Title: Text-Guided Diffusion Image Style Transfer with Contrastive Loss Fine-tuning. Abstract: Recently, diffusion models have demonstrated superior performance in text-guided image style transfer. However, there exists fundamental trade-off between transforming styles and maintaining content in diffusion models. Although a simple remedy would be using deterministic sampling scheme such as denoising diffusion implicit model (DDIM) that guarantees the perfect reconstruction, it requires the computationally expensive fining-tuning of the diffusion models. To address this, here we present a text-guided sampling scheme using a patch-wise contrastive loss fine-tuning. By exploiting the contrastive loss between the samples and the original images, our diffusion model can generate an image with the same semantic content as the source image. Experimental results demonstrate that our approach outperforms the existing methods while maintaining content and requiring no additional training on the diffusion model.