  Image super-resolution is a fundamentally ill-posed problem because multiple
valid high-resolution images exist for one low-resolution image.
Super-resolution methods based on diffusion probabilistic models can deal with
the ill-posed nature by learning the distribution of high-resolution images
conditioned on low-resolution images, avoiding the problem of blurry images in
PSNR-oriented methods. However, existing diffusion-based super-resolution
methods have high time consumption with the use of iterative sampling, while
the quality and consistency of generated images are less than ideal due to
problems like color shifting. In this paper, we propose Efficient Conditional
Diffusion Model with Probability Flow Sampling (ECDP) for image
super-resolution. To reduce the time consumption, we design a continuous-time
conditional diffusion model for image super-resolution, which enables the use
of probability flow sampling for efficient generation. Additionally, to improve
the consistency of generated images, we propose a hybrid parametrization for
the denoiser network, which interpolates between the data-predicting
parametrization and the noise-predicting parametrization for different noise
scales. Moreover, we design an image quality loss as a complement to the score
matching loss of diffusion models, further improving the consistency and
quality of super-resolution. Extensive experiments on DIV2K, ImageNet, and
CelebA demonstrate that our method achieves higher super-resolution quality
than existing diffusion-based image super-resolution methods while having lower
time consumption. Our code is available at https://github.com/Yuan-Yutao/ECDP.
