\n",
        "                rand_mag = jsd['rand_mag']\n",
        "            if jsd.__contains__('eta'):\n",
        "                eta = jsd['eta']\n",
        "            if jsd.__contains__('steps'):  \n",
        "                steps = jsd['steps']\n",
        "            if jsd.__contains__('diffusion_steps'):\n",
        "                diffusion_steps = jsd['diffusion_steps']\n",
        "            if jsd.__contains__('diffusion_sampling_mode'):\n",
        "                diffusion_sampling_mode = jsd['diffusion_sampling_mode']\n",
        "            if jsd.__contains__('cut_overview'):\n",
        "                cut_overview = jsd['cut_overview']\n",
        "            if jsd.__contains__('cut_innercut'):\n",
        "                cut_innercut = jsd['cut_innercut']\n",
        "            if jsd.__contains__('cut_ic_pow'):\n",
        "                cut_ic_pow = jsd['cut_ic_pow']\n",
        "            if jsd.__contains__('cut_icgray_p'):\n",
        "                cut_icgray_p = jsd['cut_icgray_p']\n",
        "            if jsd.__contains__('key_frames'):\n",
        "                key_frames = jsd['key_frames']\n",
        "            if jsd.__contains__('angle'):\n",
        "                angle = jsd['angle']\n",
        "            if jsd.__contains__('zoom'):\n",
        "                zoom = jsd['zoom']\n",
        "            if jsd.__contains__('translation_x'):\n",
        "                translation_x = jsd['translation_x']\n",
        "            if jsd.__contains__('translation_y'):\n",
        "                translation_y = jsd['translation_y']\n",
        "            if jsd.__contains__('translation_z'):\n",
        "                translation_z = jsd['translation_z']\n",
        "            if jsd.__contains__('translation_3d_x'):\n",
        "                translation_3d_x = jsd['translation_3d_x']\n",
        "            if jsd.__contains__('translation_3d_y'):\n",
        "                translation_3d_y = jsd['translation_3d_y']\n",
        "            if jsd.__contains__('translation_3d_z'):\n",
        "                translation_3d_z = jsd['translation_3d_z']\n",
        "            if jsd.__contains__('midas_depth_model'):\n",
        "                midas_depth_model = jsd['midas_depth_model']\n",
        "            if jsd.__contains__('midas_weight'):\n",
        "                midas_weight = jsd['midas_weight']\n",
        "            if jsd.__contains__('near_plane'):\n",
        "                near_plane = jsd['near_plane']\n",
        "            if jsd.__contains__('far_plane'):\n",
        "                far_plane = jsd['far_plane']\n",
        "            if jsd.__contains__('fov'):\n",
        "                fov = jsd['fov']\n",
        "            if jsd.__contains__('padding_mode'):\n",
        "                padding_mode = jsd['padding_mode']\n",
        "            if jsd.__contains__('sampling_mode'):\n",
        "                sampling_mode = jsd['sampling_mode']\n",
        "            if jsd.__contains__('extract_nth_frame'):\n",
        "                extract_nth_frame = jsd['extract_nth_frame']\n",
        "            if jsd.__contains__('turbo_mode'):\n",
        "                turbo_mode = jsd['turbo_mode']\n",
        "            if jsd.__contains__('turbo_steps'):\n",
        "                turbo_steps = jsd['turbo_steps']\n",
        "            if jsd.__contains__('turbo_preroll'):\n",
        "                turbo_preroll = jsd['turbo_preroll']\n",
        "            if jsd.__contains__('use_horizontal_symmetry'):\n",
        "                use_horizontal_symmetry = jsd['use_horizontal_symmetry']\n",
        "            if jsd.__contains__('use_vertical_symmetry'):\n",
        "                use_vertical_symmetry = jsd['use_vertical_symmetry']\n",
        "            if jsd.__contains__('transformation_percent'):\n",
        "                transformation_percent = jsd['transformation_percent']\n",
        "\n",
        "            if load_prompts_and_inits:\n",
        "\n",
        "                if jsd.__contains__('text_prompts'):\n",
        "                    text_prompts = jsd['text_prompts']\n",
        "                if jsd.__contains__('generate_nsp_at_runtime'):\n",
        "                    text_prompts = jsd['generate_nsp_at_runtime']\n",
        "                if jsd.__contains__('image_prompts'):\n",
        "                    image_prompts = jsd['image_prompts']\n",
        "\n",
        "                if jsd.__contains__('init_image'):\n",
        "                    if jsd['init_image'].lower() not in ['none', '']:\n",
        "                        init_image = jsd['init_image']\n",
        "                if jsd.__contains__('init_scale'):\n",
        "                    init_scale = jsd['init_scale']\n",
        "                if jsd.__contains__('skip_steps'):\n",
        "                    skip_steps = jsd['skip_steps']\n",
        "                if jsd.__contains__('do_prep_init_image'):\n",
        "                    do_prep_init_image = jsd['do_prep_init_image']\n",
        "                if jsd.__contains__('do_go_big_slice_prep'):\n",
        "                    do_go_big_slice_prep = jsd['do_go_big_slice_prep']\n",
        "                if jsd.__contains__('gaussian_blur_radius'):\n",
        "                    gassian_blur_radius = jsd['gaussian_blur_radius']\n",
        "                if jsd.__contains__('gaussian_noise_amount'):\n",
        "                    gaussian_noise_amount = jsd['gaussian_noise_amount']\n",
