Image-to-Video
Diffusers
Safetensors
Wan2.2
astronex-world
world-model
video-generation
text-to-video
diffusion
video-diffusion-transformer
interactive
real-time
camera-control
action-conditioned
embodied-ai
robotics
autonomous-driving
Instructions to use Astronex-Lab/Astronex-World with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Wan2.2
How to use Astronex-Lab/Astronex-World with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download config.json from Astronex-Lab/Astronex-World: direct link, hf CLI and curl.
- Browser
- Download file 2.37 kB
-
https://huggingface.co/Astronex-Lab/Astronex-World/resolve/main/config.json
- Command line
-
hf download hf://Astronex-Lab/Astronex-World/config.json
-
curl -L -o config.json https://huggingface.co/Astronex-Lab/Astronex-World/resolve/main/config.json
2.37 kB
| { | |
| "name": "Astronex-World-5B", | |
| "description": "Causal camera- and action-controllable video world model, Wan2.2-TI2V-5B backbone.", | |
| "architecture": "CausalWanModel", | |
| "backbone_variant": "wan2.2", | |
| "parameters": 5351000000, | |
| "dtype": "bfloat16", | |
| "latent": { | |
| "channels": 48, | |
| "compression": [ | |
| 4, | |
| 16, | |
| 16 | |
| ], | |
| "vae": "Wan2.2 VAE" | |
| }, | |
| "model_kwargs": { | |
| "model_name": "Wan2.2-TI2V-5B", | |
| "timestep_shift": 5.0, | |
| "local_attn_size": 20, | |
| "sink_size": 4, | |
| "use_camera": true, | |
| "use_action": true, | |
| "action_dim": 64, | |
| "action_output": false, | |
| "num_embodiments": 32, | |
| "prope_scale": 1.0, | |
| "prope_chunk_anchor": true, | |
| "prope_num_layers": 30, | |
| "prope_exclude_sink": false, | |
| "prope_sink_dual": false, | |
| "sink_attention_repeat": 1, | |
| "history_cross_attention": false, | |
| "is_causal": true | |
| }, | |
| "inference": { | |
| "sampler": "UniPC", | |
| "sampling_steps": 8, | |
| "num_frame_per_block": 8, | |
| "guidance_scale": 3.0, | |
| "timestep_shift": 10.0, | |
| "cond_prefill_timestep": 100, | |
| "ref_cond_scale": 1.1, | |
| "ref_sequence_conditioning": true, | |
| "rgb_chroma_stabilize_strength": 1.0, | |
| "rgb_chroma_stabilize_fade": 4, | |
| "retrieval_window": true, | |
| "retrieval_recent_frames": 4, | |
| "retrieval_min_overlap": 0.3, | |
| "retrieval_swap_per_block": 2, | |
| "note": "8 steps is the validated setting. 4 steps degrades visibly -- cabinets and signage smear -- so it is not a supported mode." | |
| }, | |
| "provenance": { | |
| "base": "logs/merged/online_unipc25_to12_step20_full.pt", | |
| "adapter": "logs/wan22_dmd_mp2/checkpoint_model_001545/model.pt (generator_ema, 600 LoRA tensors)", | |
| "merge": "W = base.weight + (lora_b @ lora_a) * 128/64, folded in fp32 then cast to bf16", | |
| "training": "bidirectional teacher -> causal student via DMD, 1545 steps, with a motion-preservation term (weight 2.0) and rolling forcing at p=0.5" | |
| }, | |
| "action_output_head": { | |
| "present": true, | |
| "tensors": 6, | |
| "shape": "LayerNorm(3072) -> Linear(3072, 3072) -> SiLU -> Linear(3072, 64)", | |
| "initialisation": "output layer zero-initialised, so installing it leaves the model unchanged", | |
| "status": "included but untrained: enable with model_kwargs.action_output=true and train it with the action-output post-training branch before reading actions from it" | |
| }, | |
| "license": "apache-2.0" | |
| } | |