This paper proposes a learning-based video compression framework that applies a conditional flow-based model for inter-frame coding and takes YUV 4:2:0 as the input format. Most learning-based video compression models use predictive coding and directly encode the residual signal, which is considered a sub-optimal solution. In addition, those models usually only operate on RGB, which is also regarded as an inefficient format. Furthermore, they require multiple models to fit on different bit rates. To solve these issues, we introduce a conditional flow-based video compression framework to improve the coding efficiency. To adapt to YUV 4:2:0 format, we incorporate lossless space-to-depth and depth-to-space transformation in our design. Lastly, we apply rate-adaption net on both I-frame and P-frame coder to achieve variable-rate coding and can further be extended to rate control applications. Our experimental results show comparable or better performance against x265 for UVG and MCL-JCV common test datasets in terms of PSNR-YUV.