DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion
  Transformer for Tuning-Free Multi-Prompt Longer Video Generation
Authors: name: Minghong Cai; name: Xiaodong Cun; name: Xiaoyu Li; name: Wenze Liu; name: Zhaoyang Zhang; name: Yong Zhang; name: Ying Shan; name: Xiangyu Yue
Published: 2024-12-24 18:51:19

Abstract: Sora-like video generation models have achieved remarkable progress with a
Multi-Modal Diffusion Transformer MM-DiT architecture. However, the current
video generation models predominantly focus on single-prompt, struggling to
generate coherent scenes with multiple sequential prompts that better reflect
real-world dynamic scenarios. While some pioneering works have explored
multi-prompt video generation, they face significant challenges including
strict training data requirements, weak prompt following, and unnatural
transitions. To address these problems, we propose DiTCtrl, a training-free
multi-prompt video generation method under MM-DiT architectures for the first
time. Our key idea is to take the multi-prompt video generation task as
temporal video editing with smooth transitions. To achieve this goal, we first
analyze MM-DiT's attention mechanism, finding that the 3D full attention
behaves similarly to that of the cross/self-attention blocks in the UNet-like
diffusion models, enabling mask-guided precise semantic control across
different prompts with attention sharing for multi-prompt video generation.
Based on our careful design, the video generated by DiTCtrl achieves smooth
transitions and consistent object motion given multiple sequential prompts
without additional training. Besides, we also present MPVBench, a new benchmark
specially designed for multi-prompt video generation to evaluate the
performance of multi-prompt generation. Extensive experiments demonstrate that
our method achieves state-of-the-art performance without additional training.