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:
SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models
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.
Usage
Here's an example of how to use the model for image sampling from the official GitHub repository (for instance erasure):
# 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 |  |
| Multi-Concept Erasure | |
| Implicit Concept Erasure | |
Citation
If you find the repo useful, please consider citing.
@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},
}