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accelerate launch examples/qwen_image/model_training/train.py \
--dataset_base_path "data/example_image_dataset" \
--dataset_metadata_path data/example_image_dataset/metadata_qwenimage_context.csv \
--data_file_keys "image,context_image" \
--max_pixels 1048576 \
--dataset_repeat 50 \
--model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
--learning_rate 1e-4 \
--num_epochs 5 \
--remove_prefix_in_ckpt "pipe.dit." \
--output_path "./models/train/Qwen-Image-In-Context-Control-Union_lora" \
--lora_base_model "dit" \
--lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
--lora_rank 64 \
--lora_checkpoint "models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union/model.safetensors" \
--extra_inputs "context_image" \
--use_gradient_checkpointing \
--find_unused_parameters
# if you want to train from scratch, you can remove the --lora_checkpoint argument
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