Abstract: Due to the complexity of noise in low light environments, it is very difficult to establish noise models. In this regard, this paper proposed a noise image generation method based on diffusion model.Firstly, this article introduced various types of noise into clean images using the forward process of diffusion models based on prior knowledge. Then, a conditional diffusion model was used to input it together with the clean image into the network. Finally, the reverse process of the cold diffusion model was used to iteratively generate noisy images. The experimental results on a low light dataset show that compared with other algorithms, the noise images generated by our algorithm have an objective KL divergence value of 0. 068, which is 0. 001 lower than the existing best methods. The subjective quality is higher, and it is closest to images in low light environments. The established noise model is the most accurate. This method successfully established a high-quality noise model in low light environments, which provided a new approach for noise modeling in low light environments, and the diffusion model was used to the field of low light noise modeling.