Deforum_Stable_Diffusion.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/meefs/art/blob/master/Deforum_Stable_Diffusion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ByGXyiHZWM_q"
      },
      "source": [
        "# **Deforum Stable Diffusion v0.7**\n",
        "[Stable Diffusion](https://github.com/CompVis/stable-diffusion) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer and the [Stability.ai](https://stability.ai/) Team. [K Diffusion](https://github.com/crowsonkb/k-diffusion) by [Katherine Crowson](https://twitter.com/RiversHaveWings). Notebook by [deforum](https://discord.gg/upmXXsrwZc)\n",
        "\n",
        "[Quick Guide](https://docs.google.com/document/d/1RrQv7FntzOuLg4ohjRZPVL7iptIyBhwwbcEYEW2OfcI/edit?usp=sharing) to Deforum v0.7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "IJjzzkKlWM_s"
      },
      "outputs": [],
      "source": [
        "#@markdown **NVIDIA GPU**\n",
        "import subprocess, os, sys\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(f\"{sub_p_res[:-1]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UA8-efH-WM_t"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "vohUiWo-I2HQ"
      },
      "outputs": [],
      "source": [
        "#@markdown **Environment Setup**\n",
        "import subprocess, time, gc, os, sys\n",
        "\n",
        "def setup_environment():\n",
        "    start_time = time.time()\n",
        "    print_subprocess = False\n",
        "    use_xformers_for_colab = True\n",
        "    try:\n",
        "        ipy = get_ipython()\n",
        "    except:\n",
        "        ipy = 'could not get_ipython'\n",
        "    if 'google.colab' in str(ipy):\n",
        "        print(\"..setting up environment\")\n",
        "\n",
        "        # weird hack\n",
        "        import torch\n",
        "        \n",
        "        all_process = [\n",
        "            ['pip', 'install', 'omegaconf', 'einops==0.4.1', 'pytorch-lightning==1.7.7', 'torchmetrics', 'transformers', 'safetensors', 'kornia'],\n",
        "            ['git', 'clone', 'https://github.com/deforum-art/deforum-stable-diffusion'],\n",
        "            ['pip', 'install', 'accelerate', 'ftfy', 'jsonmerge', 'matplotlib', 'resize-right', 'timm', 'torchdiffeq','scikit-learn','torchsde','open-clip-torch','numpngw'],\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",
        "        with open('deforum-stable-diffusion/src/k_diffusion/__init__.py', 'w') as f:\n",
        "            f.write('')\n",
        "        sys.path.extend([\n",
        "            'deforum-stable-diffusion/',\n",
        "            'deforum-stable-diffusion/src',\n",
        "        ])\n",
        "        if use_xformers_for_colab:\n",
        "\n",
        "            print(\"..installing triton and xformers\")\n",
        "\n",
        "            all_process = [['pip', 'install', 'triton==2.0.0.dev20221202', 'xformers==0.0.16rc424']]\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",
        "    else:\n",
        "        sys.path.extend([\n",
        "            'src'\n",
        "        ])\n",
        "    end_time = time.time()\n",
        "    print(f\"..environment set up in {end_time-start_time:.0f} seconds\")\n",
        "    return\n",
        "\n",
        "setup_environment()\n",
        "\n",
        "import torch\n",
        "import random\n",
        "import clip\n",
        "from IPython import display\n",
        "from types import SimpleNamespace\n",
        "from helpers.save_images import get_output_folder\n",
        "from helpers.settings import load_args\n",
        "from helpers.render import render_animation, render_input_video, render_image_batch, render_interpolation\n",
        "from helpers.model_load import make_linear_decode, load_model, get_model_output_paths\n",
        "from helpers.aesthetics import load_aesthetics_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "0D2HQO-PWM_t"
