Hugging Face Trending Papers
SaTPO: Text-to-Image Safety Alignment via Transition Preference Optimization
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arXiv Computer Vision
4d ago
SaTPO: Text-to-Image Safety Alignment via Transition Preference Optimization
arXiv:2610.19563v1 Announce Type: new Abstract: Safety alignment of text-to-image diffusion models aims to suppress harmful content without compromising useful generation capabilities. However, prefe...
arXiv AI
Jul 2
The Illusion of High Utility in Safety Alignment of Text-to-Image Diffusion Models
arXiv:2607. 00402v1 Announce Type: cross Abstract: Safety alignment of text-to-image (T2I) diffusion models aims to suppress harmful generations while preserving utility on benign prompts.
arXiv Computer Vision
3d ago
Decoupled Safety Control: A Safety-Control Algorithm for Training-Free Safety Guidance
Decoupled Safety Control (DSC) is a plug‑in algorithm that improves training‑free safety guidance for diffusion models by separating the unsafe condition from the intervention process. Unlike existing coupled safety‑control chains, DSC determines the intervention based on the current generation state, reducing residual unsafe content and preserving benign‑generation quality. Experiments on standard safety benchmarks demonstrate that DSC achieves stronger unsafe‑content suppression while maintaining efficient inference.
arXiv AI
Jun 17
Pulling The REINS: Training-Free Safety Alignment of Video Diffusion Models via Representation Steering
arXiv:2606. 17257v1 Announce Type: cross Abstract: Open-weight video diffusion models can generate photorealistic unsafe content, from violence to misinformation, yet existing defenses either require expensive safety fine-tuning that degrades general capability, or apply external filters that are trivially bypassed by adversarial prompts.
arXiv AI
Oct 5
Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation
The paper examines the limitations of global unsafety signals used in training‑free safeguards for text‑to‑image generation, showing a trade‑off between coverage and selectivity. It introduces CALM, a counterfactual adaptive local modulation method that locally corrects unsafe token representations while preserving benign content. Experiments demonstrate that CALM outperforms global removal approaches in suppressing unsafe content without compromising utility.
arXiv AI
Jun 2
Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning
arXiv:2503. 11832v5 Announce Type: replace Abstract: Recent vision language models (VLMs) have made remarkable strides in generative modeling with multimodal inputs, particularly text and images.