193 lines
8.5 KiB
Markdown
193 lines
8.5 KiB
Markdown
# Kohya's GUI
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This repository provides a Windows-focused Gradio GUI for [Kohya's Stable Diffusion trainers](https://github.com/kohya-ss/sd-scripts). The GUI allows you to set the training parameters and generate and run the required CLI commands to train the model.
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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](https://github.com/P2Enjoy/kohya_ss-docker).
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### Table of Contents
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- [Tutorials](https://github.com/jonathanzhang53/kohya_ss#tutorials)
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- [Required Dependencies](https://github.com/jonathanzhang53/kohya_ss#required-dependencies)
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- [Installation](https://github.com/jonathanzhang53/kohya_ss#installation)
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- [CUDNN 8.6](https://github.com/jonathanzhang53/kohya_ss#optional-cudnn-86)
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- [Upgrading](https://github.com/jonathanzhang53/kohya_ss#upgrading)
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- [Launching the GUI](https://github.com/jonathanzhang53/kohya_ss#launching-the-gui)
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- [Dreambooth](https://github.com/jonathanzhang53/kohya_ss#dreambooth)
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- [Finetune](https://github.com/jonathanzhang53/kohya_ss#finetune)
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- [Train Network](https://github.com/jonathanzhang53/kohya_ss#train-network)
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- [LoRA](https://github.com/jonathanzhang53/kohya_ss#lora)
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- [Troubleshooting](https://github.com/jonathanzhang53/kohya_ss#troubleshooting)
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- [Page File Limit](https://github.com/jonathanzhang53/kohya_ss#page-file-limit)
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- [No module called tkinter](https://github.com/jonathanzhang53/kohya_ss#no-module-called-tkinter)
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- [FileNotFoundError](https://github.com/jonathanzhang53/kohya_ss#filenotfounderror)
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- [Change History](https://github.com/jonathanzhang53/kohya_ss#change-history)
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## Tutorials
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[How to Create a LoRA Part 1: Dataset Preparation](https://www.youtube.com/watch?v=N4_-fB62Hwk):
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[](https://www.youtube.com/watch?v=N4_-fB62Hwk)
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[How to Create a LoRA Part 2: Training the Model](https://www.youtube.com/watch?v=k5imq01uvUY):
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[](https://www.youtube.com/watch?v=k5imq01uvUY)
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## Required Dependencies
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- Install [Python 3.10](https://www.python.org/ftp/python/3.10.9/python-3.10.9-amd64.exe)
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- make sure to tick the box to add Python to the 'PATH' environment variable
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- Install [Git](https://git-scm.com/download/win)
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- Install [Visual Studio 2015, 2017, 2019, and 2022 redistributable](https://aka.ms/vs/17/release/vc_redist.x64.exe)
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## Installation
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Give unrestricted script access to powershell so venv can work:
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- Run PowerShell as an administrator
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- Run `Set-ExecutionPolicy Unrestricted` and answer 'A'
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- Close PowerShell
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Open a regular user Powershell terminal and run the following commands:
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```powershell
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git clone https://github.com/bmaltais/kohya_ss.git
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cd kohya_ss
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python -m venv venv
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.\venv\Scripts\activate
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pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
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pip install --use-pep517 --upgrade -r requirements.txt
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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
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cp .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\
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cp .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py
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cp .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py
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accelerate config
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```
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### Optional: CUDNN 8.6
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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.
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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](https://b1.thefileditch.ch/mwxKTEtelILoIbMbruuM.zip).
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To install, simply unzip the directory and place the `cudnn_windows` folder in the root of the this repo.
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Run the following commands to install:
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```
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.\venv\Scripts\activate
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python .\tools\cudann_1.8_install.py
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```
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## Upgrading
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When a new release comes out, you can upgrade your repo with the following commands in the root directory:
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```powershell
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git pull
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.\venv\Scripts\activate
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pip install --use-pep517 --upgrade -r requirements.txt
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```
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Once the commands have completed successfully you should be ready to use the new version.