        "                if jsd.__contains__('gaussian_noise_blur_radius'):\n",
        "                    gaussian_noise_blur_radius = jsd['gaussian_noise_blur_radius']\n",
        "                if jsd.__contains__('gaussian_noise_greyscale'):\n",
        "                    gaussian_noise_greyscale = jsd['gaussian_noise_greyscale']\n",
        "                if jsd.__contains__('gaussian_noise_double'):\n",
        "                    gaussian_noise_double = jsd['gaussian_noise_double']\n",
        "\n",
        "                if jsd.__contains__('go_big'):\n",
        "                    go_big = jsd['go_big']\n",
        "                if jsd.__contains__('go_big_scale'):\n",
        "                    go_big_scale = jsd['go_big_scale']\n",
        "                if jsd.__contains__('go_big_skip_percent'):\n",
        "                    go_big_skip_percent = jsd['go_big_skip_percent']\n",
        "                if jsd.__contains__('gobig_overlap'):\n",
        "                    gobig_overlap = jsd['gobig_overlap']\n",
        "                if jsd.__contains__('gobig_maximize'):\n",
        "                    gobig_maximize = jsd['gobig_maximize']\n",
        "                if jsd.__contains__('go_big_init'):\n",
        "                    if jsd['go_big_init'].lower() not in ['none', '']:\n",
        "                        go_big_init = jsd['go_big_init']\n",
        "                if jsd.__contains__('go_big_init_skipresize'):\n",
        "                    go_big_init_skipresize = jsd['go_big_init_skipresize']\n",
        "                if jsd.__contains__('init_masked'):\n",
        "                    if jsd['init_masked'].lower() not in ['none', '']:\n",
        "                        init_masked = jsd['init_masked']\n",
        "                if jsd.__contains__('render_mask'):\n",
        "                    if jsd['render_mask'].lower() not in ['none', '']:\n",
        "                        render_mask = jsd['render_mask']\n",
        "\n",
        "                if jsd.__contains__('frames_scale'):\n",
        "                    frames_scale = jsd['frames_scale']\n",
        "                if jsd.__contains__('frames_skip_steps'):\n",
        "                    frames_skip_steps = jsd['frames_skip_steps']\n",
        "                    skip_step_ratio = int(frames_skip_steps.rstrip(\"%\")) / 100\n",
        "                    calc_frames_skip_steps = math.floor(steps * skip_step_ratio)\n",
        "                if jsd.__contains__('perlin_init'):\n",
        "                    perlin_init = jsd['perlin_init']\n",
        "                if jsd.__contains__('perlin_mode'):\n",
        "                    perlin_mode = jsd['perlin_mode']\n",
        "                if jsd.__contains__('width') and jsd.__contains__('height'):\n",
        "                    width_height = [jsd['width'], jsd['height']]\n",
        "                    side_x = jsd['width']\n",
        "                    side_y = jsd['height']\n",
        "                if jsd.__contains__('video_init_seed_continuity'):\n",
        "                    video_init_seed_continuity = jsd['video_init_seed_continuity']\n",
        "                if jsd.__contains__('video_init_steps'):\n",
        "                    video_init_steps = jsd['video_init_steps']\n",
        "                if jsd.__contains__('video_init_clip_guidance_scale'):\n",
        "                    video_init_clip_guidance_scale = jsd['video_init_clip_guidance_scale']\n",
        "                if jsd.__contains__('video_init_tv_scale'):\n",
        "                    video_init_tv_scale = jsd['video_init_tv_scale']\n",
        "                if jsd.__contains__('video_init_range_scale'):\n",
        "                    video_init_range_scale = jsd['video_init_range_scale']\n",
        "                if jsd.__contains__('video_init_sat_scale'):\n",
        "                    video_init_sat_scale = jsd['video_init_sat_scale']\n",
        "                if jsd.__contains__('video_init_cutn_batches'):\n",
        "                    video_init_cutn_batches = jsd['video_init_cutn_batches']\n",
        "                if jsd.__contains__('video_init_skip_steps'):\n",
        "                    video_init_skip_steps = jsd['video_init_skip_steps']\n",
        "                if jsd.__contains__('video_init_frames_scale'):\n",
        "                    video_init_frames_scale = jsd['video_init_frames_scale']\n",
        "                if jsd.__contains__('video_init_frames_skip_steps'):\n",
        "                    video_init_frames_skip_steps = jsd['video_init_frames_skip_steps']\n",
        "                if jsd.__contains__('video_init_flow_warp'):\n",
        "                    video_init_flow_warp = jsd['video_init_flow_warp']\n",
        "                if jsd.__contains__('video_init_flow_blend'):\n",
        "                    video_init_flow_blend = jsd['video_init_flow_blend']\n",
        "                if jsd.__contains__('video_init_check_consistency'):\n",
        "                    video_init_check_consistency = jsd['video_init_check_consistency']\n",
        "                if jsd.__contains__('video_init_blend_mode'):\n",
        "                    video_init_blend_mode = jsd['video_init_blend_mode']\n",
        "\n",
        "# n_batches patch for Go BIG\n",
        "if go_big and n_batches > 1:\n",
        "  print(\"\\33[33m\\u001b[4mWARNING:\\33[0m n_batches is above 1, but Go BIG is active! Changing n_batches to 1...\")\n",
