  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
https://github.com/Picsart-AI-Research/IPL-Zero-Shot-Generative-Model-Adaptation.
