Colab Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KaQFv5nVUdiJ"
   },
   "source": [
    "Loading the pre-trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "QQSJAmyyZZ3u",
    "outputId": "80861fe7-aac9-42af-c0a0-1ba01d470cac"
   },
   "outputs": [],
   "source": [
    "from IPython.display import display, HTML\n",
    "import time\n",
    "\n",
    "keywords = [\"art nouveau\", \"mucha-style\", \"ornate decorative frame\", \"floral border\", \"teal and peach palette\", \"flowing hair\", \"feminine portrait\", \"decorative scrollwork\", \"pastel colors\", \"botanical ornaments\", \"liberty style\", \"organic curves\", \"elegant portrait\", \"art deco elements\", \"ethereal goddess\", \"flower crown\", \"delicate linework\", \"nouveau portrait\", \"romantic illustration\", \"ornamental frame\", \"dreamy expression\", \"soft color harmony\", \"vintage poster style\", \"classical beauty\", \"detailed floral elements\"]\n",
    "\n",
    "!pip install  google-auth-oauthlib google-api-python-client\n",
    "\n",
    "# Authenticate and create the Drive API service\n",
    "from google.colab import auth\n",
    "from googleapiclient.discovery import build\n",
    "from googleapiclient.http import MediaFileUpload\n",
    "auth.authenticate_user()\n",
    "drive_service = build('drive', 'v3', cache_discovery=False)\n",
    "\n",
    "!pip install diffusers torch accelerate clip pyexiv2 tenacity\n",
    "import pkg_resources\n",
    "REQUIRED_PACKAGES = ['diffusers','torch','accelerate','clip','pyexiv2','tenacity']\n",
    "\n",
    "for package in REQUIRED_PACKAGES:\n",
    "    try:\n",
    "        dist = pkg_resources.get_distribution(package)\n",
    "        print('{} ({}) is installed'.format(dist.key, dist.version))\n",
    "\n",
    "    except pkg_resources.DistributionNotFound:\n",
    "        print('{} is NOT installed'.format(package))\n",
    "\n",
    "from diffusers import DiffusionPipeline\n",
    "import torch\n",
    "\n",
    "pipeline = DiffusionPipeline.from_pretrained(\"stabilityai/stable-diffusion-xl-base-1.0\", torch_dtype=torch.float16)\n",
    "\n",
    "from IPython.display import clear_output\n",
    "clear_output()\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    pipeline.to(\"cuda\")\n",
    "    pipeline.enable_attention_slicing()\n",
    "    print(\"Using CUDA\")\n",
    "else:\n",
    "    print(\"Using CPU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "base_uri": "https://localhost:8080/",
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    },
    "id": "Ljyzc1shpo5X",
    "outputId": "b08cc3cb-241d-4d1c-ebd3-861d96d2fca2"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "import pyexiv2\n",
    "from PIL import Image\n",
    "import google.generativeai as genai\n",
    "import time\n",
    "from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type\n",
    "\n",
    "# Number of images per prompt and number of prompts\n",
    "num_images = 2\n",
    "num_prompts = 75\n",
    "prompts = []\n",
    "\n",
    "# Gemini API Key\n",
    "GOOGLE_API_KEY = \"AIzaSyATXE22og8-HoroqLF9J5wlb1l58aHOhhU\"\n",
    "\n",
    "# Configure Google Generative AI\n",
    "genai.configure(api_key = GOOGLE_API_KEY)\n",
    "model = genai.GenerativeModel('gemini-pro')\n",
    "\n",
    "# Authenticate and create the Drive API service\n",
    "auth.authenticate_user()\n",
    "drive_service = build('drive', 'v3', cache_discovery=False)\n",
    "\n",
    "# ID of the public folder where images will be uploaded\n",
    "PUBLIC_FOLDER_ID = \"1hf_A_NStS0QcbeDwYb0SBUhLzEIuRruR\" # Add the ID of public folder here\n",
    "\n",
    "# Get the uploaded file name (adjust this if you're handling prompts differently in Colab)\n",
    "uploaded_filename = \"prompts.txt\"\n",
    "\n",
    "def clear_folder(folder_id):\n",
    "    page_token = None\n",
    "    while True:\n",
    "        response = drive_service.files().list(\n",
    "            q=f\"'{folder_id}' in parents\",\n",
    "            spaces='drive',\n",
    "            fields='nextPageToken, files(id, name)',\n",
    "            pageToken=page_token\n",
    "        ).execute()\n",
    "        for file in response.get('files', []):\n",
    "            print(f\"Deleting file: {file.get('name')}\")\n",
    "            drive_service.files().delete(fileId=file.get('id')).execute()\n",
