Awesome

Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models

:star2: :star2: ECCV 2024 | Arxiv | :hugs:Models :star2: :star2:

Authors

Chao Gong*, Kai Chen*, Zhipeng Wei, Jingjing Chen, Yu-Gang Jiang

Fudan University

Run

The edited models of RECE can be found :hugs:here.

• Run pip install -r requirements.txt to install the required packages.

• You can check scripts/ for running scripts. For example, run the following command to erase "nudity":

  python train.py \
      --concepts nudity \
      --concept_type nudity \
      --emb_computing close_regzero \
      --regular_scale 0.1 \
      --epochs 3 \
      --target_ckpt unified-concept-editing/erased-nudity-towards_uncond-preserve_false-sd_1_4-method_replace-1-1.0.pt \
      --preserve_scale 0.1 \
      --lamb 0.1 
  

  Then, generate images of I2P:

  python execs/generate_images.py \
      --prompts_path dataset/i2p.csv \
      --concept nudity \
      --save_path /ckpt2/RECE \
      --ckpt results_above.pt \
  

  Finally, evaluate the erasure performance:

  python compute_nudity_rate.py \
      --root save_path_above
  

Notes

• Configuration. We have released a new Arxiv version to state the experiment settings. For all concepts, the coefficients of Eq.3 are: $\lambda_1=0.1$ and $\lambda_2=0.1$. The regularization coefficients $\lambda$ and epochs are set as follows:

  1. Nudity and unsafe concepts(I2P concepts), $\lambda=1e-1$, with nudity for 3 epochs and unsafe concepts for 2 epochs.
  2. Artistic styles, $\lambda=1e-3$, 1 epoch.
  3. Difficult objects for UCE(e.g., church and garbage truck), $\lambda=1e-3$, 1 epoch.
  4. Easy objects for UCE(e.g., English Springer, golf ball and parachute), $\lambda=1e-1$, 1 epoch.
  5. For other objects where erasing accuracies reach 0 using UCE, RECE's further erasure is not applied.

• Red-teaming tools. Due to the open-source timeline, we used our reproduced Ring-A-Bell attack method for all baselines, available in attack_methods/. And we used the P4D attack method reproduced by UnlearnDiff.

Citation

If you find our work helpful, please leave us a star and cite our paper.

@article{gong2024reliable,
  title={Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models},
  author={Gong, Chao and Chen, Kai and Wei, Zhipeng and Chen, Jingjing and Jiang, Yu-Gang},
  journal={arXiv preprint arXiv:2407.12383},
  year={2024}
}

Acknowledgement

Some code is borrowed from UCE.