  Recently, the diffusion model with the training-free methods has succeeded in
conditional image generation tasks. However, there is an efficiency problem
because it requires calculating the gradient with high computational cost, and
previous methods make strong assumptions to solve it, sacrificing
generalization. In this work, we propose the Fisher information guided
diffusion model (FIGD). Concretely, we introduce the Fisher information to
estimate the gradient without making any additional assumptions to reduce
computation cost. Meanwhile, we demonstrate that the Fisher information ensures
the generalization of FIGD and provides new insights for training-free methods
based on the information theory. The experimental results demonstrate that FIGD
could achieve different conditional generations more quickly while maintaining
high quality.
