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VS-DRBA

Distance Ratio Based Adjuster for Animeinterp, based on https://github.com/routineLife1/DRBA and https://github.com/HolyWu/vs-rife.

This project is modified from HolyWu/vs-rife and achieves nearly the same interpolation quality as the original DRBA project.

With TensorRT integration, it achieves a 400% acceleration, enabling real-time playback on high-performance NVIDIA GPUs.

Dependencies

trt requires additional packages:

To install the latest stable version of PyTorch, Torch-TensorRT and cupy, run:

pip install -U packaging setuptools wheel
pip install -U torch torchvision torch_tensorrt --index-url https://download.pytorch.org/whl/cu126 --extra-index-url https://pypi.nvidia.com
pip install -U cupy-cuda12x

Installation

pip install -U vsdrba==1.0.2

If you want to download all models at once, run python -m vsdrba. If you prefer to only download the model you specified at first run, set auto_download=True in drba_rife().

Usage

from vsdrba import drba_rife

ret = drba_rife(clip)

See __init__.py for the description of the parameters.

Benchmarks

model scale os hardware arch fps 720 fps 1080 vram 720 vram 1080 backend verified output batch level streams threads onnx onnxslim / onnxsim onnx shape trtexec shape precision usage
rife 4.26 heavy 2x Linux 3070laptop / 12400 rife (4.26) 119 53 1.6gb 3.4gb trt 10.8, torch 20241231+cu126, torch_trt 20250102+cu126 (holywu vsrife) yes, works 1 5 - 8 - - - static RGBH rife(clip, trt=True, trt_static_shape=True, model="4.26.heavy", trt_optimization_level=5, sc=False)
drba_rife 4.26 heavy 2x Linux 3070laptop / 12400 drba_rife (4.26) 158 70 1.7gb 3.7gb trt 10.8, torch 20241231+cu126, torch_trt 20250102+cu126 (routineLife1 vsdrba) yes, works 1 5 - 8 - - - static RGBH rife(clip, trt=True, trt_static_shape=True, model="4.26.heavy", trt_optimization_level=5, sc=False)

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