Official PyTorch implementation of Inversion-based Measure of Memorization.
This repository follows the implemetations of codebases pytorch-ddpm, latent-diffusion, stable-diffusion-v1 and stable-diffusion-v2. We also modify some source codes, including introducing xformers support for stable-diffusion-v1 and fixing bugs in DDIM sampler.
Go to the specific directory and create an anaconda environment with:
cd ddpm[latent-diffusion, stable-diffusion-v1, stable-diffusion-v2]
conda env create -f environment.yamlChange Torch and xformers to appropriate versions depending on your own CUDA run time library.
CIFAR-10 can be downloaded on the official website. Obtain the IDs of 99 highly memorized images and 1000 normal images if needed.
We use the CelebAHQ-256 dataset on Kaggle and FFHQ following their official repository.
The subsets of LAION used in the paper can be downloaded here.
| Model | Dataset | Link |
|---|---|---|
| DDPM | CIFAR-10 | https://drive.google.com/file/d/1TJDmFdb6-ZwI2AqOfTCClNWn_iAGjdvN/view?usp=sharing |
| LDM | CelebAHQ FFHQ | https://drive.google.com/drive/folders/1eeO9E4zLTdy1PfPA55YhIwclS9XBF-UI?usp=sharing |
| SD v1.4 | LAION Subset | https://drive.google.com/drive/folders/1TNvSc6JMvCqZJ4-9FO-A4-bwjYVReOIc?usp=sharing |
| SD v2.1 | LAION Subset | https://drive.google.com/drive/folders/1qiFMpUfLdZdLWRV-TkmPJ1-AEMmUsF07?usp=sharing |
We use SSCD to calculate image similarity. Download the sscd_disc_large model in their official repository.
Download our pretrained DDPM and run the following command to perform inversion and calculate memorization scores:
python inversion.py --logdir logs/DDPM_CIFAR10_EPS_INVERSIONWe provide pretrained models on the subsets of CelebAHQ and FFHQ. Pretrained models on the full datasets can be found in the official repository.
To perform inversion:
python inversion -dp /path/to/dataset --ckpt_file /path/to/pretrained_modelDownload pretrained SD v1.4 and SD v2.1, and then run:
python inversion -dp /path/to/datasetEach model follows their original license.