Review for NeurIPS paper: Improved Techniques for Training Score-Based Generative Models

Weaknesses: The experimental section does not quite have the experiments I was hoping for. I was hoping to understand *which* techniques were important to scale the model to higher-resolution images. I know that using all techniques are needed for the model to learn LSUN images, but my suspicion is that only a subset of them (perhaps only EMA) is needed for regular NCSN to learn high-resolution images. Better understanding of which techniques are important would help explain what is needed for scaling the model. Moreover, for the ablation experiments provided, it does not seem that all 5 techniques are need for all datasets. Fig 5 seems to show that the simplified network is not needed on CelebA 64x64 as FID is better using the original network.