  Diffusion Morphs (DiM) are a recent state-of-the-art method for creating high
quality face morphs; however, they require a high number of network function
evaluations (NFE) to create the morphs. We propose a new DiM pipeline,
Fast-DiM, which can create morphs of a similar quality but with fewer NFE. We
investigate the ODE solvers used to solve the Probability Flow ODE and the
impact they have on the the creation of face morphs. Additionally, we employ an
alternative method for encoding images into the latent space of the Diffusion
model by solving the Probability Flow ODE as time runs forwards. Our
experiments show that we can reduce the NFE by upwards of 85% in the encoding
process while experiencing only 1.6\% reduction in Mated Morph Presentation
Match Rate (MMPMR). Likewise, we showed we could cut NFE, in the sampling
process, in half with only a maximal reduction of 0.23% in MMPMR.
