Related papers: Improving Diffusion-based Image Translation using Asymmetric Gradient Guidance

Improving Diffusion-based Image Translation using Asymmetric Gradient Guidance

URL: http://arxiv.org/abs/2306.04396v1
Date: Wed, 7 Jun 2023 12:56:56 GMT
Title: Improving Diffusion-based Image Translation using Asymmetric Gradient Guidance
Authors: Gihyun Kwon, Jong Chul Ye
Abstract summary: We present an approach that guides the reverse process of diffusion sampling by applying asymmetric gradient guidance. Our model's adaptability allows it to be implemented with both image-fusion and latent-dif models. Experiments show that our method outperforms various state-of-the-art models in image translation tasks.
Score: 51.188396199083336
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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