Deforum_Stable_Diffusion.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c442uQJ_gUgy"
      },
      "source": [
        "# **Deforum Stable Diffusion v0.5**\n",
        "[Stable Diffusion](https://github.com/CompVis/stable-diffusion) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Bj\u00f6rn Ommer and the [Stability.ai](https://stability.ai/) Team. [K Diffusion](https://github.com/crowsonkb/k-diffusion) by [Katherine Crowson](https://twitter.com/RiversHaveWings). You need to get the ckpt file and put it on your Google Drive first to use this. It can be downloaded from [HuggingFace](https://huggingface.co/CompVis/stable-diffusion).\n",
        "\n",
        "Notebook by [deforum](https://discord.gg/upmXXsrwZc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LBamKxcmNI7-"
      },
      "source": [
        "By using this Notebook, you agree to the following Terms of Use, and license:\n",
        "\n",
        "**Stablity.AI Model Terms of Use**\n",
        "\n",
        "This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\n",
        "\n",
        "The CreativeML OpenRAIL License specifies:\n",
        "\n",
        "You can't use the model to deliberately produce nor share illegal or harmful outputs or content\n",
        "CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n",
        "You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\n",
        "\n",
        "\n",
        "Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4knibRpAQ06"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2g-f7cQmf2Nt",
        "cellView": "form"
      },
      "source": [
        "#@markdown **NVIDIA GPU**\n",
        "import subprocess\n",
        "sub_p_res = subprocess.run(['nvidia-smi', '--query-gpu=name,memory.total,memory.free', '--format=csv,noheader'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "print(sub_p_res)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "TxIOPT0G5Lx1"
      },
      "source": [
        "#@markdown **Model and Output Paths**\n",
        "# ask for the link\n",
        "print(\"Local Path Variables:\\n\")\n",
        "\n",
        "models_path = \"/content/models\" #@param {type:\"string\"}\n",
        "output_path = \"/content/output\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown **Google Drive Path Variables (Optional)**\n",
        "mount_google_drive = True #@param {type:\"boolean\"}\n",
        "force_remount = False\n",
        "\n",
        "if mount_google_drive:\n",
        "    from google.colab import drive # type: ignore\n",
        "    try:\n",
        "        drive_path = \"/content/drive\"\n",
        "        drive.mount(drive_path,force_remount=force_remount)\n",
        "        models_path_gdrive = \"/content/drive/MyDrive/AI/models\" #@param {type:\"string\"}\n",
        "        output_path_gdrive = \"/content/drive/MyDrive/AI/StableDiffusion\" #@param {type:\"string\"}\n",
        "        models_path = models_path_gdrive\n",
        "        output_path = output_path_gdrive\n",
        "    except:\n",
        "        print(\"...error mounting drive or with drive path variables\")\n",
        "        print(\"...reverting to default path variables\")\n",
        "\n",
        "import os\n",
        "os.makedirs(models_path, exist_ok=True)\n",
        "os.makedirs(output_path, exist_ok=True)\n",
        "\n",
        "print(f\"models_path: {models_path}\")\n",
        "print(f\"output_path: {output_path}\")"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRNl2mfepEIe",
        "cellView": "form"
      },
      "source": [
        "#@markdown **Setup Environment**\n",
        "\n",
        "setup_environment = True #@param {type:\"boolean\"}\n",
        "print_subprocess = False #@param {type:\"boolean\"}\n",
        "\n",
        "if setup_environment:\n",
        "    import subprocess, time\n",
        "    print(\"Setting up environment...\")\n",
        "    start_time = time.time()\n",
        "    all_process = [\n",
        "        ['pip', 'install', 'torch==1.12.1+cu113', 'torchvision==0.13.1+cu113', '--extra-index-url', 'https://download.pytorch.org/whl/cu113'],\n",
        "        ['pip', 'install', 'omegaconf==2.2.3', 'einops==0.4.1', 'pytorch-lightning==1.7.4', 'torchmetrics==0.9.3', 'torchtext==0.13.1', 'transformers==4.21.2', 'kornia==0.6.7'],\n",
        "        ['git', 'clone',  'https://github.com/deforum/stable-diffusion'],\n",
        "        ['pip', 'install', '-e', 'git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers'],\n",
        "        ['pip', 'install', '-e', 'git+https://github.com/openai/CLIP.git@main#egg=clip'],\n",
        "        ['pip', 'install', 'accelerate', 'ftfy', 'jsonmerge', 'matplotlib', 'resize-right', 'timm', 'torchdiffeq'],\n",
        "        ['git', 'clone', 'https://github.com/shariqfarooq123/AdaBins.git'],\n",
        "        ['git', 'clone', 'https://github.com/isl-org/MiDaS.git'],\n",
        "        ['git', 'clone', 'https://github.com/MSFTserver/pytorch3d-lite.git'],\n",
        "    ]\n",
        "    for process in all_process:\n",
        "        running = subprocess.run(process,stdout=subprocess.PIPE).stdout.decode('utf-8')\n",
        "        if print_subprocess:\n",
        "            print(running)\n",
        "    \n",
        "    print(subprocess.run(['git', 'clone', 'https://github.com/deforum/k-diffusion/'], stdout=subprocess.PIPE).stdout.decode('utf-8'))\n",
        "    with open('k-diffusion/k_diffusion/__init__.py', 'w') as f:\n",
        "        f.write('')\n",
        "\n",
        "    end_time = time.time()\n",
        "    print(f\"Environment set up in {end_time-start_time:.0f} seconds\")"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81qmVZbrm4uu",
        "cellView": "form"
      },
      "source": [
        "#@markdown **Python Definitions**\n",
        "import json\n",
        "from IPython import display\n",
        "\n",
        "import gc, math, os, pathlib, subprocess, sys, time\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "import requests\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision.transforms as T\n",
        "import torchvision.transforms.functional as TF\n",
        "from contextlib import contextmanager, nullcontext\n",
        "from einops import rearrange, repeat\n",
        "from omegaconf import OmegaConf\n",
        "from PIL import Image\n",
        "from pytorch_lightning import seed_everything\n",
        "from skimage.exposure import match_histograms\n",
        "from torchvision.utils import make_grid\n",
        "from tqdm import tqdm, trange\n",
        "from types import SimpleNamespace\n",
        "from torch import autocast\n",
        "import re\n",
        "from scipy.ndimage import gaussian_filter\n",
        "\n",
        "sys.path.extend([\n",
        "    'src/taming-transformers',\n",
        "    'src/clip',\n",
        "    'stable-diffusion/',\n",
        "    'k-diffusion',\n",
        "    'pytorch3d-lite',\n",
        "    'AdaBins',\n",
        "    'MiDaS',\n",
        "])\n",
        "\n",
        "import py3d_tools as p3d\n",
        "\n",
        "from helpers import DepthModel, sampler_fn\n",
        "from k_diffusion.external import CompVisDenoiser\n",
        "from ldm.util import instantiate_from_config\n",
        "from ldm.models.diffusion.ddim import DDIMSampler\n",
        "from ldm.models.diffusion.plms import PLMSSampler\n",
        "\n",
        "def sanitize(prompt):\n",
        "    whitelist = set('abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ')\n",
        "    tmp = ''.join(filter(whitelist.__contains__, prompt))\n",
        "    return tmp.replace(' ', '_')\n",
        "\n",
        "from functools import reduce\n",
        "def construct_RotationMatrixHomogenous(rotation_angles):\n",
        "    assert(type(rotation_angles)==list and len(rotation_angles)==3)\n",
        "    RH = np.eye(4,4)\n",
        "    cv2.Rodrigues(np.array(rotation_angles), RH[0:3, 0:3])\n",
        "    return RH\n",
        "\n",
        "# https://en.wikipedia.org/wiki/Rotation_matrix\n",
        "def getRotationMatrixManual(rotation_angles):\n",
        "\t\n",
        "    rotation_angles = [np.deg2rad(x) for x in rotation_angles]\n",
        "    \n",
        "    phi         = rotation_angles[0] # around x\n",
        "    gamma       = rotation_angles[1] # around y\n",
        "    theta       = rotation_angles[2] # around z\n",
        "    \n",
        "    # X rotation\n",
        "    Rphi        = np.eye(4,4)\n",
        "    sp          = np.sin(phi)\n",
        "    cp          = np.cos(phi)\n",
        "    Rphi[1,1]   = cp\n",
        "    Rphi[2,2]   = Rphi[1,1]\n",
        "    Rphi[1,2]   = -sp\n",
        "    Rphi[2,1]   = sp\n",
        "    \n",
        "    # Y rotation\n",
        "    Rgamma        = np.eye(4,4)\n",
        "    sg            = np.sin(gamma)\n",
        "    cg            = np.cos(gamma)\n",
        "    Rgamma[0,0]   = cg\n",
        "    Rgamma[2,2]   = Rgamma[0,0]\n",
        "    Rgamma[0,2]   = sg\n",
        "    Rgamma[2,0]   = -sg\n",
        "    \n",
        "    # Z rotation (in-image-plane)\n",
        "    Rtheta      = np.eye(4,4)\n",
        "    st          = np.sin(theta)\n",
        "    ct          = np.cos(theta)\n",
        "    Rtheta[0,0] = ct\n",
        "    Rtheta[1,1] = Rtheta[0,0]\n",
        "    Rtheta[0,1] = -st\n",
        "    Rtheta[1,0] = st\n",
        "    \n",
        "    R           = reduce(lambda x,y : np.matmul(x,y), [Rphi, Rgamma, Rtheta]) \n",
        "    \n",
        "    return R\n",
        "\n",
        "\n",
        "def getPoints_for_PerspectiveTranformEstimation(ptsIn, ptsOut, W, H, sidelength):\n",
        "    \n",
        "    ptsIn2D      =  ptsIn[0,:]\n",
        "    ptsOut2D     =  ptsOut[0,:]\n",
        "    ptsOut2Dlist =  []\n",
        "    ptsIn2Dlist  =  []\n",
        "    \n",
        "    for i in range(0,4):\n",
        "        ptsOut2Dlist.append([ptsOut2D[i,0], ptsOut2D[i,1]])\n",
        "        ptsIn2Dlist.append([ptsIn2D[i,0], ptsIn2D[i,1]])\n",
        "    \n",
        "    pin  =  np.array(ptsIn2Dlist)   +  [W/2.,H/2.]\n",
        "    pout = (np.array(ptsOut2Dlist)  +  [1.,1.]) * (0.5*sidelength)\n",
        "    pin  = pin.astype(np.float32)\n",
        "    pout = pout.astype(np.float32)\n",
        "    \n",
        "    return pin, pout\n",
        "\n",
        "def warpMatrix(W, H, theta, phi, gamma, scale, fV):\n",
        "    \n",
        "    # M is to be estimated\n",
        "    M          = np.eye(4, 4)\n",
        "    \n",
        "    fVhalf     = np.deg2rad(fV/2.)\n",
        "    d          = np.sqrt(W*W+H*H)\n",
        "    sideLength = scale*d/np.cos(fVhalf)\n",
        "    h          = d/(2.0*np.sin(fVhalf))\n",
        "    n          = h-(d/2.0);\n",
        "    f          = h+(d/2.0);\n",
        "    \n",
        "    # Translation along Z-axis by -h\n",
        "    T       = np.eye(4,4)\n",
        "    T[2,3]  = -h\n",
        "    \n",
        "    # Rotation matrices around x,y,z\n",
        "    R = getRotationMatrixManual([phi, gamma, theta])\n",
        "    \n",
        "    \n",
        "    # Projection Matrix \n",
        "    P       = np.eye(4,4)\n",
        "    P[0,0]  = 1.0/np.tan(fVhalf)\n",
        "    P[1,1]  = P[0,0]\n",
        "    P[2,2]  = -(f+n)/(f-n)\n",
        "    P[2,3]  = -(2.0*f*n)/(f-n)\n",
        "    P[3,2]  = -1.0\n",
        "    \n",
        "    # pythonic matrix multiplication\n",
        "    F       = reduce(lambda x,y : np.matmul(x,y), [P, T, R]) \n",
        "    \n",
        "    # shape should be 1,4,3 for ptsIn and ptsOut since perspectiveTransform() expects data in this way. \n",
        "    # In C++, this can be achieved by Mat ptsIn(1,4,CV_64FC3);\n",
        "    ptsIn = np.array([[\n",
        "                 [-W/2., H/2., 0.],[ W/2., H/2., 0.],[ W/2.,-H/2., 0.],[-W/2.,-H/2., 0.]\n",
        "                 ]])\n",
        "    ptsOut  = np.array(np.zeros((ptsIn.shape), dtype=ptsIn.dtype))\n",
        "    ptsOut  = cv2.perspectiveTransform(ptsIn, F)\n",
        "    \n",
        "    ptsInPt2f, ptsOutPt2f = getPoints_for_PerspectiveTranformEstimation(ptsIn, ptsOut, W, H, sideLength)\n",
        "    \n",
        "    # check float32 otherwise OpenCV throws an error\n",
        "    assert(ptsInPt2f.dtype  == np.float32)\n",
        "    assert(ptsOutPt2f.dtype == np.float32)\n",
        "    M33 = cv2.getPerspectiveTransform(ptsInPt2f,ptsOutPt2f)\n",
        "\n",
        "    return M33, sideLength\n",
        "\n",
        "def anim_frame_warp_2d(prev_img_cv2, args, anim_args, keys, frame_idx):\n",
