VQGAN+CLIP_(z+quantize_method_with_augmentations,_user_friendly_interface).ipynb

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    "colab": {
      "name": "VQGAN+CLIP_(z+quantize_method_with_augmentations,_user_friendly_interface).ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2061ea2b-37f6-44d5-8012-58bd9abf6548"
      },
      "source": [
        "**Generate images from text phrases with VQGAN and CLIP**  (*z+quantize method with augmentations*)."
      ],
      "id": "2061ea2b-37f6-44d5-8012-58bd9abf6548"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3d7a8be-73ce-4cee-be70-e21c1210a7a6"
      },
      "source": [
        "Original Notebook made by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings).\n",
        "\n",
        "Modified by: Justin John"
      ],
      "id": "c3d7a8be-73ce-4cee-be70-e21c1210a7a6"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2505191-1756-4e5d-b9a8-33af798ad879",
        "cellView": "form"
      },
      "source": [
        "#@markdown #**Licensed under the MIT License (*Double-click me to read the license agreement*)**\n",
        "#@markdown ---\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."
      ],
      "id": "c2505191-1756-4e5d-b9a8-33af798ad879",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "809824ee-8f52-4986-a9d3-514b14df72b7",
        "cellView": "form"
      },
      "source": [
        "#@markdown #**Check GPU type**\n",
        "#@markdown ### Factory reset runtime if you don't have the desired GPU.\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#@markdown V100 = Excellent (*Available only for Colab Pro Users*)\n",
        "\n",
        "#@markdown P100 = Very Good\n",
        "\n",
        "#@markdown T4 = Good\n",
        "\n",
        "#@markdown K80 = Meh\n",
        "\n",
        "#@markdown P4 = (Not Recommended) *for heavy A.I Models like COCO, WikiArt 1024, WikiArt 16384, FacesHQ or S-FLCKR*\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "!nvidia-smi -L"
      ],
      "id": "809824ee-8f52-4986-a9d3-514b14df72b7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHyPd4oxVp_l",
        "cellView": "form"
      },
      "source": [
        "#@markdown #**Anti-Disconnect for Google Colab**\n",
        "#@markdown ## Run this to stop it from disconnecting automatically \n",
        "#@markdown  **(It will anyhow disconnect after 6 - 12 hrs for using the free version of Colab.)**\n",
        "#@markdown  *(Colab Pro users will get about 24 hrs usage time)*\n",
        "#@markdown ---\n",
        "\n",
        "import IPython\n",
        "js_code = '''\n",
        "function ClickConnect(){\n",
        "console.log(\"Working\");\n",
        "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
        "}\n",
        "setInterval(ClickConnect,60000)\n",
        "'''\n",
        "display(IPython.display.Javascript(js_code))"
      ],
      "id": "XHyPd4oxVp_l",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45cac47a-fbac-48bd-8f70-95d5099a12e9",
        "cellView": "form"
      },
      "source": [
        "#@markdown #**Installation of libraries**\n",
        "# @markdown This cell will take a little while because it has to download several libraries\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "!git clone https://github.com/openai/CLIP\n",
        "!pip install taming-transformers\n",
        "!git clone https://github.com/CompVis/taming-transformers.git\n",
        "!pip install ftfy regex tqdm omegaconf pytorch-lightning\n",
        "!pip install kornia\n",
        "!pip install imageio-ffmpeg   \n",
        "!pip install einops          \n",
        "!mkdir steps"
      ],
      "id": "45cac47a-fbac-48bd-8f70-95d5099a12e9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "af151e0b-8b57-4f8c-92dd-947f122db8c2",
