🚨 Deprecation Notice
This repository is no longer maintained. All features have been integrated and further developed in cccv. Please migrate to the new repository for future updates and support.
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.