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Out-of-Distribution Detection with a Single Unconditional Diffusion Model

preprint License: MIT Venue:NeurIPS 2024


Figure 1: Illustration of the diffusion paths of two samples from two different distributions (CIFAR10 and SVHN) obtained via DDIM integration. The paths have different first and second derivatives (rate-of-change and curvature). We propose to measure these quantities for OOD detection.

This is the official code repository for the NeurIPS 2024 paper Out-of-Distribution Detection with a Single Unconditional Diffusion Model (DiffPath). The codebase is based on openai/improved-diffusion.

Installation

It is recommended to install dependencies in a conda environment:

conda create --name diffpath python=3.8
pip install -r requirements.txt

Download Diffusion Model Checkpoints

CelebA 32x32

We provide our pretrained diffusion model checkpoint on CelebA 32x32 at https://huggingface.co/ajrheng/diffpath/tree/main. Alternatively,

wget https://huggingface.co/ajrheng/diffpath/resolve/main/celeba_ema_0.9999_499999.pt

ImageNet 64x64

Download the ImageNet-64 diffusion model checkpoint trained with L-hybrid objective from the openai/improved-diffusion repo. Alternatively,

wget https://openaipublic.blob.core.windows.net/diffusion/march-2021/imagenet64_uncond_100M_1500K.pt

OOD Detection with DiffPath

To perform OOD detection with DiffPath, first we calculate the diffusion path statistics for both train and test sets. We demonstrate the steps for the task of CIFAR10 (in-dist) vs SVHN (out-of-dist).

# calculate statistics for CIFAR10 training set on GPU ID 0 using DM with CelebA as base distribution
python save_train_statistics.py --model celeba --data_dir /path/to/cifar10/dataset --dataset cifar10 --model_path /path/to/celeba/model/checkpoint --config configs/celeba_model_config.yaml --batch_size 256 --n_ddim_steps 10 --device 0

# calculate statistics for CIFAR10 test set on GPU ID 0
python save_test_statistics.py --model celeba --data_dir /path/to/cifar10/dataset --dataset cifar10 --model_path /path/to/celeba/model/checkpoint --config configs/celeba_model_config.yaml --batch_size 256 --n_ddim_steps 10 --device 0

# calculate statistics for SVHN test set on GPU ID 0
python save_test_statistics.py --model celeba --data_dir /path/to/svhn/dataset --dataset svhn --model_path /path/to/celeba/model/checkpoint --config configs/celeba_model_config.yaml --batch_size 256 --n_ddim_steps 10 --device 0

The statistics will be saved as .npz files in train_statistics_celeba_model/ddim10 and test_statistics_celeba_model/ddim10 respectively.

Now perform OOD detection using DiffPath-6D:

python eval_6d.py --model celeba --in_dist cifar10 --out_of_dist svhn --n_ddim_steps 10

The results will be printed to the screen.

BibTeX

If you find this repository or the ideas presented in our paper useful, please consider citing our work.

@article{heng2024out,
  title={Out-of-Distribution Detection with a Single Unconditional Diffusion Model},
  author={Heng, Alvin and Thiery, Alexandre H and Soh, Harold},
  journal={arXiv preprint arXiv:2405.11881},
  year={2024}
}