  While impressive performance has been achieved in image captioning, the
limited diversity of the generated captions and the large parameter scale
remain major barriers to the real-word application of these systems. In this
work, we propose a lightweight image captioning network in combination with
continuous diffusion, called Prefix-diffusion. To achieve diversity, we design
an efficient method that injects prefix image embeddings into the denoising
process of the diffusion model. In order to reduce trainable parameters, we
employ a pre-trained model to extract image features and further design an
extra mapping network. Prefix-diffusion is able to generate diverse captions
with relatively less parameters, while maintaining the fluency and relevance of
the captions benefiting from the generative capabilities of the diffusion
model. Our work paves the way for scaling up diffusion models for image
captioning, and achieves promising performance compared with recent approaches.
