Micro-World
Collection
Action-controlled Interactive world model. • 5 items • Updated • 6
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("amd/Micro-World-2", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Micro-World 2.0
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.
The Wan2.2-TI2V-5B base weights are not in this repository; download them from Wan-AI/Wan2.2-TI2V-5B.
Checkpoints
| Path | What it is |
|---|---|
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. Faster decode with few loss of fidelity. Omit it to use the Wan VAE. |
Load order
- Wan2.2-TI2V-5B transformer, VAE, and text encoder
causal-forcing-init/model.ptcausal-loraactiondmd-lora
Training and inference code: Micro-World.
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