        "  n_batches = 1\n",
        "  import time\n",
        "  time.sleep(10)\n",
        "\n",
        "args = {\n",
        "    'batchNum': batchNum,\n",
        "    'generate_nsp_at_runtime': generate_nsp_at_runtime,\n",
        "    'prompts_series':split_prompts(text_prompts) if text_prompts else None,\n",
        "    'image_prompts_series':split_prompts(image_prompts) if image_prompts else None,\n",
        "    'seed': seed,\n",
        "    'display_rate':display_rate,\n",
        "    'n_batches':n_batches if animation_mode == 'None' else 1,\n",
        "    'batch_size':batch_size,\n",
        "    'batch_name': batch_name,\n",
        "    'steps': steps,\n",
        "    'diffusion_sampling_mode': diffusion_sampling_mode,\n",
        "    'width_height': width_height,\n",
        "    'clip_guidance_scale': clip_guidance_scale,\n",
        "    'tv_scale': tv_scale,\n",
        "    'range_scale': range_scale,\n",
        "    'sat_scale': sat_scale,\n",
        "    'cutn_batches': cutn_batches,\n",
        "    'init_image': init_image,\n",
        "    'init_scale': init_scale,\n",
        "    'skip_steps': skip_steps,\n",
        "    'do_prep_init_image': do_prep_init_image,\n",
        "    'do_go_big_slice_prep': do_go_big_slice_prep,\n",
        "    'gaussian_blur_radius': gaussian_blur_radius,\n",
        "    'gaussian_noise_amount': gaussian_noise_amount,\n",
        "    'gaussian_noise_blur_radius': gaussian_noise_blur_radius,\n",
        "    'gaussian_noise_greyscale': gaussian_noise_greyscale,\n",
        "    'gaussian_noise_double': gaussian_noise_double,\n",
        "    'go_big': go_big,\n",
        "    'go_big_scale': go_big_scale,\n",
        "    'gobig_skip_percent': gobig_skip_percent,\n",
        "    'gobig_overlap': gobig_overlap,\n",
        "    'gobig_maximize': gobig_maximize,\n",
        "    'go_big_init': go_big_init,\n",
        "    'go_big_init_skipresize': go_big_init_skipresize,\n",
        "    'init_masked': init_masked,\n",
        "    'render_mask': render_mask,\n",
        "    'side_x': side_x,\n",
        "    'side_y': side_y,\n",
        "    'timestep_respacing': timestep_respacing,\n",
        "    'diffusion_steps': diffusion_steps,\n",
        "    'animation_mode': animation_mode,\n",
        "    'video_init_path': video_init_path,\n",
        "    'extract_nth_frame': extract_nth_frame,\n",
        "    'video_init_seed_continuity': video_init_seed_continuity,\n",
        "    'key_frames': key_frames,\n",
        "    'max_frames': max_frames if animation_mode != \"None\" else 1,\n",
        "    'interp_spline': interp_spline,\n",
        "    'start_frame': start_frame,\n",
        "    'angle': angle,\n",
        "    'zoom': zoom,\n",
        "    'translation_x': translation_x,\n",
        "    'translation_y': translation_y,\n",
        "    'translation_z': translation_z,\n",
        "    'rotation_3d_x': rotation_3d_x,\n",
        "    'rotation_3d_y': rotation_3d_y,\n",
        "    'rotation_3d_z': rotation_3d_z,\n",
        "    'midas_depth_model': midas_depth_model,\n",
        "    'midas_weight': midas_weight,\n",
        "    'near_plane': near_plane,\n",
        "    'far_plane': far_plane,\n",
        "    'fov': fov,\n",
        "    'padding_mode': padding_mode,\n",
        "    'sampling_mode': sampling_mode,\n",
        "    'angle_series':angle_series,\n",
        "    'zoom_series':zoom_series,\n",
        "    'translation_x_series':translation_x_series,\n",
        "    'translation_y_series':translation_y_series,\n",
        "    'translation_z_series':translation_z_series,\n",
        "    'rotation_3d_x_series':rotation_3d_x_series,\n",
        "    'rotation_3d_y_series':rotation_3d_y_series,\n",
        "    'rotation_3d_z_series':rotation_3d_z_series,\n",
        "    'frames_scale': frames_scale,\n",
        "    'skip_step_ratio': skip_step_ratio,\n",
        "    'calc_frames_skip_steps': calc_frames_skip_steps,\n",
        "    'text_prompts': text_prompts,\n",
        "    'image_prompts': image_prompts,\n",
        "    'cut_overview': eval(cut_overview),\n",
        "    'cut_innercut': eval(cut_innercut),\n",
        "    'cut_ic_pow': eval(cut_ic_pow),\n",
        "    'cut_icgray_p': eval(cut_icgray_p),\n",
        "    'intermediate_saves': intermediate_saves,\n",
        "    'intermediates_in_subfolder': intermediates_in_subfolder,\n",
        "    'steps_per_checkpoint': steps_per_checkpoint,\n",
        "    'perlin_init': perlin_init,\n",
        "    'perlin_mode': perlin_mode,\n",
        "    'set_seed': set_seed,\n",
        "    'eta': eta,\n",
        "    'clamp_grad': clamp_grad,\n",
        "    'clamp_max': clamp_max,\n",
        "    'skip_augs': skip_augs,\n",
        "    'randomize_class': randomize_class,\n",
        "    'clip_denoised': clip_denoised,\n",
        "    'fuzzy_prompt': fuzzy_prompt,\n",
        "    'rand_mag': rand_mag,\n",
        "    'turbo_mode':turbo_mode,\n",
        "    'turbo_steps':turbo_steps,\n",
        "    'turbo_preroll':turbo_preroll,\n",
        "    'use_vertical_symmetry': use_vertical_symmetry,\n",
        "    'use_horizontal_symmetry': use_horizontal_symmetry,\n",
        "    'transformation_percent': transformation_percent,\n",
        "    'video_init_steps': video_init_steps,\n",
        "    'video_init_clip_guidance_scale': video_init_clip_guidance_scale,\n",
        "    'video_init_tv_scale': video_init_tv_scale,\n",
        "    'video_init_range_scale': video_init_range_scale,\n",
        "    'video_init_sat_scale': video_init_sat_scale,\n",
