koharu

App Featured

ML-powered manga translator, written in Rust.

Checked · Sources

Downloads
6,338
Dependents
Not applicable
Stars
6,620
Forks
492
Contributors
55
Commits
2,456
Latest release

About

From the project README ·

ML-powered manga translator, written in Rust.

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.

Read the full README

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
  • Rust 1.97.1 or later (Rust 2024 edition)
  • Bun 1.3.14 or later
  • LLVM 22.1.8 or later
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.

License
Apache-2.0
Crate facts
Not measured
  • Runs on, Native code and Python bindings read crates on crates.io. This entry lists no crate.

Compare with similar projects

Order: strongest tier first, then most downloads, then name, with unmeasured values last. A library counts downloads over 30 days. An app counts binary downloads per release. An app with none counts its crate downloads over 30 days.

koharu beside the projects that share a topic with it
ProjectDownloadsDependentsStarsLatest releaseRuns on (GPU)Native codeLicense
koharu This page App Featured

ML-powered manga translator, written in Rust.

6,338 binary downloads per release Not applicable 6,620 Not measured Not measured Apache-2.0
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usls Listed

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rust-cv-convert Listed Dormant

Type conversions among famous Rust computer vision libraries

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Why FeaturedIt passes every Featured gate.

What it meets

  • Its use signals read: binary downloads 19014, Homebrew unknown, Repology families 3, Docker growth unknown. Featured needs 2 of these to reach their limits.
  • The last activity is on 24 Oct 2026, 16 days ago. The rules expect an update within 365 days. Dormancy rule

Not measured: First release.

Exact rules: Featured app. The check on applied thresholds version 9.

Every rule outcome, as the check records it
  • expected-interval: app: E = 365 days
  • dormancy: last activity 2026-10-24, 16 days ago, E = 365: active
  • featured: featured: binary downloads 19014, Homebrew unknown, Repology families 3, Docker growth unknown

Measurements and sources

Each signal row shows the raw value, its source and the fetch date. The Sources list gathers every distinct source on this page. Movement needs more history.

Sources5 sources
Adoption6,338 binary downloads per release4 not measured
Adoption measurements
SignalValueSourceFetchedCounts
Active dependentsNot measuredYes
New dependents (365 days)Not measuredYes
Peak active dependentsNot measuredYes
Active dependents as a share of the peakNot measuredYes
Binary downloads per release6338GitHub releasesYes
MaintenanceLatest human commit: 16 days ago7 not measured
Maintenance measurements
SignalValueSourceFetchedCounts
Latest human commit2026-10-24GitHub repositoryYes
Latest release2026-09-22GitHub releasesYes
First releaseNot measuredYes
Latest versionNot measuredYes
Days the oldest outside issue waits for a reply57GitHub repositoryYes
Maintainer replies over 365 days12GitHub repositoryYes
Repository archivedfalseGitHub repositoryYes
Repository gonefalseGitHub repositoryYes
Release commit or manifest in the repoNot measuredYes
Published docsNot measuredYes
Latest release fails cargo checkNot measuredYes
Build check dateNot measuredYes
Build check toolchainNot measuredYes
SecurityNot measured
Security measurements
SignalValueSourceFetchedCounts
Unpatched vulnerabilities past the grace windowNot measuredYes
RustSec unmaintained advisoryNot measuredYes
Deprecation noticeNot measuredYes
Deprecated requirementNot measuredYes
Distribution19,014 binary downloads over the last 3 releases9 not measured
Distribution measurements
SignalValueSourceFetchedCounts
ReleasesNot measuredYes
Downloads over 30 daysNot measuredYes
Binary downloads over the last 3 releases19014GitHub releasesYes
Homebrew installs on request over 365 daysNot measuredYes
Repology repository families3Repology projectYes
Docker pull growth over 90 daysNot measuredYes
Docker pulls at the growth baselineNot measuredYes
Every version a prereleaseNot measuredYes
Every version 0.0.xNot measuredYes
Every version yankedNot measuredYes
Crate deleted from crates.ioNot measuredYes
Repository6,620 stars, 492 forks, 55 contributors, 2,456 commits. Stars alone never qualify a project.1 not measured
Repository measurements
SignalValueSourceFetchedCounts
GitHub stars6620GitHub repositoryYes — one part of the repository route
GitHub forks492GitHub repositoryYes — one part of the repository route
Contributors with a GitHub account55GitHub contributorsYes — one part of the repository route
Commits on the default branch2456GitHub repositoryNo
Repository's main crate left out of the entryNot measuredYes
Facts that never count
Facts that never count measurements
SignalValueSourceFetchedCounts
LicenseApache-2.0GitHub repositoryNo
Runs on, from listed crates and their direct dependenciesNot measuredNo
What shows each backendNot measuredNo
Native code, from listed crates and their dependenciesNot measuredNo
Python bindings, from direct dependencies of listed cratesNot measuredNo
Crates that depend on a listed crate directlyNot measuredNo

Corrections and review history

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Judgments

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