  In recent years, diffusion models have gained popularity for their ability to
generate higher-quality images in comparison to GAN models. However, like any
other large generative models, these models require a huge amount of data,
computational resources, and meticulous tuning for successful training. This
poses a significant challenge, rendering it infeasible for most individuals. As
a result, the research community has devised methods to leverage pre-trained
unconditional diffusion models with additional guidance for the purpose of
conditional image generative. These methods enable conditional image
generations on diverse inputs and, most importantly, circumvent the need for
training the diffusion model. In this paper, our objective is to reduce the
time-required and computational overhead introduced by the addition of guidance
in diffusion models -- while maintaining comparable image quality. We propose a
set of methods based on our empirical analysis, demonstrating a reduction in
computation time by approximately threefold.