      },
      "outputs": [],
      "source": [
        "#@markdown **Path Setup**\n",
        "\n",
        "def Root():\n",
        "    models_path = \"models\" #@param {type:\"string\"}\n",
        "    configs_path = \"configs\" #@param {type:\"string\"}\n",
        "    output_path = \"outputs\" #@param {type:\"string\"}\n",
        "    mount_google_drive = True #@param {type:\"boolean\"}\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",
        "\n",
        "    #@markdown **Model Setup**\n",
        "    map_location = \"cuda\" #@param [\"cpu\", \"cuda\"]\n",
        "    model_config = \"v1-inference.yaml\" #@param [\"custom\",\"v2-inference.yaml\",\"v2-inference-v.yaml\",\"v1-inference.yaml\"]\n",
        "    model_checkpoint =  \"Protogen_V2.2.ckpt\" #@param [\"custom\",\"v2-1_768-ema-pruned.ckpt\",\"v2-1_512-ema-pruned.ckpt\",\"768-v-ema.ckpt\",\"512-base-ema.ckpt\",\"Protogen_V2.2.ckpt\",\"v1-5-pruned.ckpt\",\"v1-5-pruned-emaonly.ckpt\",\"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\",\"wd-v1-3-float16.ckpt\"]\n",
        "    custom_config_path = \"\" #@param {type:\"string\"}\n",
        "    custom_checkpoint_path = \"\" #@param {type:\"string\"}\n",
        "    return locals()\n",
        "\n",
        "root = Root()\n",
        "root = SimpleNamespace(**root)\n",
        "\n",
        "root.models_path, root.output_path = get_model_output_paths(root)\n",
        "root.model, root.device = load_model(root, load_on_run_all=True, check_sha256=True, map_location=root.map_location)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JxwhBwtWM_t"
      },
      "source": [
        "# Settings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "E0tJVYA4WM_u"
      },
      "outputs": [],
      "source": [
        "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",
        "    hybrid_video_comp_alpha_schedule = \"0:(1)\" #@param {type:\"string\"}\n",
        "    hybrid_video_comp_mask_blend_alpha_schedule = \"0:(0.5)\" #@param {type:\"string\"}\n",
        "    hybrid_video_comp_mask_contrast_schedule = \"0:(1)\" #@param {type:\"string\"}\n",
        "    hybrid_video_comp_mask_auto_contrast_cutoff_high_schedule =  \"0:(100)\" #@param {type:\"string\"}\n",
        "    hybrid_video_comp_mask_auto_contrast_cutoff_low_schedule =  \"0:(0)\" #@param {type:\"string\"}\n",
        "\n",
        "    #@markdown ####**Unsharp mask (anti-blur) Parameters:**\n",
        "    kernel_schedule = \"0: (5)\"#@param {type:\"string\"}\n",
        "    sigma_schedule = \"0: (1.0)\"#@param {type:\"string\"}\n",
        "    amount_schedule = \"0: (0.2)\"#@param {type:\"string\"}\n",
        "    threshold_schedule = \"0: (0.0)\"#@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', 'Video Input'] {type:'string'}\n",
        "    color_coherence_video_every_N_frames = 1 #@param {type:\"integer\"}\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 ####**Hybrid Video for 2D/3D Animation Mode:**\n",
        "    hybrid_video_generate_inputframes = False #@param {type:\"boolean\"}\n",
        "    hybrid_video_use_first_frame_as_init_image = True #@param {type:\"boolean\"}\n",
        "    hybrid_video_motion = \"None\" #@param ['None','Optical Flow','Perspective','Affine']\n",
        "    hybrid_video_flow_method = \"Farneback\" #@param ['Farneback','DenseRLOF','SF']\n",
        "    hybrid_video_composite = False #@param {type:\"boolean\"}\n",
        "    hybrid_video_comp_mask_type = \"None\" #@param ['None', 'Depth', 'Video Depth', 'Blend', 'Difference']\n",
        "    hybrid_video_comp_mask_inverse = False #@param {type:\"boolean\"}\n",
        "    hybrid_video_comp_mask_equalize = \"None\" #@param  ['None','Before','After','Both']\n",
        "    hybrid_video_comp_mask_auto_contrast = False #@param {type:\"boolean\"}\n",
