Model Card for EAR
This is an EAR (Erasing Autoregressive Models) model trained to erase specific concepts.
Model Details
Model Description
- Developed by: IMMC
- Model type: AR model
- License: MIT
- Finetuned from model : Janus-Pro
Model Sources
Installation Guide
EAR Environment
git clone https://github.com/immc-lab/ear.git
cd ear
conda create -n ear python=3.12
conda activate ear
pip install -r requirements.txt
Janus-Pro Environment
Ensure that your environment can run Janus-Pro, refer to its official Quick Start for details.
Training Guide
After installation, follow these instructions to train EAR model for Janus-Pro.
Please run the script in train/ after checking the file path:
python train/ear_train_church.py
Generating Images with EAR
Image generation using the custom EAR model is a straightforward process. Please run the script in infer/.
For automated batch generation of evaluation images, utilize the following script:
python infer/infer_church.py
Evaluation
You can execute the following command to evaluate the generated data. Please run the script in eval/.
The specific evaluation method can be found in our paper.
python eval/eval_object.py --folder_path {args.output_dir} --topk 10 --batch_size 250
References
This repo is the code for the paper EAR: Erasing Concepts from Unified Autoregressive Models.
Thanks for the creative ideas of the pioneer researches:
- https://github.com/rohitgandikota/erasing: Erasing Concepts from Diffusion Models
- https://github.com/Con6924/SPM: One-dimentional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications
- https://github.com/koushiksrivats/robust-concept-erasing: STEREO: A Two-Stage Framework for Adversarially Robust Concept Erasing from Text-to-Image Diffusion Models
- https://github.com/OPTML-Group/Diffusion-MU-Attack: To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now
- https://github.com/deepseek-ai/Janus: Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation
- https://github.com/deepseek-ai/Janus: Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
Citing our work
The preprint can be cited as follows
@misc{fan2025earerasingconceptsunified,
title={EAR: Erasing Concepts from Unified Autoregressive Models},
author={Haipeng Fan and Shiyuan Zhang and Baohunesitu and Zihang Guo and Huaiwen Zhang},
year={2025},
eprint={2506.20151},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.20151},
}