nvidia-vfx 0.1.0.1

Python bindings for NVIDIA Video Effects SDK

nvidia-vfx Python Wheel README

nvidia-vfx

nvidia-vfx provides Python bindings for the NVIDIA VFX SDK. It includes the VideoSuperRes effect, so you can add AI-based upscaling, denoising, and deblurring to your video pipelines entirely from Python.

Features

• Video Super Resolution – upscale and enhance SDR video up to 4x while reducing compression artifacts.
• Same-resolution denoise/deblur – set output dimensions equal to input and use denoise or deblur quality levels to clean noisy or blurry footage without changing resolution.
• Zero-copy interop – exchange data with PyTorch, CuPy, and other frameworks via the DLPack protocol.
• GPU-accelerated – runs entirely on NVIDIA GPUs with Tensor Cores for real-time or near-real-time performance on supported hardware.

Requirements

• Python: 3.10 or later
• GPU: NVIDIA GPU with Tensor Cores (Turing, Ampere, Ada, Blackwell, or Hopper architecture)
• GPU driver:
  • Windows: 570.65 or later (for Tesla Compute Cluster (TCC) devices, 595 or later is required)
  • Linux: 570.190+, 580.82+, or 590.44+
• OS:
  • Windows: 64-bit Windows 10 or later
  • Linux: Ubuntu 20.04, 22.04, 24.04, Debian 12, or RHEL 8/9

Installation

pip install nvidia-vfx

This installs the Python bindings; at runtime nvidia-vfx loads the NVIDIA VFX libraries bundled in the wheel.

Quick start (PyTorch)

import torch
from nvvfx import VideoSuperRes

# Example input: 1080p RGB frame on CUDA in [0, 1] range
frame = torch.rand(3, 1080, 1920, device="cuda", dtype=torch.float32)

# Create effect with standard HIGH quality (default)
vsr = VideoSuperRes(quality=VideoSuperRes.QualityLevel.HIGH)
# Set output dimensions (e.g. 2x upscale)
vsr.output_width = 3840   # 1920 * 2
vsr.output_height = 2160  # 1080 * 2
vsr.load()

# Run Video Super Resolution; returns DLPack capsule
result = vsr.run(frame)
# IMPORTANT: convert and clone before reusing the effect
out = torch.from_dlpack(result.image).clone()
print(out.shape)   # -> (3, 2160, 3840)

Quick start (CuPy)

import cupy as cp
from nvvfx import VideoSuperRes

# Example input: 720p RGB frame on CUDA in [0, 1] range
frame = cp.random.rand(3, 720, 1280, dtype=cp.float32)

# Same-resolution denoise: output dimensions = input dimensions
vsr = VideoSuperRes(quality=VideoSuperRes.QualityLevel.DENOISE_HIGH)
vsr.output_width = 1280
vsr.output_height = 720
vsr.load()

result = vsr.run(frame)
# IMPORTANT: convert and copy before the next call
out = cp.from_dlpack(result.image).copy()
print(out.shape)   # -> (3, 720, 1280)

Output dimensions and quality overview

• Set output_width and output_height (in pixels), then call load() before run(). Input size is inferred from each frame; use output size to control upscaling or same-resolution processing.
• Upscaling (2x, 3x, 4x): Set output_width and output_height to the desired size (e.g. 2x input width and height). Use standard quality levels (e.g. VideoSuperRes.QualityLevel.HIGH) or high-bitrate levels for clean sources.
• Same-resolution (denoise/deblur): Set output_width and output_height equal to the input dimensions. Use a DENOISE_* or DEBLUR_* quality level.
• QualityLevel is an IntEnum (e.g. VideoSuperRes.QualityLevel) with values in the range 0–19, grouped into:
  • Standard upscaling modes (BICUBIC through ULTRA)
  • Denoise modes (DENOISE_LOW ... DENOISE_ULTRA)
  • Deblur modes (DEBLUR_LOW ... DEBLUR_ULTRA)
  • High-bitrate upscaling modes (HIGHBITRATE_LOW ... HIGHBITRATE_ULTRA)

Pass a QualityLevel enum member to the quality argument (e.g. VideoSuperRes.QualityLevel.HIGH).

Best practices

• Keep tensors on the GPU and in channels-first RGB (3, H, W) format with float32 values in [0.0, 1.0] for optimal performance.
• Use moderate quality levels for interactive previews, and then switch to ULTRA or high-bitrate variants for final renders.
• run() returns a VideoSuperResOutput whose .image is a DLPack capsule. Convert with torch.from_dlpack(result.image).clone() or cp.from_dlpack(result.image).copy() and keep that copy before the next run() so the underlying buffer is not overwritten.

Documentation

For the complete API reference, parameter details, and advanced usage, refer to the NVIDIA Video Effects (VFX) Python Bindings Guide.

Changelog

0.1.0.1 (first release)

• Initial release of the nvidia-vfx Python wheel.
• Internal VFX SDK version: 1.2.0.0.
• Video Super Resolution – upscale, denoise, and deblur via the VideoSuperRes effect.
• Zero-copy interop with PyTorch, CuPy, and other frameworks via the DLPack protocol.