        "cellView": "form"
      },
      "source": [
        "#@markdown #**Selection of models to download**\n",
        "#@markdown ---\n",
        "#@markdown **By default, the notebook downloads the 1024 and 16384 models from ImageNet. There are others like COCO-Stuff, WikiArt 1024, WikiArt 16384, FacesHQ or S-FLCKR, which are heavy, and if you are not going to use them it would be pointless to download them, so if you want to use them, simply select the models to download.**\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "imagenet_1024 = True #@param {type:\"boolean\"}\n",
        "imagenet_16384 = True #@param {type:\"boolean\"}\n",
        "coco = False #@param {type:\"boolean\"}\n",
        "faceshq = False #@param {type:\"boolean\"}\n",
        "wikiart_1024 = False #@param {type:\"boolean\"}\n",
        "wikiart_16384 = False #@param {type:\"boolean\"}\n",
        "sflckr = False #@param {type:\"boolean\"}\n",
        "openimages_8192 = False #@param {type:\"boolean\"}\n",
        "\n",
        "if imagenet_1024:\n",
        "  !curl -L -o vqgan_imagenet_f16_1024.yaml -C - 'http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_1024.yaml' #ImageNet 1024\n",
        "  !curl -L -o vqgan_imagenet_f16_1024.ckpt -C - 'http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_1024.ckpt'  #ImageNet 1024\n",
        "if imagenet_16384:\n",
        "  !curl -L -o vqgan_imagenet_f16_16384.yaml -C - 'http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_16384.yaml' #ImageNet 16384\n",
        "  !curl -L -o vqgan_imagenet_f16_16384.ckpt -C - 'http://mirror.io.community/blob/vqgan/vqgan_imagenet_f16_16384.ckpt' #ImageNet 16384\n",
        "if openimages_8192:\n",
        "  !curl -L -o vqgan_openimages_f16_8192.yaml -C - 'https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/files/?p=%2Fconfigs%2Fmodel.yaml&dl=1' #ImageNet 16384\n",
        "  !curl -L -o vqgan_openimages_f16_8192.ckpt -C - 'https://heibox.uni-heidelberg.de/d/2e5662443a6b4307b470/files/?p=%2Fckpts%2Flast.ckpt&dl=1' #ImageNet 16384\n",
        "\n",
        "if coco:\n",
        "  !curl -L -o coco.yaml -C - 'https://dl.nmkd.de/ai/clip/coco/coco.yaml' #COCO\n",
        "  !curl -L -o coco.ckpt -C - 'https://dl.nmkd.de/ai/clip/coco/coco.ckpt' #COCO\n",
        "if faceshq:\n",
        "  !curl -L -o faceshq.yaml -C - 'https://drive.google.com/uc?export=download&id=1fHwGx_hnBtC8nsq7hesJvs-Klv-P0gzT' #FacesHQ\n",
        "  !curl -L -o faceshq.ckpt -C - 'https://app.koofr.net/content/links/a04deec9-0c59-4673-8b37-3d696fe63a5d/files/get/last.ckpt?path=%2F2020-11-13T21-41-45_faceshq_transformer%2Fcheckpoints%2Flast.ckpt' #FacesHQ\n",
        "if wikiart_1024: \n",
        "  !curl -L -o wikiart_1024.yaml -C - 'http://mirror.io.community/blob/vqgan/wikiart.yaml' #WikiArt 1024\n",
        "  !curl -L -o wikiart_1024.ckpt -C - 'http://mirror.io.community/blob/vqgan/wikiart.ckpt' #WikiArt 1024\n",
        "if wikiart_16384: \n",
        "  !curl -L -o wikiart_16384.yaml -C - 'http://mirror.io.community/blob/vqgan/wikiart_16384.yaml' #WikiArt 16384\n",
        "  !curl -L -o wikiart_16384.ckpt -C - 'http://mirror.io.community/blob/vqgan/wikiart_16384.ckpt' #WikiArt 16384\n",
        "if sflckr:\n",
        "  !curl -L -o sflckr.yaml -C - 'https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fconfigs%2F2020-11-09T13-31-51-project.yaml&dl=1' #S-FLCKR\n",
        "  !curl -L -o sflckr.ckpt -C - 'https://heibox.uni-heidelberg.de/d/73487ab6e5314cb5adba/files/?p=%2Fcheckpoints%2Flast.ckpt&dl=1' #S-FLCKR"
      ],
      "id": "af151e0b-8b57-4f8c-92dd-947f122db8c2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "66d3b419-955d-44f7-ad87-904ee3c16ff8",
        "cellView": "form"
      },
      "source": [
        "#@markdown #**Loading libraries and definitions**\n",
