Improve model card metadata and content

Hi! I'm Niels from the Hugging Face community team.

I've opened this PR to improve the model card for Matrix-Game 3.0. Specifically, I have:
- Added the `library_name: diffusers` tag based on the presence of `model_index.json` and Diffusers versioning in the config files.
- Updated the `pipeline_tag` to `text-to-video` for better discoverability.
- Linked the model to its research paper on the Hugging Face Hub.
- Added a "Quick Start" section with installation and inference instructions sourced from the official GitHub repository.

These changes will help users find and use your model more effectively!

README.md

@@ -1,96 +1,91 @@
 ---
-license: apache-2.0
-language:
-- en
 base_model:
 - Wan-AI/Wan2.2-TI2V-5B
-pipeline_tag: image-text-to-video
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-to-video
+library_name: diffusers
 ---
+
 # Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory
+
+Matrix-Game 3.0 is an open-source, memory-augmented interactive world model designed for 720p real-time long-form video generation. It achieves up to 40 FPS real-time generation at 720p resolution with a 5B model while maintaining stable memory consistency over minute-long sequences.
+
 <div style="display: flex; justify-content: center; gap: 10px;">
   <a href="https://github.com/SkyworkAI/Matrix-Game">
     <img src="https://img.shields.io/badge/GitHub-100000?style=flat&logo=github&logoColor=white" alt="GitHub">
   </a>
-  <a href="https://github.com/SkyworkAI/Matrix-Game/blob/main/Matrix-Game-3/assets/pdf/report.pdf">
-    <img src="https://img.shields.io/badge/Technical Report-b31b1b?style=flat&logo=arxiv&logoColor=white" alt="report">
+  <a href="https://huggingface.co/papers/2604.08995">
+    <img src="https://img.shields.io/badge/Paper-b31b1b?style=flat&logo=arxiv&logoColor=white" alt="Paper">
   </a>
   <a href="https://matrix-game-v3.github.io/">
     <img src="https://img.shields.io/badge/Project%20Page-grey?style=flat&logo=huggingface&color=FFA500" alt="Project Page">
   </a>
-
-  
 </div>
 
 ## 📝 Overview
-**Matrix-Game-3.0** is an open-sourced, memory-augmented interactive world model designed for 720p real-time long-form video generation.
-
-## Framework Overview
-Our framework unifies three stages into an end-to-end pipeline: 
-- Data Engine — an industrial-scale infinite data engine integrating Unreal Engine synthetic scenes, large-scale automated AAA game collection,and real-world video augmentation to produce high-quality Video-Pose-Action-Prompt quadruplets at scale; 
-- Model Training — a memory-augmented Diffusion Transformer (DiT) with an error buffer that learns action-conditioned generation with memory-enhanced long-horizon consistency; 
-- Inference Deployment — few-step sampling, INT8 quantization, and model distillation achieving 720p@40FPS real-time generation with a 5B model.
+The Matrix-Game 3.0 framework unifies three stages into an end-to-end pipeline: 
+- **Data Engine**: An upgraded industrial-scale data engine integrating Unreal Engine synthetic data and AAA game collection to produce high-quality Video-Pose-Action-Prompt quadruplets. 
+- **Model Training**: A memory-augmented Diffusion Transformer (DiT) that learns self-correction by modeling prediction residuals and employs camera-aware memory for long-horizon consistency. 
+- **Inference Deployment**: Multi-segment autoregressive distillation (DMD), model quantization, and VAE decoder pruning to achieve efficient real-time inference.
 
 ![Model Overview](./framework.png)
 
 ## ✨ Key Features
-- 🚀 **Feature 1**: **Upgraded Data Engine**: Combines Unreal Engine-based synthetic data, large-scale automated AAA game data, and real-world video augmentation to generate high-quality Video–Pose–Action–Prompt data. 
-- 🖱️ **Feature 2**: **Long-horizon Memory & Consistency**: Uses prediction residuals and frame re-injection for self-correction, while camera-aware memory ensures long-term spatiotemporal consistency. 
-- 🎬 **Feature 3**: **Real-Time Interactivity & Open Access**: It employs a multi-segment autoregressive distillation strategy based on Distribution Matching Distillation (DMD), combined with model quantization and VAE decoder distillation to support [40fps] real-time generation at 720p resolution with a 5B model, while maintaining stable memory consistency over minute-long sequence.
-- 👍 **Feature 3**: **Scale Up 28B-MoE Model**: Scaling up to a 2×14B model further improves generation quality, dynamics, and generalization.
-
-## 🔥 Latest Updates
-
-* [2026-03] 🎉 Initial release of Matrix-Game-3.0 Model
+- 🚀 **Real-Time Performance**: Supports 720p @ 40fps generation with the 5B model.
+- 🖱️ **Long-horizon Consistency**: Stable memory consistency over sequences lasting minutes.
+- 🎬 **Scalability**: Scaling to a 28B-MoE model (2x14B) further improves quality and generalization.
 
