automatic_inpainting.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HLCV-23/Inpainting-Detection/blob/Nicola/automatic_inpainting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bzbedd6f-Wiv",
        "outputId": "d2c32476-1739-48f2-9adf-1f7a1b246684"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install diffusers\n",
        "!pip install accelerate\n",
        "!pip install xformers\n",
        "import numpy as np\n",
        "from diffusers import StableDiffusionInpaintPipeline\n",
        "from transformers import pipeline\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "import torch\n",
        "import pandas as pd\n",
        "import time\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [
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          "metadata": {}
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            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint.StableDiffusionInpaintPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda:0\")\n",
        "\n",
        "mask_generator = pipeline(\"mask-generation\", model=\"facebook/sam-vit-base\", device=device)\n",
        "\n",
        "#caption_generator = pipeline(\"image-to-text\", model=\"nlpconnect/vit-gpt2-image-captioning\")\n",
        "caption_generator = pipeline(\"image-to-text\", model=\"Salesforce/blip-image-captioning-base\")  # Is BLIP fast enough?\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"Gustavosta/MagicPrompt-Stable-Diffusion\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"Gustavosta/MagicPrompt-Stable-Diffusion\")\n",
        "prompt_generator = pipeline(\"text-generation\", model=model, tokenizer = tokenizer, device = device)\n",
        "\n",
        "inpainting_generator = StableDiffusionInpaintPipeline.from_pretrained(\n",
        "    \"runwayml/stable-diffusion-inpainting\",\n",
        "    torch_dtype=torch.float16,\n",
        "    safety_checker=None,\n",
        "    low_cpu_mem_usage = False\n",
        ").to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Avpg71aD-jHm",
        "outputId": "29a20279-0c2d-46f7-9477-40e484a5c4f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.13.1-py3-none-any.whl (486 kB)\n",
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            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
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            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.14-py310-none-any.whl (134 kB)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.13.1 dill-0.3.6 multiprocess-0.70.14 xxhash-3.2.0\n",
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) \n",
            "Token is valid (permission: read).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "from datasets import load_dataset, Dataset, Image\n",
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130,
          "referenced_widgets": [
            "59213c90c58641039e579dcdc518e9ab",
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            "e6bf45d0aae841e788995024fd065be1",
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            "23ef9c2eb4ad4daa81da3dea2ea8d082",
            "1cb69b58dab843cca02ae0fe694c070e"
          ]
        },
        "id": "cpL-zSZb-k-C",
        "outputId": "3009e7ee-a2aa-4f5f-ed94-4b367ae31d2d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/21.4k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59213c90c58641039e579dcdc518e9ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/22.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa59d976d0c24170b797302afe6afa21"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/50.0k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41b743625b6045f39764c189c4fbce4b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:No config specified, defaulting to: scene_parse_150/scene_parsing\n"
          ]
        }
      ],
      "source": [
        "dataset = load_dataset(\"scene_parse_150\", use_auth_token = True, streaming = True, split = \"train\")  # set streaming to false for scenes!\n",
        "dataset = iter(dataset.shuffle())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WiGySxK_COSd",
        "outputId": "c90bb111-c11d-400f-a134-3a884e9ec8ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jbBpsQM_-s2R",
        "outputId": "3b418a03-45d4-41a1-f1da-4050b3c3946b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "!mkdir drive/MyDrive/Imagenet_Dataset/\n",
        "!touch drive/MyDrive/Imagenet_Dataset/Prompts.csv\n",