        "    angle = keys.angle_series[frame_idx]\n",
        "    zoom = keys.zoom_series[frame_idx]\n",
        "    translation_x = keys.translation_x_series[frame_idx]\n",
        "    translation_y = keys.translation_y_series[frame_idx]\n",
        "\n",
        "    center = (args.W // 2, args.H // 2)\n",
        "    trans_mat = np.float32([[1, 0, translation_x], [0, 1, translation_y]])\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",
        "    if anim_args.flip_2d_perspective:\n",
        "        perspective_flip_theta = keys.perspective_flip_theta_series[frame_idx]\n",
        "        perspective_flip_phi = keys.perspective_flip_phi_series[frame_idx]\n",
        "        perspective_flip_gamma = keys.perspective_flip_gamma_series[frame_idx]\n",
        "        perspective_flip_fv = keys.perspective_flip_fv_series[frame_idx]\n",
        "        M,sl = warpMatrix(args.W, args.H, perspective_flip_theta, perspective_flip_phi, perspective_flip_gamma, 1., perspective_flip_fv);\n",
        "        post_trans_mat = np.float32([[1, 0, (args.W-sl)/2], [0, 1, (args.H-sl)/2]])\n",
        "        post_trans_mat = np.vstack([post_trans_mat, [0,0,1]])\n",
        "        bM = np.matmul(M, post_trans_mat)\n",
        "        xform = np.matmul(bM, rot_mat, trans_mat)\n",
        "    else:\n",
        "        xform = np.matmul(rot_mat, trans_mat)\n",
        "\n",
        "    return cv2.warpPerspective(\n",
        "        prev_img_cv2,\n",
        "        xform,\n",
        "        (prev_img_cv2.shape[1], prev_img_cv2.shape[0]),\n",
        "        borderMode=cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE\n",
        "    )\n",
        "\n",
        "def anim_frame_warp_3d(prev_img_cv2, depth, anim_args, keys, frame_idx):\n",
        "    TRANSLATION_SCALE = 1.0/200.0 # matches Disco\n",
        "    translate_xyz = [\n",
        "        -keys.translation_x_series[frame_idx] * TRANSLATION_SCALE, \n",
        "        keys.translation_y_series[frame_idx] * TRANSLATION_SCALE, \n",
        "        -keys.translation_z_series[frame_idx] * TRANSLATION_SCALE\n",
        "    ]\n",
        "    rotate_xyz = [\n",
        "        math.radians(keys.rotation_3d_x_series[frame_idx]), \n",
        "        math.radians(keys.rotation_3d_y_series[frame_idx]), \n",
        "        math.radians(keys.rotation_3d_z_series[frame_idx])\n",
        "    ]\n",
        "    rot_mat = p3d.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), \"XYZ\").unsqueeze(0)\n",
        "    result = transform_image_3d(prev_img_cv2, depth, rot_mat, translate_xyz, anim_args)\n",
        "    torch.cuda.empty_cache()\n",
        "    return result\n",
        "\n",
        "def add_noise(sample: torch.Tensor, noise_amt: float) -> torch.Tensor:\n",
        "    return sample + torch.randn(sample.shape, device=sample.device) * noise_amt\n",
        "\n",
        "def get_output_folder(output_path, batch_folder):\n",
        "    out_path = os.path.join(output_path,time.strftime('%Y-%m'))\n",
        "    if batch_folder != \"\":\n",
        "        out_path = os.path.join(out_path, batch_folder)\n",
        "    os.makedirs(out_path, exist_ok=True)\n",
        "    return out_path\n",
        "\n",
        "def load_img(path, shape, use_alpha_as_mask=False):\n",
        "    # use_alpha_as_mask: Read the alpha channel of the image as the mask image\n",
        "    if path.startswith('http://') or path.startswith('https://'):\n",
        "        image = Image.open(requests.get(path, stream=True).raw)\n",
        "    else:\n",
        "        image = Image.open(path)\n",
        "\n",
        "    if use_alpha_as_mask:\n",
        "        image = image.convert('RGBA')\n",
        "    else:\n",
        "        image = image.convert('RGB')\n",
        "\n",
        "    image = image.resize(shape, resample=Image.LANCZOS)\n",
        "\n",
        "    mask_image = None\n",
        "    if use_alpha_as_mask:\n",
        "      # Split alpha channel into a mask_image\n",
        "      red, green, blue, alpha = Image.Image.split(image)\n",
        "      mask_image = alpha.convert('L')\n",
        "      image = image.convert('RGB')\n",
        "\n",
        "    image = np.array(image).astype(np.float16) / 255.0\n",
        "    image = image[None].transpose(0, 3, 1, 2)\n",
        "    image = torch.from_numpy(image)\n",
        "    image = 2.*image - 1.\n",
        "\n",
        "    return image, mask_image\n",
        "\n",
        "def load_mask_latent(mask_input, shape):\n",
        "    # mask_input (str or PIL Image.Image): Path to the mask image or a PIL Image object\n",
        "    # shape (list-like len(4)): shape of the image to match, usually latent_image.shape\n",
        "    \n",
        "    if isinstance(mask_input, str): # mask input is probably a file name\n",
        "        if mask_input.startswith('http://') or mask_input.startswith('https://'):\n",
        "            mask_image = Image.open(requests.get(mask_input, stream=True).raw).convert('RGBA')\n",
        "        else:\n",
        "            mask_image = Image.open(mask_input).convert('RGBA')\n",
        "    elif isinstance(mask_input, Image.Image):\n",
        "        mask_image = mask_input\n",
        "    else:\n",
        "        raise Exception(\"mask_input must be a PIL image or a file name\")\n",
        "\n",
        "    mask_w_h = (shape[-1], shape[-2])\n",
        "    mask = mask_image.resize(mask_w_h, resample=Image.LANCZOS)\n",
        "    mask = mask.convert(\"L\")\n",
        "    return mask\n",
        "\n",
        "def prepare_mask(mask_input, mask_shape, mask_brightness_adjust=1.0, mask_contrast_adjust=1.0):\n",
        "    # mask_input (str or PIL Image.Image): Path to the mask image or a PIL Image object\n",
        "    # shape (list-like len(4)): shape of the image to match, usually latent_image.shape\n",
        "    # mask_brightness_adjust (non-negative float): amount to adjust brightness of the iamge, \n",
        "    #     0 is black, 1 is no adjustment, >1 is brighter\n",
        "    # mask_contrast_adjust (non-negative float): amount to adjust contrast of the image, \n",
        "    #     0 is a flat grey image, 1 is no adjustment, >1 is more contrast\n",
        "    \n",
        "    mask = load_mask_latent(mask_input, mask_shape)\n",
        "\n",
        "    # Mask brightness/contrast adjustments\n",
        "    if mask_brightness_adjust != 1:\n",
        "        mask = TF.adjust_brightness(mask, mask_brightness_adjust)\n",
        "    if mask_contrast_adjust != 1:\n",
        "        mask = TF.adjust_contrast(mask, mask_contrast_adjust)\n",
        "\n",
        "    # Mask image to array\n",
        "    mask = np.array(mask).astype(np.float32) / 255.0\n",
        "    mask = np.tile(mask,(4,1,1))\n",
        "    mask = np.expand_dims(mask,axis=0)\n",
        "    mask = torch.from_numpy(mask)\n",
        "\n",
        "    if args.invert_mask:\n",
        "        mask = ( (mask - 0.5) * -1) + 0.5\n",
        "    \n",
        "    mask = np.clip(mask,0,1)\n",
        "    return mask\n",
        "\n",
        "def maintain_colors(prev_img, color_match_sample, mode):\n",
        "    if mode == 'Match Frame 0 RGB':\n",
        "        return match_histograms(prev_img, color_match_sample, multichannel=True)\n",
        "    elif mode == 'Match Frame 0 HSV':\n",
        "        prev_img_hsv = cv2.cvtColor(prev_img, cv2.COLOR_RGB2HSV)\n",
        "        color_match_hsv = cv2.cvtColor(color_match_sample, cv2.COLOR_RGB2HSV)\n",
        "        matched_hsv = match_histograms(prev_img_hsv, color_match_hsv, multichannel=True)\n",
        "        return cv2.cvtColor(matched_hsv, cv2.COLOR_HSV2RGB)\n",
        "    else: # Match Frame 0 LAB\n",
        "        prev_img_lab = cv2.cvtColor(prev_img, cv2.COLOR_RGB2LAB)\n",
        "        color_match_lab = cv2.cvtColor(color_match_sample, cv2.COLOR_RGB2LAB)\n",
        "        matched_lab = match_histograms(prev_img_lab, color_match_lab, multichannel=True)\n",
        "        return cv2.cvtColor(matched_lab, cv2.COLOR_LAB2RGB)\n",
        "\n",
        "\n",
        "#\n",
        "# Callback functions\n",
        "#\n",
        "class SamplerCallback(object):\n",
        "    # Creates the callback function to be passed into the samplers for each step\n",
        "    def __init__(self, args, mask=None, init_latent=None, sigmas=None, sampler=None,\n",
        "                  verbose=False):\n",
        "        self.sampler_name = args.sampler\n",
        "        self.dynamic_threshold = args.dynamic_threshold\n",
        "        self.static_threshold = args.static_threshold\n",
        "        self.mask = mask\n",
        "        self.init_latent = init_latent \n",
        "        self.sigmas = sigmas\n",
        "        self.sampler = sampler\n",
        "        self.verbose = verbose\n",
        "\n",
        "        self.batch_size = args.n_samples\n",
        "        self.save_sample_per_step = args.save_sample_per_step\n",
        "        self.show_sample_per_step = args.show_sample_per_step\n",
        "        self.paths_to_image_steps = [os.path.join( args.outdir, f\"{args.timestring}_{index:02}_{args.seed}\") for index in range(args.n_samples) ]\n",
        "\n",
        "        if self.save_sample_per_step:\n",
        "            for path in self.paths_to_image_steps:\n",
        "                os.makedirs(path, exist_ok=True)\n",
        "\n",
        "        self.step_index = 0\n",
        "\n",
        "        self.noise = None\n",
        "        if init_latent is not None:\n",
        "            self.noise = torch.randn_like(init_latent, device=device)\n",
        "\n",
        "        self.mask_schedule = None\n",
        "        if sigmas is not None and len(sigmas) > 0:\n",
        "            self.mask_schedule, _ = torch.sort(sigmas/torch.max(sigmas))\n",
        "        elif len(sigmas) == 0:\n",
        "            self.mask = None # no mask needed if no steps (usually happens because strength==1.0)\n",
        "\n",
        "        if self.sampler_name in [\"plms\",\"ddim\"]: \n",
        "            if mask is not None:\n",
        "                assert sampler is not None, \"Callback function for stable-diffusion samplers requires sampler variable\"\n",
        "\n",
        "        if self.sampler_name in [\"plms\",\"ddim\"]: \n",
        "            # Callback function formated for compvis latent diffusion samplers\n",
        "            self.callback = self.img_callback_\n",
        "        else: \n",
        "            # Default callback function uses k-diffusion sampler variables\n",
        "            self.callback = self.k_callback_\n",
        "\n",
        "        self.verbose_print = print if verbose else lambda *args, **kwargs: None\n",
        "\n",
        "    def view_sample_step(self, latents, path_name_modifier=''):\n",
        "        if self.save_sample_per_step or self.show_sample_per_step:\n",
        "            samples = model.decode_first_stage(latents)\n",
        "            if self.save_sample_per_step:\n",
        "                fname = f'{path_name_modifier}_{self.step_index:05}.png'\n",
        "                for i, sample in enumerate(samples):\n",
        "                    sample = sample.double().cpu().add(1).div(2).clamp(0, 1)\n",
        "                    sample = torch.tensor(np.array(sample))\n",
        "                    grid = make_grid(sample, 4).cpu()\n",
        "                    TF.to_pil_image(grid).save(os.path.join(self.paths_to_image_steps[i], fname))\n",
        "            if self.show_sample_per_step:\n",
        "                print(path_name_modifier)\n",
        "                self.display_images(samples)\n",
        "        return\n",
        "\n",
        "    def display_images(self, images):\n",
        "        images = images.double().cpu().add(1).div(2).clamp(0, 1)\n",
        "        images = torch.tensor(np.array(images))\n",
        "        grid = make_grid(images, 4).cpu()\n",
        "        display.display(TF.to_pil_image(grid))\n",
        "        return\n",
        "\n",
        "    # The callback function is applied to the image at each step\n",
        "    def dynamic_thresholding_(self, img, threshold):\n",
        "        # Dynamic thresholding from Imagen paper (May 2022)\n",
        "        s = np.percentile(np.abs(img.cpu()), threshold, axis=tuple(range(1,img.ndim)))\n",
        "        s = np.max(np.append(s,1.0))\n",
        "        torch.clamp_(img, -1*s, s)\n",
        "        torch.FloatTensor.div_(img, s)\n",
        "\n",
