fastforks 0.1.0

ee27404 2 days ago

10.2 kB

---
library_name: "fastforks"
pipeline_tag: "depth-estimation"
license: "other"
license_name: "mit-code-weights-unstated"
license_link: "https://github.com/gangweix/IGEV-plusplus/blob/099dec97989780d151700459c682df1cc2c18e88/README.md#L23"
tags:
- "stereo"
- "stereo-matching"
- "disparity"
- "cuda"
- "pytorch"
- "fastforks"
---
# IGEV++ (fastforks)

IGEV++ (Xu et al. 2025): IGEV-Stereo with geometry encoding volumes for small, medium and large disparity ranges (48 bins each at 1/4 resolution, up to 768 px), aggregated separately and blended per pixel in the ConvGRU update, 32 iterations.

This repo is the model card only: it holds no weights. fastforks downloads the authors' weights from the links below and checks them against the SHA-256 in the model card. Code: <https://github.com/kleinicke/fastforks>. Kernels for the `kernels` backend: <https://huggingface.co/fastforks/kernels>. Original code: <https://github.com/gangweix/IGEV-plusplus> at `099dec979897`.

Task: stereo. Tier: experimental. Outputs: `disparity`. Backends: `portable`, `kernels`.

Input: a rectified left / right image pair. Output: a disparity map (pixels, left view).

## Variants

Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in [`card.toml`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/stereo/models/igev_plusplus/card.toml).

| variant | weights (author link) | SHA-256 | weights licence | commercial use |
|---|---|---|---|---|
| `igev-plusplus-eth3d@1` | [eth3d.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1gN8J9YVJMupJVjUAUMSJ0jldkqH1YJUo) | `4595ac1c164b` | MIT (assumed from code) | yes |
| `igev-plusplus-kitti12@1` | [kitti2012.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1j5grw6AKhwhJNWJXn_RfhpFfiWBYboqH) | `2e7947101eec` | MIT (assumed from code) | yes |
| `igev-plusplus-kitti15@1` | [kitti2015.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=15kWs23CO4bBDy3DJE4kNv_tlbeEvp1bk) | `42db237df5f8` | MIT (assumed from code) | yes |
| `igev-plusplus-middlebury@1` | [middlebury.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1R9a2YP4kaAv4oT0wtwTnWXrb82Shpp3O) | `eecddfa4549d` | MIT (assumed from code) | yes |
| `igev-plusplus-sceneflow@1` | [sceneflow.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1n8tu7eoByY_Xpyox5XJnjJC_bGBM2r4M) | `550227fb7b73` | MIT (assumed from code) | yes |

## Licence

Original-code strings, recorded as they are (research-use package; check them before any other use). Code: MIT. Files: `LICENSE`.

Code licence: MIT; the authors do not state a separate weights licence.

Training data: Freiburg SceneFlow (FlyingThings3D, Driving, Monkaa).

Licence evidence: <https://github.com/gangweix/IGEV-plusplus/blob/099dec97989780d151700459c682df1cc2c18e88/README.md#L23>

## Input size

any H x W >= 32, padded to a multiple of 32
Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `igev-plusplus-eth3d` not measured yet; `igev-plusplus-kitti12` not measured yet; `igev-plusplus-kitti15` not measured yet; `igev-plusplus-middlebury` not measured yet; `igev-plusplus-sceneflow` not measured yet

## Speed and memory

### Stereo, Middlebury 2014 half, 994x1482

| model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| `igev-plusplus-eth3d` | 2012 | 1278 | 1081 | 393 | 3.3x | 2.7x | 2891 / 3108 | 1594 / 1840 | 1389 / 2302 |

### Startup and first call

| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
| --- | --- | --- | --- | --- | --- | --- |
| `igev-plusplus-eth3d` | 0.42 (375x1242) | 0.20 | 0.16† | 2.33 (375x1242) | 1.10† | 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) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| `igev-plusplus-eth3d` | 994x1482 | 1081 | 715.6 | 159.0 | 494.4 | 425 | 393 | 1594 / 1840 | 952 / 1074 | 3632 |