        "    'video_init_cutn_batches': video_init_cutn_batches,\n",
        "    'video_init_skip_steps': video_init_skip_steps,\n",
        "    'video_init_frames_scale': video_init_frames_scale,\n",
        "    'video_init_frames_skip_steps': video_init_frames_skip_steps,\n",
        "    'video_init_flow_warp':video_init_flow_warp,\n",
        "    'video_init_flow_blend':video_init_flow_blend,\n",
        "    'video_init_check_consistency':video_init_check_consistency,\n",
        "    'video_init_blend_mode':video_init_blend_mode,\n",
        "    'save_metadata': save_metadata,\n",
        "    'meta_author': meta_author,\n",
        "    'meta_copyright': meta_copyright,\n",
        "}\n",
        "\n",
        "if animation_mode == 'Video Input':\n",
        "    # This isn't great in terms of what will get saved to the settings.. but it should work.\n",
        "    args['steps'] = args['video_init_steps']\n",
        "    args['clip_guidance_scale'] = args['video_init_clip_guidance_scale']\n",
        "    args['tv_scale'] = args['video_init_tv_scale']\n",
        "    args['range_scale'] = args['video_init_range_scale']\n",
        "    args['sat_scale'] = args['video_init_sat_scale']\n",
        "    args['cutn_batches'] = args['video_init_cutn_batches']\n",
        "    args['skip_steps'] = args['video_init_skip_steps']\n",
        "    args['frames_scale'] = args['video_init_frames_scale']\n",
        "    args['frames_skip_steps'] = args['video_init_frames_skip_steps']\n",
        "\n",
        "args = SimpleNamespace(**args)\n",
        "\n",
        "# Standard Run Setup\n",
        "\n",
        "if go_big_init == None:\n",
        "\n",
        "    print('Preparing model...')\n",
        "    if diffusion_model == 'custom':\n",
        "          model, diffusion = create_model_and_diffusion(**custom_model)\n",
        "          print(f'Using model: {os.path.basename(custom_path)}')\n",
        "          model.load_state_dict(torch.load(custom_path, map_location='cpu'))\n",
        "          if custom_model['use_fp16']:\n",
        "                model.convert_to_fp16()\n",
        "    else:\n",
        "          model, diffusion = create_model_and_diffusion(**model_config)\n",
        "          print(f'Using model: {get_model_filename(diffusion_model)}')\n",
        "          model.load_state_dict(torch.load(f'{model_path}/{get_model_filename(diffusion_model)}', map_location='cpu'))\n",
        "          if model_config['use_fp16']:\n",
        "                model.convert_to_fp16()\n",
        "    model.requires_grad_(False).eval().to(device)\n",
        "    for name, param in model.named_parameters():\n",
        "          if 'qkv' in name or 'norm' in name or 'proj' in name:\n",
        "              param.requires_grad_()\n",
        "\n",
        "\n",
        "# FUNCTIONS FOR GO BIG MODE\n",
        "def addalpha(im, mask):\n",
        "    imr, img, imb, ima = im.split()\n",
        "    mmr, mmg, mmb, mma = mask.split()\n",
        "    im = Image.merge('RGBA', [imr, img, imb, mma])  # we want the RGB from the original, but the transparency from the mask\n",
        "    return(im)\n",
        "\n",
        "# Alternative method composites a grid of images at the positions provided\n",
        "def grid_merge(source, slices):\n",
        "    source.convert(\"RGBA\")\n",
        "    for slice, posx, posy in slices: # go in reverse to get proper stacking\n",
        "      #print(posx, posy)\n",
        "      source.alpha_composite(slice, (posx, posy))\n",
        "    return source\n",
        "\n",
        "def grid_coords(target, original, overlap):\n",
        "    #generate a list of coordinate tuples for our sections, in order of how they'll be rendered\n",
        "    #target should be the size for the gobig result, original is the size of each chunk being rendered\n",
        "    center = []\n",
        "    target_x, target_y = target\n",
        "    center_x = int(target_x / 2)\n",
        "    center_y = int(target_y / 2)\n",
        "    original_x, original_y = original\n",
        "    x = center_x - int(original_x / 2)\n",
        "    y = center_y - int(original_y / 2)\n",
        "    center.append((x,y)) #center chunk\n",
        "    uy = y #up\n",
        "    uy_list = []\n",
        "    dy = y #down\n",
        "    dy_list = []\n",
        "    lx = x #left\n",
        "    lx_list = []\n",
        "    rx = x #right\n",
        "    rx_list = []\n",
        "    while uy > 0: #center row vertical up\n",
        "        uy = uy - original_y + overlap\n",
        "        uy_list.append((lx, uy))\n",
        "    while (dy + original_y) <= target_y: #center row vertical down\n",
        "        dy = dy + original_y - overlap\n",
        "        dy_list.append((rx, dy))\n",
        "    while lx > 0:\n",
        "        lx = lx - original_x + overlap\n",
        "        lx_list.append((lx, y))\n",
        "        uy = y\n",
        "        while uy > 0:\n",
        "            uy = uy - original_y + overlap\n",
        "            uy_list.append((lx, uy))\n",
        "        dy = y\n",
        "        while (dy + original_y) <= target_y:\n",
        "            dy = dy + original_y - overlap\n",
        "            dy_list.append((lx, dy))\n",
        "    while (rx + original_x) <= target_x:\n",
        "        rx = rx + original_x - overlap\n",
        "        rx_list.append((rx, y))\n",
        "        uy = y\n",
        "        while uy > 0:\n",
        "            uy = uy - original_y + overlap\n",
