AI_clip_ing2text.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RusMermaid/AI_Digits_Remake_Neo/blob/main/AI_clip_ing2text.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jm8RYrLqvzz"
      },
      "source": [
        "# CLIP Interrogator 2.2\n",
        "\n",
        "Want to figure out what a good prompt might be to create new images like an existing one? The CLIP Interrogator is here to get you answers!\n",
        "\n",
        "<br>\n",
        "\n",
        "For Stable Diffusion 1.X choose the **ViT-L** model and for Stable Diffusion 2.0+ choose the **ViT-H** CLIP Model.\n",
        "\n",
        "This version is specialized for producing nice prompts for use with Stable Diffusion and achieves higher alignment between generated text prompt and source image. You can try out the old [version 1](https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/v1/clip_interrogator.ipynb) to see how different CLIP models ranks terms. \n",
        "\n",
        "You can also run this on HuggingFace and Replicate<br>\n",
        "[![Generic badge](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue.svg)](https://huggingface.co/spaces/pharma/CLIP-Interrogator) [![Replicate](https://replicate.com/pharmapsychotic/clip-interrogator/badge)](https://replicate.com/pharmapsychotic/clip-interrogator)\n",
        "\n",
        "<br>\n",
        "\n",
        "If this notebook is helpful to you please consider buying me a coffee via [ko-fi](https://ko-fi.com/pharmapsychotic) or following me on [twitter](https://twitter.com/pharmapsychotic) for more cool Ai stuff. 🙂\n",
        "\n",
        "And if you're looking for more Ai art tools check out my [Ai generative art tools list](https://pharmapsychotic.com/tools.html).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "aP9FjmWxtLKJ",
        "outputId": "0057741f-47b2-4aa6-ed92-31376e0afc5d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU 0: Tesla T4 (UUID: GPU-bf77d3e1-62a2-be76-3b61-6b7a176b29f1)\n"
          ]
        }
      ],
      "source": [
        "#@title Check GPU\n",
        "!nvidia-smi -L"
      ]
    },
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            "  Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "  Downloading huggingface_hub-0.11.1-py3-none-any.whl (182 kB)\n",
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            "Installing collected packages: sentencepiece, protobuf, ftfy, huggingface-hub, open_clip_torch\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.19.6\n",
            "    Uninstalling protobuf-3.19.6:\n",
            "      Successfully uninstalled protobuf-3.19.6\n",
            "Successfully installed ftfy-6.1.1 huggingface-hub-0.11.1 open_clip_torch-2.9.1 protobuf-3.20.3 sentencepiece-0.1.97\n",
            "\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Obtaining blip from git+https://github.com/pharmapsychotic/BLIP.git@lib#egg=blip\n",
            "  Cloning https://github.com/pharmapsychotic/BLIP.git (to revision lib) to ./src/blip\n",
            "  Resolved https://github.com/pharmapsychotic/BLIP.git to commit 5b58f59454039295a87c6628b1392e6b8752e911\n",
            "  Preparing metadata (setup.py): started\n",
            "  Preparing metadata (setup.py): finished with status 'done'\n",
            "Collecting timm==0.4.12\n",
            "  Downloading timm-0.4.12-py3-none-any.whl (376 kB)\n",
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            "Collecting transformers==4.15.0\n",
            "  Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n",
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            "Collecting fairscale==0.4.4\n",
            "  Downloading fairscale-0.4.4.tar.gz (235 kB)\n",
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            "  Installing build dependencies: started\n",
            "  Installing build dependencies: finished with status 'done'\n",
            "  Getting requirements to build wheel: started\n",
            "  Getting requirements to build wheel: finished with status 'done'\n",
            "  Installing backend dependencies: started\n",
            "  Installing backend dependencies: finished with status 'done'\n",