        "    # Callback for samplers in the k-diffusion repo, called thus:\n",
        "    #   callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n",
        "    def k_callback_(self, args_dict):\n",
        "        self.step_index = args_dict['i']\n",
        "        if self.dynamic_threshold is not None:\n",
        "            self.dynamic_thresholding_(args_dict['x'], self.dynamic_threshold)\n",
        "        if self.static_threshold is not None:\n",
        "            torch.clamp_(args_dict['x'], -1*self.static_threshold, self.static_threshold)\n",
        "        if self.mask is not None:\n",
        "            init_noise = self.init_latent + self.noise * args_dict['sigma']\n",
        "            is_masked = torch.logical_and(self.mask >= self.mask_schedule[args_dict['i']], self.mask != 0 )\n",
        "            new_img = init_noise * torch.where(is_masked,1,0) + args_dict['x'] * torch.where(is_masked,0,1)\n",
        "            args_dict['x'].copy_(new_img)\n",
        "\n",
        "        self.view_sample_step(args_dict['denoised'], \"x0_pred\")\n",
        "\n",
        "    # Callback for Compvis samplers\n",
        "    # Function that is called on the image (img) and step (i) at each step\n",
        "    def img_callback_(self, img, i):\n",
        "        self.step_index = i\n",
        "        # Thresholding functions\n",
        "        if self.dynamic_threshold is not None:\n",
        "            self.dynamic_thresholding_(img, self.dynamic_threshold)\n",
        "        if self.static_threshold is not None:\n",
        "            torch.clamp_(img, -1*self.static_threshold, self.static_threshold)\n",
        "        if self.mask is not None:\n",
        "            i_inv = len(self.sigmas) - i - 1\n",
        "            init_noise = self.sampler.stochastic_encode(self.init_latent, torch.tensor([i_inv]*self.batch_size).to(device), noise=self.noise)\n",
        "            is_masked = torch.logical_and(self.mask >= self.mask_schedule[i], self.mask != 0 )\n",
        "            new_img = init_noise * torch.where(is_masked,1,0) + img * torch.where(is_masked,0,1)\n",
        "            img.copy_(new_img)\n",
        "\n",
        "        self.view_sample_step(img, \"x\")\n",
        "\n",
        "def sample_from_cv2(sample: np.ndarray) -> torch.Tensor:\n",
        "    sample = ((sample.astype(float) / 255.0) * 2) - 1\n",
        "    sample = sample[None].transpose(0, 3, 1, 2).astype(np.float16)\n",
        "    sample = torch.from_numpy(sample)\n",
        "    return sample\n",
        "\n",
        "def sample_to_cv2(sample: torch.Tensor, type=np.uint8) -> np.ndarray:\n",
        "    sample_f32 = rearrange(sample.squeeze().cpu().numpy(), \"c h w -> h w c\").astype(np.float32)\n",
        "    sample_f32 = ((sample_f32 * 0.5) + 0.5).clip(0, 1)\n",
        "    sample_int8 = (sample_f32 * 255)\n",
        "    return sample_int8.astype(type)\n",
        "\n",
        "def transform_image_3d(prev_img_cv2, depth_tensor, rot_mat, translate, anim_args):\n",
        "    # adapted and optimized version of transform_image_3d from Disco Diffusion https://github.com/alembics/disco-diffusion \n",
        "    w, h = prev_img_cv2.shape[1], prev_img_cv2.shape[0]\n",
        "\n",
        "    aspect_ratio = float(w)/float(h)\n",
        "    near, far, fov_deg = anim_args.near_plane, anim_args.far_plane, anim_args.fov\n",
        "    persp_cam_old = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, device=device)\n",
        "    persp_cam_new = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, R=rot_mat, T=torch.tensor([translate]), device=device)\n",
        "\n",
        "    # range of [-1,1] is important to torch grid_sample's padding handling\n",
        "    y,x = torch.meshgrid(torch.linspace(-1.,1.,h,dtype=torch.float32,device=device),torch.linspace(-1.,1.,w,dtype=torch.float32,device=device))\n",
        "    z = torch.as_tensor(depth_tensor, dtype=torch.float32, device=device)\n",
        "    xyz_old_world = torch.stack((x.flatten(), y.flatten(), z.flatten()), dim=1)\n",
        "\n",
        "    xyz_old_cam_xy = persp_cam_old.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]\n",
        "    xyz_new_cam_xy = persp_cam_new.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]\n",
        "\n",
        "    offset_xy = xyz_new_cam_xy - xyz_old_cam_xy\n",
        "    # affine_grid theta param expects a batch of 2D mats. Each is 2x3 to do rotation+translation.\n",
        "    identity_2d_batch = torch.tensor([[1.,0.,0.],[0.,1.,0.]], device=device).unsqueeze(0)\n",
        "    # coords_2d will have shape (N,H,W,2).. which is also what grid_sample needs.\n",
        "    coords_2d = torch.nn.functional.affine_grid(identity_2d_batch, [1,1,h,w], align_corners=False)\n",
        "    offset_coords_2d = coords_2d - torch.reshape(offset_xy, (h,w,2)).unsqueeze(0)\n",
        "\n",
        "    image_tensor = rearrange(torch.from_numpy(prev_img_cv2.astype(np.float32)), 'h w c -> c h w').to(device)\n",
        "    new_image = torch.nn.functional.grid_sample(\n",
        "        image_tensor.add(1/512 - 0.0001).unsqueeze(0), \n",
        "        offset_coords_2d, \n",
        "        mode=anim_args.sampling_mode, \n",
        "        padding_mode=anim_args.padding_mode, \n",
        "        align_corners=False\n",
        "    )\n",
        "\n",
        "    # convert back to cv2 style numpy array\n",
        "    result = rearrange(\n",
        "        new_image.squeeze().clamp(0,255), \n",
        "        'c h w -> h w c'\n",
        "    ).cpu().numpy().astype(prev_img_cv2.dtype)\n",
        "    return result\n",
        "\n",
        "def check_is_number(value):\n",
        "    float_pattern = r'^(?=.)([+-]?([0-9]*)(\\.([0-9]+))?)$'\n",
        "    return re.match(float_pattern, value)\n",
        "\n",
        "# prompt weighting with colons and number coefficients (like 'bacon:0.75 eggs:0.25')\n",
        "# borrowed from https://github.com/kylewlacy/stable-diffusion/blob/0a4397094eb6e875f98f9d71193e350d859c4220/ldm/dream/conditioning.py\n",
        "# and https://github.com/raefu/stable-diffusion-automatic/blob/unstablediffusion/modules/processing.py\n",
        "def get_uc_and_c(prompts, model, args, frame = 0):\n",
        "    prompt = prompts[0] # they are the same in a batch anyway\n",
        "\n",
        "    # get weighted sub-prompts\n",
        "    negative_subprompts, positive_subprompts = split_weighted_subprompts(\n",
        "        prompt, frame, not args.normalize_prompt_weights\n",
        "    )\n",
        "\n",
        "    uc = get_learned_conditioning(model, negative_subprompts, \"\", args, -1)\n",
        "    c = get_learned_conditioning(model, positive_subprompts, prompt, args, 1)\n",
        "\n",
        "    return (uc, c)\n",
        "\n",
        "def get_learned_conditioning(model, weighted_subprompts, text, args, sign = 1):\n",
        "    if len(weighted_subprompts) < 1:\n",
        "        log_tokenization(text, model, args.log_weighted_subprompts, sign)\n",
        "        c = model.get_learned_conditioning(args.n_samples * [text])\n",
        "    else:\n",
        "        c = None\n",
        "        for subtext, subweight in weighted_subprompts:\n",
        "            log_tokenization(subtext, model, args.log_weighted_subprompts, sign * subweight)\n",
        "            if c is None:\n",
        "                c = model.get_learned_conditioning(args.n_samples * [subtext])\n",
        "                c *= subweight\n",
        "            else:\n",
        "                c.add_(model.get_learned_conditioning(args.n_samples * [subtext]), alpha=subweight)\n",
        "        \n",
        "    return c\n",
        "\n",
        "def parse_weight(match, frame = 0)->float:\n",
        "    import numexpr\n",
        "    w_raw = match.group(\"weight\")\n",
        "    if w_raw == None:\n",
        "        return 1\n",
        "    if check_is_number(w_raw):\n",
        "        return float(w_raw)\n",
        "    else:\n",
        "        t = frame\n",
        "        if len(w_raw) < 3:\n",
        "            print('the value inside `-characters cannot represent a math function')\n",
        "            return 1\n",
        "        return float(numexpr.evaluate(w_raw[1:-1]))\n",
        "\n",
        "def normalize_prompt_weights(parsed_prompts):\n",
        "    if len(parsed_prompts) == 0:\n",
        "        return parsed_prompts\n",
        "    weight_sum = sum(map(lambda x: x[1], parsed_prompts))\n",
        "    if weight_sum == 0:\n",
        "        print(\n",
        "            \"Warning: Subprompt weights add up to zero. Discarding and using even weights instead.\")\n",
        "        equal_weight = 1 / max(len(parsed_prompts), 1)\n",
        "        return [(x[0], equal_weight) for x in parsed_prompts]\n",
        "    return [(x[0], x[1] / weight_sum) for x in parsed_prompts]\n",
        "\n",
        "def split_weighted_subprompts(text, frame = 0, skip_normalize=False):\n",
        "    \"\"\"\n",
        "    grabs all text up to the first occurrence of ':'\n",
        "    uses the grabbed text as a sub-prompt, and takes the value following ':' as weight\n",
        "    if ':' has no value defined, defaults to 1.0\n",
        "    repeats until no text remaining\n",
        "    \"\"\"\n",
        "    prompt_parser = re.compile(\"\"\"\n",
        "            (?P<prompt>         # capture group for 'prompt'\n",
        "            (?:\\\\\\:|[^:])+      # match one or more non ':' characters or escaped colons '\\:'\n",
        "            )                   # end 'prompt'\n",
        "            (?:                 # non-capture group\n",
        "            :+                  # match one or more ':' characters\n",
        "            (?P<weight>((        # capture group for 'weight'\n",
        "            -?\\d+(?:\\.\\d+)?     # match positive or negative integer or decimal number\n",
        "            )|(                 # or\n",
        "            `[\\S\\s]*?`# a math function\n",
        "            )))?                 # end weight capture group, make optional\n",
        "            \\s*                 # strip spaces after weight\n",
        "            |                   # OR\n",
        "            $                   # else, if no ':' then match end of line\n",
        "            )                   # end non-capture group\n",
        "            \"\"\", re.VERBOSE)\n",
        "    negative_prompts = []\n",
        "    positive_prompts = []\n",
        "    for match in re.finditer(prompt_parser, text):\n",
        "        w = parse_weight(match, frame)\n",
        "        if w < 0:\n",
        "            # negating the sign as we'll feed this to uc\n",
        "            negative_prompts.append((match.group(\"prompt\").replace(\"\\\\:\", \":\"), -w))\n",
        "        elif w > 0:\n",
        "            positive_prompts.append((match.group(\"prompt\").replace(\"\\\\:\", \":\"), w))\n",
        "\n",
        "    if skip_normalize:\n",
        "        return (negative_prompts, positive_prompts)\n",
        "    return (normalize_prompt_weights(negative_prompts), normalize_prompt_weights(positive_prompts))\n",
        "\n",
        "# shows how the prompt is tokenized\n",
        "# usually tokens have '</w>' to indicate end-of-word,\n",
        "# but for readability it has been replaced with ' '\n",
        "def log_tokenization(text, model, log=False, weight=1):\n",
        "    if not log:\n",
        "        return\n",
        "    tokens    = model.cond_stage_model.tokenizer._tokenize(text)\n",
        "    tokenized = \"\"\n",
        "    discarded = \"\"\n",
        "    usedTokens = 0\n",
        "    totalTokens = len(tokens)\n",
        "    for i in range(0, totalTokens):\n",
        "        token = tokens[i].replace('</w>', ' ')\n",
        "        # alternate color\n",
        "        s = (usedTokens % 6) + 1\n",
        "        if i < model.cond_stage_model.max_length:\n",
        "            tokenized = tokenized + f\"\\x1b[0;3{s};40m{token}\"\n",
        "            usedTokens += 1\n",
        "        else:  # over max token length\n",
        "            discarded = discarded + f\"\\x1b[0;3{s};40m{token}\"\n",
        "    print(f\"\\n>> Tokens ({usedTokens}), Weight ({weight:.2f}):\\n{tokenized}\\x1b[0m\")\n",
        "    if discarded != \"\":\n",
        "        print(\n",
        "            f\">> Tokens Discarded ({totalTokens-usedTokens}):\\n{discarded}\\x1b[0m\"\n",
        "        )\n",
        "\n",