    "        page_token = response.get('nextPageToken', None)\n",
    "        if page_token is None:\n",
    "            break\n",
    "    print(\"Folder cleared successfully.\")\n",
    "\n",
    "@retry(stop=stop_after_attempt(5), wait=wait_exponential(multiplier=1, min=4, max=10), retry=retry_if_exception_type((TimeoutError)))\n",
    "def get_gemini_response(question):\n",
    "    response = model.generate_content(question)\n",
    "    return response.text\n",
    "\n",
    "def generate_prompts(keywords, num_prompts):\n",
    "    global prompts\n",
    "    keyword_string = \", \".join(keywords)\n",
    "    prompt_generation_query = f\"\"\"Task: Generate {num_prompts} unique and creative image prompts based on the keywords: {keyword_string}.\n",
    "    Instructions:\n",
    "    1. Each prompt should be highly detailed and descriptive in English only, suitable for AI image generation.\n",
    "    2. Ensure each prompt is coherent and avoids logical inconsistencies or impossible scenarios.\n",
    "    3. Use vivid, sensory language to create clear mental images.\n",
    "    4. Incorporate unexpected or innovative combinations of the given keywords.\n",
    "    5. Vary the style, mood, and setting across the prompts.\n",
    "    6. Avoid any numbering, punctuation, quotation marks, or special characters in the prompts.\n",
    "    7. Do not include numbers, bullets, or any additional text beyond the prompts themselves.\n",
    "    8. Each prompt should be on a new line.\n",
    "    9. Please don't number the prompts, just plain text is enough.\n",
    "    10. Please don't leave spaces or blank lines after a prompt.\n",
    "    11. Avoid people and texts in the images.\n",
    "    Output Format:\n",
    "    - Provide the prompts as plain text, with each prompt on a new line.\n",
    "    - Do not include any additional formatting or text.\n",
    "    Example Output Format:\n",
    "    A serene mountain lake reflecting a starry night sky with a futuristic city on the distant shore\n",
    "    An ancient tree growing through the ruins of a cyberpunk metropolis its roots entwined with neon wires\n",
    "    A bioluminescent forest surrounding a clean energy power plant with floating wind turbines\n",
    "    Now, generate {num_prompts} image prompts based on these instructions.\"\"\"\n",
    "\n",
    "    response = get_gemini_response(prompt_generation_query)\n",
    "\n",
    "    lines = response.strip().split('\\n')\n",
    "    for line in lines:\n",
    "        clean_line = line.lstrip('-\"0123456789. ')\n",
    "        if clean_line:  # Only add non-empty lines\n",
    "            prompts.append(clean_line)\n",
    "\n",
    "    # Ensure we have exactly num_prompts\n",
    "    if len(prompts) < num_prompts:\n",
    "        generate_prompts(keywords, num_prompts - len(prompts))\n",
    "\n",
    "    # Write prompts to the file\n",
    "    with open(uploaded_filename, 'w') as f:\n",
    "        for prompt in prompts:\n",
    "            f.write(f\"{prompt}\\n\")\n",
    "\n",
    "def upload_file_to_drive(file_path, file_name):\n",
    "    file_metadata = {'name': file_name, 'parents': [PUBLIC_FOLDER_ID]}\n",
    "    media = MediaFileUpload(file_path, resumable=True)\n",
    "    file = drive_service.files().create(body=file_metadata, media_body=media, fields='id').execute()\n",
    "    return file.get('id')\n",
    "\n",
    "# Function to upscale image and ensure minimum file size\n",
    "def upscale_and_save_image(image, jpg_path, min_file_size=500*1024):\n",
    "    width, height = image.size\n",
    "    scale_factor = 4\n",
    "    quality = 95\n",
    "\n",
    "    while True:\n",
    "        # Upscale image\n",
    "        new_size = (width * scale_factor, height * scale_factor)\n",
    "        upscaled_image = image.resize(new_size, resample=Image.BICUBIC)\n",
    "\n",
    "        # Save image\n",
    "        upscaled_image.save(jpg_path, format=\"JPEG\", quality=quality)\n",
    "\n",
    "        # Check file size\n",
    "        if os.path.getsize(jpg_path) >= min_file_size:\n",
    "            break\n",
    "\n",
    "        # If file size is too small, increase scale factor or quality\n",
    "        if scale_factor < 8:\n",
    "            scale_factor += 1\n",
    "        elif quality < 100:\n",
    "            quality += 5\n",