 ## 🚀 Quick Start
+
 ### Installation
-Create a conda environment and install dependencies:
-```
+```bash
 conda create -n matrix-game-3.0 python=3.12 -y
 conda activate matrix-game-3.0
-# install FlashAttention
-# Our project also depends on [FlashAttention](https://github.com/Dao-AILab/flash-attention)
+# install FlashAttention and other dependencies
 git clone https://github.com/SkyworkAI/Matrix-Game-3.0.git
 cd Matrix-Game-3.0
 pip install -r requirements.txt
 ```
 
-### Model Download
-```
-pip install "huggingface_hub[cli]"
-huggingface-cli download Matrix-Game-3.0 --local-dir Matrix-Game-3.0
-```
 ### Inference
-Before running inference, you need to prepare:
-- Input image
-- Text prompt
+After downloading the pretrained weights, you can generate an interactive video with the following command:
 
-After downloading pretrained models, you can use the following command to generate an interactive video with random actions:
-``` sh
-torchrun --nproc_per_node=$NUM_GPUS generate.py --size 704*1280 --dit_fsdp --t5_fsdp --ckpt_dir Matrix-Game-3.0 --fa_version 3 --use_int8 --num_iterations 12 --num_inference_steps 3 --image demo_images/000/image.png --prompt "a vintage gas station with a classic car parked under a canopy, set against a desert landscape." --save_name test --seed 42 --compile_vae --lightvae_pruning_rate 0.5 --vae_type mg_lightvae --output_dir ./output
-# "num_iterations" refers to the number of iterations you want to generate. The total number of frames generated is given by:57 + (num_iterations - 1) * 40 
+```bash
+torchrun --nproc_per_node=$NUM_GPUS generate.py \
+    --size 704*1280 \
+    --dit_fsdp \
+    --t5_fsdp \
+    --ckpt_dir Matrix-Game-3.0 \
+    --fa_version 3 \
+    --use_int8 \
+    --num_iterations 12 \
+    --num_inference_steps 3 \
+    --image demo_images/000/image.png \
+    --prompt "a vintage gas station with a classic car parked under a canopy, set against a desert landscape." \
+    --save_name test \
+    --seed 42 \
+    --compile_vae \
+    --lightvae_pruning_rate 0.5 \
+    --vae_type mg_lightvae \
+    --output_dir ./output
 ```
-Tips: 
-If you want to use the base model, you can use "--use_base_model --num_inference_steps 50". Otherwise if you want to generating the interactive videos with your own input actions, you can use "--interactive".
-With multiple GPUs, you can pass `--use_async_vae --async_vae_warmup_iters 1` to speed up inference.
 
 ## ⭐ Acknowledgements
-- [Diffusers](https://github.com/huggingface/diffusers) for their excellent diffusion model framework
-- [Self-Forcing](https://github.com/guandeh17/Self-Forcing) for their excellent work
-- [GameFactory](https://github.com/KwaiVGI/GameFactory) for their idea of action control module
-- [LightX2V](https://github.com/ModelTC/lightx2v) for their excellent quantization framework
-- [Wan2.2](https://github.com/Wan-Video/Wan2.2) for their strong base model
-- [lingbot-world](https://github.com/Robbyant/lingbot-world) for their context parallel framework 
+- [Diffusers](https://github.com/huggingface/diffusers) for the diffusion model framework.
+- [Wan2.2](https://github.com/Wan-Video/Wan2.2) for the strong base model.
+- [Self-Forcing](https://github.com/guandeh17/Self-Forcing), [GameFactory](https://github.com/KwaiVGI/GameFactory), [LightX2V](https://github.com/ModelTC/lightx2v), and [lingbot-world](https://github.com/Robbyant/lingbot-world) for their contributions and frameworks.
 
 ## 📖 Citation
-If you find this work useful for your research, please kindly cite our paper:
+If you find this work useful for your research, please cite:
 
-```
-  @misc{2026matrix,
-    title={Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory},
-    author={{Skywork AI Matrix-Game Team}},
-    year={2026},
-    howpublished={Technical report},
-    url={https://github.com/SkyworkAI/Matrix-Game/blob/main/Matrix-Game-3/assets/pdf/report.pdf}
-  }
+```bibtex
+@misc{2026matrix,
+  title={Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory},
+  author={{Skywork AI Matrix-Game Team}},
+  year={2026},
+  howpublished={Technical report},
+  url={https://github.com/SkyworkAI/Matrix-Game/blob/main/Matrix-Game-3/assets/pdf/report.pdf}
+}
 ```