  With the rapidly growing model complexity and data volume, training deep
generative models (DGMs) for better performance has becoming an increasingly
more important challenge. Previous research on this problem has mainly focused
on improving DGMs by either introducing new objective functions or designing
more expressive model architectures. However, such approaches often introduce
significantly more computational and/or designing overhead. To resolve such
issues, we introduce in this paper a generic framework called {\em
generative-model inference} that is capable of enhancing pre-trained GANs
effectively and seamlessly in a variety of application scenarios. Our basic
idea is to efficiently infer the optimal latent distribution for the given
requirements using Wasserstein gradient flow techniques, instead of re-training
or fine-tuning pre-trained model parameters. Extensive experimental results on
applications like image generation, image translation, text-to-image
generation, image inpainting, and text-guided image editing suggest the
effectiveness and superiority of our proposed framework.
