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Kohya S
2024-08-18 16:56:50 +09:00
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@@ -9,10 +9,8 @@ __Please update PyTorch to 2.4.0. We have tested with `torch==2.4.0` and `torchv
The command to install PyTorch is as follows: The command to install PyTorch is as follows:
`pip3 install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu124` `pip3 install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu124`
Aug 18, 2024: Aug 18, 2024:
Memory-efficient training based on 2kpr's implementation is implemented in `flux_train.py`. Thanks to 2kpr. See [FLUX.1 fine-tuning](#flux1-fine-tuning) for details. Memory-efficient training based on 2kpr's implementation is implemented in `flux_train.py`. Thanks to 2kpr! See [FLUX.1 fine-tuning](#flux1-fine-tuning) for details.
Aug 17, 2024: Aug 17, 2024:
Added a script `flux_train.py` to train FLUX.1. The script is experimental and not an optimized version. It needs >28GB VRAM for training. Added a script `flux_train.py` to train FLUX.1. The script is experimental and not an optimized version. It needs >28GB VRAM for training.
@@ -118,6 +116,8 @@ accelerate launch --mixed_precision bf16 --num_cpu_threads_per_process 1 flux_t
(Combine the command into one line.) (Combine the command into one line.)
Sample image generation during training is not tested yet.
Options are almost the same as LoRA training. The difference is `--blockwise_fused_optimizer`, `--double_blocks_to_swap` and `--cpu_offload_checkpointing`. `--single_blocks_to_swap` is also available. Options are almost the same as LoRA training. The difference is `--blockwise_fused_optimizer`, `--double_blocks_to_swap` and `--cpu_offload_checkpointing`. `--single_blocks_to_swap` is also available.
`--blockwise_fused_optimizer` enables the fusing of the optimizer for each block. This is similar to `--fused_backward_pass`. Any optimizer can be used, but Adafactor is recommended for memory efficiency. `--fused_optimizer_groups` is deprecated due to the addition of this option for FLUX.1 training. `--blockwise_fused_optimizer` enables the fusing of the optimizer for each block. This is similar to `--fused_backward_pass`. Any optimizer can be used, but Adafactor is recommended for memory efficiency. `--fused_optimizer_groups` is deprecated due to the addition of this option for FLUX.1 training.