Model card synced with fastforks 7204e33f

README.md

@@ -59,6 +59,12 @@ Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `cfnet-sceneflow` 1988x2880 (8
 | --- | --- | --- | --- | --- | --- | --- |
 | `cfnet-sceneflow` | 0.39 (375x1242) | 0.94 | 0.21† | 0.24 (375x1242) | 0.48† | 0.40† |
 
+### torch.compile and TensorRT
+
+| model | input | portable ms | compile ms | compile s (cold) | TensorRT fp16 ms | ONNX + build s | kernels ms | peak MiB (alloc / reserved): portable | compile | TensorRT (device) |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| `cfnet-sceneflow` | 994x1482 | 484.31 | 249.2 | 53.0 | 170.9 | 139 | 400.67 | 3441 / 4376 | 1026 / 1398 | 1238 |
+
 ### Fixed-size speed and peak memory, all models
 
 | model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 |
@@ -66,6 +72,12 @@ Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `cfnet-sceneflow` 1988x2880 (8
 | `cfnet-sceneflow` | portable | 73.6 ms / 1070 / 1588 | 284.0 ms / 2640 / 4342 | 758.8 ms / 4508 / 5956 | OOM |
 | `cfnet-sceneflow` | kernels | 59.3 ms / 1118 / 1618 | 229.9 ms / 2778 / 4362 | 634.1 ms / 4801 / 5976 | OOM |
 
+### Apple silicon (MPS)
+
+| model | `portable` | `kernels` | speed-up |
+| --- | --- | --- | --- |
+| `cfnet-sceneflow` | 2228 ms / 5463 MiB (float16) | 758 ms / 2931 MiB | 2.94x |
+
 Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).
 
 Hardware, protocol and caveats: [benchmarks](https://github.com/kleinicke/fastforks/blob/main/docs/benchmarks.md) (RTX 5060 Ti 16 GB, sm_120). Original = the authors' code at the precision of portable (fp16, or `(fp32)` where fp16 is unsupported or not finite); original fp32 = its default fp32 run. Peak memory is the torch allocator's.
@@ -81,7 +93,7 @@ Every variant is checked against golden outputs of the original code at its pinn
 ```python
 from fastforks import stereo
 
-model = stereo.load("cfnet-sceneflow")  # portable PyTorch on the GPU if there is one, else the CPU
+model = stereo.load("cfnet-sceneflow")  # mode="auto": the preferred backend from recorded measurements
 result = model("left.png", "right.png")
 result.disparity  # [H, W] float32 tensor, input resolution
 result.backend  # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16')
@@ -92,3 +104,5 @@ Install with `pip install fastforks` (see <https://github.com/kleinicke/fastfork
 ## Kernels backend
 
 `stereo.load("cfnet-sceneflow", mode="kernels")` runs our CUDA kernels (`igev`, `cutlass_conv`, `cudnn_hacc`). It needs a prebuilt kernel for your GPU architecture (downloaded from <https://huggingface.co/fastforks/kernels> on first use) and a recent NVIDIA driver; if there is none it raises `BackendUnavailableError` with the reason and never falls back silently. `mode="auto"` picks `kernels` when it can run and logs the choice.
+
+<!-- staged from fastforks 7204e33f (2026-11-05) -->