基于μP扩展Diffusion Transformers：大规模验证 我们进一步在大规模的文生图任务上验证了μP扩展diffusion Transformers的有效性。 我们首先考虑了流行的开源文生图模型PixArt-α，我们在0.04B的代理模型上搜索学习率，并迁移到最终0.61B大小PixArt-α的训练。

本综述系统探讨了小波变换（WT）与深度学习（如CNN、Transformer、Diffusion Model）的融合策略，重点分析了其在多分辨率分析、特征提取及模型效率优化方面的协同优势，为复杂模式识别任务提供了创新解决方案。 核心概念与理论基础 小波变换（Wavelet Transform, WT ...

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 ...

Large language models like ChatGPT and Llama-2 are notorious for their extensive memory and computational demands, making them costly to run. Trimming even a small fraction of their size can lead to ...

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