Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models
Conditioned diffusion models have demonstrated state-of-the-art text-to-image
synthesis capacity. Recently, most works focus on synthesizing independent
images; While for real-world applications, it is common and necessary to
generate a series of coherent images for story-stelling. In this work, we
mainly focus on story visualization and continuation tasks and propose AR-LDM,
a latent diffusion model auto-regressively conditioned on history captions and
generated images. Moreover, AR-LDM can generalize to new characters through
adaptation. To our best knowledge, this is the first work successfully
leveraging diffusion models for coherent visual story synthesizing.
Quantitative results show that AR-LDM achieves SoTA FID scores on PororoSV,
FlintstonesSV, and the newly introduced challenging dataset VIST containing
natural images. Large-scale human evaluations show that AR-LDM has superior
performance in terms of quality, relevance, and consistency.