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--- |
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base_model: |
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- CompVis/stable-diffusion-v1-4 |
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license: apache-2.0 |
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pipeline_tag: text-to-image |
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library_name: diffusers |
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--- |
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# SPEED |
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Here are the released model checkpoints of our paper: |
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> [SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models](https://arxiv.org/abs/2503.07392) |
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**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. |
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More implementation details can be found in our [GitHub repository](https://github.com/Ouxiang-Li/SPEED). |
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## Usage |
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Here's an example of how to use the model for image sampling from the [official GitHub repository](https://github.com/Ouxiang-Li/SPEED) (for instance erasure): |
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```bash |
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# Instance Erasure |
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CUDA_VISIBLE_DEVICES=0 python sample.py \ |
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--erase_type 'instance' \ |
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--target_concept 'Snoopy, Mickey, Spongebob' \ |
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--contents 'Snoopy, Mickey, Spongebob, Pikachu, Hello Kitty' \ |
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--mode 'original, edit' \ |
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--edit_ckpt '{checkpoint_path}' \ |
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--num_samples 10 --batch_size 10 \ |
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--save_root 'logs/few-concept/instance' |
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``` |
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In the command above, you can configure the `--mode` to determine the sampling mode: |
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- `original`: Generate images using the original Stable Diffusion model. |
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- `edit`: Generate images with the erased checkpoint. |
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## Model Card |
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We provide several edited models with SPEED on Stable Diffusion v1.4. |
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| Concept Erasure Task | Edited Model | |
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|---|---| |
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| Few-Concept Erasure | <a href='https://huggingface.co/lioooox/SPEED/tree/main/few-concept' style="margin: 0 2px; text-decoration: none;"><img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'></a> | |
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| Multi-Concept Erasure | <a href='https://huggingface.co/lioooox/SPEED/tree/main/multi-concept' style="margin: 0 2px; text-decoration: none;"><img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'></a> | |
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| Implicit Concept Erasure | <a href='https://huggingface.co/lioooox/SPEED/tree/main/nudity' style="margin: 0 2px; text-decoration: none;"><img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'></a> | |
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## Citation |
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If you find the repo useful, please consider citing. |
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```bibtex |
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@misc{li2025speedscalablepreciseefficient, |
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title={SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models}, |
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author={Ouxiang Li and Yuan Wang and Xinting Hu and Houcheng Jiang and Tao Liang and Yanbin Hao and Guojun Ma and Fuli Feng}, |
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year={2025}, |
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eprint={2503.07392}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2503.07392}, |
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} |
