README.md

---
license: apache-2.0
pipeline_tag: text-to-video
inference: false
tags:
  - world-model
  - video-generation
  - text-to-video
  - image-to-video
  - interactive
  - 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/🌐_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/🤗_Paper-2608.13546-ffce1c.svg" alt="Paper page"></a>
</p>

Model weights for **EVOKE** ([paper](https://huggingface.co/papers/2608.13546)), a 3-step, CFG-free
interactive world model that generates **384 × 640 @ 24 fps** video and stays coherent over 30 s
rollouts. Code, docs and demos live in the GitHub repository — **this repository holds weights only.**

- ⚡ **3 steps, zero CFG** — 1.5 s of video every 2.11 s on one H200, one forward per step.
- 🌍 **Endless, not windowed** — scene geometry lives in an external camera-indexed world state bank,
  so the denoiser context stays bounded however long the session runs.
- 🎛️ **Re-promptable mid-flight** — change the prompt while the rollout is running, no cut, no restart.

## Contents

Every EVOKE directory is the **parent** of a `transformer/`, because it loads as
`from_pretrained(path, subfolder="transformer")`.

```
evoke-base/                                   vae / text_encoder / tokenizer / scheduler only
evoke/
├── stage1_camera_control/transformer/        multi-step camera-controllable model
├── stage2_few_step_training/transformer/     few-step distillation (3-step pyramid)
├── stage3_long_distillation/transformer/     30 s long-video distillation (post-distill init)
├── stage3_post_distillation/transformer/     the shipped model
└── evoke_teacher/{high,low}_noise/           the two DMD teacher experts -- training only
```

## Usage

```bash
git clone https://github.com/AlayaLab/Evoke && cd Evoke
pip install -r requirements.txt

hf download AlayaLab/Evoke        --local-dir models
hf download pkqbajng/ViGeo        --local-dir models/ViGeo1.1   # REQUIRED depth backend

MODE=t2v NUM_CHUNKS=20 bash scripts/inference/infer_post_distill.sh
```

`ViGeo` is a separate download and is **required** — every shipped recipe uses it as the depth backend
behind the world state bank. Depth-Anything-3 is optional. Both ship under CC-BY-NC-4.0, which is more
restrictive than this repository's Apache-2.0; check their licences before any commercial use.

Inference modes, the mode × model matrix, hour-scale rollouts and training are documented in the
GitHub repository.

## Notes

The distilled models were trained on v2v conditioning alone, so `MODE=i2v|t2v` on them is **zero-shot**;
only `stage1_camera_control` has all three modes in distribution.

The vae / text encoder / tokenizer / scheduler in `evoke-base/` come from the released
[Helios](https://github.com/PKU-YuanGroup/Helios) base, which traces them to Wan. The EVOKE teacher is
built on [LingBot-World](https://github.com/robbyant/lingbot-world).

## 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},
}
```