| # Musubi Tuner |
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| [English](./README.md) | [日本語](./README.ja.md) |
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| ## Table of Contents |
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| <details> |
| <summary>Click to expand</summary> |
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| - [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) |
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| </details> |
|
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| ## Introduction |
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| 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. |
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| This repository is unofficial and not affiliated with the official repositories of these architectures. |
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| *This repository is under development.* |
|
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| ### Sponsors |
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| We are grateful to the following companies for their generous sponsorship: |
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| <a href="https://aihub.co.jp/top-en"> |
| <img src="./images/logo_aihub.png" alt="AiHUB Inc." title="AiHUB Inc." height="100px"> |
| </a> |
|
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| ### Support the Project |
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| 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! |
|
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| ### Recent Updates |
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| 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) |
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| - 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. |
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| - 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. |
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| - 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. |
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| - 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`. |
|
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| ### Releases |
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| 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. |
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| You can find the latest release and version history in our [releases page](https://github.com/kohya-ss/musubi-tuner/releases). |
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| ### For Developers Using AI Coding Agents |
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| This repository provides recommended instructions to help AI agents like Claude and Gemini understand our project context and coding standards. |
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| To use them, you need to opt-in by creating your own configuration file in the project root. |
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| **Quick Setup:** |
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| 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): |
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| ```markdown |
| @./.ai/claude.prompt.md |
| ``` |
| |
| or for Gemini: |
| |
| ```markdown |
| @./.ai/gemini.prompt.md |
| ``` |
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| You may be also import the prompt depending on the agent you are using with the custom prompt file such as `AGENTS.md`. |
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| 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.`). |
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| 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. |
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| ## Overview |
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| ### Hardware Requirements |
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| - 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 |
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| ### Features |
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| - 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 |
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| ### Documentation |
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| For detailed information on specific architectures, configurations, and advanced features, please refer to the documentation below. |
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| **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) |
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| **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) |
|
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| ## Installation |
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| ### pip based installation |
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| Python 3.10 or later is required (verified with 3.10). |
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| Create a virtual environment and install PyTorch and torchvision matching your CUDA version. |
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| PyTorch 2.5.1 or later is required (see [note](#PyTorch-version)). |
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| ```bash |
| pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124 |
| ``` |
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| Install the required dependencies using the following command. |
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| ```bash |
| pip install -e . |
| ``` |
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| Optionally, you can use FlashAttention and SageAttention (**for inference only**; see [SageAttention Installation](#sageattention-installation) for installation instructions). |
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| 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. |
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| ```bash |
| pip install ascii-magic matplotlib tensorboard prompt-toolkit |
| ``` |
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| ### uv based installation (experimental) |
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| You can also install using uv, but installation with uv is experimental. Feedback is welcome. |
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| 1. Install uv (if not already present on your OS). |
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| #### Linux/MacOS |
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| ```sh |
| curl -LsSf https://astral.sh/uv/install.sh | sh |
| ``` |
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| Follow the instructions to add the uv path manually until you restart your session... |
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| #### Windows |
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| ```powershell |
| powershell -c "irm https://astral.sh/uv/install.ps1 | iex" |
| ``` |
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| Follow the instructions to add the uv path manually until you reboot your system... or just reboot your system at this point. |
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| ## Model Download |
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| Model download procedures vary by architecture. Please refer to the architecture-specific documents in the [Documentation](#documentation) section for instructions. |
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| ## Usage |
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| ### Dataset Configuration |
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| Please refer to [here](./docs/dataset_config.md). |
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| ### Pre-caching |
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| Pre-caching procedures vary by architecture. Please refer to the architecture-specific documents in the [Documentation](#documentation) section for instructions. |
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| ### Configuration of Accelerate |
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| 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: |
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| ```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 |
| ``` |
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| *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. |
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| ### Training and Inference |
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| 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. |
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| ## Miscellaneous |
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| ### SageAttention Installation |
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| 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. |
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| For reference, the build and installation instructions are as follows. You may need to update Microsoft Visual C++ Redistributable to the latest version. |
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| 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). |
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| 2. Install Microsoft Visual Studio 2022 or Build Tools for Visual Studio 2022, configured for C++ builds. |
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| 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. |
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| 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. |
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| ### PyTorch version |
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| If you specify `torch` for `--attn_mode`, use PyTorch 2.5.1 or later (earlier versions may result in black videos). |
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| If you use an earlier version, use xformers or SageAttention. |
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| ## Disclaimer |
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| This repository is unofficial and not affiliated with the official repositories of the supported architectures. |
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| This repository is experimental and under active development. While we welcome community usage and feedback, please note: |
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| - 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 |
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| 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 |
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| ## Contributing |
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| We welcome contributions! Please see [CONTRIBUTING.md](./CONTRIBUTING.md) for details. |
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| ## License |
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| Code under the `hunyuan_model` directory is modified from [HunyuanVideo](https://github.com/Tencent/HunyuanVideo) and follows their license. |
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| 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. |
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| 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. |
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| Code under the `frame_pack` directory is modified from [FramePack](https://github.com/lllyasviel/FramePack). The license is under the Apache License 2.0. |
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| Other code is under the Apache License 2.0. Some code is copied and modified from Diffusers. |
