In Diff-Shape method, a novel equivariant neural network architecture，named Graph ControllNet (GrCN), was proposed and it composed an unconditioned diffusion model satisfying SE (3) symmetry for 3D molecular generation and a graph control module which takes specific conditions such as the 3D shape of a template molecule. Here we first summarize the architecture of GrCN model, then how Diff-Shape extends a pre-trained molecular diffusion model, MIDI in this case, to a shape conditioned molecular generative model will be discussed.