        "\n",
        "import argparse\n",
        "import math\n",
        "from pathlib import Path\n",
        "import sys\n",
        "\n",
        "sys.path.insert(1, '/content/taming-transformers')\n",
        "from IPython import display\n",
        "from base64 import b64encode\n",
        "from omegaconf import OmegaConf\n",
        "from PIL import Image\n",
        "from taming.models import cond_transformer, vqgan\n",
        "import taming.modules \n",
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "from torchvision import transforms\n",
        "from torchvision.transforms import functional as TF\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "from CLIP import clip\n",
        "import kornia.augmentation as K\n",
        "import numpy as np\n",
        "import imageio\n",
        "from PIL import ImageFile, Image\n",
        "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
        "\n",
        "def sinc(x):\n",
        "    return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))\n",
        "\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",
        "\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",
        "\n",
        "def resample(input, size, align_corners=True):\n",
        "    n, c, h, w = input.shape\n",
        "    dh, dw = size\n",
        "\n",
        "    input = input.view([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.view([n, c, h, w])\n",
        "    return F.interpolate(input, size, mode='bicubic', align_corners=align_corners)\n",
        "\n",
        "\n",
        "class ReplaceGrad(torch.autograd.Function):\n",
        "    @staticmethod\n",
        "    def forward(ctx, x_forward, x_backward):\n",
        "        ctx.shape = x_backward.shape\n",
        "        return x_forward\n",
        "\n",
        "    @staticmethod\n",
        "    def backward(ctx, grad_in):\n",
        "        return None, grad_in.sum_to_size(ctx.shape)\n",
        "\n",
        "\n",
        "replace_grad = ReplaceGrad.apply\n",
        "\n",
        "\n",
        "class ClampWithGrad(torch.autograd.Function):\n",
        "    @staticmethod\n",
        "    def forward(ctx, input, min, max):\n",
        "        ctx.min = min\n",
        "        ctx.max = max\n",
        "        ctx.save_for_backward(input)\n",
        "        return input.clamp(min, max)\n",
        "\n",
        "    @staticmethod\n",
        "    def backward(ctx, grad_in):\n",
        "        input, = ctx.saved_tensors\n",
        "        return grad_in * (grad_in * (input - input.clamp(ctx.min, ctx.max)) >= 0), None, None\n",
        "\n",
        "\n",
        "clamp_with_grad = ClampWithGrad.apply\n",
        "\n",
        "\n",
        "def vector_quantize(x, codebook):\n",
        "    d = x.pow(2).sum(dim=-1, keepdim=True) + codebook.pow(2).sum(dim=1) - 2 * x @ codebook.T\n",
        "    indices = d.argmin(-1)\n",
        "    x_q = F.one_hot(indices, codebook.shape[0]).to(d.dtype) @ codebook\n",
        "    return replace_grad(x_q, x)\n",
        "\n",
        "\n",
        "class Prompt(nn.Module):\n",
        "    def __init__(self, embed, weight=1., stop=float('-inf')):\n",
        "        super().__init__()\n",
        "        self.register_buffer('embed', embed)\n",
        "        self.register_buffer('weight', torch.as_tensor(weight))\n",
        "        self.register_buffer('stop', torch.as_tensor(stop))\n",
        "\n",
        "    def forward(self, input):\n",
        "        input_normed = F.normalize(input.unsqueeze(1), dim=2)\n",
        "        embed_normed = F.normalize(self.embed.unsqueeze(0), dim=2)\n",
        "        dists = input_normed.sub(embed_normed).norm(dim=2).div(2).arcsin().pow(2).mul(2)\n",
        "        dists = dists * self.weight.sign()\n",
