Instructions to use RobinWZQ/sd14_ba_computer_plant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobinWZQ/sd14_ba_computer_plant with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RobinWZQ/sd14_ba_computer_plant", dtype="auto") - Notebooks
- Google Colab
- Kaggle
🛡️DAA: Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models
This repository contains artifacts and code related to the paper: Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models.
Code: https://github.com/Robin-WZQ/DAA
This study introduces a novel backdoor detection perspective from Dynamic Attention Analysis (DAA), which shows that the dynamic feature in attention maps can serve as a much better indicator for backdoor detection in text-to-image diffusion models. By examining the dynamic evolution of cross-attention maps, backdoor samples exhibit distinct feature evolution patterns compared to benign samples, particularly at the <EOS> token.
📄 Citation
If you find this project useful in your research, please consider cite:
@article{wang2025dynamicattentionanalysisbackdoor,
title={Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models},
author={Zhongqi Wang and Jie Zhang and Shiguang Shan and Xilin Chen},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year={2025},
}