README.md

# Musubi Tuner

[English](./README.md) | [日本語](./README.ja.md)

## Table of Contents

<details>
<summary>Click to expand</summary>

- [Musubi Tuner](#musubi-tuner)
  - [Table of Contents](#table-of-contents)
  - [Introduction](#introduction)
    - [Sponsors](#sponsors)
    - [Support the Project](#support-the-project)
    - [Recent Updates](#recent-updates)
    - [Releases](#releases)
    - [For Developers Using AI Coding Agents](#for-developers-using-ai-coding-agents)
  - [Overview](#overview)
    - [Hardware Requirements](#hardware-requirements)
    - [Features](#features)
    - [Documentation](#documentation)
  - [Installation](#installation)
    - [pip based installation](#pip-based-installation)
    - [uv based installation](#uv-based-installation-experimental)
    - [Linux/MacOS](#linuxmacos)
    - [Windows](#windows)
  - [Model Download](#model-download)
  - [Usage](#usage)
    - [Dataset Configuration](#dataset-configuration)
    - [Pre-caching and Training](#pre-caching-and-training)
    - [Configuration of Accelerate](#configuration-of-accelerate)
    - [Training and Inference](#training-and-inference)
  - [Miscellaneous](#miscellaneous)
    - [SageAttention Installation](#sageattention-installation)
    - [PyTorch version](#pytorch-version)
  - [Disclaimer](#disclaimer)
  - [Contributing](#contributing)
  - [License](#license)

</details>

## Introduction

This repository provides scripts for training LoRA (Low-Rank Adaptation) models with HunyuanVideo, Wan2.1/2.2, FramePack, FLUX.1 Kontext, FLUX.2 dev/klein, Qwen-Image, Z-Image, and [LTX-2](./docs/ltx_2.md) architectures. 

This repository is unofficial and not affiliated with the official repositories of these architectures.

*This repository is under development.*

### Sponsors

We are grateful to the following companies for their generous sponsorship:

<a href="https://aihub.co.jp/top-en">
  <img src="./images/logo_aihub.png" alt="AiHUB Inc." title="AiHUB Inc." height="100px">
</a>

### Support the Project

If you find this project helpful, please consider supporting its development via [GitHub Sponsors](https://github.com/sponsors/kohya-ss/). Your support is greatly appreciated!

### Recent Updates

GitHub Discussions Enabled: We've enabled GitHub Discussions for community Q&A, knowledge sharing, and technical information exchange. Please use Issues for bug reports and feature requests, and Discussions for questions and sharing experiences. [Join the conversation →](https://github.com/kohya-ss/musubi-tuner/discussions)

- February 15, 2026
    - Added support for LoHa/LoKr training. See [PR #900](https://github.com/kohya-ss/musubi-tuner/pull/900)
        - Implemented based on the LoHa/LoKr algorithms from LyCORIS. Special thanks to KohakuBlueleaf from the LyCORIS project.
        - Please refer to the [documentation](./docs/loha_lokr.md) for details.
    - Added `--block_swap_optimizer_patch_params` option to enable the use of some optimizers when using `blocks_to_swap` in Z-Image fine-tuning. See [PR #899](https://github.com/kohya-ss/musubi-tuner/pull/899)
        - Please refer to the [documentation](./docs/zimage.md#finetuning) for details.
        
- January 29, 2026
    - With the release of Z-Image-Base, we have verified that both LoRA and finetuning work correctly.
    - Updated the [related documentation](./docs/zimage.md) for Z-Image.
    - Fixed an issue where sample image generation did not work correctly in LoRA training and finetuning of Z-Image. See [PR #861](https://github.com/kohya-ss/musubi-tuner/pull/861).

- January 24, 2026
    - Fixed an issue where LoRA training for FLUX.2 [klein] did not work. Also made various bug fixes and feature additions related to FLUX.2. See [PR #858](https://github.com/kohya-ss/musubi-tuner/pull/858).
        - The `--model_version` specification has changed from `flux.2-dev` or `flux.2-klein-4b` to `dev` or `klein-4b`, etc.
        - fp8 optimization and other features also work. Please refer to the [documentation](./docs/flux_2.md) for details.
        - Since klein 9B, dev models, and training with multiple control images have not been sufficiently tested, please report any issues via Issue.

- January 21, 2026
    - Added support for LoRA training of FLUX.2 [dev]/[klein]. See [PR #841](https://github.com/kohya-ss/musubi-tuner/pull/841). Many thanks to christopher5106 from https://www.scenario.com for this contribution.
        - Please refer to the [documentation](./docs/flux_2.md) for details.

