We propose a novel diffusion model called observation-guided diffusion
probabilistic model (OGDM), which effectively addresses the trade-off 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 the conditional discriminator
on noise level, which employs 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 method 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
same inference procedure without incurring extra computational cost. We
demonstrate the effectiveness of the proposed training algorithm using diverse
inference methods on strong diffusion model baselines