Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling
arXiv:2512.12675v3 Announce Type: replace-cross Abstract: Subject-driven image generation has advanced from single- to multi-subject composition, while neglecting distinction, the ability to distinguish and generate the correct subject when inputs contain multiple candidates. This limitation restricts effectiveness in complex, realistic visual settings. We propose Scone, a unified understanding-generation method that integrates composition and distinction. Scone enables the understanding expert to act as a semantic bridge, conveying semantic information and guiding the generation expert to pre
The development of sophisticated AI models capable of nuanced image generation necessitates better control over subject specificity and distinction to handle increasingly complex prompts and visual scenarios.
Improving subject-driven image generation, especially in multi-subject contexts, is crucial for advancing AI's practical applications in design, virtual content creation, and autonomous systems, reducing ambiguity and enhancing quality.
This research introduces a method for AI to better distinguish and accurately generate specific subjects within complex inputs, moving beyond basic composition to sophisticated distinction.
- · AI model developers
- · Creative industries
- · Virtual content creators
- · AI-powered design platforms
More precise and controllable AI image generation becomes possible, reducing the need for extensive post-generation editing.
The ability to handle complex visual instructions will enable new applications for AI in fields requiring high specificity, such as product design or medical imaging.
As AI image quality and control improve, the demand for human graphic designers and illustrators may shift towards supervision and refinement rather than primary creation.
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Read at arXiv cs.AI