  Flow-based methods have demonstrated promising results in addressing the
ill-posed nature of super-resolution (SR) by learning the distribution of
high-resolution (HR) images with the normalizing flow. However, these methods
can only perform a predefined fixed-scale SR, limiting their potential in
real-world applications. Meanwhile, arbitrary-scale SR has gained more
attention and achieved great progress. Nonetheless, previous arbitrary-scale SR
methods ignore the ill-posed problem and train the model with per-pixel L1
loss, leading to blurry SR outputs. In this work, we propose "Local Implicit
Normalizing Flow" (LINF) as a unified solution to the above problems. LINF
models the distribution of texture details under different scaling factors with
normalizing flow. Thus, LINF can generate photo-realistic HR images with rich
texture details in arbitrary scale factors. We evaluate LINF with extensive
experiments and show that LINF achieves the state-of-the-art perceptual quality
compared with prior arbitrary-scale SR methods.