        "        return self.weight.abs() * replace_grad(dists, torch.maximum(dists, self.stop)).mean()\n",
        "\n",
        "\n",
        "def parse_prompt(prompt):\n",
        "    vals = prompt.rsplit(':', 2)\n",
        "    vals = vals + ['', '1', '-inf'][len(vals):]\n",
        "    return vals[0], float(vals[1]), float(vals[2])\n",
        "\n",
        "\n",
        "class MakeCutouts(nn.Module):\n",
        "    def __init__(self, cut_size, cutn, cut_pow=1.):\n",
        "        super().__init__()\n",
        "        self.cut_size = cut_size\n",
        "        self.cutn = cutn\n",
        "        self.cut_pow = cut_pow\n",
        "\n",
        "        self.augs = nn.Sequential(\n",
        "            # K.RandomHorizontalFlip(p=0.5),\n",
        "            # K.RandomVerticalFlip(p=0.5),\n",
        "            # K.RandomSolarize(0.01, 0.01, p=0.7),\n",
        "            # K.RandomSharpness(0.3,p=0.4),\n",
        "            # K.RandomResizedCrop(size=(self.cut_size,self.cut_size), scale=(0.1,1),  ratio=(0.75,1.333), cropping_mode='resample', p=0.5),\n",
        "            # K.RandomCrop(size=(self.cut_size,self.cut_size), p=0.5),\n",
        "            K.RandomAffine(degrees=15, translate=0.1, p=0.7, padding_mode='border'),\n",
        "            K.RandomPerspective(0.7,p=0.7),\n",
        "            K.ColorJitter(hue=0.1, saturation=0.1, p=0.7),\n",
        "            K.RandomErasing((.1, .4), (.3, 1/.3), same_on_batch=True, p=0.7),\n",
        "            \n",
        ")\n",
        "        self.noise_fac = 0.1\n",
        "        self.av_pool = nn.AdaptiveAvgPool2d((self.cut_size, self.cut_size))\n",
        "        self.max_pool = nn.AdaptiveMaxPool2d((self.cut_size, self.cut_size))\n",
        "\n",
        "    def forward(self, input):\n",
        "        sideY, sideX = input.shape[2:4]\n",
        "        max_size = min(sideX, sideY)\n",
        "        min_size = min(sideX, sideY, self.cut_size)\n",
        "        cutouts = []\n",
        "        \n",
        "        for _ in range(self.cutn):\n",
        "\n",
        "            # size = int(torch.rand([])**self.cut_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",
        "            # cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))\n",
        "\n",
        "            # cutout = transforms.Resize(size=(self.cut_size, self.cut_size))(input)\n",
        "            \n",
        "            cutout = (self.av_pool(input) + self.max_pool(input))/2\n",
        "            cutouts.append(cutout)\n",
        "        batch = self.augs(torch.cat(cutouts, dim=0))\n",
        "        if self.noise_fac:\n",
        "            facs = batch.new_empty([self.cutn, 1, 1, 1]).uniform_(0, self.noise_fac)\n",
        "            batch = batch + facs * torch.randn_like(batch)\n",
        "        return batch\n",
        "\n",
        "\n",
        "def load_vqgan_model(config_path, checkpoint_path):\n",
        "    config = OmegaConf.load(config_path)\n",
        "    if config.model.target == 'taming.models.vqgan.VQModel':\n",
        "        model = vqgan.VQModel(**config.model.params)\n",
        "        model.eval().requires_grad_(False)\n",
        "        model.init_from_ckpt(checkpoint_path)\n",
        "    elif config.model.target == 'taming.models.vqgan.GumbelVQ':\n",
        "        model = vqgan.GumbelVQ(**config.model.params)\n",
        "        model.eval().requires_grad_(False)\n",
        "        model.init_from_ckpt(checkpoint_path)\n",
        "    elif config.model.target == 'taming.models.cond_transformer.Net2NetTransformer':\n",
        "        parent_model = cond_transformer.Net2NetTransformer(**config.model.params)\n",
        "        parent_model.eval().requires_grad_(False)\n",
