y exploit diffusion modeling, we reinterpret the
compression process itself as a forward diffusion path governed by stochastic
differential equations (SDEs). A reverse neural network is trained to
reconstruct images by reversing the compression process directly, without
requiring Gaussian noise initialization. This approach achieves smooth rate
adjustment and photo-realistic reconstructions with only a minimal number of
sampling steps. Extensive experiments on benchmark datasets demonstrate that
our method 