  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:
https://github.com/mit-han-lab/anycost-gan.

Computer Vision and Pattern Recognition