Summary of Ditctrl: Exploring Attention Control in Multi-modal Diffusion Transformer For Tuning-free Multi-prompt Longer Video Generation, by Minghong Cai et al.
DiTCtrl: Exploring Attention Control in Multi-Modal Diffusion Transformer for Tuning-Free Multi-Prompt Longer Video Generation
by Minghong Cai, Xiaodong Cun, Xiaoyu Li, Wenze Liu, Zhaoyang Zhang, Yong Zhang, Ying Shan, Xiangyu Yue
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces DiTCtrl, a training-free method for generating coherent scenes with multiple sequential prompts using an MM-DiT architecture. The current video generation models primarily focus on single-prompt inputs, struggling to reflect real-world dynamic scenarios. To address this limitation, the authors analyze MM-DiT’s attention mechanism and design a mask-guided precise semantic control approach that enables attention sharing for multi-prompt video generation. The proposed method achieves smooth transitions and consistent object motion without additional training, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sora-like video generation models have made great progress with the MM-DiT architecture. However, these models usually focus on single prompts, making it hard to generate scenes that reflect real-world scenarios. The authors propose a new way to make videos using multiple prompts without needing extra training data or attention. They do this by analyzing how the model’s attention mechanism works and then designing a special way to control the semantic meaning of the video based on the prompts. This results in smooth transitions and consistent object motion, making it better than other methods. |
Keywords
» Artificial intelligence » Attention » Mask » Prompt
