StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video
  Generation
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.
663443af2adb91d8d9fa2b9a
Yupeng Zhou
admin_assigned
2024-05-03T07:39:20.759Z
64b54d5799ba6b130245da20
/avatars/c5281b17a4d4a565d6d189a007ac934e.svg
zhou
user
YupengZhou
663443af2adb91d8d9fa2b9b
Daquan Zhou
admin_assigned
2024-05-03T07:39:31.405Z
63fe0b160c1bbe8e29d2dd32
/avatars/bc574036287170a77057893efaa48e2d.svg
Zhou
user
DaQuan21
663443af2adb91d8d9fa2b9c
Ming-Ming Cheng
admin_assigned
2024-05-03T07:39:51.152Z
64e496ae0195913c7fa91c66
/avatars/23f274f0a3b4ef6ded35205df9bfb564.svg
chengmingming
user
mingming8688
663443af2adb91d8d9fa2b9d
Jiashi Feng
663443af2adb91d8d9fa2b9e
Qibin Hou
StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video
  Generation
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.
StoryDiffusion, combining consistent self-attention and semantic motion prediction, enables generation of coherent and stable images and videos from textual descriptions.
diffusion-based generative models self-attention Consistent Self-Attention latent spaces Semantic Motion Predictor visual story generation