Training Free Acceleration

Training-free acceleration focuses on speeding up the inference phase of various deep learning models, particularly diffusion models and Vision Transformers, without requiring additional training or model modifications. Current research explores techniques like optimized ODE solvers, feature reuse across time steps or camera angles (in 3D generation), and adaptive guidance strategies to reduce redundant computations. These advancements significantly improve the efficiency of these models, leading to faster generation times and reduced computational costs for applications ranging from image and 3D object generation to large-scale model training.