an inference lib for video frame interpolation with VapourSynth support
Make sure you have Python >= 3.9 and PyTorch >= 1.13 installed
pip install ccvfi- Install VapourSynth (optional)
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)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
It still in development, the following models are supported:
This project is licensed under the MIT - see the LICENSE file for details.
