  Recent advances in text-to-image generative models provide the ability to
generate high-quality images from short text descriptions. These foundation
models, when pre-trained on billion-scale datasets, are effective for various
downstream tasks with little or no further training. A natural question to ask
is how such models may be adapted for image compression. We investigate several
techniques in which the pre-trained models can be directly used to implement
compression schemes targeting novel low rate regimes. We show how text
descriptions can be used in conjunction with side information to generate
high-fidelity reconstructions that preserve both semantics and spatial
structure of the original. We demonstrate that at very low bit-rates, our
method can significantly improve upon learned compressors in terms of
perceptual and semantic fidelity, despite no end-to-end training.
