# FullDiT: Multi-Task Video Generative Foundation Model with Full Attention

arXiv: https://arxiv.org/abs/2503.19907 · HTML: https://mlsanity.com/paper/2503.19907/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 67 of its 70 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 30 of 67)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Chameleon: Mixed-Modal Early-Fusion Foundation Models](https://mlsanity.com/paper/2405.09818/) (2024)
- [Emu3: Next-Token Prediction is All You Need](https://mlsanity.com/paper/2409.18869/) (2024)
- [Show-o: One Single Transformer to Unify Multimodal Understanding and Generation](https://mlsanity.com/paper/2408.12528/) (2024)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [Movie Gen: A Cast of Media Foundation Models](https://mlsanity.com/paper/2410.13720/) (2024)
- [MotionCtrl: A Unified and Flexible Motion Controller for Video Generation](https://mlsanity.com/paper/2312.03641/) (2023)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [VideoPoet: A Large Language Model for Zero-Shot Video Generation](https://mlsanity.com/paper/2312.14125/) (2023)
- [Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model](https://mlsanity.com/paper/2408.11039/) (2024)
- [VideoComposer: Compositional Video Synthesis with Motion Controllability](https://mlsanity.com/paper/2306.02018/) (2023)
- [CameraCtrl: Enabling Camera Control for Text-to-Video Generation](https://mlsanity.com/paper/2404.02101/) (2024)
- [Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://mlsanity.com/paper/2410.13848/) (2024)
- [Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks](https://mlsanity.com/paper/2206.08916/) (2022)
- [OmniGen: Unified Image Generation](https://mlsanity.com/paper/2409.11340/) (2024)
- [Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers](https://mlsanity.com/paper/2402.19479/) (2024)
- [Generating Videos with Scene Dynamics](https://mlsanity.com/paper/1609.02612/) (2016)

## Built on by (most cited first, 30 of 47)

- [Controllable Video Generation: A Survey](https://mlsanity.com/paper/2507.16869/) (2025)
- [UniVideo: Unified Understanding, Generation, and Editing for Videos](https://mlsanity.com/paper/2510.08377/) (2025)
- [EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning](https://mlsanity.com/paper/2509.20360/) (2025)
- [Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation](https://mlsanity.com/paper/2509.19296/) (2025)
- [ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling](https://mlsanity.com/paper/2603.25746/) (2026)
- [VINO: A Unified Visual Generator with Interleaved OmniModal Context](https://mlsanity.com/paper/2601.02358/) (2026)
- [FullDiT2: Efficient In-Context Conditioning for Video Diffusion Transformers](https://mlsanity.com/paper/2506.04213/) (2025)
- [Phantom-Data : Towards a General Subject-Consistent Video Generation Dataset](https://mlsanity.com/paper/2506.18851/) (2025)
- [OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions](https://mlsanity.com/paper/2506.23361/) (2025)
- [Simulating the Visual World with Artificial Intelligence: A Roadmap](https://mlsanity.com/paper/2511.08585/) (2025)
- [Video-As-Prompt: Unified Semantic Control for Video Generation](https://mlsanity.com/paper/2510.20888/) (2025)
- [EasyV2V: A High-quality Instruction-based Video Editing Framework](https://mlsanity.com/paper/2512.16920/) (2025)
- [Lance: Unified Multimodal Modeling by Multi-Task Synergy](https://mlsanity.com/paper/2605.18678/) (2026)
- [Any2Caption:Interpreting Any Condition to Caption for Controllable Video Generation](https://mlsanity.com/paper/2503.24379/) (2025)
- [Frame In-N-Out: Unbounded Controllable Image-to-Video Generation](https://mlsanity.com/paper/2505.21491/) (2025)
- [GenCompositor: Generative Video Compositing with Diffusion Transformer](https://mlsanity.com/paper/2509.02460/) (2025)
- [LiveTalk: Real-Time Multimodal Interactive Video Diffusion via Improved On-Policy Distillation](https://mlsanity.com/paper/2512.23576/) (2025)
- [Plenoptic Video Generation](https://mlsanity.com/paper/2601.05239/) (2026)
- [VideoCoF: Unified Video Editing with Temporal Reasoner](https://mlsanity.com/paper/2512.07469/) (2025)
- [UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation](https://mlsanity.com/paper/2512.07831/) (2025)
- [CineScene: Implicit 3D as Effective Scene Representation for Cinematic Video Generation](https://mlsanity.com/paper/2602.06959/) (2026)
- [Evolution of Video Generative Foundations](https://mlsanity.com/paper/2604.06339/) (2026)
- [MoCha:End-to-End Video Character Replacement without Structural Guidance](https://mlsanity.com/paper/2601.08587/) (2026)
- [Omni-3DEdit: Generalized Versatile 3D Editing in One-Pass](https://mlsanity.com/paper/2603.17841/) (2026)
- [VideoCanvas: Unified Video Completion from Arbitrary Spatiotemporal Patches via In-Context Conditioning](https://mlsanity.com/paper/2510.08555/) (2025)
- [CETCAM: Camera-Controllable Video Generation via Consistent and Extensible Tokenization](https://mlsanity.com/paper/2512.19020/) (2025)
- [TeleStyle: Content-Preserving Style Transfer in Images and Videos](https://mlsanity.com/paper/2601.20175/) (2026)
- [DreamVideo-Omni: Omni-Motion Controlled Multi-Subject Video Customization with Latent Identity Reinforcement Learning](https://mlsanity.com/paper/2603.12257/) (2026)
- [Gloria: Consistent Character Video Generation via Content Anchors](https://mlsanity.com/paper/2603.29931/) (2026)
- [MMPhysVideo: Physically Plausible Video Generation Through Joint RGB-Perception Modeling](https://mlsanity.com/paper/2604.02817/) (2026)

## Abstract (arXiv)

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.