Consistent Style Transfer with ConsisLoRA

Style transfer, the process of applying the style of a reference image to the content of a target image, is a fascinating field of image processing. Recent advancements in LoRA-based methods (Low-Rank Adaptation) have demonstrated that the style of a single image can be effectively captured. Despite this progress, challenges remain, particularly concerning content consistency, accurate style transfer, and the unwanted appearance of content from the reference image (content leakage).

A recently published paper titled "ConsisLoRA: Enhancing Content and Style Consistency for LoRA-based Style Transfer" analyzes the limitations of standard diffusion parameterization, which aims to predict noise, in the context of style transfer. The authors argue that this parameterization contributes to the aforementioned problems.

To address these challenges, the researchers propose ConsisLoRA, a LoRA-based method that improves both content and style consistency. Unlike conventional approaches, ConsisLoRA optimizes the LoRA weights to predict the original image instead of the noise. This approach aims to better preserve the integrity of the content image while transferring the desired style.

Another important aspect of ConsisLoRA is the proposed two-stage training strategy. This strategy decouples the learning of content and style from the reference image. In the first stage, the model is trained to reconstruct the content of the original image. In the second stage, the style of the reference image is integrated while maintaining content consistency.

To effectively capture both the global structure and local details of the content image, the authors introduce a gradual loss transition strategy. This strategy allows the model to progressively focus on finer details after capturing the coarse structures.

Additionally, the researchers present an inference guidance method that allows continuous control over content and style strengths during inference. This provides users with greater flexibility and control over the style transfer process.

The results of the qualitative and quantitative evaluations demonstrate that ConsisLoRA achieves significant improvements in content and style consistency while reducing content leakage. The authors show that their method delivers superior results compared to existing LoRA-based style transfer methods.

Developments in the field of style transfer are promising, and ConsisLoRA represents a significant contribution. The improved consistency and control offered by this method open new possibilities for creative applications in image editing and beyond. Further research in this area will undoubtedly lead to further improvements and innovations.

Bibliography: Chen, B., Zhao, B., Xie, H., Cai, Y., Li, Q., & Mao, X. (2025). ConsisLoRA: Enhancing Content and Style Consistency for LoRA-based Style Transfer. arXiv preprint arXiv:2503.10614. B-LoRA. https://b-lora.github.io/B-LoRA/ B-LoRA Paper. https://b-lora.github.io/B-LoRA/static/source/B-LoRA.pdf ECVA Paper. https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/01549.pdf Reddit Discussion. https://www.reddit.com/r/ninjasaid13/comments/1jatjim/250310614_consislora_enhancing_content_and_style/ Paper Reading Club. http://paperreading.club/page?id=291860