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": "918d48d7-9d8e-48fa-ab32-9fbc142e0074",
        "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==0.2.22\n",
            "  Downloading min-dalle-0.2.22.tar.gz (11 kB)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from min-dalle==0.2.22) (1.11.0+cu113)\n",
            "Requirement already satisfied: typing_extensions>=4.1.0 in /usr/local/lib/python3.7/dist-packages (from min-dalle==0.2.22) (4.1.1)\n",
            "Requirement already satisfied: numpy>=1.21 in /usr/local/lib/python3.7/dist-packages (from min-dalle==0.2.22) (1.21.6)\n",
            "Requirement already satisfied: pillow>=7.1 in /usr/local/lib/python3.7/dist-packages (from min-dalle==0.2.22) (7.1.2)\n",
            "Requirement already satisfied: requests>=2.23 in /usr/local/lib/python3.7/dist-packages (from min-dalle==0.2.22) (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23->min-dalle==0.2.22) (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==0.2.22) (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==0.2.22) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23->min-dalle==0.2.22) (2022.6.15)\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-0.2.22-py3-none-any.whl size=11263 sha256=84726f3fdf87ac6ccf6731fefadcc29154efee2a26020b771dcd08d4724ccf01\n",
            "  Stored in directory: /root/.cache/pip/wheels/7b/db/df/ec2e6cb890f0f527e178401b3f2fecaa7106748f2bdbc69bb9\n",
            "Successfully built min-dalle\n",
            "Installing collected packages: min-dalle\n",
            "Successfully installed min-dalle-0.2.22\n",
            "Mon Jul  4 21:23:14 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   37C    P0    28W / 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": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8W-L2ICFGFup",
        "outputId": "e5df04d8-bae6-4d00-acd8-eeb908d2a54f"
      },
      "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 = None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "nQ0UG05dA4p2",
        "outputId": "0213d6b2-a343-42d8-df03-7dc5dc768584"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x512 at 0x7F946818F890>"
            ],
            "image/png": "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
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 33.9 s, sys: 551 ms, total: 34.4 s\n",
            "Wall time: 35.3 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 [\"2\", \"4\", \"8\", \"16\", \"None\"] {type:\"raw\"}\n",
        "display_size = 512 #@param {type:\"integer\"}\n",
        "\n",
        "image_shape = (display_size, display_size, 3)\n",
        "zero_image = Image.fromarray(numpy.zeros(image_shape, dtype=numpy.uint8))\n",
        "display(zero_image, display_id=1)\n",
        "\n",
        "def handle_intermediate_image(row_index: int, image: Image.Image):\n",
        "    image = image.resize((display_size, display_size))\n",
        "    update_display(image, display_id=1)\n",
        "\n",
        "image = model.generate_image(\n",
        "    text,\n",
        "    seed,\n",
        "    grid_size,\n",
        "    log2(intermediate_image_count),\n",
        "    handle_intermediate_image\n",
        ")"
      ]
    }
  ],
  "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
}