FreeStyle: Free Control of Style-Content Dual-Reference Generation from Community LoRA Mining
Summary
FreeStyle proposes a scalable dual-reference generation framework using community LoRA mining to construct large-scale style-content triplets, with disentanglement mechanisms to prevent content leakage, and introduces a comprehensive benchmark for evaluation.
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Paper page - FreeStyle: Free Control of Style-Content Dual-Reference Generation from Community LoRA Mining
Source: https://huggingface.co/papers/2606.20506 Authors:
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Abstract
FreeStyle is a scalable dual-reference generation framework that uses community LoRA mining to create large-scale style-content triplets while addressing content leakage through disentanglement mechanisms and a comprehensive benchmark.
Style-contentdual-reference generationaims to synthesize an image that preserves the structure and semantics of a content reference while adopting the style of a separate style reference.Despite recent progress, this setting remains challenging because models must balance content fidelity, style alignment, and instruction following avoiding semantic leakage from the style reference.A key bottleneck is the lack of large-scale triplet data with clean content-style separation and broad long-tail style coverage.In this work, we propose FreeStyle, a scalabledual-reference generationframework based on communityLoRA mining.We treat community LoRAs as compositional anchors for style and content, and design a rigorous generation and filtering pipeline to construct large-scale Style-Reference and Content-Reference triplets across multiple base models.To addresscontent leakage, we adopt a two-stage curriculum with stage-specificdisentanglement mechanisms: anattention-level enrichment constraintthat suppresses style-reference leakage in the style-transfer stage, and afrequency-aware RoPE modulationstrategy that targets positional-correspondence-based leakage in the harder dual-reference stage.We also introduce a benchmark covering both style-reference anddual-reference generation, with evaluations on style similarity, content preservation, aesthetics, instruction following, and leakage rejection. The benchmark incorporates a style-invariantContent Alignment Score(CAS) and introduces a calibrated VLM-basedRejection Scorefor evaluating generation reliability and leakage suppression.Extensive experiments show that our model achieves a strong balance among style alignment, content preservation, and leakage suppression.
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