ModelLock: Locking Your Model With a Spell

Official model repository for the paper: ModelLock: Locking Your Model With a Spell

Overview

This repository contains the locked model checkpoint for the Oxford-IIIT Pet dataset using the ModelLock framework with style-based transformation.

Checkpoint Information

Model: MAE (Masked Autoencoder) fine-tuned on Oxford-IIIT Pet dataset
Lock Type: Style lock
Dataset: Oxford-IIIT Pet (38 classes)

Model Hyperparameters

The model was locked using the following configuration:

Diffusion Model

Model: timbrooks/instruct-pix2pix (InstructPix2Pix)

Transformation Parameters

Prompt: "with oil pastel"
Alpha (blending ratio): 0.5
Inference Steps: 5
Image Guidance Scale: 1.5
Guidance Scale: 4.5

Download Checkpoint

huggingface-cli download SFTJBD/ModelLock pets_mae_style_checkpoint-best.pth --local-dir ./checkpoints

Or using Python:

from huggingface_hub import hf_hub_download
checkpoint_path = hf_hub_download(
    repo_id="SFTJBD/ModelLock", 
    filename="pets_mae_style_checkpoint-best.pth"
)

Usage

To evaluate the locked model, use the key prompt "with oil pastel" with the same hyperparameters listed above to unlock the model's full performance.

Citation

@article{gao2024modellock,
  title={ModelLock: Locking Your Model With a Spell},
  author={Gao, Yifeng and Sun, Yuhua and Ma, Xingjun and Wu, Zuxuan and Jiang, Yu-Gang},
  journal={arXiv preprint arXiv:2405.16285},
  year={2024}
}

License

MIT License

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