PartDiffuser: Part-wise 3D Mesh Generation via Discrete Diffusion

A new framework called EntPruner has been introduced to address parameter redundancy in large-scale vision generative models, specifically diffusion and flow models. This framework employs an entropy-guided automatic progressive pruning strategy, which assesses the importance of model blocks based on Conditional Entropy Deviation (CED) to optimize performance across various downstream tasks.

MoGAN has been introduced as a motion-centric post-training framework aimed at enhancing motion quality in video diffusion models, which often struggle with issues like jitter and ghosting. This framework utilizes a DiT-based optical-flow discriminator to improve motion realism without relying on reward models or human preference data.

A new study introduces a plug-and-play memory system for Diffusion Transformer-based video generation models, specifically the DiT, enhancing their ability to incorporate world knowledge and improve visual coherence. This development addresses the models' frequent violations of physical laws and commonsense dynamics, which have been a significant limitation in their application.

A new inference pipeline named Jenga has been introduced to enhance the efficiency of video generation using Video Diffusion Transformer (DiT) models. This approach addresses the computational challenges associated with self-attention and the multi-step nature of diffusion models by employing dynamic attention carving and progressive resolution generation.