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": "a1821c1c-b5b3-4f6f-a53d-abf9c716f868",
        "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 (0.2.26)\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: pillow>=7.1 in /usr/local/lib/python3.7/dist-packages (from min-dalle) (7.1.2)\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: 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: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23->min-dalle) (2.10)\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: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23->min-dalle) (3.0.4)\n",
            "Tue Jul  5 02:47:56 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   39C    P0    29W / 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": "101b43dc-196f-4d4e-bb47-3674732c0b25"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "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)\n",
        "\n",
        "- `grid_size` Size of the image grid.  3x3 works best when displaying intermediate results.  4x4 has been tested to work on T4 and P100 with intermediate_image_count = 1 \n",
        "\n",
        "- `seed` Set the seed to a positive number for reproducible results\n",
        "\n",
        "- `intermediate_image_count` Set the number of intermediate images to show while running\n",
        "\n",
        "- `supercondition_factor` Lower results in a wider variety of images but less agreement with the text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "id": "nQ0UG05dA4p2",
        "outputId": "7829c121-02d0-4c52-ac7d-a077bbff9bcb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=768x768 at 0x7F742771C350>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwAAAAMACAIAAAAc45fZAAEAAElEQVR4nFz9ecxtWXYfhv3WWnvvc+7wzW+s96rq1TwPPQ/sgWyySYrUSIqR5diKE8hKFCJOAMMwIgUZjAQGIiOAgQAJIxm2JZGy5JCUKHESyZ7Y1WNVd1d11zy+ef7m795zzt5rrfyxz9dt5/1RKHzD/e4999y9f/s3LcKDnwcCdAJfIkQMS5hDAC0AoTg4ogCNQxjZQQYrcAIIRFCBFkRDZJiDBOagABCoIDiyIzCMgYDimATAAAY7uh4cYAXMAJAV5EgCGExQDExYAqTgJUgAhwdkg0YEQ1BkhWV0S4QEBSxgEhEZBYiEIcMDGCAHRZiCHSkgAMrIABwaYT2aAhDUIYIMeEQ0sMIVqhh6IACMQpgEkMMIkbHsQAFUwAxjmIEUTYI4lFEAJhSGZcQMEJwQEgqAgOBggyvcoQojuMAD2voyGcGhCiWwgRjKcAUXpAR2KEMdLFCBL5EcxUEMilCGEhoHO8wAQh7gDDNwADlgQEAgOMMUZnAHJ1iGODgABIsgBhEAWIckAFAMiDABA4FhhiDjfVK/SAIGQADAgpyRAuBwgjG0gxiagDwAEeaQBGJ4jygoBQxYQCYQIwKRURSB4A4FyECCEoAOohCBOZzhEc6AAgXkyAphKFAcEsAKEhQgENRgBQJwRCmICXll/bf+q92LBT2jiYChFGhBYmhGCFCDAiwghylii6GDGSRAHYFRerDAZbxWTrAMd7jAMsTgBGY4oIAI3DF0SBElQxUxYVCQgxhmUAMRDGCGFuQCCdACYbijOITBAjcwoxsgEW7weqcZVMECKIpDFSIA4A4zZCAy3MGCXkEOEcABhhsIMEWKyBmg8bvuEIIpmOEEOAggggOuCDI+PVNA4AAAdQBwAxxEMAUALUgBxREExREIxRAYQsgFIoDBMV5AAszAVG8imI/3EgBVpIA8IAqcQISUoAomUIQriCD1OkeQggwuiAwroAQYmGAEzxAD1XsYMIIECDD0aBI0w+rLJxADhYXNOArlohCBOryAhEzhaCapaGEK5iZMRaFD38w4D+rkbUxWzIlBlEsJEti1z0ViDESmTlRExIwjU1Fz4uxmZsGZXIWJAjtAYDUnkDpKGdKEh6Fw/TaRG3ldfVnIofXWzSW1acglSHRnA7kWqEEZLcMyiCZ5ft+f/JIIKJN5DimVoQcjxMjiMHOWYi7gFEQECqaQGHBiM0/MJauIhwghcWGi6CQEc5CTUyAnLdkSQhYapGXEaACbKSFNJUZxObG55UtKwUJM2Hnv9jtfW9x8/RNP3fOpT37k3H33cZifWJtTK+9e3//RD16/eunNi2++9sKXQR2u4P//39mEX/+PzseFX7509aEHt1ZPzS6cP3Pm7KkO+eaNnUlMlz64vb55qpmtPPDIk8Th7IMP7e0d2jIZhyY1FsR14gyYSQJH3j/oDu/euv7arffev/7etRuql565794nn336wiPTex4+hSm6riyv7mW968NO6Q5XWim9GrJmOtgddu+Qh9S2OauIQII6rKhpUbLBAct3KedJoGa+ItRYiATuSk9E7WrZ207vvXStXd+4/6mPLFcftruHe/35f/yP//s//dL3by6260s+McVnPvXxBy48eOHC+tamTNOZpvFm3nDjMbZFchAsu8yUIiV2DhQUWcJKahzoD48OrD9SWjDtLfrdo+XlK1euXHrz0vs/wu995390bX/9f/Xk0x9Z37TpqcmwtR7u7u5/cOniO+/cfutl/PBNXF7gCDjJeORxPPbQ2rNPrD394D0nzsmp1dnWRjJ474QYjGcaZMjdYLl0R8ujQ80lZy2lzznDC5wJkODmPnR5UCFwSMnJu0G1Uw7CTMRcNJvTkLOEGYubdyga0qS4ujJAIfTZl9BMTO5iDJFofW6SZOah9835GcLTv4rA6HsEQR4AQxSYA4pkyIzsMII4isMBz3BAGCIwgQhKBjLcwBFuMII7mADHJIEIVuACI1AA2bjmwhATyECOzAChKDhDFQoIwwE3kMAVKGCABd0AjigKAEZgRl6gcWQgTpEBcZhDGG6QgDbBHC7gAFP4ALdxK4KDGA7AQBkhggjLDtSgZLDDgMDIHdjhBA9AhGXQ8fOXiAgQoTBYoBmsI5phAWHc+5mAghjghH4YoZhUPMTQDCiIwe34/B2AjzArCcihARyhA7jAFM4QhhpAcAMDATADDBlAgjoCoI5G0A9gRqj7FmMgwMEDoBgIKQAMYZhACzhDBM5QAAwSkEHq5nSMnBBhABkMI2jQDDCyopmAGEVBABMkgBSFAIYEeAYpvAABrkDEUNAEkEECbAAMaQ6ncc+uCNgVxdFW3MnghNIjDAAgCbnABMOAKDBFiBDG8hASAR53zbptm8IczOAAAJwQBCj4v/wz9BmeEROWQOMIhFwAgxOKQwRU0bkEQckZ7pCEwNEp9wOIoA4uI0TIDk6IBM2wDAWCIDtCADmGcrxPO4hAjGwAgQF19AWJ4AAZBgcYQeAYcaoxJEJovDjKiIzBxmNGxR+JMBSowx0hgQw5wxwUR0hBjt4QBOQghxqIQQABAgyGIEC9aTFishRAGMGZMHIBEYhQfHwCEmAGrjchYAoCYDCDlfE29gIiqCEEOEZoVe+revBgB3x86wUwG4EgGIGRDV5xiSIK1BEDOKAUgBAYXv8oIya0DRZLuCESyKCOyFAHGI2AgTKwQ5isWBPFmPq+BJE2cWdK5sIhxMCGfigSCOoiCgBBlgUpNtOAoy5HUnKHExEq4FJ3uDepHvIQidypFC/ZYpAY6GAYyNA2KaRgWbUYHAwnNnd3oV5JQpolOuxyhMFBICcUA0AKE2YWy+rCHMDEXNSLGhNLkkUuppRS/Y4Ng4YgRT3nAhfPFJokQZloiIF5uPe3/8bELWkhkkGVhFOMbBi4j+zqPsR2wtNVFSXtwkAcyEFEhuBADERegjDglpg9UY5CMCnMTcHgrGBJHnouC5NE85mztXmB0BX31G5M19anayk2e5O8LElu/fDwtd/+tU889mu/+LMNx/UT1IR28e6Vf/HV7/+zV97Z2ZkcXLx48ZW8/B9szCeB5x8+9ciHzz/3oQcunD+Fo5CmR+ubSYftJrWAKnkZep5MVk8+zM2pnKWNIYbYNvPF4vakidIGNe61Kz1K35SiIbHzRBimw17x3Rs3L7715g9eefPq+9t7d3fX1/HMh059+hNPPfPoxnSiTNod+rDcSWmIOLI8mPfLI8pL9HYi0sTKoeqUySCHRfKgzs4wqA3i+wHCKIQQgkViYUVoPM2kGw55c/d2F9ut5sz9R7r2h7/77t//jf+qvurpNP7UZz/xic985N6zJ2ZtkhjdDyepJQlClq3Ly17z0vqOfGhmkzZIq2gkSUyT1ZXV9a1cGmJvGjTt0C9vdYc7iuWw2F/2Qzcc3Lx1/eat6+++duNb39bvfjBe6i88lf7qX/1LTz+PE06qR91hf7B3Y3e5e+fg6PKtvVfewjsXceMGSNEyzm3gkSdw7vHzjz65+ZGn7j+zydNUt7dWlQa1vl90eaFFNfeOwax361ACu2fDkHNxLooUk6sbrFcPlNgoY8isrh0xO1jiWilOOGJWUNPnQaihwpmPMjr2rk0TtujUOCVC8XBUzLKnZBPCx/4W0KErCAwt49EtRpQCAZwQAkoFJQQI8gD68ToVkQJygWbAURxMCGFc3eqhkCIYEAGH48WOYRiRhxrcIIIQURw+AAolEGA+HgQJ6DuQgABmiEAVZmCGCczgA0AjwnAFNyAH6u/KeJ6LEVnhGV5gdcs3ECMQSkZRsIzrMjPMUDKYUAAHgqPk8bRtxwwHaFyv1RAiYkIp8Aw2qIDr6ZpBDgKGY5ZLZMR2WoEUwQgB49m9XlUWgMcTLYBSEBJCRCnwAWQoPKKrGGGKUuAYL4gIRKAGVVDFlAFCKAMMSAFGMB6BJgQBMAcRgqBXaAYRnMEEieMtkcv4kuuNwQJTZAUFMEEVVLfGgCBYZoSIJsIcpYAAEGJCEzEoyhKEccMD0LQYlmCCVtKoYiYeH
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 34.2 s, sys: 593 ms, total: 34.8 s\n",
            "Wall time: 35 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 = 16 #@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",
        "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
}