        "            uy_list.append((rx, uy))\n",
        "        dy = y\n",
        "        while (dy + original_y) <= target_y:\n",
        "            dy = dy + original_y - overlap\n",
        "            dy_list.append((rx, dy))\n",
        "    # calculate a new size that will fill the canvas, which will be optionally used in grid_slice and go_big\n",
        "    last_coordx, last_coordy = dy_list[-1:][0]\n",
        "    render_edgey = last_coordy + original_y # outer edge of the render canvas\n",
        "    render_edgex = last_coordx + original_x # outer edge of the render canvas\n",
        "    scalarx = render_edgex / target_x\n",
        "    scalary = render_edgey / target_y\n",
        "    if scalarx <= scalary:\n",
        "        new_edgex = int(target_x * scalarx)\n",
        "        new_edgey = int(target_y * scalarx)\n",
        "    else:\n",
        "        new_edgex = int(target_x * scalary)\n",
        "        new_edgey = int(target_y * scalary)\n",
        "    # now put all the chunks into one master list of coordinates (essentially reverse of how we calculated them so that the central slices will be on top)\n",
        "    result = []\n",
        "    for coords in dy_list[::-1]:\n",
        "        result.append(coords)\n",
        "    for coords in uy_list[::-1]:\n",
        "        result.append(coords)\n",
        "    for coords in rx_list[::-1]:\n",
        "        result.append(coords)\n",
        "    for coords in lx_list[::-1]:\n",
        "        result.append(coords)\n",
        "    result.append(center[0])\n",
        "    return result, (new_edgex, new_edgey)\n",
        "\n",
        "# Alternative method uses a grid of images that each equal the size of the original render\n",
        "def grid_slice(source, overlap, og_size, maximize=False): \n",
        "    width, height = og_size # size of the slices to be rendered\n",
        "    coordinates, new_size = grid_coords(source.size, og_size, overlap)\n",
        "    if maximize == True:\n",
        "        source = source.resize(new_size, get_resampling_mode()) # minor concern that we're resizing twice\n",
        "        coordinates, new_size = grid_coords(source.size, og_size, overlap) # re-do the coordinates with the new canvas size\n",
        "    # loc_width and loc_height are the center point of the goal size, and we'll start there and work our way out\n",
        "    slices = []\n",
        "    for coordinate in coordinates:\n",
        "        x, y = coordinate\n",
        "        slices.append(((source.crop((x, y, x+width, y+height))), x, y))\n",
        "    global slices_todo\n",
        "    slices_todo = len(slices) - 1\n",
        "    return slices, new_size\n",
        "\n",
        "import traceback\n",
        "\n",
        "try:\n",
        "\n",
        "  if go_big_init is not None:\n",
        "\n",
        "    progress_image = 'progress.png'\n",
        "    try:\n",
        "      go_big_temp_image = Image.open(fetch(go_big_init)).convert(\"RGBA\")\n",
        "    except AttributeError as e:\n",
        "        if \"'PIL.TiffTags' has no attribute 'IFD'\" in str(e):\n",
        "          print(\"\\33[33m\\u001b[4mWARNING:\\33[0m \\033[1mPIL.TiffTags requires a restart. Please select Runtime -> Restart Runtime, and then run all the cells again.\\033[0m\")\n",
        "          raise SystemExit\n",
        "    go_big_temp_image.save(progress_image)\n",
        "    go_big_temp_image.close()\n",
        "    #shutil.copy(go_big_init, progress_image)\n",
        "\n",
        "  else:\n",
        "\n",
        "    do_run()\n",
        "\n",
        "  if go_big:\n",
        "\n",
        "    current_time = datetime.now().strftime('%y%m%d-%H%M%S_%f')\n",
        "    # Resize initial progress.png to new size, setup some filenames\n",
        "    original_output_image = (f'{batchFolder}/{batch_name}_original_output_{current_time}.png')\n",
        "    final_output_image = (f'{batchFolder}/{batch_name}_final_output_{current_time}.png')\n",
        "    progress_image = 'progress.png'\n",
        "    input_image = Image.open(fetch(progress_image)).convert('RGBA')\n",
        "    input_image.save(original_output_image)\n",
        "    slice_image = (f'{batchFolder}/{batch_name}_slice.png')\n",
        "    slice_rmask = (f'{batchFolder}/{batch_name}_slice_rmask.png')\n",
        "    slice_imask = (f'{batchFolder}/{batch_name}_slice_imask.png')\n",
        "    if render_mask is not None:\n",
        "        source_render_mask = Image.open(render_mask).convert('RGBA')\n",
        "    else:\n",
        "        source_render_mask = None\n",
        "    if go_big_init_skipresize == False:\n",
        "      og_size = (side_x, side_y)\n",
        "      reside_x = side_x * go_big_scale\n",
        "      reside_y = side_y * go_big_scale\n",
        "      source_image = input_image.resize((reside_x, reside_y), get_resampling_mode())\n",
        "      if source_render_mask is not None:\n",
        "        source_render_mask = source_render_mask.resize((reside_x, reside_y), get_resampling_mode())\n",
        "    else:\n",
        "      source_image = input_image\n",
        "      og_size = (int(side_x / go_big_scale), int(side_y / go_big_scale)) \n",
        "    input_image.close()\n",
        "    if init_masked is not None:\n",
        "        source_imask = Image.open(init_masked).convert('RGBA')\n",
        "        source_imask = source_imask.resize(source_image.size, get_resampling_mode())\n",
        "    else:\n",
        "        source_imask = None\n",
