Structural Pruning for Diffusion Models -- Supplementary Materials -- Gongfan Fang Xinyin Ma Xinchao Wang National University of Singapore

Table 1: Finetuning pruned models with more training steps. Note that the only difference lies in the position of the summation. It is easy to observe that our model achieves convergence rapidly. The dataset size of LSUN Bedroom is 44.48GB, which is We conducted further investigations to explore the effectiveness of knowledge distillation in enhancing pruning techniques. Table 3 profiles the pre-trained and the pruned models on a single A5000, with a batch size of 1.