            "  Preparing metadata (pyproject.toml): started\n",
            "  Preparing metadata (pyproject.toml): finished with status 'done'\n",
            "Collecting pycocoevalcap\n",
            "  Downloading pycocoevalcap-1.2-py3-none-any.whl (104.3 MB)\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 104.3/104.3 MB 10.0 MB/s eta 0:00:00\n",
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            "Collecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
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            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 880.6/880.6 KB 59.8 MB/s eta 0:00:00\n",
            "  Preparing metadata (setup.py): started\n",
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            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.1.0->pycocotools>=2.0.2->pycocoevalcap->blip) (1.4.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.1.0->pycocotools>=2.0.2->pycocoevalcap->blip) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.1.0->pycocotools>=2.0.2->pycocoevalcap->blip) (0.11.0)\n",
            "Building wheels for collected packages: fairscale, sacremoses\n",
            "  Building wheel for fairscale (pyproject.toml): started\n",
            "  Building wheel for fairscale (pyproject.toml): finished with status 'done'\n",
            "  Created wheel for fairscale: filename=fairscale-0.4.4-py3-none-any.whl size=292864 sha256=e9d85152115d1353e1a967283c3b376ec9c97f426e58a75aed6e3773b902faa7\n",
            "  Stored in directory: /root/.cache/pip/wheels/b3/c3/6e/3fca67aaef3657c2266e9ee439b54f534f05967cd8774cc65b\n",
            "  Building wheel for sacremoses (setup.py): started\n",
            "  Building wheel for sacremoses (setup.py): finished with status 'done'\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=e357d7e8f039006175c93472531b01f98282c28e51d1761b63bfb470ce240460\n",
            "  Stored in directory: /root/.cache/pip/wheels/82/ab/9b/c15899bf659ba74f623ac776e861cf2eb8608c1825ddec66a4\n",
            "Successfully built fairscale sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, fairscale, transformers, timm, pycocoevalcap, blip\n",
            "  Running setup.py develop for blip\n",
            "Successfully installed blip-1.0 fairscale-0.4.4 pycocoevalcap-1.2 sacremoses-0.0.53 timm-0.4.12 tokenizers-0.10.3 transformers-4.15.0\n",
            "\n",
            "\n",
            "Download preprocessed cache files...\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Loading BLIP model...\n"
          ]
        },
        {
          "output_type": "display_data",
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth\n",
            "Loading CLIP model...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████| 933M/933M [00:09<00:00, 99.8MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded CLIP model and data in 18.94 seconds.\n"
          ]
        }
      ],
      "source": [
        "#@title Setup\n",
        "import os, subprocess\n",
        "\n",
        "def setup():\n",
        "    install_cmds = [\n",
        "        ['pip', 'install', 'gradio'],\n",
        "        ['pip', 'install', 'open_clip_torch'],\n",
        "        ['pip', 'install', '-e', 'git+https://github.com/pharmapsychotic/BLIP.git@lib#egg=blip'],\n",
        "        ['git', 'clone', 'https://github.com/pharmapsychotic/clip-interrogator.git']\n",
        "    ]\n",
        "    for cmd in install_cmds:\n",
        "        print(subprocess.run(cmd, stdout=subprocess.PIPE).stdout.decode('utf-8'))\n",
        "\n",
        "setup()\n",
        "\n",
        "\n",
        "clip_model_name = 'ViT-L-14/openai' #@param [\"ViT-L-14/openai\", \"ViT-H-14/laion2b_s32b_b79k\"]\n",
        "\n",
        "\n",
        "print(\"Download preprocessed cache files...\")\n",
        "CACHE_URLS = [\n",
        "    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-L-14_openai_artists.pkl',\n",
        "    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-L-14_openai_flavors.pkl',\n",
        "    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-L-14_openai_mediums.pkl',\n",
