StableDiffusion/KohyaSS
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
31a1c8a71a
KohyaSS/README.md
bmaltais 31a1c8a71a Merge kohya Jan 19 updates
2023-01-19 15:47:43 -05:00

205 lines
8.6 KiB
Markdown
Raw Blame History

# 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:
```powershell
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:
```powershell
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](train_db_README.md)
## Finetune
You can find the finetune solution spercific [Finetune README](fine_tune_README.md)
## Train Network
You can find the train network solution spercific [Train network README](train_network_README.md)
## 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
* 2023/01/16 (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>`
Reference in New Issue View Git Blame Copy Permalink