MAGI-1 – The First Open-Source Autoregressive Video Generation Model by Sand AI

What is MAGI-1?

MAGI-1 is the world’s first open-source autoregressive video generation model developed by Sand AI. It adopts an autoregressive architecture that generates smooth and natural videos by predicting video sequences block-by-block. It supports infinite scalability and long-form video generation with seamless continuity. The model’s native resolution reaches 1440×2568, producing videos with fluid motion and lifelike details. It also offers controllable generation capabilities, allowing smooth scene transitions and fine-grained control through block-level prompts.

Key Features of MAGI-1

• Efficient Video Generation:
  MAGI-1 can generate high-quality video clips in a short amount of time—e.g., a 5-second video can be produced in just 3 seconds, and a 1-minute video can be generated in under a minute. By using block-wise generation (24 frames per block), it performs denoising sequentially while enabling parallel processing to significantly improve generation speed.

• High-Fidelity Output:
  The generated videos feature high resolution (native 1440×2568), smooth motion, and vivid details, making them ideal for high-quality content creation.

• Infinite Extension & Timeline Control:
  MAGI-1 supports infinite video extension with seamless continuation of scenes. It provides second-level timeline control, allowing users to refine scene transitions and edits with block-level prompts.

• Controllable Generation:
  With block-based prompts, MAGI-1 enables smooth scene transitions, long-range synthesis, and fine-grained, text-driven control. It can generate video content aligned with user instructions.

• Physical Behavior Prediction:
  MAGI-1 excels in predicting realistic physical behaviors, generating scenes and actions that follow physical laws. This makes it suitable for complex, dynamic video scenarios.

• Real-Time Deployment & Flexible Inference:
  It supports real-time streaming video generation and is adaptable to various hardware configurations, including deployment on a single RTX 4090 GPU, reducing the barrier to use.

Technical Principles of MAGI-1

• Autoregressive Denoising Algorithm:
  MAGI-1 uses an autoregressive denoising approach to generate videos by dividing them into fixed-length segments (24 frames each) and denoising block-by-block. Once a block reaches a certain denoising level, the next block begins processing. This pipelined design can process up to four blocks simultaneously, significantly improving efficiency.

• Transformer-Based VAE:
  The model employs a Transformer-based Variational Autoencoder (VAE), achieving 8x spatial compression and 4x temporal compression. It features fast decoding speed and competitive reconstruction quality.

• Diffusion Model Architecture:
  Built on a Diffusion Transformer, MAGI-1 integrates several innovative techniques such as block-wise causal attention, parallel attention blocks, QK-Norm and GQA, sandwich normalization, SwiGLU, and Softcap Modulation. These enhancements improve training efficiency and stability at scale.

• Distillation Algorithm:
  MAGI-1 utilizes an efficient distillation strategy by training a speed-optimized model that supports multiple inference budgets. By enforcing self-consistency constraints (e.g., equating one large step to two small ones), the model approximates flow-matching trajectories across varying step sizes, enabling efficient inference.

Project Resources

• GitHub Repository: https://github.com/SandAI-org/MAGI-1

• Technical Paper: https://static.magi.world/static/files/MAGI_1.pdf

Application Scenarios

• Content Creation:
  MAGI-1 provides content creators with an efficient video generation tool that can quickly produce high-quality video clips based on textual instructions. Creators can generate a wide variety of scenes—such as landscapes or character actions—using simple prompts, boosting productivity.

• Film Production:
  In the film industry, MAGI-1 can be used to generate complex visual effects scenes, helping filmmakers rapidly realize creative ideas. Its “infinite video extension” capability allows for seamless content continuation, while second-level timeline control supports precise editing and scene transitions—ideal for long-form storytelling.

• Game Development:
  MAGI-1 can be used to generate dynamic backgrounds and scenes, enhancing immersion and visual appeal in games. With real-time streaming video generation, developers can achieve smoother and more natural in-game animations.

• Education:
  MAGI-1 can generate vivid educational videos, helping educators convey knowledge in a more intuitive and engaging way.

• Advertising & Marketing:
  MAGI-1 can rapidly produce high-quality advertising videos tailored to brand themes. Its high-fidelity visuals and fluid motion effectively capture audience attention and enhance advertising impact.