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README.md

@@ -1,3 +1,81 @@
 ---
-license: mit
+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/SII-YuanyangYin/Evoke"><img src="https://img.shields.io/badge/GitHub-SII--YuanyangYin/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="#"><img src="https://img.shields.io/badge/arXiv-coming_soon-b31b1b.svg" alt="arXiv"></a>
+</p>
+
+Model weights for **EVOKE**, 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/SII-YuanyangYin/Evoke && cd Evoke
+pip install -r requirements.txt
+
+huggingface-cli download SII-YuanyangYin/Evoke --local-dir models
+huggingface-cli 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},
+  year  = {2026},
+}
+```