Researchers have developed a new method called ChebBooster to speed up the inference process of Diffusion Transformers (DiTs) in image generation. Unlike other methods that require training or are prone to instability, ChebBooster uses Chebyshev polynomial theory to extrapolate model behavior and reduce computational costs. The approach involves two stages: offline weight precomputation and online application. Experiments on three DiT-based models show consistent improvements in visual quality and inference efficiency, with up to 3.68 times faster latency and 5.12 times fewer floating-point operations. --- Why it matters: This matters because it addresses a significant bottleneck in DiT-based image generation: the computationally intensive sampling process. By efficiently accelerating this process, ChebBooster enables faster and more scalable image generation, which is crucial for applications like real-time video processing or large-scale data analysis. Source: https://arxiv.org/abs/2608.23429