Abstract
Style transfer involves transferring the style from a reference image to the content of a target image. Recent advancements in LoRA-based (Low-Rank Adaptation) methods have shown promise in effectively capturing the style of a single image. However, these approaches still face significant challenges such as content inconsistency, style misalignment, and content leakage. In this paper, we comprehensively analyze the limitations of the standard diffusion parameterization, which learns to predict noise, in the context of style transfer. To address these issues, we introduce ConsisLoRA, a LoRA-based method that enhances both content and style consistency by optimizing the LoRA weights to predict the original image rather than noise. We also propose a two-step training strategy that decouples the learning of content and style from the reference image. To effectively capture both the global structure and local details of the content image, we introduce a stepwise loss transition strategy. Additionally, we present an inference guidance method that enables continuous control over content and style strengths during inference. Through both qualitative and quantitative evaluations, our method demonstrates significant improvements in content and style consistency while effectively reducing content leakage.
Abstract (translated)
样式转换涉及将参考图像的风格转移到目标图像的内容上。最近，基于LoRA（低秩适应）的方法在捕捉单张图片的风格方面显示出巨大的潜力。然而，这些方法仍然面临诸如内容不一致、风格错位和内容泄漏等重大挑战。在这篇论文中，我们全面分析了标准扩散参数化（学习预测噪声）在样式转换中的局限性。为了解决这些问题，我们提出了ConsisLoRA，这是一种基于LoRA的方法，通过优化LoRA权重以预测原图而非噪声来增强内容和风格的一致性。此外，我们还提出了一种两步训练策略，将参考图像的内容和风格的学习过程解耦。为了有效地捕捉内容图像的全局结构和局部细节，我们引入了逐步损失转换策略。另外，我们还提出了一种推理指导方法，在推理过程中实现了对内容和风格强度的连续控制。通过定性和定量评估，我们的方法在内容和样式一致性方面表现出显著改进，并有效减少了内容泄漏。
URL
https://arxiv.org/abs/2503.10614
