Kohya's GUI

This repository provides a Windows-focused Gradio GUI for Kohya's Stable Diffusion trainers. The GUI allows you to set the training parameters and generate and run the required CLI commands to train the model.

Table of Contents

• Tutorials

• Training guide - common : data preparation, options etc...
  • Chinese version
  • Dataset config
  • DreamBooth training guide
  • Step by Step fine-tuning guide:
  • Training LoRA
  • training Textual Inversion
  • Image generation
  • Model conversion

• Required Dependencies
  • Linux/macOS
• Installation
  • Docker
  • Linux/macOS
    • Default Install Locations
  • Windows
  • CUDNN 8.6
• Upgrading
  • Windows
  • Linux/macOS
• Launching the GUI
  • Windows
  • Linux/macOS
  • Direct Launch via Python Script
• Dreambooth
• Finetune
• Train Network
• LoRA
• Troubleshooting
  • Page File Limit
  • No module called tkinter
  • FileNotFoundError
• Change History

Tutorials

How to Create a LoRA Part 1: Dataset Preparation:

How to Create a LoRA Part 2: Training the Model:

Newer Tutorial: Generate Studio Quality Realistic Photos By Kohya LoRA Stable Diffusion Training:

Newer Tutorial: How To Install And Use Kohya LoRA GUI / Web UI on RunPod IO:

Required Dependencies

• Install Python 3.10
  • make sure to tick the box to add Python to the 'PATH' environment variable
• Install Git
• Install Visual Studio 2015, 2017, 2019, and 2022 redistributable

Linux and macOS dependencies

These dependencies are taken care of via setup.sh in the installation section. No additional steps should be needed unless the scripts inform you otherwise.

Installation

Runpod

Follow the instructions found in this discussion: #379

Docker

Docker is supported on Windows and Linux distributions. However this method currently only supports Nvidia GPUs. Run the following commands in your OS shell after installing git and docker:

git clone https://github.com/bmaltais/kohya_ss.git
cd kohya_ss
docker compose build
docker compose run --service-ports kohya-ss-gui

This will take a while (up to 20 minutes) on the first run.

The following limitations apply:

• All training data must be added to the dataset subdirectory, the docker container cannot access any other files
• The file picker does not work
  • Cannot select folders, folder path must be set manually like e.g. /dataset/my_lora/img
  • Cannot select config file, it must be loaded via path instead like e.g. /dataset/my_config.json
• Dialogs do not work
  • Make sure your file names are unique as this happens when asking if an existing file should be overridden
• No auto-update support. Must run update scripts outside docker manually and then rebuild with docker compose build.

If you run on Linux, there is an alternative docker container port with less limitations. You can find the project here.

Linux and macOS

In the terminal, run

git clone https://github.com/bmaltais/kohya_ss.git
cd kohya_ss
# May need to chmod +x ./setup.sh if you're on a machine with stricter security.
# There are additional options if needed for a runpod environment.
# Call 'setup.sh -h' or 'setup.sh --help' for more information.
./setup.sh

Setup.sh help included here:

Kohya_SS Installation Script for POSIX operating systems.

The following options are useful in a runpod environment,
but will not affect a local machine install.

Usage:
  setup.sh -b dev -d /workspace/kohya_ss -g https://mycustom.repo.tld/custom_fork.git
  setup.sh --branch=dev --dir=/workspace/kohya_ss --git-repo=https://mycustom.repo.tld/custom_fork.git

Options:
  -b BRANCH, --branch=BRANCH    Select which branch of kohya to check out on new installs.
  -d DIR, --dir=DIR             The full path you want kohya_ss installed to.
  -g REPO, --git_repo=REPO      You can optionally provide a git repo to check out for runpod installation. Useful for custom forks.
  -h, --help                    Show this screen.
  -i, --interactive             Interactively configure accelerate instead of using default config file.
  -n, --no-update               Do not update kohya_ss repo. No git pull or clone operations.
  -p, --public                  Expose public URL in runpod mode. Won't have an effect in other modes.
  -r, --runpod                  Forces a runpod installation. Useful if detection fails for any reason.
  -s, --skip-space-check        Skip the 10Gb minimum storage space check.
  -u, --no-gui                  Skips launching the GUI.
  -v, --verbose                 Increase verbosity levels up to 3.

Install location

The default install location for Linux is where the script is located if a previous installation is detected that location. Otherwise, it will fall to /opt/kohya_ss. If /opt is not writeable, the fallback is $HOME/kohya_ss. Lastly, if all else fails it will simply install to the current folder you are in (PWD).

On macOS and other non-Linux machines, it will first try to detect an install where the script is run from and then run setup there if that's detected. If a previous install isn't found at that location, then it will default install to $HOME/kohya_ss followed by where you're currently at if there's no access to $HOME. You can override this behavior by specifying an install directory with the -d option.

If you are using the interactive mode, our default values for the accelerate config screen after running the script answer "This machine", "None", "No" for the remaining questions. These are the same answers as the Windows install.

