FreeStyle: Free Control of Style-Content Dual-Reference Generation from Community LoRA Mining

FreeStyle mines community LoRA weights as compositional anchors to generate large-scale triplet training data with clean content-style separation, sidestepping the bottleneck of manually curated datasets. The framework trains a dual-reference model that preserves content structure while adopting a target style without semantic leakage from the style reference, achieving broad long-tail style coverage by leveraging the diversity already present in public LoRA repositories.