Improve model card: add pipeline tag, library name, usage, and additional links

README.md

@@ -1,7 +1,9 @@
 ---
-license: apache-2.0
 base_model:
 - CompVis/stable-diffusion-v1-4
+license: apache-2.0
+pipeline_tag: text-to-image
+library_name: diffusers
 ---
 
 # SPEED
@@ -13,4 +15,50 @@ Here are the released model checkpoints of our paper:
 
 **Three characteristics of our proposed method, SPEED.**  **(a) Scalable:** SPEED seamlessly scales from single-concept to large-scale multi-concept erasure (e.g., 100 celebrities) without additional design.  **(b) Precise:** SPEED precisely removes the target concept (e.g., *Snoopy*) while preserving the semantic integrity for non-target concepts (e.g., *Hello Kitty* and *SpongeBob*).  **(c) Efficient:** SPEED can immediately erase 100 concepts within 5 seconds, achieving a ×350 speedup over the state-of-the-art (SOTA) method.
 
-More implementation details can be found in our [GitHub repository](https://github.com/Ouxiang-Li/SPEED).
+More implementation details can be found in our [GitHub repository](https://github.com/Ouxiang-Li/SPEED).
+
+## Usage
+
+Here's an example of how to use the model for image sampling from the [official GitHub repository](https://github.com/Ouxiang-Li/SPEED) (for instance erasure):
+
+```bash
+# Instance Erasure
+CUDA_VISIBLE_DEVICES=0 python sample.py \
+    --erase_type 'instance' \
+    --target_concept 'Snoopy, Mickey, Spongebob' \
+    --contents 'Snoopy, Mickey, Spongebob, Pikachu, Hello Kitty' \
+    --mode 'original, edit' \
+    --edit_ckpt '{checkpoint_path}' \
+    --num_samples 10 --batch_size 10 \
+    --save_root 'logs/few-concept/instance'
+```
+
+In the command above, you can configure the `--mode` to determine the sampling mode:
+
+- `original`: Generate images using the original Stable Diffusion model.
+- `edit`: Generate images with the erased checkpoint.
+
+## Model Card
+
+We provide several edited models with SPEED on Stable Diffusion v1.4.
+
+| Concept Erasure Task | Edited Model |
+|---|---|
+| Few-Concept Erasure | <a href='https://huggingface.co/lioooox/SPEED/tree/main/few-concept' style="margin: 0 2px; text-decoration: none;"><img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'></a> |
+| Multi-Concept Erasure | <a href='https://huggingface.co/lioooox/SPEED/tree/main/multi-concept' style="margin: 0 2px; text-decoration: none;"><img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'></a> |
+| Implicit Concept Erasure | <a href='https://huggingface.co/lioooox/SPEED/tree/main/nudity' style="margin: 0 2px; text-decoration: none;"><img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'></a> |
+
+## Citation
+
+If you find the repo useful, please consider citing.
+
+```bibtex
+@misc{li2025speedscalablepreciseefficient,
+      title={SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models}, 
+      author={Ouxiang Li and Yuan Wang and Xinting Hu and Houcheng Jiang and Tao Liang and Yanbin Hao and Guojun Ma and Fuli Feng},
+      year={2025},
+      eprint={2503.07392},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2503.07392}, 
+}