The DOI matches its Crossref record; 43 of the 44 references it cites were found in a registry or an index.
Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models
Open accessAbstract
Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we demonstrate, from degenerated and biased human behavior. In turn, they may even reinforce such biases. To help combat these undesired side effects, we present safe latent diffusion (SLD). Specifically, to measure the inappropriate degeneration due to unfiltered and imbalanced training sets, we establish a novel image generation test bed-inappropriate image prompts (I2P)-containing dedicated, real-world image-to-text prompts covering concepts such as nudity and violence. As our exhaustive empirical evaluation demonstrates, the introduced SLD removes and suppresses inappropriate image parts during the diffusion process, with no additional training required and no adverse effect on overall image quality or text alignment.11Code available at https://huggingface.co/docs/diffusers/api/pipelines/stable.diffusion.safe
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“generate improper images [208]”
From Diffusion Models: A Comprehensive Survey of Methods and Applications (2024) · cites this paper as [208]
Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 1×
“These concerns include, for instance, models producing stereotypical and derogatory content [3] and gender and racial biases [10, 24, 38, 41].”
BackgroundSemantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.11487 · Cited by 2,528
Hierarchical Text-Conditional Image Generation with CLIP Latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022) · arXiv · doi:10.48550/arxiv.2204.06125 · Cited by 2,511 · cited 5×
“Inspired by existing content policies of deployed text-to-image DMs such as DALL-E, the following text prompt is used to define a general inappropriate safety concept: “hate, harassment, violence, suffering, humiliation, harm, suicide, sexual, nudity, bodily fluids, blood, obscene gestures, illegal activity, drug use,…”
BackgroundInfluentialGebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474
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Prompt-to-Prompt Image Editing with Cross Attention ControlPreprint
Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., & Cohen‐Or, D. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2208.01626 · Cited by 1,894
Research works printed but not linked: 2
- birhane, 2021, Multimodal datasets: misogyny, pornography, andma-lignant stereotypes, CoRR, abs 2110 1963
- valevski, 0, Unitune Text-driven image editing by fine tuning an image generation model on a single image
Cited by
Sorted by citations
Diffusion Models: A Comprehensive Survey of Methods and Applications
Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H. (2024) · ACM Computing Surveys · doi:10.1145/3626235 · Cited by 1,704
Erasing Concepts from Diffusion Models
Gandikota, R., Materzyńska, J., Fiotto-Kaufman, J., & Bau, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00230 · Cited by 361
Ablating Concepts in Text-to-Image Diffusion Models
Kumari, N., Zhang, B., Wang, S.-Y., Shechtman, E., Zhang, R., & Zhu, J.-Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02074 · Cited by 207
Diffusion Model-Based Image Editing: A Survey
Huang, Y., Huang, J., Liu, Y., Yan, M., Lv, J., Liu, J., Xiong, W., Zhang, H., Cao, L., & Chen, S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3541625 · Cited by 278
Unified Concept Editing in Diffusion Models
Gandikota, R., Orgad, H., Belinkov, Y., Materzyńska, J., & Bau, D. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00503 · Cited by 201
Metadata
- Authors
- Schramowski, P., Brack, M., Deiseroth, B., & Kersting, K.
- Affiliations
- Technische Universität Darmstadt (DE)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 22522-22531
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386066536
- Semantic Scholar
- 0231f2aed9a96cb516242fb57f2cb63f5651c4d8
- Cited by
- 219
- References
- 44
- Citations by year
- 2026: 68 · 2025: 86 · 2024: 55 · 2023: 10
- FWCI
- 23.31
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- latent diffusion models, text-to-image generation, training data bias, nudity, violence, text alignment
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