        "def generate(args, frame = 0, return_latent=False, return_sample=False, return_c=False):\n",
        "    seed_everything(args.seed)\n",
        "    os.makedirs(args.outdir, exist_ok=True)\n",
        "\n",
        "    sampler = PLMSSampler(model) if args.sampler == 'plms' else DDIMSampler(model)\n",
        "    model_wrap = CompVisDenoiser(model)\n",
        "    batch_size = args.n_samples\n",
        "    prompt = args.prompt\n",
        "    assert prompt is not None\n",
        "    data = [batch_size * [prompt]]\n",
        "    precision_scope = autocast if args.precision == \"autocast\" else nullcontext\n",
        "\n",
        "    init_latent = None\n",
        "    mask_image = None\n",
        "    init_image = None\n",
        "    if args.init_latent is not None:\n",
        "        init_latent = args.init_latent\n",
        "    elif args.init_sample is not None:\n",
        "        with precision_scope(\"cuda\"):\n",
        "            init_latent = model.get_first_stage_encoding(model.encode_first_stage(args.init_sample))\n",
        "    elif args.use_init and args.init_image != None and args.init_image != '':\n",
        "        init_image, mask_image = load_img(args.init_image, \n",
        "                                          shape=(args.W, args.H),  \n",
        "                                          use_alpha_as_mask=args.use_alpha_as_mask)\n",
        "        init_image = init_image.to(device)\n",
        "        init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)\n",
        "        with precision_scope(\"cuda\"):\n",
        "            init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image))  # move to latent space        \n",
        "\n",
        "    if not args.use_init and args.strength > 0 and args.strength_0_no_init:\n",
        "        print(\"\\nNo init image, but strength > 0. Strength has been auto set to 0, since use_init is False.\")\n",
        "        print(\"If you want to force strength > 0 with no init, please set strength_0_no_init to False.\\n\")\n",
        "        args.strength = 0\n",
        "\n",
        "    # Mask functions\n",
        "    if args.use_mask:\n",
        "        assert args.mask_file is not None or mask_image is not None, \"use_mask==True: An mask image is required for a mask. Please enter a mask_file or use an init image with an alpha channel\"\n",
        "        assert args.use_init, \"use_mask==True: use_init is required for a mask\"\n",
        "        assert init_latent is not None, \"use_mask==True: An latent init image is required for a mask\"\n",
        "\n",
        "\n",
        "        mask = prepare_mask(args.mask_file if mask_image is None else mask_image, \n",
        "                            init_latent.shape, \n",
        "                            args.mask_contrast_adjust, \n",
        "                            args.mask_brightness_adjust)\n",
        "        \n",
        "        if (torch.all(mask == 0) or torch.all(mask == 1)) and args.use_alpha_as_mask:\n",
        "            raise Warning(\"use_alpha_as_mask==True: Using the alpha channel from the init image as a mask, but the alpha channel is blank.\")\n",
        "        \n",
        "        mask = mask.to(device)\n",
        "        mask = repeat(mask, '1 ... -> b ...', b=batch_size)\n",
        "    else:\n",
        "        mask = None\n",
        "\n",
        "    assert not ( (args.use_mask and args.overlay_mask) and (args.init_sample is None and init_image is None)), \"Need an init image when use_mask == True and overlay_mask == True\"\n",
        "        \n",
        "    t_enc = int((1.0-args.strength) * args.steps)\n",
        "\n",
        "    # Noise schedule for the k-diffusion samplers (used for masking)\n",
        "    k_sigmas = model_wrap.get_sigmas(args.steps)\n",
        "    k_sigmas = k_sigmas[len(k_sigmas)-t_enc-1:]\n",
        "\n",
        "    if args.sampler in ['plms','ddim']:\n",
        "        sampler.make_schedule(ddim_num_steps=args.steps, ddim_eta=args.ddim_eta, ddim_discretize='fill', verbose=False)\n",
        "\n",
        "    callback = SamplerCallback(args=args,\n",
        "                            mask=mask, \n",
        "                            init_latent=init_latent,\n",
        "                            sigmas=k_sigmas,\n",
        "                            sampler=sampler,\n",
        "                            verbose=False).callback  \n",
        "\n",
        "    results = []\n",
        "    with torch.no_grad():\n",
        "        with precision_scope(\"cuda\"):\n",
        "            with model.ema_scope():\n",
        "                for prompts in data:\n",
        "                    if isinstance(prompts, tuple):\n",
        "                        prompts = list(prompts)\n",
        "                    if args.prompt_weighting:\n",
        "                        uc, c = get_uc_and_c(prompts, model, args, frame)\n",
        "                    else:\n",
        "                        uc = model.get_learned_conditioning(batch_size * [\"\"])\n",
        "                        c = model.get_learned_conditioning(prompts)\n",
        "\n",
        "\n",
        "                    if args.scale == 1.0:\n",
        "                        uc = None\n",
        "                    if args.init_c != None:\n",
        "                        c = args.init_c\n",
        "\n",
        "                    if args.sampler in [\"klms\",\"dpm2\",\"dpm2_ancestral\",\"heun\",\"euler\",\"euler_ancestral\"]:\n",
        "                        samples = sampler_fn(\n",
        "                            c=c, \n",
        "                            uc=uc, \n",
        "                            args=args, \n",
        "                            model_wrap=model_wrap, \n",
        "                            init_latent=init_latent, \n",
        "                            t_enc=t_enc, \n",
        "                            device=device, \n",
        "                            cb=callback)\n",
        "                    else:\n",
        "                        # args.sampler == 'plms' or args.sampler == 'ddim':\n",
        "                        if init_latent is not None and args.strength > 0:\n",
        "                            z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))\n",
        "                        else:\n",
        "                            z_enc = torch.randn([args.n_samples, args.C, args.H // args.f, args.W // args.f], device=device)\n",
        "                        if args.sampler == 'ddim':\n",
        "                            samples = sampler.decode(z_enc, \n",
        "                                                     c, \n",
        "                                                     t_enc, \n",
        "                                                     unconditional_guidance_scale=args.scale,\n",
        "                                                     unconditional_conditioning=uc,\n",
        "                                                     img_callback=callback)\n",
        "                        elif args.sampler == 'plms': # no \"decode\" function in plms, so use \"sample\"\n",
        "                            shape = [args.C, args.H // args.f, args.W // args.f]\n",
        "                            samples, _ = sampler.sample(S=args.steps,\n",
        "                                                            conditioning=c,\n",
        "                                                            batch_size=args.n_samples,\n",
        "                                                            shape=shape,\n",
        "                                                            verbose=False,\n",
        "                                                            unconditional_guidance_scale=args.scale,\n",
        "                                                            unconditional_conditioning=uc,\n",
        "                                                            eta=args.ddim_eta,\n",
        "                                                            x_T=z_enc,\n",
        "                                                            img_callback=callback)\n",
        "                        else:\n",
        "                            raise Exception(f\"Sampler {args.sampler} not recognised.\")\n",
        "\n",
        "                    \n",
        "                    if return_latent:\n",
        "                        results.append(samples.clone())\n",
        "\n",
        "                    x_samples = model.decode_first_stage(samples)\n",
        "\n",
        "                    if args.use_mask and args.overlay_mask:\n",
        "                        # Overlay the masked image after the image is generated\n",
        "                        if args.init_sample is not None:\n",
        "                            img_original = args.init_sample\n",
        "                        elif init_image is not None:\n",
        "                            img_original = init_image\n",
        "                        else:\n",
        "                            raise Exception(\"Cannot overlay the masked image without an init image to overlay\")\n",
        "\n",
        "                        mask_fullres = prepare_mask(args.mask_file if mask_image is None else mask_image, \n",
        "                                                    img_original.shape, \n",
        "                                                    args.mask_contrast_adjust, \n",
        "                                                    args.mask_brightness_adjust)\n",
        "                        mask_fullres = mask_fullres[:,:3,:,:]\n",
        "                        mask_fullres = repeat(mask_fullres, '1 ... -> b ...', b=batch_size)\n",
        "\n",
        "                        mask_fullres[mask_fullres < mask_fullres.max()] = 0\n",
        "                        mask_fullres = gaussian_filter(mask_fullres, args.mask_overlay_blur)\n",
        "                        mask_fullres = torch.Tensor(mask_fullres).to(device)\n",
        "\n",
        "                        x_samples = img_original * mask_fullres + x_samples * ((mask_fullres * -1.0) + 1)\n",
        "\n",
        "\n",
        "                    if return_sample:\n",
        "                        results.append(x_samples.clone())\n",
        "\n",
        "                    x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)\n",
        "\n",
        "                    if return_c:\n",
        "                        results.append(c.clone())\n",
        "\n",
        "                    for x_sample in x_samples:\n",
        "                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')\n",
        "                        image = Image.fromarray(x_sample.astype(np.uint8))\n",
        "                        results.append(image)\n",
        "    return results"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "CIUJ7lWI4v53"
      },
      "source": [
        "#@markdown **Select and Load Model**\n",
        "\n",
        "model_config = \"v1-inference.yaml\" #@param [\"custom\",\"v1-inference.yaml\"]\n",
        "model_checkpoint =  \"sd-v1-4.ckpt\" #@param [\"custom\",\"sd-v1-4-full-ema.ckpt\",\"sd-v1-4.ckpt\",\"sd-v1-3-full-ema.ckpt\",\"sd-v1-3.ckpt\",\"sd-v1-2-full-ema.ckpt\",\"sd-v1-2.ckpt\",\"sd-v1-1-full-ema.ckpt\",\"sd-v1-1.ckpt\", \"robo-diffusion-v1.ckpt\",\"waifu-diffusion-v1-3.ckpt\"]\n",
        "if model_checkpoint == \"waifu-diffusion-v1-3.ckpt\":\n",
        "    model_checkpoint = \"model-epoch05-float16.ckpt\"\n",
        "custom_config_path = \"\" #@param {type:\"string\"}\n",
        "custom_checkpoint_path = \"\" #@param {type:\"string\"}\n",
        "\n",
        "load_on_run_all = True #@param {type: 'boolean'}\n",
        "half_precision = True # check\n",
        "check_sha256 = True #@param {type:\"boolean\"}\n",
        "\n",
        "model_map = {\n",
        "    \"sd-v1-4-full-ema.ckpt\": {\n",
        "        'sha256': '14749efc0ae8ef0329391ad4436feb781b402f4fece4883c7ad8d10556d8a36a',\n",
        "        'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/blob/main/sd-v1-4-full-ema.ckpt',\n",
        "        'requires_login': True,\n",
        "        },\n",
        "    \"sd-v1-4.ckpt\": {\n",
        "        'sha256': 'fe4efff1e174c627256e44ec2991ba279b3816e364b49f9be2abc0b3ff3f8556',\n",
        "        'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt',\n",
        "        'requires_login': True,\n",
        "        },\n",
        "    \"sd-v1-3-full-ema.ckpt\": {\n",