- January 17, 2026
    - Changed to use `convert_lora.py` for converting Z-Image LoRA for ComfyUI to improve compatibility. See [PR #851](https://github.com/kohya-ss/musubi-tuner/pull/851).
        - The previous `convert_z_image_lora_to_comfy.py` can still be used, but the converted weights may not work correctly with nunchaku.
        - Please refer to the [documentation](./docs/zimage.md#converting-lora-weights-to-diffusers-format-for-comfyui--lora重みをcomfyuiで使用可能なdiffusers形式に変換する) for details.
        - Many thanks to fai-9 for providing the solution in [Issue #847](https://github.com/kohya-ss/musubi-tuner/issues/847).
    - Added `--remove_first_image_from_target` option for LoRA training of Qwen-Image-Layered. See [PR #852](https://github.com/kohya-ss/musubi-tuner/pull/852).
        - Please refer to the [documentation](./docs/qwen_image.md#lora-training--lora学習) for details.

- January 11, 2026
    - Added support for LoRA training of Qwen-Image-Layered. See [PR #816](https://github.com/kohya-ss/musubi-tuner/pull/816).
        - Please refer to the [documentation](./docs/qwen_image.md) for details.
        - In the caching, training, and inference scripts, specify `--model_version` option as `layered`.

### Releases

We are grateful to everyone who has been contributing to the Musubi Tuner ecosystem through documentation and third-party tools. To support these valuable contributions, we recommend working with our [releases](https://github.com/kohya-ss/musubi-tuner/releases) as stable reference points, as this project is under active development and breaking changes may occur.

You can find the latest release and version history in our [releases page](https://github.com/kohya-ss/musubi-tuner/releases).

### For Developers Using AI Coding Agents

This repository provides recommended instructions to help AI agents like Claude and Gemini understand our project context and coding standards.

To use them, you need to opt-in by creating your own configuration file in the project root.

**Quick Setup:**

1.  Create a `CLAUDE.md`, `GEMINI.md`, and/or `AGENTS.md` file in the project root.
2.  Add the following line to your `CLAUDE.md` to import the repository's recommended prompt (currently they are the almost same):

    ```markdown
    @./.ai/claude.prompt.md
    ```

    or for Gemini:

    ```markdown
    @./.ai/gemini.prompt.md
    ```

    You may be also import the prompt depending on the agent you are using with the custom prompt file such as `AGENTS.md`.

3.  You can now add your own personal instructions below the import line (e.g., `Always include a short summary of the change before diving into details.`).

This approach ensures that you have full control over the instructions given to your agent while benefiting from the shared project context. Your `CLAUDE.md`, `GEMINI.md` and `AGENTS.md` (as well as Claude's `.mcp.json`) are already listed in `.gitignore`, so they won't be committed to the repository.

## Overview

### Hardware Requirements

- VRAM: 12GB or more recommended for image training, 24GB or more for video training
    - *Actual requirements depend on resolution and training settings.* For 12GB, use a resolution of 960x544 or lower and use memory-saving options such as `--blocks_to_swap`, `--fp8_llm`, etc.
- Main Memory: 64GB or more recommended, 32GB + swap may work

### Features

- Memory-efficient implementation
- Windows compatibility confirmed (Linux compatibility confirmed by community)
- Multi-GPU training (using [Accelerate](https://huggingface.co/docs/accelerate/index)), documentation will be added later

### Documentation

For detailed information on specific architectures, configurations, and advanced features, please refer to the documentation below.

**Architecture-specific:**
- [HunyuanVideo](./docs/hunyuan_video.md)
- [Wan2.1/2.2](./docs/wan.md)
- [Wan2.1/2.2 (Single Frame)](./docs/wan_1f.md)
- [FramePack](./docs/framepack.md)
- [FramePack (Single Frame)](./docs/framepack_1f.md)
- [FLUX.1 Kontext](./docs/flux_kontext.md)
- [Qwen-Image](./docs/qwen_image.md)
- [Z-Image](./docs/zimage.md)
- [HunyuanVideo 1.5](./docs/hunyuan_video_1_5.md)
- [Kandinsky 5](./docs/kandinsky5.md)
- [LTX-2](./docs/ltx_2.md)
- [FLUX.2](./docs/flux_2.md)

**Common Configuration & Usage:**
- [Dataset Configuration](./docs/dataset_config.md)
- [Advanced Configuration](./docs/advanced_config.md)
- [Sampling during Training](./docs/sampling_during_training.md)
- [Tools and Utilities](./docs/tools.md)
- [Using torch.compile](./docs/torch_compile.md)

## Installation

### pip based installation

Python 3.10 or later is required (verified with 3.10).

Create a virtual environment and install PyTorch and torchvision matching your CUDA version. 

PyTorch 2.5.1 or later is required (see [note](#PyTorch-version)).

```bash
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124
```

Install the required dependencies using the following command.

```bash
pip install -e .
```

Optionally, you can use FlashAttention and SageAttention (**for inference only**; see [SageAttention Installation](#sageattention-installation) for installation instructions).

Optional dependencies for additional features:
- `ascii-magic`: Used for dataset verification
- `matplotlib`: Used for timestep visualization
- `tensorboard`: Used for logging training progress
- `prompt-toolkit`: Used for interactive prompt editing in Wan2.1 and FramePack inference scripts. If installed, it will be automatically used in interactive mode. Especially useful in Linux environments for easier prompt editing.

```bash
pip install ascii-magic matplotlib tensorboard prompt-toolkit
```

### uv based installation (experimental)

You can also install using uv, but installation with uv is experimental. Feedback is welcome.

