F3-Pruning: A Training-Free and Generalized Pruning Strategy towards Faster and Finer Text-to-Video Synthesis

Recently Text-to-Video (T2V) synthesis has undergone a breakthrough by training transformers or diffusion models on large-scale datasets. Nevertheless, inferring such large models incurs huge costs.Previous inference acceleration works either require costly retraining or are model-specific.To address this issue, instead of retraining we explore the inference process of two mainstream T2V models using transformers and diffusion models.The exploration reveals the redundancy in temporal attention modules of both models, which are commonly utilized to establish temporal relations among frames.Consequently, we propose a training-free and generalized pruning strategy called F3-Pruning to prune redundant temporal attention weights.Specifically, when aggregate temporal attention values are ranked below a certain ratio, corresponding weights will be pruned.Extensive experiments on three datasets using a classic transformer-based model CogVideo and a typical diffusion-based model Tune-A-Video verify the effectiveness of F3-Pruning in inference acceleration, quality assurance and broad applicability

Impact of Extracellularity on the Evolutionary Rate of Mammalian Proteins

It is of fundamental importance to understand the determinants of the rate of protein evolution. Eukaryotic extracellular proteins are known to evolve faster than intracellular proteins. Although this rate difference appears to be due to the lower essentiality of extracellular proteins than intracellular proteins in yeast, we here show that, in mammals, the impact of extracellularity is independent from the impact of gene essentiality. Our partial correlation analysis indicated that the impact of extracellularity on mammalian protein evolutionary rate is also independent from those of tissue-specificity, expression level, gene compactness, and the number of protein–protein interactions and, surprisingly, is the strongest among all the factors we examined. Similar results were also found from principal component regression analysis. Our findings suggest that different rules govern the pace of protein sequence evolution in mammals and yeasts