made possible by several algorithmic
innovations and a dedicated infrastructure stack. MAGI-1 facilitates
controllable generation via chunk-wise prompting and supports real-time,
memory-efficient deployment by maintaining constant peak inference cost,
regardless of video length. The largest variant of MAGI-1 comprises 24 billion
parameters and supports context lengths of up to 4 million tokens,
demonstrating the scalability and robustness of our approach. The code and
models are available at https://github.