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].

Investment Rating - The report maintains a "Buy" rating for the AI video generation industry, highlighting the accelerated commercialization and strong performance of domestic manufacturers [2]. Core Insights - The AI video generation industry is entering a commercial development fast track, with significant advancements in technology and diverse application scenarios. The global market size is projected to reach approximately 25.63 billion USD by 2032, with a compound annual growth rate (CAGR) of 20% from 2025 to 2032 [4][40]. - The industry is driven by both pricing and model capabilities, with current API prices ranging from 0.2 to 1 RMB per second. The cost advantages of AI video generation compared to traditional video production methods are substantial [46][47]. - Domestic manufacturers, such as Kuaishou and Meitu, are showing outstanding performance in the competitive landscape, with products like Kuaishou's Kling and ByteDance's Seedance leading the market [58][62]. Summary by Sections 1. Technology Path - The evolution of AI video generation technology has progressed from static image sequences to GAN, Transformer, Diffusion Model, and DiT, enhancing content richness and controllability [4][7]. - The DiT architecture, which combines diffusion models with transformers, has emerged as a key direction in the industry, validated by the Sora model's performance [23][31]. 2. AI Video Generation Industry 2.1 Driving Factors - The growth of the AI video generation industry is fueled by both pricing and performance improvements, with significant cost advantages over traditional video production methods [46][47]. - The current mainstream generation duration is 5-10 seconds, with advancements allowing for longer video generation, enhancing narrative capabilities [47]. 2.2 Industry Applications - The industry has diverse applications in B2B sectors such as film content creation, commercial advertising, e-commerce marketing, and education, as well as in C2C scenarios that enhance user engagement [51][54]. 2.3 Product and Competitive Landscape - Domestic manufacturers like Kuaishou and ByteDance are leading the market with their advanced models, achieving high usage and web traffic [58][62]. - The competitive landscape shows that products like Seedance1.0 and Veo2/3 are among the top performers, indicating a strong domestic capability in AI video generation [58][62]. 3. Investment Recommendations and Related Stocks - The report suggests focusing on Kuaishou (1024.HK) and Meitu (1357.HK) as key investment opportunities in the AI video generation sector, given their strong commercial performance and growth potential [64][75].