Improve model card: Add pipeline tag, library_name, description, usage example, and citation

README.md

@@ -1,16 +1,53 @@
 ---
-license: apache-2.0
 base_model:
 - CompVis/stable-diffusion-v1-4
+license: apache-2.0
+pipeline_tag: text-to-image
+library_name: diffusers
 ---
 
 # SPEED
-Here are the released model checkpoints of our paper:
+## Model Description
+This model (SPEED) introduces an efficient concept erasure approach that directly edits model parameters of large-scale text-to-image (T2I) diffusion models, such as `CompVis/stable-diffusion-v1-4`. SPEED searches for a null space, a model editing space where parameter updates do not affect non-target concepts, to achieve scalable and precise erasure, successfully erasing 100 concepts within only 5 seconds.
 
-> [SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models](https://arxiv.org/abs/2503.07392)
+It is based on the paper [SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models](https://arxiv.org/abs/2503.07392).
 
 ![teaser](assets/teaser.JPEG)
 
 **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).
+
+## Sample Usage
+Here's how to use the model for image sampling after concept erasure:
+
+```python
+# Image Sampling
+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.
+
+## 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}, 
+}
+```