  Sound content is an indispensable element for multimedia works such as video
games, music, and films. Recent high-quality diffusion-based sound generation
models can serve as valuable tools for the creators. However, despite producing
high-quality sounds, these models often suffer from slow inference speeds. This
drawback burdens creators, who typically refine their sounds through trial and
error to align them with their artistic intentions. To address this issue, we
introduce Sound Consistency Trajectory Models (SoundCTM). Our model enables
flexible transitioning between high-quality 1-step sound generation and
superior sound quality through multi-step generation. This allows creators to
initially control sounds with 1-step samples before refining them through
multi-step generation. While CTM fundamentally achieves flexible 1-step and
multi-step generation, its impressive performance heavily depends on an
additional pretrained feature extractor and an adversarial loss, which are
expensive to train and not always available in other domains. Thus, we reframe
CTM's training framework and introduce a novel feature distance by utilizing
the teacher's network for a distillation loss. Additionally, while distilling
classifier-free guided trajectories, we train conditional and unconditional
student models simultaneously and interpolate between these models during
inference. We also propose training-free controllable frameworks for SoundCTM,
leveraging its flexible sampling capability. SoundCTM achieves both promising
1-step and multi-step real-time sound generation without using any extra
off-the-shelf networks. Furthermore, we demonstrate SoundCTM's capability of
controllable sound generation in a training-free manner. Our codes, pretrained
models, and audio samples are available at https://github.com/sony/soundctm.
