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.
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).
These dependencies are taken care of via `setup.sh` in the installation section. No additional steps should be needed unless the scripts inform you otherwise.
The default install location for Linux is where the script is located if a previous installation is detected that location.
Otherwise, it will fall to `/opt/kohya_ss`. If /opt is not writeable, the fallback is `$HOME/kohya_ss`. Lastly, if all else fails it will simply install to the current folder you are in (PWD).
On macOS and other non-Linux machines, it will first try to detect an install where the script is run from and then run setup there if that's detected.
If a previous install isn't found at that location, then it will default install to `$HOME/kohya_ss` followed by where you're currently at if there's no access to $HOME.
You can override this behavior by specifying an install directory with the -d option.
If you are using the interactive mode, our default values for the accelerate config screen after running the script answer "This machine", "None", "No" for the remaining questions.
These are the same answers as the Windows install.
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.
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).
Once the commands have completed successfully you should be ready to use the new version. MacOS support is not tested and has been mostly taken from https://gist.github.com/jstayco/9f5733f05b9dc29de95c4056a023d645
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:
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).
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:
This will store a backup file with your current locally installed pip packages and then uninstall them. Then, redo the installation instructions within the kohya_ss venv.
- removes TensorFlow from requirements.txt for Darwin platforms as pip does not support advanced conditionals like CPU architecture. The logic is now defined in setup.sh to avoid version bump headaches, and the selection logic is in the pre-existing pip function. Additionally, the release includes the addition of the tensorflow-metal package for M1+ Macs, which enables GPU acceleration per Apple's documentation. Thanks @jstayco
- Improved linux and macos installation and updates script. See README for more details. Many thanks to @jstayco and @Galunid for the great PR!
- Fix issue with "missing library" error.
* 2023/04/01 (v21.3.9)
- Update how setup is done on Windows by introducing a setup.bat script. This will make it easier to install/re-install on Windows if needed. Many thanks to @missionfloyd for his PR: https://github.com/bmaltais/kohya_ss/pull/496
- Fix issues when `--persistent_data_loader_workers` is specified.
- The batch members of the bucket are not shuffled.
-`--caption_dropout_every_n_epochs` does not work.
- These issues occurred because the epoch transition was not recognized correctly. Thanks to u-haru for reporting the issue.
- Fix an issue that images are loaded twice in Windows environment.
- Add Min-SNR Weighting strategy. Details are in [#308](https://github.com/kohya-ss/sd-scripts/pull/308). Thank you to AI-Casanova for this great work!
- Add `--min_snr_gamma` option to training scripts, 5 is recommended by paper.
- Fix for https://github.com/bmaltais/kohya_ss/issues/230
- Added detection for Google Colab to not bring up the GUI file/folder window on the platform. Instead it will only use the file/folder path provided in the input field.
Let me know how this work. From the look of it it appear to be well thought out. I modified a few things to make it fit better with the rest of the code in the repo.
- Add support for custom user gui files. THey will be created at installation time or when upgrading is missing. You will see two files in the root of the folder. One named `gui-user.bat` and the other `gui-user.ps1`. Edit the file based on your preferred terminal. Simply add the parameters you want to pass the gui in there and execute it to start the gui with them. Enjoy!
- Add a function to load training config with `.toml` to each training script. Thanks to Linaqruf for this great contribution!
- 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.
- All sub-sections are combined to a single dictionary (the section names are ignored.)
- Omitted arguments are the default values for command line arguments.
- Command line args override the arguments in `.toml`.
- With `--output_config` option, you can output current command line options to the `.toml` specified with`--config_file`. Please use as a template.
- 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)
- Same as the optimizer.
- Add sample image generation with weight and no length limit. Thanks to mio2333! [#288](https://github.com/kohya-ss/sd-scripts/pull/288)
-`( )`, `(xxxx:1.2)` and `[ ]` can be used.
- 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)
- Add warning if you are about to overwrite an existing model: https://github.com/bmaltais/kohya_ss/issues/404