### Fixed-size speed and peak memory, all models

| model | mode | 480x640 | 720x1280 | 1080x1920 | 12 MP 3000x4000 |
| --- | --- | --- | --- | --- | --- |
| `igev-plusplus-eth3d` | portable | 201.5 ms / 354 / 614 | 606.5 ms / 987 / 1662 | 1534.8 ms / 2172 / 3496 | OOM |
| `igev-plusplus-eth3d` | kernels | 88.7 ms / 357 / 1092 | 241.2 ms / 830 / 1210 | 534.3 ms / 1799 / 2464 | 3135.8 ms (n=3) / 8419 / 9752 |
| `igev-plusplus-kitti12` | portable | 201.4 ms / 354 / 614 | 607.2 ms / 987 / 1662 | 1535.8 ms / 2172 / 3496 | OOM |
| `igev-plusplus-kitti12` | kernels | 89.1 ms / 357 / 1092 | 244.8 ms / 830 / 1210 | 535.9 ms / 1799 / 2464 | 3143.3 ms (n=3) / 8419 / 9750 |
| `igev-plusplus-kitti15` | portable | 202.7 ms / 354 / 614 | 606.9 ms / 987 / 1662 | 1535.8 ms / 2172 / 3496 | OOM |
| `igev-plusplus-kitti15` | kernels | 88.7 ms / 357 / 1092 | 240.2 ms / 830 / 1210 | 534.9 ms / 1799 / 2464 | 3141.8 ms (n=3) / 8419 / 9752 |
| `igev-plusplus-middlebury` | portable | 201.2 ms / 354 / 612 | 606.8 ms / 987 / 1662 | 1535.6 ms / 2172 / 3496 | OOM |
| `igev-plusplus-middlebury` | kernels | 88.7 ms / 357 / 1092 | 240.5 ms / 830 / 1210 | 535.0 ms / 1799 / 2464 | 3140.6 ms (n=3) / 8419 / 9752 |
| `igev-plusplus-sceneflow` | portable | 201.0 ms / 354 / 612 | 606.2 ms / 987 / 1662 | 1534.9 ms / 2172 / 3496 | OOM |
| `igev-plusplus-sceneflow` | kernels | 88.6 ms / 357 / 1092 | 240.6 ms / 830 / 1210 | 535.4 ms / 1799 / 2464 | 3143.9 ms (n=3) / 8419 / 9752 |

### Apple silicon (MPS)

| model | `portable` | `kernels` | speed-up |
| --- | --- | --- | --- |
| `igev-plusplus-sceneflow` | 2852 ms / 1783 MiB (float16) | 2054 ms / 1803 MiB | 1.39x |

Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).

- `igev-plusplus-kitti12`, `igev-plusplus-kitti15`, `igev-plusplus-middlebury`, `igev-plusplus-sceneflow`: same network and settings as `igev-plusplus-eth3d`, measured once (rows not repeated).

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.
## Accuracy
### On public datasets
Original = the authors' code, same checkpoint and protocol.
| variant | dataset | original fp32 | original fp16 | portable | kernels | note |
|---|---|---|---|---|---|---|
| `igev-plusplus-sceneflow` | ETH3D two-view train, bad > 1 px % / EPE | 4.66 / 0.356 | 4.66 / 0.355 | 4.65 / 0.355 | 4.66 / 0.356 |  |
| `igev-plusplus-sceneflow` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 10.57 / 3.120 | 10.58 / 3.120 | 10.55 / 3.128 | 10.56 / 3.153 |  |
| `igev-plusplus-kitti12` | ETH3D two-view train, bad > 1 px % / EPE | 26.75 / 1.968 | 26.77 / 1.966 | 26.73 / 1.969 | 26.78 / 1.963 |  |
| `igev-plusplus-kitti12` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 21.25 / 4.995 | 21.25 / 4.990 | 21.25 / 4.996 | 21.25 / 4.976 |  |
| `igev-plusplus-kitti15` | ETH3D two-view train, bad > 1 px % / EPE | 25.52 / 1.896 | 25.51 / 1.893 | 25.47 / 1.888 | 25.50 / 1.890 |  |
| `igev-plusplus-kitti15` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 20.69 / 4.856 | 20.70 / 4.861 | 20.71 / 4.851 | 20.69 / 4.859 |  |
| `igev-plusplus-middlebury` | ETH3D two-view train, bad > 1 px % / EPE | 18.40 / 0.779 | 18.31 / 0.778 | 18.47 / 0.780 | 18.46 / 0.782 | possibly in the training data |
| `igev-plusplus-middlebury` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 4.70 / 0.703 | 4.66 / 0.703 | 4.76 / 0.707 | 4.79 / 0.708 | trained on this dataset's public images; not a zero-shot result |
| `igev-plusplus-eth3d` | ETH3D two-view train, bad > 1 px % / EPE | 0.50 / 0.093 | 0.50 / 0.094 | 0.50 / 0.093 | 0.50 / 0.094 | trained on this dataset's public images; not a zero-shot result |
| `igev-plusplus-eth3d` | Middlebury 2014 trainingH, bad > 2 px % / EPE | 10.61 / 2.709 | 10.62 / 2.649 | 10.58 / 2.713 | 10.60 / 2.734 |  |
Measured on portable: NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-05; kernels: NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-06.
### Parity with the original code
Every variant is checked against reference outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.02 px and p99 <= 0.25 px.
## Limitations
- CUDA output (float16, kernels, float32 with PyTorch's default TF32 convolutions) differs from the CPU by ~0.06 px mean / 1.2 px p99 at 720x1280, 0.005 px with full float32: rounding amplified by 32 GRU iterations. Portable and kernels reach the same ETH3D / Middlebury accuracy.
## Usage
```python
from fastforks import stereo
model = stereo.load("igev-plusplus-eth3d")  # 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')
```
Install with `pip install fastforks` (see <https://github.com/kleinicke/fastforks>). Other variants: `igev-plusplus-eth3d`, `igev-plusplus-kitti12`, `igev-plusplus-kitti15`, `igev-plusplus-middlebury`, `igev-plusplus-sceneflow`.
## Kernels backend
`stereo.load("igev-plusplus-eth3d", mode="kernels")` runs our CUDA kernels (`igev`, `igevpp`, `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 feccac41 (2026-11-06) -->

