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# Set the CUDA devices to use
export CUDA_VISIBLE_DEVICES=0,1,2,3;
# Launch the distributed training
python -m torch.distributed.launch \
--nproc_per_node=4 \
--use_env \
--master_port=29501 \
3_train_fill50k_rescale.py \
--batch-size 32 \
--total_train_steps 50000 \
--teacher-model vit_large \
--target_model vit_base \
--model models_proteus_dinov2 \
--patch_size 14 \
--cldm_learning_rate 1e-4 \
--lambda_token 1.0 \
--lambda_fea 1.0 \
--lambda_patch 1.0 \
--finetune "/home/t2vg-a100-G4-1/projects/qiyp/1_feature_extractor/1_feature_extractor/checkpoint0160.pth" \
--log_dir '/home/t2vg-a100-G4-1/projects/qiyp/3_ControlNet/log/' \
--resume_path '/home/t2vg-a100-G4-1/projects/qiyp/3_ControlNet/models/control_sd15_fill50k.ckpt' \
--image_floder '/home/t2vg-a100-G4-1/projects/dataset/train2017' \
--txt_path '/home/t2vg-a100-G4-1/projects/dataset/annotations/captions_train2017.json' \
--log_every 25 \
--ckpt_every 2500 \
--image_every 100 \
--cldm_yaml './models/cldm_fill50k.yaml' \
--exp_dir './exp_fill50k_rescale' \
--controlnet_dir '/home/t2vg-a100-G4-1/projects/qiyp/3_ControlNet/exp_fill50k_rescale/checkpoints/0002500.pt' \
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