        "        'sha256': '54632c6e8a36eecae65e36cb0595fab314e1a1545a65209f24fde221a8d4b2ca',\n",
        "        'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-3-original/blob/main/sd-v1-3-full-ema.ckpt',\n",
        "        'requires_login': True,\n",
        "        },\n",
        "    \"sd-v1-3.ckpt\": {\n",
        "        'sha256': '2cff93af4dcc07c3e03110205988ff98481e86539c51a8098d4f2236e41f7f2f',\n",
        "        'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-3-original/resolve/main/sd-v1-3.ckpt',\n",
        "        'requires_login': True,\n",
        "        },\n",
        "    \"sd-v1-2-full-ema.ckpt\": {\n",
        "        'sha256': 'bc5086a904d7b9d13d2a7bccf38f089824755be7261c7399d92e555e1e9ac69a',\n",
        "        'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/blob/main/sd-v1-2-full-ema.ckpt',\n",
        "        'requires_login': True,\n",
        "        },\n",
        "    \"sd-v1-2.ckpt\": {\n",
        "        'sha256': '3b87d30facd5bafca1cbed71cfb86648aad75d1c264663c0cc78c7aea8daec0d',\n",
        "        'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/resolve/main/sd-v1-2.ckpt',\n",
        "        'requires_login': True,\n",
        "        },\n",
        "    \"sd-v1-1-full-ema.ckpt\": {\n",
        "        'sha256': 'efdeb5dc418a025d9a8cc0a8617e106c69044bc2925abecc8a254b2910d69829',\n",
        "        'url':'https://huggingface.co/CompVis/stable-diffusion-v-1-1-original/resolve/main/sd-v1-1-full-ema.ckpt',\n",
        "        'requires_login': True,\n",
        "        },\n",
        "    \"sd-v1-1.ckpt\": {\n",
        "        'sha256': '86cd1d3ccb044d7ba8db743d717c9bac603c4043508ad2571383f954390f3cea',\n",
        "        'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-1-original/resolve/main/sd-v1-1.ckpt',\n",
        "        'requires_login': True,\n",
        "        },\n",
        "    \"robo-diffusion-v1.ckpt\": {\n",
        "        'sha256': '244dbe0dcb55c761bde9c2ac0e9b46cc9705ebfe5f1f3a7cc46251573ea14e16',\n",
        "        'url': 'https://huggingface.co/nousr/robo-diffusion/resolve/main/models/robo-diffusion-v1.ckpt',\n",
        "        'requires_login': False,\n",
        "        },\n",
        "    \"model-epoch05-float16.ckpt\": {\n",
        "        'sha256': '26cf2a2e30095926bb9fd9de0c83f47adc0b442dbfdc3d667d43778e8b70bece',\n",
        "        'url': 'https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/model-epoch05-float16.ckpt',\n",
        "        'requires_login': False,\n",
        "        },\n",
        "}\n",
        "\n",
        "# config path\n",
        "ckpt_config_path = custom_config_path if model_config == \"custom\" else os.path.join(models_path, model_config)\n",
        "if os.path.exists(ckpt_config_path):\n",
        "    print(f\"{ckpt_config_path} exists\")\n",
        "else:\n",
        "    ckpt_config_path = \"./stable-diffusion/configs/stable-diffusion/v1-inference.yaml\"\n",
        "print(f\"Using config: {ckpt_config_path}\")\n",
        "\n",
        "# checkpoint path or download\n",
        "ckpt_path = custom_checkpoint_path if model_checkpoint == \"custom\" else os.path.join(models_path, model_checkpoint)\n",
        "ckpt_valid = True\n",
        "if os.path.exists(ckpt_path):\n",
        "    print(f\"{ckpt_path} exists\")\n",
        "elif 'url' in model_map[model_checkpoint]:\n",
        "    url = model_map[model_checkpoint]['url']\n",
        "\n",
        "    # CLI dialogue to authenticate download\n",
        "    if model_map[model_checkpoint]['requires_login']:\n",
        "        print(\"This model requires an authentication token\")\n",
        "        print(\"Please ensure you have accepted its terms of service before continuing.\")\n",
        "\n",
        "        username = input(\"What is your huggingface username?:\")\n",
        "        token = input(\"What is your huggingface token?:\")\n",
        "\n",
        "        _, path = url.split(\"https://\")\n",
        "\n",
        "        url = f\"https://{username}:{token}@{path}\"\n",
        "\n",
        "    # contact server for model\n",
        "    print(f\"Attempting to download {model_checkpoint}...this may take a while\")\n",
        "    ckpt_request = requests.get(url)\n",
        "    request_status = ckpt_request.status_code\n",
        "\n",
        "    # inform user of errors\n",
        "    if request_status == 403:\n",
        "      raise ConnectionRefusedError(\"You have not accepted the license for this model.\")\n",
        "    elif request_status == 404:\n",
        "      raise ConnectionError(\"Could not make contact with server\")\n",
        "    elif request_status != 200:\n",
        "      raise ConnectionError(f\"Some other error has ocurred - response code: {request_status}\")\n",
        "\n",
        "    # write to model path\n",
        "    with open(os.path.join(models_path, model_checkpoint), 'wb') as model_file:\n",
        "        model_file.write(ckpt_request.content)\n",
        "else:\n",
        "    print(f\"Please download model checkpoint and place in {os.path.join(models_path, model_checkpoint)}\")\n",
        "    ckpt_valid = False\n",
        "\n",
        "if check_sha256 and model_checkpoint != \"custom\" and ckpt_valid:\n",
        "    import hashlib\n",
        "    print(\"\\n...checking sha256\")\n",
        "    with open(ckpt_path, \"rb\") as f:\n",
        "        bytes = f.read() \n",
        "        hash = hashlib.sha256(bytes).hexdigest()\n",
        "        del bytes\n",
        "    if model_map[model_checkpoint][\"sha256\"] == hash:\n",
        "        print(\"hash is correct\\n\")\n",
        "    else:\n",
        "        print(\"hash in not correct\\n\")\n",
        "        ckpt_valid = False\n",
        "\n",
        "if ckpt_valid:\n",
        "    print(f\"Using ckpt: {ckpt_path}\")\n",
        "\n",
        "def load_model_from_config(config, ckpt, verbose=False, device='cuda', half_precision=True):\n",
        "    map_location = \"cuda\" #@param [\"cpu\", \"cuda\"]\n",
        "    print(f\"Loading model from {ckpt}\")\n",
        "    pl_sd = torch.load(ckpt, map_location=map_location)\n",
        "    if \"global_step\" in pl_sd:\n",
        "        print(f\"Global Step: {pl_sd['global_step']}\")\n",
        "    sd = pl_sd[\"state_dict\"]\n",
        "    model = instantiate_from_config(config.model)\n",
        "    m, u = model.load_state_dict(sd, strict=False)\n",
        "    if len(m) > 0 and verbose:\n",
        "        print(\"missing keys:\")\n",
        "        print(m)\n",
        "    if len(u) > 0 and verbose:\n",
        "        print(\"unexpected keys:\")\n",
        "        print(u)\n",
        "\n",
        "    if half_precision:\n",
        "        model = model.half().to(device)\n",
        "    else:\n",
        "        model = model.to(device)\n",
        "    model.eval()\n",
        "    return model\n",
        "\n",
        "if load_on_run_all and ckpt_valid:\n",
        "    local_config = OmegaConf.load(f\"{ckpt_config_path}\")\n",
        "    model = load_model_from_config(local_config, f\"{ckpt_path}\", half_precision=half_precision)\n",
        "    device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "    model = model.to(device)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ov3r4RD1tzsT"
      },
      "source": [
        "# Settings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0j7rgxvLvfay"
      },
      "source": [
        "### Animation Settings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "8HJN2TE3vh-J"
      },
      "source": [
        "\n",
        "def DeforumAnimArgs():\n",
        "\n",
        "    #@markdown ####**Animation:**\n",
        "    animation_mode = 'None' #@param ['None', '2D', '3D', 'Video Input', 'Interpolation'] {type:'string'}\n",
        "    max_frames = 1000 #@param {type:\"number\"}\n",
        "    border = 'replicate' #@param ['wrap', 'replicate'] {type:'string'}\n",
        "\n",
        "    #@markdown ####**Motion Parameters:**\n",
        "    angle = \"0:(0)\"#@param {type:\"string\"}\n",
        "    zoom = \"0:(1.04)\"#@param {type:\"string\"}\n",
        "    translation_x = \"0:(10*sin(2*3.14*t/10))\"#@param {type:\"string\"}\n",
        "    translation_y = \"0:(0)\"#@param {type:\"string\"}\n",
        "    translation_z = \"0:(10)\"#@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",
        "    flip_2d_perspective = False #@param {type:\"boolean\"}\n",
        "    perspective_flip_theta = \"0:(0)\"#@param {type:\"string\"}\n",
        "    perspective_flip_phi = \"0:(t%15)\"#@param {type:\"string\"}\n",
        "    perspective_flip_gamma = \"0:(0)\"#@param {type:\"string\"}\n",
        "    perspective_flip_fv = \"0:(53)\"#@param {type:\"string\"}\n",
        "    noise_schedule = \"0: (0.02)\"#@param {type:\"string\"}\n",
        "    strength_schedule = \"0: (0.65)\"#@param {type:\"string\"}\n",
        "    contrast_schedule = \"0: (1.0)\"#@param {type:\"string\"}\n",
        "\n",
        "    #@markdown ####**Coherence:**\n",
        "    color_coherence = 'Match Frame 0 LAB' #@param ['None', 'Match Frame 0 HSV', 'Match Frame 0 LAB', 'Match Frame 0 RGB'] {type:'string'}\n",
        "    diffusion_cadence = '1' #@param ['1','2','3','4','5','6','7','8'] {type:'string'}\n",
        "\n",
        "    #@markdown ####**3D Depth Warping:**\n",
        "    use_depth_warping = True #@param {type:\"boolean\"}\n",
        "    midas_weight = 0.3#@param {type:\"number\"}\n",
        "    near_plane = 200\n",
        "    far_plane = 10000\n",
        "    fov = 40#@param {type:\"number\"}\n",
        "    padding_mode = 'border'#@param ['border', 'reflection', 'zeros'] {type:'string'}\n",
        "    sampling_mode = 'bicubic'#@param ['bicubic', 'bilinear', 'nearest'] {type:'string'}\n",
        "    save_depth_maps = False #@param {type:\"boolean\"}\n",
        "\n",
        "    #@markdown ####**Video Input:**\n",
        "    video_init_path ='/content/video_in.mp4'#@param {type:\"string\"}\n",
        "    extract_nth_frame = 1#@param {type:\"number\"}\n",
        "    overwrite_extracted_frames = True #@param {type:\"boolean\"}\n",
        "    use_mask_video = False #@param {type:\"boolean\"}\n",
        "    video_mask_path ='/content/video_in.mp4'#@param {type:\"string\"}\n",
        "\n",
        "    #@markdown ####**Interpolation:**\n",
        "    interpolate_key_frames = False #@param {type:\"boolean\"}\n",
        "    interpolate_x_frames = 4 #@param {type:\"number\"}\n",
        "    \n",
        "    #@markdown ####**Resume Animation:**\n",
        "    resume_from_timestring = False #@param {type:\"boolean\"}\n",
        "    resume_timestring = \"20220829210106\" #@param {type:\"string\"}\n",
        "\n",
        "    return locals()\n",
        "\n",
        "class DeformAnimKeys():\n",
        "    def __init__(self, anim_args):\n",
        "        self.angle_series = get_inbetweens(parse_key_frames(anim_args.angle), anim_args.max_frames)\n",
        "        self.zoom_series = get_inbetweens(parse_key_frames(anim_args.zoom), anim_args.max_frames)\n",
        "        self.translation_x_series = get_inbetweens(parse_key_frames(anim_args.translation_x), anim_args.max_frames)\n",
        "        self.translation_y_series = get_inbetweens(parse_key_frames(anim_args.translation_y), anim_args.max_frames)\n",
        "        self.translation_z_series = get_inbetweens(parse_key_frames(anim_args.translation_z), anim_args.max_frames)\n",
        "        self.rotation_3d_x_series = get_inbetweens(parse_key_frames(anim_args.rotation_3d_x), anim_args.max_frames)\n",
        "        self.rotation_3d_y_series = get_inbetweens(parse_key_frames(anim_args.rotation_3d_y), anim_args.max_frames)\n",
        "        self.rotation_3d_z_series = get_inbetweens(parse_key_frames(anim_args.rotation_3d_z), anim_args.max_frames)\n",
        "        self.perspective_flip_theta_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_theta), anim_args.max_frames)\n",
        "        self.perspective_flip_phi_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_phi), anim_args.max_frames)\n",
        "        self.perspective_flip_gamma_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_gamma), anim_args.max_frames)\n",
        "        self.perspective_flip_fv_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_fv), anim_args.max_frames)\n",
        "        self.noise_schedule_series = get_inbetweens(parse_key_frames(anim_args.noise_schedule), anim_args.max_frames)\n",
