MAGI-1: Graph-Generated Video Model, Sand Al Open Source's First Autoregressive Video Generation Model

MAGI-1 is the world's first autoregression open-sourced by Sand AIVideo Generation of Large ModelsA self-regression structure is used to generate natural and fluid video by predicting the video series on a block-by-block basis, to support unlimited expansion and one-stop long video generation. Models can have a primary resolution of 1440 x 2568, generating a smooth and accurate video movement with controllable generating capacity that allows smooth landscape conversion and fine particle control through a break-in。

MAGI-1 Features

1. Efficient Video Generation: MAGI-1 can generate high-quality video clips in a short time, for example, generating a 5-second video takes only 3 seconds, and generating a 1-minute video can be accomplished in 1 minute. Generation efficiency is greatly improved by block generation (24 frames per block), block-by-block denoising and parallel processing.
2. High security output: The generated video is of high resolution (native 1440 x 2568), is fluent and concrete and suitable for many high-quality video creation needs。
3. Unlimited Expansion and Timeline Control: Supports unlimited length expansion, seamless continuation of writing to generate continuous long video scenes, with second-level timeline control capability, users can realize refined scene transitions and editing through block-by-block cueing.
4. Controlled Generation: With chunked cues, MAGI-1 supports smooth scene transitions, long-view compositing, and fine-grained text-driven control that generates video content that meets the user's needs based on textual commands.
5. Physical Behavior Prediction: excellent performance in physical behavior prediction, can generate actions and scenes that conform to physical laws, suitable for the generation of complex dynamic scenes.
6. Real-time deployment and flexible inference: Supports real-time streaming video generation while adapting to a variety of hardware configurations, including the deployment of a single RTX4090 GPU, lowering the threshold for use.

Github repository:https://github.com/SandAl-org/