Lens-less imaging is affected by twinning noise occurring in in-line holograms， and the reconstructed results continuously face poor reconstruction signal-to-noise ratio and low imaging resolution. This study proposes a lens-less imaging via a score-based generation model. In the training phase， the proposed model perturbs data distribution by gradually adding Gaussian noise by using a continuous stochastic differential equation （SDE）. A continuous time-dependent score-based function with denoising score matching is then trained and used to solve the inverse SDE required to generate object sample data. In the testing phase， a single Fresnel zone aperture is used as a mask to achieve lens-less encoding modulation under incoherent illumination. The prediction-correction method is then used to alternate iteration steps between the numerical SDE solver and data-fidelity term to achieve lens-less imaging reconstruction. Validation results on LSUN-bedroom and LSUN-church datasets show that the proposed algorithm can effectively eliminate twin image noise， and the peak signal-to-noise ratio （PSNR） and structural similarity （SSIM） of the reconstruction results can reach 25.23 dB and 0.65， respectively. The PSNR values of the reconstruction results are 17.49 dB and 7.16 dB， which is higher than that of lens-less imaging algorithms based on traditional back propagation or compressed sensing， respectively. In addition， the corresponding SSIM values were 0.42 and 0.35 higher， respectively. Therefore， the reconstruction quality of the lens-less imaging is effectively improved.