Diffusion Adversarial Post-Training for One-Step Video Generation
The diffusion models are widely used for image and video generation, but
their iterative generation process is slow and expansive. While existing
distillation approaches have demonstrated the potential for one-step generation
in the image domain, they still suffer from significant quality degradation. In
this work, we propose Adversarial Post-Training (APT) against real data
following diffusion pre-training for one-step video generation. To improve the
training stability and quality, we introduce several improvements to the model
architecture and training procedures, along with an approximated R1
regularization objective. Empirically, our experiments show that our
adversarial post-trained model, Seaweed-APT, can generate 2-second, 1280x720,
24fps videos in real time using a single forward evaluation step. Additionally,
our model is capable of generating 1024px images in a single step, achieving
quality comparable to state-of-the-art methods.
678725b38c1e7b6c4a69f88a
Shanchuan Lin
claimed_verified
2025-01-15T08:48:34.462Z
645863f7dc18eb1a9b5d29df
https://cdn-avatars.huggingface.co/v1/production/uploads/645863f7dc18eb1a9b5d29df/t49Nnyl4tbkUn7CmQqKZh.jpeg
Peter Lin
user
PeterL1n
678725b38c1e7b6c4a69f88b
Xin Xia
claimed_verified
2025-01-17T10:26:06.599Z
63089a78ff78e2aead8d10e7
/avatars/f326fd08abee7e31599a78923be30003.svg
XinXia
user
XiaXin-Aloys
678725b38c1e7b6c4a69f88c
Yuxi Ren
admin_assigned
2025-01-15T16:46:42.832Z
6618d5e83b412cdc85334ca8
/avatars/5fe356d58c4c822a60370dbee8d78a69.svg
renyuxi
user
renyuxi
678725b38c1e7b6c4a69f88d
Ceyuan Yang
admin_assigned
2025-01-15T16:46:55.730Z
64accf3fc1da7c4dbc69392d
/avatars/ec8ad2498f8a49a7a00c77a7f70d34bb.svg
Ceyuan
user
Ceyuan
678725b38c1e7b6c4a69f88e
Xuefeng Xiao
admin_assigned
2025-01-15T16:47:01.595Z
646b7f71df2609a541c1ab9f
/avatars/48b82e5fd9b06f41ff825507c36816cd.svg
Xuefeng Xiao
user
xiaoxuefeng
678725b38c1e7b6c4a69f88f
Lu Jiang
Diffusion Adversarial Post-Training for One-Step Video Generation
The diffusion models are widely used for image and video generation, but
their iterative generation process is slow and expansive. While existing
distillation approaches have demonstrated the potential for one-step generation
in the image domain, they still suffer from significant quality degradation. In
this work, we propose Adversarial Post-Training (APT) against real data
following diffusion pre-training for one-step video generation. To improve the
training stability and quality, we introduce several improvements to the model
architecture and training procedures, along with an approximated R1
regularization objective. Empirically, our experiments show that our
adversarial post-trained model, Seaweed-APT, can generate 2-second, 1280x720,
24fps videos in real time using a single forward evaluation step. Additionally,
our model is capable of generating 1024px images in a single step, achieving
quality comparable to state-of-the-art methods.
Adversarial Post-Training improves the quality and speed of one-step video and image generation using diffusion models.
diffusion models one-step generation adversarial post-training R1 regularization Seaweed-APT real-time video generation