  Multimedia generation approaches occupy a prominent place in artificial
intelligence research. Text-to-image models achieved high-quality results over
the last few years. However, video synthesis methods recently started to
develop. This paper presents a new two-stage latent diffusion text-to-video
generation architecture based on the text-to-image diffusion model. The first
stage concerns keyframes synthesis to figure the storyline of a video, while
the second one is devoted to interpolation frames generation to make movements
of the scene and objects smooth. We compare several temporal conditioning
approaches for keyframes generation. The results show the advantage of using
separate temporal blocks over temporal layers in terms of metrics reflecting
video generation quality aspects and human preference. The design of our
interpolation model significantly reduces computational costs compared to other
masked frame interpolation approaches. Furthermore, we evaluate different
configurations of MoVQ-based video decoding scheme to improve consistency and
achieve higher PSNR, SSIM, MSE, and LPIPS scores. Finally, we compare our
pipeline with existing solutions and achieve top-2 scores overall and top-1
among open-source solutions: CLIPSIM = 0.2976 and FVD = 433.054. Project page:
https://ai-forever.github.io/kandinsky-video/
