nikopueringer / CorridorKey2.0

# CorridorKey 2.0
Welcome to CorridorKey 2.0! You may rest easy, weary artist. The green beast has been slain. The CorridorKey 2.0 model was designed and trained by Zachary Harris, in direct collaboration with Niko and Corridor Digital.
# Check out our official plugin!
The most requested feature we've heard this year- "Can you make a plugin for my favorite software?
...yes! Finally!
Led by Ale Alvaro, we now have a plugin (and stand-alone app) available for almost every media software package. A one-time purchase gets you access to ALL OF THEM.
**corridorkey.com**
...And for those of you that want to experiment, tinker, and build your own python tools, welcome to the CorridorKey 2.0 repo! You may use this model and software freely, for commercial or personal work. See the license for more details, but that's it in a nutshell. Enjoy!
Any business or research inquiries for Zac can be directed to z.brooks.harris@gmail.com
## Setup
You need an NVIDIA GPU (currently RTX 30-series or newer) with at least 4 GB of VRAM
for footage up to 1440p, or 6 GB for 4K, `ffmpeg` on your PATH, and a Python
environment with CUDA-enabled PyTorch installed. On smaller cards, see Long Clips And Low VRAM.
This package was tested with:
 [code] 
From the repository root, create an environment, install PyTorch and the
remaining dependencies, and download the model weights (about 5 GB):
 [code] 
The download script fetches:
- the CorridorKey 2.0 weights from
 Hugging Face into
 `weights/`
- the three frozen foundation models CorridorKey builds on, from their original
 publishers, into `vfms/`: C-RADIOv4-SO400M (NVIDIA), MoGe-2 ViT-B
 (Microsoft), and RVM small (Google DeepMind)
After that everything loads from those local folders.
## Drag-And-Drop Launcher
The easiest way to run CorridorKey is to drag a clip, a frame-sequence folder,
or a folder of clips onto the launcher in the repository root:
- **Windows:** `CorridorKey_DRAG_CLIPS_HERE.bat`
- **Linux:** `CorridorKey_DRAG_CLIPS_HERE.sh` (or run it in a terminal and drop
 the clip into the terminal when asked)
It walks you through the inference mode, outputs, and GPU memory profile, then
runs the job. The launcher uses the `venv` created during setup if it exists.
It also picks up companion hint files next to the input automatically
(` FrameHint.png` for a first-frame hint, ` _AlphaHint.mp4` for a
full hint video).
You can also start the same guided session directly:
 [code] 
## Quick Start
From the repository root:
 [code] 
This runs native-resolution inference with no external hint. By default it
writes:
- `alpha`: predicted matte as a video preview.
- `fg`: predicted foreground RGB as a video preview.
Native resolution is always preserved. The model reflect-pads internally to its
required shape quantum and crops back to the original size for output.
## Choosing An Inference Mode
CorridorKey can run with no hints, a single first-frame hint, or a full hint
video. The right mode depends on what information you have and how