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## Launching the GUI
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To run the GUI, simply use this command:
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```
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.\gui.ps1
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```
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or you can also do:
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```
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.\venv\Scripts\activate
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python.exe .\kohya_gui.py
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```
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## Dreambooth
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You can find the dreambooth solution specific here: [Dreambooth README](train_db_README.md)
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## Finetune
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You can find the finetune solution specific here: [Finetune README](fine_tune_README.md)
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## Train Network
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You can find the train network solution specific here: [Train network README](train_network_README.md)
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## LoRA
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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:
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```
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.\venv\Scripts\activate
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python lora_gui.py
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```
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Once you have created the LoRA network, you can generate images via auto1111 by installing [this extension](https://github.com/kohya-ss/sd-webui-additional-networks).
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## Troubleshooting
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### Page File Limit
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- X error relating to `page file`: Increase the page file size limit in Windows.
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### No module called tkinter
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- Re-install [Python 3.10](https://www.python.org/ftp/python/3.10.9/python-3.10.9-amd64.exe) on your system.
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### FileNotFoundError
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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:
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1. Open a new powershell terminal and make sure no venv is active.
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2. Run the following commands:
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```
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pip freeze > uninstall.txt
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pip uninstall -r uninstall.txt
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```
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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.
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## Change History
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* 2023/02/24 (v20.8.2):
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- Fix issue https://github.com/bmaltais/kohya_ss/issues/231
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- Change default for seed to random
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- Add support for --share argument to `kohya_gui.py` and `gui.ps1`
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- 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.
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* 2023/02/23 (v20.8.1):
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- Fix instability training issue in `train_network.py`.
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- `fp16` training is probably not affected by this issue.
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- Training with `float` for SD2.x models will work now. Also training with bf16 might be improved.
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- This issue seems to have occurred in [PR#190](https://github.com/kohya-ss/sd-scripts/pull/190).
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- Add some metadata to LoRA model. Thanks to space-nuko!
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- Raise an error if optimizer options conflict (e.g. `--optimizer_type` and `--use_8bit_adam`.)
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- Support ControlNet in `gen_img_diffusers.py` (no documentation yet.)
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* 2023/02/22 (v20.8.0):
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- Add gui support for optimizers: `AdamW, AdamW8bit, Lion, SGDNesterov, SGDNesterov8bit, DAdaptation, AdaFactor`
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- Add gui support for `--noise_offset`
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- Refactor optmizer options. Thanks to mgz-dev!
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- Add `--optimizer_type` option for each training script. Please see help. Japanese documentation is [here](https://github-com.translate.goog/kohya-ss/sd-scripts/blob/main/train_network_README-ja.md?_x_tr_sl=fr&_x_tr_tl=en&_x_tr_hl=en-US&_x_tr_pto=wapp#%E3%82%AA%E3%83%97%E3%83%86%E3%82%A3%E3%83%9E%E3%82%A4%E3%82%B6%E3%81%AE%E6%8C%87%E5%AE%9A%E3%81%AB%E3%81%A4%E3%81%84%E3%81%A6).
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- `--use_8bit_adam` and `--use_lion_optimizer` options also work and will override the options above for backward compatibility.
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- Add SGDNesterov and its 8bit.
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- Add [D-Adaptation](https://github.com/facebookresearch/dadaptation) optimizer. Thanks to BootsofLagrangian and all!
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- Please install D-Adaptation optimizer with `pip install dadaptation` (it is not in requirements.txt currently.)
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- Please see https://github.com/kohya-ss/sd-scripts/issues/181 for details.
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- Add AdaFactor optimizer. Thanks to Toshiaki!
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- Extra lr scheduler settings (num_cycles etc.) are working in training scripts other than `train_network.py`.
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- Add `--max_grad_norm` option for each training script for gradient clipping. `0.0` disables clipping.
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- Symbolic link can be loaded in each training script. Thanks to TkskKurumi!
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