Add model card, pipeline tag, and links to paper/code

README.md

@@ -1,3 +1,90 @@
 ---
 license: mit
+pipeline_tag: image-to-image
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+
+# Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance
+
+[![Project Page](https://img.shields.io/badge/Project_Page-https://hustvl.github.io/Moebius-purple)](https://hustvl.github.io/Moebius)
+[![arxiv](https://img.shields.io/badge/ECCV'26-paper-orange)](https://arxiv.org/abs/2606.19195)
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/hustvl/Moebius)
+
+**Moebius** is a highly efficient, lightweight image inpainting framework that operates with a mere **0.22B parameters** (less than 2% of the size of the 11.9B FLUX.1-Fill-Dev) while delivering comparable or superior generation quality and a $>15\times$ acceleration in total inference time.
+
+<div align="center">
+    <img src="https://github.com/hustvl/Moebius/raw/main/assets/pipeline.png" width="100%" alt="Moebius Pipeline">
+</div>
+
+## Key Features
+- **Extreme Parametric Efficiency (< 2%)**: Moebius operates with a mere **0.22B (226M) parameters**, bypassing heavy-compute bottlenecks.
+- **15× Inference Speedup**: Blistering inference latency of only **26.01 ms per step** on a single GPU.
+- **10B-Level Inpainting Quality**: Performs on par with, and in certain scenarios surpasses, SOTA generalist models (*FLUX.1-Fill-Dev*, *SD3.5 Large-Inpainting*) across both natural and portrait scenes.
+
+---
+
+## 📦 Environment Setup
+
+To get started, clone the official GitHub repository, set up the environment, and install dependencies:
+
+```bash
+git clone https://github.com/hustvl/Moebius.git
+cd Moebius
+conda create -n moebius python=3.14.4
+conda activate moebius
+pip install -r requirements.txt
+```
+
+## 🗃️ Model Checkpoints Setup
+
+Organize your model weights in the `./weight` folder as follows:
+
+1. Download the VAE checkpoint from [hustvl/PixelHacker](https://huggingface.co/hustvl/PixelHacker/tree/main/vae) and put it into `./weight/vae`.
+2. Download the Moebius checkpoints (e.g., pretrained, ft_places2, ft_celebahq, ft_ffhq) and place them under `./weight/Moebius`.
+
+```bash
+├── weight
+|   ├── Moebius
+|        ├── pretrained
+|            ├── diffusion_pytorch_model.bin
+|        ├── ft_places2
+|            ├── diffusion_pytorch_model.bin
+|        ├── ft_celebahq
+|            ├── diffusion_pytorch_model.bin
+|        ├── ft_ffhq
+|            ├── diffusion_pytorch_model.bin
+|    ├── vae
+|        ├── config.json
+|        ├── diffusion_pytorch_model.bin
+```
+
+## 🔮 Inference
+
+Run the following command to perform inpainting on custom images and masks. Place your input images and masks with matching filenames under `data/images` and `data/masks` respectively:
+
+```bash
+python -m infer.infer_moebius \
+    --model-config config/model_cfg/moebius.yaml \
+    --model-weight weight/Moebius/ft_celebahq/diffusion_pytorch_model.bin \
+    --real-dir data/images \
+    --mask-dir data/masks \
+    --save-dir ./outputs \
+    --cfg 2.0 \
+    --batch-size 8 \
+    --num-workers 8
+```
+
+## 🎓 Citation
+
+If you find Moebius useful in your research, please consider citing:
+
+```bibtex
+@misc{DuanAndXu2026Moebius,
+      title={Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance},
+      author={Kangsheng Duan and Ziyang Xu and Wenyu Liu and Xiaohu Ruan and Xiaoxin Chen and Xinggang Wang},
+      year={2026},
+      eprint={2606.19195},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2606.19195}, 
+}
+```