        "        parent_model.init_from_ckpt(checkpoint_path)\n",
        "        model = parent_model.first_stage_model\n",
        "    else:\n",
        "        raise ValueError(f'unknown model type: {config.model.target}')\n",
        "    del model.loss\n",
        "    return model\n",
        "\n",
        "\n",
        "def resize_image(image, out_size):\n",
        "    ratio = image.size[0] / image.size[1]\n",
        "    area = min(image.size[0] * image.size[1], out_size[0] * out_size[1])\n",
        "    size = round((area * ratio)**0.5), round((area / ratio)**0.5)\n",
        "    return image.resize(size, Image.LANCZOS)"
      ],
      "id": "66d3b419-955d-44f7-ad87-904ee3c16ff8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f80696fb-8535-488b-b83e-a3e4928d6e6a"
      },
      "source": [
        "## Settings for this run:\n",
        "Mainly what you will have to modify will be `texts:`, there you can place the text or texts you want to generate (separated with `|`). It is a list because you can put more than one text, and so the AI ​​tries to 'mix' the images, giving the same priority to both texts.\n",
        "\n",
        "To use an initial image to the model, you just have to upload a file to the Colab environment (in the section on the left), and then modify `init_image:` putting the exact name of the file. Example: `sample.png`\n",
        "\n",
        "You can also modify the model by changing the lines that say `model:`. Currently ImageNet 1024, ImageNet 16384, WikiArt 1024, WikiArt 16384, S-FLCKR, COCO-Stuff and Open Images are available. To activate them you have to have downloaded them first, and then you can simply select it.\n",
        "\n",
        "You can also use `target_images`, which is basically putting one or more images on it that the AI ​​will take as a \"target\", fulfilling the same function as putting text on it. To put more than one you have to use `|` as a separator.\n",
        "\n",
        "**You can change the width and the height of the image that will be generated according to your liking.**"
      ],
      "id": "f80696fb-8535-488b-b83e-a3e4928d6e6a"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fccf05b3-2e0a-46a1-a377-607d151377ac",
        "cellView": "form"
      },
      "source": [
        "#@markdown #**Parameters**\n",
        "#@markdown ---\n",
        "\n",
        "texts = \"\" #@param {type:\"string\"}\n",
        "width =  300#@param {type:\"number\"}\n",
        "height = 300#@param {type:\"number\"}\n",
        "model = \"vqgan_imagenet_f16_16384\" #@param [\"vqgan_imagenet_f16_16384\", \"vqgan_imagenet_f16_1024\", \"vqgan_openimages_f16_8192\", \"wikiart_1024\", \"wikiart_16384\", \"coco\", \"faceshq\", \"sflckr\"]\n",
        "images_interval =  50#@param {type:\"number\"}\n",
        "init_image = \"\"#@param {type:\"string\"}\n",
        "target_images = \"\"#@param {type:\"string\"}\n",
        "seed = -1#@param {type:\"number\"}\n",
        "max_iterations = -1#@param {type:\"number\"}\n",
        "\n",
        "model_names={\"vqgan_imagenet_f16_16384\": 'ImageNet 16384',\"vqgan_imagenet_f16_1024\":\"ImageNet 1024\", 'vqgan_openimages_f16_8192':'OpenImages 8912',\n",
        "                 \"wikiart_1024\":\"WikiArt 1024\", \"wikiart_16384\":\"WikiArt 16384\", \"coco\":\"COCO-Stuff\", \"faceshq\":\"FacesHQ\", \"sflckr\":\"S-FLCKR\"}\n",
        "name_model = model_names[model]     \n",
        "\n",
        "if seed == -1:\n",
        "    seed = None\n",
        "if init_image == \"None\":\n",
        "    init_image = None\n",
        "if target_images == \"None\" or not target_images:\n",
        "    target_images = []\n",
        "else:\n",
        "    target_images = target_images.split(\"|\")\n",
        "    target_images = [image.strip() for image in target_images]\n",
