Headshot AI

Headshot AI is an open-source project by Astria designed to help developers and makers quickly build and deploy AI-powered applications for generating professional headshots. This starter kit leverages cutting-edge AI model training and inference from Astria, combined with a robust tech stack for a seamless development experience.

Key Features:

• AI Model Training & Inference: Powered by Astria for generating high-quality AI headshots. Supports Astria's packs API for flexible prompt management and easy migration to new models like Flux.
• Frontend: Built with Next.js for a fast and responsive user interface and landing page.
• Backend & Database: Utilizes Supabase for secure database management and user authentication (Magic Link Auth).
• Styling: Implements Shadcn components with Tailwind CSS for a modern and customizable design.
• Deployment: Optimized for deployment on Vercel, offering instant dev environments and continuous integration.
• Optional Integrations:
  • Resend: For sending email notifications to users when their headshots are ready.
  • Stripe: For implementing a credit-based billing system (e.g., 1 credit = 1 model train), with configurable pricing packages.
  • Vercel Blob: For efficient image uploads and storage.

Use Cases: Beyond professional headshots, this versatile starter kit can be adapted for various AI-driven image generation applications, including:

• AI Avatars: Generate avatars in different styles (e.g., Anime, Portraits, Story Illustrations).
• Pet Portraits: Create unique AI-generated portraits of pets.
• Product Shots: Produce high-quality product images for e-commerce.
• Food Photography: Generate appealing food images.
• Icons & Style-Consistent Assets: Create consistent visual assets for branding or UI.

The project emphasizes best practices for achieving good results, such as providing high-quality, single-subject image samples with consistent aspect ratios, and using negative prompts to avoid common AI generation artifacts. It provides a comprehensive guide for local setup, environment variable configuration (including DEPLOYMENT_URL for platform-agnostic deployments), and optional integrations, making it an ideal launchpad for building popular AI SaaS apps.