ccvfi 0.0.3

an inference lib for video frame interpolation with VapourSynth support

ccvfi

an inference lib for video frame interpolation with VapourSynth support

Install

Make sure you have Python >= 3.9 and PyTorch >= 1.13 installed

pip install ccvfi

• Install VapourSynth (optional)

Start

cv2

a simple example to use the RIFE (ECCV2022-RIFE) model to process an image sequence.

import cv2
import numpy as np

from ccvfi import AutoModel, ConfigType, VFIBaseModel

model: VFIBaseModel = AutoModel.from_pretrained(
    pretrained_model_name=ConfigType.RIFE_IFNet_v426_heavy,
)

img0 = cv2.imdecode(np.fromfile("01.jpg", dtype=np.uint8), cv2.IMREAD_COLOR)
img1 = cv2.imdecode(np.fromfile("02.jpg", dtype=np.uint8), cv2.IMREAD_COLOR)
out = model.inference_image_list(img_list=[img0, img1])[0]
cv2.imwrite("test_out.jpg", out)

VapourSynth

a simple example to use the VFI (Video Frame-Interpolation) model to process a video (DRBA)

import vapoursynth as vs
from vapoursynth import core

from ccvfi import AutoModel, BaseModelInterface, ConfigType

model: BaseModelInterface = AutoModel.from_pretrained(
    pretrained_model_name=ConfigType.DRBA_IFNet,
)

clip = core.bs.VideoSource(source="s.mp4")
clip = core.resize.Bicubic(clip=clip, matrix_in_s="709", format=vs.RGBH)
clip = model.inference_video(clip, tar_fps=60)
clip = core.resize.Bicubic(clip=clip, matrix_s="709", format=vs.YUV420P16)
clip.set_output()

See more examples in the example directory, ccvfi can register custom configurations and models to extend the functionality

Current Support

It still in development, the following models are supported:

• Architecture
• Model
• Weight(Config)

Reference

• PyTorch
• BasicSR
• mmcv
• huggingface transformers
• VapourSynth
• HolyWu's functions

License

This project is licensed under the MIT - see the LICENSE file for details.