MAGI-1: Autoregressive Video Generation at Scale

MAGI-1 AI is an advanced autoregressive video generation model developed by SandAI, designed to generate high-quality videos by predicting sequences of video chunks in an autoregressive manner. This model is trained to denoise video chunks, enabling causal temporal modeling and supporting streaming generation.

MAGI-1 AI video generation model features include a Transformer-based VAE for fast decoding and competitive reconstruction quality, an auto-regressive denoising algorithm for efficient video generation, and a diffusion model architecture that enhances training efficiency and stability at scale. It also supports controllable generation via chunk-wise prompting, enabling smooth scene transitions, long-horizon synthesis, and fine-grained text-driven control.

MAGI-1 AI generates videos chunk-by-chunk instead of as a whole. Each chunk (24 frames) is denoised holistically, and the generation of the next chunk begins as soon as the current one reaches a certain level of denoising. This pipeline design enables concurrent processing of up to four chunks for efficient video generation.

The model variants for MAGI-1 video include the 24B model optimized for high-fidelity video generation and the 4.5B model suitable for resource-constrained environments. Distilled and quantized models are also available for faster inference.

MAGI-1 AI achieves state-of-the-art performance among open-source models, excelling in instruction following and motion quality, positioning it as a strong potential competitor to closed-source commercial models such as Kling1.6. It also demonstrates superior precision in predicting physical behavior through video continuation, significantly outperforming all existing models.

MAGI-1 AI can be run using Docker or directly from source code. Docker is recommended for ease of setup. Users can control input and output by modifying parameters in the provided run.sh scripts.

MAGI-1 is released under the Apache License 2.0.

MAGI-1's 'Infinite Video Expansion' function allows seamless extension of video content, combined with 'second-level time axis control,' enabling users to achieve scene transitions and refined editing through chunk-by-chunk prompting, meeting the needs of film production and storytelling.

Thanks to the natural advantages of the autoregressive architecture, Magi achieves far superior precision in predicting physical behavior through video continuation—significantly outperforming all existing models.

MAGI-1 is designed for various applications such as content creation, game development, film post-production, and education. It offers a powerful tool for video generation that can be used in multiple scenarios.