Learning to Integrate Diffusion ODEs by Averaging the Derivatives

To accelerate diffusion model inference, numerical solvers perform poorly at extremely small steps, while distillation techniques often introduce complexity and instability. This work presents an intermediate strategy, balancing performance and cost, integral by relationship, learning ODE inspired integration by Monte using Carlo loss functions integration deri and ved Picard from iteration.