---
title: Alaya-EVOKE Text-to-Video
emoji: 🌍
colorFrom: yellow
colorTo: pink
sdk: gradio
sdk_version: 5.49.1
python_version: "3.10"
app_file: app.py
pinned: false
license: apache-2.0
short_description: Text-to-video world model (Evoke, 14B DiT, 3-step distilled)
startup_duration_timeout: 60m
models:
  - SII-YuanyangYin/Evoke
tags:
  - text-to-video
  - world-model
  - video-generation
---

# Alaya-EVOKE · Text-to-Video

Prompt-only **text-to-video** demo of the released **post-distillation** checkpoint of
[**SII-YuanyangYin/Evoke**](https://huggingface.co/SII-YuanyangYin/Evoke) — *Alaya-EVOKE: From
Linear-Scaling Supervision to Endless World*.

- **14B DiT** world model + UMT5 text encoder + Wan VAE
- Sampled in **3 steps, no CFG** (the distilled recipe) at **384×640 · 24 fps**
- This is the model's **`t2v`** mode: no camera control (the pipeline runs warp-free), so the clip
  is generated from the prompt alone. One chunk ≈ 1.5 s of video.

The demo drives the authors' own inference engine (`scripts/inference/infer_single.py`) in-process,
reproducing the `infer_post_distill.sh MODE=t2v` recipe exactly
(`STAGE2_STEPS=1 1 1`, `guidance_scale=1.0`, `vae_decode_type=persistent`).

> Note: the distilled checkpoints were trained on `v2v` conditioning, so `t2v` here is zero-shot
> extrapolation (as documented upstream) — expect it to work but not to be the model's trained mode.

Model & code © the Evoke authors, Apache-2.0.