Image-to-image translation (I2I), and particularly its subfield of appearance
transfer, which seeks to alter the visual appearance between images while
maintaining structural coherence, presents formidable challenges. Despite
significant advancements brought by diffusion models, achieving fine-grained
transfer remains complex, particularly in terms of retaining detailed
structural elements and ensuring information fidelity. This paper proposes an
innovative framework designed to surmount these challenges by integrating
various aspects of semantic matching, appearance transfer, and latent
deviation. A pivotal aspect of our approach is the strategic use of the
predicted $x_0$ space by diffusion models within the latent space of diffusion
processes. This is identified as a crucial element for the precise and natural
transfer of fine-grained details. Our framework exploits this space to
accomplish semantic alignment between source and target images, facilitating
mask-wise appearance transfer for improved feature acquisition. A significant
advancement of our method is the seamless integration of these features into
the latent space, enabling more nuanced latent deviations without necessitating
extensive model retraining or fine-tuning. The effectiveness of our approach is
demonstrated through extensive experiments, which showcase its ability to
adeptly handle fine-grained appearance transfers across a wide range of
categories and domains. We provide our code at
this https URL

提出了一个创新性框架，通过集成语义匹配、外观转换和潜在偏差的各个方面，来达到细粒度细节的精确和自然转换，其中关键的要素是扩散模型在扩散过程的潜在空间内对预测的 x0 范围的战略使用，用于实现源图像和目标图像的语义对齐，便于改善特征获取的逐掩膜外观转换。通过广泛的实验验证了这种方法的有效性，并展示了其在广泛类别和领域中处理细粒度外观转换的能力。