Local_Disco_Diffusion_v4_1.ipynb

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  "metadata": {
    "colab": {
      "name": "Local Disco Diffusion v4.1.ipynb",
      "private_outputs": true,
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Midgraph/disco-diffusion/blob/main/Local_Disco_Diffusion_v4_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YwMUyt9LHG1"
      },
      "source": [
        "# Disco Diffusion v4.1 - Now with Video Inits, Recovery, DDIM Sharpen and improved UI\n",
        "\n",
        "For issues, message [@Somnai_dreams](https://twitter.com/Somnai_dreams) or Somnai#6855\n",
        "\n",
        "Credits & Changelog.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Original notebook by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). It uses either OpenAI's 256x256 unconditional ImageNet or Katherine Crowson's fine-tuned 512x512 diffusion model (https://github.com/openai/guided-diffusion), together with CLIP (https://github.com/openai/CLIP) to connect text prompts with images.\n",
        "\n",
        "Modified by Daniel Russell (https://github.com/russelldc, https://twitter.com/danielrussruss) to include (hopefully) optimal params for quick generations in 15-100 timesteps rather than 1000, as well as more robust augmentations.\n",
        "\n",
        "Further improvements from Dango233 and nsheppard helped improve the quality of diffusion in general, and especially so for shorter runs like this notebook aims to achieve.\n",
        "\n",
        "Vark added code to load in multiple Clip models at once, which all prompts are evaluated against, which may greatly improve accuracy.\n",
        "\n",
        "The latest zoom, pan, rotation, and keyframes features were taken from Chigozie Nri's VQGAN Zoom Notebook (https://github.com/chigozienri, https://twitter.com/chigozienri)\n",
        "\n",
        "Advanced DangoCutn Cutout method is also from Dango223.\n",
        "\n",
        "--\n",
        "\n",
        "I, Somnai (https://twitter.com/Somnai_dreams), have added Diffusion Animation techniques, QoL improvements and various implementations of tech and techniques, mostly listed in the changelog below."
      ],
      "metadata": {
        "id": "wX5omb9C7Bjz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Licensed under the MIT License\n",
        "\n",
        "# Copyright (c) 2021 Katherine Crowson \n",
        "\n",
        "# Permission is hereby granted, free of charge, to any person obtaining a copy\n",
        "# of this software and associated documentation files (the \"Software\"), to deal\n",
        "# in the Software without restriction, including without limitation the rights\n",
        "# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
        "# copies of the Software, and to permit persons to whom the Software is\n",
        "# furnished to do so, subject to the following conditions:\n",
        "\n",
        "# The above copyright notice and this permission notice shall be included in\n",
        "# all copies or substantial portions of the Software.\n",
        "\n",
        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
        "# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
        "# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n",
        "# THE SOFTWARE."
      ],
      "metadata": {
        "cellView": "form",
        "id": "wDSYhyjqZQI9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title <- View Changelog\n",
        "\n",
        "skip_for_run_all = True #@param {type: 'boolean'}\n",
        "\n",
        "if skip_for_run_all == False:\n",
        "  print(\n",
        "      '''\n",
        "  v1 Update: Oct 29th 2021\n",
        "\n",
        "      QoL improvements added by Somnai (@somnai_dreams), including user friendly UI, settings+prompt saving and improved google drive folder organization.\n",
        "\n",
        "  v1.1 Update: Nov 13th 2021\n",
        "\n",
        "      Now includes sizing options, intermediate saves and fixed image prompts and perlin inits. unexposed batch option since it doesn't work\n",
        "\n",
        "  v2 Update: Nov 22nd 2021\n",
        "\n",
        "      Initial addition of Katherine Crowson's Secondary Model Method (https://colab.research.google.com/drive/1mpkrhOjoyzPeSWy2r7T8EYRaU7amYOOi#scrollTo=X5gODNAMEUCR)\n",
        "\n",
        "      Noticed settings were saving with the wrong name so corrected it. Let me know if you preferred the old scheme.\n",
        "\n",
        "  v3 Update: Dec 24th 2021\n",
        "\n",
        "      Implemented Dango's advanced cutout method\n",
        "\n",
        "      Added SLIP models, thanks to NeuralDivergent\n",
        "\n",
        "      Fixed issue with NaNs resulting in black images, with massive help and testing from @Softology\n",
        "\n",
        "      Perlin now changes properly within batches (not sure where this perlin_regen code came from originally, but thank you)\n",
        "\n",
        "  v4 Update: Jan 2021\n",
        "\n",
        "      Implemented Diffusion Zooming\n",
        "\n",
        "      Added Chigozie keyframing\n",
        "\n",
        "      Made a bunch of edits to processes\n",
        "  \n",
        "  v4.1 Update: Jan  14th 2021\n",
        "\n",
        "      Added video input mode\n",
        "\n",
        "      Added license that somehow went missing\n",
        "\n",
        "      Added improved prompt keyframing, fixed image_prompts and multiple prompts\n",
        "\n",
        "      Improved UI\n",
        "\n",
        "      Significant under the hood cleanup and improvement\n",
        "\n",
        "      Refined defaults for each mode\n",
        "\n",
        "      Added latent-diffusion SuperRes for sharpening\n",
        "\n",
        "      Added resume run mode\n",
        "\n",
        "      '''\n",
        "  )"
      ],
      "metadata": {
        "cellView": "form",
        "id": "qFB3nwLSQI8X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XTu6AjLyFQUq"
      },
      "source": [
        "#Tutorial"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YR806W0wi3He"
      },
      "source": [
        "**Diffusion settings**\n",
        "---\n",
        "\n",
        "Setting | Description | Default\n",
        "--- | --- | ---\n",
        "**Your vision:**\n",
        "`text_prompts` | A description of what you'd like the machine to generate. Think of it like writing the caption below your image on a website. | N/A\n",
        "`image_prompts` | Think of these images more as a description of their contents. | N/A\n",
        "**Image quality:**\n",
        "`clip_guidance_scale`  | Controls how much the image should look like the prompt. | 1000\n",
        "`tv_scale` |  Controls the smoothness of the final output. | 150\n",
        "`range_scale` |  Controls how far out of range RGB values are allowed to be. | 150\n",
        "`sat_scale` | Controls how much saturation is allowed. From nshepperd's JAX notebook. | 0\n",
        "`cutn` | Controls how many crops to take from the image. | 16\n",
        "`cutn_batches` | Accumulate CLIP gradient from multiple batches of cuts  | 2\n",
        "**Init settings:**\n",
        "`init_image` |   URL or local path | None\n",
        "`init_scale` |  This enhances the effect of the init image, a good value is 1000 | 0\n",
        "`skip_steps Controls the starting point along the diffusion timesteps | 0\n",
        "`perlin_init` |  Option to start with random perlin noise | False\n",
        "`perlin_mode` |  ('gray', 'color') | 'mixed'\n",
        "**Advanced:**\n",
        "`skip_augs` |Controls whether to skip torchvision augmentations | False\n",
        "`randomize_class` |Controls whether the imagenet class is randomly changed each iteration | True\n",
        "`clip_denoised` |Determines whether CLIP discriminates a noisy or denoised image | False\n",
        "`clamp_grad` |Experimental: Using adaptive clip grad in the cond_fn | True\n",
        "`seed`  | Choose a random seed and print it at end of run for reproduction | random_seed\n",
        "`fuzzy_prompt` | Controls whether to add multiple noisy prompts to the prompt losses | False\n",
        "`rand_mag` |Controls the magnitude of the random noise | 0.1\n",
        "`eta` | DDIM hyperparameter | 0.5\n",
        "\n",
        "..\n",
        "\n",
        "**Model settings**\n",
        "---\n",
        "\n",
        "Setting | Description | Default\n",
        "--- | --- | ---\n",
        "**Diffusion:**\n",
        "`timestep_respacing`  | Modify this value to decrease the number of timesteps. | ddim100\n",
        "`diffusion_steps` || 1000\n",
        "**Diffusion:**\n",
        "`clip_models`  | Models of CLIP to load. Typically the more, the better but they all come at a hefty VRAM cost. | ViT-B/32, ViT-B/16, RN50x4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_9Eg9Kf5FlfK"
      },
      "source": [
        "# 1. Set Up"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZ3rNuAWAewx",
        "cellView": "form"
      },
      "source": [
        "#@title 1.1 Check GPU Status\n",
        "!nvidia-smi -L"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZsjzwS0YGo6",
        "cellView": "form"
      },
      "source": [
        "# from google.colab import drive\n",
        "#@title 1.2 Prepare Folders\n",
        "\n",
        "# google_drive = False #@param {type:\"boolean\"}\n",
        "\n",
        "yes_please = True\n",
        "\n",
        "root_path = ''\n",
        "\n",
        "import os\n",
        "from os import path\n",
        "#Simple create paths taken with modifications from Datamosh's Batch VQGAN+CLIP notebook\n",
        "def createPath(filepath):\n",
        "    if path.exists(filepath) == False:\n",
        "      os.makedirs(filepath)\n",
        "      print(f'Made {filepath}')\n",
        "    else:\n",
        "      print(f'filepath {filepath} exists.')\n",
        "\n",
        "initDirPath = f'init_images'\n",
        "createPath(initDirPath)\n",
        "outDirPath = f'images_out'\n",
        "createPath(outDirPath)\n",
        "\n",
        "\n",
        "model_path = 'models'\n",
        "createPath(model_path)\n",
        "\n",
        "# libraries = f'{root_path}/libraries'\n",
        "# createPath(libraries)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmbrcrhpBPC6"
      },
      "source": [
        "#@title ### 2.1 Install and import dependencies\n",
        "\n",
        "model_256_downloaded = False\n",
        "model_512_downloaded = False\n",
        "model_secondary_downloaded = False\n",
        "\n",
        "!git clone https://github.com/openai/CLIP\n",
        "# !git clone https://github.com/facebookresearch/SLIP.git\n",
        "!git clone https://github.com/crowsonkb/guided-diffusion\n",
        "!git clone https://github.com/assafshocher/ResizeRight.git\n",
        "!pip install -e ./CLIP\n",
        "!pip install -e ./guided-diffusion\n",
        "!pip install lpips datetime timm\n",
        "!pip install timm\n",
        "!pip install imagemagick\n",
        "\n",
        "\n",
        "import sys\n",
        "# sys.path.append('./SLIP')\n",
        "sys.path.append('./ResizeRight')\n",
        "from dataclasses import dataclass\n",
        "from functools import partial\n",
        "import cv2\n",
        "import pandas as pd\n",
        "import gc\n",
        "import io\n",
        "import math\n",
        "import timm\n",
        "from IPython import display\n",
        "import lpips\n",
        "from PIL import Image, ImageOps\n",
        "import requests\n",
        "from glob import glob\n",
        "import json\n",
        "from types import SimpleNamespace\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "import torchvision.transforms as T\n",
        "import torchvision.transforms.functional as TF\n",
        "from tqdm.notebook import tqdm\n",
        "sys.path.append('./CLIP')\n",
        "sys.path.append('./guided-diffusion')\n",
        "import clip\n",
        "from resize_right import resize\n",
        "# from models import SLIP_VITB16, SLIP, SLIP_VITL16\n",
        "from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults\n",
        "from datetime import datetime\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "from ipywidgets import Output\n",
        "import hashlib\n",
        "\n",
        "#SuperRes\n",
        "!git clone https://github.com/CompVis/latent-diffusion.git\n",
        "!git clone https://github.com/CompVis/taming-transformers\n",
        "!pip install -e ./taming-transformers\n",
        "!pip install ipywidgets omegaconf>=2.0.0 pytorch-lightning>=1.0.8 torch-fidelity einops wandb\n",
        "\n",
        "#SuperRes\n",
        "import ipywidgets as widgets\n",
        "import os\n",
        "sys.path.append(\".\")\n",
        "sys.path.append('./taming-transformers')\n",
        "from taming.models import vqgan # checking correct import from taming\n",
        "from torchvision.datasets.utils import download_url\n",
        "%cd latent-diffusion\n",
        "from functools import partial\n",
        "from ldm.util import instantiate_from_config\n",
        "from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like\n",
        "# from ldm.models.diffusion.ddim import DDIMSampler\n",
        "from ldm.util import ismap\n",
        "%cd ..\n",
        "# from google.colab import files\n",
        "from IPython.display import Image as ipyimg\n",
        "from numpy import asarray\n",
        "from einops import rearrange, repeat\n",
        "import torch, torchvision\n",
        "import time\n",
        "from omegaconf import OmegaConf\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
        "\n",
        "\n",
        "import torch\n",
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "print('Using device:', device)\n",
        "\n",
        "if torch.cuda.get_device_capability(device) == (8,0): ## A100 fix thanks to Emad\n",
        "  print('Disabling CUDNN for A100 gpu', file=sys.stderr)\n",
        "  torch.backends.cudnn.enabled = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FpZczxnOnPIU",
        "cellView": "form"
      },
      "source": [
        "#@title 2.2 Define necessary functions\n",
        "\n",
        "# https://gist.github.com/adefossez/0646dbe9ed4005480a2407c62aac8869\n",
        "\n",
        "def interp(t):\n",
        "    return 3 * t**2 - 2 * t ** 3\n",
        "\n",
        "def perlin(width, height, scale=10, device=None):\n",
        "    gx, gy = torch.randn(2, width + 1, height + 1, 1, 1, device=device)\n",
        "    xs = torch.linspace(0, 1, scale + 1)[:-1, None].to(device)\n",
        "    ys = torch.linspace(0, 1, scale + 1)[None, :-1].to(device)\n",
        "    wx = 1 - interp(xs)\n",
        "    wy = 1 - interp(ys)\n",
        "    dots = 0\n",
        "    dots += wx * wy * (gx[:-1, :-1] * xs + gy[:-1, :-1] * ys)\n",
        "    dots += (1 - wx) * wy * (-gx[1:, :-1] * (1 - xs) + gy[1:, :-1] * ys)\n",
        "    dots += wx * (1 - wy) * (gx[:-1, 1:] * xs - gy[:-1, 1:] * (1 - ys))\n",
        "    dots += (1 - wx) * (1 - wy) * (-gx[1:, 1:] * (1 - xs) - gy[1:, 1:] * (1 - ys))\n",
        "    return dots.permute(0, 2, 1, 3).contiguous().view(width * scale, height * scale)\n",
        "\n",
        "def perlin_ms(octaves, width, height, grayscale, device=device):\n",
        "    out_array = [0.5] if grayscale else [0.5, 0.5, 0.5]\n",
        "    # out_array = [0.0] if grayscale else [0.0, 0.0, 0.0]\n",
        "    for i in range(1 if grayscale else 3):\n",
        "        scale = 2 ** len(octaves)\n",
        "        oct_width = width\n",
        "        oct_height = height\n",
        "        for oct in octaves:\n",
        "            p = perlin(oct_width, oct_height, scale, device)\n",
        "            out_array[i] += p * oct\n",
        "            scale //= 2\n",
        "            oct_width *= 2\n",
        "            oct_height *= 2\n",
        "    return torch.cat(out_array)\n",
        "\n",
        "def create_perlin_noise(octaves=[1, 1, 1, 1], width=2, height=2, grayscale=True):\n",
        "    out = perlin_ms(octaves, width, height, grayscale)\n",
        "    if grayscale:\n",
        "        out = TF.resize(size=(side_y, side_x), img=out.unsqueeze(0))\n",
        "        out = TF.to_pil_image(out.clamp(0, 1)).convert('RGB')\n",
        "    else:\n",
        "        out = out.reshape(-1, 3, out.shape[0]//3, out.shape[1])\n",
        "        out = TF.resize(size=(side_y, side_x), img=out)\n",
        "        out = TF.to_pil_image(out.clamp(0, 1).squeeze())\n",
        "\n",
        "    out = ImageOps.autocontrast(out)\n",
        "    return out\n",
        "\n",
        "def regen_perlin():\n",
        "    if perlin_mode == 'color':\n",
        "        init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)\n",
        "        init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, False)\n",
        "    elif perlin_mode == 'gray':\n",
        "        init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, True)\n",
        "        init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)\n",
        "    else:\n",
        "        init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)\n",
        "        init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)\n",
        "\n",
        "    init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device).unsqueeze(0).mul(2).sub(1)\n",
        "    del init2\n",
        "    return init.expand(batch_size, -1, -1, -1)\n",
        "\n",
        "def fetch(url_or_path):\n",
        "    if str(url_or_path).startswith('http://') or str(url_or_path).startswith('https://'):\n",
        "        r = requests.get(url_or_path)\n",
        "        r.raise_for_status()\n",
        "        fd = io.BytesIO()\n",
        "        fd.write(r.content)\n",
        "        fd.seek(0)\n",
        "        return fd\n",
        "    return open(url_or_path, 'rb')\n",
        "\n",
        "def read_image_workaround(path):\n",
        "    \"\"\"OpenCV reads images as BGR, Pillow saves them as RGB. Work around\n",
        "    this incompatibility to avoid colour inversions.\"\"\"\n",
        "    im_tmp = cv2.imread(path)\n",
        "    return cv2.cvtColor(im_tmp, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "def parse_prompt(prompt):\n",
        "    if prompt.startswith('http://') or prompt.startswith('https://'):\n",
        "        vals = prompt.rsplit(':', 2)\n",
        "        vals = [vals[0] + ':' + vals[1], *vals[2:]]\n",
        "    else:\n",
        "        vals = prompt.rsplit(':', 1)\n",
        "    vals = vals + ['', '1'][len(vals):]\n",
        "    return vals[0], float(vals[1])\n",
        "\n",
        "def sinc(x):\n",
        "    return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))\n",
        "\n",
        "def lanczos(x, a):\n",
        "    cond = torch.logical_and(-a < x, x < a)\n",
        "    out = torch.where(cond, sinc(x) * sinc(x/a), x.new_zeros([]))\n",
        "    return out / out.sum()\n",
        "\n",
        "def ramp(ratio, width):\n",
        "    n = math.ceil(width / ratio + 1)\n",
        "    out = torch.empty([n])\n",
        "    cur = 0\n",
        "    for i in range(out.shape[0]):\n",
        "        out[i] = cur\n",
        "        cur += ratio\n",
        "    return torch.cat([-out[1:].flip([0]), out])[1:-1]\n",
        "\n",
        "def resample(input, size, align_corners=True):\n",
        "    n, c, h, w = input.shape\n",
        "    dh, dw = size\n",
        "\n",
        "    input = input.reshape([n * c, 1, h, w])\n",
        "\n",
        "    if dh < h:\n",
        "        kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype)\n",
        "        pad_h = (kernel_h.shape[0] - 1) // 2\n",
        "        input = F.pad(input, (0, 0, pad_h, pad_h), 'reflect')\n",
        "        input = F.conv2d(input, kernel_h[None, None, :, None])\n",
        "\n",
        "    if dw < w:\n",
        "        kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype)\n",
        "        pad_w = (kernel_w.shape[0] - 1) // 2\n",
        "        input = F.pad(input, (pad_w, pad_w, 0, 0), 'reflect')\n",
        "        input = F.conv2d(input, kernel_w[None, None, None, :])\n",
        "\n",
        "    input = input.reshape([n, c, h, w])\n",
        "    return F.interpolate(input, size, mode='bicubic', align_corners=align_corners)\n",
        "\n",
        "class MakeCutouts(nn.Module):\n",
        "    def __init__(self, cut_size, cutn, skip_augs=False):\n",
        "        super().__init__()\n",
        "        self.cut_size = cut_size\n",
        "        self.cutn = cutn\n",
        "        self.skip_augs = skip_augs\n",
        "        self.augs = T.Compose([\n",
        "            T.RandomHorizontalFlip(p=0.5),\n",
        "            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "            T.RandomAffine(degrees=15, translate=(0.1, 0.1)),\n",
        "            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "            T.RandomPerspective(distortion_scale=0.4, p=0.7),\n",
        "            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "            T.RandomGrayscale(p=0.15),\n",
        "            T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "            # T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),\n",
        "        ])\n",
        "\n",
        "    def forward(self, input):\n",
        "        input = T.Pad(input.shape[2]//4, fill=0)(input)\n",
        "        sideY, sideX = input.shape[2:4]\n",
        "        max_size = min(sideX, sideY)\n",
        "\n",
        "        cutouts = []\n",
        "        for ch in range(self.cutn):\n",
        "            if ch > self.cutn - self.cutn//4:\n",
        "                cutout = input.clone()\n",
        "            else:\n",
        "                size = int(max_size * torch.zeros(1,).normal_(mean=.8, std=.3).clip(float(self.cut_size/max_size), 1.))\n",
        "                offsetx = torch.randint(0, abs(sideX - size + 1), ())\n",
        "                offsety = torch.randint(0, abs(sideY - size + 1), ())\n",
        "                cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]\n",
        "\n",
        "            if not self.skip_augs:\n",
        "                cutout = self.augs(cutout)\n",
        "            cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))\n",
        "            del cutout\n",
        "\n",
        "        cutouts = torch.cat(cutouts, dim=0)\n",
        "        return cutouts\n",
        "\n",
        "cutout_debug = False\n",
        "padargs = {}\n",
        "\n",
        "class MakeCutoutsDango(nn.Module):\n",
        "    def __init__(self, cut_size,\n",
        "                 Overview=4, \n",
        "                 InnerCrop = 0, IC_Size_Pow=0.5, IC_Grey_P = 0.2\n",
        "                 ):\n",
        "        super().__init__()\n",
        "        self.cut_size = cut_size\n",
        "        self.Overview = Overview\n",
        "        self.InnerCrop = InnerCrop\n",
        "        self.IC_Size_Pow = IC_Size_Pow\n",
        "        self.IC_Grey_P = IC_Grey_P\n",
        "        if args.animation_mode == 'None':\n",
        "          self.augs = T.Compose([\n",
        "              T.RandomHorizontalFlip(p=0.5),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomAffine(degrees=10, translate=(0.05, 0.05),  interpolation = T.InterpolationMode.BILINEAR),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomGrayscale(p=0.1),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),\n",
        "          ])\n",
        "        elif args.animation_mode == 'Video Input':\n",
        "          self.augs = T.Compose([\n",
        "              T.RandomHorizontalFlip(p=0.5),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomAffine(degrees=15, translate=(0.1, 0.1)),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomPerspective(distortion_scale=0.4, p=0.7),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomGrayscale(p=0.15),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              # T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),\n",
        "          ])\n",
        "        elif  args.animation_mode == '2D':\n",
        "          self.augs = T.Compose([\n",
        "              T.RandomHorizontalFlip(p=0.4),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomAffine(degrees=10, translate=(0.05, 0.05),  interpolation = T.InterpolationMode.BILINEAR),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.RandomGrayscale(p=0.1),\n",
        "              T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),\n",
        "              T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.3),\n",
        "          ])\n",
        "          \n",
        "\n",
        "    def forward(self, input):\n",
        "        cutouts = []\n",
        "        gray = T.Grayscale(3)\n",
        "        sideY, sideX = input.shape[2:4]\n",
        "        max_size = min(sideX, sideY)\n",
        "        min_size = min(sideX, sideY, self.cut_size)\n",
        "        l_size = max(sideX, sideY)\n",
        "        output_shape = [1,3,self.cut_size,self.cut_size] \n",
        "        output_shape_2 = [1,3,self.cut_size+2,self.cut_size+2]\n",
        "        pad_input = F.pad(input,((sideY-max_size)//2,(sideY-max_size)//2,(sideX-max_size)//2,(sideX-max_size)//2), **padargs)\n",
        "        cutout = resize(pad_input, out_shape=output_shape)\n",
        "\n",
        "        if self.Overview>0:\n",
        "            if self.Overview<=4:\n",
        "                if self.Overview>=1:\n",
        "                    cutouts.append(cutout)\n",
        "                if self.Overview>=2:\n",
        "                    cutouts.append(gray(cutout))\n",
        "                if self.Overview>=3:\n",
        "                    cutouts.append(TF.hflip(cutout))\n",
        "                if self.Overview==4:\n",
        "                    cutouts.append(gray(TF.hflip(cutout)))\n",
        "            else:\n",
        "                cutout = resize(pad_input, out_shape=output_shape)\n",
        "                for _ in range(self.Overview):\n",
        "                    cutouts.append(cutout)\n",
        "\n",
        "            if cutout_debug:\n",
        "                TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save(\"/content/cutout_overview0.jpg\",quality=99)\n",
        "                              \n",
        "        if self.InnerCrop >0:\n",
        "            for i in range(self.InnerCrop):\n",
        "                size = int(torch.rand([])**self.IC_Size_Pow * (max_size - min_size) + min_size)\n",
        "                offsetx = torch.randint(0, sideX - size + 1, ())\n",
        "                offsety = torch.randint(0, sideY - size + 1, ())\n",
        "                cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]\n",
        "                if i <= int(self.IC_Grey_P * self.InnerCrop):\n",
        "                    cutout = gray(cutout)\n",
        "                cutout = resize(cutout, out_shape=output_shape)\n",
        "                cutouts.append(cutout)\n",
        "            if cutout_debug:\n",
        "                TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save(\"/content/cutout_InnerCrop.jpg\",quality=99)\n",
        "        cutouts = torch.cat(cutouts)\n",
        "        if skip_augs is not True: cutouts=self.augs(cutouts)\n",
        "        return cutouts\n",
        "\n",
        "def spherical_dist_loss(x, y):\n",
        "    x = F.normalize(x, dim=-1)\n",
        "    y = F.normalize(y, dim=-1)\n",
        "    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)     \n",
        "\n",
        "def tv_loss(input):\n",
        "    \"\"\"L2 total variation loss, as in Mahendran et al.\"\"\"\n",
        "    input = F.pad(input, (0, 1, 0, 1), 'replicate')\n",
        "    x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]\n",
        "    y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]\n",
        "    return (x_diff**2 + y_diff**2).mean([1, 2, 3])\n",
        "\n",
        "\n",
        "def range_loss(input):\n",
        "    return (input - input.clamp(-1, 1)).pow(2).mean([1, 2, 3])\n",
        "\n",
        "stop_on_next_loop = False  # Make sure GPU memory doesn't get corrupted from cancelling the run mid-way through, allow a full frame to complete\n",
        "\n",
        "def do_run():\n",
        "  seed = args.seed\n",
        "  print(range(args.start_frame, args.max_frames))\n",
        "  for frame_num in range(args.start_frame, args.max_frames):\n",
        "      if stop_on_next_loop:\n",
        "        break\n",
        "      \n",
        "      display.clear_output(wait=True)\n",
        "\n",
        "      # Print Frame progress if animation mode is on\n",
        "      if args.animation_mode != \"None\":\n",
        "        batchBar = tqdm(range(args.max_frames), desc =\"Frames\")\n",
        "        batchBar.n = frame_num\n",
        "        batchBar.refresh()\n",
        "\n",
        "      \n",
        "      # Inits if not video frames\n",
        "      if args.animation_mode != \"Video Input\":\n",
        "        if args.init_image == '':\n",
        "          init_image = None\n",
        "        else:\n",
        "          init_image = args.init_image\n",
        "        init_scale = args.init_scale\n",
        "        skip_steps = args.skip_steps\n",
        "\n",
        "      if args.animation_mode == \"2D\":\n",
        "        if args.key_frames:\n",
        "          angle = args.angle_series[frame_num]\n",
        "          zoom = args.zoom_series[frame_num]\n",
        "          translation_x = args.translation_x_series[frame_num]\n",
        "          translation_y = args.translation_y_series[frame_num]\n",
        "          print(\n",
        "              f'angle: {angle}',\n",
        "              f'zoom: {zoom}',\n",
        "              f'translation_x: {translation_x}',\n",
        "              f'translation_y: {translation_y}',\n",
        "          )\n",
        "        \n",
        "        if frame_num > 0:\n",
        "          seed = seed + 1          \n",
        "          img_0 = cv2.imread('prevFrame.png')\n",
        "          center = (1*img_0.shape[1]//2, 1*img_0.shape[0]//2)\n",
        "          trans_mat = np.float32(\n",
        "              [[1, 0, translation_x],\n",
        "              [0, 1, translation_y]]\n",
        "          )\n",
        "          rot_mat = cv2.getRotationMatrix2D( center, angle, zoom )\n",
        "          trans_mat = np.vstack([trans_mat, [0,0,1]])\n",
        "          rot_mat = np.vstack([rot_mat, [0,0,1]])\n",
        "          transformation_matrix = np.matmul(rot_mat, trans_mat)\n",