        "!mkdir drive/MyDrive/Imagenet_Dataset/Images\n",
        "!mkdir drive/MyDrive/Imagenet_Dataset/Images/Modified\n",
        "!mkdir drive/MyDrive/Imagenet_Dataset/Images/Unmodified\n",
        "!mkdir drive/MyDrive/Imagenet_Dataset/Masks\n",
        "!mkdir drive/MyDrive/Imagenet_Dataset/Originals\n",
        "!pwd\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QwsX0Ph1-5To"
      },
      "outputs": [],
      "source": [
        "from PIL import Image, ImageFilter\n",
        "\n",
        "def generate_datapoint_single_mask(data, id, csv_data, path):\n",
        "    original_img = data[\"image\"].resize((512,512))\n",
        "    image_area = 512*512\n",
        "    masks = mask_generator(original_img)[\"masks\"]\n",
        "\n",
        "    # Only masks with an area not too large or too small are eligible candidates for inpainting\n",
        "    valid_masks = [mask for idx, mask in enumerate(masks) if (image_area * 0.1 <= mask.sum() <= image_area * 0.6)]\n",
        "\n",
        "    if (valid_masks != []):\n",
        "      choice = np.random.choice(np.arange(len(valid_masks)))\n",
        "      mask = valid_masks[choice]\n",
        "\n",
        "      mask_img = Image.fromarray((mask * 255).astype(np.uint8).squeeze())\n",
        "      mask_img = mask_img.convert(\"RGB\")\n",
        "\n",
        "      # Generate prompt for inpainting model\n",
        "      caption = caption_generator(original_img)[0][\"generated_text\"]\n",
        "      prompts = prompt_generator(caption)[0][\"generated_text\"]\n",
        "      prompts = prompts.replace(\". \", \", \")\n",
        "      prompts = prompts.split(\",\")\n",
        "      try:\n",
        "        prompt = prompts[0] + prompts[1]\n",
        "      except:\n",
        "        prompt = prompts[0]\n",
        "\n",
        "      # inpaint\n",
        "      inpainted_img = inpainting_generator(prompt=prompt, image=original_img, mask_image=mask_img, height=512, width=512).images[0]\n",
        "\n",
        "      modified_img = Image.composite(inpainted_img, original_img, mask_img.convert(\"1\")).convert(\"RGB\")\n",
        "\n",
        "      modified_img.save(path + f\"/Images/Modified/{id}.png\")\n",
        "      original_img.save(path + f\"/Originals/{id}.png\")\n",
        "      mask_img.save(path + f\"/Masks/{id}.png\")\n",
        "      csv_data.at[id, \"Prompt\"] = prompt\n",
        "      print(f\"Saved processed image {id}.png\")\n",
        "\n",
        "    else:\n",
        "      print(f\"No valid mask could be generated. Skipping image {id}.\")\n",
        "\n",
        "\n",
        "def save_unmodified_image(data, id, path):\n",
        "  original_img = data[\"image\"].resize((512,512))\n",
        "  original_img.save(path + f\"/Images/Unmodified/{id}.png\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z2MG_Kho_aFf"
      },
      "outputs": [],
      "source": [
        "start_time = time.time()\n",
        "\n",
        "path = \"drive/MyDrive/Imagenet_Dataset3/\"\n",
        "\n",
        "try:\n",
        "  csv_data = pd.read_csv(path + \"/Prompts.csv\")\n",
        "except:\n",
        "  with open(path + \"/Prompts.csv\", \"w+\") as f:\n",
        "    f.write(\"idx, Prompt\")\n",
        "  csv_data = pd.read_csv(path + \"/Prompts.csv\")\n",
        "\n",
        "\n",
        "# TODO: INIT ID SUCH THAT NO IMAGES ARE OVERWRITTEN!!!\n",
        "\n",
        "start_id = 2000\n",
        "num_modified = 2000\n",
        "\n",
        "for id in range(start_id, start_id + num_modified):\n",
        "  try:\n",
        "    generate_datapoint_single_mask(next(dataset), id, csv_data, path)\n",
        "    print(f\"Time passed: {time.time() - start_time}\")\n",
        "  except:\n",
        "    continue\n",
        "\n",
        "csv_data.to_csv(path + \"/Prompts.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnZM47cg_7U1",
        "outputId": "3b57873e-df2b-42bc-cabf-f25b7d460873"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2946\n"
          ]
        }
      ],
      "source": [
        "path = \"drive/MyDrive/Scenes_Dataset/\"\n",
        "\n",
        "num_unmodified = len([name for name in os.listdir(path + \"/Images/Modified/\")])\n",
        "print(num_unmodified)\n",
        "#for id in range(1655, 1655 + num_unmodified):\n",
        "#    save_unmodified_image(next(dataset), id, path)"
      ]
    },
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