Observation-Guided Diffusion Probabilistic Models

Figure 1. Comparisons of images generated by the ADM backbone on the CelebA dataset with deterministic samplers using the same initial noise but different NFEs. The entries on the leftmost column of the figure denote the combinations of the training and inference methods. (Left) The baseline model generates samples with inconsistent attributes, e.g., gender, hair, etc., by varying NFEs while our approach preserves such properties. (Right) The samples generated by the baseline method with a small number of NFEs tend to be blurry and unrealistic. Also, they have unnaturally bright and textureless areas around the chin of the person.