min-dalle-test/min_dalle.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3WL-G_f2_ld8"
      },
      "source": [
        "# min(DALL·E)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zl_ZFisFApeh"
      },
      "source": [
        "### Install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "code",
        "id": "ix_xt4X1_6F4",
        "outputId": "3b1436de-3af0-4121-8009-3672f5a03547",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting min-dalle\n",
            "  Downloading min-dalle-0.2.24.tar.gz (11 kB)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from min-dalle) (1.11.0+cu113)\n",
            "Requirement already satisfied: typing_extensions>=4.1.0 in /usr/local/lib/python3.7/dist-packages (from min-dalle) (4.1.1)\n",
            "Requirement already satisfied: numpy>=1.21 in /usr/local/lib/python3.7/dist-packages (from min-dalle) (1.21.6)\n",
            "Requirement already satisfied: pillow>=7.1 in /usr/local/lib/python3.7/dist-packages (from min-dalle) (7.1.2)\n",
            "Requirement already satisfied: requests>=2.23 in /usr/local/lib/python3.7/dist-packages (from min-dalle) (2.23.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23->min-dalle) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23->min-dalle) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23->min-dalle) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23->min-dalle) (3.0.4)\n",
            "Building wheels for collected packages: min-dalle\n",
            "  Building wheel for min-dalle (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for min-dalle: filename=min_dalle-0.2.24-py3-none-any.whl size=11198 sha256=d7c26dc9230a9b6b0e1e6d8026ec24192c248b4f3bcd61ad6fc3253449f45959\n",
            "  Stored in directory: /root/.cache/pip/wheels/25/95/af/94274bdc5e07b7aab46294f2bf36768aab35e4ed41371460c4\n",
            "Successfully built min-dalle\n",
            "Installing collected packages: min-dalle\n",
            "Successfully installed min-dalle-0.2.24\n",
            "Tue Jul  5 00:48:11 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   35C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "! pip install min-dalle\n",
        "! nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kViq2dMbGDKt"
      },
      "source": [
        "### Load Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8W-L2ICFGFup",
        "outputId": "495ab426-c385-4ea3-c4dd-63c053f684e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "initializing MinDalle\n",
            "downloading tokenizer params\n",
            "intializing TextTokenizer\n",
            "downloading encoder params\n",
            "initializing DalleBartEncoder\n",
            "downloading decoder params\n",
            "initializing DalleBartDecoder\n",
            "downloading detokenizer params\n",
            "initializing VQGanDetokenizer\n"
          ]
        }
      ],
      "source": [
        "from PIL import Image\n",
        "from IPython.display import update_display\n",
        "import numpy\n",
        "from math import log2\n",
        "from min_dalle import MinDalle\n",
        "\n",
        "model = MinDalle(is_mega=True, is_reusable=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c52TV1GbBNgS"
      },
      "source": [
        "### Generate Images\n",
        "Note: reduce the grid size if you run out of GPU memory.  4x4 has been tested to work on T4 and P100 (with intermediate_image_count = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "id": "nQ0UG05dA4p2",
        "outputId": "8bc87737-27cf-4e6f-bac3-c87826314038"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=768x768 at 0x7F3C2E493FD0>"
            ],
            "image/png": "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
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 34.2 s, sys: 524 ms, total: 34.8 s\n",
            "Wall time: 34.7 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "text = \"Dali painting of WALL·E\" #@param {type:\"string\"}\n",
        "grid_size = 3 #@param {type:\"integer\"}\n",
        "seed = -1 #@param {type:\"integer\"}\n",
        "intermediate_image_count = 8 #@param [\"1\", \"2\", \"4\", \"8\", \"16\"] {type:\"raw\"}\n",
        "\n",
        "image_stream = model.generate_image_stream(\n",
        "    text,\n",
        "    seed,\n",
        "    grid_size,\n",
        "    log2(intermediate_image_count)\n",
        ")\n",
        "\n",
        "image_shape = [256 * grid_size] * 2 + [3]\n",
        "zero_image = numpy.zeros(image_shape, dtype=numpy.uint8)\n",
        "display(Image.fromarray(zero_image), display_id=1)\n",
        "\n",
        "for image in image_stream:\n",
        "    update_display(image, display_id=1)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "Zl_ZFisFApeh"
      ],
      "name": "min-dalle",
      "provenance": [],
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}