FreeNoise: Tuning-Free Longer Video Diffusion Via Noise Rescheduling
With the availability of large-scale video datasets and the advances of
diffusion models, text-driven video generation has achieved substantial
progress. However, existing video generation models are typically trained on a
limited number of frames, resulting in the inability to generate high-fidelity
long videos during inference. Furthermore, these models only support
single-text conditions, whereas real-life scenarios often require multi-text
conditions as the video content changes over time. To tackle these challenges,
this study explores the potential of extending the text-driven capability to
generate longer videos conditioned on multiple texts. 1) We first analyze the
impact of initial noise in video diffusion models. Then building upon the
observation of noise, we propose FreeNoise, a tuning-free and time-efficient
paradigm to enhance the generative capabilities of pretrained video diffusion
models while preserving content consistency. Specifically, instead of
initializing noises for all frames, we reschedule a sequence of noises for
long-range correlation and perform temporal attention over them by window-based
function. 2) Additionally, we design a novel motion injection method to support
the generation of videos conditioned on multiple text prompts. Extensive
experiments validate the superiority of our paradigm in extending the
generative capabilities of video diffusion models. It is noteworthy that
compared with the previous best-performing method which brought about 255%
extra time cost, our method incurs only negligible time cost of approximately
17%. Generated video samples are available at our website:
http://haonanqiu.com/projects/FreeNoise.html.
65376a7a8ee17cfd440fb426
Haonan Qiu
admin_assigned
2023-10-24T08:38:34.880Z
63023b6ab002e9a4a2152890
/avatars/cae8ba0a8d61fb4e576934431f43991b.svg
Haonan Qiu
user
MoonQiu
65376a7a8ee17cfd440fb427
Menghan Xia
admin_assigned
2023-10-24T08:38:55.514Z
63401c89f81b9d101361f712
https://cdn-avatars.huggingface.co/v1/production/uploads/1665146415483-63401c89f81b9d101361f712.png
Richard
user
menghanxia
65376a7a8ee17cfd440fb428
Yong Zhang
65376a7a8ee17cfd440fb429
Yingqing He
admin_assigned
2023-10-24T08:39:52.060Z
630231bf7e137e3d6b3b0645
/avatars/dad52f8955110f0a2caeb613d6aa3ea2.svg
He
user
Yingqing
65376a7a8ee17cfd440fb42a
Xintao Wang
admin_assigned
2023-10-24T08:40:27.848Z
60e272ca6c78a8c122b12127
https://cdn-avatars.huggingface.co/v1/production/uploads/60e272ca6c78a8c122b12127/xldEGBzGrU-bX6IwAw0Ie.jpeg
Xintao Wang
user
Xintao
65376a7a8ee17cfd440fb42b
Ying Shan
65376a7a8ee17cfd440fb42c
Ziwei Liu
admin_assigned
2023-10-24T08:40:51.757Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
FreeNoise: Tuning-Free Longer Video Diffusion Via Noise Rescheduling
With the availability of large-scale video datasets and the advances of
diffusion models, text-driven video generation has achieved substantial
progress. However, existing video generation models are typically trained on a
limited number of frames, resulting in the inability to generate high-fidelity
long videos during inference. Furthermore, these models only support
single-text conditions, whereas real-life scenarios often require multi-text
conditions as the video content changes over time. To tackle these challenges,
this study explores the potential of extending the text-driven capability to
generate longer videos conditioned on multiple texts. 1) We first analyze the
impact of initial noise in video diffusion models. Then building upon the
observation of noise, we propose FreeNoise, a tuning-free and time-efficient
paradigm to enhance the generative capabilities of pretrained video diffusion
models while preserving content consistency. Specifically, instead of
initializing noises for all frames, we reschedule a sequence of noises for
long-range correlation and perform temporal attention over them by window-based
function. 2) Additionally, we design a novel motion injection method to support
the generation of videos conditioned on multiple text prompts. Extensive
experiments validate the superiority of our paradigm in extending the
generative capabilities of video diffusion models. It is noteworthy that
compared with the previous best-performing method which brought about 255%
extra time cost, our method incurs only negligible time cost of approximately
17%. Generated video samples are available at our website:
http://haonanqiu.com/projects/FreeNoise.html.
A method called FreeNoise is introduced to enhance video diffusion models for generating high-fidelity, long videos conditioned on multiple text prompts with minimal time cost.
diffusion models text-driven video generation high-fidelity long videos multi-text conditions FreeNoise initial noise window-based function temporal attention motion injection video diffusion models