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": 2,
      "metadata": {
        "cellView": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ix_xt4X1_6F4",
        "outputId": "3381aedd-4f25-4b73-b0d3-9d3675f86f02"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Jul  9 14:20:18 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    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",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting min-dalle==0.3.11\n",
            "  Downloading min-dalle-0.3.11.tar.gz (10 kB)\n",
            "Requirement already satisfied: torch>=1.11 in /usr/local/lib/python3.7/dist-packages (from min-dalle==0.3.11) (1.11.0+cu113)\n",
            "Requirement already satisfied: typing_extensions>=4.1 in /usr/local/lib/python3.7/dist-packages (from min-dalle==0.3.11) (4.1.1)\n",
            "Requirement already satisfied: numpy>=1.21 in /usr/local/lib/python3.7/dist-packages (from min-dalle==0.3.11) (1.21.6)\n",
            "Requirement already satisfied: pillow>=7.1 in /usr/local/lib/python3.7/dist-packages (from min-dalle==0.3.11) (7.1.2)\n",
            "Requirement already satisfied: requests>=2.23 in /usr/local/lib/python3.7/dist-packages (from min-dalle==0.3.11) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23->min-dalle==0.3.11) (2022.6.15)\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==0.3.11) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23->min-dalle==0.3.11) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23->min-dalle==0.3.11) (2.10)\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-0.3.11-py3-none-any.whl size=10554 sha256=72d50f11369edf356faffae4575a19840b7cf2fa92319ff34f836d566efabf65\n",
            "  Stored in directory: /root/.cache/pip/wheels/08/7c/9e/e87a42b400d85af27f9f5fda5c834262f4f20d105d91f1ffc0\n",
            "Successfully built min-dalle\n",
            "Installing collected packages: min-dalle\n",
            "  Attempting uninstall: min-dalle\n",
            "    Found existing installation: min-dalle 0.3.10\n",
            "    Uninstalling min-dalle-0.3.10:\n",
            "      Successfully uninstalled min-dalle-0.3.10\n",
            "Successfully installed min-dalle-0.3.11\n"
          ]
        }
      ],
      "source": [
        "! nvidia-smi\n",
        "! pip install min-dalle==0.3.11"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kViq2dMbGDKt"
      },
      "source": [
        "### Load Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8W-L2ICFGFup",
        "outputId": "d2f82d75-7178-4813-d4f1-0c2a09d9a550"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "intializing TextTokenizer\n",
            "initializing DalleBartEncoder\n",
            "initializing DalleBartDecoder\n",
            "initializing VQGanDetokenizer\n"
          ]
        }
      ],
      "source": [
        "from IPython.display import display, update_display\n",
        "from math import log2\n",
        "import torch\n",
        "from min_dalle import MinDalle\n",
        "\n",
        "model = MinDalle(\n",
        "    dtype=torch.float16,\n",
        "    is_mega=True, \n",
        "    is_reusable=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c52TV1GbBNgS"
      },
      "source": [
        "### Generate Images\n",
        "\n",
        "- `grid_size` Size of the image grid.  Reduce this if you run out of GPU memory.\n",
        "\n",
        "- `intermediate_outputs` Whether to show intermediate output.  Adds a small delay and increases memory usage.\n",
        "\n",
        "- `supercondition_factor` Higher values result in better agreement with the text but a narrower variety of generated images\n",
        "\n",
        "- `top_k` Each image token is sampled from the top $k$ most probable tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nQ0UG05dA4p2",
        "outputId": "598d90d3-51c7-4f4d-e12c-019924a7817d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=1280x1280 at 0x7FB4B7AF7D90>"
            ],
            "image/png": "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
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1min 23s, sys: 466 ms, total: 1min 24s\n",
            "Wall time: 1min 24s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "text = \"Dali painting of WALL·E\" #@param {type:\"string\"}\n",
        "intermediate_outputs = True #@param {type:\"boolean\"}\n",
        "grid_size = 5 #@param {type:\"integer\"}\n",
        "supercondition_factor = 16 #@param [\"2\", \"4\", \"8\", \"16\", \"32\", \"64\"] {type:\"raw\"}\n",
        "top_k = 64 #@param [\"2\", \"4\", \"8\", \"16\", \"32\", \"64\", \"128\", \"256\", \"512\", \"1024\"] {type:\"raw\"}\n",
        "log2_mid_count = 3 if intermediate_outputs else 0\n",
        "\n",
        "image_stream = model.generate_image_stream(\n",
        "    text=text,\n",
        "    seed=-1,\n",
        "    grid_size=grid_size,\n",
        "    log2_mid_count=log2_mid_count,\n",
        "    log2_k=int(log2(top_k)),\n",
        "    log2_supercondition_factor=log2(supercondition_factor)\n",
        ")\n",
        "\n",
        "is_first = True\n",
        "for image in image_stream:\n",
        "    display_image = display if is_first else update_display\n",
        "    display_image(image, display_id=1)\n",
        "    is_first = False"
      ]
    }
  ],
  "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
}