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38 lines
1.8 KiB
Bash
38 lines
1.8 KiB
Bash
# Text to image training
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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-Omni-Base:transformer/*.safetensors,Tongyi-MAI/Z-Image-Omni-Base:siglip/model.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-Omni-Base_lora" \
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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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--use_gradient_checkpointing \
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--find_unused_parameters \
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--dataset_num_workers 8
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# Image(s) to image training
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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_qwen_imgae_edit_multi.json \
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# --data_file_keys "image,edit_image" \
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# --extra_inputs "edit_image" \
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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-Omni-Base:transformer/*.safetensors,Tongyi-MAI/Z-Image-Omni-Base:siglip/model.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-Omni-Base_lora_edit" \
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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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# --use_gradient_checkpointing \
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# --find_unused_parameters \
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# --dataset_num_workers 8
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