        "        self.strength_schedule_series = get_inbetweens(parse_key_frames(anim_args.strength_schedule), anim_args.max_frames)\n",
        "        self.contrast_schedule_series = get_inbetweens(parse_key_frames(anim_args.contrast_schedule), anim_args.max_frames)\n",
        "\n",
        "\n",
        "def get_inbetweens(key_frames, max_frames, integer=False, interp_method='Linear'):\n",
        "    import numexpr\n",
        "    key_frame_series = pd.Series([np.nan for a in range(max_frames)])\n",
        "    \n",
        "    for i in range(0, max_frames):\n",
        "        if i in key_frames:\n",
        "            value = key_frames[i]\n",
        "            value_is_number = check_is_number(value)\n",
        "            # if it's only a number, leave the rest for the default interpolation\n",
        "            if value_is_number:\n",
        "                t = i\n",
        "                key_frame_series[i] = value\n",
        "        if not value_is_number:\n",
        "            t = i\n",
        "            key_frame_series[i] = numexpr.evaluate(value)\n",
        "    key_frame_series = key_frame_series.astype(float)\n",
        "    \n",
        "    if interp_method == 'Cubic' and len(key_frames.items()) <= 3:\n",
        "      interp_method = 'Quadratic'    \n",
        "    if interp_method == 'Quadratic' and len(key_frames.items()) <= 2:\n",
        "      interp_method = 'Linear'\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=interp_method.lower(), limit_direction='both')\n",
        "    if integer:\n",
        "        return key_frame_series.astype(int)\n",
        "    return key_frame_series\n",
        "\n",
        "def parse_key_frames(string, prompt_parser=None):\n",
        "    # because math functions (i.e. sin(t)) can utilize brackets \n",
        "    # it extracts the value in form of some stuff\n",
        "    # which has previously been enclosed with brackets and\n",
        "    # with a comma or end of line existing after the closing one\n",
        "    pattern = r'((?P<frame>[0-9]+):[\\s]*\\((?P<param>[\\S\\s]*?)\\)([,][\\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",
        "    if frames == {} and len(string) != 0:\n",
        "        raise RuntimeError('Key Frame string not correctly formatted')\n",
        "    return frames"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63UOJvU3xdPS"
      },
      "source": [
        "### Prompts\n",
        "`animation_mode: None` batches on list of *prompts*. `animation_mode: 2D` uses *animation_prompts* key frame sequence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ujwkGZTcGev"
      },
      "source": [
        "\n",
        "prompts = [\n",
        "    \"a beautiful forest by Asher Brown Durand, trending on Artstation\", # the first prompt I want\n",
        "    \"a beautiful portrait of a woman by Artgerm, trending on Artstation\", # the second prompt I want\n",
        "    #\"this prompt I don't want it I commented it out\",\n",
        "    #\"a nousr robot, trending on Artstation\", # use \"nousr robot\" with the robot diffusion model (see model_checkpoint setting)\n",
        "    #\"touhou 1girl komeiji_koishi portrait, green hair\", # waifu diffusion prompts can use danbooru tag groups (see model_checkpoint)\n",
        "    #\"this prompt has weights if prompt weighting enabled:2 can also do negative:-2\", # (see prompt_weighting)\n",
        "]\n",
        "\n",
        "animation_prompts = {\n",
        "    0: \"a beautiful apple, trending on Artstation\",\n",
        "    20: \"a beautiful banana, trending on Artstation\",\n",
        "    30: \"a beautiful coconut, trending on Artstation\",\n",
        "    40: \"a beautiful durian, trending on Artstation\",\n",
        "}"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8RAo2zI-vQm"
      },
      "source": [
        "# Run"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qH74gBWDd2oq",
        "cellView": "form"
      },
      "source": [
        "#@markdown **Load Settings**\n",
        "override_settings_with_file = False #@param {type:\"boolean\"}\n",
        "custom_settings_file = \"/content/drive/MyDrive/Settings.txt\"#@param {type:\"string\"}\n",
        "\n",
        "def DeforumArgs():\n",
        "    #@markdown **Image Settings**\n",
        "    W = 512 #@param\n",
        "    H = 512 #@param\n",
        "    W, H = map(lambda x: x - x % 64, (W, H))  # resize to integer multiple of 64\n",
        "\n",
        "    #@markdown **Sampling Settings**\n",
        "    seed = -1 #@param\n",
        "    sampler = 'klms' #@param [\"klms\",\"dpm2\",\"dpm2_ancestral\",\"heun\",\"euler\",\"euler_ancestral\",\"plms\", \"ddim\"]\n",
        "    steps = 50 #@param\n",
        "    scale = 7 #@param\n",
        "    ddim_eta = 0.0 #@param\n",
        "    dynamic_threshold = None\n",
        "    static_threshold = None   \n",
        "\n",
        "    #@markdown **Save & Display Settings**\n",
        "    save_samples = True #@param {type:\"boolean\"}\n",
        "    save_settings = True #@param {type:\"boolean\"}\n",
        "    display_samples = True #@param {type:\"boolean\"}\n",
        "    save_sample_per_step = False #@param {type:\"boolean\"}\n",
        "    show_sample_per_step = False #@param {type:\"boolean\"}\n",
        "\n",
        "    #@markdown **Prompt Settings**\n",
        "    prompt_weighting = False #@param {type:\"boolean\"}\n",
        "    normalize_prompt_weights = True #@param {type:\"boolean\"}\n",
        "    log_weighted_subprompts = False #@param {type:\"boolean\"}\n",
        "\n",
        "    #@markdown **Batch Settings**\n",
        "    n_batch = 1 #@param\n",
        "    batch_name = \"StableFun\" #@param {type:\"string\"}\n",
        "    filename_format = \"{timestring}_{index}_{prompt}.png\" #@param [\"{timestring}_{index}_{seed}.png\",\"{timestring}_{index}_{prompt}.png\"]\n",
        "    seed_behavior = \"iter\" #@param [\"iter\",\"fixed\",\"random\"]\n",
        "    make_grid = False #@param {type:\"boolean\"}\n",
        "    grid_rows = 2 #@param \n",
        "    outdir = get_output_folder(output_path, batch_name)\n",
        "\n",
        "    #@markdown **Init Settings**\n",
        "    use_init = False #@param {type:\"boolean\"}\n",
        "    strength = 0.0 #@param {type:\"number\"}\n",
        "    strength_0_no_init = True # Set the strength to 0 automatically when no init image is used\n",
        "    init_image = \"https://cdn.pixabay.com/photo/2022/07/30/13/10/green-longhorn-beetle-7353749_1280.jpg\" #@param {type:\"string\"}\n",
        "    # Whiter areas of the mask are areas that change more\n",
        "    use_mask = False #@param {type:\"boolean\"}\n",
        "    use_alpha_as_mask = False # use the alpha channel of the init image as the mask\n",
        "    mask_file = \"https://www.filterforge.com/wiki/images/archive/b/b7/20080927223728%21Polygonal_gradient_thumb.jpg\" #@param {type:\"string\"}\n",
        "    invert_mask = False #@param {type:\"boolean\"}\n",
        "    # Adjust mask image, 1.0 is no adjustment. Should be positive numbers.\n",
        "    mask_brightness_adjust = 1.0  #@param {type:\"number\"}\n",
        "    mask_contrast_adjust = 1.0  #@param {type:\"number\"}\n",
        "    # Overlay the masked image at the end of the generation so it does not get degraded by encoding and decoding\n",
        "    overlay_mask = True  # {type:\"boolean\"}\n",
        "    # Blur edges of final overlay mask, if used. Minimum = 0 (no blur)\n",
        "    mask_overlay_blur = 5 # {type:\"number\"}\n",
        "\n",
        "    n_samples = 1 # doesnt do anything\n",
        "    precision = 'autocast' \n",
        "    C = 4\n",
        "    f = 8\n",
        "\n",
        "    prompt = \"\"\n",
        "    timestring = \"\"\n",
        "    init_latent = None\n",
        "    init_sample = None\n",
        "    init_c = None\n",
        "\n",
        "    return locals()\n",
        "\n",
        "\n",
        "\n",
        "def next_seed(args):\n",
        "    if args.seed_behavior == 'iter':\n",
        "        args.seed += 1\n",
        "    elif args.seed_behavior == 'fixed':\n",
        "        pass # always keep seed the same\n",
        "    else:\n",
        "        args.seed = random.randint(0, 2**32 - 1)\n",
        "    return args.seed\n",
        "\n",
        "def render_image_batch(args):\n",
        "    args.prompts = {k: f\"{v:05d}\" for v, k in enumerate(prompts)}\n",
        "    \n",
        "    # create output folder for the batch\n",
        "    os.makedirs(args.outdir, exist_ok=True)\n",
        "    if args.save_settings or args.save_samples:\n",
        "        print(f\"Saving to {os.path.join(args.outdir, args.timestring)}_*\")\n",
        "\n",
        "    # save settings for the batch\n",
        "    if args.save_settings:\n",
        "        filename = os.path.join(args.outdir, f\"{args.timestring}_settings.txt\")\n",
        "        with open(filename, \"w+\", encoding=\"utf-8\") as f:\n",
        "            json.dump(dict(args.__dict__), f, ensure_ascii=False, indent=4)\n",
        "\n",
        "    index = 0\n",
        "    \n",
        "    # function for init image batching\n",
        "    init_array = []\n",
        "    if args.use_init:\n",
        "        if args.init_image == \"\":\n",
        "            raise FileNotFoundError(\"No path was given for init_image\")\n",
        "        if args.init_image.startswith('http://') or args.init_image.startswith('https://'):\n",
        "            init_array.append(args.init_image)\n",
        "        elif not os.path.isfile(args.init_image):\n",
        "            if args.init_image[-1] != \"/\": # avoids path error by adding / to end if not there\n",
        "                args.init_image += \"/\" \n",
        "            for image in sorted(os.listdir(args.init_image)): # iterates dir and appends images to init_array\n",
        "                if image.split(\".\")[-1] in (\"png\", \"jpg\", \"jpeg\"):\n",
        "                    init_array.append(args.init_image + image)\n",
        "        else:\n",
        "            init_array.append(args.init_image)\n",
        "    else:\n",
        "        init_array = [\"\"]\n",
        "\n",
        "    # when doing large batches don't flood browser with images\n",
        "    clear_between_batches = args.n_batch >= 32\n",
        "\n",
        "    for iprompt, prompt in enumerate(prompts):  \n",
        "        args.prompt = prompt\n",
        "        print(f\"Prompt {iprompt+1} of {len(prompts)}\")\n",
        "        print(f\"{args.prompt}\")\n",
        "\n",
        "        all_images = []\n",
        "\n",
        "        for batch_index in range(args.n_batch):\n",
        "            if clear_between_batches and batch_index % 32 == 0: \n",
        "                display.clear_output(wait=True)            \n",
        "            print(f\"Batch {batch_index+1} of {args.n_batch}\")\n",
        "            \n",
        "            for image in init_array: # iterates the init images\n",
        "                args.init_image = image\n",
        "                results = generate(args)\n",
        "                for image in results:\n",
        "                    if args.make_grid:\n",
        "                        all_images.append(T.functional.pil_to_tensor(image))\n",
        "                    if args.save_samples:\n",
        "                        if args.filename_format == \"{timestring}_{index}_{prompt}.png\":\n",
        "                            filename = f\"{args.timestring}_{index:05}_{sanitize(prompt)[:160]}.png\"\n",
        "                        else:\n",
        "                            filename = f\"{args.timestring}_{index:05}_{args.seed}.png\"\n",
        "                        image.save(os.path.join(args.outdir, filename))\n",
        "                    if args.display_samples:\n",
        "                        display.display(image)\n",
        "                    index += 1\n",
        "                args.seed = next_seed(args)\n",
        "\n",
        "        #print(len(all_images))\n",
        "        if args.make_grid:\n",
        "            grid = make_grid(all_images, nrow=int(len(all_images)/args.grid_rows))\n",
        "            grid = rearrange(grid, 'c h w -> h w c').cpu().numpy()\n",
        "            filename = f\"{args.timestring}_{iprompt:05d}_grid_{args.seed}.png\"\n",
        "            grid_image = Image.fromarray(grid.astype(np.uint8))\n",
        "            grid_image.save(os.path.join(args.outdir, filename))\n",
        "            display.clear_output(wait=True)            \n",
