SEED-Story: Multimodal Long Story Generation with Large Language Model
Authors: name: Shuai Yang, name: Yuying Ge, name: Yang Li, name: Yukang Chen, name: Yixiao Ge, name: Ying Shan, name: Yingcong Chen

With the remarkable advancements in image generation and open-form text
generation, the creation of interleaved image-text content has become an
increasingly intriguing field. Multimodal story generation, characterized by
producing narrative texts and vivid images in an interleaved manner, has
emerged as a valuable and practical task with broad applications. However, this
task poses significant challenges, as it necessitates the comprehension of the
complex interplay between texts and images, and the ability to generate long
sequences of coherent, contextually relevant texts and visuals. In this work,
we propose SEED-Story, a novel method that leverages a Multimodal Large
Language Model (MLLM) to generate extended multimodal stories. Our model, built
upon the powerful comprehension capability of MLLM, predicts text tokens as
well as visual tokens, which are subsequently processed with an adapted visual
de-tokenizer to produce images with consistent characters and styles. We
further propose multimodal attention sink mechanism to enable the generation of
stories with up to 25 sequences (only 10 for training) in a highly efficient
autoregressive manner. Additionally, we present a large-scale and
high-resolution dataset named StoryStream for training our model and
quantitatively evaluating the task of multimodal story generation in various
aspects.