  We present SoundLoCD, a novel text-to-sound generation framework, which
incorporates a LoRA-based conditional discrete contrastive latent diffusion
model. Unlike recent large-scale sound generation models, our model can be
efficiently trained under limited computational resources. The integration of a
contrastive learning strategy further enhances the connection between text
conditions and the generated outputs, resulting in coherent and high-fidelity
performance. Our experiments demonstrate that SoundLoCD outperforms the
baseline with greatly reduced computational resources. A comprehensive ablation
study further validates the contribution of each component within SoundLoCD.
Demo page: \url{https://XinleiNIU.github.io/demo-SoundLoCD/}.
