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license: apache-2.0
base_model: AlayaLab/Evoke
pipeline_tag: text-to-video
inference: false
tags:
- world-model
- video-generation
- text-to-video
- image-to-video
- interactive
- camera-control
- distillation
---
<h1 align="center">Alaya-EVOKE: From Linear-Scaling Supervision to Endless World</h1>
<p align="center">
<a href="https://github.com/AlayaLab/Evoke"><img src="https://img.shields.io/badge/GitHub-AlayaLab/Evoke-181717.svg?logo=github" alt="GitHub"></a>
<a href="https://evoke-world.github.io/Evoke/"><img src="https://img.shields.io/badge/%F0%9F%8C%90_Project_Page-evoke--world.github.io-1a73e8.svg" alt="Project Page"></a>
<a href="https://arxiv.org/abs/2608.13546"><img src="https://img.shields.io/badge/arXiv-2608.13546-b31b1b.svg" alt="arXiv"></a>
<a href="https://huggingface.co/papers/2608.13546"><img src="https://img.shields.io/badge/%F0%9F%A4%97_Paper-2608.13546-ffce1c.svg" alt="Paper page"></a>
</p>
**Alaya-EVOKE-Turbo** is a **controllability-optimized** version of [Alaya-EVOKE](https://huggingface.co/AlayaLab/Evoke), focusing on camera-motion following and scene/subject instruction adherence. It uses the same architecture and inference workflow: **3 steps, no CFG, 384 × 640 at 24 fps**.
## Contents
Merged Turbo transformer weights, stored in **FP32** to match Evoke. Download the shared base components and depth backend separately:
```text
models/
├── evoke-base/ # VAE / text encoder / tokenizer / scheduler; from Evoke
├── evoke-turbo/transformer/ # this release
└── ViGeo1.1/ # required depth backend
```
## Usage
```bash
git clone https://github.com/AlayaLab/Evoke && cd Evoke
pip install -r requirements.txt
hf download AlayaLab/Evoke --include "evoke-base/*" --local-dir models
hf download AlayaLab/Evoke-Turbo --local-dir models/evoke-turbo/transformer
hf download pkqbajng/ViGeo --local-dir models/ViGeo1.1 # REQUIRED depth backend
TRANSFORMER_PATH=models/evoke-turbo MODE=t2v NUM_CHUNKS=20 \
bash scripts/inference/infer_post_distill.sh
```
See [Evoke](https://github.com/AlayaLab/Evoke) for camera control, other inference modes, and long rollouts.
## WBench
Results on [WBench](https://meituan-longcat.github.io/WBench/) (158 cases; Interaction: Navigation only). **Bold** marks the best score in each column.
| Model | Video Quality | Setting | Navigation | Consistency | Physical | Avg |
|---|---:|---:|---:|---:|---:|---:|
| [Evoke](https://huggingface.co/AlayaLab/Evoke) | **82.7900** | **83.7600** | 78.6300 | 86.8700 | 72.0550 | 80.8210 |
| **Evoke-Turbo** | 81.8914 | 82.0518 | **83.8978** | **88.1469** | **74.0133** | **82.0003** |
Turbo uses seed-44 results selected from six prompt variants; Evoke uses [published reference scores](https://huggingface.co/datasets/SII-YuanyangYin/wbench_results/resolve/1e9401f827a2bb7bfe2a15b6c67df4fa8d5ae786/FINAL_SCORES.csv), with different prompts and poses.
## Notes
See [Evoke](https://huggingface.co/AlayaLab/Evoke) for base-component provenance and dependency licenses; external components retain their own licenses.
## Citation
```bibtex
@article{evoke2026,
title = {Alaya-EVOKE: From Linear-Scaling Supervision to Endless World},
author = {Yin, Yuanyang and Wang, Gongxuan and Zhan, Yifan and
Li, Chuanhao and Zhang, Kaipeng and Zhao, Feng},
journal = {arXiv preprint arXiv:2608.13546},
year = {2026},
}
```
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