Lumos-1 Generates Video Using Minimal LLM Changes and Multimodal RoPE Encoding

The creation of realistic and coherent video from simple text prompts remains a significant challenge in artificial intelligence. Now, Hangjie Yuan, Weihua Chen, Jun Cen, and colleagues at DAMO Academy, Alibaba Group, alongside Tao Feng from Tsinghua University and Yi Yang from Zhejiang University, present Lumos-1, a new autoregressive video generator that overcomes key limitations of existing models. Unlike previous approaches that require substantial architectural changes or suffer from slow processing speeds, Lumos-1 builds directly upon the established framework of large language models. The team achieves this by carefully incorporating spatiotemporal information using a novel frequency spectrum scheme and a token dependency strategy, alongside a new training technique that balances computational efficiency with high-quality video generation, ultimately delivering performance comparable to state-of-the-art models with significantly reduced resource requirements.

Unifying Vision and Language: A New Approach to Video Generation

The rapid advancement of large language models (LLMs) has revolutionised natural language processing, inspiring researchers to explore similar approaches for visual generation. The goal is to create a unified model capable of both understanding and generating images and videos, a task complicated by the challenges of developing coherent and realistic video. Researchers have now introduced Lumos-1, a new model designed to address these limitations by leveraging the established architecture of LLMs with minimal modifications.

This approach opens the door to a truly unified model that can seamlessly process and generate both text and visual content. The key to Lumos-1’s success lies in adapting techniques commonly used in LLMs to capture the complex spatiotemporal correlations inherent in video data effectively. Initial experiments revealed that standard positional encoding techniques were insufficient, prompting the development of an enhanced scheme, MM-RoPE, which better distributes frequency allocations and balances modality information.

Beyond adapting positional encoding, the team recognised that the inherent structure of video – its temporal causality and spatial redundancy – requires a unique approach to training. Lumos-1 employs a token dependency strategy that prioritises causal relationships between frames and addresses spatial information leakage during training. This is achieved through a technique called Autoregressive Discrete Diffusion Forcing (AR-DF), which uses temporal tube masking to encourage the model to learn more effectively.

By carefully considering the unique characteristics of video data and adapting proven techniques from LLMs, the researchers have created a powerful new model that represents a significant step towards a genuinely unified approach to visual and language processing. Remarkably, Lumos-1 achieves performance comparable to state-of-the-art models while being trained on a relatively modest scale, using only 48 GPUs, demonstrating its efficiency and potential for wider accessibility.

Bringing Movement to Language Models: The Methodology Behind Lumos-1

Researchers have developed Lumos-1, a new approach to video generation that successfully integrates moving images into the framework of large language models. Rather than building entirely new systems, the team focused on adapting existing LLM architectures, a strategy that offers significant advantages in terms of efficiency and scalability. The core innovation lies in how Lumos-1 understands and represents the spatiotemporal relationships inherent in video data – essentially, teaching the model to perceive movement and position within a scene. A key challenge was adapting the positional encoding technique known as RoPE for use with video.

RoPE works by embedding information about the position of elements within a sequence, allowing the model to understand order and relationships. The team recognised that standard RoPE wasn’t ideally suited to the three-dimensional nature of video, encompassing not just sequence (time), but also height and width. To address this, they developed MM-RoPE, a modified scheme that effectively incorporates these three dimensions by carefully distributing positional information across the model’s channels.

Beyond positional encoding, the researchers tackled the problem of training stability. When generating video, the model must consider information from previous frames to ensure smooth and coherent movement. However, simply feeding the model all available information can lead to imbalances during training, as frames contain redundant spatial information.

To overcome this, they introduced Autoregressive Discrete Diffusion Forcing (AR-DF). AR-DF strategically masks portions of the input during training, forcing the model to rely on temporal relationships and preventing it from overemphasising static spatial details. The team also prioritised computational efficiency by building upon existing LLM architectures and employing memory-friendly training techniques.

This allowed them to pre-train Lumos-1 on a relatively modest number of GPUs, demonstrating the power of adapting established models rather than creating entirely new ones, paving the way for more accessible and scalable video generation technologies. The result is a system that achieves performance comparable to more complex models, while remaining computationally manageable.

Motion Mapping Improves Video Generation Efficiency

Lumos-1, a novel video generation model, achieves impressive results while maintaining computational efficiency. Unlike many existing systems, Lumos-1 builds upon the established architecture of large language models, streamlining the process and reducing complexity. This allows for high-quality video creation without requiring substantial modifications to proven LLM technology. A key innovation lies in how Lumos-1 understands and incorporates motion within videos.

The team identified that effectively representing the interplay of space and time requires careful attention to the frequencies used in the model. They introduced MM-RoPE, which comprehensively maps frequencies to spatiotemporal data, enabling the model to discern more accurately and recreate realistic movement. This approach demonstrably outperforms systems using simpler frequency representations, converging to lower error rates during training and ultimately generating more coherent videos.

The team also addressed a common problem in video generation: ensuring consistency between frames. They developed Autoregressive Discrete Diffusion Forcing (AR-DF), which strategically masks portions of video frames during training. This forces the model to learn genuine temporal dynamics – how things change over time – rather than simply copying information from one frame to the next.

The results demonstrate that this technique significantly enhances video quality and reduces flickering and inconsistencies. Lumos-1 demonstrates performance comparable to leading video generation models, such as EMU3 and OpenSoraPlan, on established benchmarks. Remarkably, it achieves this level of quality using fewer computational resources – trained on just 48 GPUs – and without relying on massive datasets.

In side-by-side comparisons, Lumos-1 excels at generating natural motion, particularly in complex scenes with multiple objects, and accurately aligning generated content with text prompts. For example, the model can convincingly animate subtle ripples on water or smoothly render a skier gliding down a snowy slope, details often lost in other systems. The researchers also demonstrated that Lumos-1 can generate videos from a single initial image, a capability not explicitly trained for, further highlighting its versatility and potential. By carefully addressing the challenges of spatiotemporal representation and frame consistency, Lumos-1 represents a significant step forward in generating accessible and efficient videos.

Conclusion

This research introduces Lumos-1, a novel autoregressive video generation model based on the established architecture of large language models. The team successfully adapted this architecture for video creation by focusing on how to incorporate spatiotemporal data effectively.

Key to this achievement was the development of MM-RoPE, a modified positional encoding scheme that better captures the dynamics of video, and Autoregressive Discrete Diffusion Forcing (AR-DF). This training strategy addresses information imbalances across frames. The resulting model demonstrates performance comparable to existing state-of-the-art video generation systems, achieving strong results on established benchmarks. Lumos-1 represents a step towards a unified foundational model capable of processing and generating both text and video content.

By retaining the core principles of large language models, the researchers have created a system that is relatively efficient to train, requiring resources comparable to those used for text-based models. The authors acknowledge limitations related to the training data and potential for further refinement of the model’s capabilities. Future work will likely focus on addressing these limitations and exploring the full potential of this approach for creating high-quality, coherent video content.