196_StyleGAN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "196 StyleGAN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpbiQBCJYZj8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d5fef5a9-5caa-4eb3-b8c6-72d163b1d238"
      },
      "source": [
        "%tensorflow_version 1.x"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcwBjwGrYc_x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8cf68178-217a-4700-eff9-1601a817e469"
      },
      "source": [
        "import tensorflow\n",
        "print(tensorflow.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.15.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8sdeWUhP4I2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "bccc7814-1c5a-466d-e192-af4d44439ab9"
      },
      "source": [
        "! git clone https://github.com/iyaja/stylegan-encoder.git\n",
        "import os\n",
        "os.chdir(\"stylegan-encoder\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'stylegan-encoder'...\n",
            "remote: Enumerating objects: 154, done.\u001b[K\n",
            "remote: Total 154 (delta 0), reused 0 (delta 0), pack-reused 154\n",
            "Receiving objects: 100% (154/154), 16.13 MiB | 16.65 MiB/s, done.\n",
            "Resolving deltas: 100% (42/42), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ip3bgVGZIhZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "0029a9b3-85b5-4ee0-b8f2-25927ae29a78"
      },
      "source": [
        "import os\n",
        "import pickle\n",
        "import numpy as np\n",
        "import PIL.Image\n",
        "import dnnlib\n",
        "import dnnlib.tflib as tflib\n",
        "import config\n",
        "from random import seed, randint, random\n",
        "\n",
        "\n",
        "\n",
        "# Initialize TensorFlow.\n",
        "tflib.init_tf()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/tfutil.py:34: The name tf.Dimension is deprecated. Please use tf.compat.v1.Dimension instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/tfutil.py:74: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/tfutil.py:128: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/tfutil.py:97: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/tfutil.py:109: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/tfutil.py:132: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4aw3I793ZhCJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "e80970d1-04e6-459f-eb32-44086fd59d47"
      },
      "source": [
        "URL_FFHQ = 'https://s3-us-west-2.amazonaws.com/nanonets/blogs/karras2019stylegan-ffhq-1024x1024.pkl'\n",
        "\n",
        "tflib.init_tf()\n",
        "with dnnlib.util.open_url(URL_FFHQ, cache_dir=config.cache_dir) as f:\n",
        "    generator_network, discriminator_network, Gs = pickle.load(f)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://s3-us-west-2.amazonaws.com/nanonets/blogs/karras2019stylegan-ffhq-1024x1024.pkl ... done\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/network.py:142: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/network.py:150: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/tfutil.py:76: The name tf.VariableScope is deprecated. Please use tf.compat.v1.VariableScope instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/network.py:151: The name tf.get_variable_scope is deprecated. Please use tf.compat.v1.get_variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/network.py:154: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/network.py:182: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/stylegan-encoder/dnnlib/tflib/tfutil.py:200: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From <string>:364: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIc5L97_Zhik",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Pick latent vector.\n",
        "def pick_latent_vec():\n",
        "    value=randint(1,2**30)\n",
        "    rnd = np.random.RandomState(value)\n",
        "    latents = rnd.randn(1, Gs.input_shape[1])\n",
        "    return latents"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAT7gDnfbYgb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Generate image.\n",
        "def gen_image(latents):\n",
        "    fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)\n",
        "    images = Gs.run(latents, None, truncation_psi=0.7, randomize_noise=True, output_transform=fmt)\n",
        "    return images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PIt4QbZobZGF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "latent = pick_latent_vec()\n",
        "image = gen_image(latent)\n",
        "os.makedirs(config.result_dir, exist_ok=True)\n",
        "png_filename = os.path.join(config.result_dir, 'Stylegan_example.png')\n",
        "PIL.Image.fromarray(image[0], 'RGB').save(png_filename)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emhNnKtHeAb-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Use this cell to generate multiple images, set num_imgs to desired amount\n",
        "num_imgs = 9\n",
        "i = 0\n",
        "images = []\n",
        "while i < num_imgs:\n",
        "    latent = pick_latent_vec()\n",
        "    image = gen_image(latent)\n",
        "    os.makedirs(config.result_dir, exist_ok=True)\n",
        "    png_filename = os.path.join(config.result_dir, 'ganslide%s.png' %i)\n",
        "    PIL.Image.fromarray(image[0], 'RGB').save(png_filename)\n",
        "    i+=1"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}