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Diffusion models have shown significant progress in image translation tasksrecently. However, due to their stochastic nature, there's often a trade-offbetween style transformation and content preservation. Current strategies aimto disentangle style and content, preserving the source image's structure whilesuccessfully transitioning from a source to a target domain under text orone-shot image conditions. Yet, these methods often require computationallyintense fine-tuning of diffusion models or additional neural networks. Toaddress these challenges, here we present an approach that guides the reverseprocess of diffusion sampling by applying asymmetric gradient guidance. Thisresults in quicker and more stable image manipulation for both text-guided andimage-guided image translation. Our model's adaptability allows it to beimplemented with both image- and latent-diffusion models. Experiments show thatour method outperforms various state-of-the-art models in image translationtasks.

Improving Diffusion-based Image Translation using Asymmetric Gradient Guidance