vsrvrt 1.1.1

Vapoursynth plugin for RVRT (Recurrent Video Restoration Transformer)

VSRVRT: Vapoursynth Plugin for RVRT

A Vapoursynth plugin wrapper for RVRT (Recurrent Video Restoration Transformer), implementing state-of-the-art video denoising, deblurring, and super-resolution. Based on https://github.com/JingyunLiang/RVRT

Features

• Video Denoising: Non-blind denoising with tunable sigma parameter (0-50)
• Video Deblurring: Support for both GoPro and DVD dataset models
• Video Super-Resolution: 4x upscaling with multiple model variants (Extreme VRAM usage)
• FP16 Support: Half-precision for faster inference and 50% VRAM reduction, optional FP32
• Preview Mode: Lazy chunk processing for faster preview in vspreview (still slow though)
• Flexible Chunking: Control chunk size, overlap, and processing strategy
• Automatic Tiling: VRAM-aware automatic tiling to handle large videos (may not be perfect)
• RGBS Format: Full support for 32-bit float RGB
• Pre-built Binaries: No compilation required for PyPI installs

Requirements

• Python 3.12 or 3.13
• VapourSynth >= 60
• PyTorch >= 2.10.0 with CUDA support (see important note below)
• NVIDIA GPU with CUDA 12.8+ driver

Installation

pip

pip install vsrvrt

Arch Linux (AUR)

yay -S vsrvrt-git

Important Note: PyPI's default index only has CPU-only PyTorch. You must install the CUDA version first:

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128

Building from Source

If you need to build from source (e.g., for development or unsupported platforms):

1. Prerequisites:

   • CUDA Toolkit 12.8+
   • C++ compiler (MSVC on Windows, GCC on Linux)
   • ninja build system

2. Build:

   git clone https://github.com/Lyra-Vhess/vs-rvrt/
   cd vs-rvrt
   pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128
   pip install ninja
   python build_wheels.py
   pip install dist/vsrvrt-*.whl
   

Usage

Basic Usage

# Convert to RGB (required)
clip = clip.resize.Bicubic(format=vs.RGB24) # or RGBS

# Video Denoising (sigma: 0-50)
denoised = vsrvrt.Denoise(clip, sigma=12.0)

# Video Deblurring
deblurred = vsrvrt.Deblur(clip, model="gopro")  # or "dvd"

# Video Super-Resolution (4x)
upscaled = vsrvrt.SuperRes(clip, scale=4, model="reds")  # or "vimeo_bi", "vimeo_bd"

API Reference

Denoise

vsrvrt.Denoise(
    clip: vs.VideoNode,             # Input clip (RGB format)
    sigma: float = 12.0,            # Noise level (0-50, default: 12)
    tile_size: Tuple[int, int, int] = (64,256,256),    # (Temporal, Height, Width), None for auto
    tile_overlap: Tuple[int, int, int] = (2, 20, 20),  # Overlap for tiling
    use_fp16: [bool] = True,        # Use FP16 precision, (default: True)
    device: [str] = None,           # 'cuda', 'cpu', or auto
    chunk_size: [int] = None,       # Frames per chunk (default: 64)
    chunk_overlap: [int] = None,    # Overlapping frames (default: 16)
    use_chunking: [bool] = None,    # Whether to us chunked processing, (default: True)
    preview_mode: [bool] = False    # Lazy chunk processing for preview
) -> vs.VideoNode

Deblur

vsrvrt.Deblur(
    clip: vs.VideoNode,             # Input clip (RGB format)
    model: str = "gopro",           # 'gopro' or 'dvd'
    tile_size: Tuple[int, int, int] = None,
    tile_overlap: Tuple[int, int, int] = (2, 20, 20),
    use_fp16: [bool] = True,
    device: [str] = None,
    chunk_size: [int] = None,
    chunk_overlap: [int] = None,
    use_chunking: [bool] = None,
    preview_mode: [bool] = False
) -> vs.VideoNode

SuperRes

vsrvrt.SuperRes(
    clip: vs.VideoNode,             # Input clip (RGB format)
    scale: int = 4,                 # Must be 4 (only 4x models available)
    model: str = "reds",            # 'reds', 'vimeo_bi', or 'vimeo_bd'
    tile_size: Optional[Tuple[int, int, int]] = None,
    tile_overlap: Tuple[int, int, int] = (2, 20, 20),
    use_fp16: bool = True,
    device: Optional[str] = None,
    chunk_size: Optional[int] = None,
    chunk_overlap: Optional[int] = None,
    use_chunking: Optional[bool] = None,
    preview_mode: bool = False
) -> vs.VideoNode

Tiling Options

The tile_size parameter controls memory usage:

• None: Automatic based on available VRAM
• (0, 0, 0): No tiling (process entire video at once) - requires significant VRAM
• (T, H, W): Manual tile size (e.g., (64, 256, 256))

