StyleFactory: Towards Better Style Alignment in Image Creation through Style-Strength-Based Control and Evaluation
Authors: 
Mingxu Zhou, Dengming Zhang, Weitao You, Ziqi Yu, Yifei Wu, Chenghao Pan, Huiting Liu, Tianyu Lao, Pei ChenAuthors Info & Claims
Mingxu Zhou, Dengming Zhang, Weitao You, Ziqi Yu, Yifei Wu, Chenghao Pan, Huiting Liu, Tianyu Lao, Pei ChenAuthors Info & ClaimsArticle No.: 107, Pages 1 - 15
Abstract
Generative AI models have been widely used for image creation. However, generating images that are well-aligned with users’ personal styles on aesthetic features (e.g., color and texture) can be challenging due to the poor style expression and interpretation between humans and models. Through a formative study, we observed that participants showed a clear subjective perception of the desired style and variations in its strength, which directly inspired us to develop style-strength-based control and evaluation. Building on this, we present StyleFactory, an interactive system that helps users achieve style alignment. Our interface enables users to rank images based on their strengths in the desired style and visualizes the strength distribution of other images in that style from the model’s perspective. In this way, users can evaluate the understanding gap between themselves and the model, and define well-aligned personal styles for image creation through targeted iterations. Our technical evaluation and user study demonstrate that StyleFactory accurately generates images in specific styles, effectively facilitates style alignment in image creation workflow, stimulates creativity, and enhances the user experience in human-AI interactions.
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- StyleFactory: Towards Better Style Alignment in Image Creation through Style-Strength-Based Control and Evaluation
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