        "    # Slice source_image into overlapping slices\n",
        "    #print(f'debug: before slicing, slices_todo is {slices_todo}')\n",
        "    slices, new_canvas_size = grid_slice(source_image, gobig_overlap, og_size, gobig_maximize)\n",
        "    if gobig_maximize == True:\n",
        "      source_image = source_image.resize(new_canvas_size, get_resampling_mode())\n",
        "      slices, new_canvas_size = grid_slice(source_image, gobig_overlap, og_size)\n",
        "      print(f'Go BIG maximize is on. New size is now {source_image.size}')\n",
        "    if source_render_mask is not None:\n",
        "        rmasks = grid_slice(source_render_mask, gobig_overlap, og_size)\n",
        "    else:\n",
        "        rmasks = None\n",
        "    if source_imask is not None:\n",
        "        imasks = grid_slice(source_imask, gobig_overlap, og_size)\n",
        "    else:\n",
        "        imasks = None\n",
        "    # Render each slice, with init image paramaters, etc.\n",
        "    betterslices = []\n",
        "    i = 1\n",
        "    seed_list = []\n",
        "    init_seed = seed\n",
        "    for count, chunk_w_coords in enumerate(slices):\n",
        "        current_slice = count\n",
        "        chunk, coord_x, coord_y = chunk_w_coords\n",
        "        if rmasks is not None:\n",
        "            chunk_rmask = rmasks[0][count]\n",
        "        else:\n",
        "            chunk_rmask = None\n",
        "        if imasks is not None:\n",
        "            chunk_imask = imasks[0][count]\n",
        "        else:\n",
        "            chunk_imask = None\n",
        "\n",
        "        # Reset underlying systems for another run\n",
        "        seed = seed + 1\n",
        "        args.seed = seed\n",
        "        seed_list.append(seed)\n",
        "\n",
        "        # Go BIG Diffusion Model\n",
        "        print('Preparing model for next run...')\n",
        "        if go_big_diffusion_model != \"Inherit\":\n",
        "            model, diffusion = create_model_and_diffusion(**go_big_model_config)\n",
        "            if go_big_diffusion_model == 'custom':\n",
        "                model, diffusion = create_model_and_diffusion(**custom_model)\n",
        "                print(f'Using Custom Model: {os.path.basename(custom_path)}')\n",
        "                model.load_state_dict(torch.load(custom_path, map_location='cpu'))\n",
        "            else:\n",
        "                model, diffusion = create_model_and_diffusion(**go_big_model_config)\n",
        "                dm_name = get_model_filename(go_big_diffusion_model)\n",
        "                print(\"Using Model:\", dm_name)\n",
        "                model.load_state_dict(torch.load(f'{model_path}/{dm_name}', map_location='cpu'))\n",
        "        else:\n",
        "            if diffusion_model == 'custom':\n",
        "                model, diffusion = create_model_and_diffusion(**custom_model)\n",
        "                print(f'Using Custom Model: {os.path.basename(custom_path)}')\n",
        "                model.load_state_dict(torch.load(custom_path, map_location='cpu'))\n",
        "            else:\n",
        "                model, diffusion = create_model_and_diffusion(**model_config)\n",
        "                dm_name = get_model_filename(diffusion_model)\n",
        "                print(\"Using Model:\", dm_name)\n",
        "                model.load_state_dict(torch.load(f'{model_path}/{dm_name}', map_location='cpu'))\n",
        "        model.requires_grad_(False).eval().to(device)\n",
        "        for name, param in model.named_parameters():\n",
        "            if 'qkv' in name or 'norm' in name or 'proj' in name:\n",
        "                param.requires_grad_()\n",
        "        if model_config['use_fp16']:\n",
        "            model.convert_to_fp16()\n",
        "        gc.collect()\n",
        "        if \"cuda\" in str(device):\n",
        "          with torch.cuda.device(device):\n",
        "            torch.cuda.empty_cache()\n",
        "\n",
        "        #now do the next run\n",
        "        chunk.save(slice_image)\n",
        "        if chunk_rmask is not None:\n",
        "            chunk_rmask[0].save(slice_rmask)\n",
        "        if chunk_imask is not None:\n",
        "            chunk_imask[0].save(slice_imask)\n",
        "        args.init_image = slice_image\n",
        "        init_image = slice_image\n",
        "        if chunk_rmask is not None:\n",
        "            args.render_mask = slice_rmask\n",
        "            render_mask = slice_rmask\n",
        "        if chunk_imask is not None:\n",
        "            init_masked = slice_imask\n",
        "            args.init_masked = slice_imask\n",
        "        args.init_image = slice_image\n",
        "        args.skip_steps = int(steps * (gobig_skip_percent / 100))\n",
        "        args.side_x, args.side_y = chunk.size\n",
        "        args.perlin_init = False\n",
        "        perlin_init = False\n",
        "        args.symmetry_loss = False\n",
        "        do_run()\n",
        "        print(f'Finished run, grabbing {progress_image} and adding it to slices...')\n",
        "        resultslice = Image.open(progress_image).convert('RGBA')\n",
        "        betterslices.append((resultslice.copy(), coord_x, coord_y))\n",
        "        resultslice.save(f'{batchFolder}/{batch_name}_slice_output_{current_time}_{i}.png')\n",
        "        resultslice.close() # hopefully this will allow subsequent images to actually save.\n",
