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": 3,
      "metadata": {
        "cellView": "code",
        "id": "ix_xt4X1_6F4",
        "outputId": "4451164e-86cb-43d8-d0a2-a6ebef7d3938",
        "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",
            "Requirement already satisfied: min-dalle in /usr/local/lib/python3.7/dist-packages (0.2.25)\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: typing-extensions>=4.1.0 in /usr/local/lib/python3.7/dist-packages (from min-dalle) (4.1.1)\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: torch>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from min-dalle) (1.11.0+cu113)\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",
            "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",
            "Tue Jul  5 01:08:31 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   36C    P0    27W / 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": "f79d4490-cf78-4642-e1f1-5b74c26fb2be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "initializing MinDalle\n",
            "intializing TextTokenizer\n",
            "initializing DalleBartEncoder\n",
            "initializing DalleBartDecoder\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": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "id": "nQ0UG05dA4p2",
        "outputId": "cf14c540-6fad-4a7f-fc93-2a9a5553e02d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=768x768 at 0x7F6BCA36FB10>"
            ],
            "image/png": "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
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 34.4 s, sys: 558 ms, total: 34.9 s\n",
            "Wall time: 35.4 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",
        "supercondition_factor = 8 #@param [\"2\", \"4\", \"8\", \"16\", \"32\", \"64\"] {type:\"raw\"}\n",
        "\n",
        "image_stream = model.generate_image_stream(\n",
        "    text=text,\n",
        "    seed=seed,\n",
        "    grid_size=grid_size,\n",
        "    log2_mid_count=log2(intermediate_image_count),\n",
        "    log2_supercondition_factor=log2(supercondition_factor)\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
}