An interactive autoregressive world model with real-time camera control, prompt switching, and long-horizon memory consistency.
- [2026-07-21] Full Technical Report released.
- [2026-07-16] Inference code released and pretrained weights available on 🤗 Hugging Face. See Quick Start.
- [2026-07-08] Project page and technical report released.
- Inference code
- Pretrained weights — 🤗 AlayaLab/AlayaWorld
- Training code
- Training data (partial)
AlayaWorld is built around four core properties — interaction, consistency, stability, and runtime.
Two control channels: a rendered 3D cache with lightweight AdaLN camera modulation for grounded, trajectory-aware navigation, and chunk-level prompt switching to introduce new events mid-generation.
Two forms of complementary memory: an explicit 3D cache reprojected to the queried view for spatial recall, plus a compressed frame-history embedding for temporal continuity, so revisited places stay recognizable.
Long-horizon stability from training on drifted histories and an error bank that re-injects accumulated artifacts into both memory and target, preventing errors from compounding over minute-long rollouts.
Real-time interaction via few-step DMD distillation and short temporal chunks, with prompt switching at chunk boundaries to minimize both visual and semantic latency.
Inference is image-to-video: give the model a first-frame image, a camera trajectory, and a text prompt, and it rolls out a video chunk by chunk along the camera path (1 chunk ≈ 1.33s @ 24fps; ~45 chunks ≈ 1 minute).
1. Environment — a CUDA GPU and PyTorch ≥ 2.6 (the DiT uses flex_attention).
2. Weights — the model runs on four pieces. Only merged_infer.safetensors
(AlayaWorld's own weights) is hosted on our 🤗 repo; the text encoder and the
depth model are third-party — get them from their original sources:
Path under checkpoints/ |
Source |
|---|---|
merged_infer.safetensors — DiT + VAE + text-encoder + history-encoder bundle |
🤗 AlayaLab/AlayaWorld |
gemma-3-12b-it-qat-q4_0-unquantized/ — Gemma text encoder |
🤗 google/gemma-3-12b-it-qat-q4_0-unquantized (gated — accept Google's license first) |
Depth-Anything-3/ — DA3 code repo |
GitHub ByteDance-Seed/Depth-Anything-3 (see step 3) |
hf_cache/ — DA3 weights, in HF-cache layout |
🤗 depth-anything/DA3NESTED-GIANT-LARGE-1.1 (see step 3) |
taeltx2_3_wide.pth — optional TAEHV bank decoder (--bank-taehv) |
GitHub madebyollin/taehv |
From the repo root:
# AlayaWorld weights (this repo)
hf download AlayaLab/AlayaWorld merged_infer.safetensors --local-dir checkpoints
# Gemma text encoder (gated: log in + accept the license on its HF page first)
hf download google/gemma-3-12b-it-qat-q4_0-unquantized \
--local-dir checkpoints/gemma-3-12b-it-qat-q4_0-unquantizedTarget layout (paths: in configs/infer.yaml points here; repoint it if your
weights live elsewhere):
checkpoints/
├── merged_infer.safetensors
├── gemma-3-12b-it-qat-q4_0-unquantized/
├── Depth-Anything-3/ # step 3
├── hf_cache/ # step 3
└── taeltx2_3_wide.pth # optional
3. Depth-Anything-3 (required) — the spatial-memory branch depends on DA3, an external repo (inference errors out without it):
git clone https://github.com/ByteDance-Seed/Depth-Anything-3 checkpoints/Depth-Anything-3
pip install -e checkpoints/Depth-Anything-3 # run AFTER installing the torch stack (see requirements.txt)Its weights (depth-anything/DA3NESTED-GIANT-LARGE-1.1) load from
checkpoints/hf_cache/ — either let them download there on first run, or fetch
them ahead of time:
HF_HOME=checkpoints/hf_cache hf download depth-anything/DA3NESTED-GIANT-LARGE-1.14. Run a ready-made case (cases live under playground/).
The one-command launcher renders the bundled case1 (~1 min):
# single GPU
bash inference/run.sh
# multi-GPU (Ulysses context parallel; e.g. 2 or 4 GPUs)
GPUS=4 bash inference/run.shrun.sh just forwards to python -m inference.run (defaulting to
--input playground/case1/case1); call the module directly to run any case or
pass extra flags:
PYTORCH_ALLOC_CONF=expandable_segments:True \
python -m inference.run --input playground/case1/case1 --seed 1234The result mp4 is written under outputs/. See inference/README.md
for the full option list.
5. Use your own input — a "case" is three files sharing a prefix:
<prefix>_image.png first frame (seeds the history)
<prefix>_camera.pt camera trajectory: cam_c2w [F,4,4] + intrinsics
<prefix>_prompt.txt text prompt
Point --input at the prefix. For long (~1 min) rollouts, add --ttc to curb appearance drift. See inference/README.md for the full option list.
- Core Lead: Kaipeng Zhang
- Lead: Chuanhao Li
- Core Contributors: Chuanhao Li, Kaipeng Zhang, Yifan Zhan, Yongtao Ge, Yuanyang Yin
- Contributors: Jiaming Tan, Kang He, Liaoyuan Fan, Mingliang Zhai, Ruicong Liu, Xiaojie Xu, Xuangeng Chu, Zhen Li, Zhengyuan Lin, Zhixiang Wang, Zian Meng, Zihui Gao
For collaboration or business inquiries, contact kaipeng.zhang@shanda.com.
If you find AlayaWorld useful for your research, please cite:
@article{team2026alayaworld,
title={AlayaWorld: Long-Horizon and Playable Video World Generation},
author={Team, AlayaWorld and Zhang, Kaipeng and Li, Chuanhao and Zhan, Yifan and Ge, Yongtao and Yin, Yuanyang and Tan, Jiaming and He, Kang and Fan, Liaoyuan and Liu, Ruicong and others},
journal={arXiv preprint arXiv:2607.06291},
year={2026}
}This project is based on LTX-2 by Lightricks Ltd. Portions of the original LTX-2
codebase (flash_alaya/ltx2/) have been modified by Alaya Lab for academic and research purposes only, and the released weights
(merged_infer.safetensors) are fine-tuned from LTX-2.3. Accordingly, this
project — code and weights — is released under the
LTX-2 Community License Agreement. All original copyright, license,
patent, trademark, and attribution notices from LTX-2 are retained.
For academic research and non-commercial use only. For commercial use of LTX-2 or its derivatives, contact Lightricks Ltd. (entities with ≥ $10M annual revenue require a commercial license).
See NOTICE and THIRD_PARTY_LICENSES.md for full attribution. Third-party weights (Gemma-3 text encoder; Depth-Anything-3) are not redistributed here — obtain them from their original sources under their own licenses.