Spatial Tile Size Guidelines:

• As far as I can tell, HxW = 256x256 is ideal because the models were trained with that tile size. Experiment at your own risk.
• Higher temporal windows appear to improve quality, though there appears to be a ceiling after which you will get artifacts.
• Temporal windows from 16 to 64 seem safe
• The interaction between the temporal tiling and chunk length is not entirely clear to me, experiment at your own risk
• Automatic sizing may not be perfect, consider (16,256,256) as a good default and increase T as memory allows
• Super-Resolution is extremely memory-hungry and likely requires bare minimal tiling for 12GB GPUs of (8,256,256) with chunk sizes of 16
• Expect poor SR quality below 16GB of VRAM due to the need for extremely minimal tiling sizes

Note: Tile size must be a multiple of 8 (the model's window size).

Preview Mode (for vspreview)

Preview mode processes chunks on-demand for instant startup:

# Preview mode - faster startup, processes chunks as needed
denoised = vsrvrt.Denoise(clip, sigma=10.0, preview_mode=True)

# Normal mode - process all chunks upfront, best quality but extreme delay
denoised = vsrvrt.Denoise(clip, sigma=10.0, preview_mode=False)

Preview Mode Notes:

• Each chunk is processed independently (no recurrence from previous chunks)
• Slight quality trade-off at chunk boundaries
• Processed chunks are cached for the session
• Best for quickly checking settings before final encode

Chunk Control

Control how video is processed in chunks:

# Customize chunk processing
denoised = vsrvrt.Denoise(
    clip, 
    sigma=10.0,
    chunk_size=64,        # Frames per chunk (default: 64)
    chunk_overlap=16,     # Overlapping frames (default: 16)
    use_chunking=True     # Use chunked processing
)

# Disable chunking (process entire video at once, extreme memory usage)
denoised = vsrvrt.Denoise(clip, sigma=10.0, use_chunking=False)

Chunk Size Guidelines:

• 64 frames: Good balance (default)
• 48-96: Adjust based on VRAM
• Must be <128 to keep processing on GPU

Model Info

These are the datasets used in training the models, choose the model based on how closely your video matches the dataset.

Dataset	Task	Resolution	Content Type	Best For
REDS	Super-Resolution	1280×720	Real diverse scenes	General upscaling, natural motion
Vimeo-90K	Super-Resolution	448×256	Real web videos	Web content, user videos
GoPro	Deblurring	1280×720	Synthetic blur from real scenes	Camera shake, dynamic motion
DVD	Deblurring	~1280×720	Hand-held camera blur	Smartphone videos, hand shake
DAVIS	Denoising	1080p/480p	High-quality footage	Tunable denoising (sigma 0-50)

Troubleshooting

"requires PyTorch with CUDA support, but you have the CPU-only version"

This means pip installed the CPU-only PyTorch from the default PyPI index. Fix:

pip uninstall torch torchvision -y
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128

"CUDA is not available" (but you have PyTorch with CUDA)

This usually means:

1. You don't have an NVIDIA GPU
2. Your GPU driver is too old
3. CUDA drivers aren't installed

Update your NVIDIA drivers from https://www.nvidia.com/Download/index.aspx

Performance Tips

1. Use FP16: Enable for 2x speedup and 50% VRAM reduction
2. Preview Mode: Use preview_mode=True in vspreview for instant feedback
3. Adjust Tiling: If you get OOM errors, reduce tile_size
4. Chunk Size: Larger chunks = better quality but more VRAM. Default 64 is a good balance.

Project Structure

vsrvrt/
├── __init__.py             # Package initialization
├── _binary/                # Pre-built CUDA extension binaries
│   ├── __init__.py         # Binary loader
│   ├── win_amd64/          # Windows binaries
│   └── manylinux_x86_64/   # Linux binaries
├── model_configs.py        # Model configurations
├── rvrt_core.py            # Core inference wrapper
├── rvrt_filter.py          # Vapoursynth filter functions
├── models/                 # Downloaded model weights (auto-populated)
├── utils/                  # Utility functions
└── rvrt_src/               # RVRT source code
    ├── models/
    │   ├── network_rvrt.py
    │   └── op/             # CUDA extension source
    └── utils/

Citation

@article{liang2022rvrt,
    title={Recurrent Video Restoration Transformer with Guided Deformable Attention},
    author={Liang, Jingyun and Fan, Yuchen and Xiang, Xiaoyu and Ranjan, Rakesh and Ilg, Eddy  and Green, Simon and Cao, Jiezhang and Zhang, Kai and Timofte, Radu and Van Gool, Luc},
    journal={arXiv preprint arXiv:2206.02146},
    year={2022}
}

License

This plugin follows the same license as RVRT (CC-BY-NC-4.0 for non-commercial use).