  Image compression at extremely low bitrates (below 0.1 bits per pixel (bpp))
is a significant challenge due to substantial information loss. In this work,
we propose a novel two-stage extreme image compression framework that exploits
the powerful generative capability of pre-trained diffusion models to achieve
realistic image reconstruction at extremely low bitrates. In the first stage,
we treat the latent representation of images in the diffusion space as
guidance, employing a VAE-based compression approach to compress images and
initially decode the compressed information into content variables. The second
stage leverages pre-trained stable diffusion to reconstruct images under the
guidance of content variables. Specifically, we introduce a small control
module to inject content information while keeping the stable diffusion model
fixed to maintain its generative capability. Furthermore, we design a space
alignment loss to force the content variables to align with the diffusion space
and provide the necessary constraints for optimization. Extensive experiments
demonstrate that our method significantly outperforms state-of-the-art
approaches in terms of visual performance at extremely low bitrates.
