De-Diffusion Makes Text a Strong Cross-Modal Interface
Chen Wei Chenxi Liu Siyuan Qiao Zhishuai Zhang Alan Yuille Jiahui Yu
We demonstrate text as a strong cross-modal interface. Rather than relying on
deep embeddings to connect image and language as the interface representation,
our approach represents an image as text, from which we enjoy the
interpretability and flexibility inherent to natural language. We employ an
autoencoder that uses a pre-trained text-to-image diffusion model for decoding.
The encoder is trained to transform an input image into text, which is then fed
into the fixed text-to-image diffusion decoder to reconstruct the original
input -- a process we term De-Diffusion. Experiments validate both the
precision and comprehensiveness of De-Diffusion text representing images, such
that it can be readily ingested by off-the-shelf text-to-image tools and LLMs
for diverse multi-modal tasks. For example, a single De-Diffusion model can
generalize to provide transferable prompts for different text-to-image tools,
and also achieves a new state of the art on open-ended vision-language tasks by
simply prompting large language models with few-shot examples.