A rhythm action game is a music-based video game in which the player is challenged to issue commands at the right timings during a music session. The timings are rendered in the chart, which consists of visual symbols, called notes, flying through the screen. KLab Inc., a Japan-based video game developer, has operated rhythm action games including a title for the "Love Live!" franchise, which became a hit across Asia and beyond. Before this work, the company generated the charts manually, which resulted in a costly business operation. This paper presents how KLab applied a deep generative model for synthesizing charts, and shows how it has improved the chart production process, reducing the business cost by half. Existing generative models generated poor quality charts for easier difficulty modes. We report how we overcame this challenge through a multi-scaling model dedicated to rhythm actions, by considering beats among other things. Our model, named Gen\'eLive!, is evaluated using production datasets at KLab as well as open datasets.