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update for sdxl

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Kohya S
2023-06-26 21:18:22 +09:00
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@@ -18,6 +18,38 @@ This repository contains the scripts for:
__Stable Diffusion web UI now seems to support LoRA trained by ``sd-scripts``.__ Thank you for great work!!!
## About SDXL training
The feature of SDXL training is now available in sdxl branch as an experimental feature.
Summary of the feature:
- `sdxl_train.py` is a script for SDXL fine-tuning. The usage is almost the same as `fine_tune.py`.
- `sdxl_train_network.py` is a script for LoRA training for SDXL. The usage is almost the same as `train_network.py`.
- Both scripts has following additional options:
- `--cache_text_encoder_outputs`: Cache the outputs of the text encoders. This option is useful to reduce the GPU memory usage. This option cannot be used with options for shuffling or dropping the captions.
- `--no_half_vae`: Disable the half-precision (mixed-precision) VAE. VAE for SDXL seems to produce NaNs in some cases. This option is useful to avoid the NaNs.
### Tips for SDXL training
- The default resolution of SDXL is 1024x1024.
- The fine-tuning can be done with 24GB GPU memory with the batch size of 1.
- Train U-Net only.
- Use gradient checkpointing.
- Use `--cache_text_encoder_outputs` option and caching latents.
- Use Adafactor optimizer. RMSprop 8bit or Adagrad 8bit may work. AdamW 8bit doesn't seem to work.
- The LoRA training can be done with 12GB GPU memory.
- `--train_unet_only` option is highly recommended for SDXL LoRA. Because SDXL has two text encoders, the result of the training will be unexpected.
Example of the optimizer settings for Adafactor with the fixed learning rate:
```
optimizer_type = "adafactor"
optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False" ]
lr_scheduler = "constant_with_warmup"
lr_warmup_steps = 100
learning_rate = 4e-7 # SDXL original learning rate
```
## About requirements.txt
These files do not contain requirements for PyTorch. Because the versions of them depend on your environment. Please install PyTorch at first (see installation guide below.)