	---
	library_name: "fastforks"
	pipeline_tag: "depth-estimation"
	license: "other"
	license_name: "mit-code-weights-unstated"
	license_link: "https://github.com/gangweix/IGEV-plusplus/blob/099dec97989780d151700459c682df1cc2c18e88/README.md#L23"
	tags:
	- "stereo"
	- "stereo-matching"
	- "disparity"
	- "cuda"
	- "pytorch"
	- "fastforks"
	---
	# IGEV++ (fastforks)

	IGEV++ (Xu et al. 2025): IGEV-Stereo with geometry encoding volumes for small, medium and large disparity ranges (48 bins each at 1/4 resolution, up to 768 px), aggregated separately and blended per pixel in the ConvGRU update, 32 iterations.

	This repo is the model card only: it holds no weights. fastforks downloads the authors' weights from the links below and checks them against the SHA-256 in the model card. Code: <https://github.com/kleinicke/fastforks>. Kernels for the `kernels` backend: <https://huggingface.co/fastforks/kernels>. Original code: <https://github.com/gangweix/IGEV-plusplus> at `099dec979897`.

	Task: stereo. Tier: experimental. Outputs: `disparity`. Backends: `portable`, `kernels`.

	Input: a rectified left / right image pair. Output: a disparity map (pixels, left view).

	## Variants

	Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in [`card.toml`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/stereo/models/igev_plusplus/card.toml).

	\| variant \| weights (author link) \| SHA-256 \| weights licence \| commercial use \|
	\|---\|---\|---\|---\|---\|
	\| `igev-plusplus-eth3d@1` \| [eth3d.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1gN8J9YVJMupJVjUAUMSJ0jldkqH1YJUo) \| `4595ac1c164b` \| MIT (assumed from code) \| yes \|
	\| `igev-plusplus-kitti12@1` \| [kitti2012.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1j5grw6AKhwhJNWJXn_RfhpFfiWBYboqH) \| `2e7947101eec` \| MIT (assumed from code) \| yes \|
	\| `igev-plusplus-kitti15@1` \| [kitti2015.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=15kWs23CO4bBDy3DJE4kNv_tlbeEvp1bk) \| `42db237df5f8` \| MIT (assumed from code) \| yes \|
	\| `igev-plusplus-middlebury@1` \| [middlebury.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1R9a2YP4kaAv4oT0wtwTnWXrb82Shpp3O) \| `eecddfa4549d` \| MIT (assumed from code) \| yes \|
	\| `igev-plusplus-sceneflow@1` \| [sceneflow.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1n8tu7eoByY_Xpyox5XJnjJC_bGBM2r4M) \| `550227fb7b73` \| MIT (assumed from code) \| yes \|

	## Licence

	Original-code strings, recorded as they are (research-use package; check them before any other use). Code: MIT. Files: `LICENSE`.

	Code licence: MIT; the authors do not state a separate weights licence.

