CyberTimon/RapidRAW

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 simple, beautiful editing experience in a lightweight package (under 20MB) for Windows, macOS, Linux, and Android.

I started developing this project as a personal challenge when I was 18. My goal was to create a high-performance tool for my own photography workflow while deepening my understanding of React, WGSL and Rust.

Download RapidRAW Get the latest release for Windows, macOS, Linux, and Android. Packaged and ready to run. Download Latest Version →

Read the Docs Learn how RapidRAW works with step-by-step tutorials, from adjustments to masking. View Tutorials & Docs →

Watch RapidRAW in action:

Library & Culling View

Editor View

Explore example edits processed entirely within RapidRAW. You can download the .rrdata sidecar file for each image to inspect or apply the exact adjustment parameters.

Before & After Edits

Edits & Sidecar File	Before	After
1. Frankfurt Skyline Sunset color grading, bridge and shadow recovery, tone curve adjustment, and urban warmth enhancement. Download Frankfurt.rrdata
2. Gotthard Pass Atmospheric contrast, cloud dehaze, highlight compression, and selective warm split-toning on the terrain. Download Gotthard.rrdata
3. Saint Tropez Portrait AI Subject Masking for targeted portrait exposure lift, skin tone calibration, and golden hour coastal warmth. Download Saint_Tropez.rrdata
4. Mallorca Coast HSL color mixer adjustments for rich ocean teals, local clarity boost on coastal rocks, and subtle exposure lift. Download Mallorca.rrdata
5. Moutathal Waterfall Moody forest grade with desaturated harsh greens, localized waterfall mask pop, and overall exposure reduction. Download Moutathal.rrdata
6. Nufenen Pass AgX tone mapping to recover sky highlights, shadow recovery on the car and road, and sunset peak illumination. Download Nufenen.rrdata

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

Timon Käch Lucerne, Switzerland Developer & Photographer

As a photography enthusiast, I often found existing software to be sluggish and resource-heavy on my machine. I wanted a faster, simpler photo editing experience, so I decided 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.

RapidRAW supports a wide array of camera RAW formats, non-RAW image files, and automatic lens corrections.

Here is an outlook on what is actively being developed and planned for the coming months:

• Astrophotography Stacking: Implementing GPU-accelerated star alignment and stacking (mean, median, sigma-clipping) to merge multi-exposure night sky sequences into 32-bit linear composites.
• Bug Fixes & Stability: Continuous memory optimization, resolving OS-specific crashes (such as Linux Wayland/WebKit edge cases), and refining GPU processing backend auto-selection for seamless performance across all platforms.
• Cloud AI Inpainting: Launching the optional cloud generative AI integration to deliver high-quality object removal and generative replace without requiring local ComfyUI setups or heavy GPU hardware.
• Performance & Algorithm Refinements: Further optimizing processing speed on older GPU architectures, refining Fujifilm X-Trans sensor demosaicing algorithms, and expanding UI responsiveness on mobile/Android devices.

I've designed RapidRAW's AI architecture with absolute flexibility, privacy, and user choice in mind. Whether you want a purely traditional RAW workflow without any neural networks, fast local automation, powerful self-hosting, or simple cloud convenience, you have four distinct ways to use RapidRAW:

These features are integrated directly into RapidRAW and run entirely on your local machine using your CPU or GPU. They are fast, completely private, free, and require zero setup:

• AI Masking: Instantly detect and mask subjects, skies, foregrounds, and depth maps (powered by SAM 2, U-2-Net, and Depth Anything V2).
• Automatic Tagging: The image library is automatically indexed with keywords using a local CLIP model for effortless search.
• Simple Generative Replace: A lightweight, CPU-based inpainting tool (via LaMa) for removing sensor dust, blemishes, and small distractions.

For users with a capable GPU who want maximum control over generative features, RapidRAW connects directly to your local ComfyUI server through the RapidRAW-AI-Connector.

Why this approach? The AI Connector acts as a high-performance middleware that handles image caching, workflow injection, and coordination. Instead of transferring the entire high-resolution image for every edit, the full image is cached once; subsequent generative tweaks transfer only the tiny mask and prompt.

