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: magic-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)}
}