	Training data: Freiburg SceneFlow (FlyingThings3D, Driving, Monkaa).

	Licence evidence: <https://github.com/gangweix/IGEV-plusplus/blob/099dec97989780d151700459c682df1cc2c18e88/README.md#L23>

	## Input size

	any H x W >= 32, padded to a multiple of 32
	Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `igev-plusplus-eth3d` not measured yet; `igev-plusplus-kitti12` not measured yet; `igev-plusplus-kitti15` not measured yet; `igev-plusplus-middlebury` not measured yet; `igev-plusplus-sceneflow` not measured yet

	## Speed and memory

	### Stereo, Middlebury 2014 half, 994x1482

	\| model \| time (ms): original fp32 \| original \| portable \| kernels \| kernels speed-up: vs original \| vs portable \| peak memory (MiB, allocated / reserved): original \| portable \| kernels \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| `igev-plusplus-eth3d` \| 2012 \| 1278 \| 1081 \| 393 \| 3.3x \| 2.7x \| 2891 / 3108 \| 1594 / 1840 \| 1389 / 2302 \|

	### Startup and first call

	\| model \| load (s): original \| portable \| kernels \| first call (s): original \| portable \| kernels \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| `igev-plusplus-eth3d` \| 0.42 (375x1242) \| 0.20 \| 0.16† \| 2.33 (375x1242) \| 1.10† \| 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) \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| `igev-plusplus-eth3d` \| 994x1482 \| 1081 \| 715.6 \| 159.0 \| 494.4 \| 425 \| 393 \| 1594 / 1840 \| 952 / 1074 \| 3632 \|

	### Fixed-size speed and peak memory, all models

	\| model \| mode \| 480x640 \| 720x1280 \| 1080x1920 \| 12 MP 3000x4000 \|
	\| --- \| --- \| --- \| --- \| --- \| --- \|
	\| `igev-plusplus-eth3d` \| portable \| 201.5 ms / 354 / 614 \| 606.5 ms / 987 / 1662 \| 1534.8 ms / 2172 / 3496 \| OOM \|
	\| `igev-plusplus-eth3d` \| kernels \| 88.7 ms / 357 / 1092 \| 241.2 ms / 830 / 1210 \| 534.3 ms / 1799 / 2464 \| 3135.8 ms (n=3) / 8419 / 9752 \|
	\| `igev-plusplus-kitti12` \| portable \| 201.4 ms / 354 / 614 \| 607.2 ms / 987 / 1662 \| 1535.8 ms / 2172 / 3496 \| OOM \|
	\| `igev-plusplus-kitti12` \| kernels \| 89.1 ms / 357 / 1092 \| 244.8 ms / 830 / 1210 \| 535.9 ms / 1799 / 2464 \| 3143.3 ms (n=3) / 8419 / 9750 \|
	\| `igev-plusplus-kitti15` \| portable \| 202.7 ms / 354 / 614 \| 606.9 ms / 987 / 1662 \| 1535.8 ms / 2172 / 3496 \| OOM \|
	\| `igev-plusplus-kitti15` \| kernels \| 88.7 ms / 357 / 1092 \| 240.2 ms / 830 / 1210 \| 534.9 ms / 1799 / 2464 \| 3141.8 ms (n=3) / 8419 / 9752 \|
	\| `igev-plusplus-middlebury` \| portable \| 201.2 ms / 354 / 612 \| 606.8 ms / 987 / 1662 \| 1535.6 ms / 2172 / 3496 \| OOM \|
	\| `igev-plusplus-middlebury` \| kernels \| 88.7 ms / 357 / 1092 \| 240.5 ms / 830 / 1210 \| 535.0 ms / 1799 / 2464 \| 3140.6 ms (n=3) / 8419 / 9752 \|
	\| `igev-plusplus-sceneflow` \| portable \| 201.0 ms / 354 / 612 \| 606.2 ms / 987 / 1662 \| 1534.9 ms / 2172 / 3496 \| OOM \|
	\| `igev-plusplus-sceneflow` \| kernels \| 88.6 ms / 357 / 1092 \| 240.6 ms / 830 / 1210 \| 535.4 ms / 1799 / 2464 \| 3143.9 ms (n=3) / 8419 / 9752 \|

	### Apple silicon (MPS)

	\| model \| `portable` \| `kernels` \| speed-up \|
	\| --- \| --- \| --- \| --- \|
	\| `igev-plusplus-sceneflow` \| 2852 ms / 1783 MiB (float16) \| 2054 ms / 1803 MiB \| 1.39x \|

	Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).