        "          img_0 = cv2.warpPerspective(\n",
        "              img_0,\n",
        "              transformation_matrix,\n",
        "              (img_0.shape[1], img_0.shape[0]),\n",
        "              borderMode=cv2.BORDER_WRAP\n",
        "          )\n",
        "          cv2.imwrite('prevFrameScaled.png', img_0)\n",
        "          init_image = 'prevFrameScaled.png'\n",
        "          init_scale = args.frames_scale\n",
        "          skip_steps = args.calc_frames_skip_steps\n",
        "\n",
        "      if  args.animation_mode == \"Video Input\":\n",
        "        seed = seed + 1  \n",
        "        init_image = f'{videoFramesFolder}/{frame_num+1:04}.jpg'\n",
        "        init_scale = args.frames_scale\n",
        "        skip_steps = args.calc_frames_skip_steps\n",
        "\n",
        "      loss_values = []\n",
        "  \n",
        "      if seed is not None:\n",
        "          np.random.seed(seed)\n",
        "          random.seed(seed)\n",
        "          torch.manual_seed(seed)\n",
        "          torch.cuda.manual_seed_all(seed)\n",
        "          torch.backends.cudnn.deterministic = True\n",
        "  \n",
        "      target_embeds, weights = [], []\n",
        "      \n",
        "      if args.prompts_series is not None and frame_num >= len(args.prompts_series):\n",
        "        frame_prompt = args.prompts_series[-1]\n",
        "      elif args.prompts_series is not None:\n",
        "        frame_prompt = args.prompts_series[frame_num]\n",
        "      else:\n",
        "        frame_prompt = []\n",
        "      \n",
        "      print(args.image_prompts_series)\n",
        "      if args.image_prompts_series is not None and frame_num >= len(args.image_prompts_series):\n",
        "        image_prompt = args.image_prompts_series[-1]\n",
        "      elif args.image_prompts_series is not None:\n",
        "        image_prompt = args.image_prompts_series[frame_num]\n",
        "      else:\n",
        "        image_prompt = []\n",
        "\n",
        "      print(f'Frame Prompt: {frame_prompt}')\n",
        "\n",
        "      model_stats = []\n",
        "      for clip_model in clip_models:\n",
        "            cutn = 16\n",
        "            model_stat = {\"clip_model\":None,\"target_embeds\":[],\"make_cutouts\":None,\"weights\":[]}\n",
        "            model_stat[\"clip_model\"] = clip_model\n",
        "            \n",
        "            \n",
        "            for prompt in frame_prompt:\n",
        "                txt, weight = parse_prompt(prompt)\n",
        "                txt = clip_model.encode_text(clip.tokenize(prompt).to(device)).float()\n",
        "\n",
        "            if args.fuzzy_prompt:\n",
        "                for i in range(25):\n",
        "                    model_stat[\"target_embeds\"].append((txt + torch.randn(txt.shape).cuda() * args.rand_mag).clamp(0,1))\n",
        "                    model_stat[\"weights\"].append(weight)\n",
        "            else:\n",
        "                model_stat[\"target_embeds\"].append(txt)\n",
        "                model_stat[\"weights\"].append(weight)\n",
        "        \n",
        "            if image_prompt:\n",
        "              model_stat[\"make_cutouts\"] = MakeCutouts(clip_model.visual.input_resolution, cutn, skip_augs=skip_augs) \n",
        "              for prompt in image_prompt:\n",
        "                  path, weight = parse_prompt(prompt)\n",
        "                  img = Image.open(fetch(path)).convert('RGB')\n",
        "                  img = TF.resize(img, min(side_x, side_y, *img.size), T.InterpolationMode.LANCZOS)\n",
        "                  batch = model_stat[\"make_cutouts\"](TF.to_tensor(img).to(device).unsqueeze(0).mul(2).sub(1))\n",
        "                  embed = clip_model.encode_image(normalize(batch)).float()\n",
        "                  if fuzzy_prompt:\n",
        "                      for i in range(25):\n",
        "                          model_stat[\"target_embeds\"].append((embed + torch.randn(embed.shape).cuda() * rand_mag).clamp(0,1))\n",
        "                          weights.extend([weight / cutn] * cutn)\n",
        "                  else:\n",
        "                      model_stat[\"target_embeds\"].append(embed)\n",
        "                      model_stat[\"weights\"].extend([weight / cutn] * cutn)\n",
        "        \n",
        "            model_stat[\"target_embeds\"] = torch.cat(model_stat[\"target_embeds\"])\n",
        "            model_stat[\"weights\"] = torch.tensor(model_stat[\"weights\"], device=device)\n",
        "            if model_stat[\"weights\"].sum().abs() < 1e-3:\n",
        "                raise RuntimeError('The weights must not sum to 0.')\n",
        "            model_stat[\"weights\"] /= model_stat[\"weights\"].sum().abs()\n",
        "            model_stats.append(model_stat)\n",
        "  \n",
        "      init = None\n",
        "      if init_image is not None:\n",
        "          init = Image.open(fetch(init_image)).convert('RGB')\n",
        "          init = init.resize((args.side_x, args.side_y), Image.LANCZOS)\n",
        "          init = TF.to_tensor(init).to(device).unsqueeze(0).mul(2).sub(1)\n",
        "      \n",
        "      if args.perlin_init:\n",
        "          if args.perlin_mode == 'color':\n",
        "              init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)\n",
        "              init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, False)\n",
        "          elif args.perlin_mode == 'gray':\n",
        "            init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, True)\n",
        "            init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)\n",
        "          else:\n",
        "            init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)\n",
        "            init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)\n",
        "          # init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device)\n",
        "          init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device).unsqueeze(0).mul(2).sub(1)\n",
        "          del init2\n",
        "  \n",
        "      cur_t = None\n",
        "  \n",
        "      def cond_fn(x, t, y=None):\n",
        "          with torch.enable_grad():\n",
        "              x_is_NaN = False\n",
        "              x = x.detach().requires_grad_()\n",
        "              n = x.shape[0]\n",
        "              if use_secondary_model is True:\n",
        "                alpha = torch.tensor(diffusion.sqrt_alphas_cumprod[cur_t], device=device, dtype=torch.float32)\n",
        "                sigma = torch.tensor(diffusion.sqrt_one_minus_alphas_cumprod[cur_t], device=device, dtype=torch.float32)\n",
        "                cosine_t = alpha_sigma_to_t(alpha, sigma)\n",
        "                out = secondary_model(x, cosine_t[None].repeat([n])).pred\n",
        "                fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]\n",
        "                x_in = out * fac + x * (1 - fac)\n",
        "                x_in_grad = torch.zeros_like(x_in)\n",
        "              else:\n",
        "                my_t = torch.ones([n], device=device, dtype=torch.long) * cur_t\n",
        "                out = diffusion.p_mean_variance(model, x, my_t, clip_denoised=False, model_kwargs={'y': y})\n",
        "                fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]\n",
        "                x_in = out['pred_xstart'] * fac + x * (1 - fac)\n",
        "                x_in_grad = torch.zeros_like(x_in)\n",
        "              for model_stat in model_stats:\n",
        "                for i in range(args.cutn_batches):\n",
        "                    t_int = int(t.item())+1 #errors on last step without +1, need to find source\n",
        "                    #when using SLIP Base model the dimensions need to be hard coded to avoid AttributeError: 'VisionTransformer' object has no attribute 'input_resolution'\n",
        "                    try:\n",
        "                        input_resolution=model_stat[\"clip_model\"].visual.input_resolution\n",
        "                    except:\n",
        "                        input_resolution=224\n",
        "\n",
        "                    cuts = MakeCutoutsDango(input_resolution,\n",
        "                            Overview= args.cut_overview[1000-t_int], \n",
        "                            InnerCrop = args.cut_innercut[1000-t_int], IC_Size_Pow=args.cut_ic_pow, IC_Grey_P = args.cut_icgray_p[1000-t_int]\n",
        "                            )\n",
        "                    clip_in = normalize(cuts(x_in.add(1).div(2)))\n",
        "                    image_embeds = model_stat[\"clip_model\"].encode_image(clip_in).float()\n",
        "                    dists = spherical_dist_loss(image_embeds.unsqueeze(1), model_stat[\"target_embeds\"].unsqueeze(0))\n",
        "                    dists = dists.view([args.cut_overview[1000-t_int]+args.cut_innercut[1000-t_int], n, -1])\n",
        "                    losses = dists.mul(model_stat[\"weights\"]).sum(2).mean(0)\n",
        "                    loss_values.append(losses.sum().item()) # log loss, probably shouldn't do per cutn_batch\n",
        "                    x_in_grad += torch.autograd.grad(losses.sum() * clip_guidance_scale, x_in)[0] / cutn_batches\n",
        "              tv_losses = tv_loss(x_in)\n",
        "              if use_secondary_model is True:\n",
        "                range_losses = range_loss(out)\n",
        "              else:\n",
        "                range_losses = range_loss(out['pred_xstart'])\n",
        "              sat_losses = torch.abs(x_in - x_in.clamp(min=-1,max=1)).mean()\n",
        "              loss = tv_losses.sum() * tv_scale + range_losses.sum() * range_scale + sat_losses.sum() * sat_scale\n",
        "              if init is not None and args.init_scale:\n",
        "                  init_losses = lpips_model(x_in, init)\n",
        "                  loss = loss + init_losses.sum() * args.init_scale\n",
        "              x_in_grad += torch.autograd.grad(loss, x_in)[0]\n",
        "              if torch.isnan(x_in_grad).any()==False:\n",
        "                  grad = -torch.autograd.grad(x_in, x, x_in_grad)[0]\n",
        "              else:\n",
        "                # print(\"NaN'd\")\n",
        "                x_is_NaN = True\n",
        "                grad = torch.zeros_like(x)\n",
        "          if args.clamp_grad and x_is_NaN == False:\n",
        "              magnitude = grad.square().mean().sqrt()\n",
        "              return grad * magnitude.clamp(max=args.clamp_max) / magnitude  #min=-0.02, min=-clamp_max, \n",
        "          return grad\n",
        "  \n",
        "      if model_config['timestep_respacing'].startswith('ddim'):\n",
        "          sample_fn = diffusion.ddim_sample_loop_progressive\n",
        "      else:\n",
        "          sample_fn = diffusion.p_sample_loop_progressive\n",
        "    \n",
        "\n",
        "      image_display = Output()\n",
        "      for i in range(args.n_batches):\n",
        "          if args.animation_mode == 'None':\n",
        "            display.clear_output(wait=True)\n",
        "            batchBar = tqdm(range(args.n_batches), desc =\"Batches\")\n",
        "            batchBar.n = i\n",
        "            batchBar.refresh()\n",
        "          print('')\n",
        "          display.display(image_display)\n",
        "          gc.collect()\n",
        "          torch.cuda.empty_cache()\n",
        "          cur_t = diffusion.num_timesteps - skip_steps - 1\n",
        "          total_steps = cur_t\n",
        "\n",
        "          if perlin_init:\n",
        "              init = regen_perlin()\n",
        "\n",
        "          if model_config['timestep_respacing'].startswith('ddim'):\n",
        "              samples = sample_fn(\n",
        "                  model,\n",
        "                  (batch_size, 3, args.side_y, args.side_x),\n",
        "                  clip_denoised=clip_denoised,\n",
        "                  model_kwargs={},\n",
        "                  cond_fn=cond_fn,\n",
        "                  progress=True,\n",
        "                  skip_timesteps=skip_steps,\n",
        "                  init_image=init,\n",
        "                  randomize_class=randomize_class,\n",
        "                  eta=eta,\n",
        "              )\n",
        "          else:\n",
        "              samples = sample_fn(\n",
        "                  model,\n",
        "                  (batch_size, 3, args.side_y, args.side_x),\n",
        "                  clip_denoised=clip_denoised,\n",
        "                  model_kwargs={},\n",
        "                  cond_fn=cond_fn,\n",
        "                  progress=True,\n",
        "                  skip_timesteps=skip_steps,\n",
        "                  init_image=init,\n",
        "                  randomize_class=randomize_class,\n",
        "              )\n",
        "          \n",
        "          \n",
        "          # with run_display:\n",
        "          # display.clear_output(wait=True)\n",
        "          imgToSharpen = None\n",
        "          for j, sample in enumerate(samples):    \n",
        "            cur_t -= 1\n",
        "            intermediateStep = False\n",
        "            if args.steps_per_checkpoint is not None:\n",
        "                if j % steps_per_checkpoint == 0 and j > 0:\n",
        "                  intermediateStep = True\n",
        "            elif j in args.intermediate_saves:\n",
        "              intermediateStep = True\n",
        "            with image_display:\n",
        "              if j % args.display_rate == 0 or cur_t == -1 or intermediateStep == True:\n",
        "                  for k, image in enumerate(sample['pred_xstart']):\n",
        "                      # tqdm.write(f'Batch {i}, step {j}, output {k}:')\n",
        "                      current_time = datetime.now().strftime('%y%m%d-%H%M%S_%f')\n",
        "                      percent = math.ceil(j/total_steps*100)\n",
        "                      if args.n_batches > 0:\n",
        "                        #if intermediates are saved to the subfolder, don't append a step or percentage to the name\n",
        "                        if cur_t == -1 and args.intermediates_in_subfolder is True:\n",
        "                          save_num = f'{frame_num:04}' if animation_mode != \"None\" else i\n",
        "                          filename = f'{args.batch_name}({args.batchNum})_{save_num}.png'\n",
        "                        else:\n",
        "                          #If we're working with percentages, append it\n",
        "                          if args.steps_per_checkpoint is not None:\n",
        "                            filename = f'{args.batch_name}({args.batchNum})_{i:04}-{percent:02}%.png'\n",
        "                          # Or else, iIf we're working with specific steps, append those\n",
        "                          else:\n",
        "                            filename = f'{args.batch_name}({args.batchNum})_{i:04}-{j:03}.png'\n",
        "                      image = TF.to_pil_image(image.add(1).div(2).clamp(0, 1))\n",
