The paper introduces a lightweight information multi-distillation network (IMDN) for single image super-resolution (SISR). The proposed method aims to balance performance and applicability, addressing the limitations of existing deep CNN-based SISR methods, which often suffer from high computational costs and memory footprints. The IMDN is designed to be efficient and can handle images of any scale factor using a single model. It consists of cascaded information multi-distillation blocks (IMDBs) that extract hierarchical features step-by-step and a contrast-aware channel attention (CCA) layer to aggregate these features. The adaptive cropping strategy (ACS) is also introduced to process images of arbitrary sizes by scaling them to the target resolution and then cropping them into appropriate patches. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art SISR algorithms in terms of visual quality, memory usage, and inference time. The code for the method is available at https://github.com/Zheng222/IMDN.The paper introduces a lightweight information multi-distillation network (IMDN) for single image super-resolution (SISR). The proposed method aims to balance performance and applicability, addressing the limitations of existing deep CNN-based SISR methods, which often suffer from high computational costs and memory footprints. The IMDN is designed to be efficient and can handle images of any scale factor using a single model. It consists of cascaded information multi-distillation blocks (IMDBs) that extract hierarchical features step-by-step and a contrast-aware channel attention (CCA) layer to aggregate these features. The adaptive cropping strategy (ACS) is also introduced to process images of arbitrary sizes by scaling them to the target resolution and then cropping them into appropriate patches. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art SISR algorithms in terms of visual quality, memory usage, and inference time. The code for the method is available at https://github.com/Zheng222/IMDN.