CoReDiT: Spatial Coherence-Guided Token Pruning and Reconstruction for Efficient Diffusion Transformers · DeepSignal
CoReDiT: Spatial Coherence-Guided Token Pruning and Reconstruction for Efficient Diffusion Transformers arXiv cs.CV · Zhuojin Li, Hsin-Pai Cheng, Hong Cai, Shizhong Han, Fatih Porikli 2d ago · · · · CoReDiT enhances Diffusion Transformers by optimizing token pruning for efficiency and quality.
Key Points Introduces structured token pruning for Diffusion Transformers. Achieves up to 55% reduction in self-attention FLOPs. Increases on-device memory for higher-resolution generation. Reader Mode unavailable (could not extract clean content).
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Technical impact 30%
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≥75 high · 50–74 medium · <50 low
Why Featured
CoReDiT's optimization of token pruning in Diffusion Transformers signals improved efficiency and quality, crucial for developers and PMs focusing on resource management and performance in AI applications.