Volterra generative models propose a continuous-time score-based framework using fractional kernels to inject path-dependent noise, avoiding memoryless noising in traditional diffusion models. The approach introduces finite-dimensional Markovian lifts and proves squared error bounds, demonstrating improved generation on MNIST and potential for natural images, with a bridge sampler enhancing stability for larger models.