FreeInit: Bridging Initialization Gap in Video Diffusion Models
Though diffusion-based video generation has witnessed rapid progress, the
inference results of existing models still exhibit unsatisfactory temporal
consistency and unnatural dynamics. In this paper, we delve deep into the noise
initialization of video diffusion models, and discover an implicit
training-inference gap that attributes to the unsatisfactory inference quality.
Our key findings are: 1) the spatial-temporal frequency distribution of the
initial latent at inference is intrinsically different from that for training,
and 2) the denoising process is significantly influenced by the low-frequency
components of the initial noise. Motivated by these observations, we propose a
concise yet effective inference sampling strategy, FreeInit, which
significantly improves temporal consistency of videos generated by diffusion
models. Through iteratively refining the spatial-temporal low-frequency
components of the initial latent during inference, FreeInit is able to
compensate the initialization gap between training and inference, thus
effectively improving the subject appearance and temporal consistency of
generation results. Extensive experiments demonstrate that FreeInit
consistently enhances the generation results of various text-to-video
generation models without additional training.
657924094d989b0a68bf85f2
Tianxing Wu
admin_assigned
2023-12-13T08:34:09.020Z
63185a8973e9e4aa51dbc0ce
https://cdn-avatars.huggingface.co/v1/production/uploads/1676359176854-63185a8973e9e4aa51dbc0ce.jpeg
Tianxing Wu
user
TianxingWu
657924094d989b0a68bf85f3
Chenyang Si
admin_assigned
2023-12-13T08:34:18.858Z
635f8ed47c05eb9f59963d3a
https://cdn-avatars.huggingface.co/v1/production/uploads/635f8ed47c05eb9f59963d3a/uQf4p9N9pSaFy87Wg9v4k.jpeg
ChenyangSi
user
ChenyangSi
657924094d989b0a68bf85f4
Yuming Jiang
admin_assigned
2023-12-13T08:33:46.802Z
629c95b7a5d6f5fe10e6ed45
/avatars/c9a5f0e7fae3a079d26db266e9ff90a3.svg
Yuming Jiang
user
yumingj
657924094d989b0a68bf85f5
Ziqi Huang
claimed_verified
2023-12-13T08:32:45.214Z
60efe7fa0d920bc7805cada5
https://cdn-avatars.huggingface.co/v1/production/uploads/60efe7fa0d920bc7805cada5/2LBrJBjSCOP5ilZIpWLHl.png
Ziqi Huang
user
Ziqi
657924094d989b0a68bf85f6
Ziwei Liu
admin_assigned
2023-12-13T08:34:27.265Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
FreeInit: Bridging Initialization Gap in Video Diffusion Models
Though diffusion-based video generation has witnessed rapid progress, the
inference results of existing models still exhibit unsatisfactory temporal
consistency and unnatural dynamics. In this paper, we delve deep into the noise
initialization of video diffusion models, and discover an implicit
training-inference gap that attributes to the unsatisfactory inference quality.
Our key findings are: 1) the spatial-temporal frequency distribution of the
initial latent at inference is intrinsically different from that for training,
and 2) the denoising process is significantly influenced by the low-frequency
components of the initial noise. Motivated by these observations, we propose a
concise yet effective inference sampling strategy, FreeInit, which
significantly improves temporal consistency of videos generated by diffusion
models. Through iteratively refining the spatial-temporal low-frequency
components of the initial latent during inference, FreeInit is able to
compensate the initialization gap between training and inference, thus
effectively improving the subject appearance and temporal consistency of
generation results. Extensive experiments demonstrate that FreeInit
consistently enhances the generation results of various text-to-video
generation models without additional training.
FreeInit addresses the temporal consistency and unnatural dynamics issues in diffusion-based video generation by refining spatial-temporal low-frequency components during inference, improving subject appearance and consistency.
diffusion-based video generation temporal consistency inference quality noise initialization spatial-temporal frequency distribution initial latent denoising process low-frequency components FreeInit