StyleFactory: Towards Better Style Alignment in Image Creation through Style-Strength-Based Control and Evaluation

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.

Authors

Institutions

Publication Details

Published
2024-10-11
DOI
https://doi.org/10.1145/3654777.3676370
Citations
5
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
1.25
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

StyleFactory: Towards Better Style Alignment in Image Creation through Style-Strength-Based Control and Evaluation

H.L. Liu, Pei Chen, Weitao You, Dengming Zhang et al.
5 citations
Generative Adversarial Networks and Image Synthesis
1.25
article

StyleFactory: Towards Better Style Alignment in Image Creation through Style-Strength-Based Control and Evaluation

H.L. Liu, Pei Chen, Weitao You, Dengming Zhang, Ziqi Yu, Yifei Wu, Mingxu Zhou, Tianyu Lao, Chenghao Pan
article en
5 citations

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.

Zhejiang University of Science and Technology (CN), Zhejiang University (CN)
Openalex Percentile: Top 19%
Generative Adversarial Networks and Image Synthesis
1.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.