LUMAFORGE.

A professional, open-source color grading and optics engine built natively for the web.

[](https://opensource.org/licenses/MIT) [](https://reactjs.org/) [](https://supabase.com/)

Lumaforge is a client-side photo editing environment designed to deliver advanced color control and precise image manipulation directly in the browser. It bypasses the need for heavy desktop software, offering a seamless, non-destructive editing experience.

Key Features:

• Steganographic Presets: Lumaforge invisibly embeds your entire mathematical node tree into the metadata of your exported PNGs. Re-uploading any Lumaforge image back into the canvas seamlessly reconstructs your exact edit history and slider states.
• Professional Optics: Precision tools including non-linear RGB spline curves, cinematic split-toning, and high-dynamic-range exposure controls.
• Universal .CUBE Export: Build your aesthetic in the browser and export custom grades as industry-standard .CUBE LUTs for immediate integration into Premiere Pro, DaVinci Resolve, or After Effects.
• The Uplink: A built-in, community-driven platform. Publish your custom grades, browse public presets, and seamlessly "Fork & Remix" other creators' math directly onto your own local canvas.
• Client-Side Processing: 100% local browser rendering utilizing the Canvas API, featuring a 30-step non-destructive Undo/Redo history stack and lightning-fast WebP image compression.

Tech Stack:

• Frontend: React.js, WebGL / Canvas API
• Backend & Database: Supabase (PostgreSQL)
• Authentication & Storage: Supabase Auth, Supabase Storage

Getting Started:

To run Lumaforge locally on your machine:

1. Clone the repository:
   bash git clone [https://github.com/sganeshe/lumaforge.git](https://github.com/sganeshe/lumaforge.git) cd lumaforge

2. Install dependencies:
   bash npm install

3. Configure Environment Variables:
   Create a .env file in the root directory and add your Supabase credentials:
   env VITE_SUPABASE_URL=your_supabase_project_url VITE_SUPABASE_ANON_KEY=your_supabase_anon_key

4. Start the development server:
   bash npm run dev

Roadmap:

The current architecture is actively being developed to support advanced capabilities, including:

• Integration of native Computer Vision processing pipelines.
• Implementation of Machine Learning-driven semantic image segmentation and automated spatial masking.

Credits & Acknowledgements:

Designed and engineered by Saumya Ganeshe.

Special thanks to the open-source communities behind React and Supabase for providing the foundational infrastructure to make browser-based compute accessible.

License:

This project is open-source and available under the MIT License.