Add README.md

README.md

@@ -1,3 +1,52 @@
 ---
-license: mit
+license: apache-2.0
+base_model:
+  - hustvl/Moebius
+tags:
+  - executorch
+  - react-native
+  - on-device
+  - inpainting
+  - coreml
+  - xnnpack
 ---
+
+# react-native-executorch demo models
+
+ExecuTorch exports of models used in [react-native-executorch](https://github.com/software-mansion/react-native-executorch) demo apps.
+
+## moebius-eraser
+
+[Moebius](https://github.com/hustvl/Moebius) (Duan and Xu et al., ECCV 2026), a 0.22B latent-diffusion inpainting model from HUST and VIVO AI Lab, exported to ExecuTorch for on-device object removal.
+
+| file | backend | precision | size |
+|---|---|---|---|
+| `moebius-eraser/coreml/moebius_vae_encoder.pte` | CoreML | fp16 | 69 MB |
+| `moebius-eraser/coreml/moebius_unet.pte` | CoreML | fp16 | 447 MB |
+| `moebius-eraser/coreml/moebius_vae_decoder.pte` | CoreML | fp16 | 100 MB |
+| `moebius-eraser/xnnpack/moebius_vae_encoder.pte` | XNNPACK | fp32 | 137 MB |
+| `moebius-eraser/xnnpack/moebius_unet.pte` | XNNPACK | fp32 | 905 MB |
+| `moebius-eraser/xnnpack/moebius_vae_decoder.pte` | XNNPACK | fp32 | 198 MB |
+
+The three graphs are meant to be driven by a DDIM sampling loop on the JavaScript side.
+
+- `moebius_vae_encoder`: `(1,3,512,512)` image in [-1,1] -> `(1,4,64,64)` latents, already scaled by 0.13025
+- `moebius_unet`: `(noisy_latents, mask64, masked_latents, timestep, guidance)` -> `(1,4,64,64)` predicted noise. Classifier-free guidance is baked into the graph as an internal batch of 2, so one call per denoising step.
+- `moebius_vae_decoder`: `(1,4,64,64)` latents -> `(1,3,512,512)` image in [0,1]
+
+Spatial dimensions are static at 512x512 (64x64 latent): the cross-attention positional embedding is a learned parameter tied to the training resolution.
+
+Reference implementation, export script and sampler: [magic-eraser demo app](https://github.com/nklockiewicz/magic-eraser).
+
+## License and credit
+
+Moebius weights and code are Apache 2.0, and these exports inherit that license. The VAE comes from [PixelHacker](https://huggingface.co/hustvl/PixelHacker) by the same authors.
+
+```
+@inproceedings{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},
+      booktitle={European Conference on Computer Vision (ECCV)}
+}
+```