A defining feature of MAGI-1 is its chunk-wise prompting system, which empowers users to modify video parameters and content at each segment, providing fine-grained control over motion, style, and scene transitions. This enables creators to extend videos indefinitely, achieve smooth narrative flow, and apply real-time edits or effects as the video is generated. The model supports multiple generation modes, including text-to-video, image-to-video, and video-to-video, all while maintaining high temporal consistency and realistic physical dynamics. MAGI-1’s architecture incorporates advanced diffusion techniques, block-causal attention, and parallel attention blocks, resulting in superior motion quality, spatial coherence, and efficient decoding.
MAGI-1 is fully open-source and distributed under the Apache 2.0 license, making it freely accessible for both personal and commercial projects. The repository includes pre-trained weights, inference code, and comprehensive documentation, allowing developers and creators to integrate the model into their workflows with ease. With support for HD video (up to 1280×720 at 24 FPS), output in MP4 format, and the ability to generate videos up to 30 seconds in length per run, MAGI-1 stands out as a leading solution for anyone seeking advanced, customizable video generation tools. Its robust performance in physical behavior prediction and instruction following positions it as a strong competitor to both open and closed-source alternatives.
