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README.md
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license: gpl-3.0
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In this work, we introduce Micro-World, an action-controlled interactive world model designed to generate high-quality, open-domain scenes. Built on top of the Wan2.1 family of models, we train both image-to-
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For action injection, we favor adaLN for its lightweight parameter footprint, and ControlNet for its strong empirical stability during training.
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Note that released
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More info please refer to code.
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<div style="margin: 0; padding: 0; text-align: center;">
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<img src="https://github.com/user-attachments/assets/680b87ac-0c95-4a27-b4fd-fcafb9fdf609" alt="
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<img src="https://github.com/user-attachments/assets/c9cd8d9e-9555-42d3-b884-04705d1e329c" alt="
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</div>
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license: gpl-3.0
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In this work, we introduce Micro-World, an action-controlled interactive world model designed to generate high-quality, open-domain scenes. Built on top of the Wan2.1 family of models, we train both image-to-world (I2W) and text-to-world (T2W) variants to support a wide range of use cases. To foster open research and practical adoption in the community, we release the model weights, full training and inference code, as well as a curated dataset specifically tailored for controllable world modeling.
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For action injection, we favor adaLN for its lightweight parameter footprint, and ControlNet for its strong empirical stability during training.
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Note that released I2W model is trained using AdaLN architecture.
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More info please refer to [code](https://github.com/AMD-AGI/Micro-World).
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<div style="margin: 0; padding: 0; text-align: center;">
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<img src="https://github.com/user-attachments/assets/680b87ac-0c95-4a27-b4fd-fcafb9fdf609" alt="model architecture" title="model architecture" class="model architecture">
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<img src="https://github.com/user-attachments/assets/c9cd8d9e-9555-42d3-b884-04705d1e329c" alt="model architecture" title="model architecture" class="model architecture">
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</div>
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