Kohya's GUI

This repository repository is providing a Gradio GUI for kohya's Stable Diffusion trainers found here: https://github.com/kohya-ss/sd-scripts. The GUI allow you to set the training parameters and generate and run the required CLI command to train the model.

Required Dependencies

Python 3.10.6+ and Git:

• Python 3.10.6+: https://www.python.org/ftp/python/3.10.9/python-3.10.9-amd64.exe
• git: https://git-scm.com/download/win

Installation

Give unrestricted script access to powershell so venv can work:

• Open an administrator powershell window
• Type Set-ExecutionPolicy Unrestricted and answer A
• Close admin powershell window

Open a regular user Powershell terminal and type the following inside:

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

python -m venv --system-site-packages 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 4090 owners...

Due to the filesize 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) you can download them from here: https://b1.thefileditch.ch/mwxKTEtelILoIbMbruuM.zip

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

Run the following command to install:

.\venv\Scripts\activate
python .\tools\cudann_1.8_install.py

Upgrade

When a new release comes out you can upgrade your repo with the following command:

cd kohya_ss
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 you simply use this command:

.\gui.ps1

or you can alsi do:

.\venv\Scripts\activate
python.exe .\kohya_gui.py

Dreambooth

You can find the dreambooth solution spercific Dreambooth README

Finetune

You can find the finetune solution spercific Finetune README

Train Network

You can find the train network solution spercific Train network README

LoRA

Training a LoRA currently use the train_network.py python code. You can create 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 the extension found here: https://github.com/kohya-ss/sd-webui-additional-networks

Troubleshooting

Page file limit

• if get X error relating to page file, increase page file size limit in Windows

No module called tkinter

• Re-install python 3.10.x on your system: https://www.python.org/ftp/python/3.10.9/python-3.10.9-amd64.exe

Change history

• 2023/01/22 (v20.4.0):
  • Add support for network_alpha under the Training tab and support for --training_comment under the Folders tab.
  • Add --network_alpha option to specify alpha value to prevent underflows for stable training. Thanks to CCRcmcpe!
    • Details of the issue are described in https://github.com/kohya-ss/sd-webui-additional-networks/issues/49 .
    • The default value is 1, scale 1 / rank (or dimension). Set same value as network_dim for same behavior to old version.
    • LoRA with a large dimension (rank) seems to require a higher learning rate with alpha=1 (e.g. 1e-3 for 128-dim, still investigating).　
  • For generating images in Web UI, the latest version of the extension sd-webui-additional-networks (v0.3.0 or later) is required for the models trained with this release or later.
  • Add logging for the learning rate for U-Net and Text Encoder independently, and for running average epoch loss. Thanks to mgz-dev!
  • Add more metadata such as dataset/reg image dirs, session ID, output name etc... See https://github.com/kohya-ss/sd-scripts/pull/77 for details. Thanks to space-nuko!
    • Now the metadata includes the folder name (the basename of the folder contains image files, not fullpath). If you do not want it, disable metadata storing with --no_metadata option.
  • Add --training_comment option. You can specify an arbitrary string and refer to it by the extension.

It seems that the Stable Diffusion web UI now supports image generation using the LoRA model learned in this repository.

Note: At this time, it appears that models learned with version 0.4.0 are not supported. If you want to use the generation function of the web UI, please continue to use version 0.3.2. Also, it seems that LoRA models for SD2.x are not supported.

• 2023/01/16 (v20.3.0):
  • Fix a part of LoRA modules are not trained when gradient_checkpointing is enabled.
  • Add --save_last_n_epochs_state option. You can specify how many state folders to keep, apart from how many models to keep. Thanks to shirayu!
  • Fix Text Encoder training stops at max_train_steps even if max_train_epochs is set in `train_db.py``.
  • Added script to check LoRA weights. You can check weights by python networks\check_lora_weights.py <model file>. If some modules are not trained, the value is 0.0 like following.
    • lora_te_text_model_encoder_layers_11_* is not trained with clip_skip=2, so 0.0 is okay for these modules.

