  Face image super resolution (face hallucination) usually relies on facial
priors to restore realistic details and preserve identity information. Recent
advances can achieve impressive results with the help of GAN prior. They either
design complicated modules to modify the fixed GAN prior or adopt complex
training strategies to finetune the generator. In this work, we propose a
generative and controllable face SR framework, called GCFSR, which can
reconstruct images with faithful identity information without any additional
priors. Generally, GCFSR has an encoder-generator architecture. Two modules
called style modulation and feature modulation are designed for the
multi-factor SR task. The style modulation aims to generate realistic face
details and the feature modulation dynamically fuses the multi-level encoded
features and the generated ones conditioned on the upscaling factor. The simple
and elegant architecture can be trained from scratch in an end-to-end manner.
For small upscaling factors (<=8), GCFSR can produce surprisingly good results
with only adversarial loss. After adding L1 and perceptual losses, GCFSR can
outperform state-of-the-art methods for large upscaling factors (16, 32, 64).
During the test phase, we can modulate the generative strength via feature
modulation by changing the conditional upscaling factor continuously to achieve
various generative effects.
