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Recent advances in diffusion models bring state-of-the-art performance onimage generation tasks. However, empirical results from previous research indiffusion models imply an inverse correlation between density estimation andsample generation performances. This paper investigates with sufficientempirical evidence that such inverse correlation happens because densityestimation is significantly contributed by small diffusion time, whereas samplegeneration mainly depends on large diffusion time. However, training a scorenetwork well across the entire diffusion time is demanding because the lossscale is significantly imbalanced at each diffusion time. For successfultraining, therefore, we introduce Soft Truncation, a universally applicabletraining technique for diffusion models, that softens the fixed and statictruncation hyperparameter into a random variable. In experiments, SoftTruncation achieves state-of-the-art performance on CIFAR-10, CelebA, CelebA-HQ256x256, and STL-10 datasets.

Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation