Enhancing User-Controlled Text-to-Image Generation with Layout-Aware Personalization
Authors: Hongliang Luo, Wei Xi
Accepted at: acl25


Reviewer paper summaries: This paper introduces a novel layout-controllable framework that aims to achieve more precise and fine-grained control in personalized text-to-image generation. Specifically, this approach is built around two core design goals: maintaining the integrity of subject-specific details, and enhancing the model's layout adaptability to meet different user needs. To cope with it, they propose a variational detail-aware feature extractor that can effectively capture subtle changes in subject details, ensuring that the generated images accurately reflect the reference images while preserving essential unique features. Complementary to this, a dual layout control mechanism can flexibly integrate layout conditions, giving users control over image composition and spatial arrangement.
Summary of strengths: - Clear motivation. This paper clearly states their motivation, which is to enhance the controllability of layout in the personalized text-to-image generation task. 
- Clear structure and fluent writing. The writing and figures of this article well present the ideas of the proposed method.
- A solid approach. In this study, tthey present a novel approach for layout-controllable, personalized diffusion that combines two key innovations: a Variational Detail-Aware Feature Extractor and a Dual Layout Control Mechanism. The feature extractor captures intricate details from reference subjects, ensuring high fidelity in the generated images, while the layout control mechanism allows users to embed specific layout constraints directly into the generation process.
Summary of weaknesses: - About the quantitative comparison results in multi-object scenes. Do Table 3 and Table 1 use different evaluation datasets? If so, please indicate this in the caption. In addition, Table 1 seems to lack comparisons with similar methods, which I hope can be supplemented.
- FlexiDiffusion seems to overstate its contribution, for example, ``To the best of our knowledge, this work is pioneering in enabling users to achieve a new level of personalization, allowing them to create anything, anywhere.'' which has been extensively explored in Layout-to-Image generation such as LayoutDiffusion.
- Qualitative evaluation of multiple objectives is lacking in the evaluation in this paper.
Comments, suggestions and typos: See weaknesses.
Overall assessment scores: 3