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GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection.

Official implementation of GLAD

Requirements

This repository is implemented and tested on Python 3.10 and PyTorch 2.0.1. To install requirements:

pip install -r requirements.txt

Train and Evaluation of the Model

First, you should download the pretrained stable diffusion model from pretrained model, and datasets. (In addition, DTD dataset is required for anomaly synthesis)

To train the UNet of stable diffusion, modify the settings in the train.sh and train the model on different categories:

bash train.sh

Model checkpoints trained by us are Baidu Netdisk

To evaluate and test the model, modify the path of models in the main.py and test.sh, and run:

bash test.sh

In particular, considering the large differences between the visa dataset and the pre-trained model, we fine-tune VAE of stable diffusion and DINO. You can refer to the DiffAD for fine-tuning VAE.

To fine-tune DINO, run:

python train-dino.py --dataset VisA

To test DINO, run:

python test-dino.py --dataset VisA

Citation

@article{yao2024glad,
  title={GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection},
  author={Yao, Hang and Liu, Ming and Wang, Haolin and Yin, Zhicun and Yan, Zifei and Hong, Xiaopeng and Zuo, Wangmeng},
  journal={arXiv preprint arXiv:2406.07487},
  year={2024}
}

Feedback

For any feedback or inquiries, please contact yaohang_1@outlook.com