Score-based diffusion models have shown promising results in unpaired image-to-image translation (I2I). However, the existing methods only perform unpaired I2I in pixel space, which requires high computation costs. To this end, we propose guiding stochastic differential equations in latent space (Latent-SDE) that extracts domain-specific and domain-independent features of the image in the latent space to calculate the loss and guides the inference process of a pretrained SDE in the latent space for unpaired I2I. To refine the image in the latent space, we propose a latent time-travel strategy that increases the sampling timestep. Empirically, we compare Latent-SDE to the baseline of the score-based diffusion model on three widely adopted unpaired I2I tasks under two metrics. Latent-SDE achieves state-of-the-art on Cat \(\rightarrow \) Dog and is competitive on the other two tasks. Our code will be freely available for public use upon acceptance at https://github.com/zhangXJ147/Latent-SDE.

基于分数的扩散模型在不成对的图像到图像转换（I2I）方面显示出了有希望的结果。然而，现有方法仅在像素空间中执行不成对的I2I，这需要较高的计算成本。为此，我们提出了潜在空间中的引导随机微分方程（Latent-SDE），它提取潜在空间中图像的特定于域和域无关的特征来计算损失，并指导预训练的 SDE 的推理过程。未配对 I2I 的潜在空间。为了细化潜在空间中的图像，我们提出了一种增加采样时间步长的潜在时间旅行策略。根据经验，我们在两个指标下的三个广泛采用的不配对 I2I 任务上将 Latent-SDE 与基于分数的扩散模型的基线进行比较。 Latent-SDE 在 Cat \(\rightarrow \) Dog 上达到了最先进的水平，并且在其他两项任务上具有竞争力。我们的代码在 https://github.com/zhangXJ147/Latent-SDE 被接受后将免费供公众使用。