        "        i += 1\n",
        "    # For each slice, use addalpha to add an alpha mask\n",
        "    alpha_gradient = Image.new('L', (args.side_x, 1), color=0xFF)\n",
        "    a = 0\n",
        "    for x in range(args.side_x):\n",
        "        a +=4 # add 4 to alpha at each pixel, to give us a 64 pixel overlap gradient\n",
        "        if a < 255:\n",
        "            alpha_gradient.putpixel((x, 0), a)\n",
        "        else:\n",
        "            alpha_gradient.putpixel((x, 0), 255)\n",
        "        # print '{}, {:.2f}, {}'.format(x, float(x) / width, a)\n",
        "    # generate an alpha mask for compositing the chunks\n",
        "    alpha = Image.new('L', (args.side_x, args.side_y), color=0xFF)\n",
        "    alpha_gradient = ImageDraw.Draw(alpha)\n",
        "    a = 0\n",
        "    i = 0\n",
        "    a_overlap = int(gobig_overlap / 2)\n",
        "    shape = ((args.side_x, args.side_y), (0,0))\n",
        "    while i < gobig_overlap:\n",
        "        alpha_gradient.rectangle(shape, fill = a)\n",
        "        a += int(255 / a_overlap)\n",
        "        a = 255 if a > 255 else a\n",
        "        i += 1\n",
        "        shape = ((args.side_x - i, args.side_y - i), (i,i))\n",
        "    if a != 255:\n",
        "      # in case we didn't get to full opaque, this ensures we do\n",
        "      alpha_gradient.rectangle(shape, fill = 255)\n",
        "    mask = Image.new('RGBA', (args.side_x, args.side_y), color=0)\n",
        "    mask.putalpha(alpha)\n",
        "    finished_slices = []\n",
        "    for betterslice, x, y in betterslices:\n",
        "        finished_slice = addalpha(betterslice, mask)\n",
        "        finished_slices.append((finished_slice, x, y))\n",
        "\n",
        "    # Once we have all our images, mergeimgs back onto source.png, then save\n",
        "    final_output = grid_merge(source_image, finished_slices)\n",
        "    final_output.save(final_output_image)\n",
        "    print(f'\\n\\n\\033[32mGo BIG is Complete!\\33[0m\\n\\n\\33[33m\\u001b[4mNote:\\33[0m Your output is saved as {final_output_image}!')\n",
        "\n",
        "    \n",
        "  if go_big:\n",
        "    print(f\"\\nInitial Render Seed: {init_seed}\\nSlice Seeds:\")\n",
        "    try:\n",
        "        pprint(seed_list, indent=4)\n",
        "    except Exception as e:\n",
        "        pass\n",
        "  else:\n",
        "    print('Base Seed:', seed)\n",
        "\n",
        "except RuntimeError as e:\n",
        "\n",
        "  if 'out of memory' in str(e):\n",
        "    print(f\"\\u001b[31m\\u001b[1m\\u001b[4mCRITICAL ERROR\\u001b[0m: {gpu_name} ran out of memory! Flushing memory to save session...\")\n",
        "    if gpu_warning:\n",
        "      print(gpu_warning)\n",
        "    print('\\nSeed used for this failed run:', seed)\n",
        "    pass\n",
        "  else:\n",
        "    raise e\n",
        "\n",
        "except KeyboardInterrupt as e:\n",
        "\n",
        "  print('\\33[33mExecution interrupted by user.\\33[0m')\n",
        "  print('\\nSeed used for this exited run:', seed)\n",
        "  pass\n",
        "\n",
        "#except Exception as e:\n",
        "#\n",
        "#  print(f\"\\n\\33[31mAn exxception has occured:\\33[0m\\n {str(e)}\\n\")\n",
        "#\n",
        "#  if gpu_warning:\n",
        "#    print(gpu_warning+\"\\n\")\n",
        "#\n",
        "#  errors = io.StringIO()\n",
        "#  traceback.print_exc(file=errors)\n",
        "#  contents = str(errors.getvalue())\n",
        "#  print(contents)\n",
        "#  errors.close()\n",
        "#  \n",
        "#  print(\"\\nAttempting to continue...\")\n",
        "#\n",
        "#  pass\n",
        "\n",
        "finally:\n",
        "\n",
        "    print(\"\\n:recycling_symbol: Cleaning up...\")\n",
        "    current_slice = None\n",
        "    go_big_prompts = None\n",
        "    runtime_prompts = []\n",
        "    gc.collect()\n",
        "    torch.cuda.empty_cache()\n",
        "    print(\":check_mark_button: Cleanup complete.\")"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CreateVid",
        "cellView": "form"
      },
      "source": [
        "import PIL\n",
        "# @title ### **Render Video Frame Sequence**\n",
        "#@markdown Video file will save in the same folder as your images.\n",
        "from tqdm.notebook import trange\n",
        "skip_video_for_run_all = True #@param {type: 'boolean'}\n",
        "\n",
        "if animation_mode == 'Video Input':\n",
        "    frames = sorted(glob(in_path+'/*.*'));\n",
        "    if len(frames)==0: \n",
        "        sys.exit(\"ERROR: 0 frames found.\\nPlease check your video input path and rerun the video settings cell.\")\n",
        "    flows = glob(flo_folder+'/*.*')\n",
        "    if (len(flows)==0) and video_init_flow_warp:\n",
        "        sys.exit(\"ERROR: 0 flow files found.\\nPlease rerun the flow generation cell.\")\n",
        "\n",
        "blend =  0.5#@param {type: 'number'}\n",
        "video_init_check_consistency = False #@param {type: 'boolean'}\n",
        "if skip_video_for_run_all == True:\n",
        "    print('Skipping video creation, uncheck skip_video_for_run_all if you want to run it')\n",
        "\n",
        "else:\n",
        "    # import subprocess in case this cell is run without the above cells\n",
        "    import subprocess\n",
        "    from base64 import b64encode\n",
        "\n",
        "    latest_run = batchNum\n",
        "\n",
