  We propose a novel diffusion-based image generation method called the
observation-guided diffusion probabilistic model (OGDM), which effectively
addresses the tradeoff between quality control and fast sampling. Our approach
reestablishes the training objective by integrating the guidance of the
observation process with the Markov chain in a principled way. This is achieved
by introducing an additional loss term derived from the observation based on a
conditional discriminator on noise level, which employs a Bernoulli
distribution indicating whether its input lies on the (noisy) real manifold or
not. This strategy allows us to optimize the more accurate negative
log-likelihood induced in the inference stage especially when the number of
function evaluations is limited. The proposed training scheme is also
advantageous even when incorporated only into the fine-tuning process, and it
is compatible with various fast inference strategies since our method yields
better denoising networks using the exactly the same inference procedure
without incurring extra computational cost. We demonstrate the effectiveness of
our training algorithm using diverse inference techniques on strong diffusion
model baselines. Our implementation is available at
https://github.com/Junoh-Kang/OGDM_edm.