        "                      if j % args.display_rate == 0 or cur_t == -1:\n",
        "                        image.save('progress.png')\n",
        "                        display.clear_output(wait=True)\n",
        "                        display.display(display.Image('progress.png'))\n",
        "                      if args.steps_per_checkpoint is not None:\n",
        "                        if j % args.steps_per_checkpoint == 0 and j > 0:\n",
        "                          if args.intermediates_in_subfolder is True:\n",
        "                            image.save(f'{partialFolder}/{filename}')\n",
        "                          else:\n",
        "                            image.save(f'{batchFolder}/{filename}')\n",
        "                      else:\n",
        "                        if j in args.intermediate_saves:\n",
        "                          if args.intermediates_in_subfolder is True:\n",
        "                            image.save(f'{partialFolder}/{filename}')\n",
        "                          else:\n",
        "                            image.save(f'{batchFolder}/{filename}')\n",
        "                      if cur_t == -1:\n",
        "                        if frame_num == 0:\n",
        "                          save_settings()\n",
        "                        if args.animation_mode != \"None\":\n",
        "                          image.save('prevFrame.png')\n",
        "                        if args.sharpen_preset != \"Off\" and animation_mode == \"None\":\n",
        "                          imgToSharpen = image\n",
        "                          if args.keep_unsharp is True:\n",
        "                            image.save(f'{unsharpenFolder}/{filename}')\n",
        "                        else:\n",
        "                          image.save(f'{batchFolder}/{filename}')\n",
        "                        # if frame_num != args.max_frames-1:\n",
        "                        #   display.clear_output()\n",
        "\n",
        "          with image_display:   \n",
        "            if args.sharpen_preset != \"Off\" and animation_mode == \"None\":\n",
        "              print('Starting Diffusion Sharpening...')\n",
        "              do_superres(imgToSharpen, f'{batchFolder}/{filename}')\n",
        "              display.clear_output()\n",
        "          \n",
        "          plt.plot(np.array(loss_values), 'r')\n",
        "\n",
        "def save_settings():\n",
        "  setting_list = {\n",
        "    'text_prompts': text_prompts,\n",
        "    'image_prompts': image_prompts,\n",
        "    'clip_guidance_scale': clip_guidance_scale,\n",
        "    'tv_scale': tv_scale,\n",
        "    'range_scale': range_scale,\n",
        "    'sat_scale': sat_scale,\n",
        "    # 'cutn': cutn,\n",
        "    'cutn_batches': cutn_batches,\n",
        "    'max_frames': max_frames,\n",
        "    'interp_spline': interp_spline,\n",
        "    # 'rotation_per_frame': rotation_per_frame,\n",
        "    'init_image': init_image,\n",
        "    'init_scale': init_scale,\n",
        "    'skip_steps': skip_steps,\n",
        "    # 'zoom_per_frame': zoom_per_frame,\n",
        "    'frames_scale': frames_scale,\n",
        "    'frames_skip_steps': frames_skip_steps,\n",
        "    'perlin_init': perlin_init,\n",
        "    'perlin_mode': perlin_mode,\n",
        "    'skip_augs': skip_augs,\n",
        "    'randomize_class': randomize_class,\n",
        "    'clip_denoised': clip_denoised,\n",
        "    'clamp_grad': clamp_grad,\n",
        "    'clamp_max': clamp_max,\n",
        "    'seed': seed,\n",
        "    'fuzzy_prompt': fuzzy_prompt,\n",
        "    'rand_mag': rand_mag,\n",
        "    'eta': eta,\n",
        "    'width': width_height[0],\n",
        "    'height': width_height[1],\n",
        "    'diffusion_model': diffusion_model,\n",
        "    'use_secondary_model': use_secondary_model,\n",
        "    'steps': steps,\n",
        "    'diffusion_steps': diffusion_steps,\n",
        "    'ViTB32': ViTB32,\n",
        "    'ViTB16': ViTB16,\n",
        "    'RN101': RN101,\n",
        "    'RN50': RN50,\n",
        "    'RN50x4': RN50x4,\n",
        "    'RN50x16': RN50x16,\n",
        "    'cut_overview': str(cut_overview),\n",
        "    'cut_innercut': str(cut_innercut),\n",
        "    'cut_ic_pow': cut_ic_pow,\n",
        "    'cut_icgray_p': str(cut_icgray_p),\n",
        "    'key_frames': key_frames,\n",
        "    'max_frames': max_frames,\n",
        "    'angle': angle,\n",
        "    'zoom': zoom,\n",
        "    'translation_x': translation_x,\n",
        "    'translation_y': translation_y,\n",
        "    'video_init_path':video_init_path,\n",
        "    'extract_nth_frame':extract_nth_frame,\n",
        "  }\n",
        "  # print('Settings:', setting_list)\n",
        "  with open(f\"{batchFolder}/{batch_name}({batchNum})_settings.txt\", \"w+\") as f:   #save settings\n",
        "    json.dump(setting_list, f, ensure_ascii=False, indent=4)\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "TI4oAu0N4ksZ"
      },
      "source": [
        "#@title 2.3 Define the secondary diffusion model\n",
        "\n",
        "def append_dims(x, n):\n",
        "    return x[(Ellipsis, *(None,) * (n - x.ndim))]\n",
        "\n",
        "\n",
        "def expand_to_planes(x, shape):\n",
        "    return append_dims(x, len(shape)).repeat([1, 1, *shape[2:]])\n",
        "\n",
        "\n",
        "def alpha_sigma_to_t(alpha, sigma):\n",
        "    return torch.atan2(sigma, alpha) * 2 / math.pi\n",
        "\n",
        "\n",
        "def t_to_alpha_sigma(t):\n",
        "    return torch.cos(t * math.pi / 2), torch.sin(t * math.pi / 2)\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class DiffusionOutput:\n",
        "    v: torch.Tensor\n",
        "    pred: torch.Tensor\n",
        "    eps: torch.Tensor\n",
        "\n",
        "\n",
        "class ConvBlock(nn.Sequential):\n",
        "    def __init__(self, c_in, c_out):\n",
        "        super().__init__(\n",
        "            nn.Conv2d(c_in, c_out, 3, padding=1),\n",
        "            nn.ReLU(inplace=True),\n",
        "        )\n",
        "\n",
        "\n",
        "class SkipBlock(nn.Module):\n",
        "    def __init__(self, main, skip=None):\n",
        "        super().__init__()\n",
        "        self.main = nn.Sequential(*main)\n",
        "        self.skip = skip if skip else nn.Identity()\n",
        "\n",
        "    def forward(self, input):\n",
        "        return torch.cat([self.main(input), self.skip(input)], dim=1)\n",
        "\n",
        "\n",
        "class FourierFeatures(nn.Module):\n",
        "    def __init__(self, in_features, out_features, std=1.):\n",
        "        super().__init__()\n",
        "        assert out_features % 2 == 0\n",
        "        self.weight = nn.Parameter(torch.randn([out_features // 2, in_features]) * std)\n",
        "\n",
        "    def forward(self, input):\n",
        "        f = 2 * math.pi * input @ self.weight.T\n",
        "        return torch.cat([f.cos(), f.sin()], dim=-1)\n",
        "\n",
        "\n",
        "class SecondaryDiffusionImageNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        c = 64  # The base channel count\n",
        "\n",
        "        self.timestep_embed = FourierFeatures(1, 16)\n",
        "\n",
        "        self.net = nn.Sequential(\n",
        "            ConvBlock(3 + 16, c),\n",
        "            ConvBlock(c, c),\n",
        "            SkipBlock([\n",
        "                nn.AvgPool2d(2),\n",
        "                ConvBlock(c, c * 2),\n",
        "                ConvBlock(c * 2, c * 2),\n",
        "                SkipBlock([\n",
        "                    nn.AvgPool2d(2),\n",
        "                    ConvBlock(c * 2, c * 4),\n",
        "                    ConvBlock(c * 4, c * 4),\n",
        "                    SkipBlock([\n",
        "                        nn.AvgPool2d(2),\n",
        "                        ConvBlock(c * 4, c * 8),\n",
        "                        ConvBlock(c * 8, c * 4),\n",
        "                        nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),\n",
        "                    ]),\n",
        "                    ConvBlock(c * 8, c * 4),\n",
        "                    ConvBlock(c * 4, c * 2),\n",
        "                    nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),\n",
        "                ]),\n",
        "                ConvBlock(c * 4, c * 2),\n",
        "                ConvBlock(c * 2, c),\n",
        "                nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),\n",
        "            ]),\n",
        "            ConvBlock(c * 2, c),\n",
        "            nn.Conv2d(c, 3, 3, padding=1),\n",
        "        )\n",
        "\n",
        "    def forward(self, input, t):\n",
        "        timestep_embed = expand_to_planes(self.timestep_embed(t[:, None]), input.shape)\n",
        "        v = self.net(torch.cat([input, timestep_embed], dim=1))\n",
        "        alphas, sigmas = map(partial(append_dims, n=v.ndim), t_to_alpha_sigma(t))\n",
        "        pred = input * alphas - v * sigmas\n",
        "        eps = input * sigmas + v * alphas\n",
        "        return DiffusionOutput(v, pred, eps)\n",
        "\n",
        "\n",
        "class SecondaryDiffusionImageNet2(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        c = 64  # The base channel count\n",
        "        cs = [c, c * 2, c * 2, c * 4, c * 4, c * 8]\n",
        "\n",
        "        self.timestep_embed = FourierFeatures(1, 16)\n",
        "        self.down = nn.AvgPool2d(2)\n",
        "        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)\n",
        "\n",
        "        self.net = nn.Sequential(\n",
        "            ConvBlock(3 + 16, cs[0]),\n",
        "            ConvBlock(cs[0], cs[0]),\n",
        "            SkipBlock([\n",
        "                self.down,\n",
        "                ConvBlock(cs[0], cs[1]),\n",
        "                ConvBlock(cs[1], cs[1]),\n",
        "                SkipBlock([\n",
        "                    self.down,\n",
        "                    ConvBlock(cs[1], cs[2]),\n",
        "                    ConvBlock(cs[2], cs[2]),\n",
        "                    SkipBlock([\n",
        "                        self.down,\n",
        "                        ConvBlock(cs[2], cs[3]),\n",
        "                        ConvBlock(cs[3], cs[3]),\n",
        "                        SkipBlock([\n",
        "                            self.down,\n",
        "                            ConvBlock(cs[3], cs[4]),\n",
        "                            ConvBlock(cs[4], cs[4]),\n",
        "                            SkipBlock([\n",
        "                                self.down,\n",
        "                                ConvBlock(cs[4], cs[5]),\n",
        "                                ConvBlock(cs[5], cs[5]),\n",
        "                                ConvBlock(cs[5], cs[5]),\n",
        "                                ConvBlock(cs[5], cs[4]),\n",
        "                                self.up,\n",
        "                            ]),\n",
        "                            ConvBlock(cs[4] * 2, cs[4]),\n",
        "                            ConvBlock(cs[4], cs[3]),\n",
        "                            self.up,\n",
        "                        ]),\n",
        "                        ConvBlock(cs[3] * 2, cs[3]),\n",
        "                        ConvBlock(cs[3], cs[2]),\n",
        "                        self.up,\n",
        "                    ]),\n",
        "                    ConvBlock(cs[2] * 2, cs[2]),\n",
        "                    ConvBlock(cs[2], cs[1]),\n",
        "                    self.up,\n",
        "                ]),\n",
        "                ConvBlock(cs[1] * 2, cs[1]),\n",
        "                ConvBlock(cs[1], cs[0]),\n",
        "                self.up,\n",
        "            ]),\n",
        "            ConvBlock(cs[0] * 2, cs[0]),\n",
        "            nn.Conv2d(cs[0], 3, 3, padding=1),\n",
        "        )\n",
        "\n",
        "    def forward(self, input, t):\n",
        "        timestep_embed = expand_to_planes(self.timestep_embed(t[:, None]), input.shape)\n",
        "        v = self.net(torch.cat([input, timestep_embed], dim=1))\n",
        "        alphas, sigmas = map(partial(append_dims, n=v.ndim), t_to_alpha_sigma(t))\n",
        "        pred = input * alphas - v * sigmas\n",
        "        eps = input * sigmas + v * alphas\n",
        "        return DiffusionOutput(v, pred, eps)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 2.4 SuperRes Define\n",
        "class DDIMSampler(object):\n",
        "    def __init__(self, model, schedule=\"linear\", **kwargs):\n",
        "        super().__init__()\n",
        "        self.model = model\n",
        "        self.ddpm_num_timesteps = model.num_timesteps\n",
        "        self.schedule = schedule\n",
        "\n",
        "    def register_buffer(self, name, attr):\n",
        "        if type(attr) == torch.Tensor:\n",
        "            if attr.device != torch.device(\"cuda\"):\n",
        "                attr = attr.to(torch.device(\"cuda\"))\n",
        "        setattr(self, name, attr)\n",
        "\n",
        "    def make_schedule(self, ddim_num_steps, ddim_discretize=\"uniform\", ddim_eta=0., verbose=True):\n",
        "        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,\n",
        "                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)\n",
        "        alphas_cumprod = self.model.alphas_cumprod\n",
        "        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'\n",
        "        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)\n",
        "\n",
        "        self.register_buffer('betas', to_torch(self.model.betas))\n",
        "        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))\n",
        "        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))\n",
        "\n",
        "        # calculations for diffusion q(x_t | x_{t-1}) and others\n",
        "        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))\n",
        "        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))\n",
        "        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))\n",
        "        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))\n",
        "        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))\n",
        "\n",
        "        # ddim sampling parameters\n",
        "        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),\n",
        "                                                                                   ddim_timesteps=self.ddim_timesteps,\n",
        "                                                                                   eta=ddim_eta,verbose=verbose)\n",
        "        self.register_buffer('ddim_sigmas', ddim_sigmas)\n",
        "        self.register_buffer('ddim_alphas', ddim_alphas)\n",
        "        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)\n",
        "        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))\n",
        "        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(\n",