        "    folder = batch_name #@param\n",
        "    run = latest_run #@param\n",
        "    final_frame = 'final_frame'\n",
        "\n",
        "\n",
        "    init_frame = 1#@param {type:\"number\"} This is the frame where the video will start\n",
        "    last_frame = final_frame#@param {type:\"number\"} You can change i to the number of the last frame you want to generate. It will raise an error if that number of frames does not exist.\n",
        "    fps = 12#@param {type:\"number\"}\n",
        "    # view_video_in_cell = True #@param {type: 'boolean'}\n",
        "\n",
        "    frames = []\n",
        "    # tqdm.write('Generating video...')\n",
        "\n",
        "    if last_frame == 'final_frame':\n",
        "        last_frame = len(glob(batchFolder+f\"/{folder}({run})_*.png\"))\n",
        "        print(f'Total frames: {last_frame}')\n",
        "\n",
        "    image_path = f\"{outDirPath}/{folder}/{folder}({run})_%04d.png\"\n",
        "    filepath = f\"{outDirPath}/{folder}/{folder}({run}).mp4\"\n",
        "\n",
        "    if (video_init_blend_mode == 'optical flow') and (animation_mode == 'Video Input'):\n",
        "        image_path = f\"{outDirPath}/{folder}/flow/{folder}({run})_%04d.png\"\n",
        "        filepath = f\"{outDirPath}/{folder}/{folder}({run})_flow.mp4\"\n",
        "        if last_frame == 'final_frame':\n",
        "            last_frame = len(glob(batchFolder+f\"/flow/{folder}({run})_*.png\"))\n",
        "        flo_out = batchFolder+f\"/flow\"\n",
        "        createPath(flo_out)\n",
        "        frames_in = sorted(glob(batchFolder+f\"/{folder}({run})_*.png\"))\n",
        "        shutil.copy(frames_in[0], flo_out)\n",
        "        for i in trange(init_frame, min(len(frames_in), last_frame)):\n",
        "            frame1_path = frames_in[i-1]\n",
        "            frame2_path = frames_in[i]\n",
        "  \n",
        "            frame1 = PIL.Image.open(frame1_path)\n",
        "            frame2 = PIL.Image.open(frame2_path)\n",
        "            frame1_stem = f\"{(int(frame1_path.split('/')[-1].split('_')[-1][:-4])+1):04}.jpg\"\n",
        "            flo_path = f\"/{flo_folder}/{frame1_stem}.npy\"\n",
        "            weights_path = None\n",
        "            if video_init_check_consistency:\n",
        "                # TBD\n",
        "                pass\n",
        "            warp(frame1, frame2, flo_path, blend=blend, weights_path=weights_path).save(batchFolder+f\"/flow/{folder}({run})_{i:04}.png\")\n",
        "    if video_init_blend_mode == 'linear':\n",
        "        image_path = f\"{outDirPath}/{folder}/blend/{folder}({run})_%04d.png\"\n",
        "        filepath = f\"{outDirPath}/{folder}/{folder}({run})_blend.mp4\"\n",
        "        if last_frame == 'final_frame':\n",
        "            last_frame = len(glob(batchFolder+f\"/blend/{folder}({run})_*.png\"))\n",
        "        blend_out = batchFolder+f\"/blend\"\n",
        "        createPath(blend_out)\n",
        "        frames_in = glob(batchFolder+f\"/{folder}({run})_*.png\")\n",
        "        shutil.copy(frames_in[0], blend_out)\n",
        "        for i in trange(1, len(frames_in)):\n",
        "            frame1_path = frames_in[i-1]\n",
        "            frame2_path = frames_in[i]\n",
        "    \n",
        "            frame1 = PIL.Image.open(frame1_path)\n",
        "            frame2 = PIL.Image.open(frame2_path)\n",
        "          \n",
        "            frame = PIL.Image.fromarray((np.array(frame1)*(1-blend) + np.array(frame2)*(blend)).astype('uint8')).save(batchFolder+f\"/blend/{folder}({run})_{i:04}.png\")\n",
        "\n",
        "\n",
        "    cmd = [\n",
        "        'ffmpeg',\n",
        "        '-y',\n",
        "        '-vcodec',\n",
        "        'png',\n",
        "        '-r',\n",
        "        str(fps),\n",
        "        '-start_number',\n",
        "        str(init_frame),\n",
        "        '-i',\n",
        "        image_path,\n",
        "        '-frames:v',\n",
        "        str(last_frame+1),\n",
        "        '-c:v',\n",
        "        'libx264',\n",
        "        '-vf',\n",
        "        f'fps={fps}',\n",
        "        '-pix_fmt',\n",
        "        'yuv420p',\n",
        "        '-crf',\n",
        "        '17',\n",
        "        '-preset',\n",
        "        'veryslow',\n",
        "        filepath\n",
        "    ]\n",
        "\n",
        "    process = subprocess.Popen(cmd, cwd=f'{batchFolder}', stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
        "    stdout, stderr = process.communicate()\n",
        "    if process.returncode != 0:\n",
        "        print(stderr, defaultprint=True)\n",
        "        raise RuntimeError(stderr)\n",
        "    else:\n",
        "        print(\"The video is ready and saved to the images folder\")\n",
        "\n",
        "    # if view_video_in_cell:\n",
        "    #     mp4 = open(filepath,'rb').read()\n",
        "    #     data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "    #     display.HTML(f'<video width=400 controls><source src=\"{data_url}\" type=\"video/mp4\"></video>')"
      ],
      "outputs": [],
      "execution_count": null
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "TutorialTop",
        "CustModelTop",
        "AnimSetTop",
        "ExtraSetTop"
      ],
      "machine_shape": "hm",
      "private_outputs": true,
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.1"
    },
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}