roop-unleashed.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "collapsed_sections": [
        "UdQ1VHdI8lCf"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/C0untFloyd/roop-unleashed/blob/gradio/roop-unleashed.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Colab for roop-unleashed - Gradio version\n",
        "https://github.com/C0untFloyd/roop-unleashed\n"
      ],
      "metadata": {
        "id": "G9BdiCppV6AS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Installing & preparing requirements"
      ],
      "metadata": {
        "id": "0ZYRNb0AWLLW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t1yPuhdySqCq"
      },
      "outputs": [],
      "source": [
        "!git clone -b gradio --single-branch https://github.com/C0untFloyd/roop-unleashed.git\n",
        "%cd roop-unleashed\n",
        "!pip install pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running roop-unleashed with GPU Support"
      ],
      "metadata": {
        "id": "u_4JQiSlV9Fi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python run.py --execution-provider cuda"
      ],
      "metadata": {
        "id": "Is6U2huqSzLE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download generated images folder"
      ],
      "metadata": {
        "id": "UdQ1VHdI8lCf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "import os\n",
        "from google.colab import files\n",
        "\n",
        "def zip_directory(directory_path, zip_path):\n",
        "    shutil.make_archive(zip_path, 'zip', directory_path)\n",
        "\n",
        "# Set the directory path you want to download\n",
        "directory_path = '/content/roop-unleashed/output'\n",
        "\n",
        "# Set the zip file name\n",
        "zip_filename = 'fake_output.zip'\n",
        "\n",
        "# Zip the directory\n",
        "zip_directory(directory_path, zip_filename)\n",
        "\n",
        "# Download the zip file\n",
        "files.download(zip_filename+'.zip')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "oYjWveAmw10X",
        "outputId": "5b4c3650-f951-434a-c650-5525a8a70c1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_789eab11-93d2-4880-adf3-6aceee0cc5f9\", \"fake_output.zip.zip\", 80125)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}