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README.md

@@ -1,3 +1,76 @@
 ---
-license: mit
+language:
+  - en
+tags:
+  - image-generation
+  - class-conditional
+  - diffusion
+  - mixture-of-experts
+  - pytorch
 ---
+
+# PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts
+
+<div align="center">
+
+[![arXiv](https://img.shields.io/badge/arXiv-2610.18857-b31b1b.svg)](https://arxiv.org/abs/2610.18857)&nbsp;
+[![Hugging Face](https://img.shields.io/badge/HuggingFace-PixelMoE-yellow)](https://huggingface.co/KlingTeam/PixelMoE)&nbsp;
+
+</div>
+
+![Images generated by PixelMoE](assets/visual.png)
+
+## 📖 Introduction
+
+**PixelMoE** brings standard LLM-style Mixture-of-Experts (MoE) to pixel-space diffusion for class-conditional ImageNet generation at **256×256** and **512×512** resolution. Each image token activates **4 of 32 routed experts**, alongside a shared expert, with bias-based load balancing. Pixel-Space Hierarchical Flow Matching generates images in two stages, first establishing the overall structure at low resolution, then refining details at the target resolution.
+
+PixelMoE achieves FID **1.47** at 256×256 and **1.53** at 512×512 on class-conditional ImageNet-1K, without representation alignment or perceptual losses.
+
+## 📦 Checkpoints
+
+| Model | 256×256 | FID ↓ | 512×512 | FID ↓ |
+|---|---|---|---|---|
+| PixelMoE-B-E32A4 | [Download](https://huggingface.co/KlingTeam/PixelMoE/resolve/main/pixelmoe_b_e32a4_256.pth) | 1.88 | [Download](https://huggingface.co/KlingTeam/PixelMoE/resolve/main/pixelmoe_b_e32a4_512.pth) | 2.05 |
+| PixelMoE-L-E32A4 | [Download](https://huggingface.co/KlingTeam/PixelMoE/resolve/main/pixelmoe_l_e32a4_256.pth) | 1.54 | [Download](https://huggingface.co/KlingTeam/PixelMoE/resolve/main/pixelmoe_l_e32a4_512.pth) | 1.58 |
+| PixelMoE-H-E32A4 | [Download](https://huggingface.co/KlingTeam/PixelMoE/resolve/main/pixelmoe_h_e32a4_256.pth) | **1.47** | [Download](https://huggingface.co/KlingTeam/PixelMoE/resolve/main/pixelmoe_h_e32a4_512.pth) | **1.53** |
+
+FID scores are the ImageNet-1K results reported in the paper. B, L, and H denote Base, Large, and Huge; E32A4 denotes 32 routed experts with 4 activated per token.
+
+## 🚀 Download Models
+
+Download a checkpoint with the [Hugging Face CLI](https://huggingface.co/docs/huggingface_hub/guides/cli):
+
+```bash
+pip install -U huggingface_hub
+hf download KlingTeam/PixelMoE pixelmoe_h_e32a4_256.pth --local-dir checkpoints
+```
+
+To download the entire model repository instead:
+
+```bash
+hf download KlingTeam/PixelMoE --local-dir checkpoints
+```
+
+## 📌 Note
+
+This repository hosts the pretrained checkpoints of **PixelMoE**.
+
+## 📖 Citation
+
+If you find PixelMoE useful in your research, please cite our paper:
+
+```bibtex
+@misc{dong2026pixelmoescalingpixelspacediffusion,
+  title={PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts}, 
+  author={Ruixiao Dong and Jianmin Bao and Bozhi Luan and Zijie Meng and Xin Tao and Pengfei Wan and Kun Gai and Li Li and Houqiang Li},
+  year={2026},
+  eprint={2610.18857},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2610.18857}, 
+}
+```
+
+## Contact
+
+For questions, please contact [dongruixiao@kuaishou.com](mailto:dongruixiao@kuaishou.com).

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