        "\n",
        "texts = [phrase.strip() for phrase in texts.split(\"|\")]\n",
        "if texts == ['']:\n",
        "    texts = []\n",
        "\n",
        "\n",
        "args = argparse.Namespace(\n",
        "    prompts=texts,\n",
        "    image_prompts=target_images,\n",
        "    noise_prompt_seeds=[],\n",
        "    noise_prompt_weights=[],\n",
        "    size=[width, height],\n",
        "    init_image=init_image,\n",
        "    init_weight=0.,\n",
        "    clip_model='ViT-B/32',\n",
        "    vqgan_config=f'{model}.yaml',\n",
        "    vqgan_checkpoint=f'{model}.ckpt',\n",
        "    step_size=0.1,\n",
        "    cutn=32,\n",
        "    cut_pow=1.,\n",
        "    display_freq=images_interval,\n",
        "    seed=seed,\n",
        ")"
      ],
      "id": "fccf05b3-2e0a-46a1-a377-607d151377ac",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a09f280f-bc39-4e33-b35c-510a38fbc7bf",
        "cellView": "form"
      },
      "source": [
        "#@markdown #**Fire up the AI**\n",
        "\n",
        "#@markdown ---\n",
        "from urllib.request import urlopen\n",
        "\n",
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "print('Using device:', device)\n",
        "if texts:\n",
        "    print('Using texts:', texts)\n",
        "if target_images:\n",
        "    print('Using image prompts:', target_images)\n",
        "if args.seed is None:\n",
        "    seed = torch.seed()\n",
        "else:\n",
        "    seed = args.seed\n",
        "torch.manual_seed(seed)\n",
        "print('Using seed:', seed)\n",
        "\n",
        "model = load_vqgan_model(args.vqgan_config, args.vqgan_checkpoint).to(device)\n",
        "perceptor = clip.load(args.clip_model, jit=False)[0].eval().requires_grad_(False).to(device)\n",
        "# clock=deepcopy(perceptor.visual.positional_embedding.data)\n",
        "# perceptor.visual.positional_embedding.data = clock/clock.max()\n",
        "# perceptor.visual.positional_embedding.data=clamp_with_grad(clock,0,1)\n",
        "\n",
        "cut_size = perceptor.visual.input_resolution\n",
        "\n",
        "f = 2**(model.decoder.num_resolutions - 1)\n",
        "make_cutouts = MakeCutouts(cut_size, args.cutn, cut_pow=args.cut_pow)\n",
        "\n",
        "toksX, toksY = args.size[0] // f, args.size[1] // f\n",
        "sideX, sideY = toksX * f, toksY * f\n",
        "\n",
        "if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt':\n",
        "    e_dim = 256\n",
        "    n_toks = model.quantize.n_embed\n",
        "    z_min = model.quantize.embed.weight.min(dim=0).values[None, :, None, None]\n",
        "    z_max = model.quantize.embed.weight.max(dim=0).values[None, :, None, None]\n",
        "else:\n",
        "    e_dim = model.quantize.e_dim\n",
        "    n_toks = model.quantize.n_e\n",
        "    z_min = model.quantize.embedding.weight.min(dim=0).values[None, :, None, None]\n",
        "    z_max = model.quantize.embedding.weight.max(dim=0).values[None, :, None, None]\n",
        "# z_min = model.quantize.embedding.weight.min(dim=0).values[None, :, None, None]\n",
        "# z_max = model.quantize.embedding.weight.max(dim=0).values[None, :, None, None]\n",
        "\n",
        "# normalize_imagenet = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
        "#                                            std=[0.229, 0.224, 0.225])\n",
        "\n",
        "if args.init_image:\n",
        "    if 'http' in args.init_image:\n",
        "      img = Image.open(urlopen(args.init_image))\n",
        "    else:\n",
        "      img = Image.open(args.init_image)\n",
        "    pil_image = img.convert('RGB')\n",
        "    pil_image = pil_image.resize((sideX, sideY), Image.LANCZOS)\n",
