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KohyaSS/README.md

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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.

If you run on Linux and would like to use the GUI, there is now a port of it as a docker container. You can find the project here.

Table of Contents

Tutorials

How to Create a LoRA Part 1: Dataset Preparation:

LoRA Part 1 Tutorial

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

LoRA Part 2 Tutorial

Required Dependencies

Installation

Ubuntu

In the terminal, run

git clone https://github.com/bmaltais/kohya_ss.git
cd kohya_ss
bash ubuntu_setup.sh

then configure accelerate with the same answers as in the Windows instructions when prompted.

Windows

Give unrestricted script access to powershell so venv can work:

  • Run PowerShell as an administrator
  • Run Set-ExecutionPolicy Unrestricted and answer 'A'
  • Close PowerShell

Open a regular user Powershell terminal and run the following commands:

git clone https://github.com/bmaltais/kohya_ss.git
cd kohya_ss

python -m venv venv
.\venv\Scripts\activate

pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
pip install --use-pep517 --upgrade -r requirements.txt
pip install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl

cp .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\
cp .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py
cp .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py

accelerate config

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

Upgrading

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

git pull

.\venv\Scripts\activate

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

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

Launching the GUI

To run the GUI, simply use this command:

.\gui.ps1

or you can also do:

.\venv\Scripts\activate

python.exe .\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.

Troubleshooting

Page File Limit

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

No module called tkinter

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 your 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

  • 2023/03/09 (v21.2.0):

    • Fix issue https://github.com/bmaltais/kohya_ss/issues/335

    • Add option to print LoRA trainer command without executing it

    • Add support for samples during trainin via a new Sample images config accordion in the Training parameters tab.

    • Added new Additional parameters under the Advanced Configuration section of the Training parameters tab to allow for the specifications of parameters not handles by the GUI.

    • Added support for sample as a new Accordion under the Training parameters tab. More info about the prompt options can be found here: https://github.com/kohya-ss/sd-scripts/issues/256#issuecomment-1455005709

    • There may be problems due to major changes. If you cannot revert back to the previous version when problems occur, please do not update for a while.

    • Minimum metadata (module name, dim, alpha and network_args) is recorded even with --no_metadata, issue https://github.com/kohya-ss/sd-scripts/issues/254

    • train_network.py supports LoRA for Conv2d-3x3 (extended to conv2d with a kernel size not 1x1).

      • Same as a current version of LoCon. Thank you very much KohakuBlueleaf for your help!
      • LoCon will be enhanced in the future. Compatibility for future versions is not guaranteed.
      • Specify --network_args option like: --network_args "conv_dim=4" "conv_alpha=1"
      • Additional Networks extension version 0.5.0 or later is required to use 'LoRA for Conv2d-3x3' in Stable Diffusion web UI.
      • Stable Diffusion web UI built-in LoRA does not support 'LoRA for Conv2d-3x3' now. Consider carefully whether or not to use it.

    • Merging/extracting scripts also support LoRA for Conv2d-3x3.

    • Free CUDA memory after sample generation to reduce VRAM usage, issue https://github.com/kohya-ss/sd-scripts/issues/260

    • Empty caption doesn't cause error now, issue https://github.com/kohya-ss/sd-scripts/issues/258

    • Fix sample generation is crashing in Textual Inversion training when using templates, or if height/width is not divisible by 8.

    • Update documents (Japanese only).

    • Dependencies are updated, Please upgrade the repo.

    • Add detail dataset config feature by extra config file. Thanks to fur0ut0 for this great contribution!

      • Documentation is here (only in Japanese currently.)
      • Specify .toml file with --dataset_config option.
      • The options supported under the previous release can be used as is instead of the .toml config file.
      • There might be bugs due to the large scale of update, please report any problems if you find at https://github.com/kohya-ss/sd-scripts/issues.

    • Add feature to generate sample images in the middle of training for each training scripts.

      • --sample_every_n_steps and --sample_every_n_epochs options: frequency to generate.
      • --sample_prompts option: the file contains prompts (each line generates one image.)
      • The prompt is subset of gen_img_diffusers.py. The prompt options w, h, d, l, s, n are supported.
      • --sample_sampler option: sampler (scheduler) for generating, such as ddim or k_euler. See help for useable samplers.

    • Add --tokenizer_cache_dir to each training and generation scripts to cache Tokenizer locally from Diffusers.

      • Scripts will support offline training/generation after caching.

    • Support letents upscaling for highres. fix, and VAE batch size in gen_img_diffusers.py (no documentation yet.)

    • Sample image generation: 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 not working.

    Please read Releases for recent updates.

  • 2023/03/05 (v21.1.5):

    • Add replace underscore with space option to WD14 captioning. Thanks @sALTaccount!
    • Improve how custom preset is set and handles.
    • Add support for --listen argument. This allow gradio to listen for connections from other devices on the network (or internet). For example: gui.ps1 --listen "0.0.0.0" will allow anyone to connect to the gradio webui.
    • Updated Resize LoRA tab to support LoCon resizing. Added new resize

  • 2023/03/05 (v21.1.4):

    • Removing legacy and confusing use 8bit adam chackbox. It is now configured using the Optimiser drop down list. It will be set properly based on legacy config files.

  • 2023/03/04 (v21.1.3):

    • Fix progress bar being displayed when not required.
    • Add support for linux, thank you @devNegative-asm

  • 2023/03/03 (v21.1.2):

  • 2023/03/02 (v21.1.1):

  • 2023/03/02 (v21.1.0):

  • 2023/03/01 (v21.0.1):

    • Add warning to tensorboard start if the log information is missing
    • Fix issue with 8bitadam on older config file load

  • 2023/02/27 (v21.0.0):

    • Add tensorboard start and stop support to the GUI

  • 2023/02/26 (v20.8.2):

    • Fix issue https://github.com/bmaltais/kohya_ss/issues/231
    • Change default for seed to random
    • Add support for --share argument to kohya_gui.py and gui.ps1
    • Implement 8bit adam login to help with the legacy Use 8bit adam checkbox that is now superceided by the Optimizer dropdown selection. This field will be eventually removed. Kept for now for backward compatibility.

  • 2023/02/23 (v20.8.1):

    • Fix instability training issue in train_network.py.
      • fp16 training is probably not affected by this issue.
      • Training with float for SD2.x models will work now. Also training with bf16 might be improved.
      • This issue seems to have occurred in PR#190.
    • Add some metadata to LoRA model. Thanks to space-nuko!
    • Raise an error if optimizer options conflict (e.g. --optimizer_type and --use_8bit_adam.)
    • Support ControlNet in gen_img_diffusers.py (no documentation yet.)

  • 2023/02/22 (v20.8.0):

    • Add gui support for optimizers: AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, DAdaptation, AdaFactor
    • Add gui support for --noise_offset
    • Refactor optmizer options. Thanks to mgz-dev!
      • Add --optimizer_type option for each training script. Please see help. Japanese documentation is here.
      • --use_8bit_adam and --use_lion_optimizer options also work and will override the options above for backward compatibility.
    • Add SGDNesterov and its 8bit.
    • Add D-Adaptation optimizer. Thanks to BootsofLagrangian and all!
    • Add AdaFactor optimizer. Thanks to Toshiaki!
    • Extra lr scheduler settings (num_cycles etc.) are working in training scripts other than train_network.py.
    • Add --max_grad_norm option for each training script for gradient clipping. 0.0 disables clipping.
    • Symbolic link can be loaded in each training script. Thanks to TkskKurumi!