Diffusion models have recently shown the ability to generate high-quality images. However, controlling its generation process still poses challenges. The image style transfer task is one of those challenges that transfers the visual attributes of a style image to another content image. Typical obstacle of this task is the requirement of additional training of a pre-trained model. We propose a training-free style transfer algorithm, Style Tracking Reverse Diffusion Process (STRDP) for a pretrained Latent Diffusion Model (LDM). Our algorithm employs Adaptive Instance Normalization (AdaIN) function in a distinct manner during the reverse diffusion process of an LDM while tracking the encoding history of the style image. This algorithm enables style transfer in the latent space of LDM for reduced computational cost, and provides compatibility for various LDM models. Through a series of experiments and a user study, we show that our method can quickly transfer the style of an image without additional training. The speed, compatibility, and training-free aspect of our algorithm facilitates agile experiments with combinations of styles and LDMs for extensive application.

本文解决了图像风格迁移中传统模型需要额外训练的问题。通过提出一种无训练的风格迁移算法——风格跟踪反向扩散过程（STRDP），该算法在潜在扩散模型的反向扩散过程中以独特方式使用自适应实例正则化（AdaIN）函数，同时跟踪风格图像的编码历史。实验表明，该方法能够快速实现图像风格迁移，具有速度快、兼容性强和无训练的优点，促进了风格与隐空间模型的灵活组合实验。 

利用潜在扩散模型进行无训练图像风格迁移