Self-Forcing++: Towards Minute-Scale High-Quality Video Generation
Diffusion models have revolutionized image and video generation, achieving
unprecedented visual quality. However, their reliance on transformer
architectures incurs prohibitively high computational costs, particularly when
extending generation to long videos. Recent work has explored autoregressive
formulations for long video generation, typically by distilling from
short-horizon bidirectional teachers. Nevertheless, given that teacher models
cannot synthesize long videos, the extrapolation of student models beyond their
training horizon often leads to pronounced quality degradation, arising from
the compounding of errors within the continuous latent space. In this paper, we
propose a simple yet effective approach to mitigate quality degradation in
long-horizon video generation without requiring supervision from long-video
teachers or retraining on long video datasets. Our approach centers on
exploiting the rich knowledge of teacher models to provide guidance for the
student model through sampled segments drawn from self-generated long videos.
Our method maintains temporal consistency while scaling video length by up to
20x beyond teacher's capability, avoiding common issues such as over-exposure
and error-accumulation without recomputing overlapping frames like previous
methods. When scaling up the computation, our method shows the capability of
generating videos up to 4 minutes and 15 seconds, equivalent to 99.9% of the
maximum span supported by our base model's position embedding and more than 50x
longer than that of our baseline model. Experiments on standard benchmarks and
our proposed improved benchmark demonstrate that our approach substantially
outperforms baseline methods in both fidelity and consistency. Our long-horizon
videos demo can be found at https://self-forcing-plus-plus.github.io/
68df2b55df49fb0df1e03be2
Justin Cui
68df2b55df49fb0df1e03be3
Jie Wu
claimed_verified
2025-10-03T12:32:59.026Z
6381c5d63680a7cf34e08ca9
/avatars/731467e2d80d0ae163c4a00a9e3ff9e5.svg
wujie10558@gmail.com
user
wujie10
68df2b55df49fb0df1e03be4
Ming Li
claimed_verified
2025-10-03T12:33:02.772Z
637f0eb22438d7485b8ef5d7
https://cdn-avatars.huggingface.co/v1/production/uploads/637f0eb22438d7485b8ef5d7/70h7dekqj7LuBobOXckmJ.jpeg
Ming Li
user
limingcv
68df2b55df49fb0df1e03be5
Tao Yang
68df2b55df49fb0df1e03be6
Xiaojie Li
68df2b55df49fb0df1e03be7
Rui Wang
claimed_verified
2025-10-05T12:47:22.629Z
67e463ddeba83bcebfb5055b
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Sb3QDTTcXWViTpqrnHJr7.png
Rui Wang
user
ruiwang2uiuc
68df2b55df49fb0df1e03be8
Andrew Bai
68df2b55df49fb0df1e03be9
Yuanhao Ban
68df2b55df49fb0df1e03bea
Cho-Jui Hsieh
Self-Forcing++: Towards Minute-Scale High-Quality Video Generation
Diffusion models have revolutionized image and video generation, achieving
unprecedented visual quality. However, their reliance on transformer
architectures incurs prohibitively high computational costs, particularly when
extending generation to long videos. Recent work has explored autoregressive
formulations for long video generation, typically by distilling from
short-horizon bidirectional teachers. Nevertheless, given that teacher models
cannot synthesize long videos, the extrapolation of student models beyond their
training horizon often leads to pronounced quality degradation, arising from
the compounding of errors within the continuous latent space. In this paper, we
propose a simple yet effective approach to mitigate quality degradation in
long-horizon video generation without requiring supervision from long-video
teachers or retraining on long video datasets. Our approach centers on
exploiting the rich knowledge of teacher models to provide guidance for the
student model through sampled segments drawn from self-generated long videos.
Our method maintains temporal consistency while scaling video length by up to
20x beyond teacher's capability, avoiding common issues such as over-exposure
and error-accumulation without recomputing overlapping frames like previous
methods. When scaling up the computation, our method shows the capability of
generating videos up to 4 minutes and 15 seconds, equivalent to 99.9% of the
maximum span supported by our base model's position embedding and more than 50x
longer than that of our baseline model. Experiments on standard benchmarks and
our proposed improved benchmark demonstrate that our approach substantially
outperforms baseline methods in both fidelity and consistency. Our long-horizon
videos demo can be found at https://self-forcing-plus-plus.github.io/
A method is proposed to enhance long-horizon video generation by using sampled segments from self-generated long videos to guide student models, maintaining quality and consistency without additional supervision or retraining.
diffusion models transformer architectures autoregressive formulations bidirectional teachers latent space quality degradation temporal consistency position embedding