FullDiT: Multi-Task Video Generative Foundation Model with Full Attention
This addresses the need for fine-grained video content creation with multiple controls, offering a scalable solution that reduces parameter overhead and avoids conflicts, though it is incremental in improving existing adapter-based approaches.
The paper tackles the problem of limited control in video generative foundation models by introducing FullDiT, a unified model that integrates multiple conditions via full-attention mechanisms, achieving state-of-the-art results in multi-task video generation.
Current video generative foundation models primarily focus on text-to-video tasks, providing limited control for fine-grained video content creation. Although adapter-based approaches (e.g., ControlNet) enable additional controls with minimal fine-tuning, they encounter challenges when integrating multiple conditions, including: branch conflicts between independently trained adapters, parameter redundancy leading to increased computational cost, and suboptimal performance compared to full fine-tuning. To address these challenges, we introduce FullDiT, a unified foundation model for video generation that seamlessly integrates multiple conditions via unified full-attention mechanisms. By fusing multi-task conditions into a unified sequence representation and leveraging the long-context learning ability of full self-attention to capture condition dynamics, FullDiT reduces parameter overhead, avoids conditions conflict, and shows scalability and emergent ability. We further introduce FullBench for multi-task video generation evaluation. Experiments demonstrate that FullDiT achieves state-of-the-art results, highlighting the efficacy of full-attention in complex multi-task video generation.