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  license: apache-2.0
 
 
 
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  ---
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  ![magi-logo](figures/logo_black.png)
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  We present MAGI-1, a world model that generates videos by ***autoregressively*** predicting a sequence of video chunks, defined as fixed-length segments of consecutive frames. Trained to denoise per-chunk noise that increases monotonically over time, MAGI-1 enables causal temporal modeling and naturally supports streaming generation. It achieves strong performance on image-to-video (I2V) tasks conditioned on text instructions, providing high temporal consistency and scalability, which are made possible by several algorithmic innovations and a dedicated infrastructure stack. MAGI-1 further supports controllable generation via chunk-wise prompting, enabling smooth scene transitions, long-horizon synthesis, and fine-grained text-driven control. We believe MAGI-1 offers a promising direction for unifying high-fidelity video generation with flexible instruction control and real-time deployment.
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- <div align="center">
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- <video src="https://github.com/user-attachments/assets/5cfa90e0-f6ed-476b-a194-71f1d309903a
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- " width="70%" poster=""> </video>
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- </div>
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  ## 2. Model Summary
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  ## 8. Contact
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- If you have any questions, please feel free to raise an issue or contact us at [[email protected]]([email protected]) .
 
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  license: apache-2.0
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+ language:
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+ pipeline_tag: image-to-video
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  ![magi-logo](figures/logo_black.png)
 
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  We present MAGI-1, a world model that generates videos by ***autoregressively*** predicting a sequence of video chunks, defined as fixed-length segments of consecutive frames. Trained to denoise per-chunk noise that increases monotonically over time, MAGI-1 enables causal temporal modeling and naturally supports streaming generation. It achieves strong performance on image-to-video (I2V) tasks conditioned on text instructions, providing high temporal consistency and scalability, which are made possible by several algorithmic innovations and a dedicated infrastructure stack. MAGI-1 further supports controllable generation via chunk-wise prompting, enabling smooth scene transitions, long-horizon synthesis, and fine-grained text-driven control. We believe MAGI-1 offers a promising direction for unifying high-fidelity video generation with flexible instruction control and real-time deployment.
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  ## 2. Model Summary
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  ## 8. Contact
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+ If you have any questions, please feel free to raise an issue or contact us at [[email protected]]([email protected]) .