        "    hybrid_video_comp_save_extra_frames = False #@param {type:\"boolean\"}\n",
        "    hybrid_video_use_video_as_mse_image = False #@param {type:\"boolean\"}\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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i9fly1RIWM_u"
      },
      "outputs": [],
      "source": [
        "prompts = [\n",
        "    \"a beautiful lake 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",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "XVzhbmizWM_u"
      },
      "outputs": [],
      "source": [
        "#@markdown **Load Settings**\n",
        "override_settings_with_file = False #@param {type:\"boolean\"}\n",
        "settings_file = \"custom\" #@param [\"custom\", \"512x512_aesthetic_0.json\",\"512x512_aesthetic_1.json\",\"512x512_colormatch_0.json\",\"512x512_colormatch_1.json\",\"512x512_colormatch_2.json\",\"512x512_colormatch_3.json\"]\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",
        "    bit_depth_output = 8 #@param [8, 16, 32] {type:\"raw\"}\n",
        "\n",
        "    #@markdown **Sampling Settings**\n",
        "    seed = -1 #@param\n",
        "    sampler = 'ddim' #@param [\"klms\",\"dpm2\",\"dpm2_ancestral\",\"heun\",\"euler\",\"euler_ancestral\",\"plms\", \"ddim\", \"dpm_fast\", \"dpm_adaptive\", \"dpmpp_2s_a\", \"dpmpp_2m\"]\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 = True #@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\",\"ladder\",\"alternate\"]\n",
        "    seed_iter_N = 1 #@param {type:'integer'}\n",
        "    make_grid = False #@param {type:\"boolean\"}\n",
        "    grid_rows = 2 #@param \n",
        "    outdir = get_output_folder(root.output_path, batch_name)\n",
        "\n",
        "    #@markdown **Init Settings**\n",
        "    use_init = False #@param {type:\"boolean\"}\n",
        "    strength = 0.1 #@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",
        "    #@markdown **Exposure/Contrast Conditional Settings**\n",
        "    mean_scale = 0 #@param {type:\"number\"}\n",
        "    var_scale = 0 #@param {type:\"number\"}\n",
        "    exposure_scale = 0 #@param {type:\"number\"}\n",
        "    exposure_target = 0.5 #@param {type:\"number\"}\n",
        "\n",
        "    #@markdown **Color Match Conditional Settings**\n",
        "    colormatch_scale = 0 #@param {type:\"number\"}\n",
        "    colormatch_image = \"https://www.saasdesign.io/wp-content/uploads/2021/02/palette-3-min-980x588.png\" #@param {type:\"string\"}\n",
        "    colormatch_n_colors = 4 #@param {type:\"number\"}\n",
        "    ignore_sat_weight = 0 #@param {type:\"number\"}\n",
        "\n",
        "    #@markdown **CLIP\\Aesthetics Conditional Settings**\n",
        "    clip_name = 'ViT-L/14' #@param ['ViT-L/14', 'ViT-L/14@336px', 'ViT-B/16', 'ViT-B/32']\n",
        "    clip_scale = 0 #@param {type:\"number\"}\n",
        "    aesthetics_scale = 0 #@param {type:\"number\"}\n",
        "    cutn = 1 #@param {type:\"number\"}\n",
        "    cut_pow = 0.0001 #@param {type:\"number\"}\n",
        "\n",
        "    #@markdown **Other Conditional Settings**\n",
        "    init_mse_scale = 0 #@param {type:\"number\"}\n",
        "    init_mse_image = \"https://cdn.pixabay.com/photo/2022/07/30/13/10/green-longhorn-beetle-7353749_1280.jpg\" #@param {type:\"string\"}\n",
        "\n",
        "    blue_scale = 0 #@param {type:\"number\"}\n",
        "    \n",
        "    #@markdown **Conditional Gradient Settings**\n",
        "    gradient_wrt = 'x0_pred' #@param [\"x\", \"x0_pred\"]\n",
        "    gradient_add_to = 'both' #@param [\"cond\", \"uncond\", \"both\"]\n",
        "    decode_method = 'linear' #@param [\"autoencoder\",\"linear\"]\n",
        "    grad_threshold_type = 'dynamic' #@param [\"dynamic\", \"static\", \"mean\", \"schedule\"]\n",
        "    clamp_grad_threshold = 0.2 #@param {type:\"number\"}\n",
