Recently, CLIP-guided image synthesis has shown appealing performance on
adapting a pre-trained source-domain generator to an unseen target domain. It
does not require any target-domain samples but only the textual domain labels.
The training is highly efficient, e.g., a few minutes. However, existing
methods still have some limitations in the quality of generated images and may
suffer from the mode collapse issue. A key reason is that a fixed adaptation
direction is applied for all cross-domain image pairs, which leads to identical
supervision signals. To address this issue, we propose an Image-specific Prompt
Learning (IPL) method, which learns specific prompt vectors for each
source-domain image. This produces a more precise adaptation direction for
every cross-domain image pair, endowing the target-domain generator with
greatly enhanced flexibility. Qualitative and quantitative evaluations on
various domains demonstrate that IPL effectively improves the quality and
diversity of synthesized images and alleviates the mode collapse. Moreover, IPL
is independent of the structure of the generative model, such as generative
adversarial networks or diffusion models. Code is available at
this https URL

该研究项目提出了一种名为图像特定提示学习（IPL）的方法，能够为每个源域图像学习特定的提示向量，从而为每个跨域图像对提供更准确的适应方向，增强目标域生成器的灵活性，并改善合成图像的质量和多样性。