Audio super-resolution is a fundamental task that predicts high-frequency components for low-resolution audio, enhancing audio quality in digital applications. Previous methods have limitations such as the limited scope of audio types (e.g., music, speech)  and specific bandwidth settings they can handle (e.g.,kHz tokHz) . In this paper, we introduce a diffusion-based generative model,AudioSR, that is capable of performing robust audio super-resolution on versatile audio types, including sound effects, music, and speech. Specifically,AudioSRcan upsample any input audio signal within the bandwidth range ofkHz tokHz to a high-resolution audio signal atkHz bandwidth with a sampling rate ofkHz. Extensive objective evaluation on various audio super-resolution benchmarks demonstrates the strong result achieved by the proposed model. In addition, our subjective evaluation shows thatAudioSRcan acts as a plug-and-play module to enhance the generation quality of a wide range of audio generative models, including AudioLDM, Fastspeech2, and MusicGen. Our code and demo are available athttps://audioldm.github.io/audiosr.