from optim import adamw
model = Initialize_model()
optimizer = adamw(model,threshold=2e-3)   # modify this line only
for data in dataloader:
    model.train()
model.save()

optimizer = adamw(model,threshold=2e-3)
optimizer.load($path_to_save)
torch.save(optimizer.save_params(),$path_to_save)

dataset_name
   ├── name1.png
   ├── name2.png
   ├── ...
   ├── metadata.jsonl

cd examples
python3 finetune.py \
   --config=configs/Barbie.json \
   --output_dir=$path_to_save \
   --sd_version=1.5 \
   --threshold=2e-3 \
   --lr_scheduler=cosine \
   --progressive_iter=2500 \
   --lambda_rank=0.0005\

cd examples
python3 inference.py --config=configs/Barbie.json --sara_path=$path_to_the_saved_sara_checkpoints --threshold=2e-3

cd evaluation
python3 evaluation.py --target_dir=$path_to_the_generated_image_folder --config=../examples/configs/Barbie.json

@article{hu2024sara,
  title={SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation},
  author={Hu, Teng and Zhang, Jiangning and Yi, Ran and Huang, Hongrui and Wang, Yabiao and Ma, Lizhuang},
  journal={arXiv preprint arXiv:2409.06633},
  year={2024}
}