Generative adversarial networks (GANs) have enabled photorealistic image
synthesis and editing. However, due to the high computational cost of
large-scale generators (e.g., StyleGAN2), it usually takes seconds to see the
results of a single edit on edge devices, prohibiting interactive user
experience. In this paper, we take inspirations from modern rendering software
and propose Anycost GAN for interactive natural image editing. We train the
Anycost GAN to support elastic resolutions and channels for faster image
generation at versatile speeds. Running subsets of the full generator produce
outputs that are perceptually similar to the full generator, making them a good
proxy for preview. By using sampling-based multi-resolution training,
adaptive-channel training, and a generator-conditioned discriminator, the
anycost generator can be evaluated at various configurations while achieving
better image quality compared to separately trained models. Furthermore, we
develop new encoder training and latent code optimization techniques to
encourage consistency between the different sub-generators during image
projection. Anycost GAN can be executed at various cost budgets (up to 10x
computation reduction) and adapt to a wide range of hardware and latency
requirements. When deployed on desktop CPUs and edge devices, our model can
provide perceptually similar previews at 6-12x speedup, enabling interactive
image editing. The code and demo are publicly available:
this https URL

本文提出了可以按照不同成本预算执行的 Anycost GAN，通过多分辨率训练、自适应通道训练和生成器有条件的鉴别器，能够在不同配置下进行评估，并且相比单独训练的模型获得更好的图像质量，并且借鉴现代渲染软件的灵感，使其能够进行交互式自然图像编辑并实现较高速度的实时渲染。