This is a PyTorch/GPU implementation of the paper Machine Unlearning for Image-to-Image Generative Models (ICLR 2024). This work is a general framework to remove some concepts/knowledge from a pretained image-to-image generation models.

Installation

Download the repo

git clone https://github.com/jpmorganchase/i2i-generator-unlearning.git
cd i2i-generator-unlearning

git clone -b i2i https://github.com/jpmorganchase/i2i_clean-fid.git
git clone -b i2i https://github.com/jpmorganchase/i2i_Palette-Image-to-Image-Diffusion-Models.git
git clone -b i2i https://github.com/jpmorganchase/i2i_mage.git

A suitable conda environment named mul can be created and activated with:

conda create --name mul python=3.9
conda activate mul
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
pip install -r environment.yaml

• This repo is based on timm==0.3.2, for which a fix is needed to work with PyTorch 1.8.1+.

Dataset Preparation

Run mkdir ./dataset to create a folder, and follow the steps below to prepare the datasets:

• Download ImageNet dataset from web or through Kaggle and put them under dataset.
• Download COCO-2017 dataset from web and put them under dataset.
• Download the Places-365 dataset from link or through Kaggle and put them under dataset.
  • Note: you can download the Small images (256 * 256) to save your space.
  • Please move dataset/places365/train_256_places365standard/data_256 to dataset/places365/train_256 and dataset/places365/val_256/val_256 to dataset/places365/val_256.
  • Then, run python ./i2i_Palette-Image-to-Image-Diffusion-Models/datasets/place365/process_data.py to cluster the valiation images with labels.

After downloading the three datasets, the folder dataset should be organized as:

i2i-generator-unlearning/
  i2i_mage/
  i2i_Palette-Image-to-Image-Diffusion-Models/
  i2i_clean-fid/
  dataset/
    imagenet1k/
        train/
        val/
        test/
    coco/
        train2017/
        val2017/
    places365/
        train_256/
        val_256/
        test_256/

Usage

We provide the details of running different type of models seperately:

• For diffusion models, please check i2i_Palette-Image-to-Image-Diffusion-Models for more details.
• For VQ-GAN models, please check i2i_mage for more details.
• The folder i2i_clean-fid is used to compute the Fréchet inception distance (FID) between the generated images. Please follow i2i_clean-fid for installation and usage.

Results

• Results in Table 1 and Table 2:

  • Under each folder of models (VQ-GAN in i2i_mage and Diffusion in i2i_Palette-Image-to-Image-Diffusion-Models), we need to unlearn then test these models

  • Following the instructions in the document, unlearn the original model with our approach (with and without proxy $D_R$), multiple baselines (MAX LOSS, NOISY LABEL, RETAIN LABEL, RANDOM ENCODER).

  • In total, you will have seven models:

    • Original model, i.e., before unlearning
    • MAX LOSS (Baseline1)
    • NOISY LABEL (Baseline2)
    • RETAIN LABEL (Baseline3)
    • RANDOM ENCODER (Baseline4)
    • Ours
    • Ours (Proxy $D_R$)

  • Following the instructions in the document, we next conduct the evaluation and compute the FID, IS, and CLIP scores.

  • FID and IS score:

    • Results are recorded in fid_is_eval_{MODE}.csv, where {MODE} is determined by specific models.

    • Each row in this CSV contains seven elements:

      CKPT+Config	FID (Retain)	Mean IS (Retain)	Std of IS (Retain)	FID (Forget)	Mean IS (Forget)	Std of IS (Forget)

      CKPT is the model checkpoint evaluated.

      Config is determined by the test parameters for various inputs, such as cropping size or random/center cropping.

  • CLIP score:

    • Results are recorded in clip_cosine_{MODE}.csv, where {MODE} is determined by specific models.

    • Each row in this CSV contains three elements:

      CKPT+Config	CLIP (Retain)	CLIP (Forget)

• Results in Figure 1 and Figure 3:

  • When geting the results of Table 1 and Table 2, it will automatically generate various images for both the forget set and the retain set
  • You can compare the generated images by different methods

• Results of T-SNE in Figure 4:

  • Following the instructions in the document, run the T-SNE analysis
  • You can find the results in the {MODEL}_tsne.pdf, where {MODEL} is decided by the different types of models

• Results of Ablation study in Table 3:

  • Vary the --forget_alpha parameter when doing unlearning with our methods
  • Test and evaluate the unlearned models under various forget_alpha values

Citation

@article{li2024machine_unlearn,
  title={Machine Unlearning for Image-to-Image Generative Models},
  author={Li, Guihong and Hsu, Hsiang and Chen, Chun-Fu (Richard), and Marculescu, Radu},
  journal={International Conference on Learning Representations},
  year={2024}
}

Contact

If you have any questions, feel free to contact us through email (richard.cf.chen@jpmchase.com, hsiang.hsu@jpmchase.com). Enjoy!