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131 lines
5.6 KiB
Markdown
131 lines
5.6 KiB
Markdown
This repository contains training, generation and utility scripts for Stable Diffusion.
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## Updates
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- 17 Jan. 2023, 2023/1/17
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- __Important Notice__
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It seems that only a part of LoRA modules are trained when ``gradient_checkpointing`` is enabled. The cause is under investigation, but for the time being, please train without ``gradient_checkpointing``.
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- __重要なお知らせ__
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``gradient_checkpointing`` を有効にすると LoRA モジュールの一部しか学習されないようです。原因は調査中ですが当面は ``gradient_checkpointing`` を指定せずに学習してください。
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- 15 Jan. 2023, 2023/1/15
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- Added ``--max_train_epochs`` and ``--max_data_loader_n_workers`` option for each training script.
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- If you specify the number of training epochs with ``--max_train_epochs``, the number of steps is calculated from the number of epochs automatically.
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- 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).
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- ``--max_train_epochs`` と ``--max_data_loader_n_workers`` のオプションが学習スクリプトに追加されました。
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- ``--max_train_epochs`` で学習したいエポック数を指定すると、必要なステップ数が自動的に計算され設定されます。
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- ``--max_data_loader_n_workers`` で DataLoader の worker 数が指定できます（デフォルトは8）。値を小さくするとメインメモリの使用量が減り、エポック間の待ち時間も短くなるようです。ただしデータ読み込み（学習時間）は長くなる可能性があります。
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Please read [release version 0.3.0](https://github.com/kohya-ss/sd-scripts/releases/tag/v0.3.0) for recent updates.
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最近の更新情報は [release version 0.3.0](https://github.com/kohya-ss/sd-scripts/releases/tag/v0.3.0) をご覧ください。
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##
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[日本語版README](./README-ja.md)
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For easier use (GUI and PowerShell scripts etc...), please visit [the repository maintained by bmaltais](https://github.com/bmaltais/kohya_ss). Thanks to @bmaltais!
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This repository contains the scripts for:
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* DreamBooth training, including U-Net and Text Encoder
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* fine-tuning (native training), including U-Net and Text Encoder
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* LoRA training
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* image generation
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* model conversion (supports 1.x and 2.x, Stable Diffision ckpt/safetensors and Diffusers)
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## About requirements.txt
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These files do not contain requirements for PyTorch. Because the versions of them depend on your environment. Please install PyTorch at first (see installation guide below.)
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The scripts are tested with PyTorch 1.12.1 and 1.13.0, Diffusers 0.10.2.
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## Links to how-to-use documents
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All documents are in Japanese currently, and CUI based.
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* [DreamBooth training guide](./train_db_README-ja.md)
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* [Step by Step fine-tuning guide](./fine_tune_README_ja.md):
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Including BLIP captioning and tagging by DeepDanbooru or WD14 tagger
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* [training LoRA](./train_network_README-ja.md)
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* note.com [Image generation](https://note.com/kohya_ss/n/n2693183a798e)
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* note.com [Model conversion](https://note.com/kohya_ss/n/n374f316fe4ad)
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## Windows Required Dependencies
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Python 3.10.6 and Git:
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- Python 3.10.6: https://www.python.org/ftp/python/3.10.6/python-3.10.6-amd64.exe
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- git: https://git-scm.com/download/win
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Give unrestricted script access to powershell so venv can work:
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- Open an administrator powershell window
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- Type `Set-ExecutionPolicy Unrestricted` and answer A
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- Close admin powershell window
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## Windows Installation
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Open a regular Powershell terminal and type the following inside:
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```powershell
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git clone https://github.com/kohya-ss/sd-scripts.git
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cd sd-scripts
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python -m venv --system-site-packages 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 --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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Answers to accelerate config:
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```txt
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- This machine
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- No distributed training
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- NO
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- NO
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- NO
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- all
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- fp16
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```
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note: Some user reports ``ValueError: fp16 mixed precision requires a GPU`` is occurred in training. In this case, answer `0` for the 6th question:
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``What GPU(s) (by id) should be used for training on this machine as a comma-separated list? [all]:``
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(Single GPU with id `0` will be used.)
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## Upgrade
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When a new release comes out you can upgrade your repo with the following command:
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```powershell
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cd sd-scripts
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git pull
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.\venv\Scripts\activate
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pip install --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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## Credits
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The implementation for LoRA is based on [cloneofsimo's repo](https://github.com/cloneofsimo/lora). Thank you for great work!!!
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## License
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The majority of scripts is licensed under ASL 2.0 (including codes from Diffusers, cloneofsimo's), however portions of the project are available under separate license terms:
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[Memory Efficient Attention Pytorch](https://github.com/lucidrains/memory-efficient-attention-pytorch): MIT
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[bitsandbytes](https://github.com/TimDettmers/bitsandbytes): MIT
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[BLIP](https://github.com/salesforce/BLIP): BSD-3-Clause
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