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CausalWMv1

CausalWMv1 takes one RGB observation and a text instruction, then generates optical flow, camera-frame XYZ pointmaps, and future RGB video through a stage-causal chain.

Code: AetherLabsAI/CausalWM
Paper: CausalWM: Causal Chain-of-Thought Reasoning for Embodied World Model
Checkpoint: CausalWMv1.safetensors

Files and requirements

The checkpoint contains the complete CoT Transformer state in BF16. It is not a standalone pipeline: inference additionally needs ltx-2.3-22b-dev.safetensors from Lightricks/LTX-2.3 and the Gemma-3-12B text encoder. See model-manifest.json for the file size, SHA-256, tensor count, and runtime metadata. Verify the download using sha256sum -c SHA256SUMS.

Inference

Follow the repository installation instructions for TI2V inference, then run:

python inference.py \
  --image /path/to/first_frame.png \
  --prompt "The robot arm picks up the red block and places it in the box." \
  --checkpoint /path/to/CausalWMv1.safetensors \
  --base-ckpt /path/to/ltx-2.3-22b-dev.safetensors \
  --text-encoder-dir /path/to/gemma-3-12b \
  --out-dir outputs/demo

Default settings are 121 frames, 640×480, 16 FPS, four steps per stage, guidance 1.0, and seed 42. The input frame is resized without cropping. Pointmaps use a relative camera-frame scale normalized by the first frame's median depth, not metres. Generated motion and geometry are predictions rather than measured ground truth.

License and attribution

See LICENSE and NOTICE for the LTX-2 license and attribution carried with this release. Base model and text encoder downloads remain subject to their respective terms. This package contains inference weights, not optimizer or training state.

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