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24 lines
1.2 KiB
Bash
24 lines
1.2 KiB
Bash
# Z-Image-Turbo is a distilled model.
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# After training, it loses its distillation-based acceleration capability,
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# leading to degraded generation quality at fewer inference steps.
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# This issue can be mitigated by using a pre-trained LoRA model to assist the training process.
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# https://www.modelscope.cn/models/ostris/zimage_turbo_training_adapter
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accelerate launch examples/z_image/model_training/train.py \
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--dataset_base_path data/example_image_dataset \
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--dataset_metadata_path data/example_image_dataset/metadata.csv \
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--max_pixels 1048576 \
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--dataset_repeat 50 \
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--model_id_with_origin_paths "Tongyi-MAI/Z-Image-Turbo:transformer/*.safetensors,Tongyi-MAI/Z-Image-Turbo:text_encoder/*.safetensors,Tongyi-MAI/Z-Image-Turbo:vae/diffusion_pytorch_model.safetensors" \
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--learning_rate 1e-4 \
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--num_epochs 5 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Z-Image-Turbo_lora_differential" \
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--lora_base_model "dit" \
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--lora_target_modules "to_q,to_k,to_v,to_out.0,w1,w2,w3" \
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--lora_rank 32 \
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--preset_lora_path "models/ostris/zimage_turbo_training_adapter/zimage_turbo_training_adapter_v1.safetensors" \
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--preset_lora_model "dit" \
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--use_gradient_checkpointing \
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--dataset_num_workers 8
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