- Noise offset is recorded to the metadata. Thanks to space-nuko!
- Show the moving average loss to prevent loss jumping in `train_network.py` and `train_db.py`. Thanks to shirayu!
- ``lora_interrogator.py`` is added in ``networks`` folder. See ``python networks\lora_interrogator.py -h`` for usage.
- For LoRAs where the activation word is unknown, this script compares the output of Text Encoder after applying LoRA to that of unapplied to find out which token is affected by LoRA. Hopefully you can figure out the activation word. LoRA trained with captions does not seem to be able to interrogate.
- Batch size can be large (like 64 or 128).
- ``train_textual_inversion.py`` now supports multiple init words.
- Following feature is reverted to be the same as before. Sorry for confusion:
> Now the number of data in each batch is limited to the number of actual images (not duplicated). Because a certain bucket may contain smaller number of actual images, so the batch may contain same (duplicated) images.
- Add new tool to sort, group and average crop image in a dataset
- Caption dropout is supported in ``train_db.py``, ``fine_tune.py`` and ``train_network.py``. Thanks to forestsource!
- ``--caption_dropout_rate`` option specifies the dropout rate for captions (0~1.0, 0.1 means 10% chance for dropout). If dropout occurs, the image is trained with the empty caption. Default is 0 (no dropout).
- ``--caption_dropout_every_n_epochs`` option specifies how many epochs to drop captions. If ``3`` is specified, in epoch 3, 6, 9 ..., images are trained with all captions empty. Default is None (no dropout).
- ``--caption_tag_dropout_rate`` option specified the dropout rate for tags (comma separated tokens) (0~1.0, 0.1 means 10% chance for dropout). If dropout occurs, the tag is removed from the caption. If ``--keep_tokens`` option is set, these tokens (tags) are not dropped. Default is 0 (no droupout).
- The bulk image downsampling script is added. Documentation is [here](https://github.com/kohya-ss/sd-scripts/blob/main/train_network_README-ja.md#%E7%94%BB%E5%83%8F%E3%83%AA%E3%82%B5%E3%82%A4%E3%82%BA%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%97%E3%83%88) (in Jpanaese). Thanks to bmaltais!
- Typo check is added. Thanks to shirayu!
- Add option to autolaunch the GUI in a browser and set the server_port. USe either `gui.ps1 --inbrowser --server_port 3456`or `gui.cmd -inbrowser -server_port 3456`
- Caption dropout is supported in ``train_db.py``, ``fine_tune.py`` and ``train_network.py``. Thanks to forestsource!
- ``--caption_dropout_rate`` option specifies the dropout rate for captions (0~1.0, 0.1 means 10% chance for dropout). If dropout occurs, the image is trained with the empty caption. Default is 0 (no dropout).
- ``--caption_dropout_every_n_epochs`` option specifies how many epochs to drop captions. If ``3`` is specified, in epoch 3, 6, 9 ..., images are trained with all captions empty. Default is None (no dropout).
- ``--caption_tag_dropout_rate`` option specified the dropout rate for tags (comma separated tokens) (0~1.0, 0.1 means 10% chance for dropout). If dropout occurs, the tag is removed from the caption. If ``--keep_tokens`` option is set, these tokens (tags) are not dropped. Default is 0 (no droupout).
- The bulk image downsampling script is added. Documentation is [here](https://github.com/kohya-ss/sd-scripts/blob/main/train_network_README-ja.md#%E7%94%BB%E5%83%8F%E3%83%AA%E3%82%B5%E3%82%A4%E3%82%BA%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%97%E3%83%88) (in Jpanaese). Thanks to bmaltais!
- Typo check is added. Thanks to shirayu!
- Add option to autolaunch the GUI in a browser and set the server_port. USe either `gui.ps1 --inbrowser --server_port 3456`or `gui.cmd -inbrowser -server_port 3456`
