koharu README

The project's README from github.com/koharu-rs/koharu. As one markdown file: README.md.

Koharu

ML-powered manga translator, written in Rust.

GitHub Downloads (all assets, all releases)

koharu-rs%2Fkoharu | Trendshift

Getting Started · Docs · Bug reports · Discord

日本語 | 简体中文

Koharu introduces a local-first workflow for manga translation, utilizing the power of ML to automate the process. It combines the capabilities of object detection, OCR, inpainting, and LLMs to create a seamless translation experience.

[!NOTE] Koharu runs its vision models and LLMs locally on your machine to keep your data private and secure.


screenshot

[!NOTE] Join our Discord server for support and discussion.

Features

Hardware Acceleration

Koharu supports CUDA and ROCm / HIP on Windows and Linux, Metal on Apple silicon, and Vulkan on Windows and Linux. Keep your graphics driver current; a full CUDA or ROCm SDK installation is not required. See Runtime and hardware requirements for model-specific guidance.

CUDA

CUDA 13.3 requires an NVIDIA Turing-class or newer GPU and an R610 or newer driver. Check NVIDIA's official CUDA toolkit, driver, and architecture matrix and install the latest NVIDIA driver.

ROCm / HIP

ROCm 10.0 support depends on the exact AMD GPU, operating system, and driver combination. Check AMD's official ROCm 10.0.0 compatibility matrix and install a compatible AMD driver.

Metal

Metal is available on Apple silicon Macs.

Vulkan

Vulkan is available on Windows and Linux as an alternative to CUDA and ROCm / HIP.

WebGPU

The editor canvas uses WebGPU and requires a current graphics driver even when inference runs on the CPU.

CPU

CPU inference is available for supported workloads but is substantially slower.

Machine Learning Models

Koharu uses separate models for detection, OCR, inpainting, and translation. Vision and inpainting and translation and generation have separate model settings.

Computer Vision Models

Detection, OCR, and inpainting models are selected separately.

Detection and Layout

The detection model finds text regions, speech bubbles, and segmentation masks.

OCR

OCR reads source text from detected regions.

Inpainting

Inpainting reconstructs the image behind source text before the translation is rendered.

Large Language Models

Translation can use a local language model or a remote API.

General-Purpose Local Models

Uncensored Local Models

Cloud Providers

Hosted LLM providers: OpenAI, Gemini, Claude, Grok, MiniMax, DeepSeek, and OpenRouter.

Machine Translation Providers

Machine-translation providers: DeepL, Google Cloud Translation, and Caiyun.

OpenAI-Compatible Providers

OpenAI-compatible endpoints are also supported.

Installation

Download release builds from the releases page. Installation requirements and first launch vary by operating system.

Builds are available for Windows, macOS, and Linux.

WinGet

Install on Windows with winget:

winget install koharu

Homebrew

Install on macOS with Homebrew:

brew install --cask koharu

Troubleshooting

Startup, runtime, model, and provider errors are covered in Troubleshooting. Set RUST_LOG to debug or trace for verbose logs:

# macOS / Linux
RUST_LOG=debug koharu
# Windows (PowerShell)
$env:RUST_LOG="debug"; koharu.exe

Development

Platform dependencies and validation commands for local builds are listed in Development Setup.

Prerequisites

Install dependencies

bun install

Development

bun dev

Build

bun run build

The executable is written to target/release.

Sponsorship

If Koharu is useful in your workflow, consider sponsoring the project.

sponsors

Contributors ❤️

Thanks to all the contributors who have helped make Koharu better!

License

Copyright 2025-2026 Mayo Takanashi and Koharu contributors.

Koharu is dual-licensed under the MIT License or the Apache License, Version 2.0, at your option.

koharu (koharu-rs/koharu)

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6.4K ·GitHub
What is this?Koharu is an ML-powered manga translator written in Rust that combines object detection, OCR, inpainting, and LLMs to automate translating manga pages while running everything locally.
Is this a command-line tool or a full desktop app?It is a desktop GUI application with a WebGPU-based canvas for manual cleanup, text placement, and page composition, not a library or CLI tool.
How do I get it running?Download a prebuilt release for Windows, macOS, or Linux from the releases page, or install on Windows with WinGet or on macOS with Homebrew.
Any catches with hardware acceleration or performance?CPU inference works but is described as substantially slower, and GPU acceleration depends on exact hardware/driver combinations — CUDA needs a Turing-class or newer NVIDIA GPU, and ROCm support varies by AMD GPU, OS, and driver. The editor canvas also needs a current graphics driver via WebGPU even when inference itself runs on CPU.
Does my manga data leave my machine?No, by default vision models and LLMs run locally so your manga pages stay on your machine, though you can optionally opt into cloud LLM or machine-translation providers.
What file formats can I bring into a project?Koharu supports raster images, archives, and PDFs, with page sequencing for multi-format project management.
What happens to the original Japanese text after translation?Generative inpainting models (like FLUX.2 Klein or LaMa) remove the source text and reconstruct the artwork underneath before the translated text is rendered on top.
Can I export the finished translation for further editing?Yes, Koharu offers layered PSD export so you can either flatten the result for delivery or keep editing the layers in another program.
What is the agent-based workflow mentioned in the features?It lets an agent inspect, edit, and drive the detection, OCR, translation, and inpainting pipeline across a project automatically instead of you running each step manually.
Creator
koharu-rs
Size
98.8 MB
Stars
6.4K
Age
1y
Commits · 90 Days