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The paper introduces Composer, a framework for aesthetic image generation that explicitly models composition in a semantic-agnostic manner using diffusion models. Composer enables composition transfer from reference images via a conditional guidance module and supports theme-driven composition retrieval using large vision-language models for explicit composition planning. Through a newly curated 2M image-text dataset and text-to-composition fine-tuning, Composer achieves state-of-the-art aesthetic quality and personalized composition control in text-to-image generation.
Stop letting semantics dictate composition: Composer unlocks semantic-agnostic control over image aesthetics, letting you transfer and plan compositions with unprecedented precision.
Composition is a cornerstone of visual aesthetics, influencing the appeal of an image. While its principles operate independently of specific content, in practice, composition is often coupled with semantics. As a result, existing methods often enhance composition either through implicit learning or by semantics-based layout control, rather than explicitly modeling composition itself. To address this gap, we introduce Composer, a framework rooted in aesthetic theory, designed to model composition in a semantic-agnostic manner. First, it supports composition transfer by extracting key composition-aware representations from a reference image and leveraging a tailored conditional guidance module to control composition based on pre-trained diffusion models. Second, when users specify only text themes without a composition reference, Composer supports theme-driven composition retrieval by leveraging the in-context learning capabilities of Large Vision-Language Models (LVLMs), achieving explicit composition planning. To enhance composition in a reference-free mode, we conduct text-to-composition fine-tuning on the trained control module to enable implicit composition planning. Furthermore, we curated a high-quality dataset comprising 2 million image-text pairs using state-of-the-art generative models to support model training. Experimental results demonstrate that Composer significantly enhances aesthetic quality in text-to-image tasks and facilitates personalized composition control and transfer, offering users precision and flexibility in the creative process.
