AI Diffusion Model Based on Stochastic Differential Equations: Noise Scheduling Variable Discrete Approximation

Recently, diffusion probabilistic models have shown great success in image synthesis, but their performance relies heavily on the pre-defined noise schedules, which have a direct impact on the convergence speed, stability, and quality of the generated images. Moreover, the traditional score-based SDE model is very sensitive to the choice of the step size in the numerical integration process. To overcome the above limitations, a variable discrete noise scheduling method combined with a Fully Connected Neural Network (FC-NN) denoiser is proposed in the SDE diffusion model. Experimental analysis on the CIFAR-10 dataset shows that the proposed Variable SDE model results in a final FID value of 8.7, which is better than DDPM (11.5), DDIM (10.9), and Score SDE (10.2), and an Inception Score of 9.4, which is better than 8.6 for the fixed scheduling approach. The proposed approach results in a reduction of convergence time by 20-25% and peak discretization error by 30-35%. The t-SNE analysis shows that the overlap between the real and generated distributions is good, and there is no mode collapse. The proposed approach provides faster convergence, better numerical stability, and improved generative quality compared to existing standard diffusion models.