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]
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Diffusion Models: A Comprehensive Survey of Methods and Applications
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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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