• example result of check_lora_weights.py, Text Encoder and a part of U-Net are not trained:

number of LoRA-up modules: 264
lora_te_text_model_encoder_layers_0_mlp_fc1.lora_up.weight,0.0
lora_te_text_model_encoder_layers_0_mlp_fc2.lora_up.weight,0.0
lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_up.weight,0.0
:
lora_unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj.lora_up.weight,0.0
lora_unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2.lora_up.weight,0.0
lora_unet_mid_block_attentions_0_proj_in.lora_up.weight,0.003503334941342473
lora_unet_mid_block_attentions_0_proj_out.lora_up.weight,0.004308608360588551
:

• all modules are trained:

number of LoRA-up modules: 264
lora_te_text_model_encoder_layers_0_mlp_fc1.lora_up.weight,0.0028684409335255623
lora_te_text_model_encoder_layers_0_mlp_fc2.lora_up.weight,0.0029794853180646896
lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_up.weight,0.002507600700482726
lora_te_text_model_encoder_layers_0_self_attn_out_proj.lora_up.weight,0.002639499492943287
:

• 2023/01/16 (v20.2.1):

  • Merging latest code update from kohya
  • Added --max_train_epochs and --max_data_loader_n_workers option for each training script.
  • If you specify the number of training epochs with --max_train_epochs, the number of steps is calculated from the number of epochs automatically.
  • You can set the number of workers for DataLoader with --max_data_loader_n_workers, default is 8. The lower number may reduce the main memory usage and the time between epochs, but may cause slower dataloading (training).
  • Fix loading some VAE or .safetensors as VAE is failed for --vae option. Thanks to Fannovel16!
  • Add negative prompt scaling for gen_img_diffusers.py You can set another conditioning scale to the negative prompt with --negative_scale option, and --nl option for the prompt. Thanks to laksjdjf!
  • Refactoring of GUI code and fixing mismatch... and possibly introducing bugs...

• 2023/01/11 (v20.2.0):

  • Add support for max token lenght

• 2023/01/10 (v20.1.1):

  • Fix issue with LoRA config loading

• 2023/01/10 (v20.1):

  • Add support for --output_name to trainers
  • Refactor code for easier maintenance

• 2023/01/10 (v20.0):

  • Update code base to match latest kohys_ss code upgrade in https://github.com/kohya-ss/sd-scripts

• 2023/01/09 (v19.4.3):

  • Add vae support to dreambooth GUI
  • Add gradient_checkpointing, gradient_accumulation_steps, mem_eff_attn, shuffle_caption to finetune GUI
  • Add gradient_accumulation_steps, mem_eff_attn to dreambooth lora gui

• 2023/01/08 (v19.4.2):

  • Add find/replace option to Basic Caption utility
  • Add resume training and save_state option to finetune UI

• 2023/01/06 (v19.4.1):

  • Emergency fix for new version of gradio causing issues with drop down menus. Please run pip install -U -r requirements.txt to fix the issue after pulling this repo.

• 2023/01/06 (v19.4):

  • Add new Utility to Extract a LoRA from a finetuned model

• 2023/01/06 (v19.3.1):

  • Emergency fix for dreambooth_ui no longer working, sorry
  • Add LoRA network merge too GUI. Run pip install -U -r requirements.txt after pulling this new release.

• 2023/01/05 (v19.3):

  • Add support for --clip_skip option
  • Add missing detect_face_rotate.py to tools folder
  • Add gui.cmd for easy start of GUI

• 2023/01/02 (v19.2) update:

  • Finetune, add xformers, 8bit adam, min bucket, max bucket, batch size and flip augmentation support for dataset preparation
  • Finetune, add "Dataset preparation" tab to group task specific options

• 2023/01/01 (v19.2) update:

  • add support for color and flip augmentation to "Dreambooth LoRA"

• 2023/01/01 (v19.1) update:

  • merge kohys_ss upstream code updates
  • rework Dreambooth LoRA GUI
  • fix bug where LoRA network weights were not loaded to properly resume training

• 2022/12/30 (v19) update:

  • support for LoRA network training in kohya_gui.py.

• 2022/12/23 (v18.8) update:

  • Fix for conversion tool issue when the source was an sd1.x diffuser model
  • Other minor code and GUI fix

• 2022/12/22 (v18.7) update:

  • Merge dreambooth and finetune is a common GUI
  • General bug fixes and code improvements

• 2022/12/21 (v18.6.1) update:

  • fix issue with dataset balancing when the number of detected images in the folder is 0

• 2022/12/21 (v18.6) update:

  • add optional GUI authentication support via: python fine_tune.py --username=<name> --password=<password>