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pip install -r requirements.txtgit clone https://github.com/MrDongdongLin/EW-LoRA1conda create -n ewlora python==3.8.18
2conda activate ewlora
3pip install -r requirements.txtdata folder.1coco2017
2└── train
3 ├── img1.jpg
4 ├── img2.jpg
5 └── img3.jpg
6└── test
7 ├── img4.jpg
8 ├── img5.jpg
9 └── img6.jpg1cd ./watermarker/stable_signature
2CUDA_VISIBLE_DEVICES=0 python train_SS.py --num_keys 1 \
3--train_dir ./Datasets/coco2017/train2017 \
4--val_dir ./Datasets/coco2017/val2017 \
5--ldm_config ./watermarker/stable_signature/configs/stable-diffusion/v1-inference.yaml \
6--ldm_ckpt ../models/ldm_ckpts/sd-v1-4-full-ema.ckpt \
7--msg_decoder_path ../models/wm_encdec/hidden/ckpts/dec_48b_whit.torchscript.pt \
8--output_dir ./watermarker/stable_signature/outputs/ \
9--task_name train_SS_fix_weights \
10--do_validation \
11--val_frep 50 \
12--batch_size 4 \
13--lambda_i 1.0 --lambda_w 0.2 \
14--steps 20000 --val_size 100 \
15--warmup_steps 20 \
16--save_img_freq 100 \
17--log_freq 1 --debug1@article{linEfficientWatermarkingMethod2024,
2 title = {An Efficient Watermarking Method for Latent Diffusion Models via Low-Rank Adaptation},
3 author = {Lin, Dongdong and Li, Yue and Tondi, Benedetta and Li, Bin and Barni, Mauro},
4 year = {2024},
5 month = oct,
6 number = {arXiv:2410.20202},
7 eprint = {2410.20202},
8}