Diffusion models have shown great promise in text-guided image style
transfer, but there is a trade-off between style transformation and content
preservation due to their stochastic nature. Existing methods require
computationally expensive fine-tuning of diffusion models or additional neural
network. To address this, here we propose a zero-shot contrastive loss for
diffusion models that doesn't require additional fine-tuning or auxiliary
networks. By leveraging patch-wise contrastive loss between generated samples
and original image embeddings in the pre-trained diffusion model, our method
can generate images with the same semantic content as the source image in a
zero-shot manner. Our approach outperforms existing methods while preserving
content and requiring no additional training, not only for image style transfer
but also for image-to-image translation and manipulation. Our experimental
results validate the effectiveness of our proposed method.

本文提出了一种零样式迁移的扩散模型，该零样式迁移使用零额外训练或辅助网络的对比损失来处理内容保留问题，并在图像样式转换和图像到图像翻译中取得了优于现有方法的效果。