

Ditctrl
Overview :
DiTCtrl is a video generation model based on the Multimodal Diffusion Transformer (MM-DiT) architecture, focusing on generating coherent scene videos with multiple continuous prompts without additional training. By analyzing the attention mechanism of MM-DiT, this model achieves precise semantic control and attention sharing between different prompts, producing videos with smooth transitions and cohesive object movement. The main advantages of DiTCtrl include no training requirement, capability to handle multi-prompt video generation tasks, and showcasing cinematic transition effects. Additionally, DiTCtrl introduces a new benchmark called MPVBench specifically designed for evaluating the performance of multi-prompt video generation.
Target Users :
The target audience includes video creators, content creators, and researchers who need to generate video content with multiple prompts and dynamic scenes. DiTCtrl is suitable for them as it provides a method to produce high-quality, coherent video content without the need for complex training processes. It also facilitates video editing and long video generation, significantly enhancing the efficiency and flexibility of video production.
Use Cases
Generate a video about 'a cat watching a black mouse', showing smooth transitions between different prompts.
Use DiTCtrl to create a long video featuring 'fish in the ocean', demonstrating coherence and dynamic effects.
Edit a video with DiTCtrl, replacing 'white SUV' with 'red sports car', while maintaining the original composition of the video.
Features
? Un-tuned multi-prompt video generation: DiTCtrl can generate videos based on multiple continuous prompts without additional training.
? Smooth transitions and consistency: Coherence in object movement and smooth transitions between scenes are achieved during the video generation process.
? Multimodal diffusion transformer architecture: Based on the MM-DiT architecture, DiTCtrl exhibits self-attention mechanisms similar to UNet while enhancing temporal modeling capabilities.
? Precise semantic control: Through the analysis of the attention mechanism, DiTCtrl achieves accurate semantic control between different prompts.
? Video editing features: DiTCtrl can be applied to video editing tasks, such as text replacement and video re-weighting.
? Long video generation: DiTCtrl naturally accommodates single prompt long video generation by maintaining the same continuous prompts.
? Cinematic transition effects: DiTCtrl can showcase cinematic transition effects, such as depicting a sequence of a boy riding.
How to Use
1. Prepare multiple continuous video prompts as input for video generation.
2. Use the DiTCtrl model to input these prompts into the model.
3. The model will analyze the semantic content of each prompt and perform attention mechanism calculations internally.
4. The model generates an initial latent representation of the video, incorporating content from multiple prompts.
5. Through the model's denoising process, global attention is converted into a mask-guided key-value sharing strategy to query video content from the source video.
6. Synthesize a content-consistent video based on the modified target prompts.
7. Observe the generated video and check the smoothness of transitions and coherence of object movement.
8. If necessary, further edit the generated video, such as replacing text or re-weighting video segments.
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