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  1. README.md +31 -0
  2. action/config.json +30 -0
  3. action/diffusion_pytorch_model-00001-of-00002.safetensors +3 -0
  4. action/diffusion_pytorch_model-00002-of-00002.safetensors +3 -0
  5. action/diffusion_pytorch_model.safetensors.index.json +908 -0
  6. causal-forcing-init/model.pt +3 -0
  7. causal-lora/lora_diffusion_pytorch_model.safetensors +3 -0
  8. dmd-lora/lora_diffusion_pytorch_model.safetensors +3 -0
  9. light_vae/config.json +1 -0
  10. light_vae/diffusion_pytorch_model.safetensors +3 -0
README.md ADDED
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+ ---
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+ license: openrail
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+ ---
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+
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+ # Micro-World 2.0
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+
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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 with `guidance_scale` 1.0.
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+
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+ `causal-forcing-init` is our own initialization checkpoint, trained by us. It is not the official Causal-Forcing release. 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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+
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+ ## Checkpoints
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+
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+ | Path | What it is |
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+ | --- | --- |
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+ | `causal-forcing-init/model.pt` | Our causal-forcing initialization. Loaded onto the Wan2.2 transformer before the LoRAs. |
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+ | `causal-lora/lora_diffusion_pytorch_model.safetensors` | Causal LoRA. Adapts the backbone to open-domain 352×640 video. |
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+ | `action/` | Action module. Inference reads `action_preprocess` and `action_adaLN_modulation` from this checkpoint. |
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+ | `dmd-lora/lora_diffusion_pytorch_model.safetensors` | DMD student LoRA used for 4-step generation. |
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+ | `light_vae/` | Optional TurboVAED decoder from [amd/HummingbirdXT](https://huggingface.co/amd/HummingbirdXT/tree/main/vae/wan22_v1_tiling_16_12). Faster decode with some loss of fidelity. Omit it to use the Wan VAE. |
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+
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+ ## Load order
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+
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+ 1. Wan2.2-TI2V-5B transformer, VAE, and text encoder
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+ 2. `causal-forcing-init/model.pt`
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+ 3. `causal-lora`
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+ 4. `action`
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+ 5. `dmd-lora`
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+
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+ Chunk size is 3, the local attention window is 21, and the sink size is 1.
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+
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+ Training and inference code: [Micro-World](https://github.com/AMD-AGI/Micro-World).
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+ }
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