        "            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (\n",
        "                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))\n",
        "        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)\n",
        "\n",
        "    @torch.no_grad()\n",
        "    def sample(self,\n",
        "               S,\n",
        "               batch_size,\n",
        "               shape,\n",
        "               conditioning=None,\n",
        "               callback=None,\n",
        "               normals_sequence=None,\n",
        "               img_callback=None,\n",
        "               quantize_x0=False,\n",
        "               eta=0.,\n",
        "               mask=None,\n",
        "               x0=None,\n",
        "               temperature=1.,\n",
        "               noise_dropout=0.,\n",
        "               score_corrector=None,\n",
        "               corrector_kwargs=None,\n",
        "               verbose=True,\n",
        "               x_T=None,\n",
        "               log_every_t=100,\n",
        "               **kwargs\n",
        "               ):\n",
        "        if conditioning is not None:\n",
        "            if isinstance(conditioning, dict):\n",
        "                cbs = conditioning[list(conditioning.keys())[0]].shape[0]\n",
        "                if cbs != batch_size:\n",
        "                    print(f\"Warning: Got {cbs} conditionings but batch-size is {batch_size}\")\n",
        "            else:\n",
        "                if conditioning.shape[0] != batch_size:\n",
        "                    print(f\"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}\")\n",
        "\n",
        "        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)\n",
        "        # sampling\n",
        "        C, H, W = shape\n",
        "        size = (batch_size, C, H, W)\n",
        "        # print(f'Data shape for DDIM sampling is {size}, eta {eta}')\n",
        "\n",
        "        samples, intermediates = self.ddim_sampling(conditioning, size,\n",
        "                                                    callback=callback,\n",
        "                                                    img_callback=img_callback,\n",
        "                                                    quantize_denoised=quantize_x0,\n",
        "                                                    mask=mask, x0=x0,\n",
        "                                                    ddim_use_original_steps=False,\n",
        "                                                    noise_dropout=noise_dropout,\n",
        "                                                    temperature=temperature,\n",
        "                                                    score_corrector=score_corrector,\n",
        "                                                    corrector_kwargs=corrector_kwargs,\n",
        "                                                    x_T=x_T,\n",
        "                                                    log_every_t=log_every_t\n",
        "                                                    )\n",
        "        return samples, intermediates\n",
        "\n",
        "    @torch.no_grad()\n",
        "    def ddim_sampling(self, cond, shape,\n",
        "                      x_T=None, ddim_use_original_steps=False,\n",
        "                      callback=None, timesteps=None, quantize_denoised=False,\n",
        "                      mask=None, x0=None, img_callback=None, log_every_t=100,\n",
        "                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):\n",
        "        device = self.model.betas.device\n",
        "        b = shape[0]\n",
        "        if x_T is None:\n",
        "            img = torch.randn(shape, device=device)\n",
        "        else:\n",
        "            img = x_T\n",
        "\n",
        "        if timesteps is None:\n",
        "            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps\n",
        "        elif timesteps is not None and not ddim_use_original_steps:\n",
        "            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1\n",
        "            timesteps = self.ddim_timesteps[:subset_end]\n",
        "\n",
        "        intermediates = {'x_inter': [img], 'pred_x0': [img]}\n",
        "        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)\n",
        "        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]\n",
        "        print(f\"Running DDIM Sharpening with {total_steps} timesteps\")\n",
        "\n",
        "        iterator = tqdm(time_range, desc='DDIM Sharpening', total=total_steps)\n",
        "\n",
        "        for i, step in enumerate(iterator):\n",
        "            index = total_steps - i - 1\n",
        "            ts = torch.full((b,), step, device=device, dtype=torch.long)\n",
        "\n",
        "            if mask is not None:\n",
        "                assert x0 is not None\n",
        "                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?\n",
        "                img = img_orig * mask + (1. - mask) * img\n",
        "\n",
        "            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,\n",
        "                                      quantize_denoised=quantize_denoised, temperature=temperature,\n",
        "                                      noise_dropout=noise_dropout, score_corrector=score_corrector,\n",
        "                                      corrector_kwargs=corrector_kwargs)\n",
        "            img, pred_x0 = outs\n",
        "            if callback: callback(i)\n",
        "            if img_callback: img_callback(pred_x0, i)\n",
        "\n",
        "            if index % log_every_t == 0 or index == total_steps - 1:\n",
        "                intermediates['x_inter'].append(img)\n",
        "                intermediates['pred_x0'].append(pred_x0)\n",
        "\n",
        "        return img, intermediates\n",
        "\n",
        "    @torch.no_grad()\n",
        "    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,\n",
        "                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):\n",
        "        b, *_, device = *x.shape, x.device\n",
        "        e_t = self.model.apply_model(x, t, c)\n",
        "        if score_corrector is not None:\n",
        "            assert self.model.parameterization == \"eps\"\n",
        "            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)\n",
        "\n",
        "        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas\n",
        "        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev\n",
        "        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas\n",
        "        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas\n",
        "        # select parameters corresponding to the currently considered timestep\n",
        "        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)\n",
        "        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)\n",
        "        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)\n",
        "        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)\n",
        "\n",
        "        # current prediction for x_0\n",
        "        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\n",
        "        if quantize_denoised:\n",
        "            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)\n",
        "        # direction pointing to x_t\n",
        "        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t\n",
        "        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature\n",
        "        if noise_dropout > 0.:\n",
        "            noise = torch.nn.functional.dropout(noise, p=noise_dropout)\n",
        "        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise\n",
        "        return x_prev, pred_x0\n",
        "\n",
        "\n",
        "def download_models(mode):\n",
        "\n",
        "    if mode == \"superresolution\":\n",
        "        # this is the small bsr light model\n",
        "        url_conf = 'https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1'\n",
        "        url_ckpt = 'https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1'\n",
        "\n",
        "        path_conf = f'{model_path}/superres/'\n",
        "        path_ckpt = f'{model_path}/superres/'\n",
        "\n",
        "        download_url(url_conf, path_conf, 'project.yaml')\n",
        "        download_url(url_ckpt, path_ckpt, 'last.ckpt')\n",
        "\n",
        "        path_conf = path_conf + 'project.yaml' # fix it\n",
        "        path_ckpt = path_ckpt + 'last.ckpt' # fix it\n",
        "        return path_conf, path_ckpt\n",
        "\n",
        "    else:\n",
        "        raise NotImplementedError\n",
        "\n",
        "\n",
        "def load_model_from_config(config, ckpt):\n",
        "    print(f\"Loading model from {ckpt}\")\n",
        "    pl_sd = torch.load(ckpt, map_location=\"cpu\")\n",
        "    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",
        "    model.cuda()\n",
        "    model.eval()\n",
        "    return {\"model\": model}, global_step\n",
        "\n",
        "\n",
        "def get_model(mode):\n",
        "    path_conf, path_ckpt = download_models(mode)\n",
        "    config = OmegaConf.load(path_conf)\n",
        "    model, step = load_model_from_config(config, path_ckpt)\n",
        "    return model\n",
        "\n",
        "\n",
        "def get_custom_cond(mode):\n",
        "    dest = \"data/example_conditioning\"\n",
        "\n",
        "    if mode == \"superresolution\":\n",
        "        uploaded_img = files.upload()\n",
        "        filename = next(iter(uploaded_img))\n",
        "        name, filetype = filename.split(\".\") # todo assumes just one dot in name !\n",
        "        os.rename(f\"{filename}\", f\"{dest}/{mode}/custom_{name}.{filetype}\")\n",
        "\n",
        "    elif mode == \"text_conditional\":\n",
        "        w = widgets.Text(value='A cake with cream!', disabled=True)\n",
        "        display.display(w)\n",
        "\n",
        "        with open(f\"{dest}/{mode}/custom_{w.value[:20]}.txt\", 'w') as f:\n",
        "            f.write(w.value)\n",
        "\n",
        "    elif mode == \"class_conditional\":\n",
        "        w = widgets.IntSlider(min=0, max=1000)\n",
        "        display.display(w)\n",
        "        with open(f\"{dest}/{mode}/custom.txt\", 'w') as f:\n",
        "            f.write(w.value)\n",
        "\n",
        "    else:\n",
        "        raise NotImplementedError(f\"cond not implemented for mode{mode}\")\n",
        "\n",
        "\n",
        "def get_cond_options(mode):\n",
        "    path = \"data/example_conditioning\"\n",
        "    path = os.path.join(path, mode)\n",
        "    onlyfiles = [f for f in sorted(os.listdir(path))]\n",
        "    return path, onlyfiles\n",
        "\n",
        "\n",
        "def select_cond_path(mode):\n",
        "    path = \"data/example_conditioning\"  # todo\n",
        "    path = os.path.join(path, mode)\n",
        "    onlyfiles = [f for f in sorted(os.listdir(path))]\n",
        "\n",
        "    selected = widgets.RadioButtons(\n",
        "        options=onlyfiles,\n",
        "        description='Select conditioning:',\n",
        "        disabled=False\n",
        "    )\n",
        "    display.display(selected)\n",
        "    selected_path = os.path.join(path, selected.value)\n",
        "    return selected_path\n",
        "\n",
        "\n",
        "def get_cond(mode, img):\n",
        "    example = dict()\n",
        "    if mode == \"superresolution\":\n",
        "        up_f = 4\n",
        "        # visualize_cond_img(selected_path)\n",
        "\n",
        "        c = img\n",
        "        c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)\n",
        "        c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True)\n",
        "        c_up = rearrange(c_up, '1 c h w -> 1 h w c')\n",
        "        c = rearrange(c, '1 c h w -> 1 h w c')\n",
        "        c = 2. * c - 1.\n",
        "\n",
        "        c = c.to(torch.device(\"cuda\"))\n",
        "        example[\"LR_image\"] = c\n",
        "        example[\"image\"] = c_up\n",
        "\n",
        "    return example\n",
        "\n",
        "\n",
        "def visualize_cond_img(path):\n",
        "    display.display(ipyimg(filename=path))\n",
        "\n",
        "\n",
        "def sr_run(model, img, task, custom_steps, eta, resize_enabled=False, classifier_ckpt=None, global_step=None):\n",
        "    # global stride\n",
        "\n",
        "    example = get_cond(task, img)\n",
        "\n",
        "    save_intermediate_vid = False\n",
        "    n_runs = 1\n",
        "    masked = False\n",
        "    guider = None\n",
        "    ckwargs = None\n",
        "    mode = 'ddim'\n",
        "    ddim_use_x0_pred = False\n",
        "    temperature = 1.\n",
        "    eta = eta\n",
        "    make_progrow = True\n",
        "    custom_shape = None\n",
        "\n",
        "    height, width = example[\"image\"].shape[1:3]\n",
        "    split_input = height >= 128 and width >= 128\n",
        "\n",
        "    if split_input:\n",
        "        ks = 128\n",
        "        stride = 64\n",
        "        vqf = 4  #\n",
        "        model.split_input_params = {\"ks\": (ks, ks), \"stride\": (stride, stride),\n",
        "                                    \"vqf\": vqf,\n",
        "                                    \"patch_distributed_vq\": True,\n",
        "                                    \"tie_braker\": False,\n",
        "                                    \"clip_max_weight\": 0.5,\n",
        "                                    \"clip_min_weight\": 0.01,\n",
        "                                    \"clip_max_tie_weight\": 0.5,\n",
        "                                    \"clip_min_tie_weight\": 0.01}\n",
        "    else:\n",
        "        if hasattr(model, \"split_input_params\"):\n",
        "            delattr(model, \"split_input_params\")\n",
        "\n",
        "    invert_mask = False\n",
        "\n",
        "    x_T = None\n",
        "    for n in range(n_runs):\n",
        "        if custom_shape is not None:\n",
        "            x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)\n",
        "            x_T = repeat(x_T, '1 c h w -> b c h w', b=custom_shape[0])\n",
        "\n",
        "        logs = make_convolutional_sample(example, model,\n",
        "                                         mode=mode, custom_steps=custom_steps,\n",
        "                                         eta=eta, swap_mode=False , masked=masked,\n",
        "                                         invert_mask=invert_mask, quantize_x0=False,\n",
        "                                         custom_schedule=None, decode_interval=10,\n",
        "                                         resize_enabled=resize_enabled, custom_shape=custom_shape,\n",
        "                                         temperature=temperature, noise_dropout=0.,\n",
        "                                         corrector=guider, corrector_kwargs=ckwargs, x_T=x_T, save_intermediate_vid=save_intermediate_vid,\n",
