Safety alignment of text-to-image diffusion models aims to suppress harmful content without compromising useful generation capabilities. However, preferences over completed images compress multidimens...
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: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: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:2605. 08116v2 Announce Type: replace-cross Abstract: Recent work on text diffusion models offers a promising alternative to autoregressive generation, but controlling their safety remains underexplored.
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:2608.29997v1 Announce Type: new Abstract: We propose Discrete Diffusion Bridges (DDB), a novel framework designed to resolve the fundamental spatiotemporal misalignment of standard discrete dif...
arXiv:2511. 19558v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models can emit copyrighted, unsafe, or private content.
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.
The paper introduces Spectral Correction Guidance, a training‑free method that uses spectral alignment to detect and correct deviations in guided diffusion trajectories. By comparing intermediate states to an analytic reference spectrum, the approach improves consistency with the forward process and enhances image generation quality. Experiments show consistent gains over baseline guidance in text‑to‑image tasks and on ImageNet, with benefits across guidance scales and fewer denoising steps.
arXiv:2608. 03284v1 Announce Type: cross Abstract: Ensuring safety and policy compliance in text-to-image diffusion models remains a critical challenge, as benign or adversarial prompts can often elicit prohibited content, e.
Visual autoregressive (VAR) models have emerged as a fast, high-quality alternative to diffusion for text-to-image generation, but like diffusion models they exhibit persistent compositional failures,...