        "            display.display(grid_image)\n",
        "\n",
        "\n",
        "def render_animation(args, anim_args):\n",
        "    # animations use key framed prompts\n",
        "    args.prompts = animation_prompts\n",
        "\n",
        "    # expand key frame strings to values\n",
        "    keys = DeformAnimKeys(anim_args)\n",
        "\n",
        "    # resume animation\n",
        "    start_frame = 0\n",
        "    if anim_args.resume_from_timestring:\n",
        "        for tmp in os.listdir(args.outdir):\n",
        "            if tmp.split(\"_\")[0] == anim_args.resume_timestring:\n",
        "                start_frame += 1\n",
        "        start_frame = start_frame - 1\n",
        "\n",
        "    # create output folder for the batch\n",
        "    os.makedirs(args.outdir, exist_ok=True)\n",
        "    print(f\"Saving animation frames to {args.outdir}\")\n",
        "\n",
        "    # save settings for the batch\n",
        "    settings_filename = os.path.join(args.outdir, f\"{args.timestring}_settings.txt\")\n",
        "    with open(settings_filename, \"w+\", encoding=\"utf-8\") as f:\n",
        "        s = {**dict(args.__dict__), **dict(anim_args.__dict__)}\n",
        "        json.dump(s, f, ensure_ascii=False, indent=4)\n",
        "        \n",
        "    # resume from timestring\n",
        "    if anim_args.resume_from_timestring:\n",
        "        args.timestring = anim_args.resume_timestring\n",
        "\n",
        "    # expand prompts out to per-frame\n",
        "    prompt_series = pd.Series([np.nan for a in range(anim_args.max_frames)])\n",
        "    for i, prompt in animation_prompts.items():\n",
        "        prompt_series[i] = prompt\n",
        "    prompt_series = prompt_series.ffill().bfill()\n",
        "\n",
        "    # check for video inits\n",
        "    using_vid_init = anim_args.animation_mode == 'Video Input'\n",
        "\n",
        "    # load depth model for 3D\n",
        "    predict_depths = (anim_args.animation_mode == '3D' and anim_args.use_depth_warping) or anim_args.save_depth_maps\n",
        "    if predict_depths:\n",
        "        depth_model = DepthModel(device)\n",
        "        depth_model.load_midas(models_path)\n",
        "        if anim_args.midas_weight < 1.0:\n",
        "            depth_model.load_adabins()\n",
        "    else:\n",
        "        depth_model = None\n",
        "        anim_args.save_depth_maps = False\n",
        "\n",
        "    # state for interpolating between diffusion steps\n",
        "    turbo_steps = 1 if using_vid_init else int(anim_args.diffusion_cadence)\n",
        "    turbo_prev_image, turbo_prev_frame_idx = None, 0\n",
        "    turbo_next_image, turbo_next_frame_idx = None, 0\n",
        "\n",
        "    # resume animation\n",
        "    prev_sample = None\n",
        "    color_match_sample = None\n",
        "    if anim_args.resume_from_timestring:\n",
        "        last_frame = start_frame-1\n",
        "        if turbo_steps > 1:\n",
        "            last_frame -= last_frame%turbo_steps\n",
        "        path = os.path.join(args.outdir,f\"{args.timestring}_{last_frame:05}.png\")\n",
        "        img = cv2.imread(path)\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "        prev_sample = sample_from_cv2(img)\n",
        "        if anim_args.color_coherence != 'None':\n",
        "            color_match_sample = img\n",
        "        if turbo_steps > 1:\n",
        "            turbo_next_image, turbo_next_frame_idx = sample_to_cv2(prev_sample, type=np.float32), last_frame\n",
        "            turbo_prev_image, turbo_prev_frame_idx = turbo_next_image, turbo_next_frame_idx\n",
        "            start_frame = last_frame+turbo_steps\n",
        "\n",
        "    args.n_samples = 1\n",
        "    frame_idx = start_frame\n",
        "    while frame_idx < anim_args.max_frames:\n",
        "        print(f\"Rendering animation frame {frame_idx} of {anim_args.max_frames}\")\n",
        "        noise = keys.noise_schedule_series[frame_idx]\n",
        "        strength = keys.strength_schedule_series[frame_idx]\n",
        "        contrast = keys.contrast_schedule_series[frame_idx]\n",
        "        depth = None\n",
        "        \n",
        "        # emit in-between frames\n",
        "        if turbo_steps > 1:\n",
        "            tween_frame_start_idx = max(0, frame_idx-turbo_steps)\n",
        "            for tween_frame_idx in range(tween_frame_start_idx, frame_idx):\n",
        "                tween = float(tween_frame_idx - tween_frame_start_idx + 1) / float(frame_idx - tween_frame_start_idx)\n",
        "                print(f\"  creating in between frame {tween_frame_idx} tween:{tween:0.2f}\")\n",
        "\n",
        "                advance_prev = turbo_prev_image is not None and tween_frame_idx > turbo_prev_frame_idx\n",
        "                advance_next = tween_frame_idx > turbo_next_frame_idx\n",
        "\n",
        "                if depth_model is not None:\n",
        "                    assert(turbo_next_image is not None)\n",
        "                    depth = depth_model.predict(turbo_next_image, anim_args)\n",
        "\n",
        "                if anim_args.animation_mode == '2D':\n",
        "                    if advance_prev:\n",
        "                        turbo_prev_image = anim_frame_warp_2d(turbo_prev_image, args, anim_args, keys, tween_frame_idx)\n",
        "                    if advance_next:\n",
        "                        turbo_next_image = anim_frame_warp_2d(turbo_next_image, args, anim_args, keys, tween_frame_idx)\n",
        "                else: # '3D'\n",
        "                    if advance_prev:\n",
        "                        turbo_prev_image = anim_frame_warp_3d(turbo_prev_image, depth, anim_args, keys, tween_frame_idx)\n",
        "                    if advance_next:\n",
        "                        turbo_next_image = anim_frame_warp_3d(turbo_next_image, depth, anim_args, keys, tween_frame_idx)\n",
        "                turbo_prev_frame_idx = turbo_next_frame_idx = tween_frame_idx\n",
        "\n",
        "                if turbo_prev_image is not None and tween < 1.0:\n",
        "                    img = turbo_prev_image*(1.0-tween) + turbo_next_image*tween\n",
        "                else:\n",
        "                    img = turbo_next_image\n",
        "\n",
        "                filename = f\"{args.timestring}_{tween_frame_idx:05}.png\"\n",
        "                cv2.imwrite(os.path.join(args.outdir, filename), cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_RGB2BGR))\n",
        "                if anim_args.save_depth_maps:\n",
        "                    depth_model.save(os.path.join(args.outdir, f\"{args.timestring}_depth_{tween_frame_idx:05}.png\"), depth)\n",
        "            if turbo_next_image is not None:\n",
        "                prev_sample = sample_from_cv2(turbo_next_image)\n",
        "\n",
        "        # apply transforms to previous frame\n",
        "        if prev_sample is not None:\n",
        "            if anim_args.animation_mode == '2D':\n",
        "                prev_img = anim_frame_warp_2d(sample_to_cv2(prev_sample), args, anim_args, keys, frame_idx)\n",
        "            else: # '3D'\n",
        "                prev_img_cv2 = sample_to_cv2(prev_sample)\n",
        "                depth = depth_model.predict(prev_img_cv2, anim_args) if depth_model else None\n",
        "                prev_img = anim_frame_warp_3d(prev_img_cv2, depth, anim_args, keys, frame_idx)\n",
        "\n",
        "            # apply color matching\n",
        "            if anim_args.color_coherence != 'None':\n",
        "                if color_match_sample is None:\n",
        "                    color_match_sample = prev_img.copy()\n",
        "                else:\n",
        "                    prev_img = maintain_colors(prev_img, color_match_sample, anim_args.color_coherence)\n",
        "\n",
        "            # apply scaling\n",
        "            contrast_sample = prev_img * contrast\n",
        "            # apply frame noising\n",
        "            noised_sample = add_noise(sample_from_cv2(contrast_sample), noise)\n",
        "\n",
        "            # use transformed previous frame as init for current\n",
        "            args.use_init = True\n",
        "            if half_precision:\n",
        "                args.init_sample = noised_sample.half().to(device)\n",
        "            else:\n",
        "                args.init_sample = noised_sample.to(device)\n",
        "            args.strength = max(0.0, min(1.0, strength))\n",
        "\n",
        "        # grab prompt for current frame\n",
        "        args.prompt = prompt_series[frame_idx]\n",
        "        print(f\"{args.prompt} {args.seed}\")\n",
        "        if not using_vid_init:\n",
        "            print(f\"Angle: {keys.angle_series[frame_idx]} Zoom: {keys.zoom_series[frame_idx]}\")\n",
        "            print(f\"Tx: {keys.translation_x_series[frame_idx]} Ty: {keys.translation_y_series[frame_idx]} Tz: {keys.translation_z_series[frame_idx]}\")\n",
        "            print(f\"Rx: {keys.rotation_3d_x_series[frame_idx]} Ry: {keys.rotation_3d_y_series[frame_idx]} Rz: {keys.rotation_3d_z_series[frame_idx]}\")\n",
        "\n",
        "        # grab init image for current frame\n",
        "        if using_vid_init:\n",
        "            init_frame = os.path.join(args.outdir, 'inputframes', f\"{frame_idx+1:05}.jpg\")            \n",
        "            print(f\"Using video init frame {init_frame}\")\n",
        "            args.init_image = init_frame\n",
        "            if anim_args.use_mask_video:\n",
        "                mask_frame = os.path.join(args.outdir, 'maskframes', f\"{frame_idx+1:05}.jpg\")\n",
        "                args.mask_file = mask_frame\n",
        "\n",
        "        # sample the diffusion model\n",
        "        sample, image = generate(args, frame_idx, return_latent=False, return_sample=True)\n",
        "        if not using_vid_init:\n",
        "            prev_sample = sample\n",
        "\n",
        "        if turbo_steps > 1:\n",
        "            turbo_prev_image, turbo_prev_frame_idx = turbo_next_image, turbo_next_frame_idx\n",
        "            turbo_next_image, turbo_next_frame_idx = sample_to_cv2(sample, type=np.float32), frame_idx\n",
        "            frame_idx += turbo_steps\n",
        "        else:    \n",
        "            filename = f\"{args.timestring}_{frame_idx:05}.png\"\n",
        "            image.save(os.path.join(args.outdir, filename))\n",
        "            if anim_args.save_depth_maps:\n",
        "                if depth is None:\n",
        "                    depth = depth_model.predict(sample_to_cv2(sample), anim_args)\n",
        "                depth_model.save(os.path.join(args.outdir, f\"{args.timestring}_depth_{frame_idx:05}.png\"), depth)\n",
        "            frame_idx += 1\n",
        "\n",
        "        display.clear_output(wait=True)\n",
        "        display.display(image)\n",
        "\n",
        "        args.seed = next_seed(args)\n",
        "\n",
        "def vid2frames(video_path, frames_path, n=1, overwrite=True):      \n",
        "    if not os.path.exists(frames_path) or overwrite: \n",
        "      try:\n",
        "          for f in pathlib.Path(video_in_frame_path).glob('*.jpg'):\n",
        "              f.unlink()\n",
        "      except:\n",
        "          pass\n",
        "      assert os.path.exists(video_path), f\"Video input {video_path} does not exist\"\n",
        "          \n",
        "      vidcap = cv2.VideoCapture(video_path)\n",
        "      success,image = vidcap.read()\n",
        "      count = 0\n",
        "      t=1\n",
        "      success = True\n",
        "      while success:\n",
        "        if count % n == 0:\n",
        "            cv2.imwrite(frames_path + os.path.sep + f\"{t:05}.jpg\" , image)     # save frame as JPEG file\n",
        "            t += 1\n",
        "        success,image = vidcap.read()\n",
        "        count += 1\n",
        "      print(\"Converted %d frames\" % count)\n",
        "    else: print(\"Frames already unpacked\")\n",
        "\n",
        "def render_input_video(args, anim_args):\n",
        "    # create a folder for the video input frames to live in\n",
        "    video_in_frame_path = os.path.join(args.outdir, 'inputframes') \n",
        "    os.makedirs(video_in_frame_path, exist_ok=True)\n",