        "                                         make_progrow=make_progrow,ddim_use_x0_pred=ddim_use_x0_pred\n",
        "                                         )\n",
        "    return logs\n",
        "\n",
        "\n",
        "@torch.no_grad()\n",
        "def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,\n",
        "                    mask=None, x0=None, quantize_x0=False, img_callback=None,\n",
        "                    temperature=1., noise_dropout=0., score_corrector=None,\n",
        "                    corrector_kwargs=None, x_T=None, log_every_t=None\n",
        "                    ):\n",
        "\n",
        "    ddim = DDIMSampler(model)\n",
        "    bs = shape[0]  # dont know where this comes from but wayne\n",
        "    shape = shape[1:]  # cut batch dim\n",
        "    # print(f\"Sampling with eta = {eta}; steps: {steps}\")\n",
        "    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,\n",
        "                                         normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,\n",
        "                                         mask=mask, x0=x0, temperature=temperature, verbose=False,\n",
        "                                         score_corrector=score_corrector,\n",
        "                                         corrector_kwargs=corrector_kwargs, x_T=x_T)\n",
        "\n",
        "    return samples, intermediates\n",
        "\n",
        "\n",
        "@torch.no_grad()\n",
        "def make_convolutional_sample(batch, model, mode=\"vanilla\", custom_steps=None, eta=1.0, swap_mode=False, masked=False,\n",
        "                              invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000,\n",
        "                              resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,\n",
        "                              corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,ddim_use_x0_pred=False):\n",
        "    log = dict()\n",
        "\n",
        "    z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,\n",
        "                                        return_first_stage_outputs=True,\n",
        "                                        force_c_encode=not (hasattr(model, 'split_input_params')\n",
        "                                                            and model.cond_stage_key == 'coordinates_bbox'),\n",
        "                                        return_original_cond=True)\n",
        "\n",
        "    log_every_t = 1 if save_intermediate_vid else None\n",
        "\n",
        "    if custom_shape is not None:\n",
        "        z = torch.randn(custom_shape)\n",
        "        # print(f\"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}\")\n",
        "\n",
        "    z0 = None\n",
        "\n",
        "    log[\"input\"] = x\n",
        "    log[\"reconstruction\"] = xrec\n",
        "\n",
        "    if ismap(xc):\n",
        "        log[\"original_conditioning\"] = model.to_rgb(xc)\n",
        "        if hasattr(model, 'cond_stage_key'):\n",
        "            log[model.cond_stage_key] = model.to_rgb(xc)\n",
        "\n",
        "    else:\n",
        "        log[\"original_conditioning\"] = xc if xc is not None else torch.zeros_like(x)\n",
        "        if model.cond_stage_model:\n",
        "            log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)\n",
        "            if model.cond_stage_key =='class_label':\n",
        "                log[model.cond_stage_key] = xc[model.cond_stage_key]\n",
        "\n",
        "    with model.ema_scope(\"Plotting\"):\n",
        "        t0 = time.time()\n",
        "        img_cb = None\n",
        "\n",
        "        sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,\n",
        "                                                eta=eta,\n",
        "                                                quantize_x0=quantize_x0, img_callback=img_cb, mask=None, x0=z0,\n",
        "                                                temperature=temperature, noise_dropout=noise_dropout,\n",
        "                                                score_corrector=corrector, corrector_kwargs=corrector_kwargs,\n",
        "                                                x_T=x_T, log_every_t=log_every_t)\n",
        "        t1 = time.time()\n",
        "\n",
        "        if ddim_use_x0_pred:\n",
        "            sample = intermediates['pred_x0'][-1]\n",
        "\n",
        "    x_sample = model.decode_first_stage(sample)\n",
        "\n",
        "    try:\n",
        "        x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)\n",
        "        log[\"sample_noquant\"] = x_sample_noquant\n",
        "        log[\"sample_diff\"] = torch.abs(x_sample_noquant - x_sample)\n",
        "    except:\n",
        "        pass\n",
        "\n",
        "    log[\"sample\"] = x_sample\n",
        "    log[\"time\"] = t1 - t0\n",
        "\n",
        "    return log\n",
        "\n",
        "sr_diffMode = 'superresolution'\n",
        "sr_model = get_model('superresolution')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def do_superres(img, filepath):\n",
        "\n",
        "  if args.sharpen_preset == 'Faster':\n",
        "      sr_diffusion_steps = \"25\" \n",
        "      sr_pre_downsample = '1/2' \n",
        "  if args.sharpen_preset == 'Fast':\n",
        "      sr_diffusion_steps = \"100\" \n",
        "      sr_pre_downsample = '1/2' \n",
        "  if args.sharpen_preset == 'Slow':\n",
        "      sr_diffusion_steps = \"25\" \n",
        "      sr_pre_downsample = 'None' \n",
        "  if args.sharpen_preset == 'Very Slow':\n",
        "      sr_diffusion_steps = \"100\" \n",
        "      sr_pre_downsample = 'None' \n",
        "\n",
        "\n",
        "  sr_post_downsample = 'Original Size'\n",
        "  sr_diffusion_steps = int(sr_diffusion_steps)\n",
        "  sr_eta = 1.0 \n",
        "  sr_downsample_method = 'Lanczos' \n",
        "\n",
        "  gc.collect()\n",
        "  torch.cuda.empty_cache()\n",
        "\n",
        "  im_og = img\n",
        "  width_og, height_og = im_og.size\n",
        "\n",
        "  #Downsample Pre\n",
        "  if sr_pre_downsample == '1/2':\n",
        "    downsample_rate = 2\n",
        "  elif sr_pre_downsample == '1/4':\n",
        "    downsample_rate = 4\n",
        "  else:\n",
        "    downsample_rate = 1\n",
        "\n",
        "  width_downsampled_pre = width_og//downsample_rate\n",
        "  height_downsampled_pre = height_og//downsample_rate\n",
        "\n",
        "  if downsample_rate != 1:\n",
        "    # print(f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')\n",
        "    im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)\n",
        "    # im_og.save('/content/temp.png')\n",
        "    # filepath = '/content/temp.png'\n",
        "\n",
        "  logs = sr_run(sr_model[\"model\"], im_og, sr_diffMode, sr_diffusion_steps, sr_eta)\n",
        "\n",
        "  sample = logs[\"sample\"]\n",
        "  sample = sample.detach().cpu()\n",
        "  sample = torch.clamp(sample, -1., 1.)\n",
        "  sample = (sample + 1.) / 2. * 255\n",
        "  sample = sample.numpy().astype(np.uint8)\n",
        "  sample = np.transpose(sample, (0, 2, 3, 1))\n",
        "  a = Image.fromarray(sample[0])\n",
        "\n",
        "  #Downsample Post\n",
        "  if sr_post_downsample == '1/2':\n",
        "    downsample_rate = 2\n",
        "  elif sr_post_downsample == '1/4':\n",
        "    downsample_rate = 4\n",
        "  else:\n",
        "    downsample_rate = 1\n",
        "\n",
        "  width, height = a.size\n",
        "  width_downsampled_post = width//downsample_rate\n",
        "  height_downsampled_post = height//downsample_rate\n",
        "\n",
        "  if sr_downsample_method == 'Lanczos':\n",
        "    aliasing = Image.LANCZOS\n",
        "  else:\n",
        "    aliasing = Image.NEAREST\n",
        "\n",
        "  if downsample_rate != 1:\n",
        "    # print(f'Downsampling from [{width}, {height}] to [{width_downsampled_post}, {height_downsampled_post}]')\n",
        "    a = a.resize((width_downsampled_post, height_downsampled_post), aliasing)\n",
        "  elif sr_post_downsample == 'Original Size':\n",
        "    # print(f'Downsampling from [{width}, {height}] to Original Size [{width_og}, {height_og}]')\n",
        "    a = a.resize((width_og, height_og), aliasing)\n",
        "\n",
        "  display.display(a)\n",
        "  a.save(filepath)\n",
        "  return\n",
        "  print(f'Processing finished!')\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "NJS2AUAnvn-D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQVtY1Ixnqx4"
      },
      "source": [
        "# 3. Diffusion and CLIP model settings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fpbody2NCR7w",
        "cellView": "form"
      },
      "source": [
        "#@markdown ####**Models Settings:**\n",
        "diffusion_model = \"512x512_diffusion_uncond_finetune_008100\" #@param [\"256x256_diffusion_uncond\", \"512x512_diffusion_uncond_finetune_008100\"]\n",
        "use_secondary_model = True #@param {type: 'boolean'}\n",
        "\n",
        "timestep_respacing = '50' # param ['25','50','100','150','250','500','1000','ddim25','ddim50', 'ddim75', 'ddim100','ddim150','ddim250','ddim500','ddim1000']  \n",
        "diffusion_steps = 1000 # param {type: 'number'}\n",
        "use_checkpoint = True #@param {type: 'boolean'}\n",
        "ViTB32 = True #@param{type:\"boolean\"}\n",
        "ViTB16 = True #@param{type:\"boolean\"}\n",
        "RN101 = False #@param{type:\"boolean\"}\n",
        "RN50 = True #@param{type:\"boolean\"}\n",
        "RN50x4 = False #@param{type:\"boolean\"}\n",
        "RN50x16 = False #@param{type:\"boolean\"}\n",
        "SLIPB16 = False #@param{type:\"boolean\"}\n",
        "SLIPL16 = False #@param{type:\"boolean\"}\n",
        "\n",
        "#@markdown If you're having issues with model downloads, check this to compare SHA's:\n",
        "check_model_SHA = False #@param{type:\"boolean\"}\n",
        "\n",
        "model_256_SHA = '983e3de6f95c88c81b2ca7ebb2c217933be1973b1ff058776b970f901584613a'\n",
        "model_512_SHA = '9c111ab89e214862b76e1fa6a1b3f1d329b1a88281885943d2cdbe357ad57648'\n",
        "model_secondary_SHA = '983e3de6f95c88c81b2ca7ebb2c217933be1973b1ff058776b970f901584613a'\n",
        "\n",
        "model_256_link = 'https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt'\n",
        "model_512_link = 'http://batbot.tv/ai/models/guided-diffusion/512x512_diffusion_uncond_finetune_008100.pt'\n",
        "model_secondary_link = 'https://v-diffusion.s3.us-west-2.amazonaws.com/secondary_model_imagenet_2.pth'\n",
        "\n",
        "model_256_path = f'{model_path}/256x256_diffusion_uncond.pt'\n",
        "model_512_path = f'{model_path}/512x512_diffusion_uncond_finetune_008100.pt'\n",
        "model_secondary_path = f'{model_path}/secondary_model_imagenet_2.pth'\n",
        "\n",
        "# Download the diffusion model\n",
        "if diffusion_model == '256x256_diffusion_uncond':\n",
        "  if os.path.exists(model_256_path) and check_model_SHA:\n",
        "    print('Checking 256 Diffusion File')\n",
        "    with open(model_256_path,\"rb\") as f:\n",
        "        bytes = f.read() \n",
        "        hash = hashlib.sha256(bytes).hexdigest();\n",
        "    if hash == model_256_SHA:\n",
        "      print('256 Model SHA matches')\n",
        "      model_256_downloaded = True\n",
        "    else: \n",
        "      print(\"256 Model SHA doesn't match, redownloading...\")\n",
        "      !wget --continue {model_256_link} -P {model_path}\n",
        "      model_256_downloaded = True\n",
        "  elif os.path.exists(model_256_path) and not check_model_SHA or model_256_downloaded == True:\n",
        "    print('256 Model already downloaded, check check_model_SHA if the file is corrupt')\n",
        "  else:  \n",
        "    !wget --continue {model_256_link} -P {model_path}\n",
        "    model_256_downloaded = True\n",
        "elif diffusion_model == '512x512_diffusion_uncond_finetune_008100':\n",
        "  if os.path.exists(model_512_path) and check_model_SHA:\n",
        "    print('Checking 512 Diffusion File')\n",
        "    with open(model_512_path,\"rb\") as f:\n",
        "        bytes = f.read() \n",
        "        hash = hashlib.sha256(bytes).hexdigest();\n",
        "    if hash == model_512_SHA:\n",
        "      print('512 Model SHA matches')\n",
        "      model_512_downloaded = True\n",
        "    else:  \n",
        "      print(\"512 Model SHA doesn't match, redownloading...\")\n",
        "      !wget --continue {model_512_link} -P {model_path}\n",
        "      model_512_downloaded = True\n",
        "  elif os.path.exists(model_512_path) and not check_model_SHA or model_512_downloaded == True:\n",
        "    print('512 Model already downloaded, check check_model_SHA if the file is corrupt')\n",
        "  else:  \n",
        "    !wget --continue {model_512_link} -P {model_path}\n",
        "    model_512_downloaded = True\n",
        "\n",
        "\n",
        "# Download the secondary diffusion model v2\n",
        "if use_secondary_model == True:\n",
        "  if os.path.exists(model_secondary_path) and check_model_SHA:\n",
        "    print('Checking Secondary Diffusion File')\n",
        "    with open(model_secondary_path,\"rb\") as f:\n",
        "        bytes = f.read() \n",
        "        hash = hashlib.sha256(bytes).hexdigest();\n",
        "    if hash == model_secondary_SHA:\n",
        "      print('Secondary Model SHA matches')\n",
        "      model_secondary_downloaded = True\n",
        "    else:  \n",
        "      print(\"Secondary Model SHA doesn't match, redownloading...\")\n",
        "      !wget --continue {model_secondary_link} -P {model_path}\n",
        "      model_secondary_downloaded = True\n",
        "  elif os.path.exists(model_secondary_path) and not check_model_SHA or model_secondary_downloaded == True:\n",
        "    print('Secondary Model already downloaded, check check_model_SHA if the file is corrupt')\n",
        "  else:  \n",
        "    !wget --continue {model_secondary_link} -P {model_path}\n",
        "    model_secondary_downloaded = True\n",
        "\n",
        "model_config = model_and_diffusion_defaults()\n",
        "if diffusion_model == '512x512_diffusion_uncond_finetune_008100':\n",
        "    model_config.update({\n",
        "        'attention_resolutions': '32, 16, 8',\n",
        "        'class_cond': False,\n",
        "        'diffusion_steps': diffusion_steps,\n",
        "        'rescale_timesteps': True,\n",
        "        'timestep_respacing': timestep_respacing,\n",
        "        'image_size': 512,\n",
        "        'learn_sigma': True,\n",
        "        'noise_schedule': 'linear',\n",
        "        'num_channels': 256,\n",
        "        'num_head_channels': 64,\n",
        "        'num_res_blocks': 2,\n",
        "        'resblock_updown': True,\n",
        "        'use_checkpoint': use_checkpoint,\n",
        "        'use_fp16': True,\n",
        "        'use_scale_shift_norm': True,\n",
        "    })\n",
        "elif diffusion_model == '256x256_diffusion_uncond':\n",
        "    model_config.update({\n",
        "        'attention_resolutions': '32, 16, 8',\n",
        "        'class_cond': False,\n",
