AnimateDiff-Lightning: Cross-Model Diffusion Distillation
We present AnimateDiff-Lightning for lightning-fast video generation. Our
model uses progressive adversarial diffusion distillation to achieve new
state-of-the-art in few-step video generation. We discuss our modifications to
adapt it for the video modality. Furthermore, we propose to simultaneously
distill the probability flow of multiple base diffusion models, resulting in a
single distilled motion module with broader style compatibility. We are pleased
to release our distilled AnimateDiff-Lightning model for the community's use.
65fa41407fcb33a703f37eeb
Shanchuan Lin
claimed_verified
2024-03-21T08:27:51.111Z
645863f7dc18eb1a9b5d29df
https://cdn-avatars.huggingface.co/v1/production/uploads/645863f7dc18eb1a9b5d29df/t49Nnyl4tbkUn7CmQqKZh.jpeg
Peter Lin
user
PeterL1n
65fa41407fcb33a703f37eec
Xiao Yang
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2024-07-03T07:42:09.552Z
62b3d2490385d6048985b42b
/avatars/1c4e534d086d53ef1057fbc00334350a.svg
Xiao Yang
user
seravee008
AnimateDiff-Lightning: Cross-Model Diffusion Distillation
We present AnimateDiff-Lightning for lightning-fast video generation. Our
model uses progressive adversarial diffusion distillation to achieve new
state-of-the-art in few-step video generation. We discuss our modifications to
adapt it for the video modality. Furthermore, we propose to simultaneously
distill the probability flow of multiple base diffusion models, resulting in a
single distilled motion module with broader style compatibility. We are pleased
to release our distilled AnimateDiff-Lightning model for the community's use.
AnimateDiff-Lightning uses progressive adversarial diffusion distillation for efficient video generation, enhancing style compatibility through joint distillation of multiple diffusion models.
progressive adversarial diffusion distillation few-step video generation motion module