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.
- Install Python 3.10 using https://www.python.org/ftp/python/3.10.9/python-3.10.9-amd64.exe (make sure to tick the box to add Python to the environment path)
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.
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:
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
This is usually related to an installation issue. Make sure you do not have python modules installed locally that could conflict with the ones installed in the venv:
1. Open a new powershell terminal and make sure no venv is active.
2. Run the following commands
```
pip freeze > uninstall.txt
pip uninstall -r uninstall.txt
```
Then redo the installation instruction within the kohya_ss venv.
- Add support for `network_alpha` under the Training tab and support for `--training_comment` under the Folders tab.
- Add ``--network_alpha`` option to specify ``alpha`` value to prevent underflows for stable training. Thanks to CCRcmcpe!
- Details of the issue are described in https://github.com/kohya-ss/sd-webui-additional-networks/issues/49 .
- The default value is ``1``, scale ``1 / rank (or dimension)``. Set same value as ``network_dim`` for same behavior to old version.
- LoRA with a large dimension (rank) seems to require a higher learning rate with ``alpha=1`` (e.g. 1e-3 for 128-dim, still investigating).
- For generating images in Web UI, __the latest version of the extension ``sd-webui-additional-networks`` (v0.3.0 or later) is required for the models trained with this release or later.__
- Add logging for the learning rate for U-Net and Text Encoder independently, and for running average epoch loss. Thanks to mgz-dev!
- Add more metadata such as dataset/reg image dirs, session ID, output name etc... See https://github.com/kohya-ss/sd-scripts/pull/77 for details. Thanks to space-nuko!
- __Now the metadata includes the folder name (the basename of the folder contains image files, not fullpath).__ If you do not want it, disable metadata storing with ``--no_metadata`` option.
- Add ``--training_comment`` option. You can specify an arbitrary string and refer to it by the extension.
It seems that the Stable Diffusion web UI now supports image generation using the LoRA model learned in this repository.
Note: At this time, it appears that models learned with version 0.4.0 are not supported. If you want to use the generation function of the web UI, please continue to use version 0.3.2. Also, it seems that LoRA models for SD2.x are not supported.
- 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 <modelfile>``. 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:
- 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!
- 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.
