221 lines
9.3 KiB
Markdown
221 lines
9.3 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/bmaltais/kohya_ss#tutorials)
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- [Required Dependencies](https://github.com/bmaltais/kohya_ss#required-dependencies)
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- [Installation](https://github.com/bmaltais/kohya_ss#installation)
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- [CUDNN 8.6](https://github.com/bmaltais/kohya_ss#optional-cudnn-86)
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- [Upgrading](https://github.com/bmaltais/kohya_ss#upgrading)
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- [Launching the GUI](https://github.com/bmaltais/kohya_ss#launching-the-gui)
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- [Dreambooth](https://github.com/bmaltais/kohya_ss#dreambooth)
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- [Finetune](https://github.com/bmaltais/kohya_ss#finetune)
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- [Train Network](https://github.com/bmaltais/kohya_ss#train-network)
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- [LoRA](https://github.com/bmaltais/kohya_ss#lora)
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- [Troubleshooting](https://github.com/bmaltais/kohya_ss#troubleshooting)
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- [Page File Limit](https://github.com/bmaltais/kohya_ss#page-file-limit)
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- [No module called tkinter](https://github.com/bmaltais/kohya_ss#no-module-called-tkinter)
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- [FileNotFoundError](https://github.com/bmaltais/kohya_ss#filenotfounderror)
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- [Change History](https://github.com/bmaltais/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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### Runpod
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Follow the instructions found in this discussion: https://github.com/bmaltais/kohya_ss/discussions/379
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### Ubuntu
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In the terminal, run
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```
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git clone https://github.com/bmaltais/kohya_ss.git
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cd kohya_ss
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bash ubuntu_setup.sh
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```
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then configure accelerate with the same answers as in the Windows instructions when prompted.
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### Windows
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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 using gui.bat or gui.ps1
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The script can be run with several optional command line arguments:
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--listen: the IP address to listen on for connections to Gradio.
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--username: a username for authentication.
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--password: a password for authentication.
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--server_port: the port to run the server listener on.
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--inbrowser: opens the Gradio UI in a web browser.
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--share: shares the Gradio UI.
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These command line arguments can be passed to the UI function as keyword arguments. To launch the Gradio UI, run the script in a terminal with the desired command line arguments, for example:
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`gui.ps1 --listen 127.0.0.1 --server_port 7860 --inbrowser --share`
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or
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`gui.bat --listen 127.0.0.1 --server_port 7860 --inbrowser --share`
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## Launching the GUI using kohya_gui.py
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To run the GUI, simply use this command:
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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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* 2023/03/23 (v21.3.2)
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- Fix issue reported: https://github.com/bmaltais/kohya_ss/issues/439
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* 2023/03/23 (v21.3.1)
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- Merge PR to fix refactor naming issue for basic captions. Thank @zrma
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* 2023/03/22 (v21.3.0)
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- Add a function to load training config with `.toml` to each training script. Thanks to Linaqruf for this great contribution!
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- Specify `.toml` file with `--config_file`. `.toml` file has `key=value` entries. Keys are same as command line options. See [#241](https://github.com/kohya-ss/sd-scripts/pull/241) for details.
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- All sub-sections are combined to a single dictionary (the section names are ignored.)
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- Omitted arguments are the default values for command line arguments.
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- Command line args override the arguments in `.toml`.
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- With `--output_config` option, you can output current command line options to the `.toml` specified with`--config_file`. Please use as a template.
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- Add `--lr_scheduler_type` and `--lr_scheduler_args` arguments for custom LR scheduler to each training script. Thanks to Isotr0py! [#271](https://github.com/kohya-ss/sd-scripts/pull/271)
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- Same as the optimizer.
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- Add sample image generation with weight and no length limit. Thanks to mio2333! [#288](https://github.com/kohya-ss/sd-scripts/pull/288)
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- `( )`, `(xxxx:1.2)` and `[ ]` can be used.
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- Fix exception on training model in diffusers format with `train_network.py` Thanks to orenwang! [#290](https://github.com/kohya-ss/sd-scripts/pull/290)
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- Add warning if you are about to overwrite an existing model: https://github.com/bmaltais/kohya_ss/issues/404
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- Add `--vae_batch_size` for faster latents caching to each training script. This batches VAE calls.
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- Please start with`2` or `4` depending on the size of VRAM.
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- Fix a number of training steps with `--gradient_accumulation_steps` and `--max_train_epochs`. Thanks to tsukimiya!
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- Extract parser setup to external scripts. Thanks to robertsmieja!
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- Fix an issue without `.npz` and with `--full_path` in training.
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- Support extensions with upper cases for images for not Windows environment.
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- Fix `resize_lora.py` to work with LoRA with dynamic rank (including `conv_dim != network_dim`). Thanks to toshiaki!
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- Fix issue: https://github.com/bmaltais/kohya_ss/issues/406
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- Add device support to LoRA extract.
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