CyberTimon/RapidRAW: A beautiful, non-destructive, and GPU-accelerated RAW image editor built with performance in mind.

A beautiful, non-destructive, and GPU-accelerated RAW image editor built with performance in mind.

RapidRAW is a modern, high-performance alternative to Adobe Lightroom. It delivers a feature-rich, beautiful editing experience in a lightweight package (under 30MB) for Windows, macOS, and Linux.

I developed this project as a personal challenge at the age of 18. My goal was to create a high-performance tool for my own photography workflow while deepening my understanding of both React and Rust, with the support from Google Gemini.

Have fun!

Table of Contents

Here’s RapidRAW in action.

The main editor interface in action.

If you like the theme images and want to see more of my own images, checkout my Instagram: @timonkaech.photography

As a photography enthusiast, I often found existing software to be sluggish and resource-heavy on my machine. Born from the desire for a more responsive and streamlined photo editing experience, I set out to build my own. The goal was to create a tool that was not only fast but also helped me learn the details of digital image processing and camera technology.

I set an ambitious goal to rapidly build a functional, feature-rich application from an empty folder. This personal challenge pushed me to learn quickly and focus intensely on the core architecture and user experience.

The foundation is built on Rust for its safety and performance, and Tauri for its ability to create lightweight, cross-platform desktop apps with a web frontend. The entire image processing pipeline is offloaded to the GPU via WGPU and a custom WGSL shader, ensuring that even on complex edits with multiple masks, the UI remains fluid.

I am immensely grateful for Google’s Gemini suite of AI models. As an 18-year-old without a formal background in advanced mathematics or image science, the AI Studio’s free tier was an invaluable assistant, helping me research and implement concepts like the Menon demosaicing algorithm.

While the core functionality is in place, I’m actively working on improving several key areas. Here’s a transparent look at the current focus:

RapidRAW features a two-tier approach to AI to provide both speed and power. It distinguishes between lightweight, integrated tools and heavy, optional generative features.

1. Built-in AI Masking: The core application includes lightweight, fast and open source AI models (SAM from Meta) for intelligent masking (e.g., Subject and Foreground selection). These tools run locally, are always available, and are designed to accelerate your standard editing workflow.

2. Optional Generative AI: For computationally intensive tasks like inpainting (Generative Replace), RapidRAW connects to an external ComfyUI backend. This keeps the main application small and fast, while offloading heavy processing to a dedicated, user-run server.

The Built-in AI Masking is fully functional for all users.

The Optional Generative AI features, however, currently require a manual setup of a ComfyUI backend. The official, easy-to-use Docker container is not yet provided.

This means the generative tools are considered a developer preview and are not ready for general, out-of-the-box use.

The initial work on generative AI focused on building a connection to the ComfyUI backend and implementing the first key features.

• Modular Backend: RapidRAW connects to a local ComfyUI server, which acts as the inference engine.
• Generative Replace (Inpainting): Users can paint a mask over an area of the image (or use the AI masking tool to create a precise selection) and provide a text prompt to fill that area with generated content.
• Non-Destructive Patches: Each generative edit is stored as a separate “patch” layer. These can be toggled, re-ordered, or deleted at any time, consistent with RapidRAW’s non-destructive philosophy.

This project began as an intensive sprint to build the core functionality. Here’s a summary of the initial progress and key milestones:

You have two options to run RapidRAW:

1. Download the Latest Release (Recommended)

Grab the pre-built installer or application bundle for your operating system from the Releases page.

2. Build from Source

If you want to build the project yourself, you’ll need to have Rust and Node.js installed.

# 1. Clone the repository
git clone https://github.com/CyberTimon/RapidRAW.git
cd RapidRAW

# 2. Install frontend dependencies
npm install

# 3. Build and run the application in development mode
# Use --release for a build that runs much faster (image loading etc.)
npx tauri dev --release

Contributions are welcome and highly appreciated! Whether it’s reporting a bug, suggesting a feature, or submitting a pull request, your help makes this project better. Please feel free to open an issue to discuss your ideas.

A huge thank you to the following projects and tools that were very important in the development of RapidRAW:

• Google AI Studio: For providing amazing assistance in researching, implementing image processing algorithms and giving an overall speed boost.
• rawler: For the excellent Rust crate that provides the foundation for RAW file processing in this project.

As an 18-year-old developer balancing this project with an apprenticeship, your support means the world. If you find RapidRAW useful or exciting, please consider donating to help me dedicate more time to its development and cover any associated costs.

• Ko-fi: Donate on Ko-fi
• Crypto:
  • BTC: 36yHjo2dkBwQ63p3YwtqoYAohoZhhUTkCJ (min. 0.0001 because of broker)
  • ETH: 0x597e6bdb97f3d0f1602b5efc8f3b7beb21eaf74a (min. 0.005 because of broker)
  • SOL: CkXM3C777S8iJX9h3MGSfwGxb85Yx7GHmynQUFSbZXUL (min. 0.01 because of broker)

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). I chose this license to ensure that RapidRAW and any of its derivatives will always remain open-source and free for the community. It protects the project from being used in closed-source commercial software, ensuring that improvements benefit everyone.

See the LICENSE file for more details.

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