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今天的主题是：LiT: Delving into a Simplified Linear Diffusion Transformer for Image Generation

Summary

The provided document introduces LiT, a Linear Diffusion Transformer, designed for efficient image generation. LiT simplifies linear attention mechanisms and employs a novel training strategy involving weight inheritance and hybrid knowledge distillation. This approach allows LiT to achieve competitive image generation results with significantly reduced training steps, rivaling other methods like Mamba or Gated Linear Attention. Experiments demonstrate LiT's capability to generate high-resolution, photorealistic images, even on resource-limited devices like laptops. The research explores architectural refinements and optimization strategies to improve the performance of linear Diffusion Transformers. This work is aimed at cost-effectively training a linear DiT for photorealistic image generation by focusing on linear attention design, weight inheritance, and knowledge distillation.

该文档介绍了 LiT（Linear Diffusion Transformer），一种专为高效图像生成设计的线性扩散变换器。LiT 简化了线性注意力机制，并采用了一种新颖的训练策略，包括权重继承和混合知识蒸馏，使其在大幅减少训练步骤的同时，仍能实现与 Mamba 或 Gated Linear Attention 等方法相媲美的图像生成效果。实验表明，LiT 能够生成高分辨率、逼真的图像，即使在笔记本电脑等资源受限的设备上也能运行。研究还探讨了架构优化和训练策略，以提升线性扩散变换器的性能。本研究的目标是通过专注于线性注意力设计、权重继承和知识蒸馏，以更低的成本训练出高质量的 LiT 模型，实现逼真的图像生成。

原文链接：https://arxiv.org/abs/2501.12976