        "    pil_tensor = TF.to_tensor(pil_image)\n",
        "    z, *_ = model.encode(pil_tensor.to(device).unsqueeze(0) * 2 - 1)\n",
        "else:\n",
        "    one_hot = F.one_hot(torch.randint(n_toks, [toksY * toksX], device=device), n_toks).float()\n",
        "    # z = one_hot @ model.quantize.embedding.weight\n",
        "    if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt':\n",
        "        z = one_hot @ model.quantize.embed.weight\n",
        "    else:\n",
        "        z = one_hot @ model.quantize.embedding.weight\n",
        "    z = z.view([-1, toksY, toksX, e_dim]).permute(0, 3, 1, 2) \n",
        "    z = torch.rand_like(z)*2\n",
        "z_orig = z.clone()\n",
        "z.requires_grad_(True)\n",
        "opt = optim.Adam([z], lr=args.step_size)\n",
        "\n",
        "normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],\n",
        "                                  std=[0.26862954, 0.26130258, 0.27577711])\n",
        "\n",
        "\n",
        "\n",
        "pMs = []\n",
        "\n",
        "for prompt in args.prompts:\n",
        "    txt, weight, stop = parse_prompt(prompt)\n",
        "    embed = perceptor.encode_text(clip.tokenize(txt).to(device)).float()\n",
        "    pMs.append(Prompt(embed, weight, stop).to(device))\n",
        "\n",
        "for prompt in args.image_prompts:\n",
        "    path, weight, stop = parse_prompt(prompt)\n",
        "    img = Image.open(path)\n",
        "    pil_image = img.convert('RGB')\n",
        "    img = resize_image(pil_image, (sideX, sideY))\n",
        "    batch = make_cutouts(TF.to_tensor(img).unsqueeze(0).to(device))\n",
        "    embed = perceptor.encode_image(normalize(batch)).float()\n",
        "    pMs.append(Prompt(embed, weight, stop).to(device))\n",
        "\n",
        "for seed, weight in zip(args.noise_prompt_seeds, args.noise_prompt_weights):\n",
        "    gen = torch.Generator().manual_seed(seed)\n",
        "    embed = torch.empty([1, perceptor.visual.output_dim]).normal_(generator=gen)\n",
        "    pMs.append(Prompt(embed, weight).to(device))\n",
        "\n",
        "def synth(z):\n",
        "    if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt':\n",
        "        z_q = vector_quantize(z.movedim(1, 3), model.quantize.embed.weight).movedim(3, 1)\n",
        "    else:\n",
        "        z_q = vector_quantize(z.movedim(1, 3), model.quantize.embedding.weight).movedim(3, 1)\n",
        "    return clamp_with_grad(model.decode(z_q).add(1).div(2), 0, 1)\n",
        "\n",
        "@torch.no_grad()\n",
        "def checkin(i, losses):\n",
        "    losses_str = ', '.join(f'{loss.item():g}' for loss in losses)\n",
        "    tqdm.write(f'i: {i}, loss: {sum(losses).item():g}, losses: {losses_str}')\n",
        "    out = synth(z)\n",
        "    TF.to_pil_image(out[0].cpu()).save('progress.png')\n",
        "    display.display(display.Image('progress.png'))\n",
        "\n",
        "def ascend_txt():\n",
        "    global i\n",
        "    out = synth(z)\n",
        "    iii = perceptor.encode_image(normalize(make_cutouts(out))).float()\n",
        "    \n",
        "    result = []\n",
        "\n",
        "    if args.init_weight:\n",
        "        # result.append(F.mse_loss(z, z_orig) * args.init_weight / 2)\n",
        "        result.append(F.mse_loss(z, torch.zeros_like(z_orig)) * ((1/torch.tensor(i*2 + 1))*args.init_weight) / 2)\n",
        "    for prompt in pMs:\n",
        "        result.append(prompt(iii))\n",
        "    img = np.array(out.mul(255).clamp(0, 255)[0].cpu().detach().numpy().astype(np.uint8))[:,:,:]\n",
        "    img = np.transpose(img, (1, 2, 0))\n",
        "    imageio.imwrite('./steps/' + str(i) + '.png', np.array(img))\n",
        "\n",
        "    return result\n",
