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Kohya S
2023-07-07 21:17:56 +09:00
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README.md
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@@ -25,7 +25,7 @@ The feature of SDXL training is now available in sdxl branch as an experimental
Summary of the 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`, but it also supports DreamBooth dataset. - `sdxl_train.py` is a script for SDXL fine-tuning. The usage is almost the same as `fine_tune.py`, but it also supports DreamBooth dataset.
- __`prepare_buckets_latents.py` does not support SDXL fine-tuning. Please use DreamBooth dataset, or the metadata without bucketing.__ - `prepare_buckets_latents.py` now supports SDXL fine-tuning.
- `sdxl_train_network.py` is a script for LoRA training for SDXL. The usage is almost the same as `train_network.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: - 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. - `--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.
@@ -39,7 +39,7 @@ Summary of the feature:
### Tips for SDXL training ### Tips for SDXL training
- The default resolution of SDXL is 1024x1024. - The default resolution of SDXL is 1024x1024.
- The fine-tuning can be done with 24GB GPU memory with the batch size of 1. - The fine-tuning can be done with 24GB GPU memory with the batch size of 1. For 24GB GPU, the following options are recommended:
- Train U-Net only. - Train U-Net only.
- Use gradient checkpointing. - Use gradient checkpointing.
- Use `--cache_text_encoder_outputs` option and caching latents. - Use `--cache_text_encoder_outputs` option and caching latents.