        "    clamp_start = 0.2 #@param\n",
        "    clamp_stop = 0.01 #@param\n",
        "    grad_inject_timing = list(range(1,10)) #@param\n",
        "\n",
        "    #@markdown **Speed vs VRAM Settings**\n",
        "    cond_uncond_sync = True #@param {type:\"boolean\"}\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_sample_raw = None\n",
        "    mask_sample = None\n",
        "    init_c = None\n",
        "    seed_internal = 0\n",
        "\n",
        "    return locals()\n",
        "\n",
        "args_dict = DeforumArgs()\n",
        "anim_args_dict = DeforumAnimArgs()\n",
        "\n",
        "if override_settings_with_file:\n",
        "    load_args(args_dict, anim_args_dict, settings_file, custom_settings_file, verbose=False)\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",
        "# Load clip model if using clip guidance\n",
        "if (args.clip_scale > 0) or (args.aesthetics_scale > 0):\n",
        "    root.clip_model = clip.load(args.clip_name, jit=False)[0].eval().requires_grad_(False).to(root.device)\n",
        "    if (args.aesthetics_scale > 0):\n",
        "        root.aesthetics_model = load_aesthetics_model(args, root)\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, animation_prompts, root)\n",
        "elif anim_args.animation_mode == 'Video Input':\n",
        "    render_input_video(args, anim_args, animation_prompts, root)\n",
        "elif anim_args.animation_mode == 'Interpolation':\n",
        "    render_interpolation(args, anim_args, animation_prompts, root)\n",
        "else:\n",
        "    render_image_batch(args, prompts, root)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJ88kZ2-WM_v"
      },
      "source": [
        "# Create Video From Frames"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "XQGeqaGAWM_v"
      },
      "outputs": [],
      "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/2023-01/StableFun/20230101212135_%05d.png\" #@param {type:\"string\"}\n",
        "mp4_path = \"/content/drive/MyDrive/AI/StableDiffusion/2023-01/StableFun/20230101212135.mp4\" #@param {type:\"string\"}\n",
        "render_steps = False  #@param {type: 'boolean'}\n",
        "path_name_modifier = \"x0_pred\" #@param [\"x0_pred\",\"x\"]\n",
        "make_gif = False\n",
        "bitdepth_extension = \"exr\" if args.bit_depth_output == 32 else \"png\"\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.{bitdepth_extension}\")\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', bitdepth_extension,\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>') )\n",
        "    \n",
        "    if make_gif:\n",
        "         gif_path = os.path.splitext(mp4_path)[0]+'.gif'\n",
        "         cmd_gif = [\n",
        "             'ffmpeg',\n",
        "             '-y',\n",
        "             '-i', mp4_path,\n",
        "             '-r', str(fps),\n",
        "             gif_path\n",
        "         ]\n",
        "         process_gif = subprocess.Popen(cmd_gif, stdout=subprocess.PIPE, stderr=subprocess.PIPE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "MMpAcyrYWM_v"
      },
      "outputs": [],
      "source": [
        "skip_disconnect_for_run_all = True #@param {type: 'boolean'}\n",
        "\n",
        "if skip_disconnect_for_run_all == True:\n",
        "    print('Skipping disconnect, uncheck skip_disconnect_for_run_all if you want to run it')\n",
        "else:\n",
        "    from google.colab import runtime\n",
        "    runtime.unassign()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3.10.6 ('dsd')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.8"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "b7e04c8a9537645cbc77fa0cbde8069bc94e341b0d5ced104651213865b24e58"
      }
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  },
  "nbformat": 4,
  "nbformat_minor": 0
}