这篇论文主要围绕MeanFlow框架的改进展开，核心贡献在于提出了更稳定的训练目标和更灵活的引导机制，使得单步生成模型在ImageNet 256x256数据集上达到了 1.72的FID ，相较于原版MeanFlow有了 50%的性能提升 ，且无需蒸馏，这一成果让单步生成模型与多步模型的差距显著缩小。

A novel FlowViT-Diff framework that integrates a Vision Transformer (ViT) with an enhanced denoising diffusion probabilistic model (DDPM) for super-resolution reconstruction of high-resolution flow ...

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. In today’s column, I will identify and discuss an important AI ...

Artificial intelligence is no longer confined to data crunching and automation; it's making profound inroads into the creative industries. Generative models like generative adversarial networks (GANs) ...

In brief: Companies involved in the generative AI business keep making outrageous promises about unprecedented productivity improvements and cost cuts. Meta is now focusing on 3D model creation, which ...

Picture a budget meeting at a government agency or a boardroom in a multinational firm. A generative AI system has drafted a cash-flow projection, summarized a 200-page policy report, and ...