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update docs and help text

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Kohya S
2024-10-11 22:23:15 +09:00
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10
README.md
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@@ -11,6 +11,16 @@ The command to install PyTorch is as follows:
### Recent Updates
Oct 11, 2024:
- ControlNet training for SDXL has been implemented in this branch. Please use `sdxl_train_control_net.py`.
- For details on defining the dataset, see [here](docs/train_lllite_README.md#creating-a-dataset-configuration-file).
- The learning rate for the copy part of the U-Net is specified by `--learning_rate`. The learning rate for the added modules in ControlNet is specified by `--control_net_lr`. The optimal value is still unknown, but try around U-Net `1e-5` and ControlNet `1e-4`.
- If you want to generate sample images, specify the control image as `--cn path/to/control/image`.
- The trained weights are automatically converted and saved in Diffusers format. It should be available in ComfyUI.
- Weighting of prompts (captions) during training in SDXL is now supported (e.g., `(some text)`, `[some text]`, `(some text:1.4)`, etc.). The function is enabled by specifying `--weighted_captions`.
- The default is `False`. It is same as before, and the parentheses are used as normal text.
- If `--weighted_captions` is specified, please use `\` to escape the parentheses in the prompt. For example, `\(some text:1.4\)`.
Oct 6, 2024:
- In FLUX.1 LoRA training and fine-tuning, the specified weight file (*.safetensors) is automatically determined to be dev or schnell. This allows schnell models to be loaded correctly. Note that LoRA training with schnell models and fine-tuning with schnell models are unverified.
- FLUX.1 LoRA training and fine-tuning can now load weights in Diffusers format in addition to BFL format (a single *.safetensors file). Please specify the parent directory of `transformer` or `diffusion_pytorch_model-00001-of-00003.safetensors` with the full path. However, Diffusers format CLIP/T5XXL is not supported. Saving is supported only in BFL format.

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docs/train_lllite_README.md
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@@ -185,7 +185,7 @@ for img_file in img_files:
### Creating a dataset configuration file
You can use the command line arguments of `sdxl_train_control_net_lllite.py` to specify the conditioning image directory. However, if you want to use a `.toml` file, specify the conditioning image directory in `conditioning_data_dir`.
You can use the command line argument `--conditioning_data_dir` of `sdxl_train_control_net_lllite.py` to specify the conditioning image directory. However, if you want to use a `.toml` file, specify the conditioning image directory in `conditioning_data_dir`.
```toml
[general]

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sdxl_train_control_net.py
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@@ -705,7 +705,7 @@ def setup_parser() -> argparse.ArgumentParser:
"--control_net_lr",
type=float,
default=1e-4,
help="learning rate for controlnet / controlnetの学習率",
help="learning rate for controlnet modules / controlnetモジュールの学習率",
)
return parser