        "    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-L-14_openai_movements.pkl',\n",
        "    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-L-14_openai_trendings.pkl',\n",
        "] if clip_model_name == 'ViT-L-14/openai' else [\n",
        "    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_artists.pkl',\n",
        "    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_flavors.pkl',\n",
        "    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_mediums.pkl',\n",
        "    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_movements.pkl',\n",
        "    'https://huggingface.co/pharma/ci-preprocess/resolve/main/ViT-H-14_laion2b_s32b_b79k_trendings.pkl',\n",
        "]\n",
        "os.makedirs('cache', exist_ok=True)\n",
        "for url in CACHE_URLS:\n",
        "    print(subprocess.run(['wget', url, '-P', 'cache'], stdout=subprocess.PIPE).stdout.decode('utf-8'))\n",
        "\n",
        "\n",
        "import sys\n",
        "sys.path.append('src/blip')\n",
        "sys.path.append('clip-interrogator')\n",
        "\n",
        "import gradio as gr\n",
        "from clip_interrogator import Config, Interrogator\n",
        "\n",
        "config = Config()\n",
        "config.blip_num_beams = 64\n",
        "config.blip_offload = False\n",
        "config.clip_model_name = clip_model_name\n",
        "ci = Interrogator(config)\n",
        "\n",
        "def inference(image, mode, best_max_flavors=32):\n",
        "    ci.config.chunk_size = 2048 if ci.config.clip_model_name == \"ViT-L-14/openai\" else 1024\n",
        "    ci.config.flavor_intermediate_count = 2048 if ci.config.clip_model_name == \"ViT-L-14/openai\" else 1024\n",
        "    image = image.convert('RGB')\n",
        "    if mode == 'best':\n",
        "        return ci.interrogate(image, max_flavors=int(best_max_flavors))\n",
        "    elif mode == 'classic':\n",
        "        return ci.interrogate_classic(image)\n",
        "    else:\n",
        "        return ci.interrogate_fast(image)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "UocXY5OgNzKV",
        "outputId": "71128da2-3795-41ba-d5cb-358dfe57ac0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "MessageError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mMessageError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-d5df0069828e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m   \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m   return _mount(\n\u001b[0m\u001b[1;32m    102\u001b[0m       \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    122\u001b[0m       'TBE_EPHEM_CREDS_ADDR'] if ephemeral else _os.environ['TBE_CREDS_ADDR']\n\u001b[1;32m    123\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mephemeral\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m     _message.blocking_request(\n\u001b[0m\u001b[1;32m    125\u001b[0m         'request_auth', request={'authType': 'dfs_ephemeral'}, timeout_sec=None)\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    169\u001b[0m   request_id = send_request(\n\u001b[1;32m    170\u001b[0m       request_type, request, parent=parent, expect_reply=True)\n\u001b[0;32m--> 171\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m    100\u001b[0m         reply.get('colab_msg_id') == message_id):\n\u001b[1;32m    101\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;34m'error'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mMessageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mMessageError\u001b[0m: Error: credential propagation was unsuccessful"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "id": "Pf6qkFG6MPRj",
        "outputId": "a5962c2b-6ad4-4302-90ac-cf5ee3cd9ba4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7860, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "#@title Image to prompt! 🖼️ -> 📝\n",
        "   \n",
        "inputs = [\n",
        "    gr.inputs.Image(type='pil'),\n",
        "    gr.Radio(['best', 'fast'], label='', value='best'),\n",
        "    gr.Number(value=16, label='best mode max flavors'),\n",
        "]\n",
        "outputs = [\n",
        "    gr.outputs.Textbox(label=\"Output\"),\n",
        "]\n",
        "\n",