1. Install uv (if not already present on your OS).

#### Linux/MacOS

```sh
curl -LsSf https://astral.sh/uv/install.sh | sh
```

Follow the instructions to add the uv path manually until you restart your session...

#### Windows

```powershell
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
```

Follow the instructions to add the uv path manually until you reboot your system... or just reboot your system at this point.

## Model Download

Model download procedures vary by architecture. Please refer to the architecture-specific documents in the [Documentation](#documentation) section for instructions.

## Usage


### Dataset Configuration

Please refer to [here](./docs/dataset_config.md).

### Pre-caching

Pre-caching procedures vary by architecture. Please refer to the architecture-specific documents in the [Documentation](#documentation) section for instructions.

### Configuration of Accelerate

Run `accelerate config` to configure Accelerate. Choose appropriate values for each question based on your environment (either input values directly or use arrow keys and enter to select; uppercase is default, so if the default value is fine, just press enter without inputting anything). For training with a single GPU, answer the questions as follows:

```txt
- In which compute environment are you running?: This machine
- Which type of machine are you using?: No distributed training
- Do you want to run your training on CPU only (even if a GPU / Apple Silicon / Ascend NPU device is available)?[yes/NO]: NO
- Do you wish to optimize your script with torch dynamo?[yes/NO]: NO
- Do you want to use DeepSpeed? [yes/NO]: NO
- What GPU(s) (by id) should be used for training on this machine as a comma-seperated list? [all]: all
- Would you like to enable numa efficiency? (Currently only supported on NVIDIA hardware). [yes/NO]: NO
- Do you wish to use mixed precision?: bf16
```

*Note*: In some cases, you may encounter the error `ValueError: fp16 mixed precision requires a GPU`. If this happens, answer "0" to the sixth question (`What GPU(s) (by id) should be used for training on this machine as a comma-separated list? [all]:`). This means that only the first GPU (id `0`) will be used.

### Training and Inference

Training and inference procedures vary significantly by architecture. Please refer to the architecture-specific documents in the [Documentation](#documentation) section and the various configuration documents for detailed instructions.

## Miscellaneous

### SageAttention Installation

sdbsd has provided a Windows-compatible SageAttention implementation and pre-built wheels here:  https://github.com/sdbds/SageAttention-for-windows. After installing triton, if your Python, PyTorch, and CUDA versions match, you can download and install the pre-built wheel from the [Releases](https://github.com/sdbds/SageAttention-for-windows/releases) page. Thanks to sdbsd for this contribution.

For reference, the build and installation instructions are as follows. You may need to update Microsoft Visual C++ Redistributable to the latest version.

1. Download and install triton 3.1.0 wheel matching your Python version from [here](https://github.com/woct0rdho/triton-windows/releases/tag/v3.1.0-windows.post5).

2. Install Microsoft Visual Studio 2022 or Build Tools for Visual Studio 2022, configured for C++ builds.

3. Clone the SageAttention repository in your preferred directory:
    ```shell
    git clone https://github.com/thu-ml/SageAttention.git
    ```

4. Open `x64 Native Tools Command Prompt for VS 2022` from the Start menu under Visual Studio 2022.

5. Activate your venv, navigate to the SageAttention folder, and run the following command. If you get a DISTUTILS not configured error, set `set DISTUTILS_USE_SDK=1` and try again:
    ```shell
    python setup.py install
    ```

This completes the SageAttention installation.

### PyTorch version

If you specify `torch` for `--attn_mode`, use PyTorch 2.5.1 or later (earlier versions may result in black videos).

If you use an earlier version, use xformers or SageAttention.

## Disclaimer

This repository is unofficial and not affiliated with the official repositories of the supported architectures. 

This repository is experimental and under active development. While we welcome community usage and feedback, please note:

- This is not intended for production use
- Features and APIs may change without notice
- Some functionalities are still experimental and may not work as expected
- Video training features are still under development

If you encounter any issues or bugs, please create an Issue in this repository with:
- A detailed description of the problem
- Steps to reproduce
- Your environment details (OS, GPU, VRAM, Python version, etc.)
- Any relevant error messages or logs

## Contributing

We welcome contributions! Please see [CONTRIBUTING.md](./CONTRIBUTING.md) for details.

## License

Code under the `hunyuan_model` directory is modified from [HunyuanVideo](https://github.com/Tencent/HunyuanVideo) and follows their license.

Code under the `hunyuan_video_1_5` directory is modified from [HunyuanVideo 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5) and follows their license.

Code under the `wan` directory is modified from [Wan2.1](https://github.com/Wan-Video/Wan2.1). The license is under the Apache License 2.0.

Code under the `frame_pack` directory is modified from [FramePack](https://github.com/lllyasviel/FramePack). The license is under the Apache License 2.0.

Other code is under the Apache License 2.0. Some code is copied and modified from Diffusers.