---
title: "AlayaWorld"
description: "AlayaWorld: image-to-video and camera-controlled-video; Verified."
modelId: "alayaworld"
pageSource: authored
---
AlayaWorld is a camera-controlled interactive world model for long video: from an image it rolls out world video autoregressively with action control and DA3-backed spatial memory. In this catalog it is checkpoint/GPU validated — the released 33.6 GB merged checkpoint passed exact size and SHA-256 validation, and the unified in-tree pipeline generated the official case1 first chunk (32 H.264 frames at 544x960, 24 fps) on an A100 with spatial memory enabled.
The recorded paper is [AlayaWorld: Long-Horizon and Playable Video World Generation](https://arxiv.org/abs/2607.06291) (2026).
Project page: [alaya-lab.github.io/AlayaWorld](https://alaya-lab.github.io/AlayaWorld/). Code: [AlayaLab/AlayaWorld](https://github.com/AlayaLab/AlayaWorld).
The official paper is [AlayaWorld: Long-Horizon and Playable Video World Generation](https://arxiv.org/abs/2607.06291) (arXiv 2607.06291). Project: [alaya-lab.github.io/AlayaWorld](https://alaya-lab.github.io/AlayaWorld/). Code: [AlayaLab/AlayaWorld](https://github.com/AlayaLab/AlayaWorld). Weights: `AlayaLab/AlayaWorld` (revision `e3f1adeb`). License: **ltx-2-community-license**.
What this repository checked: the released **33.6 GB** merged checkpoint passed exact size and SHA-256; transformer / history / Gemma connector keys matched the safetensors header; the official **case1** first chunk ran on an **A100** with DA3 memory on — **32** H.264 frames at **544×960**, **24 fps**. Multi-rank Ulysses tensor parity is validated; a true NCCL multi-GPU artifact is **pending**.
## What it is for
`alayaworld` is the recorded camera-control route. Start here to follow a catalog camera path.
The runtime is a thin extension of WorldFoundry's canonical in-tree LTX-2.3 model — Transformer, VAE, Gemma connector, RoPE, patchifier, attention dispatcher, and distributed helpers are all reused; Alaya-specific tensors map only to action AdaLN and the temporal-history encoder. Spatial memory reuses the in-tree Depth Anything 3 model (paper arXiv 2607.06291).
Catalog tasks include image-to-video, camera-controlled-video, interactive-world-model, and long-video-generation. Catalog aliases include `alaya-world` and `alaya_world`. Documented modalities: image in, video out.
Recorded evaluation entry points include [CameraBench](/docs/evaluation/benchmark-hub/camerabench) (Camera-control quality of interactive rollouts), [MemoBench](/docs/evaluation/benchmark-hub/memobench) (Long-horizon spatial-memory consistency evaluation).
## Runtime contract
Optional `prompt`, `input_path`, `frames`, `fps`, and `height`; artifacts `generated_world_video` and `alayaworld.mp4`.
CLI task profiles: `default`. WorldFoundry binds `AlayaWorldPipeline`.
Family status: **Verified**.
Attention dispatch is capability-based; multi-GPU Ulysses context parallelism uses WorldFoundry core collectives. Multi-rank Ulysses tensor parity is validated, but a true NCCL multi-GPU artifact run remains pending multi-GPU availability.
The runtime reuses WorldFoundry's existing LTX-2.3 Transformer, VAE, Gemma connector, RoPE, patchifier, attention dispatcher, and distributed helpers.
## Running in WorldFoundry
Launch `worldfoundry-eval run alayaworld --pipeline.task-profile default` to follow the camera path. The command builder above switches `--pipeline.task-profile` among recorded tasks and still emits `worldfoundry-eval run`.
It runs in the unified environment `worldfoundry-unified-cu128` (Python 3.11, CUDA 12.8, torch>=2.6).
Recorded weight repositories include `AlayaLab/AlayaWorld`, `google/gemma-3-12b-it-qat-q4_0-unquantized`, and `depth-anything/DA3NESTED-GIANT-LARGE-1.1`. Prepare the environment and check local assets with `bash scripts/inference/prepare_model_infer.sh alayaworld`. Add `--download` to fetch missing public checkpoints.
Gated `google/gemma-3-12b-it-qat-q4_0-unquantized` — accept upstream terms before download; status: **Verified**.