Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion
  Model and Implicit Neural Decoder
Jinseok Kim Tae-Kyun Kim
Super-resolution (SR) and image generation are important tasks in computer
vision and are widely adopted in real-world applications. Most existing
methods, however, generate images only at fixed-scale magnification and suffer
from over-smoothing and artifacts. Additionally, they do not offer enough
diversity of output images nor image consistency at different scales. Most
relevant work applied Implicit Neural Representation (INR) to the denoising
diffusion model to obtain continuous-resolution yet diverse and high-quality SR
results. Since this model operates in the image space, the larger the
resolution of image is produced, the more memory and inference time is
required, and it also does not maintain scale-specific consistency. We propose
a novel pipeline that can super-resolve an input image or generate from a
random noise a novel image at arbitrary scales. The method consists of a
pretrained auto-encoder, a latent diffusion model, and an implicit neural
decoder, and their learning strategies. The proposed method adopts diffusion
processes in a latent space, thus efficient, yet aligned with output image
space decoded by MLPs at arbitrary scales. More specifically, our
arbitrary-scale decoder is designed by the symmetric decoder w/o up-scaling
from the pretrained auto-encoder, and Local Implicit Image Function (LIIF) in
series. The latent diffusion process is learnt by the denoising and the
alignment losses jointly. Errors in output images are backpropagated via the
fixed decoder, improving the quality of output images. In the extensive
experiments using multiple public benchmarks on the two tasks i.e. image
super-resolution and novel image generation at arbitrary scales, the proposed
method outperforms relevant methods in metrics of image quality, diversity and
scale consistency. It is significantly better than the relevant prior-art in
the inference speed and memory usage.