StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video
  Generation
Authors: name: Yupeng Zhou, name: Daquan Zhou, name: Ming-Ming Cheng, name: Jiashi Feng, name: Qibin Hou

For recent diffusion-based generative models, maintaining consistent content
across a series of generated images, especially those containing subjects and
complex details, presents a significant challenge. In this paper, we propose a
new way of self-attention calculation, termed Consistent Self-Attention, that
significantly boosts the consistency between the generated images and augments
prevalent pretrained diffusion-based text-to-image models in a zero-shot
manner. To extend our method to long-range video generation, we further
introduce a novel semantic space temporal motion prediction module, named
Semantic Motion Predictor. It is trained to estimate the motion conditions
between two provided images in the semantic spaces. This module converts the
generated sequence of images into videos with smooth transitions and consistent
subjects that are significantly more stable than the modules based on latent
spaces only, especially in the context of long video generation. By merging
these two novel components, our framework, referred to as StoryDiffusion, can
describe a text-based story with consistent images or videos encompassing a
rich variety of contents. The proposed StoryDiffusion encompasses pioneering
explorations in visual story generation with the presentation of images and
videos, which we hope could inspire more research from the aspect of
architectural modifications. Our code is made publicly available at
https://github.com/HVision-NKU/StoryDiffusion.