MAGI-1 – Sand AI’s First Open-Source Autoregressive Video Generation Model

What is MAGI-1?

MAGI-1 is the world’s first open-source autoregressive video generation large model developed by Sand AI. Using an autoregressive architecture, it generates smooth and natural videos through block-by-block prediction of video sequences, supporting unlimited expansion and single-take long video generation. The model achieves native resolutions up to 1440×2568, producing videos with fluid motion and realistic details while offering controllable generation capabilities through block-based prompts for smooth scene transitions and fine-grained control.

Key Features of MAGI-1

• Efficient Video Generation: MAGI-1 can generate high-quality video clips quickly—5-second videos in just 3 seconds, and 1-minute videos within a minute. Using block-based generation (24 frames per block) with parallel processing significantly improves efficiency.
• High-Fidelity Output: Videos are generated at high resolution (native 1440×2568) with smooth motion and detailed realism, suitable for various high-quality video production needs.
• Unlimited Expansion & Timeline Control: Supports infinite length expansion for seamless continuous long video scenes, with second-level timeline control allowing precise scene transitions and editing through block-based prompts.
• Controllable Generation: Block-based prompts enable smooth scene transitions, long-range synthesis, and fine-grained text-driven control to generate videos that meet user requirements.
• Physical Behavior Prediction: Excels at predicting physical behaviors, generating actions and scenes that follow physical laws, suitable for complex dynamic scenarios.
• Real-Time Deployment & Flexible Inference: Supports real-time streaming video generation and adapts to various hardware configurations, including single RTX 4090 GPU deployment, lowering the barrier to use.

Technical Principles of MAGI-1

• Autoregressive Denoising Algorithm: MAGI-1 generates videos through autoregressive denoising, dividing videos into fixed-length segments (24 frames per block) and processing them block by block. When a segment reaches a certain denoising level, the next segment begins generation. This pipeline design allows up to four segments to be processed simultaneously, greatly improving efficiency.
• Transformer-Based VAE: The model uses a Transformer-based Variational Autoencoder (VAE) with 8× spatial compression and 4× temporal compression, delivering fast decoding and competitive reconstruction quality.
• Diffusion Model Architecture: Built on Diffusion Transformer, MAGI-1 incorporates innovations like block-causal attention, parallel attention blocks, QK-Norm and GQA, sandwich normalization, SwiGLU, and Softcap Modulation, enhancing large-scale training efficiency and stability.
• Distillation Algorithm: MAGI-1 employs an efficient distillation method, training a speed-based model that supports different inference budgets. By enforcing self-consistency constraints (equating one large step to two small steps), the model approximates flow-matching trajectories across multiple step ranges for efficient inference.

MAGI-1 Project Links

• GitHub Repository: https://github.com/SandAI-org/MAGI-1
• Technical Paper: https://static.magi.world/static/files/MAGI_1.pdf
• Official Website: Sand.ai

Applications of MAGI-1

• Content Creation: Provides efficient video generation tools for creators, enabling quick production of high-quality videos from text prompts for scenes like landscapes or character actions.
• Film Production: Generates complex special effects scenes, helping filmmakers quickly realize creative ideas. The “unlimited video expansion” feature allows seamless scene extensions with precise transitions for long narratives.
• Game Development: Creates dynamic backgrounds and scenes to enhance immersion and visual effects, with real-time streaming generation for natural animations.
• Education: Generates vivid educational videos to help educators present knowledge more intuitively.
• Advertising & Marketing: Quickly produces high-quality ad videos tailored to brand themes, with high-fidelity output and fluid motion to capture audience attention.