Personalized Safety Alignment for Text-to-Image Diffusion Models
Text-to-image diffusion models have revolutionized visual content generation,
but current safety mechanisms apply uniform standards that often fail to
account for individual user preferences. These models overlook the diverse
safety boundaries shaped by factors like age, mental health, and personal
beliefs. To address this, we propose Personalized Safety Alignment (PSA), a
framework that allows user-specific control over safety behaviors in generative
models. PSA integrates personalized user profiles into the diffusion process,
adjusting the model's behavior to match individual safety preferences while
preserving image quality. We introduce a new dataset, Sage, which captures
user-specific safety preferences and incorporates these profiles through a
cross-attention mechanism. Experiments show that PSA outperforms existing
methods in harmful content suppression and aligns generated content better with
user constraints, achieving higher Win Rate and Pass Rate scores. Our code,
data, and models are publicly available at
https://torpedo2648.github.io/PSAlign/.
68917c7af01a094725f834db
Yu Lei
68917c7af01a094725f834dc
Jinbin Bai
68917c7af01a094725f834dd
Qingyu Shi
68917c7af01a094725f834de
Aosong Feng
68917c7af01a094725f834df
Kaidong Yu
Personalized Safety Alignment for Text-to-Image Diffusion Models
Text-to-image diffusion models have revolutionized visual content generation,
but current safety mechanisms apply uniform standards that often fail to
account for individual user preferences. These models overlook the diverse
safety boundaries shaped by factors like age, mental health, and personal
beliefs. To address this, we propose Personalized Safety Alignment (PSA), a
framework that allows user-specific control over safety behaviors in generative
models. PSA integrates personalized user profiles into the diffusion process,
adjusting the model's behavior to match individual safety preferences while
preserving image quality. We introduce a new dataset, Sage, which captures
user-specific safety preferences and incorporates these profiles through a
cross-attention mechanism. Experiments show that PSA outperforms existing
methods in harmful content suppression and aligns generated content better with
user constraints, achieving higher Win Rate and Pass Rate scores. Our code,
data, and models are publicly available at
https://torpedo2648.github.io/PSAlign/.
A personalized safety alignment framework integrates user-specific profiles into text-to-image diffusion models to better align generated content with individual safety preferences.
text-to-image diffusion models Personalized Safety Alignment (PSA) user-specific profiles cross-attention mechanism harmful content suppression Win Rate Pass Rate