        "        'diffusion_steps': diffusion_steps,\n",
        "        'rescale_timesteps': True,\n",
        "        'timestep_respacing': timestep_respacing,\n",
        "        'image_size': 256,\n",
        "        'learn_sigma': True,\n",
        "        'noise_schedule': 'linear',\n",
        "        'num_channels': 256,\n",
        "        'num_head_channels': 64,\n",
        "        'num_res_blocks': 2,\n",
        "        'resblock_updown': True,\n",
        "        'use_checkpoint': use_checkpoint,\n",
        "        'use_fp16': True,\n",
        "        'use_scale_shift_norm': True,\n",
        "    })\n",
        "\n",
        "secondary_model_ver = 2\n",
        "model_default = model_config['image_size']\n",
        "\n",
        "\n",
        "\n",
        "if secondary_model_ver == 2:\n",
        "    secondary_model = SecondaryDiffusionImageNet2()\n",
        "    secondary_model.load_state_dict(torch.load(f'{model_path}/secondary_model_imagenet_2.pth', map_location='cpu'))\n",
        "secondary_model.eval().requires_grad_(False).to(device)\n",
        "\n",
        "clip_models = []\n",
        "if ViTB32 is True: clip_models.append(clip.load('ViT-B/32', jit=False)[0].eval().requires_grad_(False).to(device)) \n",
        "if ViTB16 is True: clip_models.append(clip.load('ViT-B/16', jit=False)[0].eval().requires_grad_(False).to(device) ) \n",
        "if RN50 is True: clip_models.append(clip.load('RN50', jit=False)[0].eval().requires_grad_(False).to(device))\n",
        "if RN50x4 is True: clip_models.append(clip.load('RN50x4', jit=False)[0].eval().requires_grad_(False).to(device)) \n",
        "if RN50x16 is True: clip_models.append(clip.load('RN50x16', jit=False)[0].eval().requires_grad_(False).to(device)) \n",
        "if RN101 is True: clip_models.append(clip.load('RN101', jit=False)[0].eval().requires_grad_(False).to(device)) \n",
        "\n",
        "if SLIPB16:\n",
        "  SLIPB16model = SLIP_VITB16(ssl_mlp_dim=4096, ssl_emb_dim=256)\n",
        "  if not os.path.exists(f'{model_path}/slip_base_100ep.pt'):\n",
        "    !wget https://dl.fbaipublicfiles.com/slip/slip_base_100ep.pt -P {model_path}\n",
        "  sd = torch.load(f'{model_path}/slip_base_100ep.pt')\n",
        "  real_sd = {}\n",
        "  for k, v in sd['state_dict'].items():\n",
        "    real_sd['.'.join(k.split('.')[1:])] = v\n",
        "  del sd\n",
        "  SLIPB16model.load_state_dict(real_sd)\n",
        "  SLIPB16model.requires_grad_(False).eval().to(device)\n",
        "\n",
        "  clip_models.append(SLIPB16model)\n",
        "\n",
        "if SLIPL16:\n",
        "  SLIPL16model = SLIP_VITL16(ssl_mlp_dim=4096, ssl_emb_dim=256)\n",
        "  if not os.path.exists(f'{model_path}/slip_large_100ep.pt'):\n",
        "    !wget https://dl.fbaipublicfiles.com/slip/slip_large_100ep.pt -P {model_path}\n",
        "  sd = torch.load(f'{model_path}/slip_large_100ep.pt')\n",
        "  real_sd = {}\n",
        "  for k, v in sd['state_dict'].items():\n",
        "    real_sd['.'.join(k.split('.')[1:])] = v\n",
        "  del sd\n",
        "  SLIPL16model.load_state_dict(real_sd)\n",
        "  SLIPL16model.requires_grad_(False).eval().to(device)\n",
        "\n",
        "  clip_models.append(SLIPL16model)\n",
        "\n",
        "normalize = T.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])\n",
        "lpips_model = lpips.LPIPS(net='vgg').to(device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjtsXaszn-bB"
      },
      "source": [
        "# 4. Settings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0PwzFZbLfcy",
        "cellView": "form"
      },
      "source": [
        "#@markdown ####**Basic Settings:**\n",
        "batch_name = 'TimeToDiscoLocally' #@param{type: 'string'}\n",
        "steps = 250 #@param [25,50,100,150,250,500,1000]{type: 'raw', allow-input: true}\n",
        "width_height = [1920, 1080]#@param{type: 'raw'}\n",
        "clip_guidance_scale = 5000 #@param{type: 'number'}\n",
        "tv_scale =  0#@param{type: 'number'}\n",
        "range_scale =   150#@param{type: 'number'}\n",
        "sat_scale =   0#@param{type: 'number'}\n",
        "cutn_batches = 6  #@param{type: 'number'}\n",
        "skip_augs = True#@param{type: 'boolean'}\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ####**Init Settings:**\n",
        "init_image = None #@param{type: 'string'}\n",
        "init_scale = 1000 #@param{type: 'integer'}\n",
        "skip_steps = 0 #@param{type: 'integer'}\n",
        "\n",
        "#Get corrected sizes\n",
        "side_x = (width_height[0]//64)*64;\n",
        "side_y = (width_height[1]//64)*64;\n",
        "if side_x != width_height[0] or side_y != width_height[1]:\n",
        "  print(f'Changing output size to {side_x}x{side_y}. Dimensions must by multiples of 64.')\n",
        "\n",
        "#Update Model Settings\n",
        "timestep_respacing = f'ddim{steps}'\n",
        "diffusion_steps = (1000//steps)*steps if steps < 1000 else steps\n",
        "model_config.update({\n",
        "    'timestep_respacing': timestep_respacing,\n",
        "    'diffusion_steps': diffusion_steps,\n",
        "})\n",
        "\n",
        "#Make folder for batch\n",
        "batchFolder = f'{outDirPath}/{batch_name}'\n",
        "createPath(batchFolder)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Animation Settings"
      ],
      "metadata": {
        "id": "CnkTNXJAPzL2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ####**Animation Mode:**\n",
        "animation_mode = \"None\" #@param['None', '2D', 'Video Input']\n",
        "#@markdown *For animation, you probably want to turn `cutn_batches` to 1 to make it quicker.*\n",
        "\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ####**Video Input Settings:**\n",
        "video_init_path = \"/content/training.mp4\" #@param {type: 'string'}\n",
        "extract_nth_frame = 2 #@param {type:\"number\"} \n",
        "\n",
        "if animation_mode == \"Video Input\":\n",
        "  videoFramesFolder = f'/content/videoFrames'\n",
        "  createPath(videoFramesFolder)\n",
        "  print(f\"Exporting Video Frames (1 every {extract_nth_frame})...\")\n",
        "  try:\n",
        "    !rm {videoFramesFolder}/*.jpg\n",
        "  except:\n",
        "    print('')\n",
        "  vf = f'\"select=not(mod(n\\,{extract_nth_frame}))\"'\n",
        "  !ffmpeg -i {video_init_path} -vf {vf} -vsync vfr -q:v 2 -loglevel error -stats {videoFramesFolder}/%04d.jpg\n",
        "\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ####**2D Animation Settings:**\n",
        "#@markdown `zoom` is a multiplier of dimensions, 1 is no zoom.\n",
        "\n",
        "key_frames = True #@param {type:\"boolean\"}\n",
        "max_frames = 10000#@param {type:\"number\"}\n",
        "\n",
        "if animation_mode == \"Video Input\":\n",
        "  max_frames = len(glob(f'{videoFramesFolder}/*.jpg'))\n",
        "\n",
        "interp_spline = 'Linear' #Do not change, currently will not look good. param ['Linear','Quadratic','Cubic']{type:\"string\"}\n",
        "angle = \"0:(0)\"#@param {type:\"string\"}\n",
        "zoom = \"0: (1), 10: (1.05)\"#@param {type:\"string\"}\n",
        "translation_x = \"0: (0)\"#@param {type:\"string\"}\n",
        "translation_y = \"0: (0)\"#@param {type:\"string\"}\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ####**Coherency Settings:**\n",
        "#@markdown `frame_scale` tries to guide the new frame to looking like the old one. A good default is 1500.\n",
        "frames_scale = 1500 #@param{type: 'integer'}\n",
        "#@markdown `frame_skip_steps` will blur the previous frame - higher values will flicker less but struggle to add enough new detail to zoom into.\n",
        "frames_skip_steps = '60%' #@param ['40%', '50%', '60%', '70%', '80%'] {type: 'string'}\n",
        "\n",
        "\n",
        "def parse_key_frames(string, prompt_parser=None):\n",
        "    \"\"\"Given a string representing frame numbers paired with parameter values at that frame,\n",
        "    return a dictionary with the frame numbers as keys and the parameter values as the values.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    string: string\n",
        "        Frame numbers paired with parameter values at that frame number, in the format\n",
        "        'framenumber1: (parametervalues1), framenumber2: (parametervalues2), ...'\n",
        "    prompt_parser: function or None, optional\n",
        "        If provided, prompt_parser will be applied to each string of parameter values.\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    dict\n",
        "        Frame numbers as keys, parameter values at that frame number as values\n",
        "\n",
        "    Raises\n",
        "    ------\n",
        "    RuntimeError\n",
        "        If the input string does not match the expected format.\n",
        "    \n",
        "    Examples\n",
        "    --------\n",
        "    >>> parse_key_frames(\"10:(Apple: 1| Orange: 0), 20: (Apple: 0| Orange: 1| Peach: 1)\")\n",
        "    {10: 'Apple: 1| Orange: 0', 20: 'Apple: 0| Orange: 1| Peach: 1'}\n",
        "\n",
        "    >>> parse_key_frames(\"10:(Apple: 1| Orange: 0), 20: (Apple: 0| Orange: 1| Peach: 1)\", prompt_parser=lambda x: x.lower()))\n",
        "    {10: 'apple: 1| orange: 0', 20: 'apple: 0| orange: 1| peach: 1'}\n",
        "    \"\"\"\n",
        "    import re\n",
        "    pattern = r'((?P<frame>[0-9]+):[\\s]*[\\(](?P<param>[\\S\\s]*?)[\\)])'\n",
        "    frames = dict()\n",
        "    for match_object in re.finditer(pattern, string):\n",
        "        frame = int(match_object.groupdict()['frame'])\n",
        "        param = match_object.groupdict()['param']\n",
        "        if prompt_parser:\n",
        "            frames[frame] = prompt_parser(param)\n",
        "        else:\n",
        "            frames[frame] = param\n",
        "\n",
        "    if frames == {} and len(string) != 0:\n",
        "        raise RuntimeError('Key Frame string not correctly formatted')\n",
        "    return frames\n",
        "\n",
        "def get_inbetweens(key_frames, integer=False):\n",
        "    \"\"\"Given a dict with frame numbers as keys and a parameter value as values,\n",
        "    return a pandas Series containing the value of the parameter at every frame from 0 to max_frames.\n",
        "    Any values not provided in the input dict are calculated by linear interpolation between\n",
        "    the values of the previous and next provided frames. If there is no previous provided frame, then\n",
        "    the value is equal to the value of the next provided frame, or if there is no next provided frame,\n",
        "    then the value is equal to the value of the previous provided frame. If no frames are provided,\n",
        "    all frame values are NaN.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    key_frames: dict\n",
        "        A dict with integer frame numbers as keys and numerical values of a particular parameter as values.\n",
        "    integer: Bool, optional\n",
        "        If True, the values of the output series are converted to integers.\n",
        "        Otherwise, the values are floats.\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    pd.Series\n",
        "        A Series with length max_frames representing the parameter values for each frame.\n",
        "    \n",
        "    Examples\n",
        "    --------\n",
        "    >>> max_frames = 5\n",
        "    >>> get_inbetweens({1: 5, 3: 6})\n",
        "    0    5.0\n",
        "    1    5.0\n",
        "    2    5.5\n",
        "    3    6.0\n",
        "    4    6.0\n",
        "    dtype: float64\n",
        "\n",
        "    >>> get_inbetweens({1: 5, 3: 6}, integer=True)\n",
        "    0    5\n",
        "    1    5\n",
        "    2    5\n",
        "    3    6\n",
        "    4    6\n",
        "    dtype: int64\n",
        "    \"\"\"\n",
        "    key_frame_series = pd.Series([np.nan for a in range(max_frames)])\n",
        "\n",
        "    for i, value in key_frames.items():\n",
        "        key_frame_series[i] = value\n",
        "    key_frame_series = key_frame_series.astype(float)\n",
        "    \n",
        "    interp_method = interp_spline\n",
        "\n",
        "    if interp_method == 'Cubic' and len(key_frames.items()) <=3:\n",
        "      interp_method = 'Quadratic'\n",
        "    \n",
        "    if interp_method == 'Quadratic' and len(key_frames.items()) <= 2:\n",
        "      interp_method = 'Linear'\n",
        "      \n",
        "    \n",
        "    key_frame_series[0] = key_frame_series[key_frame_series.first_valid_index()]\n",
        "    key_frame_series[max_frames-1] = key_frame_series[key_frame_series.last_valid_index()]\n",
        "    # key_frame_series = key_frame_series.interpolate(method=intrp_method,order=1, limit_direction='both')\n",
        "    key_frame_series = key_frame_series.interpolate(method=interp_method.lower(),limit_direction='both')\n",
        "    if integer:\n",
        "        return key_frame_series.astype(int)\n",
        "    return key_frame_series\n",
        "\n",
        "def split_prompts(prompts):\n",
        "  prompt_series = pd.Series([np.nan for a in range(max_frames)])\n",
        "  for i, prompt in prompts.items():\n",
        "    prompt_series[i] = prompt\n",
        "  # prompt_series = prompt_series.astype(str)\n",
        "  prompt_series = prompt_series.ffill().bfill()\n",
        "  return prompt_series\n",
        "\n",
        "if key_frames:\n",
        "    try:\n",
        "        angle_series = get_inbetweens(parse_key_frames(angle))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `angle` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `angle` as \"\n",
        "            f'\"0: ({angle})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        angle = f\"0: ({angle})\"\n",
        "        angle_series = get_inbetweens(parse_key_frames(angle))\n",
        "\n",
        "    try:\n",
        "        zoom_series = get_inbetweens(parse_key_frames(zoom))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `zoom` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `zoom` as \"\n",
        "            f'\"0: ({zoom})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        zoom = f\"0: ({zoom})\"\n",
        "        zoom_series = get_inbetweens(parse_key_frames(zoom))\n",
        "\n",
        "    try:\n",
        "        translation_x_series = get_inbetweens(parse_key_frames(translation_x))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `translation_x` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `translation_x` as \"\n",
        "            f'\"0: ({translation_x})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        translation_x = f\"0: ({translation_x})\"\n",
        "        translation_x_series = get_inbetweens(parse_key_frames(translation_x))\n",
        "\n",
        "    try:\n",
        "        translation_y_series = get_inbetweens(parse_key_frames(translation_y))\n",
        "    except RuntimeError as e:\n",
        "        print(\n",
        "            \"WARNING: You have selected to use key frames, but you have not \"\n",
        "            \"formatted `translation_y` correctly for key frames.\\n\"\n",
        "            \"Attempting to interpret `translation_y` as \"\n",
        "            f'\"0: ({translation_y})\"\\n'\n",
