nklockiewicz/react-native-executorch-demo-models

react-native-executorch demo models

ExecuTorch exports of models used in react-native-executorch demo apps.

moebius-eraser

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.

License and credit

Moebius weights and code are Apache 2.0, and these exports inherit that license. The VAE comes from 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 (ByteDance Seed, ICLR 2026) any-view models, for the on-device 3D scanner demo app: 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}
}