Alaya-EVOKE-Turbo is a controllability-optimized version of Alaya-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:
models/
├── evoke-base/ # VAE / text encoder / tokenizer / scheduler; from Evoke
├── evoke-turbo/transformer/ # this release
└── ViGeo1.1/ # required depth backend
Usage
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 for camera control, other inference modes, and long rollouts.
WBench
Results on WBench (158 cases; Interaction: Navigation only). Bold marks the best score in each column.
| Model | Video Quality | Setting | Navigation | Consistency | Physical | Avg |
|---|---|---|---|---|---|---|
| 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, with different prompts and poses.
Notes
See Evoke for base-component provenance and dependency licenses; external components retain their own licenses.
Citation
@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},
}