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longpipe-onnx
Longpipe matting models converted to ONNX for use as ffrwd model pins. The network and weights are sb2702/longpipe, MIT licensed (code and weights; see their licensing page for dataset provenance). This repo holds faithful conversions, nothing more.
| file | preset | input | sha256 |
|---|---|---|---|
| matting_xl_temporal_f32.onnx | xl system | see notes | 67f79a367c4014d9df631afb002c99bf6efb1aa4ae122df175b45f2f92d611b6 |
| matting_xl_refined_f32.onnx | xl system | see notes | 54b73fcc90335f4ef5274039f7ae09bf5462b2091172695c399bd8078b6b4a69 |
| flow_xl_f32.onnx | xl system | see notes | e39ee98cb8ecff36e416ad234bb90b5301faa302becf4e82fec61869aa218df3 |
| face_xl_f32.onnx | xl system | see notes | fb3d2ef4f6de97cc51708ed56ec9fb6e9fff8a5a0daeebf952a0f7ef8905cbb3 |
| landmark_mesh_f32.onnx | xl system | see notes | e930cc43f06288ba53ba2a3251e2fd3c42995a5c39324c56988648cc32e62edc |
| matting_large_f32.onnx | large | 1x3x160x256 RGB [0,1] | 5007ee2a9fe53dbdebfd2d6c9baba270199aaee9ad1189df9227030b51f21b70 |
| matting_xl_f32.onnx | xl | 1x3x192x320 RGB [0,1] | 996cf2779c5b7c690b35ee1ef5e8890f8d2497e12dea97dc13ddd18e4132d04c |
| matting_small_f32.onnx | small | 1x3x112x192 RGB [0,1] | d5a8c23461c253366ef9b87c59448e3d8c93062a65e25db6591b8def997f6afc |
Converted from the f32 weight blobs served at cdn.longpipe.dev/models/v/0.0.5/ (small blob 56430de7...b03394, large 5294fbbf...4e121f5, xl c1ea11f3...80217032) by reconstruction in PyTorch: the blob's own JSON index locates each tensor, the WGSL mat4x4 packing inverts to dense OIHW (the inverse of longpipe's own scripts/generate_fixtures.py packer), and the export holds PyTorch-to-ONNX parity under 1e-4. Conversion scripts live with the ffrwd project.
The xl system files: matting_xl_refined_f32.onnx is base+wrapper
in one graph (frame in, refined alpha out, canvas 1280x768);
matting_xl_temporal_f32.onnx is the whole per-frame production
step with the optical-flow stabilizer, state as explicit inputs and
outputs (previous frame, four encoder taps, alpha and envelope);
flow_xl_f32.onnx is the flow net alone; face_xl_f32.onnx emits
five facial heatmaps at base/8; landmark_mesh_f32.onnx regresses
478 landmarks from a 256x256 ImageNet-normalized crop (its blob is
their separate model_landmark_mesh.bin, sha ca534744...3b87).
MIT, matching upstream.