SPEED

This is the official Pytorch implementation of our paper:

SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models

Ouxiang Li, Yuan Wang, Xinting Hu, Houcheng Jiang, Tao Liang, Yanbin Hao, Guojun Ma, Fuli Feng

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.

News

• 2025/03 🌟 Code and checkpoints are released.

Requirements

Install the environment as follows:

# create conda environment
conda create -n SPEED -y python=3.10
conda activate SPEED
# install pytorch 
pip install torch==2.3.0 torchvision==0.18.0
# install other dependencies
pip install -r requirements.txt

Concept Erasure with SPEED

Model Editing

# Instance Erasure
CUDA_VISIBLE_DEVICES=0 python train_erase_null.py \
    --target_concepts "Snoopy, Mickey, Spongebob" --anchor_concepts "" \
    --retain_path "data/instance.csv" --heads "concept"

# Artistic Style Erasure
CUDA_VISIBLE_DEVICES=0 python train_erase_null.py \
    --target_concepts "Van Gogh" --anchor_concepts "art" \
    --retain_path "data/style.csv" --heads "concept"

# 10-Celebrity Erasure
CUDA_VISIBLE_DEVICES=0 python train_erase_null.py \
    --target_concepts "Adam Driver, Adriana Lima, Amber Heard, Amy Adams, Andrew Garfield, Angelina Jolie, Anjelica Huston, Anna Faris, Anna Kendrick, Anne Hathaway" \
    --anchor_concepts "person" \
    --retain_scale 0.05 --disable_filter \
    --retain_path "data/10_celebrity.csv" --heads "concept"

Since the retain set for multi-concept erasure includes only 100 celebrities following MACE, which does not reach rank saturation, we specify --disable_filter to disable the initial filter with IPF. This results in $\mathbf{R}_\text{refine} = \mathbf{R} \cup \left(\mathbf{R}\right)^{\text{aug}}_f$.

The edited checkpoints will be saved at logs/checkpoints by default, you can alternatively specify --save_path to your preferred path.

Image Sampling

# 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'

# Artistic Style Erasure
CUDA_VISIBLE_DEVICES=0 python sample.py \
    --erase_type 'style' \
    --target_concept 'Van Gogh' \
    --contents 'Van Gogh, Picasso, Monet, Paul Gauguin, Caravaggio' \
    --mode 'original, edit' \
    --edit_ckpt '{checkpoint_path}' \
    --num_samples 10 --batch_size 10 \
    --save_root 'logs/few-concept/style'

# 10-Celebrity Erasure
CUDA_VISIBLE_DEVICES=0 python sample2.py \
    --erase_type "10_celebrity" \
    --target_concept "10_celebrity" \
    --contents "erase, retain" \
    --mode "original, edit" \
    --edit_ckpt '{checkpoint_path}' \
    --num_samples 1 --batch_size 10 \
    --save_root "logs/multi_celebrity"

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.

Metrics Evaluation

Pre-trained Model Sampling

We first generate images from the pre-trained model as a baseline for subsequent comparisons, and these images are also used to calculate FID.

bash pretrain/pretrain_sample.sh

This script facilitates to sample the original images on few-concept erasure (instance, style), multi-concept (10/50/100 celebrities) erasure, and MS-COCO generations (coco).

On Few-Concept Erasure

You can directly reproduce our results using this script, which consists three steps: (1) model editing, (2) image sampling, and (3) evaluation (CS and FID).

bash scripts/eval_few.sh

On Multi-Concept Erasure

This script consists of two steps: (1) model editing and (2) image sampling.

bash scripts/eval_multi.sh

In evaluation, we follow MACE's setup by introduing GIPHY Celebrity Detector, you can refer to the GCD installation guideline and then calculate the GCD accuracy for both erase and retain sets.

conda activate GCD
CUDA_VISIBLE_DEVICES=0 python metrics/evaluate_by_GCD.py --image_folder '{image_folder_path}'

On Implicit Concept Erasure

You can directly run the following script for fast evaluation, which consists of: (1) model editing, (2) I2P & MS-COCO image sampling, and (3) NudeNet detection & MS-COCO evaluation.

bash scripts/eval_nudity.sh

Model Card

We provide several edited models with SPEED on Stable Diffusion v1.4.

Concept Erasure Task	Edited Model	Hugging Face
Few-Concept Erasure	Cloud Drive Link
Multi-Concept Erasure	Cloud Drive Link
Implicit Concept Erasure	Cloud Drive Link

More Applications

Our method can adapt to other T2I models across versatile applications.

• Composite Concept Erasure on Community Versions

  

• Knowledge Editing on SDXL

  

• Instance Erasure and Prior Preservation on SDv3

  

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}, 
}