license: apache-2.0
tags:
- onnx
- inpainting
- android
- qnn
Moebius inpainting — ONNX models prepared for Android
Derived from simonw/Moebius-ONNX (ONNX export of hustvl/Moebius, Apache-2.0)
for the native Android app in the onnx-android project. Files are downloaded by the app and checked against sha256 values compiled into the APK.
| Set | Files | For |
|---|---|---|
gpu+htp |
hyb1.json + hyb1.s* (batch-1 hybrid chain) + unet.b1.fp16x.onnx + the vae_*.static_ctx files |
Snapdragon 8 Elite, default there. The two classifier-free-guidance branches run in parallel: uncond on the NPU+CPU hybrid chain, cond on the GPU (both batch 1) |
gpu |
unet.fp16x.onnx + the vae_*.static_ctx files of htp |
Snapdragon 8 Elite (SM8750) only, default there. UNet (fp16, cross-attention kept fp32) runs whole on the QNN GPU backend (Adreno 830); the app builds the GPU context binary on the device on first run. VAE on the NPU |
htp |
hyb.json + hyb.s*.cpu.onnx (CPU segments) + hyb.s*.htp_ctx.onnx / _ctx_qnn.bin (QNN HTP context binaries), vae_*.static_ctx.onnx / _ctx_qnn.bin |
Snapdragon 8 Elite (SM8750, HTP v79) only. UNet split into 13 segments: 64×64 attention blocks and the timestep embedding on CPU, the rest on the NPU. Context binaries compiled offline with onnxruntime-qnn 2.7.0 (QNN 2.51); the device needs qnn-runtime ≥ 2.51 |
cpu-int8 |
unet.int8r.onnx + the VAE of cpu |
Any device, optional: convolutions statically quantized to int8 (QDQ; first/last and residual-block convs kept fp32). About 2× faster on ARM CPUs without dot-product instructions, slightly smoother inpainted texture |
cpu |
unet.einsum.onnx, vae_encoder.static.onnx, vae_decoder.static.onnx |
Any device, CPU EP, fp32. Static shapes (UNet batch 2), Einsum rewritten to MatMul, condition embedding replaced by a context input (2×10×3072, shipped in the APK) |
Changes from the original graphs: static shapes, input_ids → context input, Einsum → Transpose/Reshape/MatMul, normalization pre-scaling (HTP fp16 range), graph splitting. Weights are unchanged except for the fp16 conversion of unet.fp16x.onnx.
app/ — Android code that runs these models
app/ holds the parts of the Android app that load and run the models above: the inpainting pipeline (noise, DDIM, classifier-free guidance, VAE scaling), image preparation and paste-back, ONNX Runtime / QNN session setup (HTP context binaries, the hybrid NPU+CPU chain, the GPU context cache, the parallel CFG branches), the download/sha256 manifest, and the small inputs the graphs need that are not model files (context.bin, benchmark tensors).
It is reference code, not a complete buildable project (the UI, resources and build tooling are left out). See app/README-HF.md for how the pieces fit together and the file list.