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.

翻译：我们提出一种名为"观察引导扩散概率模型"（OGDM）的新型扩散模型，该模型有效解决了质量控制与快速采样之间的权衡问题。我们的方法通过将观察过程的引导与马尔可夫链以严谨方式相结合，重新构建了训练目标。这一目标通过引入一个基于噪声水平条件判别器的额外损失项实现——该判别器依据伯努利分布判断其输入是否位于（含噪）真实流形上。这种策略使我们能够在推理阶段（尤其是函数评估次数受限时）优化更精确的负对数似然。所提出的训练方法即便仅融入微调过程也具有优势，并且与多种快速推理策略兼容，因为我们的方法在不增加额外计算成本的前提下，通过完全相同的推理流程获得了更优的去噪网络。我们通过多种推理方法在强扩散模型基线上验证了所提训练算法的有效性。