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export MODEL_NAME="../sdmodel/v2-1" # In this study, Stable diffusion v2-1 is used as the pre-training model
export DATASET_NAME="../dataset/thyroid_image/training_data"
export PROMPT="ultrasound of papillary thyroid carcinoma, malignancy, wider-than-tall, shape" # Prompt to verify the effect of model generation
CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' accelerate launch --multi_gpu --num_processes=8 Tiger Model/Coarse-Training.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$DATASET_NAME \
--caption_column="text" \
--resolution=512 \
--random_flip \
--gradient_accumulation_steps=4 \
--train_batch_size=24 \
--num_train_epochs=4000 \
--checkpointing_steps=1000 \
--learning_rate=1e-04 \
--lr_scheduler="constant" \
--lr_warmup_steps=500 \
--seed=2024 \
--output_dir="./modelsaved/Tiger-Corase" \ # Model saving
--validation_epochs=1 \
--validation_prompt=$PROMPT
--report_to="wandb"
# For the description of parameter meanings, see Appendix
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