        "            \"Please read the instructions to find out how to use key frames \"\n",
        "            \"correctly.\\n\"\n",
        "        )\n",
        "        translation_y = f\"0: ({translation_y})\"\n",
        "        translation_y_series = get_inbetweens(parse_key_frames(translation_y))\n",
        "\n",
        "else:\n",
        "    angle = float(angle)\n",
        "    zoom = float(zoom)\n",
        "    translation_x = float(translation_x)\n",
        "    translation_y = float(translation_y)\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "djPY2_4kHgV2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Extra Settings\n",
        " Partial Saves, Diffusion Sharpening, Advanced Settings, Cutn Scheduling"
      ],
      "metadata": {
        "id": "u1VHzHvNx5fd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown ####**Saving:**\n",
        "\n",
        "intermediate_saves = 0#@param{type: 'raw'}\n",
        "intermediates_in_subfolder = True #@param{type: 'boolean'}\n",
        "#@markdown Intermediate steps will save a copy at your specified intervals. You can either format it as a single integer or a list of specific steps \n",
        "\n",
        "#@markdown A value of `2` will save a copy at 33% and 66%. 0 will save none.\n",
        "\n",
        "#@markdown A value of `[5, 9, 34, 45]` will save at steps 5, 9, 34, and 45. (Make sure to include the brackets)\n",
        "\n",
        "\n",
        "if type(intermediate_saves) is not list:\n",
        "  if intermediate_saves:\n",
        "    steps_per_checkpoint = math.floor((steps - skip_steps - 1) // (intermediate_saves+1))\n",
        "    steps_per_checkpoint = steps_per_checkpoint if steps_per_checkpoint > 0 else 1\n",
        "    print(f'Will save every {steps_per_checkpoint} steps')\n",
        "  else:\n",
        "    steps_per_checkpoint = steps+10\n",
        "else:\n",
        "  steps_per_checkpoint = None\n",
        "\n",
        "if intermediate_saves and intermediates_in_subfolder is True:\n",
        "  partialFolder = f'{batchFolder}/partials'\n",
        "  createPath(partialFolder)\n",
        "\n",
        "  #@markdown ---\n",
        "\n",
        "#@markdown ####**SuperRes Sharpening:**\n",
        "#@markdown *Sharpen each image using latent-diffusion. Does not run in animation mode. `keep_unsharp` will save both versions.*\n",
        "sharpen_preset = 'Off' #@param ['Off', 'Faster', 'Fast', 'Slow', 'Very Slow']\n",
        "keep_unsharp = True #@param{type: 'boolean'}\n",
        "\n",
        "if sharpen_preset != 'Off' and keep_unsharp is True:\n",
        "  unsharpenFolder = f'{batchFolder}/unsharpened'\n",
        "  createPath(unsharpenFolder)\n",
        "\n",
        "\n",
        "  #@markdown ---\n",
        "\n",
        "#@markdown ####**Advanced Settings:**\n",
        "#@markdown *There are a few extra advanced settings available if you double click this cell.*\n",
        "\n",
        "#@markdown *Perlin init will replace your init, so uncheck if using one.*\n",
        "\n",
        "perlin_init = False  #@param{type: 'boolean'}\n",
        "perlin_mode = 'mixed' #@param ['mixed', 'color', 'gray']\n",
        "set_seed = 'random_seed' #@param{type: 'string'}\n",
        "eta = 0.8#@param{type: 'number'}\n",
        "clamp_grad = True #@param{type: 'boolean'}\n",
        "clamp_max = 0.05 #@param{type: 'number'}\n",
        "\n",
        "\n",
        "### EXTRA ADVANCED SETTINGS:\n",
        "randomize_class = True\n",
        "clip_denoised = False\n",
        "fuzzy_prompt = False\n",
        "rand_mag = 0.05\n",
        "\n",
        "\n",
        " #@markdown ---\n",
        "\n",
        "#@markdown ####**Cutn Scheduling:**\n",
        "#@markdown Format: `[40]*400+[20]*600` = 40 cuts for the first 400 /1000 steps, then 20 for the last 600/1000\n",
        "\n",
        "#@markdown cut_overview and cut_innercut are cumulative for total cutn on any given step. Overview cuts see the entire image and are good for early structure, innercuts are your standard cutn.\n",
        "\n",
        "cut_overview = \"[12]*400+[4]*600\" #@param {type: 'string'}       \n",
        "cut_innercut =\"[4]*400+[12]*600\"#@param {type: 'string'}  \n",
        "cut_ic_pow = 1#@param {type: 'number'}  \n",
        "cut_icgray_p = \"[0.2]*400+[0]*600\"#@param {type: 'string'}  \n",
        "\n"
      ],
      "metadata": {
        "id": "lCLMxtILyAHA",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIwh5RvNpk4K"
      },
      "source": [
        "###Prompts\n",
        "`animation_mode: None` will only use the first set. `animation_mode: 2D / Video` will run through them per the set frames and hold on the last one."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGBzhk3dpcGO"
      },
      "source": [
        "text_prompts = {\n",
        "    0: [\"A beautiful highly detailed raytraced render of a fantasy landscape by james gurney, rendered in octane with CGI lighting and reflections, Trending on cgsociety. #pixelart\"],\n",
        "    # 100: [\"This set of prompts start at frame 100\", \"This prompt has weight five:5\"],\n",
        "}\n",
        "\n",
        "image_prompts = {\n",
        "    # 0:['ImagePromptsWorkButArentVeryGood.png:2',],\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nf9hTc8YLoLx"
      },
      "source": [
        "# 5. Diffuse!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHLiO56OfwgD",
        "cellView": "form"
      },
      "source": [
        "#@title Do the Run!\n",
        "#@markdown `n_batches` ignored with animation modes.\n",
        "display_rate =  50 #@param{type: 'number'}\n",
        "n_batches =  1000 #@param{type: 'number'}\n",
        "\n",
        "batch_size = 1 \n",
        "\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "\n",
        "resume_run = False #@param{type: 'boolean'}\n",
        "run_to_resume = 'latest' #@param{type: 'string'}\n",
        "resume_from_frame = 'latest' #@param{type: 'string'}\n",
        "retain_overwritten_frames = False #@param{type: 'boolean'}\n",
        "if retain_overwritten_frames is True:\n",
        "  retainFolder = f'{batchFolder}/retained'\n",
        "  createPath(retainFolder)\n",
        "\n",
        "\n",
        "skip_step_ratio = int(frames_skip_steps.rstrip(\"%\")) / 100\n",
        "calc_frames_skip_steps = math.floor(steps * skip_step_ratio)\n",
        "\n",
        "\n",
        "if steps <= calc_frames_skip_steps:\n",
        "  sys.exit(\"ERROR: You can't skip more steps than your total steps\")\n",
        "\n",
        "if resume_run:\n",
        "  if run_to_resume == 'latest':\n",
        "    try:\n",
        "      batchNum\n",
        "    except:\n",
        "      batchNum = len(glob(f\"{batchFolder}/{batch_name}(*)_settings.txt\"))-1\n",
        "  else:\n",
        "    batchNum = int(run_to_resume)\n",
        "  if resume_from_frame == 'latest':\n",
        "    start_frame = len(glob(batchFolder+f\"/{batch_name}({batchNum})_*.png\"))\n",
        "  else:\n",
        "    start_frame = int(resume_from_frame)+1\n",
        "    if retain_overwritten_frames is True:\n",
        "      existing_frames = len(glob(batchFolder+f\"/{batch_name}({batchNum})_*.png\"))\n",
        "      frames_to_save = existing_frames - start_frame\n",
        "      print(f'Moving {frames_to_save} frames to the Retained folder')\n",
        "      move_files(start_frame, existing_frames, batchFolder, retainFolder)\n",
        "else:\n",
        "  start_frame = 0\n",
        "  batchNum = len(glob(batchFolder+\"/*.txt\"))\n",
        "  while path.isfile(f\"{batchFolder}/{batch_name}({batchNum})_settings.txt\") is True or path.isfile(f\"{batchFolder}/{batch_name}-{batchNum}_settings.txt\") is True:\n",
        "    batchNum += 1\n",
        "\n",
        "print(f'Starting Run: {batch_name}({batchNum}) at frame {start_frame}')\n",
        "\n",
        "if set_seed == 'random_seed':\n",
        "    random.seed()\n",
        "    seed = random.randint(0, 2**32)\n",
        "    # print(f'Using seed: {seed}')\n",
        "else:\n",
        "    seed = int(set_seed)\n",
        "\n",
        "args = {\n",
        "    'batchNum': batchNum,\n",
        "    'prompts_series':split_prompts(text_prompts) if text_prompts else None,\n",
        "    'image_prompts_series':split_prompts(image_prompts) if image_prompts else None,\n",
        "    'seed': seed,\n",
        "    'display_rate':display_rate,\n",
        "    'n_batches':n_batches if animation_mode == 'None' else 1,\n",
        "    'batch_size':batch_size,\n",
        "    'batch_name': batch_name,\n",
        "    'steps': steps,\n",
        "    'width_height': width_height,\n",
        "    'clip_guidance_scale': clip_guidance_scale,\n",
        "    'tv_scale': tv_scale,\n",
        "    'range_scale': range_scale,\n",
        "    'sat_scale': sat_scale,\n",
        "    'cutn_batches': cutn_batches,\n",
        "    'init_image': init_image,\n",
        "    'init_scale': init_scale,\n",
        "    'skip_steps': skip_steps,\n",
        "    'sharpen_preset': sharpen_preset,\n",
        "    'keep_unsharp': keep_unsharp,\n",
        "    'side_x': side_x,\n",
        "    'side_y': side_y,\n",
        "    'timestep_respacing': timestep_respacing,\n",
        "    'diffusion_steps': diffusion_steps,\n",
        "    'animation_mode': animation_mode,\n",
        "    'video_init_path': video_init_path,\n",
        "    'extract_nth_frame': extract_nth_frame,\n",
        "    'key_frames': key_frames,\n",
        "    'max_frames': max_frames if animation_mode != \"None\" else 1,\n",
        "    'interp_spline': interp_spline,\n",
        "    'start_frame': start_frame,\n",
        "    'angle': angle,\n",
        "    'zoom': zoom,\n",
        "    'translation_x': translation_x,\n",
        "    'translation_y': translation_y,\n",
        "    'angle_series':angle_series,\n",
        "    'zoom_series':zoom_series,\n",
        "    'translation_x_series':translation_x_series,\n",
        "    'translation_y_series':translation_y_series,\n",
        "    'frames_scale': frames_scale,\n",
        "    'calc_frames_skip_steps': calc_frames_skip_steps,\n",
        "    'skip_step_ratio': skip_step_ratio,\n",
        "    'calc_frames_skip_steps': calc_frames_skip_steps,\n",
        "    'text_prompts': text_prompts,\n",
        "    'image_prompts': image_prompts,\n",
        "    'cut_overview': eval(cut_overview),\n",
        "    'cut_innercut': eval(cut_innercut),\n",
        "    'cut_ic_pow': cut_ic_pow,\n",
        "    'cut_icgray_p': eval(cut_icgray_p),\n",
        "    'intermediate_saves': intermediate_saves,\n",
        "    'intermediates_in_subfolder': intermediates_in_subfolder,\n",
        "    'steps_per_checkpoint': steps_per_checkpoint,\n",
        "    'perlin_init': perlin_init,\n",
        "    'perlin_mode': perlin_mode,\n",
        "    'set_seed': set_seed,\n",
        "    'eta': eta,\n",
        "    'clamp_grad': clamp_grad,\n",
        "    'clamp_max': clamp_max,\n",
        "    'skip_augs': skip_augs,\n",
        "    'randomize_class': randomize_class,\n",
        "    'clip_denoised': clip_denoised,\n",
        "    'fuzzy_prompt': fuzzy_prompt,\n",
        "    'rand_mag': rand_mag,\n",
        "}\n",
        "\n",
        "args = SimpleNamespace(**args)\n",
        "\n",
        "print('Prepping model...')\n",
        "model, diffusion = create_model_and_diffusion(**model_config)\n",
        "model.load_state_dict(torch.load(f'{model_path}/{diffusion_model}.pt', map_location='cpu'))\n",
        "model.requires_grad_(False).eval().to(device)\n",
        "for name, param in model.named_parameters():\n",
        "    if 'qkv' in name or 'norm' in name or 'proj' in name:\n",
        "        param.requires_grad_()\n",
        "if model_config['use_fp16']:\n",
        "    model.convert_to_fp16()\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "try:\n",
        "  do_run()\n",
        "except KeyboardInterrupt:\n",
        "    pass\n",
        "finally:\n",
        "    print('Seed used:', seed)\n",
        "    gc.collect()\n",
        "    torch.cuda.empty_cache()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZUg3bfzazgW"
      },
      "source": [
        "# 6. Create the video"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ### **Create video**\n",
        "#@markdown Video file will save in the same folder as your images.\n",
        "\n",
        "skip_video_for_run_all = True #@param {type: 'boolean'}\n",
        "\n",
        "if skip_video_for_run_all == False:\n",
        "  # import subprocess in case this cell is run without the above cells\n",
        "  import subprocess\n",
        "  from base64 import b64encode\n",
        "\n",
        "  latest_run = batchNum\n",
        "\n",
        "  folder = batch_name #@param\n",
        "  run = latest_run #@param\n",
        "  final_frame = 'final_frame'\n",
        "\n",
        "\n",
        "  init_frame = 1#@param {type:\"number\"} This is the frame where the video will start\n",
        "  last_frame = final_frame#@param {type:\"number\"} You can change i to the number of the last frame you want to generate. It will raise an error if that number of frames does not exist.\n",
        "  fps = 12#@param {type:\"number\"}\n",
        "  view_video_in_cell = False #@param {type: 'boolean'}\n",
        "\n",
        "  frames = []\n",
        "  # tqdm.write('Generating video...')\n",
        "\n",
        "  if last_frame == 'final_frame':\n",
        "    last_frame = len(glob(batchFolder+f\"/{folder}({run})_*.png\"))\n",
        "    print(f'Total frames: {last_frame}')\n",
        "\n",
        "  image_path = f\"{outDirPath}/{folder}/{folder}({run})_%04d.png\"\n",
        "  filepath = f\"{outDirPath}/{folder}/{folder}({run}).mp4\"\n",
        "\n",
        "\n",
        "  cmd = [\n",
        "      'ffmpeg',\n",
        "      '-y',\n",
        "      '-vcodec',\n",
        "      'png',\n",
        "      '-r',\n",
        "      str(fps),\n",
        "      '-start_number',\n",
        "      str(init_frame),\n",
        "      '-i',\n",
        "      image_path,\n",
        "      '-frames:v',\n",
        "      str(last_frame+1),\n",
        "      '-c:v',\n",
        "      'libx264',\n",
        "      '-vf',\n",
        "      f'fps={fps}',\n",
        "      '-pix_fmt',\n",
        "      'yuv420p',\n",
        "      '-crf',\n",
        "      '17',\n",
        "      '-preset',\n",
        "      'veryslow',\n",
        "      filepath\n",
        "  ]\n",
        "\n",
        "  process = subprocess.Popen(cmd, cwd=f'{batchFolder}', stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
        "  stdout, stderr = process.communicate()\n",
        "  if process.returncode != 0:\n",
        "      print(stderr)\n",
        "      raise RuntimeError(stderr)\n",
        "  else:\n",
        "      print(\"The video is ready\")\n",
        "\n",
        "  if view_video_in_cell:\n",
        "      mp4 = open(filepath,'rb').read()\n",
        "      data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "      display.HTML(\"\"\"\n",
        "      <video width=400 controls>\n",
        "            <source src=\"%s\" type=\"video/mp4\">\n",
        "      </video>\n",
        "      \"\"\" % data_url)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "HV54fuU3pMzJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}