Fit any garment onto any person photo in seconds with photorealistic AI. A production-ready, self-hostable Next.js SaaS boilerplate built for fashion brands, e-commerce stores, stylists, and DTC apparel lines. Replaces $40–$100/mo paid try-on tools. A free open-source alternative to Botika, Lalaland.ai, ZMO.ai, WearView, and Vue.ai — powered by the MuAPI AI engine.

Tech stack: Next.js 14 (App Router) · Prisma · PostgreSQL · NextAuth (Google OAuth) · Stripe · Tailwind CSS · MuAPI · Webhook-backed async delivery Use cases: Fashion e-commerce on-model photography · Virtual fitting rooms · Outfit visualization · Shopify clothing stores · Apparel marketing · Personal styling apps · Wardrobe planning · Streetwear product shots · Boutique catalog generation

🎨 Explore 50+ more open-source AI apps →

GitHub Repository: github.com/SamurAIGPT/ai-tryon

Live Demo Preview: ai-tryon-smoky.vercel.app

TryOn AI is a production-ready, highly-optimized AI web application. Out of the box, it seamlessly manages User Authentication, Credits & Billing, Image Persistence, and asynchronous AI fitting using a sleek Next.js (App Router) architecture. It empowers fashion enthusiasts, styling brands, and e-commerce stores to render garments realistically onto different body silhouettes — all without photoshoots.

Why use TryOn AI?

• Production-Ready SaaS — Complete with Google OAuth and Stripe Checkout workflows built-in.
• Virtual Dressing Studio — Upload portrait photos and outfit images, select aspect ratios, auto-generate or customize prompts, and see results instantly.
• Webhook-Backed AI Delivery — MuAPI async webhook delivers results directly into the database (/api/webhook/muapi), keeping API routes non-blocking and preventing request timeouts.
• Personal Wardrobe Dashboard — All outfit fittings are saved to PostgreSQL. Users can review, compare, download, and delete their designs from /dashboard.
• Responsive Screen-Fitting — Designed with a fluid layout that fits perfectly on all screens (mobile, tablet, desktop) using stacked adaptive grids on mobile and viewport-locked scrolling on desktop.

• Dual image upload via file picker or drag-and-drop. Real-time preview shown instantly.
• Custom dropdown for Aspect Ratio: Auto Detect, 1:1 (Square), 9:16 (Story), 3:4 (Portrait), 4:3 (Landscape), 16:9 (Widescreen).
• Customizable smart prompt with a Reset button to restore the smart default instructions.
• Cost: 18 credits per AI Try-On generation.

• Visual card grid of all generated outfit try-ons.
• Cards show a thumbnail, aspect ratio, prompt summary, creation date, and status (processing / completed / failed).
• Full-screen viewer modal with a floating overlay of the input photos for reference, along with Download High-Res and Delete Outfit actions.
• Auto-polls every 4 seconds for any items still in processing state.

• Four credit packs based on a $1 = 200 credits conversion rate:
  • Basic Pack ($5 / 1,000 credits)
  • Standard Pack ($10 / 2,000 credits)
  • Professional Pack ($20 / 4,000 credits — Most Popular)
  • Business Pack ($50 / 10,000 credits)
• No recurring subscriptions — pay once, use at your own pace.
• Credit balance is automatically topped up via Stripe webhook on checkout completion.

• NextAuth Google provider with Prisma adapter — user sessions, credit balances, and galleries are all persisted per account.
• Credits displayed live in the Navbar with a pulsing coin icon.

This architecture is engineered explicitly for Vercel serverless environments.

Live App: ai-tryon-smoky.vercel.app

To successfully deploy and run, you must populate the following environment variables in your Vercel project settings:

1. Database Provisioning: Create a new Postgres database (via Supabase or Neon). Retrieve the connection string (DATABASE_URL).
2. Project Creation: Import your GitHub fork into the Vercel dashboard.
3. Configure Environment Variables: Copy the variables above into the Vercel project settings environment tab.
4. Deploy: Hit "Deploy". Vercel will automatically run the build steps (npm run build).
5. Database Push: Run npx prisma db push to synchronize database models before launching.
6. Integrations Setup:
   • Establish a Google Cloud OAuth app, enabling the callback URL: https://your-app.vercel.app/api/auth/callback/google
   • Setup a Stripe Webhook, pointing to https://your-app.vercel.app/api/stripe/webhook and selecting the checkout.session.completed event.
   • Register a MuAPI Webhook pointing to https://your-app.vercel.app/api/webhook/muapi to receive async generation results.

Ready to iterate locally? Setup is straightforward.

• Node.js (v18 or higher)
• A local PostgreSQL instance or a free cloud Database URL.
• ngrok (optional, for local MuAPI webhook testing)

# 1. Clone the repository
git clone https://github.com/SamurAIGPT/ai-tryon
cd ai-tryon

# 2. Install dependencies
npm install

# 3. Setup Environment
cp .env.example .env
# Open .env and insert your specific keys.

# 4. Initialize Database Schema
# Note: Because the database is shared, see the Safety Warning below!
npx prisma generate
npx prisma db push

# 5. Start the Development Server
npm run dev

The console should now be active on http://localhost:3000.

Webhook Tip: For local MuAPI webhook testing, run ngrok http 3000 and set WEBHOOK_URL to the generated HTTPS URL in your .env.

The workspace database is shared with other applications. Running npx prisma db push on a clean, empty schema will drop tables belonging to other applications. Always follow the Pull-Declare-Push-Cleanup sequence:

1. Run npx prisma db pull to fetch all database tables.
2. Declare your TryOn table and update the relations on the User model.
3. Run npx prisma db push to add your changes safely.
4. Clean up schema.prisma to keep only NextAuth models, TryOn, and the updated User relations.
5. Run npx prisma generate to rebuild the type-safe client.

ai-tryon/
├── prisma/
│   └── schema.prisma           # Postgres schema (User, Account, Session, TryOn)
├── src/
│   ├── app/                    # Next.js App Router
│   │   ├── page.js             # Main Studio Workspace (Uploads, Aspect Ratio, Prompt)
│   │   ├── dashboard/          # Personal gallery with review modal & polling
│   │   ├── pricing/            # 4-Plan credit pricing grid ($1 = 200 credits)
│   │   └── api/
│   │       ├── auth/           # NextAuth handler
│   │       ├── upload/         # MuAPI file upload proxy
│   │       ├── tryon/          # Credit deduction + MuAPI trigger endpoint
│   │       ├── tryons/         # GET / DELETE try-on history (per user)
│   │       ├── webhook/muapi/  # MuAPI async webhook callback handler
│   │       └── stripe/         # Stripe checkout creation + checkout webhook
│   ├── components/
│   │   ├── Providers.js        # NextAuth SessionProvider wrapper
│   │   └── layout/Navbar.jsx   # Sticky header with Vercel Deploy button & credit balance
│   └── lib/
│       ├── auth.js             # NextAuth config with Prisma adapter
│       ├── config.js           # Central config mapping Google, Stripe, MuAPI keys
│       ├── prisma.js           # Cached Prisma client singleton
│       ├── stripe.js           # Stripe instance initializer
│       └── services/
│           ├── user.js         # Credit management service (18 credits per run)
│           └── billing.js      # Stripe checkout and payment webhook parser
└── next.config.mjs             # Next.js configuration

MIT Licensed.

TryOn AI: A premium, high-contrast, fully responsive virtual outfit fitting room built for fashion creators, brands, and styling teams.