ControlSR: Taming Diffusion Models for Consistent Real-World Image Super Resolution

Understand

We present ControlSR, a new method that can tame Diffusion Models for consistent real-world image super-resolution (Real-ISR).

• Previous Real-ISR models mostly focus on how to activate more generative priors of text-to-image diffusion models to make the output high-resolution (HR) images look better.
• However, since these methods rely too much on the generative priors, the content of the output images is often inconsistent with the input LR ones.
• To mitigate the above issue, in this work, we tame Diffusion Models by effectively utilizing LR information to impose stronger constraints on the control signals from ControlNet in the latent space.