        "\n",
        "def train(i):\n",
        "    opt.zero_grad()\n",
        "    lossAll = ascend_txt()\n",
        "    if i % args.display_freq == 0:\n",
        "        checkin(i, lossAll)\n",
        "       \n",
        "    loss = sum(lossAll)\n",
        "    loss.backward()\n",
        "    opt.step()\n",
        "    with torch.no_grad():\n",
        "        z.copy_(z.maximum(z_min).minimum(z_max))\n",
        "\n",
        "i = 0\n",
        "try:\n",
        "    with tqdm() as pbar:\n",
        "        while True:\n",
        "            train(i)\n",
        "            if i == max_iterations:\n",
        "                break\n",
        "            i += 1\n",
        "            pbar.update()\n",
        "except KeyboardInterrupt:\n",
        "    pass"
      ],
      "id": "a09f280f-bc39-4e33-b35c-510a38fbc7bf",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c053240e-8a05-448d-9977-8308179b6d09",
        "cellView": "form"
      },
      "source": [
        "#@markdown **Generate a video with the result (You can edit frame rate and stuff by double-clicking this tab)**\n",
        "init_frame = 1 #This is the frame where the video will start\n",
        "last_frame = i #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",
        "\n",
        "min_fps = 10\n",
        "max_fps = 60\n",
        "\n",
        "total_frames = last_frame-init_frame\n",
        "\n",
        "length = 15 #Desired time of the video in seconds\n",
        "\n",
        "frames = []\n",
        "tqdm.write('Generating video...')\n",
        "for i in range(init_frame,last_frame): #\n",
        "    frames.append(Image.open(\"./steps/\"+ str(i) +'.png'))\n",
        "\n",
        "#fps = last_frame/10\n",
        "fps = np.clip(total_frames/length,min_fps,max_fps)\n",
        "\n",
        "from subprocess import Popen, PIPE\n",
        "p = Popen(['ffmpeg', '-y', '-f', 'image2pipe', '-vcodec', 'png', '-r', str(fps), '-i', '-', '-vcodec', 'libx264', '-r', str(fps), '-pix_fmt', 'yuv420p', '-crf', '17', '-preset', 'veryslow', 'video.mp4'], stdin=PIPE)\n",
        "for im in tqdm(frames):\n",
        "    im.save(p.stdin, 'PNG')\n",
        "p.stdin.close()\n",
        "p.wait()\n",
        " \n",
        "print(\"The video is now being compressed, wait ...\")\n",
        "p.wait()\n",
        "print(\"The video is ready\")"
      ],
      "id": "c053240e-8a05-448d-9977-8308179b6d09",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcSE6X9Knp3g",
        "cellView": "form"
      },
      "source": [
        "#@markdown **View video in browser**\n",
        "\n",
        "# @markdown *This process may take a little longer. If you don't want to wait, download it by executing the next cell instead of using this cell.*\n",
        "mp4 = open('video.mp4','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)"
      ],
      "id": "jcSE6X9Knp3g",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZpYoLNWn10M",
        "cellView": "form"
      },
      "source": [
        "#@markdown #**Download the result video**\n",
        "from google.colab import files\n",
        "files.download(\"video.mp4\")"
      ],
      "id": "fZpYoLNWn10M",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUmwey4oV4zH"
      },
      "source": [
        "JS to prevent idle timeout:\n",
        "\n",
        "Press F12 OR CTRL + SHIFT + I OR right click on this website -> inspect.\n",
        "Then click on the console tab and paste in the following code.\n",
        "\n",
        "```javascript\n",
        "function ClickConnect(){\n",
        "console.log(\"Working\");\n",
        "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
        "}\n",
        "setInterval(ClickConnect,60000)\n",
        "```"
      ],
      "id": "DUmwey4oV4zH"
    }
  ]
}