• Full Control: Use your own local hardware and any custom Diffusion model or checkpoint.
• Cost-Free Power: Utilize your existing GPU for heavy generative edits without subscriptions.
• Custom Workflow Selection: Import your own ComfyUI node graphs and workflows into RapidRAW-AI-Connector.

To be clear: core software functionality is never locked behind a paywall. All features remain free if you use built-in tools or choose to self-host.

However, not everyone has access to a powerful GPU or wants the technical overhead of configuring and maintaining a local ComfyUI server. For users who prefer seamless convenience, RapidRAW offers an optional $TBD/month cloud subscription:

• Turnkey Setup: No local server or model weights to download. Just log in and run high-end inpainting/generative tools immediately.
• Support Indie Development: Subscribing is the best way to directly fund continuous development, updates, and maintenance.

For purists, contest photographers, or those with ethical or privacy-related objections to AI tools, RapidRAW can function as a purely traditional, non-AI RAW editor with a single toggle in Settings → General:

• Decluttered UI: All AI-related buttons, generative patch menus, AI selection tabs, and AI-driven lens blur tools are hidden from the interface.
• Algorithmic Fallbacks: Features like Denoise automatically fall back to classic algorithmic filters (such as BM3D) and disable neural-based models.
• Non-Destructive Compatibility: Any existing AI masks or edits in previously edited sidecar files (.rrdata) will still render faithfully without breaking your library.

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)

Windows & macOS:

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

Linux:

• The official Flatpak package supports all Linux distributions and is available on Flathub.
• On Debian-based distributions, install the .deb package from the Releases page.
• On Arch-based distributions, use the rapidraw-bin package from the AUR.

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
npm start

To build the release build:

# 1. Make the release build
npm run tauri build

# 2. Run the release build
./src-tauri/target/release/RapidRAW

# macOS (Homebrew)
brew install libgphoto2 pkg-config

# Linux (Ubuntu / Debian)
sudo apt-get install -y libgphoto2-dev pkg-config

# Development mode with Tethering
npm run start:tethering
# or: npm start -- -- --features tethering

# Release build with Tethering
npm run tauri build -- --features tethering

RapidRAW includes camera tethering for studio, portrait, and product photography workflows. Connect your camera via USB to control exposure settings, monitor your shot in real time, and automatically ingest files directly into your workspace.

Key Capabilities Real-Time Live View: High-frame-rate live view with composition guides, 90° rotation, and horizontal flip. Full Camera Control: Adjust Aperture, Shutter Speed, ISO, White Balance, Exposure Compensation, Exposure Mode, and Metering Mode directly from RapidRAW. Autofocus Control: Trigger autofocus directly from RapidRAW. Ghost Overlay: Overlay previous captures with adjustable opacity to maintain consistent framing and perspective. Battery Monitoring: View the connected camera's battery level directly in RapidRAW. Automatic Presets: Automatically apply a selected preset to newly captured images. Instant Ingestion: Captured images are automatically saved to your active library, indexed, and optionally opened in the editor.

Live Camera Tethering Sony α7 III connected with real-time Live View

Tethering is powered by libgphoto2 and supports over 2,500+ camera models across Canon, Nikon, Sony, Fujifilm, Olympus, Panasonic, and other manufacturers.

• View the Full List of Supported Cameras

Note: Ensure your camera's USB connection mode is set to PC Remote, Tether Shooting, or PTP in the camera settings.

Tethering is supported on macOS and Linux. Windows and Android are not supported.

To run the _tethering release binaries (or build from source), you must install libgphoto2 on your machine first.

brew install libgphoto2 pkg-config

sudo apt-get update
sudo apt-get install -y libgphoto2-dev pkg-config

sudo pacman -S libgphoto2 pkgconf

sudo dnf install libgphoto2-devel pkgconf-pkg-config

After installing the system dependencies, download the corresponding _tethering build from the Releases page, or run/build from source using the tethering feature flag.

