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-license: apache-2.0
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+---
+license: apache-2.0
+---
+# Dreamer-MC: A Real-Time Autoregressive World Model for Infinite Video Generation
+
+<div align="center">
+
+<br>
+
+[![License](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
+[![Python](https://img.shields.io/badge/Python-3.10%2B-brightgreen)](https://www.python.org/)
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.8%2B-orange)](https://pytorch.org/)
+[![Blog](https://img.shields.io/badge/Project-Blog-blue)](https://findlamp.github.io/dreamer-mc.github.io/)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model%20Zoo-yellow)](https://huggingface.co/)
+
+[**📖 Introduction**](#-introduction) | [**🏰 Model Zoo**](#-model-zoo) | [**🛠️ Installation**](#-installation) | [**💻 Quick Start**](#-quick-start)
+
+</div>
+
+---
+
+## 📖 Introduction
+
+This repository contains the **Inference Code** for the Minecraft Autoregressive World Model.
+This project serves as an **open-source reproduction of the [DreamerV4](https://danijar.com/project/dreamerv4/) architecture**, tailored specifically for high-fidelity simulation in the Minecraft environment. Our model utilizes a **MAE (Masked Autoencoder)** for efficient video compression and a **DiT (Diffusion Transformer)** architecture to autoregressively predict future game frames based on history and action inputs in the latent space.
+ This codebase is streamlined for **deployment and generation**, supporting long-context inference and real-time interaction.
+
+### Key Features
+* **Inference Only**: Lightweight codebase focused on generation, stripped of complex training logic.
+* **Long Context Support**: Capable of loading Long-Context models to recall events from 12 seconds prior.
+* **Fast Inference Backend**: Built-in optimized inference pipeline designed for high-performance, real-time next-frame prediction.
+* **Infinite Generation**: Supports infinite generation without image quality degradation during long-term rollouts.
+* **Complex Interaction**: Supports a variety of interactions within the Minecraft world, such as eating food, collecting water, using weapons, etc.
+
+## 🏰 Model Zoo
+
+Please download the pre-trained weights and place them in the `checkpoints/` directory before running the code.
+
+| Model Name | Params | VRAM Req | Description |
+| :--- | :---: | :---: | :--- |
+| **MAE-Tokenizer** | 430M | >2GB | Handles video encoding and decoding. |
+| **Dynamic Model** | 1.7B | 9GB | Generates the next frame based on history and action. |
+
+> 🔗 **Download**: [HuggingFace Collection](https://huggingface.co/your-username/minecraft-world-model)
+
+## 🛠️ Installation
+
+We recommend using Python 3.10+ and CUDA 12.1+.
+
+```bash
+# 1. Clone the repository
+git clone https://github.com/IamCreateAI/Dreamerv4-MC.git
+cd Dreamerv4-MC
+
+# 2. Create a virtual environment
+conda create -n dreamer python=3.12 -y
+conda activate dreamer
+
+# 3. Install PyTorch (Adjust index-url for your CUDA version)
+pip install torch torchvision --index-url [https://download.pytorch.org/whl/cu121](https://download.pytorch.org/whl/cu121)
+
+# 4. Install dependencies
+pip install -r requirements.txt
+MAX_JOBS=4 pip install flash-attn --no-build-isolation
+pip install -e .
+```
+## 💻 Quick-Start
+```bash
+python ui/inference_ui.py --dynamic_path=/path/to/dynamic_model \
+ --tokenizer_path=/path/to/tokenizer/ \
+ --record_video_output_path=output/
+```
+## 🎮 Controls
+
+| Key | Action |
+| :--- | :--- |
+| **W / A / S / D** | Move |
+| **Space** | Jump |
+| **Left Click** | Attack / Destroy |
+| **Right Click** | Place / Use Item |
+| **E** | Open/Close Inventory (Simulation) |
+| **1 - 9** | Select Hotbar Slot |
+| **R** | start/stop record the video|
+| **V** | refresh into new scene |
+| **left Shift** |Sneak|
+| **left ctrl** |Sprint|
+
+
+## 📜 Citation
+If you use this codebase in your research, please consider citing us as:
+```bash
+@article{hafner2025dreamerv4,
+    title   = {Dreamer-MC: A Real-Time Autoregressive World Model for Infinite Video Generation},
+    author  = {Ming Gao, Yan Yan, ShengQu Xi, Yu Duan, ShengQian Li, Feng Wang},
+    year    = {2026},
+    url     = {https://findlamp.github.io/dreamer-mc.github.io/}
+}
+```
+as well as the original Dreamer 4 paper:
+```bash
+   @misc{Hafner2025TrainingAgents,
+    title={Training Agents Inside of Scalable World Models}, 
+    author={Danijar Hafner and Wilson Yan and Timothy Lillicrap},
+    year={2025},
+    eprint={2509.24527},
+    archivePrefix={arXiv},
+    primaryClass={cs.AI},
+    url={https://arxiv.org/abs/2509.24527}, 
+}
+```
+
+
+## 📚 References
+This project is built upon the following foundational works:
+* **MaeTok**: [Masked Autoencoders Are Effective Tokenizers for Diffusion Models](https://arxiv.org/abs/2502.03444) (Chen et al., ICML 2025)
+* **DreamerV4**: [Training Agents Inside of Scalable World Models](https://danijar.com/project/dreamerv4/) (Hafner et al., 2025)
+* **CausVid**: [From Slow Bidirectional to Fast Autoregressive Video Diffusion Models](https://arxiv.org/abs/2412.07772) (Yin et al., CVPR 2025)