  Diffusion models have shown significant progress in image translation tasks
recently. However, due to their stochastic nature, there's often a trade-off
between style transformation and content preservation. Current strategies aim
to disentangle style and content, preserving the source image's structure while
successfully transitioning from a source to a target domain under text or
one-shot image conditions. Yet, these methods often require computationally
intense fine-tuning of diffusion models or additional neural networks. To
address these challenges, here we present an approach that guides the reverse
process of diffusion sampling by applying asymmetric gradient guidance. This
results in quicker and more stable image manipulation for both text-guided and
image-guided image translation. Our model's adaptability allows it to be
implemented with both image- and latent-diffusion models. Experiments show that
our method outperforms various state-of-the-art models in image translation
tasks.