Windows

• Install Python 3.10
  • make sure to tick the box to add Python to the 'PATH' environment variable
• Install Git
• Install Visual Studio 2015, 2017, 2019, and 2022 redistributable

In the terminal, run:

git clone https://github.com/bmaltais/kohya_ss.git
cd kohya_ss
.\setup.bat

If this is a 1st install answer No when asked Do you want to uninstall previous versions of torch and associated files before installing.

Then configure accelerate with the same answers as in the MacOS instructions when prompted.

Optional: CUDNN 8.6

This step is optional but can improve the learning speed for NVIDIA 30X0/40X0 owners. It allows for larger training batch size and faster training speed.

Due to the file size, I can't host the DLLs needed for CUDNN 8.6 on Github. I strongly advise you download them for a speed boost in sample generation (almost 50% on 4090 GPU) you can download them here.

To install, simply unzip the directory and place the cudnn_windows folder in the root of the this repo.

Run the following commands to install:

.\venv\Scripts\activate

python .\tools\cudann_1.8_install.py

Once the commands have completed successfully you should be ready to use the new version. MacOS support is not tested and has been mostly taken from https://gist.github.com/jstayco/9f5733f05b9dc29de95c4056a023d645

Upgrading

The following commands will work from the root directory of the project if you'd prefer to not run scripts. These commands will work on any OS.

git pull

.\venv\Scripts\activate

pip install --use-pep517 --upgrade -r requirements.txt

Windows Upgrade

When a new release comes out, you can upgrade your repo with the following commands in the root directory:

upgrade.bat

Linux and macOS Upgrade

You can cd into the root directory and simply run

# Refresh and update everything
./setup.sh

# This will refresh everything, but NOT clone or pull the git repo.
./setup.sh --no-git-update

Once the commands have completed successfully you should be ready to use the new version.

Starting GUI Service

The following command line arguments can be passed to the scripts on any OS to configure the underlying service.

--listen: the IP address to listen on for connections to Gradio.
--username: a username for authentication. 
--password: a password for authentication. 
--server_port: the port to run the server listener on. 
--inbrowser: opens the Gradio UI in a web browser. 
--share: shares the Gradio UI.

Launching the GUI on Windows

The two scripts to launch the GUI on Windows are gui.ps1 and gui.bat in the root directory. You can use whichever script you prefer.

To launch the Gradio UI, run the script in a terminal with the desired command line arguments, for example:

gui.ps1 --listen 127.0.0.1 --server_port 7860 --inbrowser --share

or

gui.bat --listen 127.0.0.1 --server_port 7860 --inbrowser --share

Launching the GUI on Linux and macOS

Run the launcher script with the desired command line arguments similar to Windows. gui.sh --listen 127.0.0.1 --server_port 7860 --inbrowser --share

Launching the GUI directly using kohya_gui.py

To run the GUI directly bypassing the wrapper scripts, simply use this command from the root project directory:

.\venv\Scripts\activate

python .\kohya_gui.py

Dreambooth

You can find the dreambooth solution specific here: Dreambooth README

Finetune

You can find the finetune solution specific here: Finetune README

Train Network

You can find the train network solution specific here: Train network README

LoRA

Training a LoRA currently uses the train_network.py code. You can create a LoRA network by using the all-in-one gui.cmd or by running the dedicated LoRA training GUI with:

.\venv\Scripts\activate

python lora_gui.py

Once you have created the LoRA network, you can generate images via auto1111 by installing this extension.

Naming of LoRA

The LoRA supported by train_network.py has been named to avoid confusion. The documentation has been updated. The following are the names of LoRA types in this repository.

1. LoRA-LierLa : (LoRA for Li n e a r La yers)

   LoRA for Linear layers and Conv2d layers with 1x1 kernel

2. LoRA-C3Lier : (LoRA for C olutional layers with 3 x3 Kernel and Li n e a r layers)

   In addition to 1., LoRA for Conv2d layers with 3x3 kernel

LoRA-LierLa is the default LoRA type for train_network.py (without conv_dim network arg). LoRA-LierLa can be used with our extension for AUTOMATIC1111's Web UI, or with the built-in LoRA feature of the Web UI.

To use LoRA-C3Lier with Web UI, please use our extension.

Sample image generation during training

A prompt file might look like this, for example

# prompt 1
masterpiece, best quality, (1girl), in white shirts, upper body, looking at viewer, simple background --n low quality, worst quality, bad anatomy,bad composition, poor, low effort --w 768 --h 768 --d 1 --l 7.5 --s 28

# prompt 2
masterpiece, best quality, 1boy, in business suit, standing at street, looking back --n (low quality, worst quality), bad anatomy,bad composition, poor, low effort --w 576 --h 832 --d 2 --l 5.5 --s 40

Lines beginning with # are comments. You can specify options for the generated image with options like --n after the prompt. The following can be used.

• --n Negative prompt up to the next option.
• --w Specifies the width of the generated image.
• --h Specifies the height of the generated image.
• --d Specifies the seed of the generated image.
• --l Specifies the CFG scale of the generated image.
• --s Specifies the number of steps in the generation.

The prompt weighting such as ( ) and [ ] are working.

