This study investigates the adoption of Generative AI text-to-image tools in the creative industry using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. The objective is to assess how brand recognition and trust, alongside UTAUT factors such as performance expectancy, effort expectancy, facilitating conditions, and social influence, influence behavioral intentions to adopt Generative AI tools. Additionally, it evaluates how prior experience with such tools moderates these relationships. While prior research underscores the significance of UTAUT factors in technology adoption, the role of brand equity factors—crucial for evaluating quality in industries with limited technological expertise—remains underexplored. This study addresses this gap by incorporating brand recognition and trust as key variables. Data were collected via standardized questionnaires from 208 creative professionals in the US and Spain, distributed through online creative communities. Partial Least Squares Structural Equation Modeling was employed to validate the hypotheses. Results indicate that performance expectancy, facilitating conditions, and brand trust positively influence behavioral intention, while brand recognition negatively impacts it. Social influence and effort expectancy showed no significant effects. Importantly, prior experience with Generative AI tools significantly moderates the relationship between brand trust and behavioral intention, with a stronger influence among experienced users, though it has limited effects on other variables. The findings enhance understanding of Generative AI adoption and offer practical insights for creative professionals and marketers, emphasizing the importance of brand trust and experience in driving adoption within the creative industry.