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).
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
--listen: the IP address to listen on for connections to Gradio.
--username: a username for authentication.
--password: a password for authentication.
--server_port: the port to run the server listener on.
--inbrowser: opens the Gradio UI in a web browser.
--share: shares the Gradio UI.
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:
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 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.
- 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.
- The Min SNR gamma fiels can be found unser the advanced training tab in all trainers.
- 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.
- Added untested support for MacOS base on this gist: https://gist.github.com/jstayco/9f5733f05b9dc29de95c4056a023d645
Let me know how this work. From the look of it it appear to be well tought 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 prefered 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