Objective To address the issue that most style transfer methods fail to achieve effective transfer in the domain of Chinese ink wash painting a Chinese ink wash painting style transfer method based on an improved diffusion model sampling algorithm is proposed to achieve high-quality ink painting style transfer effects. Methods A denoising UNet network was trained on a Chinese ink wash painting image dataset to endow the diffusion model with the capability to generate authentic ink painting images. A loss-driven diffusion model sampling algorithm was designed first noise was added to the content image to obtain noisy data containing partial structural information then the diffusion model performed step-by-step denoising of this data guided by content loss style loss and semantic separation loss from both content and style perspectives ultimately generating the stylized result. Results Qualitative and quantitative comparative experimental results demonstrated that the proposed method outperforms other arbitrary style transfer methods in Chinese ink wash painting style transfer tasks producing higher-quality ink wash stylized images. Ablation experiments proved that using two different computation methods for content loss and employing mean-variance color loss in style loss is reasonable and effective. Conclusion The model generates appropriate and authentic ink wash textures while preserving the basic content structure providing an effective method for high-quality Chinese ink wash painting style transfer. The generated results further demonstrate great potential for the development of diffusion model technology under the demand for high-quality style transfer.