        "    \n",
        "    # save the video frames from input video\n",
        "    print(f\"Exporting Video Frames (1 every {anim_args.extract_nth_frame}) frames to {video_in_frame_path}...\")\n",
        "    vid2frames(anim_args.video_init_path, video_in_frame_path, anim_args.extract_nth_frame, anim_args.overwrite_extracted_frames)\n",
        "\n",
        "    # determine max frames from length of input frames\n",
        "    anim_args.max_frames = len([f for f in pathlib.Path(video_in_frame_path).glob('*.jpg')])\n",
        "    args.use_init = True\n",
        "    print(f\"Loading {anim_args.max_frames} input frames from {video_in_frame_path} and saving video frames to {args.outdir}\")\n",
        "\n",
        "    if anim_args.use_mask_video:\n",
        "        # create a folder for the mask video input frames to live in\n",
        "        mask_in_frame_path = os.path.join(args.outdir, 'maskframes') \n",
        "        os.makedirs(mask_in_frame_path, exist_ok=True)\n",
        "\n",
        "        # save the video frames from mask video\n",
        "        print(f\"Exporting Video Frames (1 every {anim_args.extract_nth_frame}) frames to {mask_in_frame_path}...\")\n",
        "        vid2frames(anim_args.video_mask_path, mask_in_frame_path, anim_args.extract_nth_frame, anim_args.overwrite_extracted_frames)\n",
        "        args.use_mask = True\n",
        "        args.overlay_mask = True\n",
        "\n",
        "    render_animation(args, anim_args)\n",
        "\n",
        "def render_interpolation(args, anim_args):\n",
        "    # animations use key framed prompts\n",
        "    args.prompts = animation_prompts\n",
        "\n",
        "    # create output folder for the batch\n",
        "    os.makedirs(args.outdir, exist_ok=True)\n",
        "    print(f\"Saving animation frames to {args.outdir}\")\n",
        "\n",
        "    # save settings for the batch\n",
        "    settings_filename = os.path.join(args.outdir, f\"{args.timestring}_settings.txt\")\n",
        "    with open(settings_filename, \"w+\", encoding=\"utf-8\") as f:\n",
        "        s = {**dict(args.__dict__), **dict(anim_args.__dict__)}\n",
        "        json.dump(s, f, ensure_ascii=False, indent=4)\n",
        "    \n",
        "    # Interpolation Settings\n",
        "    args.n_samples = 1\n",
        "    args.seed_behavior = 'fixed' # force fix seed at the moment bc only 1 seed is available\n",
        "    prompts_c_s = [] # cache all the text embeddings\n",
        "\n",
        "    print(f\"Preparing for interpolation of the following...\")\n",
        "\n",
        "    for i, prompt in animation_prompts.items():\n",
        "      args.prompt = prompt\n",
        "\n",
        "      # sample the diffusion model\n",
        "      results = generate(args, return_c=True)\n",
        "      c, image = results[0], results[1]\n",
        "      prompts_c_s.append(c) \n",
        "      \n",
        "      # display.clear_output(wait=True)\n",
        "      display.display(image)\n",
        "      \n",
        "      args.seed = next_seed(args)\n",
        "\n",
        "    display.clear_output(wait=True)\n",
        "    print(f\"Interpolation start...\")\n",
        "\n",
        "    frame_idx = 0\n",
        "\n",
        "    if anim_args.interpolate_key_frames:\n",
        "      for i in range(len(prompts_c_s)-1):\n",
        "        dist_frames = list(animation_prompts.items())[i+1][0] - list(animation_prompts.items())[i][0]\n",
        "        if dist_frames <= 0:\n",
        "          print(\"key frames duplicated or reversed. interpolation skipped.\")\n",
        "          return\n",
        "        else:\n",
        "          for j in range(dist_frames):\n",
        "            # interpolate the text embedding\n",
        "            prompt1_c = prompts_c_s[i]\n",
        "            prompt2_c = prompts_c_s[i+1]  \n",
        "            args.init_c = prompt1_c.add(prompt2_c.sub(prompt1_c).mul(j * 1/dist_frames))\n",
        "\n",
        "            # sample the diffusion model\n",
        "            results = generate(args)\n",
        "            image = results[0]\n",
        "\n",
        "            filename = f\"{args.timestring}_{frame_idx:05}.png\"\n",
        "            image.save(os.path.join(args.outdir, filename))\n",
        "            frame_idx += 1\n",
        "\n",
        "            display.clear_output(wait=True)\n",
        "            display.display(image)\n",
        "\n",
        "            args.seed = next_seed(args)\n",
        "\n",
        "    else:\n",
        "      for i in range(len(prompts_c_s)-1):\n",
        "        for j in range(anim_args.interpolate_x_frames+1):\n",
        "          # interpolate the text embedding\n",
        "          prompt1_c = prompts_c_s[i]\n",
        "          prompt2_c = prompts_c_s[i+1]  \n",
        "          args.init_c = prompt1_c.add(prompt2_c.sub(prompt1_c).mul(j * 1/(anim_args.interpolate_x_frames+1)))\n",
        "\n",
        "          # sample the diffusion model\n",
        "          results = generate(args)\n",
        "          image = results[0]\n",
        "\n",
        "          filename = f\"{args.timestring}_{frame_idx:05}.png\"\n",
        "          image.save(os.path.join(args.outdir, filename))\n",
        "          frame_idx += 1\n",
        "\n",
        "          display.clear_output(wait=True)\n",
        "          display.display(image)\n",
        "\n",
        "          args.seed = next_seed(args)\n",
        "\n",
        "    # generate the last prompt\n",
        "    args.init_c = prompts_c_s[-1]\n",
        "    results = generate(args)\n",
        "    image = results[0]\n",
        "    filename = f\"{args.timestring}_{frame_idx:05}.png\"\n",
        "    image.save(os.path.join(args.outdir, filename))\n",
        "\n",
        "    display.clear_output(wait=True)\n",
        "    display.display(image)\n",
        "    args.seed = next_seed(args)\n",
        "\n",
        "    #clear init_c\n",
        "    args.init_c = None\n",
        "\n",
        "\n",
        "args_dict = DeforumArgs()\n",
        "anim_args_dict = DeforumAnimArgs()\n",
        "\n",
        "if override_settings_with_file:\n",
        "    print(f\"reading custom settings from {custom_settings_file}\")\n",
        "    if not os.path.isfile(custom_settings_file):\n",
        "        print('The custom settings file does not exist. The in-notebook settings will be used instead')\n",
        "    else:\n",
        "        with open(custom_settings_file, \"r\") as f:\n",
        "            jdata = json.loads(f.read())\n",
        "            animation_prompts = jdata[\"prompts\"]\n",
        "            for i, k in enumerate(args_dict):\n",
        "                if k in jdata:\n",
        "                    args_dict[k] = jdata[k]\n",
        "                else:\n",
        "                    print(f\"key {k} doesn't exist in the custom settings data! using the default value of {args_dict[k]}\")\n",
        "            for i, k in enumerate(anim_args_dict):\n",
        "                if k in jdata:\n",
        "                    anim_args_dict[k] = jdata[k]\n",
        "                else:\n",
        "                    print(f\"key {k} doesn't exist in the custom settings data! using the default value of {anim_args_dict[k]}\")\n",
        "            print(args_dict)\n",
        "            print(anim_args_dict)\n",
        "\n",
        "args = SimpleNamespace(**args_dict)\n",
        "anim_args = SimpleNamespace(**anim_args_dict)\n",
        "\n",
        "args.timestring = time.strftime('%Y%m%d%H%M%S')\n",
        "args.strength = max(0.0, min(1.0, args.strength))\n",
        "\n",
        "if args.seed == -1:\n",
        "    args.seed = random.randint(0, 2**32 - 1)\n",
        "if not args.use_init:\n",
        "    args.init_image = None\n",
        "if args.sampler == 'plms' and (args.use_init or anim_args.animation_mode != 'None'):\n",
        "    print(f\"Init images aren't supported with PLMS yet, switching to KLMS\")\n",
        "    args.sampler = 'klms'\n",
        "if args.sampler != 'ddim':\n",
        "    args.ddim_eta = 0\n",
        "\n",
        "if anim_args.animation_mode == 'None':\n",
        "    anim_args.max_frames = 1\n",
        "elif anim_args.animation_mode == 'Video Input':\n",
        "    args.use_init = True\n",
        "\n",
        "# clean up unused memory\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "# dispatch to appropriate renderer\n",
        "if anim_args.animation_mode == '2D' or anim_args.animation_mode == '3D':\n",
        "    render_animation(args, anim_args)\n",
        "elif anim_args.animation_mode == 'Video Input':\n",
        "    render_input_video(args, anim_args)\n",
        "elif anim_args.animation_mode == 'Interpolation':\n",
        "    render_interpolation(args, anim_args)\n",
        "else:\n",
        "    render_image_batch(args)    "
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zV0J_YbMCTx"
      },
      "source": [
        "# Create video from frames"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "no2jP8HTMBM0"
      },
      "source": [
        "skip_video_for_run_all = True #@param {type: 'boolean'}\n",
        "fps = 12 #@param {type:\"number\"}\n",
        "#@markdown **Manual Settings**\n",
        "use_manual_settings = False #@param {type:\"boolean\"}\n",
        "image_path = \"/content/drive/MyDrive/AI/StableDiffusion/2022-09/20220903000939_%05d.png\" #@param {type:\"string\"}\n",
        "mp4_path = \"/content/drive/MyDrive/AI/StableDiffu'/content/drive/MyDrive/AI/StableDiffusion/2022-09/sion/2022-09/20220903000939.mp4\" #@param {type:\"string\"}\n",
        "render_steps = False  #@param {type: 'boolean'}\n",
        "path_name_modifier = \"x0_pred\" #@param [\"x0_pred\",\"x\"]\n",
        "\n",
        "\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",
        "else:\n",
        "    import os\n",
        "    import subprocess\n",
        "    from base64 import b64encode\n",
        "\n",
        "    print(f\"{image_path} -> {mp4_path}\")\n",
        "\n",
        "    if use_manual_settings:\n",
        "        max_frames = \"200\" #@param {type:\"string\"}\n",
        "    else:\n",
        "        if render_steps: # render steps from a single image\n",
        "            fname = f\"{path_name_modifier}_%05d.png\"\n",
        "            all_step_dirs = [os.path.join(args.outdir, d) for d in os.listdir(args.outdir) if os.path.isdir(os.path.join(args.outdir,d))]\n",
        "            newest_dir = max(all_step_dirs, key=os.path.getmtime)\n",
        "            image_path = os.path.join(newest_dir, fname)\n",
        "            print(f\"Reading images from {image_path}\")\n",
        "            mp4_path = os.path.join(newest_dir, f\"{args.timestring}_{path_name_modifier}.mp4\")\n",
        "            max_frames = str(args.steps)\n",
        "        else: # render images for a video\n",
        "            image_path = os.path.join(args.outdir, f\"{args.timestring}_%05d.png\")\n",
        "            mp4_path = os.path.join(args.outdir, f\"{args.timestring}.mp4\")\n",
        "            max_frames = str(anim_args.max_frames)\n",
        "\n",
        "    # make video\n",
        "    cmd = [\n",
        "        'ffmpeg',\n",
        "        '-y',\n",
        "        '-vcodec', 'png',\n",
        "        '-r', str(fps),\n",
        "        '-start_number', str(0),\n",
        "        '-i', image_path,\n",
        "        '-frames:v', max_frames,\n",
        "        '-c:v', 'libx264',\n",
        "        '-vf',\n",
        "        f'fps={fps}',\n",
        "        '-pix_fmt', 'yuv420p',\n",
        "        '-crf', '17',\n",
        "        '-preset', 'veryfast',\n",
        "        '-pattern_type', 'sequence',\n",
        "        mp4_path\n",
        "    ]\n",
        "    process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
        "    stdout, stderr = process.communicate()\n",
        "    if process.returncode != 0:\n",
        "        print(stderr)\n",
        "        raise RuntimeError(stderr)\n",
        "\n",
        "    mp4 = open(mp4_path,'rb').read()\n",
        "    data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "    display.display( display.HTML(f'<video controls loop><source src=\"{data_url}\" type=\"video/mp4\"></video>') )"
      ],
      "outputs": [],
      "execution_count": null
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "private_outputs": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}