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| license: openrail |
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| # Micro-World 2.0 |
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| Action-controlled causal world-model checkpoints. Micro-World 2.0 generates 352×640 video from a first frame, a text prompt, and keyboard/mouse actions. The distilled student denoises each chunk in 4 steps. |
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| The Wan2.2-TI2V-5B base weights are not in this repository; download them from [Wan-AI/Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B). |
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| ## Checkpoints |
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| | Path | What it is | |
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| | `causal-forcing-init/model.pt` | Causal-forcing initialization. Loaded onto the Wan2.2 transformer before the LoRAs. | |
| | `causal-lora/lora_diffusion_pytorch_model.safetensors` | Causal LoRA. Adapts the backbone to open-domain 352×640 video. | |
| | `action/` | Action module. Inference reads `action_preprocess` and `action_adaLN_modulation` from this checkpoint. | |
| | `dmd-lora/lora_diffusion_pytorch_model.safetensors` | DMD student LoRA used for 4-step generation. | |
| | `light_vae/` | Optional TurboVAED decoder from [amd/HummingbirdXT](https://huggingface.co/amd/HummingbirdXT/tree/main/vae/wan22_v1_tiling_16_12). Faster decode with few loss of fidelity. Omit it to use the Wan VAE. | |
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| ## Load order |
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| 1. Wan2.2-TI2V-5B transformer, VAE, and text encoder |
| 2. `causal-forcing-init/model.pt` |
| 3. `causal-lora` |
| 4. `action` |
| 5. `dmd-lora` |
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| Training and inference code: [Micro-World](https://github.com/AMD-AGI/Micro-World). |
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