sand-ai
/

MAGI-1

@@ -1,5 +1,8 @@
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 license: apache-2.0
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 ![magi-logo](figures/logo_black.png)
@@ -34,11 +37,6 @@ This repository contains the code for the MAGI-1 model, pre-trained weights and
 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.
-<div align="center">
-  <video src="https://github.com/user-attachments/assets/5cfa90e0-f6ed-476b-a194-71f1d309903a
-" width="70%" poster=""> </video>
-</div>
 ## 2. Model Summary
@@ -220,4 +218,4 @@ If you find our code or model useful in your research, please cite:
 ## 8. Contact
-If you have any questions, please feel free to raise an issue or contact us at [support@sand.ai](support@sand.ai) .

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 license: apache-2.0
+language:
+- en
+pipeline_tag: image-to-video
 ---
 ![magi-logo](figures/logo_black.png)
 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.
 ## 2. Model Summary
 ## 8. Contact
+If you have any questions, please feel free to raise an issue or contact us at [support@sand.ai](support@sand.ai) .