        "io = gr.Interface(\n",
        "    inference, \n",
        "    inputs, \n",
        "    outputs, \n",
        "    allow_flagging=False,\n",
        ")\n",
        "io.launch(debug=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "OGmvkzITN4Hz"
      },
      "outputs": [],
      "source": [
        "#@title Batch process a folder of images 📁 -> 📝\n",
        "\n",
        "#@markdown This will generate prompts for every image in a folder and either save results \n",
        "#@markdown to a desc.csv file in the same folder or rename the files to contain their prompts.\n",
        "#@markdown The renamed files work well for [DreamBooth extension](https://github.com/d8ahazard/sd_dreambooth_extension)\n",
        "#@markdown in the [Stable Diffusion Web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui).\n",
        "#@markdown You can use the generated csv in the [Stable Diffusion Finetuning](https://colab.research.google.com/drive/1vrh_MUSaAMaC5tsLWDxkFILKJ790Z4Bl?usp=sharing)\n",
        "\n",
        "import csv\n",
        "import os\n",
        "from IPython.display import clear_output, display\n",
        "from PIL import Image\n",
        "from tqdm import tqdm\n",
        "\n",
        "folder_path = \"/content/JmWIHj__5do.jpg\" #@param {type:\"string\"}\n",
        "prompt_mode = 'best' #@param [\"best\",\"fast\"]\n",
        "output_mode = 'rename' #@param [\"desc.csv\",\"rename\"]\n",
        "max_filename_len = 128 #@param {type:\"integer\"}\n",
        "best_max_flavors = 32 #@param {type:\"integer\"}\n",
        "\n",
        "\n",
        "def sanitize_for_filename(prompt: str, max_len: int) -> str:\n",
        "    name = \"\".join(c for c in prompt if (c.isalnum() or c in \",._-! \"))\n",
        "    name = name.strip()[:(max_len-4)] # extra space for extension\n",
        "    return name\n",
        "\n",
        "ci.config.quiet = True\n",
        "\n",
        "files = [f for f in os.listdir(folder_path) if f.endswith('.jpg') or f.endswith('.png')] if os.path.exists(folder_path) else []\n",
        "prompts = []\n",
        "for idx, file in enumerate(tqdm(files, desc='Generating prompts')):\n",
        "    if idx > 0 and idx % 100 == 0:\n",
        "        clear_output(wait=True)\n",
        "\n",
        "    image = Image.open(os.path.join(folder_path, file)).convert('RGB')\n",
        "    prompt = inference(image, prompt_mode, best_max_flavors=best_max_flavors)\n",
        "    prompts.append(prompt)\n",
        "\n",
        "    print(prompt)\n",
        "    thumb = image.copy()\n",
        "    thumb.thumbnail([256, 256])\n",
        "    display(thumb)\n",
        "\n",
        "    if output_mode == 'rename':\n",
        "        name = sanitize_for_filename(prompt, max_filename_len)\n",
        "        ext = os.path.splitext(file)[1]\n",
        "        filename = name + ext\n",
        "        idx = 1\n",
        "        while os.path.exists(os.path.join(folder_path, filename)):\n",
        "            print(f'File {filename} already exists, trying {idx+1}...')\n",
        "            filename = f\"{name}_{idx}{ext}\"\n",
        "            idx += 1\n",
        "        os.rename(os.path.join(folder_path, file), os.path.join(folder_path, filename))\n",
        "\n",
        "if len(prompts):\n",
        "    if output_mode == 'desc.csv':\n",
        "        csv_path = os.path.join(folder_path, 'desc.csv')\n",
        "        with open(csv_path, 'w', encoding='utf-8', newline='') as f:\n",
        "            w = csv.writer(f, quoting=csv.QUOTE_MINIMAL)\n",
        "            w.writerow(['image', 'prompt'])\n",
        "            for file, prompt in zip(files, prompts):\n",
        "                w.writerow([file, prompt])\n",
        "\n",
        "        print(f\"\\n\\n\\n\\nGenerated {len(prompts)} prompts and saved to {csv_path}, enjoy!\")\n",
        "    else:\n",
        "        print(f\"\\n\\n\\n\\nGenerated {len(prompts)} prompts and renamed your files, enjoy!\")\n",
        "else:\n",
        "    print(f\"Sorry, I couldn't find any images in {folder_path}\")\n"
      ]
    }
  ],
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