creative_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/creative_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": "EjeP04OJEjof",
    "outputId": "7e356f76-422f-413f-dc78-c5afa0fd7e81"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xalgsN9oFKpF",
    "outputId": "7f7e4c1c-e8af-4be0-df79-811d0428ba08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\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": "-YA5Sj4tFB9R",
    "outputId": "56fd1352-b925-4586-9041-86b6164bac16"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "text_encoder/model.safetensors not found\n",
      "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-huge\", 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": {
    "id": "ZiTTdIbvFEez"
   },
   "outputs": [],
   "source": [
    "from PIL import Image, ImageFilter\n",
    "\n",
    "def generate_datapoint_single_mask(data, id, path):\n",
    "    original_img = data\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",
    "    else:\n",
    "      mask_ids = []\n",
    "      pixels_covered = 0.\n",
    "      for i in range(len(masks)):\n",
    "        mask_ids.append(i)\n",
    "\n",
    "        area = np.sum(masks[i])\n",
    "\n",
    "        pixels_covered += area/(512**2)\n",
    "\n",
    "        if pixels_covered > 0.2 or i == len(masks) - 1:\n",
    "          break\n",
    "\n",
    "      # combine these masks into a single mask\n",
    "      mask = masks[mask_ids[0]]\n",
    "      for i in range(len(mask_ids)):\n",
    "        mask_id = mask_ids[i]\n",
    "        curr_mask = masks[mask_id]\n",
    "        mask = np.logical_or(mask, curr_mask)\n",
    "\n",
    "    mask_img = Image.fromarray((mask * 255).astype(np.uint8).squeeze())\n",
    "    mask_img = mask_img.convert(\"RGB\")\n",
    "    mask_img.show()\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",
    "    print(prompt)\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",
    "    modified_img.show()\n",
    "\n",
    "    modified_img.save(path + f\"/{id}.png\")\n",
    "    print(f\"Saved processed image {id}.png\")\n",
    "\n",
    "    return modified_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZV6HqoiBFeP7"
   },
   "outputs": [],
   "source": [
    "!mkdir drive/MyDrive/Creative_Test5/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "background_save": true,
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    "id": "WJ-G2fqLFN_1",
    "outputId": "58d70383-4425-444f-8f1c-1d0257f0879c"
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   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "path = \"drive/MyDrive/Creative_Test5\"\n",
    "\n",
    "\n",
    "#img_url = \"https://cdn.i-scmp.com/sites/default/files/d8/images/methode/2020/12/25/082b5d7a-440f-11eb-be92-09cd005df0bf_image_hires_235916.jpg\"  # Trump\n",
    "#img_url = \"https://evermotion.org/files/publications_images/0002_RGB_big_thumb.jpg\"  # City\n",
    "#img_url = \"https://assets1.ignimgs.com/2017/11/06/starwarsshipscenes-2-yt-1510004191258_160w.jpg?width=1280\"  # Star Wars\n",
    "#img_url = \"https://wallpaperaccess.com/full/2117775.jpg\"  # mountains\n",
    "img_url = \"https://cdn.wallpapersafari.com/94/57/FiWP4u.jpg\"  # beach\n",
    "image = Image.open(requests.get(img_url, stream=True).raw).convert(\"RGB\").resize((512,512))\n",
    "image.save(path + \"/0.png\")\n",
    "image.show()\n",
    "\n",
    "for id in range(1, 80):\n",
    "  image = generate_datapoint_single_mask(image, id, path)\n",
    "  print(f\"Time passed: {time.time() - start_time}\")\n",
    "\n"
   ]
  },
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    "colab": {
     "background_save": true
    },
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    "from PIL import Image\n",
    "import glob\n",
    "\n",
    "# Create the frames\n",
    "frames = []\n",
    "imgs = glob.glob(\"drive/MyDrive/Creative_Test5/*.png\")\n",
    "for i in imgs:\n",
    "    new_frame = Image.open(i)\n",
    "    frames.append(new_frame)\n",
    "\n",
    "# Save into a GIF file that loops forever\n",
    "frames[0].save('png_to_gif4.gif', format='GIF',\n",
    "               append_images=frames[1:],\n",
    "               save_all=True,\n",
    "               duration=300, loop=0)"
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