Update README.md

README.md

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-This is an EAR (Erasing Autoregressive Models) model trained to erase specific concepts.
-
 ---
 license: mit
-base_model:
-- deepseek-ai/Janus-Pro-7B
----
+---
+
+# Model Card for EAR
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This is an EAR (Erasing Autoregressive Models) model trained to erase specific concepts.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** IMMC
+- **Model type:** AR model
+- **License:** MIT
+- **Finetuned from model :** Janus-Pro
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [[link](https://github.com/immc-lab/ear)]
+- **Paper:** [[link](https://arxiv.org/abs/2506.20151)]
+
+## Installation Guide
+
+### EAR Environment
+
+```shell
+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](https://github.com/deepseek-ai/Janus) 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:
+
+```shell
+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:
+
+```shell
+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](https://arxiv.org/pdf/2506.20151).
+
+```shell
+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
+
+```bibtex
+@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}, 
+}
+```