	- `igev-plusplus-kitti12`, `igev-plusplus-kitti15`, `igev-plusplus-middlebury`, `igev-plusplus-sceneflow`: same network and settings as `igev-plusplus-eth3d`, measured once (rows not repeated).

	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.

	## Accuracy

	### On public datasets

	Original = the authors' code, same checkpoint and protocol.

	\| variant \| dataset \| original fp32 \| original fp16 \| portable \| kernels \| note \|
	\|---\|---\|---\|---\|---\|---\|---\|
	\| `igev-plusplus-sceneflow` \| ETH3D two-view train, bad > 1 px % / EPE \| 4.66 / 0.356 \| 4.66 / 0.355 \| 4.65 / 0.355 \| 4.66 / 0.356 \| \|
	\| `igev-plusplus-sceneflow` \| Middlebury 2014 trainingH, bad > 2 px % / EPE \| 10.57 / 3.120 \| 10.58 / 3.120 \| 10.55 / 3.128 \| 10.56 / 3.153 \| \|
	\| `igev-plusplus-kitti12` \| ETH3D two-view train, bad > 1 px % / EPE \| 26.75 / 1.968 \| 26.77 / 1.966 \| 26.73 / 1.969 \| 26.78 / 1.963 \| \|
	\| `igev-plusplus-kitti12` \| Middlebury 2014 trainingH, bad > 2 px % / EPE \| 21.25 / 4.995 \| 21.25 / 4.990 \| 21.25 / 4.996 \| 21.25 / 4.976 \| \|
	\| `igev-plusplus-kitti15` \| ETH3D two-view train, bad > 1 px % / EPE \| 25.52 / 1.896 \| 25.51 / 1.893 \| 25.47 / 1.888 \| 25.50 / 1.890 \| \|
	\| `igev-plusplus-kitti15` \| Middlebury 2014 trainingH, bad > 2 px % / EPE \| 20.69 / 4.856 \| 20.70 / 4.861 \| 20.71 / 4.851 \| 20.69 / 4.859 \| \|
	\| `igev-plusplus-middlebury` \| ETH3D two-view train, bad > 1 px % / EPE \| 18.40 / 0.779 \| 18.31 / 0.778 \| 18.47 / 0.780 \| 18.46 / 0.782 \| possibly in the training data \|
	\| `igev-plusplus-middlebury` \| Middlebury 2014 trainingH, bad > 2 px % / EPE \| 4.70 / 0.703 \| 4.66 / 0.703 \| 4.76 / 0.707 \| 4.79 / 0.708 \| trained on this dataset's public images; not a zero-shot result \|
	\| `igev-plusplus-eth3d` \| ETH3D two-view train, bad > 1 px % / EPE \| 0.50 / 0.093 \| 0.50 / 0.094 \| 0.50 / 0.093 \| 0.50 / 0.094 \| trained on this dataset's public images; not a zero-shot result \|
	\| `igev-plusplus-eth3d` \| Middlebury 2014 trainingH, bad > 2 px % / EPE \| 10.61 / 2.709 \| 10.62 / 2.649 \| 10.58 / 2.713 \| 10.60 / 2.734 \| \|

	Measured on portable: NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-05; kernels: NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-06.

	### Parity with the original code

	Every variant is checked against reference outputs of the original code at its pinned commit. CPU, float32: max \|diff\| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean \|diff\| <= 0.02 px and p99 <= 0.25 px.

	## Limitations

	- CUDA output (float16, kernels, float32 with PyTorch's default TF32 convolutions) differs from the CPU by ~0.06 px mean / 1.2 px p99 at 720x1280, 0.005 px with full float32: rounding amplified by 32 GRU iterations. Portable and kernels reach the same ETH3D / Middlebury accuracy.

	## Usage

	```python
	from fastforks import stereo

	model = stereo.load("igev-plusplus-eth3d") # 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')
	```

	Install with `pip install fastforks` (see <https://github.com/kleinicke/fastforks>). Other variants: `igev-plusplus-eth3d`, `igev-plusplus-kitti12`, `igev-plusplus-kitti15`, `igev-plusplus-middlebury`, `igev-plusplus-sceneflow`.

	## Kernels backend

	`stereo.load("igev-plusplus-eth3d", mode="kernels")` runs our CUDA kernels (`igev`, `igevpp`, `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 feccac41 (2026-11-06) -->