Text-to-Image (T2I) has witnessed significant advancements, demonstrating superior performance for various generative tasks. However, the presence of stereotypes in T2I introduces harmful biases that require urgent attention as the T2I technology becomes more prominent.Previous work for stereotype mitigation mainly concentrated on mitigating stereotypes engendered with individual objects within images, which failed to address stereotypes engendered by the association of multiple objects, referred to as Association-Engendered Stereotypes. For example, mentioning "black people" and "houses" separately in prompts may not exhibit stereotypes. Nevertheless, when these two objects are associated in prompts, the association of "black people" with "poorer houses" becomes more pronounced. To tackle this issue, we propose a novel framework, MAS, to Mitigate Association-engendered Stereotypes. This framework models the stereotype problem as a probability distribution alignment problem, aiming to align the stereotype probability distribution of the generated image with the stereotype-free distribution. The MAS framework primarily consists of the Text-Image-Text CLIP (TIT CLIP) and Sensitive Transformer. The TIT CLIP learns the association between prompts, images, and stereotypes, which can establish the mapping of prompts to stereotypes. The Sensitive Transformer produces the sensitive constraints, which guide the stereotyped image distribution to align with the stereotype-free probability distribution. Moreover, recognizing that existing metrics are insufficient for accurately evaluating association-engendered stereotypes, we propose a novel metric, Stereotype-Distribution-Total-Variation (SDTV), to evaluate stereotypes in T2I. Comprehensive experiments demonstrate that our framework effectively mitigates association-engendered stereotypes.