ICCV 2025 | EPD-Solver:西湖大学发布并行加速扩散采样算法

Core Viewpoint - The article discusses the advancements in diffusion models, particularly the introduction of the Ensemble Parallel Direction Solver (EPD-Solver), which enhances the efficiency and quality of image generation while addressing the latency issues associated with traditional methods [2][3][27]. Group 1: Diffusion Models Overview - Diffusion models have rapidly become mainstream technologies for generating images, videos, audio, and 3D content due to their high-quality output [2]. - The core mechanism of diffusion models involves a "denoising" process that iteratively refines a random image into a clear one, which, while ensuring quality, leads to significant inference delays [2]. Group 2: Acceleration Strategies - Researchers proposed three main acceleration strategies: using ODE solvers to reduce iteration steps, model distillation to compress multi-step processes, and parallel computing to speed up inference [3]. - Each method has limitations, such as quality loss with fewer iterations, high costs of retraining models, and underutilization of parallelism in low-step scenarios [3]. Group 3: EPD-Solver Innovation - The EPD-Solver combines the advantages of the aforementioned strategies, utilizing a numerical solver framework, lightweight distillation for a small set of learnable parameters, and parallel computation of gradients [3][4]. - This method effectively reduces numerical integration errors without significant modifications to the model or additional latency, achieving high-quality image generation with only 3-5 sampling steps [3][4]. Group 4: Performance and Results - EPD-Solver can be integrated as a "plugin" into existing solvers, significantly enhancing their generation quality and efficiency [4]. - Experimental results show that EPD-Solver outperforms baseline solvers in various benchmarks like CIFAR-10, FFHQ, and ImageNet, demonstrating its potential in low-latency, high-quality generation tasks [21][25]. Group 5: Key Advantages - The method offers parallel efficiency and precision improvements by introducing multiple gradient evaluations, which significantly enhance ODE integration accuracy while maintaining zero additional inference delay [28]. - EPD-Solver is lightweight and can be easily integrated into existing ODE samplers, avoiding the costly retraining of diffusion models [28].