Koharu

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.

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

Features

• Multi-format project management for raster images, archives, and PDFs with page sequencing
• Selective pipeline for detection, OCR, translation, and inpainting at page or project scope
• Detection and segmentation for text regions, speech bubbles, and cleanup regions
• Multimodal OCR for dialogue, captions, and general page text
• Local GGUF inference and hosted providers for LLM and machine-translation workflows
• Generative inpainting for source-text removal and artwork reconstruction
• Proofreading for correcting OCR and translation output
• WebGPU-based canvas for manual cleanup, text placement, and page composition
• Multilingual text shaping and layout with automatic fitting, font fallback, vertical CJK, and right-to-left text
• Layered PSD export for flattened delivery and layered editing
• Agent-based workflow for project inspection, editing, and pipeline control

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 13.0 requires an NVIDIA Turing-class or newer GPU and an R580 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.

• Koharu Layout RF-DETR Seg 2XL

OCR

OCR reads source text from detected regions.

• PaddleOCR VL 1.6
• Manga OCR
• Baberu OCR
• Hayai OCR

Inpainting

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

• FLUX.2 Klein
• RORem mixed
• LaMa
• AOT GAN

Large Language Models

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

General-Purpose Local Models

• LFM 2.5: lfm2.5-1.2b-instruct
• Ministral 3: ministral-3-8b-instruct
• Gemma 4: gemma4-e2b-it, gemma4-e4b-it, gemma4-12b-it, gemma4-26b-a4b-it, gemma4-31b-it
• Qwen 3.5: qwen3.5-0.8b, qwen3.5-2b, qwen3.5-4b, qwen3.5-9b, qwen3.5-27b, qwen3.5-35b-a3b
• Qwen 3.6: qwen3.6-27b, qwen3.6-35b-a3b
• Qwen 3.8: qwen3.8-27b

Uncensored Local Models

• Gemma 4 uncensored: gemma4-e2b-uncensored, gemma4-e4b-uncensored, gemma4-12b-uncensored, gemma4-26b-a4b-uncensored, gemma4-31b-uncensored
• Qwen 3.5 uncensored: qwen3.5-2b-uncensored, qwen3.5-4b-uncensored, qwen3.5-9b-uncensored
• Qwen 3.6 uncensored: qwen3.6-27b-uncensored, qwen3.6-35b-a3b-uncensored
• Qwen 3.8 uncensored: qwen3.8-27b-uncensored

Cloud Providers

Hosted LLM providers: Atlas Cloud, 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.

• GitHub Sponsors
• Patreon

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.