  Diffusion-based Deep Generative Models (DDGMs) offer state-of-the-art
performance in generative modeling. Their main strength comes from their unique
setup in which a model (the backward diffusion process) is trained to reverse
the forward diffusion process, which gradually adds noise to the input signal.
Although DDGMs are well studied, it is still unclear how the small amount of
noise is transformed during the backward diffusion process. Here, we focus on
analyzing this problem to gain more insight into the behavior of DDGMs and
their denoising and generative capabilities. We observe a fluid transition
point that changes the functionality of the backward diffusion process from
generating a (corrupted) image from noise to denoising the corrupted image to
the final sample. Based on this observation, we postulate to divide a DDGM into
two parts: a denoiser and a generator. The denoiser could be parameterized by a
denoising auto-encoder, while the generator is a diffusion-based model with its
own set of parameters. We experimentally validate our proposition, showing its
pros and cons.