    "        else:\n",
    "            print(f\"Warning: Unable to reach {min_file_size/1024}KB for {jpg_path} even after significant upscaling.\")\n",
    "            break\n",
    "\n",
    "    return upscaled_image\n",
    "\n",
    "@retry(stop=stop_after_attempt(5), wait=wait_exponential(multiplier=1, min=4, max=10))\n",
    "def generate_image(pipeline, prompt):\n",
    "    return pipeline(prompt).images[0]\n",
    "\n",
    "# Clear the folder before starting\n",
    "print(\"Clearing the folder...\")\n",
    "clear_folder(PUBLIC_FOLDER_ID)\n",
    "\n",
    "# Generate prompts using Gemini\n",
    "generate_prompts(keywords, num_prompts)\n",
    "\n",
    "# Check if prompts list is not empty\n",
    "if not prompts:\n",
    "    raise ValueError(\"Prompts list is empty.\")\n",
    "\n",
    "print(f\"Generated {len(prompts)} prompts:\")\n",
    "for i, prompt in enumerate(prompts, 1):\n",
    "    print(f\"{i}. {prompt}\")\n",
    "\n",
    "prompt_titles = {}\n",
    "\n",
    "# Iterate over each prompt\n",
    "for prompt in prompts[:]:  # Create a copy of the list to iterate over\n",
    "    prompt = prompt.replace('.', '')\n",
    "\n",
    "    try:\n",
    "        # Generate keywords\n",
    "        question = f\"Generate at least 30 relevant, simple and most-searched keywords in a single line each separated by comma as delimiter for the given prompt. Make sure the keywords are single words with no special characters in them. Generate the keywords taking the prompt as reference: {prompt}\"\n",
    "        keywords = get_gemini_response(question)\n",
    "        keywords = keywords.split(',')\n",
    "        keywords = [keyword.strip() for keyword in keywords]\n",
    "        keywords.append('_ai_generated')\n",
    "\n",
    "        # Generate title once for each prompt\n",
    "        title_question = f\"Generate a simple, catchy and detailed title (less than 100 characters) for an image based on this prompt: {prompt}\"\n",
    "        title = get_gemini_response(title_question).strip()\n",
    "        prompt_titles[prompt] = title\n",
    "\n",
    "        print(f\"Prompt: {prompt}\")\n",
    "        print(f\"Title: {title}\")\n",
    "\n",
    "        images_generated = 0\n",
    "        for i in range(num_images):\n",
    "            try:\n",
    "                # Generate image\n",
    "                image = generate_image(pipeline, prompt)\n",
    "\n",
    "                # Upscale and save the image locally (temporary)\n",
    "                temp_jpg_path = f\"/tmp/image-{title}-{i}.jpg\"\n",
    "                upscaled_image = upscale_and_save_image(image, temp_jpg_path)\n",
    "\n",
    "                # Add keywords and title to file metadata\n",
    "                image = pyexiv2.Image(temp_jpg_path)\n",
    "                image.modify_iptc({\n",
    "                    'Iptc.Application2.Keywords': keywords,\n",
    "                    'Iptc.Application2.Headline': title\n",
    "                })\n",
    "                image.close()\n",
    "\n",
    "                # Upload the file to Google Drive\n",
    "                file_name = f\"image-{title}-{i}.jpg\"\n",
    "                file_id = upload_file_to_drive(temp_jpg_path, file_name)\n",
    "                print(f\"Uploaded {file_name}\")\n",
    "\n",
    "                # Remove the temporary local file\n",
    "                os.remove(temp_jpg_path)\n",
    "\n",
    "                images_generated += 1\n",
    "\n",
    "            except Exception as e:\n",
    "                print(f\"Error generating/uploading image {i+1} for prompt '{prompt}': {e}\")\n",
    "                continue\n",
    "\n",
    "        prompts.pop(0)\n",
    "\n",
    "        # Update the prompts.txt file\n",
    "        with open(uploaded_filename, 'w') as f:\n",
    "            for remaining_prompt in prompts:\n",
    "                f.write(f\"{remaining_prompt}\\n\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"Error processing prompt '{prompt}': {e}\")\n",
    "        time.sleep(30)  # Wait for 30 seconds before trying the next prompt\n",
    "        continue\n",
    "\n",
    "    time.sleep(5)  # Add a small delay between prompts\n",
    "\n",
    "print(\"Image generation and upload process completed.\")"
   ]
  }
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