EasyCache：无需训练的视频扩散模型推理加速——极简高效的视频生成提速方案

Core Viewpoint - The article discusses the development of EasyCache, a new framework for accelerating video diffusion models without requiring training or structural changes to the model, significantly improving inference efficiency while maintaining video quality [7][27]. Group 1: Research Background and Motivation - The application of diffusion models and diffusion Transformers in video generation has led to significant improvements in the quality and coherence of AI-generated videos, transforming digital content creation and multimedia entertainment [3]. - However, issues such as slow inference and high computational costs have emerged, with examples like HunyuanVideo taking 2 hours to generate a 5-second video at 720P resolution, limiting the technology's application in real-time and large-scale scenarios [4][5]. Group 2: Methodology and Innovations - EasyCache operates by dynamically detecting the "stable period" of model outputs during inference, allowing for the reuse of historical computation results to reduce redundant inference steps [7][16]. - The framework measures the "transformation rate" during the diffusion process, which indicates the sensitivity of current outputs to inputs, revealing that outputs can be approximated using previous results in later stages of the process [8][12][15]. - EasyCache is designed to be plug-and-play, functioning entirely during the inference phase without the need for model retraining or structural modifications [16]. Group 3: Experimental Results and Visual Analysis - Systematic experiments on mainstream video generation models like OpenSora, Wan2.1, and HunyuanVideo demonstrated that EasyCache achieves a speedup of 2.2 times on HunyuanVideo, with a 36% increase in PSNR and a 14% increase in SSIM, while maintaining video quality [20][26]. - In image generation tasks, EasyCache also provided a 4.6 times speedup, improving FID scores, indicating its effectiveness across different applications [21][22]. - Visual comparisons showed that EasyCache retains high visual fidelity, with generated videos closely matching the original model outputs, unlike other methods that exhibited varying degrees of quality loss [24][25]. Group 4: Conclusion and Future Outlook - EasyCache presents a minimalistic and efficient paradigm for accelerating inference in video diffusion models, laying a solid foundation for practical applications of diffusion models [27]. - The expectation is to further approach the goal of "real-time video generation" as models and acceleration technologies continue to evolve [27].