ORT's CoreML EP GatherOpBuilder::IsOpSupportedImpl explicitly rejects
rank-0 (scalar) index tensors. StyleGAN-derived models (GFPGAN's 1024
variant has 16 of them, one per style-code slice) hit this in the
generator, and the resulting CPU fallbacks split the CoreML subgraph
into multiple partitions with boundary crossings on every inference.
Add a load-time ONNX rewrite that promotes each scalar index to [1] and
squeezes the added axis on the Gather output — semantically identical
but CoreML-compatible. GFPGAN now runs as a single CoreML partition with
zero CPU-fallback nodes; inference drops from ~87 ms to ~81 ms on an
M-series Mac.
The fix has been filed upstream as microsoft/onnxruntime#28180 — the
existing code comment in gather_op_builder.cc already describes this
exact workaround, it just isn't applied. Once the upstream fix ships
and the ORT floor is raised, this pass can be deleted.
Bundles CoreML graph rewrites, GPU-accelerated pipeline work, Windows CUDA
fixes, and Mac/Windows runtime routing into a single drop.
CoreML (Apple Silicon):
- Decompose Pad(reflect) → Slice+Concat in inswapper_128 so the model
runs in one CoreML partition instead of 14 (TEMPORARY: fixed upstream
in microsoft/onnxruntime#28073, drop when ORT >= 1.26.0).
- Fold Shape/Gather chains to constants in det_10g (21ms → 4ms).
- Decompose Split(axis=1) → Slice pairs in GFPGAN (155ms → 89ms).
- Route detection model to GPU so the ANE is free for the swap model.
- Centralize provider/config selection in create_onnx_session.
Pipeline (all platforms):
- Parallelize face landmark + recognition post-detection; skip landmark_2d_106
when only face_swapper is active.
- Pipeline face detection with swap for ANE overlap.
- GPU-accelerated paste_back, MJPEG capture, zero-copy display path.
- Standalone pipeline benchmark script.
Windows / CUDA:
- CUDA graphs + FP16 model + all-GPU pipeline for 1080p 60 FPS.
- Auto-detect GPU provider and fix DLL discovery for Windows CUDA execution.
Cross-platform:
- platform_info helper for Mac/Windows runtime routing.
- GFPGAN 30 fps + MSMF camera 60 fps with adaptive pipeline tuning.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Max Buckley