  Tuning-free long video diffusion has been proposed to generate
extended-duration videos with enriched content by reusing the knowledge from
pre-trained short video diffusion model without retraining. However, most works
overlook the fine-grained long-term video consistency modeling, resulting in
limited scene consistency (i.e., unreasonable object or background
transitions), especially with multiple text inputs. To mitigate this, we
propose the Consistency Noise Injection, dubbed CoNo, which introduces the
"look-back" mechanism to enhance the fine-grained scene transition between
different video clips, and designs the long-term consistency regularization to
eliminate the content shifts when extending video contents through noise
prediction. In particular, the "look-back" mechanism breaks the noise
scheduling process into three essential parts, where one internal noise
prediction part is injected into two video-extending parts, intending to
achieve a fine-grained transition between two video clips. The long-term
consistency regularization focuses on explicitly minimizing the pixel-wise
distance between the predicted noises of the extended video clip and the
original one, thereby preventing abrupt scene transitions. Extensive
experiments have shown the effectiveness of the above strategies by performing
long-video generation under both single- and multi-text prompt conditions. The
project has been available in https://wxrui182.github.io/CoNo.github.io/.
