|
|
--- |
|
|
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}, |
|
|
} |
|
|
``` |
