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base_model:
- CompVis/stable-diffusion-v1-4
license: apache-2.0
pipeline_tag: text-to-image
library_name: diffusers
---
# SPEED
## 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.
It is based on the paper [SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models](https://arxiv.org/abs/2503.07392).
**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).
## 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},
}
``` |