[
    {
        "id": "osp-18071",
        "type": "article-journal",
        "title": "Salvation Lies Within: Eliciting Inherent Style Transfer in Step-Distilled Diffusion Models",
        "author": [
            {
                "family": "Sun",
                "given": "Shengyin"
            },
            {
                "family": "Li",
                "given": "Yiming"
            },
            {
                "family": "Lian",
                "given": "Yingzhao"
            },
            {
                "family": "Li",
                "given": "Xing"
            },
            {
                "family": "Zhou",
                "given": "Xingzhi"
            },
            {
                "family": "Tian",
                "given": "Anxin"
            },
            {
                "family": "Wang",
                "given": "Zhili"
            },
            {
                "family": "Li",
                "given": "Haoyang"
            },
            {
                "family": "Cui",
                "given": "Ziqiang"
            },
            {
                "family": "Ma",
                "given": "Chen"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/salvation-lies-within-eliciting-inherent-style-transfer-in-step-distilled-diffusion-models",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Adapting step-distilled text-to-image (T2I) models through post-training incurs additional computational costs and affects native few-step generation behavior. This motivates a complementary route beyond style-specific adaptation: drawing on the visual knowledge already encoded in step-distilled T2I models to elicit stylistic capabilities through language. Pursuing this direction requires textual guidance that captures how visual attributes jointly define a style and remain applicable as the depicted content changes. To explore this approach, we introduce StyleForge, a fully automatic, training-free framework that expresses reference styles as reusable rendering instructions. By integrating overall rendering characteristics with local color and lighting behavior, StyleForge organizes visual evidence from reference images into a coherent specification of how the target style should be expressed. The specification is then compiled into textual guidance that can be reused across content prompts, enabling frozen step-distilled T2I models to render different subjects and scenes in the reference style while retaining native few-step generation. Extensive experiments show relative gains of up to 29.47\\% in generation quality scores over the strongest baseline, while Pareto analysis indicates that improved stylization is accompanied by strong adherence to the requested content."
    }
]