Downloading sd-scripts-main.zip

This repository contains training, generation and utility scripts for Stable Diffusion. [__Change History__](#change-history) is moved to the bottom of the page. 更新履歴は[ページ末尾](#change-history)に移しました。 [日本語版READMEはこちら](./README-ja.md) For easier use (GUI and PowerShell scripts etc...), please visit [the repository maintained by bmaltais](https://github.com/bmaltais/kohya_ss). Thanks to @bmaltais! This repository contains the scripts for: * DreamBooth training, including U-Net and Text Encoder * Fine-tuning (native training), including U-Net and Text Encoder * LoRA training * Textual Inversion training * Image generation * Model conversion (supports 1.x and 2.x, Stable Diffision ckpt/safetensors and Diffusers) ## About requirements.txt The file does not contain requirements for PyTorch. Because the version of PyTorch depends on the environment, it is not included in the file. Please install PyTorch first according to the environment. See installation instructions below. The scripts are tested with Pytorch 2.1.2. 2.0.1 and 1.12.1 is not tested but should work. ## Links to usage documentation Most of the documents are written in Japanese. [English translation by darkstorm2150 is here](https://github.com/darkstorm2150/sd-scripts#links-to-usage-documentation). Thanks to darkstorm2150! * [Training guide - common](./docs/train_README-ja.md) : data preparation, options etc... * [Chinese version](./docs/train_README-zh.md) * [SDXL training](./docs/train_SDXL-en.md) (English version) * [Dataset config](./docs/config_README-ja.md) * [English version](./docs/config_README-en.md) * [DreamBooth training guide](./docs/train_db_README-ja.md) * [Step by Step fine-tuning guide](./docs/fine_tune_README_ja.md): * [Training LoRA](./docs/train_network_README-ja.md) * [Training Textual Inversion](./docs/train_ti_README-ja.md) * [Image generation](./docs/gen_img_README-ja.md) * note.com [Model conversion](https://note.com/kohya_ss/n/n374f316fe4ad) ## Windows Required Dependencies Python 3.10.6 and Git: - Python 3.10.6: https://www.python.org/ftp/python/3.10.6/python-3.10.6-amd64.exe - git: https://git-scm.com/download/win 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 ## Windows Installation Open a regular Powershell terminal and type the following inside: ```powershell git clone https://github.com/kohya-ss/sd-scripts.git cd sd-scripts python -m venv venv .\venv\Scripts\activate pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu118 pip install --upgrade -r requirements.txt pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu118 accelerate config ``` If `python -m venv` shows only `python`, change `python` to `py`. __Note:__ Now `bitsandbytes==0.43.0`, `prodigyopt==1.0` and `lion-pytorch==0.0.6` are included in the requirements.txt. If you'd like to use the another version, please install it manually. This installation is for CUDA 11.8. If you use a different version of CUDA, please install the appropriate version of PyTorch and xformers. For example, if you use CUDA 12, please install `pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121` and `pip install xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu121`. <!-- 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 --> Answers to accelerate config: ```txt - This machine - No distributed training - NO - NO - NO - all - fp16 ``` If you'd like to use bf16, please answer `bf16` to the last question. Note: Some user reports ``ValueError: fp16 mixed precision requires a GPU`` is occurred in training. In this case, answer `0` for the 6th question: ``What GPU(s) (by id) should be used for training on this machine as a comma-separated list? [all]:`` (Single GPU with id `0` will be used.) ## Upgrade When a new release comes out you can upgrade your repo with the following command: ```powershell cd sd-scripts 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. ### Upgrade PyTorch If you want to upgrade PyTorch, you can upgrade it with `pip install` command in [Windows Installation](#windows-installation) section. `xformers` is also required to be upgraded when PyTorch is upgraded. ## Credits The implementation for LoRA is based on [cloneofsimo's repo](https://github.com/cloneofsimo/lora). Thank you for great work! The LoRA expansion to Conv2d 3x3 was initially released by cloneofsimo and its effectiveness was demonstrated at [LoCon](https://github.com/KohakuBlueleaf/LoCon) by KohakuBlueleaf. Thank you so much KohakuBlueleaf! ## License The majority of scripts is licensed under ASL 2.0 (including codes from Diffusers, cloneofsimo's and LoCon), however portions of the project are available under separate license terms: [Memory Efficient Attention Pytorch](https://github.com/lucidrains/memory-efficient-attention-pytorch): MIT [bitsandbytes](https://github.com/TimDettmers/bitsandbytes): MIT [BLIP](https://github.com/salesforce/BLIP): BSD-3-Clause ## Change History ### Sep 13, 2024 / 2024-09-13: - `sdxl_merge_lora.py` now supports OFT. Thanks to Maru-mee for the PR [#1580](https://github.com/kohya-ss/sd-scripts/pull/1580). - `svd_merge_lora.py` now supports LBW. Thanks to terracottahaniwa. See PR [#1575](https://github.com/kohya-ss/sd-scripts/pull/1575) for details. - `sdxl_merge_lora.py` also supports LBW. - See [LoRA Block Weight](https://github.com/hako-mikan/sd-webui-lora-block-weight) by hako-mikan for details on LBW. - These will be included in the next release. - `sdxl_merge_lora.py` が OFT をサポートされました。PR [#1580](https://github.com/kohya-ss/sd-scripts/pull/1580) Maru-mee 氏に感謝します。 - `svd_merge_lora.py` で LBW がサポートされました。PR [#1575](https://github.com/kohya-ss/sd-scripts/pull/1575) terracottahaniwa 氏に感謝します。 - `sdxl_merge_lora.py` でも LBW がサポートされました。 - LBW の詳細は hako-mikan 氏の [LoRA Block Weight](https://github.com/hako-mikan/sd-webui-lora-block-weight) をご覧ください。 - 以上は次回リリースに含まれます。 ### Jun 23, 2024 / 2024-06-23: - Fixed `cache_latents.py` and `cache_text_encoder_outputs.py` not working. (Will be included in the next release.) - `cache_latents.py` および `cache_text_encoder_outputs.py` が動作しなくなっていたのを修正しました。（次回リリースに含まれます。） ### Apr 7, 2024 / 2024-04-07: v0.8.7 - The default value of `huber_schedule` in Scheduled Huber Loss is changed from `exponential` to `snr`, which is expected to give better results. - Scheduled Huber Loss の `huber_schedule` のデフォルト値を `exponential` から、より良い結果が期待できる `snr` に変更しました。 ### Apr 7, 2024 / 2024-04-07: v0.8.6 #### Highlights - The dependent libraries are updated. Please see [Upgrade](#upgrade) and update the libraries. - Especially `imagesize` is newly added, so if you cannot update the libraries immediately, please install with `pip install imagesize==1.4.1` separately. - `bitsandbytes==0.43.0`, `prodigyopt==1.0`, `lion-pytorch==0.0.6` are included in the requirements.txt. - `bitsandbytes` no longer requires complex procedures as it now officially supports Windows. - Also, the PyTorch version is updated to 2.1.2 (PyTorch does not need to be updated immediately). In the upgrade procedure, PyTo