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RS-Corrector: Correcting the Racial Stereotypes in Latent Diffusion Models
Recent text-conditioned image generation models have demonstrated an
exceptional capacity to produce diverse and creative imagery with high visual
quality. However, when pre-trained on billion-sized datasets randomly collected
from the Internet, where potential biased human preferences exist, these models
tend to produce images with common and recurring stereotypes, particularly for
certain racial groups. In this paper, we conduct an initial analysis of the
publicly available Stable Diffusion model and its derivatives, highlighting the
presence of racial stereotypes. These models often generate distorted or biased
images for certain racial groups, emphasizing stereotypical characteristics. To
address these issues, we propose a framework called "RS-Corrector", designed to
establish an anti-stereotypical preference in the latent space and update the
latent code for refined generated results. The correction process occurs during
the inference stage without requiring fine-tuning of the original model.
Extensive empirical evaluations demonstrate that the introduced \themodel
effectively corrects the racial stereotypes of the well-trained Stable
Diffusion model while leaving the original model unchanged.Comment: 16 pages, 15 figures, conferenc
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