CorridorKey for Nuke

A TensorRT-accelerated Nuke plugin for CorridorKey — the AI green screen keyer by Niko Pueringer / Corridor Digital that produces physically accurate unmixed foreground color and alpha.

This plugin runs the CorridorKey neural network directly inside Nuke as a native node (TRTCorridorKey), powered by NVIDIA TensorRT for real-time inference on the GPU.

What It Does

CorridorKey takes a green screen plate and a coarse alpha hint, and produces a clean straight foreground color and linear alpha matte — preserving hair, motion blur, and semi-transparent edges that traditional keyers destroy.

This plugin wraps that model into a two-input Nuke node:

• Input 0 (plate): RGB green screen footage
• Input 1 (mask): Coarse alpha hint (rough chroma key or AI roto)
• Output: RGBA — unmixed FG color (sRGB) in RGB + linear alpha in A

The TensorRT engine runs at ~300ms per frame at 2048×2048 FP16 on an RTX A5000 (24 GB).

Requirements

• Linux x86_64
• NVIDIA GPU with 24+ GB VRAM (tested on RTX A5000)
• CUDA 13.1 (or 12.9)
• TensorRT 10.15.1
• Nuke 17.0 (NDK)
• ~128 GB+ system RAM for the ONNX export step

Build Guide

The full pipeline is: .pth → ONNX → TensorRT engine → Nuke plugin. Each step only needs to be done once.

Step 1: Export CorridorKey to ONNX

Clone the original CorridorKey repo and set up its environment:

git clone https://github.com/nikopueringer/CorridorKey.git
cd CorridorKey
uv sync
uv pip install onnx onnxruntime onnxscript

Download the model weights (~300 MB) into the weights/ folder:

mkdir -p weights
wget -O weights/CorridorKey_v1.0.pth \
    https://huggingface.co/nikopueringer/CorridorKey_v1.0/resolve/main/CorridorKey_v1.0.pth

Copy export_corridorkey_onnx.py from this repo into the CorridorKey directory, then export:

source .venv/bin/activate
uv run python export_corridorkey_onnx.py --img-size 2048 --no-verify

RAM note: The ONNX trace at 2048×2048 requires ~140 GB of memory. If you have 128 GB RAM, add temporary swap:

sudo fallocate -l 64G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile
sync && sudo sh -c 'echo 3 > /proc/sys/vm/drop_caches'
uv run python export_corridorkey_onnx.py --img-size 2048 --no-verify
sudo swapoff /swapfile
sudo rm /swapfile

Alternatively, export at 1024 (--img-size 1024) which needs much less RAM. The model still works well — the Nuke plugin resizes the input to model resolution anyway.

This produces weights/CorridorKey_v1.0.onnx.

Step 2: Build the TensorRT Engine

Download the pre-built TensorRT 10.15.1 and extract it to /opt/TensorRT-10.15.1.29 (or wherever you prefer).

export LD_LIBRARY_PATH=/opt/TensorRT-10.15.1.29/lib:/usr/local/cuda-13.1/lib64:$LD_LIBRARY_PATH

/opt/TensorRT-10.15.1.29/bin/trtexec \
    --onnx=weights/CorridorKey_v1.0.onnx \
    --saveEngine=weights/CorridorKey_v1.0_fp16.engine \
    --fp16 \
    --memPoolSize=workspace:2G

FP16 is recommended — CorridorKey already uses float16 autocast during inference. This takes about 5 minutes and produces a ~198 MB engine file.

Step 3: Build the Nuke Plugin

export PATH=/usr/local/cuda-13.1/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-13.1/lib64:$LD_LIBRARY_PATH

rm -rf build && mkdir build && cd build
cmake .. \
    -DNUKE_ROOT=/opt/Nuke17.0v1 \
    -DTENSORRT_ROOT=/opt/TensorRT-10.15.1.29
make -j8

Copy TRTCorridorKey.so to your Nuke plugin path (e.g. ~/.nuke/).

Usage in Nuke

1. Create a TRTCorridorKey node (found under AI menu)
2. Connect your green screen plate to the plate input
3. Connect a coarse alpha hint to the mask input (a rough Keylight/Primatte key or AI roto works. Shuffle to Alpha to RGBA)
4. Point the Engine File knob to your .engine file
5. Set Resolution to match your engine (2048×2048 or 1024×1024)

Knobs

Knob	Description
Engine File	Path to the TensorRT .engine file
Resolution	Must match the ONNX export resolution
Output	RGBA (FG + Alpha), Alpha Only, or FG Only
Invert Matte	Inverts the predicted alpha
GPU Device	GPU index for multi-GPU systems

Output Details

• RGB channels: Straight (unpremultiplied) foreground color in sRGB
• Alpha channel: Linear alpha matte

To composite properly in Nuke, convert the FG from sRGB to linear, then premultiply:

TRTCorridorKey → Colorspace (sRGB to linear) → Premult → Merge (over)

Model Details

The CorridorKey engine uses:

• Input: [1, 4, 2048, 2048] — ImageNet-normalized RGB concatenated with the raw alpha hint
• Outputs: alpha [1, 1, 2048, 2048] and fg [1, 3, 2048, 2048], both post-sigmoid
• Architecture: Hiera Base Plus backbone (from timm) with dual decoder heads and a CNN refiner
• Native resolution: 2048×2048 (positional embeddings are baked at export resolution)

Files in This Repo

├── TRTCorridorKey.cpp          # Nuke plugin source
├── CMakeLists.txt              # Build configuration
├── export_corridorkey_onnx.py  # PyTorch → ONNX export script
├── test_trt_engine.py          # Standalone TRT engine test
├── LICENSE                     # License
└── README.md                   # This file

Credits

• CorridorKey model by Niko Pueringer / Corridor Digital — CC BY-NC-SA 4.0
• TRTCorridorKey Nuke plugin by Peter Mercell
• Built with TensorRT and the Nuke NDK

License

This plugin code is released under the MIT License. See LICENSE for details.

The CorridorKey model weights and architecture are licensed under CC BY-NC-SA 4.0 by Corridor Digital. You must comply with their license when using the model. See the CorridorKey repository for full terms.