---
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: [on-device-eraser demo app](https://github.com/NorbertKlockiewicz/on-device-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)}
}
```

## da3-scanner

ExecuTorch exports of [Depth Anything 3](https://github.com/bytedance-seed/depth-anything-3) (ByteDance Seed, ICLR 2026) any-view models, for the [on-device 3D scanner demo app](https://github.com/NorbertKlockiewicz/on-device-3d-scanner): 8 photos in, per-view depth, confidence and camera poses out in one forward pass, no SfM and no per-scene optimization.

Layout: `da3-scanner/coreml/` (iOS) and `da3-scanner/xnnpack/` (Android and CPU). All graphs are static at 8 views of 560x420 (portrait), ImageNet-normalized RGB, input `(1,8,3,560,420)`:

- `da3_base_8v_560x420_coreml_fp16_gpu.pte` (480MB): DA3-BASE, fp16, compute units pinned to CPU_AND_GPU. The pin is deliberate: the ANE compiler crashes the host process on this graph, while the GPU runs it in ~3.1s on an iPhone 16 Pro.
- `da3_small_8v_560x420_coreml_fp32.pte` (382MB): DA3-SMALL, ~2.0s on the same phone, ANE-safe.
- `da3_base_8v_560x420_xnnpack.pte` (413MB) and `da3_small_8v_560x420_xnnpack.pte` (105MB): fp32 CPU builds.

Outputs per view: depth `(1,8,560,420)`, confidence `(1,8,560,420)`, and a 9-float pose encoding (translation, quaternion xyzw, vertical/horizontal FOV) relative to view 0. Export script and the pose decoding: see the demo repo.

DA3-SMALL and DA3-BASE weights and code are Apache 2.0, and these exports inherit that license.

```
@article{depthanything3,
      title={Depth Anything 3: Recovering the visual space from any views},
      author={Haotong Lin and Sili Chen and Jun Hao Liew and Donny Y. Chen and Zhenyu Li and Guang Shi and Jiashi Feng and Bingyi Kang},
      journal={arXiv preprint arXiv:2511.10647},
      year={2025}
}
```