Introduction
AI-powered image generation has taken creative applications to new heights, allowing users to generate stunning visuals from simple text prompts. In this blog, we will explore how I built a Gemini Image Generator, a Node.js application that leverages Google’s Generative AI (Gemini API) to create images based on user input.
Project Overview
The Gemini Image Generator is a lightweight REST API that allows users to send text prompts and receive AI-generated images. It is built using Node.js, Express, and Google’s Generative AI SDK.
Key Features:
✅ Accepts user text prompts to generate images.
✅ Uses Google Gemini API for AI-based image generation.
✅ Saves generated images on the server.
✅ REST API endpoints for easy integration with other applications.
Tech Stack
The project is built with:
- Node.js - Backend runtime.
- Express.js - Lightweight web framework.
- Google Generative AI SDK - AI-powered image generation.
- dotenv - Environment variable management.
- cors - Cross-origin support.
Getting Started
1. Clone the Repository
git clone https://github.com/manthanank/gemini-image-generator.git
cd gemini-image-generator
2. Install Dependencies
npm install
3. Configure Environment Variables
Create a .env file in the root directory and add:
GEMINI_API_KEY=your_google_gemini_api_key
PORT=5000
4. Start the Server
npm start
Your server will run at http://localhost:5000 🚀
API Endpoints
Generate an Image
📌 Endpoint: POST /api/image/generate
📌 Request Body:
{
"prompt": "a futuristic cityscape with neon lights"
}
📌 Response:
{
"message": "Image generated successfully",
"imagePath": "temp/generated_image.png"
}
Project Structure
gemini-image-generator/
├── controllers/ # Business logic
├── routes/ # API routes
├── services/ # Google Gemini AI logic
├── temp/ # Generated images
├── server.js # Entry point
├── package.json # Dependencies
└── .env # Environment variables
Core Implementation
1. Setting Up the Express Server
The server.js file initializes the app and listens for requests:
const app = require("./app");
const { port } = require("./config/env");
app.listen(port, () => {
console.log(`Server running on http://localhost:${port}`);
});
2. Handling Image Generation Requests
The imageController.js file manages requests:
const { generateImage } = require("../services/geminiService");
async function generateImageController(req, res) {
const { prompt } = req.body;
if (!prompt) {
return res.status(400).json({ error: "Prompt is required" });
}
try {
const imagePath = await generateImage(prompt);
res.status(200).json({ message: "Image generated successfully", imagePath });
} catch (error) {
res.status(500).json({ error: error.message });
}
}
module.exports = { generateImageController };
3. Integrating the Gemini API
The geminiService.js file calls Google's AI API:
const { GoogleGenerativeAI } = require("@google/generative-ai");
const fs = require("fs");
const path = require("path");
const { geminiApiKey } = require("../config/env");
const genAI = new GoogleGenerativeAI(geminiApiKey);
async function generateImage(prompt) {
const model = genAI.getGenerativeModel({
model: "gemini-2.0-flash-exp-image-generation",
generationConfig: { responseModalities: ['Text', 'Image'] }
});
try {
const response = await model.generateContent(prompt);
for (const part of response.response.candidates[0].content.parts) {
if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, 'base64');
const filePath = path.join(__dirname, '../temp/generated_image.png');
fs.writeFileSync(filePath, buffer);
return filePath;
}
}
} catch (error) {
console.error("Error generating image:", error);
throw new Error("Failed to generate image");
}
}
module.exports = { generateImage };
Conclusion
By integrating Google’s Gemini API with Node.js, we’ve built an AI-powered image generation API that can transform text into creative visuals. This project can be expanded to support image style selection, real-time previews, and cloud storage integration.
If you found this useful, ⭐ star the repo and feel free to contribute! 🚀
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