| ## SDXL training | |
| The documentation will be moved to the training documentation in the future. The following is a brief explanation of the training scripts for SDXL. | |
| ### Training scripts for SDXL | |
| - `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. | |
| - `--full_bf16` option is added. Thanks to KohakuBlueleaf! | |
| - This option enables the full bfloat16 training (includes gradients). This option is useful to reduce the GPU memory usage. | |
| - The full bfloat16 training might be unstable. Please use it at your own risk. | |
| - The different learning rates for each U-Net block are now supported in sdxl_train.py. Specify with `--block_lr` option. Specify 23 values separated by commas like `--block_lr 1e-3,1e-3 ... 1e-3`. | |
| - 23 values correspond to `0: time/label embed, 1-9: input blocks 0-8, 10-12: mid blocks 0-2, 13-21: output blocks 0-8, 22: out`. | |
| - `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`. | |
| - Both scripts has following additional options: | |
| - `--cache_text_encoder_outputs` and `--cache_text_encoder_outputs_to_disk`: 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. | |
| - `--weighted_captions` option is not supported yet for both scripts. | |
| - `sdxl_train_textual_inversion.py` is a script for Textual Inversion training for SDXL. The usage is almost the same as `train_textual_inversion.py`. | |
| - `--cache_text_encoder_outputs` is not supported. | |
| - There are two options for captions: | |
| 1. Training with captions. All captions must include the token string. The token string is replaced with multiple tokens. | |
| 2. Use `--use_object_template` or `--use_style_template` option. The captions are generated from the template. The existing captions are ignored. | |
| - See below for the format of the embeddings. | |
| - `--min_timestep` and `--max_timestep` options are added to each training script. These options can be used to train U-Net with different timesteps. The default values are 0 and 1000. | |
| ### Utility scripts for SDXL | |
| - `tools/cache_latents.py` is added. This script can be used to cache the latents to disk in advance. | |
| - The options are almost the same as `sdxl_train.py'. See the help message for the usage. | |
| - Please launch the script as follows: | |
| `accelerate launch --num_cpu_threads_per_process 1 tools/cache_latents.py ...` | |
| - This script should work with multi-GPU, but it is not tested in my environment. | |
| - `tools/cache_text_encoder_outputs.py` is added. This script can be used to cache the text encoder outputs to disk in advance. | |
| - The options are almost the same as `cache_latents.py` and `sdxl_train.py`. See the help message for the usage. | |
| - `sdxl_gen_img.py` is added. This script can be used to generate images with SDXL, including LoRA, Textual Inversion and ControlNet-LLLite. See the help message for the usage. | |
| ### 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. For 24GB GPU, the following options are recommended __for the fine-tuning with 24GB GPU memory__: | |
| - 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 8GB GPU memory (10GB recommended). For reducing the GPU memory usage, the following options are recommended: | |
| - Train U-Net only. | |
| - Use gradient checkpointing. | |
| - Use `--cache_text_encoder_outputs` option and caching latents. | |
| - Use one of 8bit optimizers or Adafactor optimizer. | |
| - Use lower dim (4 to 8 for 8GB GPU). | |
| - `--network_train_unet_only` option is highly recommended for SDXL LoRA. Because SDXL has two text encoders, the result of the training will be unexpected. | |
| - PyTorch 2 seems to use slightly less GPU memory than PyTorch 1. | |
| - `--bucket_reso_steps` can be set to 32 instead of the default value 64. Smaller values than 32 will not work for SDXL training. | |
| Example of the optimizer settings for Adafactor with the fixed learning rate: | |
| ```toml | |
| 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 | |
| ``` | |
| ### Format of Textual Inversion embeddings for SDXL | |
| ```python | |
| from safetensors.torch import save_file | |
| state_dict = {"clip_g": embs_for_text_encoder_1280, "clip_l": embs_for_text_encoder_768} | |
| save_file(state_dict, file) | |
| ``` | |
| ### ControlNet-LLLite | |
| ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See [documentation](./docs/train_lllite_README.md) for details. | |