Troubleshooting

Page File Limit

• X error relating to page file: Increase the page file size limit in Windows.

No module called tkinter

• Re-install Python 3.10 on your system.

FileNotFoundError

This is usually related to an installation issue. Make sure you do not have any python modules installed locally that could conflict with the ones installed in the venv:

1. Open a new powershell terminal and make sure no venv is active.
2. Run the following commands:

pip freeze > uninstall.txt
pip uninstall -r uninstall.txt

This will store a backup file with your current locally installed pip packages and then uninstall them. Then, redo the installation instructions within the kohya_ss venv.

Change History

• 2023/05/28 (v21.5.15)

• Show warning when image caption file does not exist during training. PR #533 Thanks to TingTingin!
  • Warning is also displayed when using class+identifier dataset. Please ignore if it is intended.
• train_network.py now supports merging network weights before training. PR #542 Thanks to u-haru!
  • --base_weights option specifies LoRA or other model files (multiple files are allowed) to merge.
  • --base_weights_multiplier option specifies multiplier of the weights to merge (multiple values are allowed). If omitted or less than base_weights, 1.0 is used.
  • This is useful for incremental learning. See PR for details.
• Show warning and continue training when uploading to HuggingFace fails.

• 2023/05/28 (v21.5.14)

• Add Create Groupo tool and GUI

• 2023/05/24 (v21.5.13)

• Upgrade gradio release to fix issue with UI refresh on config load.
• D-Adaptation v3.0 is now supported. PR #530 Thanks to sdbds!
  • --optimizer_type now accepts DAdaptAdamPreprint, DAdaptAdanIP, and DAdaptLion.
  • DAdaptAdam is now new. The old DAdaptAdam is available with DAdaptAdamPreprint.
  • Simply specifying DAdaptation will use DAdaptAdamPreprint (same behavior as before).
  • You need to install D-Adaptation v3.0. After activating venv, please do pip install -U dadaptation.
  • See PR and D-Adaptation documentation for details.

• 2023/05/22 (v21.5.12)

• Fixed several bugs.
  • The state is saved even when the --save_state option is not specified in fine_tune.py and train_db.py. PR #521 Thanks to akshaal!
  • Cannot load LoRA without alpha. PR #527 Thanks to Manjiz!
  • Minor changes to console output during sample generation. PR #515 Thanks to yanhuifair!
• The generation script now uses xformers for VAE as well.
• Fixed an issue where an error would occur if the encoding of the prompt file was different from the default. PR #510 Thanks to sdbds!
  • Please save the prompt file in UTF-8.

• 2023/05/15 (v21.5.11)
  • Added an option --dim_from_weights to train_network.py to automatically determine the dim(rank) from the weight file. PR #491 Thanks to AI-Casanova!
    • It is useful in combination with resize_lora.py. Please see the PR for details.
  • Fixed a bug where the noise resolution was incorrect with Multires noise. PR #489 Thanks to sdbds!
    • Please see the PR for details.
  • The image generation scripts can now use img2img and highres fix at the same time.
  • Fixed a bug where the hint image of ControlNet was incorrectly BGR instead of RGB in the image generation scripts.
  • Added a feature to the image generation scripts to use the memory-efficient VAE.
    • If you specify a number with the --vae_slices option, the memory-efficient VAE will be used. The maximum output size will be larger, but it will be slower. Please specify a value of about 16 or 32.
    • The implementation of the VAE is in library/slicing_vae.py.
  • Fix for wandb #ebabchick
  • Added English translation of documents by darkstorm2150. Thank you very much!
  • The prompt for sample generation during training can now be specified in .toml or .json. PR #504 Thanks to Linaqruf!
    • For details on prompt description, please see the PR.
• 2023/04/07 (v21.5.10)
  • Fix issue #734
  • The documentation has been moved to the docs folder. If you have links, please change them.
  • DAdaptAdaGrad, DAdaptAdan, and DAdaptSGD are now supported by DAdaptation. PR#455 Thanks to sdbds!
    • DAdaptation needs to be installed. Also, depending on the optimizer, DAdaptation may need to be updated. Please update with pip install --upgrade dadaptation.
  • Added support for pre-calculation of LoRA weights in image generation scripts. Specify --network_pre_calc.
    • The prompt option --am is available. Also, it is disabled when Regional LoRA is used.
  • Added Adaptive noise scale to each training script. Specify a number with --adaptive_noise_scale to enable it.
    • Experimental option. It may be removed or changed in the future.
    • This is an original implementation that automatically adjusts the value of the noise offset according to the absolute value of the mean of each channel of the latents. It is expected that appropriate noise offsets will be set for bright and dark images, respectively.
    • Specify it together with --noise_offset.
    • The actual value of the noise offset is calculated as noise_offset + abs(mean(latents, dim=(2,3))) * adaptive_noise_scale. Since the latent is close to a normal distribution, it may be a good idea to specify a value of about 1/10 to the same as the noise offset.
    • Negative values can also be specified, in which case the noise offset will be clipped to 0 or more.
  • Other minor fixes.