Ouroboros-Diffusion: Exploring Consistent Content Generation in
  Tuning-free Long Video Diffusion
Authors: name: Jingyuan Chen, name: Fuchen Long, name: Jie An, name: Zhaofan Qiu, name: Ting Yao, name: Jiebo Luo, name: Tao Mei

The first-in-first-out (FIFO) video diffusion, built on a pre-trained
text-to-video model, has recently emerged as an effective approach for
tuning-free long video generation. This technique maintains a queue of video
frames with progressively increasing noise, continuously producing clean frames
at the queue's head while Gaussian noise is enqueued at the tail. However,
FIFO-Diffusion often struggles to keep long-range temporal consistency in the
generated videos due to the lack of correspondence modeling across frames. In
this paper, we propose Ouroboros-Diffusion, a novel video denoising framework
designed to enhance structural and content (subject) consistency, enabling the
generation of consistent videos of arbitrary length. Specifically, we introduce
a new latent sampling technique at the queue tail to improve structural
consistency, ensuring perceptually smooth transitions among frames. To enhance
subject consistency, we devise a Subject-Aware Cross-Frame Attention (SACFA)
mechanism, which aligns subjects across frames within short segments to achieve
better visual coherence. Furthermore, we introduce self-recurrent guidance.
This technique leverages information from all previous cleaner frames at the
front of the queue to guide the denoising of noisier frames at the end,
fostering rich and contextual global information interaction. Extensive
experiments of long video generation on the VBench benchmark demonstrate the
superiority of our Ouroboros-Diffusion, particularly in terms of subject
consistency, motion smoothness, and temporal consistency.