Enhancing low-light images to achieve proper exposure and clean visual effects poses a significant challenge in computational photography, while leveraging the generative capacity of diffusion models to yield satisfactory outcomes is a viable solution. Nonetheless, the performance of diffusion models in image restoration tasks tends to be unpredictable, often resulting in blurry details. In response to these issues, we proposed a robust and effective low-light image enhancement method via blueprint separable convolution (BSConv) and wavelet-diffusion model, called BSDiff. Specifically, we take advantage of wavelet transform to preserve the detail information in the sub-bands and utilize the generative capacity of conditional diffusion models. To substantially reduce randomness in the inference process, we introduce a novel auxiliary loss function, the Charbonnier penalty, enabling the model to yield visually pleasing results. Furthermore, a high-frequency sub-band feature enhancement module based on BSConv is designed to stabilize the denoising capability of the model and effectively correct details and color discrepancies in images. Extensive experiments conducted on publicly available real-world benchmarks demonstrate that our method outperforms existing state-of-the-art methods in both objective performance and subjective visual quality.