RapidRAW includes a headless export tool for batch processing photos in automated scripts, terminal pipelines, or server environments without opening the GUI:

# Export an entire folder using edits found in .rrdata sidecar files
rapidraw export /path/to/photos --output /path/to/output_dir --format jpeg --quality 90

# Export a single image directly to a specific target file
rapidraw export /path/to/photo.raw --output /path/to/output.png --format png

# Export a true 16-bit TIFF (the TIFF default; use 8 for an RGB8 TIFF)
rapidraw export /path/to/photo.raw --output /path/to/output.tiff --format tiff --tiff-bit-depth 16

# Batch export a folder using a custom adjustments JSON file to override sidecars
rapidraw export /path/to/photos --output /path/to/output_dir --adjustments /path/to/preset.json

Note: By default, headless export automatically detects and applies edits stored in .rrdata sidecar files located alongside your source images. You can override sidecars for all exported images by passing a custom JSON file using the --adjustments flag.

The community has created dedicated standalone companion tools designed to integrate directly with RapidRAW's non-destructive editing workflow and .rrdata sidecars:

• RapidTimelapse by @crispin81: An LRTimelapse-style keyframe ramping and deflicker utility. Edit key turning-point frames in RapidRAW, mark them with 5 stars, and RapidTimelapse will smoothly calculate and write interpolated adjustments straight to intermediate .rrdata sidecars.
• RapidReady by @on370 (Website): A high-speed digital asset manager (DAM), ingest, and culling tool. It offers zero-latency browsing through instant embedded preview extraction, Difference-of-Gradients focus peaking, GPS geotagging, virtual albums, and bidirectional rating and metadata synchronization with RapidRAW.

Developed a tool, script, or workflow integration for RapidRAW? Please open a pull request to have it listed here!

RapidRAW is built to be lightweight and cross-platform. The minimum (tested) requirements are:

Operating System:

• Windows: Windows 10 or newer
• macOS: macOS 13 (Ventura) or newer
• Linux: Ubuntu 22.04+ or a compatible modern distribution

Hardware Recommendations:

• RAM: 16GB or more is highly recommended. While the application may run on systems with less memory, performance is best with 16GB+ to handle high-resolution RAW files, undo history, and complex layer masking without slowdowns.
• GPU: A dedicated GPU is recommended. RapidRAW relies heavily on GPU acceleration for its processing pipeline. Very old GPU architectures (generally pre-2015) or older integrated graphics may struggle, leading to instability or graphical artifacts.

WEBKIT_DISABLE_DMABUF_RENDERER=1 RapidRAW

WEBKIT_DISABLE_COMPOSITING_MODE=1 RapidRAW

I’m really grateful for any contributions you make to RapidRAW! Whether you’re reporting a bug, suggesting a new feature, or submitting a pull request - your input helps shape the project and makes it better for everyone. Don’t hesitate to open an issue or share your ideas.

If your camera’s RAW files aren’t supported, please open a issue here first: rawler issues. Once support is added in rawler, create a issue for RapidRAW so I can update the packages and keep everything in sync.

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.
• lensfun: For its invaluable open-source library and comprehensive database for automatic lens correction.
• LaMa: For the powerful & simple image inpainting model, which enables content-aware fill and object removal.
• SAM 2: For providing the foundation model used for the AI subject detection capabilities.
• U-2-Net: For providing the robust architecture used for the AI sky and foreground detection capabilities.
• Depth Anything V2: For the powerful monocular depth estimation model that enables the AI depth masking capabilities.
• nind-denoise: For providing AI models that power the AI noise reduction capabilities in RapidRAW.
• NegPy: For the inspiration behind the negative conversion logic, particularly the mathematical approach to film inversion using characteristic curves.
• pixls.us: For being an incredible community full of knowledgeable people who offered inspiration, advice, and ideas.
• darktable & co.: For some reference implementations that guided parts of this work.
• libgphoto2: For the comprehensive camera communication library powering RapidRAW's tethering and remote capture subsystem.
• spektrafilm: For the spectrally-based film emulation LUTs by Andrea Volpato, used to power RapidRAW's built-in film emulations.
• You: For using and supporting RapidRAW. Your interest keeps this project alive and evolving.

As a young 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)
  • ETH: 0x597e6bdb97f3d0f1602b5efc8f3b7beb21eaf74a (min. 0.005)
  • SOL: CkXM3C777S8iJX9h3MGSfwGxb85Yx7GHmynQUFSbZXUL (min. 0.01)

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.